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PiSugar
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2022-11-13 22:16:03 UTC
2022-11-13 22:23:03 UTC
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2022-11-13 22:16:03 UTC
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metricbeat-system
metricbeat-system
System metrics from metricbeat to elasticsearch by nasuyun
2022-11-21 10:38:37 UTC
2022-11-25 13:26:34 UTC
0
metricbeat for nasuyun cloud elasticsearch by nasuyun
78,864
78,865
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17,451
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"steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "ms" }, "overrides": [] }, "gridPos": { "h": 9, "w": 12, "x": 12, "y": 33 }, "id": 30, "links": [], "options": { "legend": { "calcs": [ "mean", "lastNotNull", "max", "min" ], "displayMode": "table", "placement": "bottom" }, "tooltip": { "mode": "multi", "sort": "none" } }, "pluginVersion": "8.5.9", "targets": [ { "alias": "await", "bucketAggs": [ { "field": "@timestamp", "id": "2", "settings": { "interval": "$Interval", "min_doc_count": 0, "trimEdges": 0 }, "type": "date_histogram" } ], "datasource": { "type": "elasticsearch", "uid": "${DS_METRICBEAT}" }, "dsType": "elasticsearch", "hide": false, "metrics": [ { "field": "system.diskio.iostat.await", "id": "1", "meta": {}, "pipelineAgg": "select metric", "settings": {}, "type": "avg" } ], "query": "metricset.module:system AND metricset.name:diskio AND beat.name:$hostname", "refId": "A", "timeField": "@timestamp" }, { "alias": "service", "bucketAggs": [ { "field": "@timestamp", "id": "2", "settings": { "interval": "$Interval", "min_doc_count": 0, "trimEdges": 0 }, "type": "date_histogram" } ], "datasource": { "type": "elasticsearch", "uid": "${DS_METRICBEAT}" }, "dsType": "elasticsearch", "hide": false, "metrics": [ { "field": "system.diskio.iostat.service_time", "id": "1", "meta": {}, "pipelineAgg": "select metric", "settings": {}, "type": "avg" } ], "query": "metricset.module:system AND metricset.name:diskio AND beat.name:$hostname", "refId": "B", "timeField": "@timestamp" }, { "alias": "busy", "bucketAggs": [ { "field": "@timestamp", "id": "2", "settings": { "interval": "$Interval", "min_doc_count": 0, "trimEdges": 0 }, "type": "date_histogram" } ], "datasource": { "type": "elasticsearch", "uid": "${DS_METRICBEAT}" }, "dsType": "elasticsearch", "hide": false, "metrics": [ { "field": "system.diskio.iostat.busy", "id": "1", "meta": {}, "pipelineAgg": "select metric", "settings": {}, "type": "avg" } ], "query": "metricset.module:system AND metricset.name:diskio AND beat.name:$hostname", "refId": "C", "timeField": "@timestamp" } ], "title": "磁盘IO的响应时间", "type": "timeseries" }, { "datasource": { "type": "elasticsearch", "uid": "${DS_METRICBEAT}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": true, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 42 }, "id": 25, "links": [], "options": { "legend": { "calcs": [ "lastNotNull" ], "displayMode": "table", "placement": "bottom" }, "tooltip": { "mode": "multi", "sort": "none" } }, "pluginVersion": "8.5.9", "targets": [ { "alias": "iostat.read.request", "bucketAggs": [ { "field": "@timestamp", "id": "2", "settings": { "interval": "$Interval", "min_doc_count": 0, "trimEdges": 0 }, "type": "date_histogram" } ], "datasource": { "type": "elasticsearch", "uid": "${DS_METRICBEAT}" }, "dsType": "elasticsearch", "hide": false, "metrics": [ { "field": "system.diskio.iostat.read.request.per_sec", "id": "1", "meta": {}, "pipelineAgg": "select metric", "settings": {}, "type": "avg" } ], "query": "metricset.module:system AND metricset.name:diskio AND beat.name:$hostname", "refId": "A", "timeField": "@timestamp" }, { "alias": "iostat.write.request", "bucketAggs": [ { "field": "@timestamp", "id": "2", "settings": { "interval": "$Interval", "min_doc_count": 0, "trimEdges": 0 }, "type": "date_histogram" } ], "datasource": { "type": "elasticsearch", "uid": "${DS_METRICBEAT}" }, "dsType": "elasticsearch", "hide": false, "metrics": [ { "field": "system.diskio.iostat.write.request.per_sec", "id": "1", "meta": {}, "pipelineAgg": "select metric", "settings": {}, "type": "avg" } ], "query": "metricset.module:system AND metricset.name:diskio AND beat.name:$hostname", "refId": "B", "timeField": "@timestamp" } ], "title": "Diskio", "type": "timeseries" }, { "datasource": { "type": "elasticsearch", "uid": "${DS_METRICBEAT}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": true, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": 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beat.name:$hostname", "refId": "A", "timeField": "@timestamp" }, { "alias": "rrqm/s", "bucketAggs": [ { "field": "@timestamp", "id": "2", "settings": { "interval": "$Interval", "min_doc_count": 0, "trimEdges": 0 }, "type": "date_histogram" } ], "datasource": { "type": "elasticsearch", "uid": "${DS_METRICBEAT}" }, "dsType": "elasticsearch", "hide": false, "metrics": [ { "field": "system.diskio.iostat.read.request.merges_per_sec", "id": "1", "meta": {}, "pipelineAgg": "select metric", "settings": {}, "type": "avg" } ], "query": "metricset.module:system AND metricset.name:diskio AND beat.name:$hostname", "refId": "B", "timeField": "@timestamp" } ], "title": "每秒磁盘相关的读写请求有多少被Merge", "type": "timeseries" }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "elasticsearch", "uid": "${DS_METRICBEAT}" }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 10, "w": 24, "x": 0, "y": 51 }, "hiddenSeries": false, "id": 33, "legend": { "alignAsTable": true, 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"settings": {}, "type": "avg" } ], "query": "metricset.module:system AND metricset.name:process AND beat.name:$hostname", "refId": "A", "timeField": "@timestamp" } ], "thresholds": [], "timeRegions": [], "title": "top5进程使用cpu", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "none", "logBase": 1, "show": true }, { "format": "none", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "elasticsearch", "uid": "${DS_METRICBEAT}" }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 10, "w": 24, "x": 0, "y": 61 }, "hiddenSeries": false, "id": 34, "legend": { "alignAsTable": true, "avg": false, "current": true, "max": true, "min": false, "rightSide": true, "show": true, "total": false, "values": true }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": 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"timeRegions": [], "title": "top5进程使用mem", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "decbytes", "logBase": 1, "show": true }, { "format": "decbytes", "logBase": 1, "show": true } ], "yaxis": { "align": false } } ], "refresh": false, "schemaVersion": 36, "style": "dark", "tags": [], "templating": { "list": [ { "current": {}, "datasource": { "type": "elasticsearch", "uid": "${DS_METRICBEAT}" }, "definition": "{\"find\": \"terms\", \"field\": \"beat.name\"}", "hide": 0, "includeAll": false, "label": "", "multi": false, "name": "hostname", "options": [], "query": "{\"find\": \"terms\", \"field\": \"beat.name\"}", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 3, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "auto": false, "auto_count": 30, "auto_min": "10s", "current": { "selected": false, "text": "1m", "value": "1m" }, "hide": 0, "name": "Interval", "options": [ { "selected": false, "text": "5s", "value": "5s" }, { "selected": false, "text": "10s", "value": "10s" }, { "selected": true, "text": "1m", "value": "1m" }, { "selected": false, "text": "2m", "value": "2m" } ], "query": "5s,10s,1m,2m", "queryValue": "", "refresh": 2, "skipUrlSync": false, "type": "interval" }, { "datasource": { "uid": "metricbeat" }, "filters": [], "hide": 0, "name": "Filter", "skipUrlSync": false, "type": "adhoc" } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "browser", "title": "metricbeat-system", "uid": "ubFKKpdVz3", "version": 3, "weekStart": "" }
2022-11-21 10:38:37 UTC
2022-11-25 13:26:19 UTC
1
5
18,017
1
417,484
27,634
0
vmware-overview
VMware Overview
Uses prometheus queries via pryorda/vmware_exporter
2023-02-01 02:58:17 UTC
2023-02-02 05:56:35 UTC
0
Referenced from:https://grafana.com/grafana/dashboards/11243-vmware-stats/ How to instan vmware_exporter: ## 1.vim docker-comose.yml ``` version: '3' services: vmware_exporter: image: pryorda/vmware_exporter:latest restart: always hostname: vmware_exporter container_name: vmware_exporter environment: - [email protected] - VSPHERE_PASSWORD=123456 - VSPHERE_HOST=192.168.1.1 - VSPHERE_IGNORE_SSL=True - VSPHERE_SPECS_SIZE=2000 ports: - 9272:9272 ``` ## 2.pull image and run ``` docker-compose -f docker-compose.yml up -d ```
81,220
81,221
null
null
0
0
27,634
1
18,017
1
0
Uses prometheus queries via pryorda/vmware_exporter
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"name": "vm", "options": [], "query": { "query": "label_values(vmware_vm_power_state{dc_name=~\"$dc\",host_name=~\"$host\"}, vm_name)", "refId": "StandardVariableQuery" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS-VICTORIAMETRICS}" }, "definition": "label_values(vmware_vm_power_state{dc_name=~\"$dc\",host_name=~\"$host\",vm_name=~\"$vm\"}, ds_name)", "hide": 0, "includeAll": true, "label": "datastore", "multi": true, "name": "ds", "options": [], "query": { "query": "label_values(vmware_vm_power_state{dc_name=~\"$dc\",host_name=~\"$host\",vm_name=~\"$vm\"}, ds_name)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ] }, "timezone": "browser", "title": "VMware Overview", "uid": "q1yCDNbWz", "version": 13, "weekStart": "" }
2023-02-01 02:58:17 UTC
null
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geth-multiple
Geth: Multiple
Various metrics of multiple geth instances
2023-03-17 09:13:23 UTC
2023-03-17 09:15:23 UTC
0
This dashboard shows metrics for multiple [geth](https://geth.ethereum.org/) instances. Read the [metrics documentation](https://geth.ethereum.org/docs/monitoring/metrics) for further information on how to setup metrics.
82,524
82,525
null
null
0
0
28,077
1
18,291
1
0
null
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2023-03-17 09:13:23 UTC
2023-03-17 09:15:21 UTC
0
0
18,483
1
428,770
28,357
0
mirror-nodes-comparison-maciej
Mirror nodes comparison [Maciej]
Dashboard to track Filecoin Lotus node API endpoints.
2023-04-10 11:18:54 UTC
2023-04-10 11:19:37 UTC
0
null
0
0
null
null
0
0
28,357
1
18,483
1
0
Dashboard to track Filecoin Lotus node API endpoints.
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2023-04-10 11:18:54 UTC
null
0
0
18,696
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851,960
28,678
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kubernetes-events-custom-controller
Kubernetes Events (Custom Controller)
Kubernetes Events from custom Controller
2023-05-08 13:52:42 UTC
2023-05-08 13:58:48 UTC
0
null
84,127
84,128
Other
null
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2023-05-08 13:52:42 UTC
2023-05-08 13:56:58 UTC
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cosmos-dashboard
Cosmos Dashboard
A Grafana dashboard compatible with all the cosmos-sdk and tendermint based blockchains.
2022-11-30 17:01:39 UTC
2022-12-01 20:31:04 UTC
0
null
0
0
collectd
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26,900
1
17,516
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0
A Grafana dashboard compatible with all the cosmos-sdk and tendermint based blockchains.
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2022-11-30 17:01:39 UTC
2022-12-01 02:32:45 UTC
0
0
17,850
1
774,390
27,378
0
pv-power-ahoy
PV Power Ahoy
Visualise power metrics coming from AHOY-DTU a
2023-01-10 21:36:14 UTC
2023-01-10 21:47:26 UTC
0
null
0
0
null
null
0
0
27,378
1
17,850
1
0
Visualise power metrics coming from AHOY-DTU and OpenWeatherMap
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"legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "decimals": 0, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "blue", "value": null }, { "color": "yellow", "value": 50 }, { "color": "orange", "value": 200 }, { "color": "red", "value": 600 } ] }, "unit": "watt" }, "overrides": [ { "matcher": { "id": "byName", "options": "Power Ost" }, "properties": [ { "id": "color", "value": { "fixedColor": "orange", "mode": "fixed", "seriesBy": "last" } }, { "id": "custom.fillOpacity", "value": 20 } ] }, { "matcher": { "id": "byName", "options": "Power West" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "Irridation West" }, "properties": [ { "id": "color", "value": { "fixedColor": "#8AB8FF", "mode": "fixed" } }, { "id": "custom.axisPlacement", "value": "right" }, { "id": "unit", "value": "percent" }, { "id": "custom.lineInterpolation", "value": "smooth" }, { "id": "custom.axisSoftMin", "value": 0 }, { "id": "custom.axisSoftMax", "value": 100 } ] }, { "matcher": { "id": "byName", "options": "Irridation Ost" }, "properties": [ { "id": "color", "value": { "fixedColor": "purple", "mode": "fixed" } }, { "id": "custom.axisPlacement", "value": "right" }, { "id": "unit", "value": "percent" }, { "id": "custom.lineInterpolation", "value": "smooth" }, { "id": "custom.axisSoftMin", "value": 0 }, { "id": "custom.axisSoftMax", "value": 100 } ] } ] }, "gridPos": { "h": 19, "w": 24, "x": 0, "y": 27 }, "id": 64, "interval": "10s", "options": { "legend": { "calcs": [ "max" ], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "targets": [ { "alias": "Power West", "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "mqtt_consumer", "orderByTime": "ASC", "policy": "default", "refId": "B", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "mean" } ] ], "tags": [ { "key": "topic", "operator": "=", "value": "inverter/Hoymiles/ch1/P_DC" } ] }, { "alias": "Power Ost", "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "mqtt_consumer", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "mean" } ] ], "tags": [ { "key": "topic", "operator": "=", "value": "inverter/Hoymiles/ch2/P_DC" } ] }, { "alias": "Irridation West", "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "mqtt_consumer", "orderByTime": "ASC", "policy": "default", "refId": "D", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "mean" } ] ], "tags": [ { "key": "topic", "operator": "=", "value": "inverter/Hoymiles/ch1/Irradiation" } ] }, { "alias": "Irridation Ost", "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "mqtt_consumer", "orderByTime": "ASC", "policy": "default", "refId": "C", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "mean" } ] ], "tags": [ { "key": "topic", "operator": "=", "value": "inverter/Hoymiles/ch2/Irradiation" } ] } ], "title": "Power", "transformations": [], "type": "timeseries" } ], "title": "Details", "type": "row" } ], "refresh": "1m", "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [ { "current": { "selected": false, "text": "0.23", "value": "0.23" }, "hide": 2, "includeAll": false, "multi": false, "name": "kwhPrice", "options": [ { "selected": true, "text": "0.23", "value": "0.23" } ], "query": "0.23", "skipUrlSync": false, "type": "custom" }, { "current": {}, "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "definition": "SHOW TAG VALUES FROM \"weather\" WITH KEY = \"country\"", "hide": 0, "includeAll": true, "label": "Country", "multi": true, "name": "country", "options": [], "query": "SHOW TAG VALUES FROM \"weather\" WITH KEY = \"country\"", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "type": "query" }, { "current": {}, "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_@ INFLUXDB}" }, "definition": "SHOW TAG VALUES FROM \"weather\" WITH KEY = \"city\"", "hide": 0, "includeAll": true, "label": "City", "multi": true, "name": "city", "options": [], "query": "SHOW TAG VALUES FROM \"weather\" WITH KEY = \"city\"", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "type": "query" } ] }, "time": { "from": "now/d", "to": "now/d" }, "timepicker": {}, "timezone": "", "title": "PV Power Ahoy", "uid": "opBBFjWVz", "version": 31, "weekStart": "", "gnetId": 17850, "description": "Visualise power metrics coming from AHOY-DTU a" }
2023-01-10 21:36:14 UTC
2023-01-10 21:47:26 UTC
0
0
17,931
1
738,351
27,494
0
traefik-dashboard
Traefik Dashboard
Visualizing Traefik Reverse Proxy Stats
2023-01-22 22:01:03 UTC
2023-01-22 22:05:10 UTC
0
Grafana dashboard to visualize various stats of a Traefik reverse proxy.
80,924
80,925
null
null
0
0
27,494
1
17,931
1
0
Visualizing Traefik Reverse Proxy Stats
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"1m,10m,60m", "skipUrlSync": false, "type": "custom" }, { "current": {}, "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB-TRAEFIK}" }, "definition": "select * from \"traefik.backend.requests.total\"", "hide": 0, "includeAll": true, "label": "Backend", "multi": false, "name": "backend", "options": [], "query": "select * from \"traefik.backend.requests.total\"", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": {}, "timezone": "browser", "title": "Traefik Dashboard", "uid": "4lvBTAo4k", "version": 4, "weekStart": "monday", "gnetId": 17931 }
2023-01-22 22:01:03 UTC
2023-01-22 22:03:14 UTC
0
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redpanda-default-dashboard
Redpanda Default Dashboard
Redpanda Default Dashboard
2023-02-18 00:08:04 UTC
2023-03-06 13:49:52 UTC
0
This is the default dashboard for Redpanda. There are many charts on this page, so instead of listing each one here are the sections: - Redpanda summary - internal RPC latency - throughput - errors - IO queue - memory - others (build info, uptime, number of configured brokers, etc.) - raft - scheduler - storage
82,222
82,223
null
null
0
0
27,840
1
18,134
1
0
null
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"mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] }, { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 0, "y": 31 }, "id": 43, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_node_status_rpcs_received{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}", "refId": "", "step": 10 } ], "title": "Number of node status RPCs received by this node", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] }, { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 8, "y": 31 }, "id": 44, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_node_status_rpcs_sent{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}", "refId": "", "step": 10 } ], "title": "Number of node status RPCs sent by this node", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] }, { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 16, "y": 31 }, "id": 45, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_node_status_rpcs_timed_out{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}", "refId": "", "step": 10 } ], "title": "Number of timed out node status RPCs from this node", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] }, { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 0, "y": 31 }, "id": 51, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_rest_proxy_request_latency_seconds{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}", "refId": "", "step": 10 } ], "title": "Internal latency of request for rest_proxy", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] }, { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 8, "y": 31 }, "id": 52, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_rpc_active_connections{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}, redpanda_server: {{redpanda_server}}", "refId": "", "step": 10 } ], "title": "Count of currently active connections", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] }, { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 16, "y": 31 }, "id": 54, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_rpc_request_latency_seconds{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}, redpanda_server: {{redpanda_server}}", "refId": "", "step": 10 } ], "title": "RPC latency", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] }, { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 0, "y": 31 }, "id": 58, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_schema_registry_request_latency_seconds{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}", "refId": "", "step": 10 } ], "title": "Internal latency of request for schema_registry", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] } ], "span": 0, "targets": [ { "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "refId": "A" } ], "title": "others", "type": "row" }, { "collapsed": true, "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "editable": true, "error": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 26 }, "id": 47, "panels": [ { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 0, "y": 32 }, "id": 46, "interval": "1m", "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(redpanda_raft_leadership_changes{instance=~\"$node\"}[$__rate_interval])) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}, redpanda_namespace: {{redpanda_namespace}}, redpanda_topic: {{redpanda_topic}}", "refId": "", "step": 10 } ], "title": "Rate - Number of leadership changes across all partitions of a given topic", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "ops", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] }, { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 8, "y": 32 }, "id": 48, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_raft_recovery_partition_movement_available_bandwidth{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}, shard: {{shard}}", "refId": "", "step": 10 } ], "title": "Bandwidth available for partition movement. bytes/sec", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] } ], "span": 0, "targets": [ { "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "refId": "A" } ], "title": "raft", "type": "row" }, { "collapsed": true, "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "editable": true, "error": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 27 }, "id": 56, "panels": [ { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 0, "y": 33 }, "id": 55, "interval": "1m", "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(redpanda_scheduler_runtime_seconds_total{instance=~\"$node\"}[$__rate_interval])) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}, redpanda_scheduling_group: {{redpanda_scheduling_group}}, shard: {{shard}}", "refId": "", "step": 10 } ], "title": "Rate - Accumulated runtime of task queue associated with this scheduling group", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "percentunit", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] } ], "span": 0, "targets": [ { "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "refId": "A" } ], "title": "scheduler", "type": "row" }, { "collapsed": true, "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "editable": true, "error": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 28 }, "id": 60, "panels": [ { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 0, "y": 34 }, "id": 59, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_storage_disk_free_bytes{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}", "refId": "", "step": 10 } ], "title": "Disk storage bytes free.", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] }, { "aliasColors": {}, "bars": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": 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false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editable": true, "error": false, "fill": 1, "gridPos": { "h": 6, "w": 8, "x": 16, "y": 34 }, "id": 62, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 2, "nullPointMode": "null", "renderer": "flot", "span": 4, "steppedLine": true, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(redpanda_storage_disk_total_bytes{instance=~\"$node\"}) by ($aggr_criteria)", "format": "time_series", "intervalFactor": 2, "legendFormat": "node: {{instance}}", "refId": "", "step": 10 } ], "title": "Total size of attached storage, in bytes.", "tooltip": { "msResolution": true, "shared": true, "value_type": "cumulative" }, "transparent": false, "type": "graph", "xaxis": { "format": "", "logBase": 0, "mode": "time", "show": true }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "min": 0, "show": true } ] } ], "span": 0, "targets": [ { "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "refId": "A" } ], "title": "storage", "type": "row" } ], "refresh": "10s", "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [ { "current": { "selected": false, "text": "Prometheus", "value": "Prometheus" }, "hide": 0, "includeAll": false, "label": "Data Source", "multi": false, "name": "DS_PROMETHEUS", "options": [], "query": "prometheus", "refresh": 1, "regex": "", "skipUrlSync": false, "type": "datasource" }, { "allFormat": "", "allValue": ".*", "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "", "hide": 0, "includeAll": true, "label": "Node", "multi": true, "multiFormat": "", "name": "node", "options": [], "query": { "query": "label_values(instance)", "refId": "Prometheus-node-Variable-Query" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "type": "query" }, { "allFormat": "", "allValue": ".*", "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "", "hide": 0, "includeAll": true, "label": "Shard", "multi": true, "multiFormat": "", "name": "node_shard", "options": [], "query": { "query": "label_values(shard)", "refId": "Prometheus-node_shard-Variable-Query" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "type": "query" }, { "allFormat": "", "allValue": "", "current": { "selected": false, "text": "Cluster", "value": "cluster" }, "datasource": "${DS_PROMETHEUS}", "hide": 0, "includeAll": false, "label": "Aggregate by", "multi": false, "multiFormat": "", "name": "aggr_criteria", "options": [ { "selected": true, "text": "Cluster", "value": "cluster" }, { "selected": false, "text": "Instance", "value": "instance" }, { "selected": false, "text": "Instance,Shard", "value": "instance,shard" } ], "query": "Cluster : cluster,Instance : instance,Instance\\,Shard : instance\\,shard", "refresh": 1, "skipUrlSync": false, "sort": 1, "type": "custom" } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "utc", "title": "Redpanda Default Dashboard", "uid": "d9gY-aJ4k", "version": 1, "weekStart": "", "gnetId": 18134, "description": "Redpanda Default Dashboard" }
2023-02-18 00:08:04 UTC
2023-02-18 00:08:46 UTC
0
0
18,294
1
813,964
28,080
0
apc-ups
APC UPS
APS UPS monitoring with Prometheus and apcupsd exporter
2023-03-17 12:11:34 UTC
2023-03-17 12:18:59 UTC
0
Using docker https://hub.docker.com/r/jangrewe/apcupsd-exporter **docker-compose.yml** (to run apcupsd_exporter) ``` version: '3' services: apcupsd: image: jangrewe/apcupsd-exporter restart: unless-stopped network_mode: "host" ``` **prometheus.yml** ``` scrape_configs: - job_name: "apcupsd" static_configs: - targets: ["192.168.0.11:9162"] ```
82,535
82,536
Other
null
0
0
28,080
1
18,294
1
0
null
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2023-03-17 12:11:34 UTC
2023-03-17 12:18:58 UTC
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18,528
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jmeter-dashboard
JMeter Dashboard
Monitor your JMeter load test in real time with InfluxDB and Grafana.
2023-04-15 14:22:38 UTC
2023-04-15 14:22:45 UTC
0
null
0
0
null
null
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28,413
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Monitor your JMeter load test in real time with InfluxDB and Grafana.
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Times - $transaction", "tooltip": { "msResolution": false, "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "ms", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": false } ] } ], "repeat": null, "repeatIteration": null, "repeatRowId": null, "showTitle": true, "title": "Individual Transaction - $transaction", "titleSize": "h6" } ], "schemaVersion": 14, "style": "dark", "tags": [ "load_test" ], "templating": { "list": [ { "current": { "text": "jmeter", "value": "jmeter" }, "hide": 0, "label": "", "name": "data_source", "options": [], "query": "influxdb", "refresh": 1, "regex": "", "type": "datasource" }, { "allValue": null, "current": {}, "datasource": "$data_source", "hide": 0, "includeAll": false, "label": "", "multi": false, "name": "application", "options": [], "query": "SHOW TAG VALUES FROM \"jmeter\" WITH KEY = \"application\"", "refresh": 1, "regex": "", "sort": 0, "tagValuesQuery": null, "tags": [], "tagsQuery": null, "type": "query", "useTags": false }, { "allValue": null, "current": {}, "datasource": "$data_source", "hide": 0, "includeAll": false, "label": "", "multi": false, "name": "transaction", "options": [], "query": "SHOW TAG VALUES FROM \"jmeter\" WITH KEY = \"transaction\" WHERE \"application\" =~ /^$application$/ AND \"transaction\" != 'internal' AND \"transaction\" != 'all'", "refresh": 1, "regex": "", "sort": 0, "tagValuesQuery": null, "tags": [], "tagsQuery": null, "type": "query", "useTags": false }, { "current": { "value": "${VAR_MEASUREMENT_NAME}", "text": "${VAR_MEASUREMENT_NAME}" }, "hide": 2, "label": "Measurement name", "name": "measurement_name", "options": [ { "value": "${VAR_MEASUREMENT_NAME}", "text": "${VAR_MEASUREMENT_NAME}" } ], "query": "${VAR_MEASUREMENT_NAME}", "type": "constant" }, { "current": { "value": "${VAR_SEND_INTERVAL}", "text": "${VAR_SEND_INTERVAL}" }, "hide": 2, "label": "Backend send interval in second", "name": "send_interval", "options": [ { "value": "${VAR_SEND_INTERVAL}", "text": "${VAR_SEND_INTERVAL}" } ], "query": "${VAR_SEND_INTERVAL}", "type": "constant" } ] }, "time": { "from": "now-30m", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "browser", "title": "JMeter Dashboard", "version": 4 }
2023-04-15 14:22:38 UTC
null
0
0
18,548
1
838,138
28,435
0
avid-pay-slo-monitoring
Avid Pay SLO Monitoring
Testing import AvidPay SLO Monitoring dashboard
2023-04-17 17:58:03 UTC
2023-04-17 17:59:28 UTC
0
null
0
0
null
null
0
0
28,435
1
18,548
1
0
null
{ "__inputs": [ { "name": "DS_AZURE_MONITOR - SAAS_INFRA", "label": "Azure Monitor - SaaS_Infra", "description": "", "type": "datasource", "pluginId": "grafana-azure-monitor-datasource", "pluginName": "Azure Monitor" } ], "__elements": {}, "__requires": [ { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.3.2" }, { "type": "datasource", "id": "grafana-azure-monitor-datasource", "name": "Azure Monitor", "version": "1.0.0" }, { "type": "panel", "id": "stat", "name": "Stat", "version": "" }, { "type": "panel", "id": "text", "name": "Text", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "grafana", "uid": "-- Grafana --" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 1, "id": null, "links": [], "liveNow": false, "panels": [ { "datasource": { "type": "datasource", "uid": "grafana" }, "description": "", "gridPos": { "h": 4, "w": 24, "x": 0, "y": 0 }, "id": 39, "options": { "code": { "language": "plaintext", "showLineNumbers": false, "showMiniMap": false }, "content": "WIKI Document - https://dev.azure.com/avidxchange/technology-references/_wiki/wikis/tech-refs/37861/AvidPay-SLO-Dashboard <br/>\n<font size=\"3px\" color=\"green\"><b>CS-Creation-001 -</b></font> 100% of all check payments queued for batch processing are successfully transformed and corresponding output files are staged for delivery to the check printer(s) by 10PM each business day <br/>\n<font size=\"3px\" color=\"green\"><b>CS-Creation-002 -</b></font> 100% of all check payments queued for batch processing are successfully transformed and corresponding output files are staged for delivery to the check printer(s) by 10PM each business day <br/>\n<font size=\"3px\" color=\"green\"><b>CS-Creation-003 -</b></font> 100% of all check payments queued for batch processing are successfully transformed and corresponding output files are staged for delivery to the check printer(s) by 9PM each business day <br/>\n\n<font size=\"3px\" color=\"green\"><b>FS-Aggregation-001 -</b></font> ​​100% of all payments in ACH Requested that are eligible for Aggregation and require ONST approval are moved to the correct payment status by 5PM each business day <br/>\n<font size=\"3px\" color=\"green\"><b>FS-Aggregation-002 -</b></font> ​​100% of all payments in ACH Requested that are eligible for Aggregation and do not require ONST approval are moved to the correct payment status by 7PM each business day <br/>\n<font size=\"3px\" color=\"green\"><b>FS-Bai2-001 -</b></font> ​​100% of all BAI2 files are consumed by the BAI2 Parser by 6:30AM Monday - Friday <br/>\n<font size=\"3px\" color=\"green\"><b>FS-Bai2-002 -</b></font> 100% of all BAI2 files consumed by the BAI2 parser are sent to the Enterprise Reconciliation Tool by 7:00AM Tool Monday - Friday <br/>\n<font size=\"3px\" color=\"green\"><b>FS-Bai2-003 -</b></font> 100% of all ACH and Check Clearing requests are processed by 10AM Monday - Friday <br/>\n<font size=\"3px\" color=\"green\"><b>FS-Remittance-001 -</b></font> 100% of all remittance emails are successfully delivered to customers by 9AM each business day <br/>\n\n<font size=\"3px\" color=\"green\"><b>VCS-Creation-001 -</b></font> 100% of all virtual cards queued on each business day are created by midnight each business day <br/>\n<font size=\"3px\" color=\"green\"><b>VCS-Delivery-002 -</b></font> 100% of all virtual cards created on each business day are e-mailed to suppliers by 2PM the following business day <br/>\n<font size=\"3px\" color=\"green\"><b>VCS-WEXTransaction-001 -</b></font> 100% of WEX transaction are inserted into AvidPay database by 3 AM EST <br/>", "mode": "markdown" }, "pluginVersion": "9.3.2", "title": "AvidPay Daily Monitoring SLO Dashboard - Overview", "type": "text" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 4 }, "id": 12, "panels": [], "title": "Check Services", "type": "row" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 5, "x": 0, "y": 5 }, "id": 20, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n | where $__timeFilter(timestamp)\r\n | where isnotempty(customDimensions)\r\n //filter for custom events for CottonWood \r\n | where name has \"Cottonwood:BatchGenerating\" or name has \"Cottonwood:BatchCompleted\"\r\n | extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n | summarize \r\n //Get the count of BatchGenerating events\r\n CountGenerating=countif(name has \"Cottonwood:BatchGenerating\"),\r\n\t//Get the count of BatchGenerating comppleted\r\n CountCompleted=countif(name has \"Cottonwood:BatchCompleted\"),\r\n\t//Get the count of BatchCompleted past 10 pm EST\r\n CountPast10PM=countif(name has \"Cottonwood:BatchCompleted\" and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 22)\r\n\t//Case condition will check \r\n\t//Check for Count of BatchGenerating or BatchCompleted is not 0\r\n\t//BatchGenerating count is same as BatchCompleted\r\n\t//Check for any batch completed after 10pm EST \r\n | project PercentCompletedBefore10PM= case(CountPast10PM !=0, 1-(CountPast10PM/todouble(CountCompleted)), CountGenerating != CountCompleted, 1-(CountCompleted/todouble(CountGenerating)), CountGenerating ==0,0.0,CountCompleted==0,0.0, 1.0) ", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/Ax-Ae1-Payment-Legacy-Rg/providers/Microsoft.Insights/components/Ax-Ae1-Pr-CheckLegacy-Insights" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "CS-Creation-001 - %Files Delivered", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 5, "x": 5, "y": 5 }, "id": 40, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/^PercentCompletedAfter10PM$/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n | where $__timeFilter(timestamp)\r\n //filter for custom events for SunGard\r\n | where name has \"CheckBatchProcessor:CheckFileCreated\" or name has \"CheckBatchProcessor:CheckFileSent\" and customDimensions.CheckPrinterId == 1\r\n | extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n | summarize \r\n //Get the count of check file created for SunGard\r\n CountQueued=countif(name has \"CheckBatchProcessor:CheckFileCreated\" and d.CheckPrinterId == 1),\r\n //Get the count of check file sent for SunGard \r\n CountSent=countif(name has \"CheckBatchProcessor:CheckFileSent\" and d.CheckPrinterId == 1),\r\n //Get the count of check file sent for SunGard post 10 PM EST\r\n CountPast10PM=countif(name has \"CheckBatchProcessor:CheckFileSent\" and d.CheckPrinterId == 1 and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 22)\r\n //Case condition will check \r\n\t//Check for Count of Check File Created or Check File Sent is not 0\r\n\t//Check file created count is same as Check File sent\r\n\t//Check for any check file sent after 10pm EST \r\n| project PercentCompletedAfter10PM=case(CountPast10PM !=0, 1-(CountPast10PM/todouble(CountSent)), CountQueued != CountSent,(CountSent/todouble(CountQueued)),CountSent ==0,0.0,1.0)", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Legacy-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Legacy-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "CS-Creation-002 - %Files Delivered", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 5, "x": 10, "y": 5 }, "id": 41, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/^PercentCompletedAfter10PM$/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n | where $__timeFilter(timestamp)\r\n //filter for custom events for StrongRoom\r\n | where name has \"CheckBatchProcessor:CheckFileCreated\" or name has \"CheckBatchProcessor:CheckFileSent\" and customDimensions.CheckPrinterId == 2\r\n | extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n | summarize \r\n //Get the count of check file created for StrongRoom\r\n CountQueued=countif(name has \"CheckBatchProcessor:CheckFileCreated\" and d.CheckPrinterId == 2),\r\n //Get the count of check file sent for StrongRoom\r\n CountSent=countif(name has \"CheckBatchProcessor:CheckFileSent\" and d.CheckPrinterId == 2),\r\n //Get the count of check file sent for SunGard post 9 PM EST\r\n CountPast9PM=countif(name has \"CheckBatchProcessor:CheckFileSent\" and d.CheckPrinterId == 2 and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 21)\r\n //Case condition will check \r\n\t//Check for Count of Check File Created or Check File Sent is not 0\r\n\t//Check file created count is same as Check File sent\r\n\t//Check for any check file sent after 9pm EST \r\n| project PercentCompletedAfter9PM=case(CountPast9PM !=0, 1-(CountPast9PM/todouble(CountSent)), CountQueued != CountSent, 1-(CountSent/todouble(CountQueued)), CountSent ==0, 0.0, 1.0)", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Legacy-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Legacy-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "CS-Creation-003 - %Files Delivered", "type": "stat" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 8 }, "id": 4, "panels": [], "title": "Funding & Settlement", "type": "row" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "percentage", "steps": [ { "color": "red", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "PercentBatchApprovalCompleted" }, "properties": [ { "id": "unit", "value": "percentunit" } ] } ] }, "gridPos": { "h": 3, "w": 8, "x": 0, "y": 9 }, "id": 31, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n| extend d = parse_json(customDimensions)\r\n //filter for custom events for Aggregation Batch Creation and Created. And filter custom dimensions for AggregationType Approval\r\n| where name has \"Aggregation:Batch:Creation:Created\" and d.AggregationType == \"Approval\"\r\n //Converting the UTC datetime to EST datetime\r\n| extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.Date),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n| summarize \r\n //Get the count of Aggregation Creation and Created events. AggregationType Approval custom dimensions events\r\n TotalBatchCreated = countif(name has \"Aggregation:Batch:Creation:Created\" and d.AggregationType == \"Approval\"),\r\n //Get the count of Aggregation Creation and Created events. AggregationType Approval custom dimensions events and ONST approval are moved to the correct payment status by 5PM \r\n TotalBatchCreatedPast5PM = countif(name has \"Aggregation:Batch:Creation:Created\" and d.AggregationType == \"Approval\" and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 17)\r\n\t//Case condition will check \r\n\t//Check for Count of Batch Created and AggregationType Approval is not 0\r\n //Check for any batch created after 05pm EST\r\n| project PercentBatchApprovalCompleted = case(TotalBatchCreatedPast5PM != 0, 1-(TotalBatchCreatedPast5PM/todouble(TotalBatchCreated)), TotalBatchCreated == 0,0.0,1.0) ", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Legacy-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Legacy-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "FS-Aggregation-001 - %ApprovalACHRequestedAggregation", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": "" }, { "color": "#EAB839", "value": "" }, { "color": "green", "value": "" } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "PercentBatchAutoApprovalCompleted" }, "properties": [ { "id": "unit", "value": "percentunit" } ] } ] }, "gridPos": { "h": 3, "w": 9, "x": 8, "y": 9 }, "id": 33, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n| extend d = parse_json(customDimensions)\r\n //filter for custom events for Aggregation Batch Creation and Created. And filter custom dimensions for AggregationType Non Approval\r\n| where name has \"Aggregation:Batch:Creation:Created\" and d.AggregationType == \"Non Approval\"\r\n //Converting the UTC datetime to EST datetime\r\n| extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.Date),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n| summarize\r\n //Get the count of Aggregation Creation and Created events. AggregationType Non Approval custom dimensions events\r\n TotalBatchCreated = countif(name has \"Aggregation:Batch:Creation:Created\" and d.AggregationType == \"Non Approval\"),\r\n //Get the count of Aggregation Creation and Created events. AggregationType Approval custom dimensions events and ONST approval are moved to the correct payment status by 5PM\r\n TotalBatchCreatedPast7PM = countif(name has \"Aggregation:Batch:Creation:Created\" and d.AggregationType == \"Non Approval\" and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 19)\r\n //Case condition will check \r\n\t//Check for Count of Batch Created and AggregationType Non Approval is not 0\r\n //Check for any batch created past 07pm EST\r\n| project PercentBatchAutoApprovalCompleted = case(TotalBatchCreatedPast7PM != 0, 1-(TotalBatchCreatedPast7PM/todouble(TotalBatchCreated)), TotalBatchCreated == 0, 0.0,1.0) ", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Legacy-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Legacy-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "FS-Aggregation-002 - %AutoApprovalACHRequestedAggregation", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Percent" }, "properties": [ { "id": "unit", "value": "percentunit" } ] } ] }, "gridPos": { "h": 3, "w": 5, "x": 0, "y": 12 }, "id": 6, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "app(\"AX-Ae1-Pr-Payment-Hub-INSIGHTS\").customEvents\r\n| where $__timeFilter(timestamp)\r\n //filter for custom events for BeginParsingFile. And filter custom dimensions for FileName: BOA_AVDC(Bank of america), AVDC_BAIPD(Fifth Third) and OldNSpd(KeyBank)\r\n| where name == \"Bai2Parser:BeginParsingFile\" and ((toupper(customDimensions.FileName) contains \"BOA_AVDC\") \r\nor (toupper(customDimensions.FileName) contains \"AVDC_BAIPD\") or (customDimensions.FileName contains \"OldNSpd\"))\r\n| extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n //Converting the UTC datetime to EST datetime for timestamp\r\n | extend TransmissionDateTime = format_datetime(datetime_utc_to_local(timestamp, \"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n //get the Expected Date for all BAI2 files are consumed by the BAI2 Parser by 6:30AM\r\n | extend ExpectedDate = format_datetime(todatetime(strcat(format_datetime(todatetime(TransmissionDateTime), \"MM/dd/yyyy\"), \" \",\"06:30:00 AM\")),\"MM/dd/yyyy hh:mm:ss tt\")\r\n //get the Expected Date for BOA_AVDC files are consumed by the BAI2 Parser by 11:30AM ,\r\n // Note: need add more logic on this BOA_AVDC files for time because these files receiving multiple times in day.\r\n | extend BOA_ExpectedDate = format_datetime(todatetime(strcat(format_datetime(todatetime(TransmissionDateTime), \"MM/dd/yyyy\"), \" \",\"23:30:00 PM\")),\"MM/dd/yyyy hh:mm:ss tt\")\r\n //Get Status should be good if file is uploaded less than 6:30 AM EST and Delayed if greater than 6:30 AM\r\n | extend Status = iff(customDimensions.FileName contains \"BOA_AVDC\", \r\n (iff(todatetime(CustomDimensionsEventDateTime) > todatetime(BOA_ExpectedDate), \"Delayed\", \"Good\")),\r\n (iff(todatetime(CustomDimensionsEventDateTime) > todatetime(ExpectedDate), \"Delayed\", \"Good\")))\r\n// Get Total Files Uploaded for all BAI2 files\r\n| summarize TotalFilesUploaded = count(),\r\n//Get Percent should be good if file is uploaded less than 6:30 AM EST\r\n Percent = countif(Status == \"Good\")/todouble(count()) \r\n| project Percent", "resource": "/subscriptions/d5d4da15-d34e-4cf3-a14b-e565ec53a1e5/resourceGroups/ax-ae1-saas-pr-observability-rg/providers/microsoft.operationalinsights/workspaces/ax-ae1-pr-observability-la" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "FS-BAI2-001 - %Bai2Consumed", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Percent" }, "properties": [ { "id": "unit", "value": "percentunit" } ] } ] }, "gridPos": { "h": 3, "w": 4, "x": 5, "y": 12 }, "id": 8, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "app(\"AX-Ae1-Pr-Payment-Hub-INSIGHTS\").customEvents\r\n| where $__timeFilter(timestamp)\r\n//filter for custom events for UploadToReconStorage. And filter custom dimensions for FileName: BOA_AVDC(Bank of america), AVDC_BAIPD(Fifth Third) and OldNSpd(KeyBank)\r\n| where name == \"Bai2Parser:UploadToReconStorage\" and (toupper(customDimensions.FileName) contains \"BOA_AVDC\")\r\n or (toupper(customDimensions.FileName) contains \"AVDC_BAIPD\") or (customDimensions.FileName contains \"OldNSpd\") \r\n| extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n //Converting the UTC datetime to EST datetime for timestamp\r\n | extend TransmissionDateTime = format_datetime(datetime_utc_to_local(timestamp, \"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n //get the Expected Date for all BAI2 files are consumed by the BAI2 Parser by 6:30AM\r\n | extend ExpectedDate = format_datetime(todatetime(strcat(format_datetime(todatetime(TransmissionDateTime), \"MM/dd/yyyy\"), \" \",\"06:30:00 AM\")),\"MM/dd/yyyy hh:mm:ss tt\")\r\n //Get Status should be good if file is uploaded less than 6:30 AM EST and Delayed if greater than 6:30 AM\r\n | extend Status = iff(todatetime(CustomDimensionsEventDateTime) > todatetime(ExpectedDate), \"Delayed\", \"Good\")\r\n// Get Total Files Uploaded for all BAI2 files\r\n| summarize TotalFilesUploaded = count(),\r\n//Get Percent should be good if file is uploaded less than 6:30 AM EST\r\n Percent = countif(Status == \"Good\")/todouble(count()) \r\n| project Percent", "resource": "/subscriptions/d5d4da15-d34e-4cf3-a14b-e565ec53a1e5/resourceGroups/ax-ae1-saas-pr-observability-rg/providers/microsoft.operationalinsights/workspaces/ax-ae1-pr-observability-la" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "FS-BAI2-002 - %Bai2Sent", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 5, "x": 9, "y": 12 }, "id": 18, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n | where $__timeFilter(timestamp)\r\n | where isnotempty(customDimensions)\r\n //filter for custom events for AchClearing Success and Exception\r\n | where name has \"Bai2Listener:AchClearing:Success\" or name has \"Bai2Listener:AchClearing:Exception\"\r\n | extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n | summarize \r\n //Get the count of Ach Clearing Exception\r\n TotalACHClearingFailureCount = countif(name has \"Bai2Listener:AchClearing:Exception\"),\r\n //Get the count of Ach Clearing Success \r\n TotalACHClearingSuccessCount = countif(name has \"Bai2Listener:AchClearing:Success\"),\r\n //Get the count of Ach Clearing Success and Check Clearing requests are processed by 10AM\r\n TotalCountACHClearingSuccessPast10AM = countif(name has \"Bai2Listener:AchClearing:Success\" and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 10) \r\n //Case condition will check \r\n //Check Total Count ACH Clearing by Success for Past 10AM is not zero then get Percent ACH Completed Success fully\r\n //Check Total ACH Clearing Failure Count is not zero \r\n | project PercentACHCompletedSuccessfully = case(TotalCountACHClearingSuccessPast10AM !=0, 1-(TotalCountACHClearingSuccessPast10AM/todouble(TotalACHClearingSuccessCount))\r\n ,TotalACHClearingFailureCount !=0, 1-(TotalACHClearingFailureCount/todouble(TotalACHClearingSuccessCount)), TotalACHClearingSuccessCount ==0,0.0, \r\n 1.0)", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Hub-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Hub-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "hide": false, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "FS-BAI2-003 -%ACH Clearing", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 0, "y": 15 }, "id": 19, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n | where $__timeFilter(timestamp)\r\n | where isnotempty(customDimensions)\r\n //filter for custom events for StdCheckClearing Success and Exception\r\n | where name has \"Bai2Listener:StdCheckClearing:Success\" or name has \"Bai2Listener:StdCheckClearing:Exception\"\r\n | extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n | summarize \r\n //Get Total Std Check Clearing Failure(Exception) Count\r\n TotalStdCheckClearingFailureCount = countif(name has \"Bai2Listener:StdCheckClearing:Exception\"),\r\n //Get Total Std Check Clearing Success Count \r\n TotalStdCheckClearingSuccessCount = countif(name has \"Bai2Listener:StdCheckClearing:Success\"),\r\n //Get Total Std Check Clearing Success and Check Clearing requests are processed by 10AM count\r\n TotalCountStdCheckClearingSuccessPast10AM = countif(name has \"Bai2Listener:StdCheckClearing:Success\" and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 10) \r\n | project \r\n //Case condition will check \r\n\t//Check for Count of StdCheck Clearing Success Past 10AM not 0 \r\n //then get Percent Std Check Completed Successfully\r\n //check TotalStdCheckClearingFailureCount not 0 then Total Std Check Clearing Failure Count\r\n PercentStdCheckCompletedSuccessfully = case(TotalCountStdCheckClearingSuccessPast10AM !=0,1-(TotalCountStdCheckClearingSuccessPast10AM/todouble(TotalStdCheckClearingSuccessCount)),\r\n TotalStdCheckClearingFailureCount !=0,1-(TotalStdCheckClearingFailureCount/todouble(TotalStdCheckClearingSuccessCount)),TotalStdCheckClearingSuccessCount ==0, 0.0,\r\n 1.0)\r\n \r\n //iif(TotalStdCheckClearingFailureCount ==0 and TotalCountStdCheckClearingSuccessPast10AM ==0, 1.0,1-(TotalCountStdCheckClearingSuccessPast10AM/todouble(TotalStdCheckClearingSuccessCount)))", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Hub-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Hub-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "hide": false, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "FS-BAI2-003 - %Check Clearing", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "percentage", "steps": [ { "color": "red", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Percentage" }, "properties": [ { "id": "unit", "value": "percentunit" } ] } ] }, "gridPos": { "h": 3, "w": 6, "x": 6, "y": 15 }, "id": 35, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n| extend d = parse_json(customDimensions)\r\n| where $__timeFilter(timestamp)\r\n //filter custom events for Remittance 'Apd Credit','IA Apd Credit' and 'Apd Debit'\r\n| where name == \"Remittance:Apd Credit\" or name == \"Remittance:IA Apd Credit\" or name == \"Remittance:Apd Debit\" \r\n//filter custom events for 'Payment Identified', 'ayment Remittance Sent' and 'Missed'\r\nand ( d.Message contains \"Payment Identified\" or d.Message contains \"Payment Remittance Sent\" or d.Message contains \"Missed\")\r\n|summarize \r\n//Get email sent Total for Payment Remittance Sent\r\nTotalEmailSent = countif(d.Message contains \"Payment Remittance Sent\"),\r\n//Get Total for remittance emails are sent successfully delivered to customers by 9AM\r\n CountPast9AMEmailSent = countif( d.Message contains \"Payment Remittance Sent\" and datetime_part(\"hour\", timestamp) > 9)\r\n| project \r\n//Case condition will check for Count of Past9AMEmailSent 09AM not 0 \r\n//then get Percent remittance emails are sent successfully delivered to customers by 9AM\r\nPercentage = case(CountPast9AMEmailSent != 0, 1-(CountPast9AMEmailSent/todouble(TotalEmailSent)),TotalEmailSent == 0, 0.0,1.0)", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Legacy-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Legacy-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "FS-Remittance-001 - %RemittanceEmails", "type": "stat" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 18 }, "id": 25, "panels": [], "title": "Virtual Card", "type": "row" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 0, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 5, "x": 0, "y": 19 }, "id": 28, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents \r\n | where isnotempty(customDimensions) \r\n //filter for custom events for VCC cards\r\n | where name has \"HubCardCreation:CardQueued\" or name has \"HubVcRouting:cardCreated\" \r\n | extend d = parse_json(customDimensions) \r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(d.EventTime),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\") \r\n | summarize \r\n\t\t\t//Get total count of VCC cards created\r\n\t\t\tTotalCardCreatedCount = sumif(toint(itemCount),name has \"HubVcRouting:cardCreated\"), \r\n //Get total count of VCC cards queued\r\n\t\t\tTotalCardQueuedCount = sumif(toint(customDimensions.ProcessedTotal),name has \"HubCardCreation:CardQueued\"),\r\n\t\t\t//Get total count of cards that got sent past 12 AM EST\r\n\t\t\tTotalCountPast12AMCardCreated = sumif(toint(itemCount),name has \"HubVcRouting:cardCreated\" and datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 24)\r\n //Case condition checks for \r\n //Checks for VCC cards got sent out post 12 AM EST\r\n //Checks for Card queued count is 0, which means no cards are stuck\r\n //Checks for count of card sent is not 0\r\n //Checks for VCC Queued < Card Created count \r\n | project PercentCompletedAfter12AM= case(TotalCardCreatedCount ==0, 0.0, TotalCardQueuedCount==0,1.0,TotalCardQueuedCount < TotalCardCreatedCount,1.0, TotalCountPast12AMCardCreated !=0, 1-(TotalCountPast12AMCardCreated/todouble(TotalCardCreatedCount)), 1.0)", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Hub-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Hub-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "VCS-Creation-001 - %Card Created", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 0, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 7, "x": 5, "y": 19 }, "id": 36, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n | where $__timeFilter(timestamp) \r\n //filter for custom events for WEX transactions\r\n | where isnotempty(customDimensions) and name has \"WexETLService:FileProcessed\"\r\n | extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(timestamp),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n | summarize \r\n\t//Get the count of expected amount\r\n\tTotalExpectedTransaction = sum(toint(customDimensions[\"ExpectedTransactionCount\"])),\r\n\t//Get the count of actual amount processed\r\n\tTotalActualTransaction = sum(toint(customDimensions[\"ActualTransactionCount\"])),\r\n\t//Get the count of actual amount processed after 3 am EST\r\n TotalTransactionPast3AM = sumif(toint(customDimensions[\"ActualTransactionCount\"]), datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 3 )\r\n //Case condition checks for \r\n //Checks for transaction completed after 3 AM\r\n //Check of acutal and expected amounts are matching\r\n //Check for count of actual and expected amount is not 0 \r\n | project PercentWEXTransaction = case(TotalTransactionPast3AM != 0, 1-(TotalTransactionPast3AM/todouble(TotalActualTransaction)),TotalActualTransaction != TotalExpectedTransaction,1-(TotalActualTransaction/todouble(TotalExpectedTransaction)), TotalActualTransaction ==0,0.0,TotalExpectedTransaction ==0,0.0, 1.0)\r\n ", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Hub-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Hub-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "VCS-WEXCreation-001 - %WEXTransactions", "type": "stat" }, { "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 0, "mappings": [ { "options": { "from": 0.5, "result": { "color": "green", "index": 0 }, "to": 1 }, "type": "range" }, { "options": { "from": 0, "result": { "color": "red", "index": 1 }, "to": 0.5 }, "type": "range" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 12, "x": 0, "y": 22 }, "id": 37, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": true }, "textMode": "auto" }, "pluginVersion": "9.3.2", "targets": [ { "azureLogAnalytics": { "query": "customEvents\r\n //| where $__timeFilter(timestamp) \r\n //filter for custom events for Remittance emails\r\n | where name has \"HubCardDistribution:ProcessingSendMessage\" or name has \"HubCardDistribution:ProcessingSendingMessage\"\r\n | extend d = parse_json(customDimensions)\r\n //Converting the UTC datetime to EST datetime\r\n | extend CustomDimensionsEventDateTime = format_datetime(datetime_utc_to_local(todatetime(timestamp),\"US/Eastern\"), \"MM/dd/yyyy hh:mm:ss tt\")\r\n | summarize \r\n\t//Get count of email queued for sending\r\n\tTotalEmailsQueued = sumif(toint(itemCount),name has \"HubCardDistribution:ProcessingSendingMessage\"),\r\n //Get count of email sent\r\n\tTotalEmailSent = sumif(toint(itemCount),name has \"HubCardDistribution:ProcessingSendMessage\"),\r\n\t//Get count of email sent out after 2 pm EST\r\n TotalEmailSentPast2PM = sumif(toint(name has \"HubCardDistribution:ProcessingSendMessage\"), datetime_part(\"hour\", todatetime(CustomDimensionsEventDateTime)) > 14 )\r\n\t//case conditions checks for\r\n\t//Checks for email sent out after 2 pm EST\r\n\t//Checks for count of email sent and email queued are same\r\n\t//Checks for count of email queued > sent is same calculate the difference\r\n | project PercentEmailSent = case(TotalEmailSentPast2PM != 0,1-(TotalEmailSentPast2PM/todouble(TotalEmailSent)),TotalEmailsQueued > TotalEmailSent,1-(TotalEmailSent/todouble(TotalEmailsQueued)),TotalEmailsQueued==0,0.0,TotalEmailSent==0,0.0, 1.0)", "resource": "/subscriptions/656ab744-0db9-48ac-b23a-4b3737472e94/resourceGroups/AX-Ae1-Payment-Hub-RG/providers/Microsoft.Insights/components/AX-Ae1-Pr-Payment-Hub-INSIGHTS" }, "azureMonitor": { "allowedTimeGrainsMs": [], "timeGrain": "auto" }, "datasource": { "type": "grafana-azure-monitor-datasource", "uid": "${DS_AZURE_MONITOR - SAAS_INFRA}" }, "queryType": "Azure Log Analytics", "refId": "A" } ], "title": "VCS-Delivery-002 - %DeliveryRemittanceEmail", "type": "stat" } ], "refresh": false, "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [] }, "time": { "from": "now-30d", "to": "now" }, "timepicker": {}, "timezone": "America/New_York", "title": "Avid Pay SLO Monitoring", "uid": "iOxGpJx4k", "version": 311, "weekStart": "", "gnetId": 18548, "description": "Testing import AvidPay SLO Monitoring dashboard" }
2023-04-17 17:58:03 UTC
2023-04-17 17:59:28 UTC
0
0
17,234
1
377,006
26,470
0
medidores
MEDIDORES
test 1
2022-10-20 13:25:50 UTC
2022-10-20 13:27:30 UTC
0
null
0
0
null
null
0
0
26,470
1
17,234
1
0
null
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"color": { "mode": "thresholds" }, "decimals": 1, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [] }, "gridPos": { "h": 5, "w": 9, "x": 0, "y": 4 }, "id": 15, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "value" }, "pluginVersion": "9.1.3", "targets": [ { "datasource": { "type": "mysql", "uid": "${DS_MYSQL}" }, "format": "table", "group": [], "metricColumn": "none", "rawQuery": true, "rawSql": "SELECT eaf FROM ${medidores:raw}a", "refId": "A", "select": [ [ { "params": [ "id" ], "type": "column" } ] ], "table": "c1ea", "timeColumn": "FH_T", "timeColumnType": "timestamp", "where": [ { "name": "$__timeFilter", "params": [], "type": "macro" } ] } ], "title": "ENERGIA ACTIVA [ kWh ]", "type": "stat" }, { "datasource": { "type": "mysql", "uid": "${DS_MYSQL}" }, 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"", "axisPlacement": "auto", "axisSoftMin": 0, "fillOpacity": 80, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineWidth": 1, "scaleDistribution": { "type": "linear" } }, "decimals": 2, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 14, "w": 24, "x": 0, "y": 104 }, "hideTimeOverride": true, "id": 66, "interval": "1h", "options": { "barRadius": 0, "barWidth": 0.97, "groupWidth": 0.7, "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": false }, "orientation": "vertical", "showValue": "always", "stacking": "none", "tooltip": { "mode": "multi", "sort": "none" }, "xField": "AÑO", "xTickLabelRotation": -45, "xTickLabelSpacing": 0 }, "pluginVersion": "8.3.5", "targets": [ { "datasource": { "type": "mysql", "uid": "${DS_MYSQL}" }, "format": "table", "group": [], "metricColumn": "none", "rawQuery": true, "rawSql": "SELECT\n YYYY AS 'AÑO',\n MAX(ENERGIA_ACTIVA)-MIN(ENERGIA_ACTIVA) AS 'ENERGIA ACTIVA'\nFROM ${medidores:raw}e\nWHERE ENERGIA_ACTIVA > 0\nGROUP BY YYYY\nORDER BY YYYY DESC\nLIMIT 24", "refId": "A", "select": [ [ { "params": [ "id" ], "type": "column" } ] ], "table": "c1fase1h", "timeColumn": "FH_T", "timeColumnType": "timestamp", "where": [ { "name": "$__timeFilter", "params": [], "type": "macro" } ] } ], "timeFrom": "24h", "title": "ENERGIA ACTIVA x AÑO [kWh]", "transformations": [], "type": "barchart" } ], "refresh": "5s", "schemaVersion": 37, "style": "dark", "tags": [ "General" ], "templating": { "list": [ { "current": {}, "datasource": { "type": "mysql", "uid": "${DS_MYSQL}" }, "definition": "SELECT DISTINCT area FROM pmstations1", "hide": 0, "includeAll": false, "label": "AREA", "multi": false, "name": "area", "options": [], "query": "SELECT DISTINCT area FROM pmstations1", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "mysql", "uid": "${DS_MYSQL}" }, "definition": "SELECT nombre FROM pmstations1 WHERE area='$area'", "hide": 0, "includeAll": false, "label": "MAQUINA", "multi": false, "name": "pmstations", "options": [], "query": "SELECT nombre FROM pmstations1 WHERE area='$area'", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "mysql", "uid": "${DS_MYSQL}" }, "definition": "SELECT station FROM pmstations1 WHERE nombre='$pmstations'", "hide": 2, "includeAll": false, "label": "", "multi": false, "name": "medidores", "options": [], "query": "SELECT station FROM pmstations1 WHERE nombre='$pmstations'", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "MEDIDORES", "uid": "dHQcnxe7k", "version": 33, "weekStart": "", "gnetId": 17234, "description": "test 1" }
2022-10-20 13:25:50 UTC
2022-10-20 13:27:30 UTC
0
0
17,377
1
740,566
26,705
0
stable-diffusion-monitor
Stable Diffusion Monitor
A dashboard for monitoring a Windows Desktop/Gaming PC. Using Prometheus, windows_exporter, and OhmGraphite.
2022-11-09 03:47:05 UTC
2022-11-09 03:56:23 UTC
0
This dashboard uses the following exporters: windows_exporter: https://github.com/prometheus-community/windows_exporter OhmGraphite: https://github.com/nickbabcock/OhmGraphite Note: instances values are set manually due to same host, different ports.
0
0
Other
null
0
0
26,705
1
17,377
1
0
A dashboard for monitoring a Windows Desktop/Gaming PC. Using Prometheus, wmi_exporter, and OhmGraphite.
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"intervalFactor": 1, "legendFormat": "Silicon Power NVMe (H:)", "range": true, "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "ohm_hdd_celsius{hardware=\"Samsung SSD 860 EVO 1TB\"}", "format": "time_series", "hide": false, "intervalFactor": 1, "legendFormat": "Samsung SSD (E:)", "range": true, "refId": "C" } ], "title": "SSD Temperature", "type": "timeseries" }, { "collapsed": false, "datasource": { "type": "prometheus", "uid": "7_8f-5v4z" }, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 61 }, "id": 56, "panels": [], "targets": [ { "datasource": { "type": "prometheus", "uid": "7_8f-5v4z" }, "refId": "A" } ], "title": "Extended CPU Info", "type": "row" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, 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"time_series", "intervalFactor": 1, "legendFormat": "CPU Package", "range": true, "refId": "A" } ], "title": "Power", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "rothz" }, "overrides": [] }, "gridPos": { "h": 10, "w": 11, "x": 10, "y": 62 }, "id": 72, 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"intervalFactor": 1, "legendFormat": "OS Processes", "range": true, "refId": "A" } ], "title": "# of Processes", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 9, "w": 7, "x": 7, "y": 73 }, "id": 66, "links": [], "options": { "legend": { "calcs": [ "last" ], "displayMode": "table", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "pluginVersion": "9.2.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(windows_service_state{state=\"running\"})", "format": "time_series", "intervalFactor": 1, "legendFormat": "State: Running", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(windows_service_state{state=\"stopped\"})", "format": "time_series", "hide": false, "intervalFactor": 1, "legendFormat": "State: Stopped", "range": true, "refId": "B" } ], "title": "Services", "type": "timeseries" } ], "refresh": "5s", "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [ { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(wmi_system_system_up_time, instance)", "hide": 0, "includeAll": false, "label": "Host", "multi": false, "name": "instance", "options": [], "query": { "query": "label_values(wmi_system_system_up_time, instance)", "refId": "Prometheus-instance-Variable-Query" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "browser", "title": "Stable Diffusion Monitor", "uid": "woRrCAriz", "version": 5, "weekStart": "" }
2022-11-09 03:47:05 UTC
2022-11-09 03:56:22 UTC
0
0
17,547
1
638,729
26,941
0
opnsense-ids-ips
OPNsense & IDS/IPS
Dashboards to monitor Firewall Rules & IDS/IPS potential Attacks.
2022-12-06 14:26:22 UTC
2022-12-06 14:49:07 UTC
0
## Monitoring OPNsense with Loki Prerequisites to monitor OPNsense Firewall- & IDS/IPS Logs with Loki: - **Loadbalancer** to create virtual IP and to publish syslog ports (for example [MetalLB](https://metallb.universe.tf/)) - **Promtail or Grafana Agent** to listen on these ports and ship the logs to Loki - **Loki** to index the logs - **Grafana** to visualize the logs (datasource Loki configured) - **OPNsense** syslog target configured ### Important! The Grafana Dashboard only works when your OPNsense IDS/IPS logs has a label `job: syslog-ids` and all other Firewall logs has a label `job: syslog`. Promtail Helm chart example values: ```yaml config: lokiAddress: "http://loki-gateway/loki/api/v1/push" snippets: extraScrapeConfigs: | # Add an additional scrape config for syslog - job_name: syslog syslog: listen_address: 0.0.0.0:514 idle_timeout: 60s label_structured_data: yes labels: job: "syslog" relabel_configs: - source_labels: ['__syslog_message_hostname'] target_label: 'host' # Add an additional scrape config for OPNsense IDS alerts shipped over syslog - job_name: syslog-ids syslog: listen_address: 0.0.0.0:1514 idle_timeout: 60s label_structured_data: yes labels: job: "syslog-ids" relabel_configs: - source_labels: ['__syslog_message_hostname'] target_label: 'host' extraPorts: # Add an additional port for syslog # serviceType "LoadBalancer" only works if you have deployed an Loadbalancer for example MetalLB syslog: name: tcp-syslog containerPort: 514 protocol: TCP service: type: LoadBalancer #clusterIP: null port: 514 #externalIPs: [] #nodePort: 32682 #annotations: {} #labels: {} #loadBalancerIP: null #loadBalancerSourceRanges: [] #externalTrafficPolicy: null # Add an additional port for OPNsense IDS alerts shipped over syslog # serviceType "LoadBalancer" only works if you have deployed an Loadbalancer for example MetalLB syslog-ids: name: tcp-syslog-ids containerPort: 1514 protocol: TCP service: type: LoadBalancer #clusterIP: null port: 1514 #externalIPs: [] #nodePort: 32682 #annotations: {} #labels: {} #loadBalancerIP: null #loadBalancerSourceRanges: [] #externalTrafficPolicy: null ```
79,407
79,408
Other
null
0
0
26,941
1
17,547
1
0
null
{ "__inputs": [ { "name": "DS_LOKI", "label": "loki", "description": "OPNsense IDS/IPS logs must have label `job: syslog-ids` and all other Firewall logs must habe label `job: syslog`.", "type": "datasource", "pluginId": "loki", "pluginName": "Loki" } ], "__elements": {}, "__requires": [ { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.2.4" }, { "type": "panel", "id": "logs", "name": "Logs", "version": "" }, { "type": "datasource", "id": "loki", "name": "Loki", "version": "1.0.0" }, { "type": "panel", "id": "piechart", "name": "Pie chart", "version": "" }, { "type": "panel", "id": "stat", "name": "Stat", "version": "" }, { "type": "panel", "id": "table", "name": "Table", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "grafana", "uid": "-- Grafana --" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "links": [], "liveNow": false, "panels": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "gridPos": { "h": 1, "w": 12, "x": 0, "y": 0 }, "id": 23, "options": { "dedupStrategy": "none", "enableLogDetails": true, "prettifyLogMessage": false, "showCommonLabels": false, "showLabels": false, "showTime": false, "sortOrder": "Descending", "wrapLogMessage": false }, "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "{job=\"syslog\", host=\"$host\"}\n|~ \"block|pass\" # filter to get only firewall logs\n!= \"icmp\" # filter only not ICMP packages\n| pattern `<_>,<_>,<_>,<RuleID>,<Interface>,<Reason>,<Action>,<Direction>,<IPVersion>,<_>,<_>,<TTL>,<ID>,<Offset>,<IPFlags>,<ProtocolNr>,<Protocol>,<Length>,<SourceIP>,<DestinationIP>,<SourcePort>,<DestinationPort>,<_>,<TCPFlags>,<Seq>,<_>,<_>`", "hide": false, "queryType": "range", "refId": "A" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "{job=\"syslog\", host=\"$host\"}\n|~ \"block|pass\" # filter to get only firewall logs\n|= \"icmp\" # filter only ICMP packages\n| pattern `<_>,<_>,<_>,<RuleID>,<Interface>,<Reason>,<Action>,<Direction>,<IPVersion>,<_>,<_>,<TTL>,<ID>,<Offset>,<IPFlags>,<ProtocolNr>,<Protocol>,<Length>,<SourceIP>,<DestinationIP>,<_>`", "hide": false, "queryType": "range", "refId": "B" } ], "title": "Firewall Base Search (do not delete)", "transformations": [], "type": "logs" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "gridPos": { "h": 1, "w": 12, "x": 12, "y": 0 }, "id": 20, "options": { "dedupStrategy": "none", "enableLogDetails": true, "prettifyLogMessage": false, "showCommonLabels": false, "showLabels": false, "showTime": false, "sortOrder": "Descending", "wrapLogMessage": false }, "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "{job=\"syslog-ids\",host=\"$host\"} != \"Drop\" |~ \"$searchregex\"\n|= \"Priority\" # filter to exclude not IDS/IPS events\n| pattern `[<_>:<ruleId>:<_>] <rule> [Classification: <classification>] [Priority: <priority>] {<protocol>} <sourceIP>:<sourcePort> -> <destinationIP>:<destinationPort>`\n| protocol!~\"IPv6.*\" ", "hide": false, "queryType": "range", "refId": "IPv4-NoDrop-Query" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "{job=\"syslog-ids\",host=\"$host\"} |= \"Drop\" |~ \"$searchregex\"\n|= \"Priority\" # filter to exclude not IDS/IPS events\n| pattern `[<action>] [<_>:<ruleId>:<_>] <rule> [Classification: <classification>] [Priority: <priority>] {<protocol>} <sourceIP>:<sourcePort> -> <destinationIP>:<destinationPort>`\n| protocol!~\"IPv6.*\" ", "queryType": "range", "refId": "IPv4-Drop-Query" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "{job=\"syslog-ids\",host=\"$host\"} != \"Drop\" |~ \"$searchregex\"\n|= \"Priority\" # filter to exclude not IDS/IPS events\n| pattern `[<action>] [<_>:<ruleId>:<_>] <rule> [Classification: <classification>] [Priority: <priority>] {<protocol>} <_>`\n| protocol=~\"IPv6.*\" # filter only IPv6 logs because pattern() cannot easy handle IPv6 that's why the following regex is used\n| regexp `(?P<sourceIP>(([0-9a-fA-F]{1,4}:){7,7}[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,7}:|([0-9a-fA-F]{1,4}:){1,6}:[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,5}(:[0-9a-fA-F]{1,4}){1,2}|([0-9a-fA-F]{1,4}:){1,4}(:[0-9a-fA-F]{1,4}){1,3}|([0-9a-fA-F]{1,4}:){1,3}(:[0-9a-fA-F]{1,4}){1,4}|([0-9a-fA-F]{1,4}:){1,2}(:[0-9a-fA-F]{1,4}){1,5}|[0-9a-fA-F]{1,4}:((:[0-9a-fA-F]{1,4}){1,6})|:((:[0-9a-fA-F]{1,4}){1,7}|:)|fe80:(:[0-9a-fA-F]{0,4}){0,4}%[0-9a-zA-Z]{1,}|::(ffff(:0{1,4}){0,1}:){0,1}((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])|([0-9a-fA-F]{1,4}:){1,4}:((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9]))):(?P<sourcePort>\\d+) -> (?P<destinationIP>(([0-9a-fA-F]{1,4}:){7,7}[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,7}:|([0-9a-fA-F]{1,4}:){1,6}:[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,5}(:[0-9a-fA-F]{1,4}){1,2}|([0-9a-fA-F]{1,4}:){1,4}(:[0-9a-fA-F]{1,4}){1,3}|([0-9a-fA-F]{1,4}:){1,3}(:[0-9a-fA-F]{1,4}){1,4}|([0-9a-fA-F]{1,4}:){1,2}(:[0-9a-fA-F]{1,4}){1,5}|[0-9a-fA-F]{1,4}:((:[0-9a-fA-F]{1,4}){1,6})|:((:[0-9a-fA-F]{1,4}){1,7}|:)|fe80:(:[0-9a-fA-F]{0,4}){0,4}%[0-9a-zA-Z]{1,}|::(ffff(:0{1,4}){0,1}:){0,1}((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])|([0-9a-fA-F]{1,4}:){1,4}:((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9]))):(?P<destinationPort>\\d+)`\n| label_format sourceIP=`{{ regexReplaceAllLiteral \"\\\\b(?:0+:){2,}\" .sourceIP \":\"}}`\n| label_format sourceIP=`{{ regexReplaceAllLiteral \"(:0+){1}\" .sourceIP \":\"}}`\n| label_format destinationIP=`{{ regexReplaceAllLiteral \"\\\\b(?:0+:){2,}\" .destinationIP \":\"}}`\n| label_format destinationIP=`{{ regexReplaceAllLiteral \"(:0+){1}\" .destinationIP \":\"}}`", "hide": false, "queryType": "range", "refId": "IPv6-NoDrop-Query" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "{job=\"syslog-ids\",host=\"$host\"} |= \"Drop\" |~ \"$searchregex\"\n|= \"Priority\" # filter to exclude not IDS/IPS events\n| pattern `[<action>] [<_>:<ruleId>:<_>] <rule> [Classification: <classification>] [Priority: <priority>] {<protocol>} <_>`\n| protocol=~\"IPv6.*\" # filter only IPv6 logs because pattern() cannot easy handle IPv6 that's why the following regex is used\n| regexp `(?P<sourceIP>(([0-9a-fA-F]{1,4}:){7,7}[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,7}:|([0-9a-fA-F]{1,4}:){1,6}:[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,5}(:[0-9a-fA-F]{1,4}){1,2}|([0-9a-fA-F]{1,4}:){1,4}(:[0-9a-fA-F]{1,4}){1,3}|([0-9a-fA-F]{1,4}:){1,3}(:[0-9a-fA-F]{1,4}){1,4}|([0-9a-fA-F]{1,4}:){1,2}(:[0-9a-fA-F]{1,4}){1,5}|[0-9a-fA-F]{1,4}:((:[0-9a-fA-F]{1,4}){1,6})|:((:[0-9a-fA-F]{1,4}){1,7}|:)|fe80:(:[0-9a-fA-F]{0,4}){0,4}%[0-9a-zA-Z]{1,}|::(ffff(:0{1,4}){0,1}:){0,1}((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])|([0-9a-fA-F]{1,4}:){1,4}:((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9]))):(?P<sourcePort>\\d+) -> (?P<destinationIP>(([0-9a-fA-F]{1,4}:){7,7}[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,7}:|([0-9a-fA-F]{1,4}:){1,6}:[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,5}(:[0-9a-fA-F]{1,4}){1,2}|([0-9a-fA-F]{1,4}:){1,4}(:[0-9a-fA-F]{1,4}){1,3}|([0-9a-fA-F]{1,4}:){1,3}(:[0-9a-fA-F]{1,4}){1,4}|([0-9a-fA-F]{1,4}:){1,2}(:[0-9a-fA-F]{1,4}){1,5}|[0-9a-fA-F]{1,4}:((:[0-9a-fA-F]{1,4}){1,6})|:((:[0-9a-fA-F]{1,4}){1,7}|:)|fe80:(:[0-9a-fA-F]{0,4}){0,4}%[0-9a-zA-Z]{1,}|::(ffff(:0{1,4}){0,1}:){0,1}((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])|([0-9a-fA-F]{1,4}:){1,4}:((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9]))):(?P<destinationPort>\\d+)`\n| label_format sourceIP=`{{ regexReplaceAllLiteral \"\\\\b(?:0+:){2,}\" .sourceIP \":\"}}`\n| label_format sourceIP=`{{ regexReplaceAllLiteral \"(:0+){1}\" .sourceIP \":\"}}`\n| label_format destinationIP=`{{ regexReplaceAllLiteral \"\\\\b(?:0+:){2,}\" .destinationIP \":\"}}`\n| label_format destinationIP=`{{ regexReplaceAllLiteral \"(:0+){1}\" .destinationIP \":\"}}`", "hide": false, "queryType": "range", "refId": "IPv6-Drop-Query" } ], "title": "IDS Base Search (do not delete)", "transformations": [], "type": "logs" }, { "gridPos": { "h": 1, "w": 24, "x": 0, "y": 1 }, "id": 27, "title": "Overview", "type": "row" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 4, "w": 12, "x": 0, "y": 2 }, "id": 13, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.2.4", "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "count_over_time({job=\"syslog\",host=\"$host\"}[$__range])", "queryType": "range", "refId": "A" } ], "title": "Firewall Logs", "type": "stat" }, { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 4, "w": 12, "x": 12, "y": 2 }, "id": 19, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.2.4", "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "count_over_time({job=\"syslog-ids\", host=\"$host\"} |~ \"$searchregex\" [$__range])", "queryType": "range", "refId": "A" } ], "title": "IDS Events", "type": "stat" }, { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "hideFrom": { "legend": false, "tooltip": false, "viz": false } }, "mappings": [] }, "overrides": [ { "matcher": { "id": "byName", "options": "block" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 8, "w": 4, "x": 0, "y": 6 }, "id": 21, "options": { "displayLabels": [ "name" ], "legend": { "displayMode": "table", "placement": "right", "showLegend": true, "values": [ "value", "percent" ] }, "pieType": "pie", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "values": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.1.7", "targets": [ { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "panelId": 23, "refId": "A" } ], "title": "Firewall Connections", "transformations": [ { "id": "merge", "options": {} }, { "id": "extractFields", "options": { "format": "kvp", "replace": false, "source": "labels" } }, { "id": "groupBy", "options": { "fields": { "Action": { "aggregations": [], "operation": "groupby" }, "Line": { "aggregations": [ "count" ], "operation": "aggregate" }, "destinationIP": { "aggregations": [], "operation": "groupby" }, "sourceIP": { "aggregations": [] }, "sourcePort": { "aggregations": [] } } } } ], "type": "piechart" }, { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "hideFrom": { "legend": false, "tooltip": false, "viz": false } }, "mappings": [] }, "overrides": [ { "matcher": { "id": "byName", "options": "block" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 8, "w": 4, "x": 4, "y": 6 }, "id": 30, "options": { "displayLabels": [ "name" ], "legend": { "displayMode": "table", "placement": "right", "showLegend": false, "values": [ "value", "percent" ] }, "pieType": "pie", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "values": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.1.7", "targets": [ { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "panelId": 23, "refId": "A" } ], "title": "Firewall Connections", "transformations": [ { "id": "merge", "options": {} }, { "id": "extractFields", "options": { "format": "kvp", "replace": false, "source": "labels" } }, { "disabled": true, "id": "filterByValue", "options": { "filters": [ { "config": { "id": "equal", "options": { "value": "Drop" } }, "fieldName": "action" } ], "match": "any", "type": "include" } }, { "id": "groupBy", "options": { "fields": { "Action": { "aggregations": [] }, "Direction": { "aggregations": [], "operation": "groupby" }, "Line": { "aggregations": [ "count" ], "operation": "aggregate" }, "destinationIP": { "aggregations": [], "operation": "groupby" }, "sourceIP": { "aggregations": [] }, "sourcePort": { "aggregations": [] } } } } ], "type": "piechart" }, { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "description": "Top 10 dropped inbound Firewall connections.", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "hideFrom": { "legend": false, "tooltip": false, "viz": false } }, "mappings": [] }, "overrides": [ { "matcher": { "id": "byName", "options": "block" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 8, "w": 4, "x": 8, "y": 6 }, "id": 31, "options": { "displayLabels": [ "name" ], "legend": { "displayMode": "table", "placement": "right", "showLegend": false, "values": [ "value", "percent" ] }, "pieType": "pie", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "limit": 10, "values": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.1.7", "targets": [ { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "panelId": 23, "refId": "A" } ], "title": "Firewall Connections by DestinationPort", "transformations": [ { "id": "merge", "options": {} }, { "id": "extractFields", "options": { "format": "kvp", "replace": false, "source": "labels" } }, { "id": "filterByValue", "options": { "filters": [ { "config": { "id": "equal", "options": { "value": "in" } }, "fieldName": "Direction" }, { "config": { "id": "equal", "options": { "value": "block" } }, "fieldName": "Action" }, { "config": { "id": "isNotNull", "options": {} }, "fieldName": "DestinationPort" } ], "match": "all", "type": "include" } }, { "id": "groupBy", "options": { "fields": { "Action": { "aggregations": [] }, "DestinationPort": { "aggregations": [], "operation": "groupby" }, "Direction": { "aggregations": [] }, "Line": { "aggregations": [ "count" ], "operation": "aggregate" }, "SourceIP": { "aggregations": [] }, "destinationIP": { "aggregations": [], "operation": "groupby" }, "sourceIP": { "aggregations": [] }, "sourcePort": { "aggregations": [] } } } }, { "id": "sortBy", "options": { "fields": {}, "sort": [ { "desc": true, "field": "Line (count)" } ] } } ], "type": "piechart" }, { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "hideFrom": { "legend": false, "tooltip": false, "viz": false } }, "mappings": [ { "options": { "match": "empty", "result": { "index": 0, "text": "Allow" } }, "type": "special" } ] }, "overrides": [ { "matcher": { "id": "byName", "options": "Drop" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 8, "w": 4, "x": 12, "y": 6 }, "id": 24, "options": { "displayLabels": [ "name" ], "legend": { "displayMode": "table", "placement": "right", "showLegend": false }, "pieType": "pie", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.1.7", "targets": [ { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "panelId": 20, "refId": "A" } ], "title": "Potential Attacks", "transformations": [ { "id": "merge", "options": {} }, { "id": "extractFields", "options": { "format": "kvp", "replace": false, "source": "labels" } }, { "disabled": true, "id": "filterByValue", "options": { "filters": [ { "config": { "id": "isNull", "options": {} }, "fieldName": "action" }, { "config": { "id": "equal", "options": { "value": "drop" } }, "fieldName": "action" } ], "match": "any", "type": "include" } }, { "id": "groupBy", "options": { "fields": { "Count": { "aggregations": [] }, "Line": { "aggregations": [ "count" ], "operation": "aggregate" }, "action": { "aggregations": [], "operation": "groupby" }, "destinationIP": { "aggregations": [] }, "sourceIP": { "aggregations": [] }, "sourcePort": { "aggregations": [] } } } } ], "type": "piechart" }, { "datasource": { "type": "datasource", "uid": "-- Dashboard --" }, "description": "Top 5 dropped 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"wrapLogMessage": false }, "targets": [ { "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "editorMode": "code", "expr": "{job=\"syslog-ids\",host=\"$host\"} \n|~ \"$searchregex\"\n!~ \"\\\\[\\\\d:\\\\d+:\\\\d\\\\]\"", "queryType": "range", "refId": "A" } ], "title": "Other IDS related Logs", "type": "logs" } ], "title": "Others", "type": "row" } ], "refresh": false, "schemaVersion": 37, "style": "dark", "tags": [ "Security", "Firewall", "IDS/IPS", "OPNsense" ], "templating": { "list": [ { "current": {}, "datasource": { "type": "loki", "uid": "${DS_LOKI}" }, "definition": "label_values({job=\"syslog-ids\"}, host)", "hide": 0, "includeAll": false, "label": "Host", "multi": false, "name": "host", "options": [], "query": "label_values({job=\"syslog-ids\"}, host)", "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "type": "query" }, { "current": { "selected": false, "text": "", "value": "" }, "hide": 0, "label": "Search-Regex", "name": "searchregex", "options": [ { "selected": true, "text": "", "value": "" } ], "query": "", "skipUrlSync": false, "type": "textbox" } ] }, "time": { "from": "now-24h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "OPNsense & IDS/IPS", "uid": "5aBjqZKVk", "version": 1, "weekStart": "", "gnetId": 17547, "description": "Dashboards to monitor Firewall Rules & IDS/IPS potential Attacks." }
2022-12-06 14:26:22 UTC
2022-12-06 14:49:07 UTC
0
0
17,288
1
724,048
26,578
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kubernetes-compute-resources-namespace-workloads
EKS Workloads by Namespace
Kubernetes / Compute Resources / Namespace (Workloads)
2022-10-28 09:41:45 UTC
2022-10-28 09:44:25 UTC
0
This dashboard is part of the [AWS Observability Accelerator](https://aws.amazon.com/blogs/mt/announcing-aws-observability-accelerator-to-configure-comprehensive-observability-for-amazon-eks/) and enables you to monitor EKS workloads by namespace based on Prometheus metrics. For an automated end-to-end setup based on Terraform as well as to provide feedback or report an issue with the dashboard, please visit the [AWS Observability Accelerator GitHub repo](https://github.com/aws-observability/terraform-aws-observability-accelerator/issues).
0
0
Other
null
0
0
26,578
1
17,288
1
0
null
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"type": "number", "unit": "short" }, { "alias": "Workload Type", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "workload_type", "thresholds": [], "type": "number", "unit": "short" }, { "alias": "", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "pattern": "/.*/", "thresholds": [], "type": "string", "unit": "short" } ], "targets": [ { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "count(namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}) by (workload, workload_type)", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "A", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n node_namespace_pod_container:container_cpu_usage_seconds_total:sum_irate{cluster=\"$cluster\", namespace=\"$namespace\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "B", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n kube_pod_container_resource_requests{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"cpu\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "C", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n node_namespace_pod_container:container_cpu_usage_seconds_total:sum_irate{cluster=\"$cluster\", namespace=\"$namespace\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n/sum(\n kube_pod_container_resource_requests{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"cpu\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "D", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n kube_pod_container_resource_limits{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"cpu\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "E", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n node_namespace_pod_container:container_cpu_usage_seconds_total:sum_irate{cluster=\"$cluster\", namespace=\"$namespace\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n/sum(\n kube_pod_container_resource_limits{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"cpu\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "F", "step": 10 } ], "thresholds": [], "title": "CPU Quota", "tooltip": { "shared": false, "sort": 2, "value_type": "individual" }, "transform": "table", "type": "table-old", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "show": false } ] }, { "collapsed": false, "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "gridPos": { "h": 1, "w": 24, "x": 0, "y": 16 }, "id": 16, "panels": [], "targets": [ { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "refId": "A" } ], "title": "Memory Usage", "type": "row" }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "fill": 10, "fillGradient": 0, "gridPos": { "h": 7, "w": 24, "x": 0, "y": 17 }, "hiddenSeries": false, "id": 3, "interval": "1m", "legend": { "alignAsTable": true, "avg": false, "current": false, "max": false, "min": false, "rightSide": true, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 0, "links": [], "nullPointMode": "null as zero", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.4.7", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "quota - requests", "color": "#F2495C", "dashes": true, "fill": 0, "hiddenSeries": true, "hideTooltip": true, "legend": true, "linewidth": 2, "stack": false }, { "alias": "quota - limits", "color": "#FF9830", "dashes": true, "fill": 0, "hiddenSeries": true, "hideTooltip": true, "legend": true, "linewidth": 2, "stack": false } ], "spaceLength": 10, "stack": true, "steppedLine": false, "targets": [ { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n container_memory_working_set_bytes{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\", container!=\"\", image!=\"\"}\n * on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "time_series", "intervalFactor": 2, "legendFormat": "{{workload}} - {{workload_type}}", "refId": "A", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "scalar(kube_resourcequota{cluster=\"$cluster\", namespace=\"$namespace\", type=\"hard\",resource=\"requests.memory\"})", "format": "time_series", "intervalFactor": 2, "legendFormat": "quota - requests", "refId": "B", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "scalar(kube_resourcequota{cluster=\"$cluster\", namespace=\"$namespace\", type=\"hard\",resource=\"limits.memory\"})", "format": "time_series", "intervalFactor": 2, "legendFormat": "quota - limits", "refId": "C", "step": 10 } ], "thresholds": [], "timeRegions": [], "title": "Memory Usage", "tooltip": { "shared": false, "sort": 2, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "bytes", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "show": false } ], "yaxis": { "align": false } }, { "collapsed": false, "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "gridPos": { "h": 1, "w": 24, "x": 0, "y": 24 }, "id": 17, "panels": [], "targets": [ { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "refId": "A" } ], "title": "Memory Quota", "type": "row" }, { "aliasColors": {}, "bars": false, "columns": [], "dashLength": 10, "dashes": false, "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "fill": 1, "fontSize": "100%", "gridPos": { "h": 7, "w": 24, "x": 0, "y": 25 }, "id": 4, "interval": "1m", "legend": { "alignAsTable": true, "avg": false, "current": false, "max": false, "min": false, "rightSide": true, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null as zero", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "showHeader": true, "sort": { "col": 0, "desc": true }, "spaceLength": 10, "stack": false, "steppedLine": false, "styles": [ { "alias": "Time", "align": "auto", "dateFormat": "YYYY-MM-DD HH:mm:ss", "pattern": "Time", "type": "hidden" }, { "alias": "Running Pods", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 0, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #A", "thresholds": [], "type": "number", "unit": "short" }, { "alias": "Memory Usage", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #B", "thresholds": [], "type": "number", "unit": "bytes" }, { "alias": "Memory Requests", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #C", "thresholds": [], "type": "number", "unit": "bytes" }, { "alias": "Memory Requests %", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #D", "thresholds": [], "type": "number", "unit": "percentunit" }, { "alias": "Memory Limits", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #E", "thresholds": [], "type": "number", "unit": "bytes" }, { "alias": "Memory Limits %", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #F", "thresholds": [], "type": "number", "unit": "percentunit" }, { "alias": "Workload", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": true, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "/d/a164a7f0339f99e89cea5cb47e9be617/k8s-resources-workload?var-cluster=$cluster&var-namespace=$namespace&var-workload=$__cell&var-type=$__cell_2", "pattern": "workload", "thresholds": [], "type": "number", "unit": "short" }, { "alias": "Workload Type", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "workload_type", "thresholds": [], "type": "number", "unit": "short" }, { "alias": "", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "pattern": "/.*/", "thresholds": [], "type": "string", "unit": "short" } ], "targets": [ { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "count(namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}) by (workload, workload_type)", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "A", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n container_memory_working_set_bytes{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\", container!=\"\", image!=\"\"}\n * on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "B", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n kube_pod_container_resource_requests{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"memory\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "C", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n container_memory_working_set_bytes{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\", container!=\"\", image!=\"\"}\n * on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n/sum(\n kube_pod_container_resource_requests{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"memory\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "D", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n kube_pod_container_resource_limits{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"memory\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "E", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "sum(\n container_memory_working_set_bytes{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\", container!=\"\", image!=\"\"}\n * on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n/sum(\n kube_pod_container_resource_limits{job=\"kube-state-metrics\", cluster=\"$cluster\", namespace=\"$namespace\", resource=\"memory\"}\n* on(namespace,pod)\n group_left(workload, workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}\n) by (workload, workload_type)\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "F", "step": 10 } ], "thresholds": [], "title": "Memory Quota", "tooltip": { "shared": false, "sort": 2, "value_type": "individual" }, "transform": "table", "type": "table-old", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "min": 0, "show": true }, { "format": "short", "logBase": 1, "show": false } ] }, { "collapsed": false, "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "gridPos": { "h": 1, "w": 24, "x": 0, "y": 32 }, "id": 18, "panels": [], "targets": [ { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "refId": "A" } ], "title": "Current Network Usage", "type": "row" }, { "aliasColors": {}, "bars": false, "columns": [], "dashLength": 10, "dashes": false, "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "fill": 1, "fontSize": "100%", "gridPos": { "h": 7, "w": 24, "x": 0, "y": 33 }, "id": 5, "interval": "1m", "legend": { "alignAsTable": true, "avg": false, "current": false, "max": false, "min": false, "rightSide": true, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null as zero", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "showHeader": true, "sort": { "col": 0, "desc": true }, "spaceLength": 10, "stack": false, "steppedLine": false, "styles": [ { "alias": "Time", "align": "auto", "dateFormat": "YYYY-MM-DD HH:mm:ss", "pattern": "Time", "type": "hidden" }, { "alias": "Current Receive Bandwidth", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #A", "thresholds": [], "type": "number", "unit": "Bps" }, { "alias": "Current Transmit Bandwidth", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #B", "thresholds": [], "type": "number", "unit": "Bps" }, { "alias": "Rate of Received Packets", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #C", "thresholds": [], "type": "number", "unit": "pps" }, { "alias": "Rate of Transmitted Packets", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #D", "thresholds": [], "type": "number", "unit": "pps" }, { "alias": "Rate of Received Packets Dropped", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #E", "thresholds": [], "type": "number", "unit": "pps" }, { "alias": "Rate of Transmitted Packets Dropped", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "Value #F", "thresholds": [], "type": "number", "unit": "pps" }, { "alias": "Workload", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": true, "linkTargetBlank": false, "linkTooltip": "Drill down to pods", "linkUrl": "/d/a164a7f0339f99e89cea5cb47e9be617/k8s-resources-workload?var-cluster=$cluster&var-namespace=$namespace&var-workload=$__cell&var-type=$type", "pattern": "workload", "thresholds": [], "type": "number", "unit": "short" }, { "alias": "Workload Type", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTargetBlank": false, "linkTooltip": "Drill down", "linkUrl": "", "pattern": "workload_type", "thresholds": [], "type": "number", "unit": "short" }, { "alias": "", "align": "auto", "colors": [], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "pattern": "/.*/", "thresholds": [], "type": "string", "unit": "short" } ], "targets": [ { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "(sum(irate(container_network_receive_bytes_total{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\"}[$__rate_interval])\n* on (namespace,pod)\ngroup_left(workload,workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}) by (workload))\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "A", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "(sum(irate(container_network_transmit_bytes_total{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\"}[$__rate_interval])\n* on (namespace,pod)\ngroup_left(workload,workload_type) namespace_workload_pod:kube_pod_owner:relabel{cluster=\"$cluster\", namespace=\"$namespace\", workload_type=\"$type\"}) by (workload))\n", "format": "table", "instant": true, "intervalFactor": 2, "legendFormat": "", "refId": "B", "step": 10 }, { "datasource": "${DS_AWS-OBSERVABILITY-ACCELERATOR}", "expr": "(sum(irate(container_network_receive_packets_total{job=\"kubelet\", cluster=\"$cluster\", namespace=\"$namespace\"}[$__rate_interval])\n* on (namespace,pod)\ngroup_left(workload,workload_type) 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2022-10-28 09:41:45 UTC
2022-10-28 09:44:25 UTC
0
0
18,202
1
806,130
27,946
0
nextcloud
Nextcloud
Dashboard for Nextcloud exporter
2023-03-01 13:24:39 UTC
2023-03-01 13:25:41 UTC
0
Needs node-exporter, cadvisor, and nextcloud-exporter.
0
0
null
null
0
0
27,946
1
18,202
1
0
null
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"thresholds": [], "timeRegions": [], "title": "Disk IO", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "$$hashKey": "object:37", "format": "binbps", "logBase": 1, "show": true }, { "$$hashKey": "object:38", "format": "binbps", "logBase": 1, "show": true } ], "yaxis": { "align": true } } ], "refresh": "30s", "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [ { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(nextcloud_up, instance)", "hide": 0, "includeAll": false, "multi": false, "name": "instance", "options": [], "query": { "query": "label_values(nextcloud_up, instance)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Nextcloud", 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2023-03-01 13:24:39 UTC
2023-03-01 13:25:41 UTC
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hello-observability
Hello Observability
Hello Observability
2023-03-17 09:35:42 UTC
2023-03-17 09:38:17 UTC
0
null
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null
0
0
28,079
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2023-03-17 09:35:42 UTC
2023-03-17 09:38:17 UTC
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influxdb-2-x-telegraf-docker-dashboard
InfluxDB 2.x Telegraf Docker Dashboard
Basic docker dashboard for InfluxDB 2.x and telegraf collector.
2023-03-30 04:17:51 UTC
2023-03-30 04:22:06 UTC
0
# Overview. This dashboard is designed to visualize the sensor data collected using Telegraf `inputs.docker`.
82,885
82,886
Telegraf
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28,215
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Basic docker dashboard for InfluxDB 2.x and telegraf collector.
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"liveNow": false, "panels": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 5, "w": 3, "x": 0, "y": 0 }, "id": 8, "options": { "colorMode": "none", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.7", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker\")\n |> filter(fn: (r) => r[\"_field\"] == \"n_cpus\")\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Total Cpus", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 5, "w": 3, "x": 3, "y": 0 }, "id": 11, "options": { "colorMode": "none", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.7", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker\")\n |> filter(fn: (r) => r[\"_field\"] == \"n_images\")\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Images", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "decbytes" }, "overrides": [] }, "gridPos": { "h": 5, "w": 3, "x": 6, "y": 0 }, "id": 7, "options": { "colorMode": "none", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.7", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker\")\n |> filter(fn: (r) => r[\"_field\"] == \"memory_total\")\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Total Memory", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 5, "w": 3, "x": 9, "y": 0 }, "id": 4, "options": { "colorMode": "value", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.7", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker\")\n |> filter(fn: (r) => r[\"_field\"] == \"n_containers_running\")\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Running Containers", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "orange", "value": null } ] } }, "overrides": [] }, "gridPos": { "h": 5, "w": 3, "x": 12, "y": 0 }, "id": 5, "options": { "colorMode": "value", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.7", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker\")\n |> filter(fn: (r) => r[\"_field\"] == \"n_containers_paused\")\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Paused Containers", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "red", "value": null } ] } }, "overrides": [] }, "gridPos": { "h": 5, "w": 3, "x": 15, "y": 0 }, "id": 6, "options": { "colorMode": "value", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": {}, "textMode": "auto" }, "pluginVersion": "9.4.7", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker\")\n |> filter(fn: (r) => r[\"_field\"] == \"n_containers_stopped\")\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Paused Containers", "type": "stat" }, { "collapsed": true, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 5 }, "id": 14, "panels": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "displayName": "${__field.labels.container_name}", "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "percent" }, "overrides": [] }, "gridPos": { "h": 10, "w": 12, "x": 0, "y": 6 }, "id": 2, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker_container_cpu\")\n |> filter(fn: (r) => r[\"_field\"] == \"usage_percent\")\n |> filter(fn: (r) => r[\"container_name\"] =~ /${container:regex}/)\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Cpu Usage", "transparent": true, "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "displayName": "${__field.labels.container_name}", "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "bytes" }, "overrides": [] }, "gridPos": { "h": 10, "w": 12, "x": 12, "y": 6 }, "id": 10, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker_container_mem\")\n |> filter(fn: (r) => r[\"_field\"] == \"usage\")\n |> filter(fn: (r) => r[\"container_name\"] =~ /${container:regex}/)\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")", "refId": "A" } ], "title": "Memory Usage", "transparent": true, "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "displayName": "${__field.labels.container_name}:${__field.name}", "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "bytes" }, "overrides": [] }, "gridPos": { "h": 10, "w": 12, "x": 0, "y": 16 }, "id": 9, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker_container_blkio\")\n |> filter(fn: (r) => r[\"_field\"] == \"io_service_bytes_recursive_read\" or r[\"_field\"] == \"io_service_bytes_recursive_write\")\n |> filter(fn: (r) => r[\"container_name\"] =~ /${container:regex}/)\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")\n", "refId": "A" } ], "title": "Disk Usage", "transparent": true, "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "displayName": "${__field.labels.container_name}:${__field.name}", "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "bytes" }, "overrides": [] }, "gridPos": { "h": 10, "w": 12, "x": 12, "y": 16 }, "id": 12, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker_container_net\")\n |> filter(fn: (r) => r[\"_field\"] == \"rx_bytes\" or r[\"_field\"] == \"tx_bytes\")\n |> filter(fn: (r) => r[\"container_name\"] =~ /${container:regex}/)\n |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)\n |> yield(name: \"mean\")\n", "refId": "A" } ], "title": "Network Usage", "transparent": true, "type": "timeseries" } ], "repeat": "container", "repeatDirection": "h", "title": "${container}", "type": "row" } ], "refresh": "", "revision": 1, "schemaVersion": 38, "style": "dark", "tags": [ "docker", "influxdb2.0", "telegraf" ], "templating": { "list": [ { "current": {}, "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "definition": "buckets()", "hide": 0, "includeAll": false, "multi": false, "name": "bucket", "options": [], "query": "buckets()", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "influxdb", "uid": "${DS_DROPET-INFLUXDB}" }, "definition": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker_container_cpu\")\n |> keyValues(keyColumns: [\"container_name\"])\n |> group()\n |> keep(columns: [\"container_name\"])\n |> distinct(column: \"container_name\")", "hide": 0, "includeAll": true, "multi": true, "name": "container", "options": [], "query": "from(bucket: \"${bucket}\")\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\n |> filter(fn: (r) => r[\"_measurement\"] == \"docker_container_cpu\")\n |> keyValues(keyColumns: [\"container_name\"])\n |> group()\n |> keep(columns: [\"container_name\"])\n |> distinct(column: \"container_name\")", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-24h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "InfluxDB 2.x Telegraf Docker Dashboard", "uid": "YIIzB274z", "version": 5, "weekStart": "" }
2023-03-30 04:17:51 UTC
2023-03-30 04:22:05 UTC
0
0
18,507
1
502,618
28,385
0
home-fortiswitches
Home FortiSwitches
I will display all the switches added to Zabbix... manually entry of each port if you want add more... so far this diaply the main interface and 10 ports
2023-04-12 20:59:43 UTC
2023-04-12 21:13:28 UTC
0
null
83,310
83,311
null
null
0
0
28,385
1
18,507
1
0
null
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"w": 6, "x": 5, "y": 63 }, "id": 21, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "timezone": [ "America/Toronto" ], "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "application": { "filter": "" }, "datasource": { "type": "alexanderzobnin-zabbix-datasource", "uid": "${DS_ZABBIX}" }, "functions": [], "group": { "filter": "SNMP Devices" }, "host": { "filter": "Den-Switch" }, "item": { "filter": "Download internal" }, "itemTag": { "filter": "" }, "options": { "disableDataAlignment": false, "showDisabledItems": false, "skipEmptyValues": false, "useZabbixValueMapping": false }, "proxy": { "filter": "" }, "queryType": "0", "refId": "A", "resultFormat": "time_series", "table": { "skipEmptyValues": false }, "tags": { "filter": "" }, "trigger": { "filter": "" }, "triggers": { "acknowledged": 2, "count": true, "minSeverity": 3 } }, { "application": { "filter": "" }, "datasource": { "type": "alexanderzobnin-zabbix-datasource", "uid": "${DS_ZABBIX}" }, "functions": [], "group": { "filter": "SNMP Devices" }, "hide": false, "host": { "filter": "Den-Switch" }, "item": { "filter": "Upload internal" }, "itemTag": { "filter": "" }, "options": { "disableDataAlignment": false, "showDisabledItems": false, "skipEmptyValues": false, "useZabbixValueMapping": false }, "proxy": { "filter": "" }, "queryType": "0", "refId": "B", "resultFormat": "time_series", "table": { "skipEmptyValues": false }, "tags": { "filter": "" }, "trigger": { "filter": "" }, "triggers": { "acknowledged": 2, "count": true, "minSeverity": 3 } } ], "title": "Primary Interface - Upload/Download", "type": "timeseries" }, { "datasource": { "type": "alexanderzobnin-zabbix-datasource", "uid": "${DS_ZABBIX}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "Bits", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 25, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 3, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "bits" }, "overrides": [] }, "gridPos": { "h": 7, "w": 6, "x": 11, "y": 63 }, "id": 53, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "timezone": [ "America/Toronto" ], "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "application": { "filter": "" }, "datasource": { "type": "alexanderzobnin-zabbix-datasource", "uid": "${DS_ZABBIX}" }, "functions": [], "group": { "filter": "Fortiswitch" }, "host": { "filter": "$Switch" }, "item": { "filter": "Interface internal(primary): Inbound packets discarded" }, "itemTag": { "filter": "" }, "options": { "disableDataAlignment": false, "showDisabledItems": false, "skipEmptyValues": false, "useZabbixValueMapping": false }, "proxy": { "filter": "" }, "queryType": "0", "refId": "A", "resultFormat": "time_series", "table": { "skipEmptyValues": false }, "tags": { "filter": "" }, "trigger": { "filter": "" }, "triggers": { "acknowledged": 2, "count": true, "minSeverity": 3 } }, { "application": { "filter": "" }, "datasource": { "type": "alexanderzobnin-zabbix-datasource", "uid": "${DS_ZABBIX}" }, "functions": [], "group": { "filter": "Fortiswitch" }, "hide": false, "host": { "filter": "$Switch" }, "item": { "filter": "Interface internal(primary): Outbound packets discarded" }, "itemTag": { "filter": "" }, "options": { "disableDataAlignment": false, "showDisabledItems": false, "skipEmptyValues": false, "useZabbixValueMapping": false }, "proxy": { "filter": "" }, "queryType": "0", "refId": "B", "resultFormat": "time_series", "table": { "skipEmptyValues": false }, "tags": { "filter": "" }, "trigger": { "filter": "" }, "triggers": { "acknowledged": 2, "count": true, "minSeverity": 3 } } ], "title": "Primary - Packedt Discarded", "type": "timeseries" } ], "refresh": "5s", "revision": 1, "schemaVersion": 38, "style": "dark", "tags": [ "Fortiswitch", "Fortigate", "Zabbix Agent" ], "templating": { "list": [ { "current": {}, "datasource": { "type": "alexanderzobnin-zabbix-datasource", "uid": "${DS_ZABBIX}" }, "definition": "Zabbix - host", "hide": 0, "includeAll": false, "multi": false, "name": "Switch", "options": [], "query": { "application": "", "group": "Fortiswitch", "host": "/.*/", "item": "", "itemTag": "", "queryType": "host" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "type": "query" } ] }, "time": { "from": "now-12h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Home FortiSwitches", "uid": "fH3sKNf4z", "version": 26, "weekStart": "", "gnetId": 18507, "description": "I will display all the switches added to Zabbix... manually entry of each port if you want add more... so far this diaply the main interface and 10 ports" }
2023-04-12 20:59:43 UTC
2023-04-12 21:13:12 UTC
0
0
17,481
1
722,446
26,843
0
bitcoin-node
Bitcoin Node
Monitor your Bitcoin core node and aspects of the network.
2022-11-25 02:13:04 UTC
2022-11-25 02:13:26 UTC
0
null
0
0
null
null
0
0
26,843
1
17,481
1
0
Monitor your Bitcoin core node and aspects of the network.
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"avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "dataLinks": [] }, "percentage": false, "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "floor(sum(rate(bitcoin_blocks[3h])) * 3600 * 24)", "hide": false, "legendFormat": "3h", "refId": "C" }, { "expr": "floor(sum(rate(bitcoin_blocks[6h])) * 3600 * 24)", "hide": false, "legendFormat": "6h", "refId": "E" }, { "expr": "floor(sum(rate(bitcoin_blocks[12h])) * 3600 * 24)", "hide": false, "legendFormat": "12h", "refId": "A" }, { "expr": "floor(sum(rate(bitcoin_blocks[7d])) * 3600 * 24)", "legendFormat": "7d", "refId": "B" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Block Creation Rate", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "Difficulty value", "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 0 }, "id": 6, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": false, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "dataLinks": [] }, "percentage": false, "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "avg(bitcoin_difficulty)", "format": "time_series", "intervalFactor": 1, "legendFormat": "difficulty", "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Difficulty", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "decimals": 1, "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "Estimated network hashes per second", "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 8 }, "id": 8, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": 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"stack": false, "steppedLine": false, "targets": [ { "expr": "avg(bitcoin_num_chaintips)", "format": "time_series", "intervalFactor": 1, "legendFormat": "tips", "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Chain Branches", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "decimals": 0, "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "decimals": 0, "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "Sent and received traffic rates", "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 16 }, "id": 10, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "dataLinks": [] }, "percentage": false, "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "sum(irate(bitcoin_total_bytes_sent[5m]))", "format": "time_series", "intervalFactor": 1, "legendFormat": "sent", "refId": "A" }, { "expr": "-sum(irate(bitcoin_total_bytes_recv[10m]))", "format": "time_series", "intervalFactor": 1, "legendFormat": "received", "refId": "B" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Network traffic", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "decimals": 0, "format": "Bps", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "The estimated smart fee for confirmation of transactions in a given number of blocks.", "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 24 }, "id": 4, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "dataLinks": [] }, "percentage": false, "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "avg(bitcoin_est_smart_fee_2)", "format": "time_series", "interval": "2m", "intervalFactor": 1, "legendFormat": "2-blocks", "refId": "A" }, { "expr": "avg(bitcoin_est_smart_fee_20)", "format": "time_series", "intervalFactor": 1, "legendFormat": "20-blocks", "refId": "B" }, { "expr": "avg(bitcoin_est_smart_fee_3)", "format": "time_series", "intervalFactor": 1, "legendFormat": "3-blocks", "refId": "C" }, { "expr": "avg(bitcoin_est_smart_fee_5)", "format": "time_series", "intervalFactor": 1, "legendFormat": "5-blocks", "refId": "D" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Estimated Fee", "tooltip": { "shared": true, "sort": 2, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "The Bitcoin protocol and server versions.", "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 24 }, "id": 14, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "dataLinks": [] }, "percentage": false, "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "protocol", "yaxis": 1 }, { "alias": "server", "yaxis": 2 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "avg(bitcoin_protocol_version)", "format": "time_series", "intervalFactor": 1, "legendFormat": "protocol", "refId": "A" }, { "expr": "avg(bitcoin_server_version)", "format": "time_series", "intervalFactor": 1, "legendFormat": "server", "refId": "B" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Versions", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "decimals": null, "format": "none", "label": "protocol version", "logBase": 1, "max": null, "min": null, "show": true }, { "format": "none", "label": "server version", "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "Number of peers that have been banned and the reason for the ban.", "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 32 }, "id": 16, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "dataLinks": [] }, "percentage": false, "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "count(bitcoin_banned_until) by (reason)", "format": "time_series", "intervalFactor": 1, "legendFormat": "ban - {{ reason }}", "refId": "B" }, { "expr": "sum(bitcoin_peers)", "format": "time_series", "intervalFactor": 1, "legendFormat": "peers", "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Peers", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "decimals": 0, "format": "none", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } } ], "refresh": "5m", "schemaVersion": 20, "style": "dark", "tags": [ "bitcoin" ], "templating": { "list": [] }, "time": { "from": "now-1w", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "Bitcoin Node", "uid": "OTLC2jHWz", "version": 1, "description": "Monitor your Bitcoin core node and aspects of the network." }
2022-11-25 02:13:04 UTC
null
0
0
17,832
1
705,056
27,346
0
nyrr
NYRR
Visualization of NYRR.org race result export . Note, this is not an official NYRR product.
2023-01-08 21:22:12 UTC
2023-01-08 22:15:01 UTC
0
# About this dashboard: 1. You can search your [race search results](https://results.nyrr.org/home) and then: 1. Save them to Excel. 2. Then export to CSV (Make sure the dates are normalized to ISO format) 3. Make them available to Grafana CSV plugin as a file or from the file system You can make a very simple Docker/ PODMAN container to share your NYRR.org race results as follows. You could use the following Dockerfile to make a very simple container: ```Dockerfile # This is not a high performance HTTP server, also it is not recommended for production. # Please check: https://docs.python.org/3/library/http.server.html#http-server-security # Author Jose Vicente Nunez <[email protected]> FROM fedora:37 ARG HTDOCS=/var/www/htdocs ARG UID=1000 ARG USER=www ARG GRP=www EXPOSE 8080/tcp RUN dnf install -y htop curl && \ dnf clean -y all RUN useradd --comment 'WebServer' --home-dir $HTDOCS --create-home --uid $UID --user-group $USER USER $USER:$GRP WORKDIR $HTDOCS CMD exec python3 -m http.server 8080 HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=1 CMD curl --fail --silent --show-error http://localhost:8080 || exit 1 LABEL HOW_TO_BUILD="BUILDKIT=y docker build --file Dockerfile --tag static_server ." LABEL HOW_TO_RUN="docker run --init --detach --name home_fitness --publish 8080:8080 --restart=always --volume /home/josevnz/fitness:/var/www/htdocs:ro static_server" ``` You can see a [demonstration video here](https://youtu.be/RQYhDJw9wtI)
80,462
80,463
Other
null
0
0
27,346
1
17,832
1
0
Visualization of NYRR.org race result export
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2023-01-08 21:22:12 UTC
2023-01-08 21:30:31 UTC
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pg-overview
PG Overview
This dashboard works with postgres_exporter for prometheus
2023-03-20 02:55:29 UTC
2023-04-10 07:59:21 UTC
0
# PostgreSQL_exporter ## 1.下载和配置 ​ 下载文件:postgres_exporter-0.10.0.linux-amd64.tar.gz ​ 解压: ```shell tar -C /opt -xvf postgres_exporter-0.10.0.linux-amd64.tar.gz cd /opt && mv postgres_exporter-0.10.0.linux-amd64 postgres_exporter cd postgres_exporter ``` 添加脚本: start.sh ```shell sudo -u postgres DATA_SOURCE_NAME="user=postgres host=/var/run/postgresql/ sslmode=disable" postgres_exporter #需要通过用户postgres启动 ``` 添加至systemd: ```shell vim /usr/lib/systemd/system/postgres_exporter.service [Unit] Description=postgres_exporter After=network.target [Service] Type=simple ExecStart=/PATH/TO/start.sh Restart=on-failure [Install] WantedBy=multi-user.target ``` ​ 启动: ```shell systemctl daemon-reload systemctl start postgres_exporter systemctl status postgres_exporter systemctl enable postgres_exporte ``` ## 2.接入prometheus edit `prometheus.yml` ```yaml - job_name: "postgresql" file_sd_configs: - files: - targets/postgresql.yml refresh_interval: 10m ``` `targets/postgresql.yml` ```yaml - targets: ["10.1xx.1.1xx:9187"] labels: instance: 10.1xx.1.1xx_postgresql targets: "10.1xx.1.1xx:9187" ```
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This dashboard works with postgres_exporter for prometheus
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2023-03-20 02:55:29 UTC
2023-04-10 07:59:21 UTC
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18,379
1
819,593
28,205
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kubernetes-cluster-monitoring-via-prometheus
Kubernetes cluster monitoring (via Prometheus)
Monitors Kubernetes cluster using Prometheus. Shows overall cluster CPU / Memory / Filesystem usage as well as individual pod, containers, systemd services statistics. Uses cAdvisor metrics only.
2023-03-29 08:22:52 UTC
2023-03-29 08:23:08 UTC
0
null
0
0
null
null
0
0
28,205
1
18,379
1
0
Monitors Kubernetes cluster using Prometheus. Shows overall cluster CPU / Memory / Filesystem usage as well as individual pod, containers, systemd services statistics. Uses cAdvisor metrics only.
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2023-03-29 08:22:52 UTC
null
0
0
18,459
1
831,881
28,324
0
celestia-appd
Celestia Appd
Then most important metrics.
2023-04-07 10:01:55 UTC
2023-04-10 02:21:11 UTC
0
Set your Prometheus job (the one connect to celestia-appd) name to 'validator', then it should work.
83,221
83,222
Other
null
0
0
28,324
1
18,459
1
0
Golang internal statistics.
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"defaults": { "color": { "fixedColor": "red", "mode": "fixed" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [] }, "gridPos": { "h": 9, "w": 4, "x": 0, "y": 10 }, "id": 34, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "exemplar": false, "expr": "tendermint_consensus_validator_missed_blocks{job=\"validator\"}", "instant": true, "legendFormat": "__auto", "range": false, "refId": "A" } ], "title": "Missed Blocks", "type": "stat" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": 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"id": "byRegexp", "options": "BlockResponse - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-purple", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "StatusRequest - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-blue", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "StatusResponse - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-blue", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "BlockPart - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-green", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "HasVote - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-green", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "NewRoundStep - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-yellow", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "NewValidBlock - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-yellow", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "Proposal - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-orange", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "Vote - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-orange", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "VoteSetBits - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-red", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "VoteSetMaj23 - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "super-light-red", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "Txs - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "#808080", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "PexAddrs - .*" }, "properties": [ { "id": "color", "value": { "fixedColor": "#ffffff", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 7, "w": 10, "x": 14, "y": 15 }, "id": 43, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "right", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "label_replace(rate(tendermint_p2p_message_receive_bytes_total{job=\"validator\"}[$__rate_interval]), \"type\", \"$1 - received\", \"message_type\", \".*_(.*)\")", "interval": "", "legendFormat": "{{type}}", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "-label_replace(rate(tendermint_p2p_message_send_bytes_total{job=\"validator\"}[$__rate_interval]), \"type\", \"$1 - sent\", \"message_type\", \".*_(.*)\")", "hide": false, "legendFormat": "{{type}}", "range": true, "refId": "B" } ], "title": "P2P Message", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "description": "", "fieldConfig": { "defaults": { "color": { "fixedColor": "green", "mode": "fixed" }, "decimals": 3, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 9, "w": 4, "x": 0, "y": 19 }, "id": 36, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "exemplar": false, "expr": "sum(tendermint_consensus_validator_power{job=\"validator\"})/sum(tendermint_consensus_validators_power{job=\"validator\"})*10", "instant": true, "legendFormat": "__auto", "range": false, "refId": "A" } ], "title": "Power Share", "type": "stat" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 6, "w": 10, "x": 4, "y": 22 }, "id": 32, "options": { "legend": { "calcs": [], "displayMode": "list", 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{ "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Tx Size" }, "properties": [ { "id": "unit", "value": "bytes" } ] } ] }, "gridPos": { "h": 6, "w": 10, "x": 14, "y": 22 }, "id": 38, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "tendermint_mempool_size{job=\"validator\"}", "legendFormat": "Pool Size", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "increase(tendermint_mempool_tx_size_bytes_sum{job=\"validator\"}[$__rate_interval])", "hide": false, "legendFormat": "Tx Size", "range": true, "refId": "B" } ], "title": "Memory Pool", "type": "timeseries" }, { "gridPos": { "h": 1, "w": 24, "x": 0, "y": 28 }, "id": 10, "title": "Go", "type": "row" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "s" }, "overrides": [ { "matcher": { "id": "byName", "options": "Allocated Memory" }, "properties": [ { "id": "unit", "value": "bytes" } ] } ] }, "gridPos": { "h": 8, "w": 24, "x": 0, "y": 29 }, "id": 28, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "rate(process_cpu_seconds_total{job=\"validator\"}[$__rate_interval])", "legendFormat": "CPU Time", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "go_memstats_alloc_bytes{job=\"validator\"}", "hide": false, "legendFormat": "Allocated Memory", "range": true, "refId": "B" } ], "title": "CPU and Memory", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", 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"legendFormat": "quantile={{quantile}}", "range": true, "refId": "A" } ], "title": "GC Duration (s)", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "axisSoftMin": 0, "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Threads" }, "properties": [ { "id": "custom.axisPlacement", "value": "right" } ] } ] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 37 }, "id": 6, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "go_goroutines{job=\"validator\"}", "legendFormat": "Routines", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "go_threads{job=\"validator\"}", "hide": false, "legendFormat": "Threads", "range": true, "refId": "B" } ], "title": "Go Routines and Threads", "type": "timeseries" } ], "refresh": "10s", "revision": 1, "schemaVersion": 38, "style": "dark", "tags": [ "Celestia" ], "templating": { "list": [] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Celestia Appd", "uid": "q9cFcDenk", "version": 10, "weekStart": "", "gnetId": 18459 }
2023-04-07 10:01:55 UTC
2023-04-07 10:13:11 UTC
0
0
18,504
1
835,147
28,382
0
autometrics-function-explorer
Autometrics Function Explorer
Dig into the metrics for specific functions instrumented with Autometrics
2023-04-12 17:54:58 UTC
2023-04-12 18:00:54 UTC
0
This is a dashboard that will work for any project instrumented with [Autometrics](https://autometrics.dev).
83,306
83,307
Other
null
0
0
28,382
1
18,504
1
0
null
{ "__inputs": [ { "name": "DS_PROMETHEUS", "label": "Prometheus", "description": "", "type": "datasource", "pluginId": "prometheus", "pluginName": "Prometheus" } ], "__elements": {}, "__requires": [ { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.4.3" }, { "type": "datasource", "id": "prometheus", "name": "Prometheus", "version": "1.0.0" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "datasource", "uid": "grafana" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "description": "Dig into the metrics for specific functions instrumented with Autometrics", "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "links": [], "liveNow": false, "panels": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "description": "Calls per second is calculated as the average over a 5-minute window", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "Calls per Second", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 8, "w": 24, "x": 0, "y": 0 }, "id": 4, "options": { "legend": { "calcs": [ "lastNotNull", "max" ], "displayMode": "table", "placement": "right", "showLegend": true, "sortBy": "Max", "sortDesc": true }, "tooltip": { "mode": "multi", "sort": "desc" } }, "pluginVersion": "9.4.1", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum by (function, module) (rate(function_calls_count{function=~\"${function}\"}[5m])) > 0", "format": "time_series", "instant": false, "interval": "", "legendFormat": "", "refId": "A" } ], "title": "Request Rate", "transformations": [], "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic", "seriesBy": "max" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "Error Rate", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 8, "w": 24, "x": 0, "y": 8 }, "id": 2, "options": { "legend": { "calcs": [ "lastNotNull", "max" ], "displayMode": "table", "placement": "right", "showLegend": true, "sortBy": "Max", "sortDesc": true }, "tooltip": { "mode": "multi", "sort": "desc" } }, "pluginVersion": "9.4.1", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "(sum by(function, module) (rate(function_calls_count{result=\"error\", function=~\"${function}\"}[5m])) > 0)\n/ (sum by(function, module) (rate(function_calls_count{function=~\"${function}\"}[5m])) > 0)", "interval": "", "legendFormat": "", "range": true, "refId": "A" } ], "title": "Error Rate", "type": "timeseries" } ], "refresh": "5m", "revision": 1, "schemaVersion": 38, "style": "dark", "tags": [ "autometrics" ], "templating": { "list": [ { "allValue": "__none__", "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(function_calls_count, function)", "hide": 0, "includeAll": false, "label": "Show Function(s)", "multi": true, "name": "function", "options": [], "query": { "query": "label_values(function_calls_count, function)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Autometrics Function Explorer", "uid": "UmP_Puf4z", "version": 6, "weekStart": "", "gnetId": 18504 }
2023-04-12 17:54:58 UTC
2023-04-12 18:00:27 UTC
0
0
17,472
1
749,837
26,834
0
jmeter-test-results-influxdb2-standart-backend-listener
JMeter test results influxdb2 standart backend listener
Dashboard for visualizing JMeter metrics in influxdb2
2022-11-24 15:19:31 UTC
2022-11-28 06:58:20 UTC
0
The dashboard allows to visualize JMeter metrics stored in Influxdb2. Dependencies: - Grafana 9.0.3^ - InfluxDB 2.0^ - BackendListener: org.apache.jmeter.visualizers.backend.influxdb.InfluxdbBackendListenerClient *How to configure:* 1. Create new InfluxDB Bucket and Token. 2. Configure JMeter backend listener. ``` - Backend listener implementation: org.apache.jmeter.visualizers.backend.influxdb.InfluxdbBackendListenerClient - influxdbUrl: http://YOUR_INFLUXDB_IP:8086/api/v2/write?org=YOUR_ORG_NAME&bucket=YOUR_BUCKET_NAME - application: YOUR_APP_NAME - measurement: jmeter - summaryOnly: false - samplersRegex: .* - percentiles: 99;95;90;50 - testTitle: YOUR_TEST_ID (For example: ${__time(yyyyMMdd-HHmm)}-appname) - influxdbToken: YOUR_TOKEN (Note: it should be added manually using add button) ``` 3. OPTIONAL: Configure backend listener in jmeter.properties or user.properties files: Let's say we need to generate around 6000 rps in our test, we can set up sending the test result once every 10 seconds by changing the «backend_influxdb.send_interval» parameter. Then we need to change «QUEUE_SIZE» and «backend_metrics_large_window» to 60000 (6000 x 10 s). For good performance it better to use «timed» window mode. That completely cleans up the set of collected metrics after sending, while fixed constantly stores this queue, adds new values to the end and deletes the old ones. ``` QUEUE_SIZE=60000 //default: 5000 backend_metrics_window_mode=timed //default: fixed backend_metrics_large_window=60000 //default: 5000 backend_influxdb.send_interval=10 //default: 5 seconds ``` 4. Add InfluxDB as a data source in Grafana using your bucket and token. 5. Create a folder for your project if it doesn't already exist. 6. Import Grafana dashboard
78,999
79,000
Other
null
1
5
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1
17,472
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null
{ "__inputs": [ { "name": "DS_TEMPLATE-2", "label": "TEMPLATE-2", "description": "InfluxDB data source name", "type": "datasource", "pluginId": "influxdb", "pluginName": "InfluxDB" }, { "name": "VAR_SEND_INTERVAL", "type": "constant", "label": "send_interval", "value": "10", "description": "Send metrics in specified time range" } ], "__elements": {}, "__requires": [ { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.0.3" }, { "type": "datasource", "id": "influxdb", "name": "InfluxDB", "version": "1.0.0" }, { "type": "panel", "id": "stat", "name": "Stat", "version": "" }, { "type": "panel", "id": "table", "name": "Table", "version": "" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "grafana", "uid": "-- Grafana --" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "iteration": 1669302251189, "links": [], "liveNow": false, "panels": [ { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 0 }, "id": 24, "panels": [], "title": "Summary", "type": "row" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 0, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 4, "w": 4, "x": 0, "y": 1 }, "id": 12, "options": { "colorMode": "value", "graphMode": "none", "justifyMode": "auto", "orientation": "horizontal", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "values": false }, "text": {}, "textMode": "auto" }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> sum()", "refId": "A" } ], "title": "Total Requests", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [] }, "gridPos": { "h": 4, "w": 4, "x": 4, "y": 1 }, "id": 13, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "valueSize": 50 }, "textMode": "auto" }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"countError\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> group(columns: [\"_field\"])\r\n |> sum()", "refId": "A" } ], "title": "Total Errors", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 4, "w": 4, "x": 8, "y": 1 }, "id": 38, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "valueSize": 50 }, "textMode": "auto" }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "failuresPercentages = () => {\r\n\r\ncountResponseTime=from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"application\"])\r\n |> sum()\r\n |> keep(columns: [\"_value\", \"application\"])\r\n \r\nsumerrorCount=from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"countError\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"application\"])\r\n |> sum()\r\n |> keep(columns: [\"_value\", \"application\"])\r\n \r\n return join(\r\n tables:{countResponseTime:countResponseTime, sumerrorCount:sumerrorCount},\r\n on:[\"application\"]\r\n )\r\n |> map(fn:(r) => ({\r\n \r\n percentage: r._value_sumerrorCount / r._value_countResponseTime \r\n })) \r\n}\r\n\r\nfailuresPercentages()", "refId": "B" } ], "title": "Error Rate", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 4, "w": 4, "x": 12, "y": 1 }, "id": 39, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "valueSize": 50 }, "textMode": "auto" }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "successPercentage = () => {\r\n\r\nallCount=from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"application\"])\r\n |> sum()\r\n |> keep(columns: [\"_value\", \"application\"])\r\n \r\nokCount=from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"ok\")\r\n |> group(columns: [\"application\"])\r\n |> sum()\r\n |> keep(columns: [\"_value\", \"application\"])\r\n \r\n return join(\r\n tables:{allCount:allCount, okCount:okCount},\r\n on:[\"application\"]\r\n )\r\n |> map(fn:(r) => ({\r\n \r\n percentage: r._value_okCount / r._value_allCount \r\n })) \r\n}\r\n\r\nsuccessPercentage()", "refId": "B" } ], "title": "Success Rate", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "bytes" }, "overrides": [] }, "gridPos": { "h": 4, "w": 4, "x": 16, "y": 1 }, "id": 14, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "values": false }, "text": { "valueSize": 50 }, "textMode": "auto" }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"rb\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> sum()", "refId": "A" } ], "title": "Received Bytes", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "bytes" }, "overrides": [] }, "gridPos": { "h": 4, "w": 4, "x": 20, "y": 1 }, "id": 15, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "sum" ], "fields": "", "values": false }, "text": { "valueSize": 50 }, "textMode": "auto" }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"sb\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> sum()", "refId": "A" } ], "title": "Sent Bytes", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "left", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 4, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineStyle": { "fill": "solid" }, "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "reqps" }, "overrides": [ { "matcher": { "id": "byFrameRefID", "options": "C" }, "properties": [ { "id": "custom.drawStyle", "value": "bars" }, { "id": "custom.lineWidth", "value": 0 }, { "id": "custom.barAlignment", "value": 0 }, { "id": "custom.showPoints", "value": "auto" }, { "id": "custom.fillOpacity", "value": 76 } ] }, { "matcher": { "id": "byName", "options": "Errors Per Second" }, "properties": [ { "id": "color", "value": { "fixedColor": "dark-red", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "Transations Per Second" }, "properties": [ { "id": "color", "value": { "fixedColor": "blue", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "Active Threads" }, "properties": [ { "id": "color", "value": { "fixedColor": "green", "mode": "fixed" } }, { "id": "custom.axisPlacement", "value": "right" }, { "id": "unit", "value": "threads" } ] } ] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 5 }, "id": 2, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"maxAT\")\r\n |> keep(columns: [\"_field\", \"_value\", \"_time\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: last, createEmpty: true)\r\n |> set(key: \"_field\", value: \"Active Threads\")", "refId": "B" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> keep(columns: [\"_field\", \"_value\", \"_time\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: sum, createEmpty: true)\r\n |> map(fn: (r) => ({ r with _value: float(v: r._value / float(v: ${send_interval}))}))\r\n |> set(key: \"_field\", value: \"Transations Per Second\")", "refId": "D" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"countError\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: sum, createEmpty: true)\r\n |> map(fn: (r) => ({ r with _value: float(v: r._value / float(v: ${send_interval}))}))\r\n |> set(key: \"_field\", value: \"Errors Per Second\")", "refId": "C" } ], "title": "Total Throughput", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "binBps" }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 5 }, "id": 25, "options": { "legend": { "calcs": [ "mean" ], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"sb\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: sum, createEmpty: true)\r\n |> map(fn: (r) => ({ r with _value: float(v: r._value / float(v: ${send_interval}))}))\r\n |> set(key: \"_field\", value: \"Sent Bytes\")", "refId": "A" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"rb\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: sum, createEmpty: true)\r\n |> map(fn: (r) => ({ r with _value: float(v: r._value / float(v: ${send_interval}))}))\r\n |> set(key: \"_field\", value: \"Received Bytes\")", "refId": "B" } ], "title": "Network Traffic", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "ms" }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 13 }, "id": 7, "options": { "legend": { "calcs": [ "mean" ], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"avg\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: mean, createEmpty: true)", "refId": "A" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"pct50.0\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: median, createEmpty: true)", "refId": "B" } ], "title": "Avg Response time", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "ms" }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 13 }, "id": 20, "options": { "legend": { "calcs": [ "mean" ], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"pct90.0\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: mean, createEmpty: true)", "refId": "A" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"pct95.0\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] == \"all\")\r\n |> group(columns: [\"_field\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: mean, createEmpty: true)", "refId": "B" } ], "title": "pct90 Response time", "type": "timeseries" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 21 }, "id": 22, "panels": [], "title": "Per sample", "type": "row" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "displayName": "${__field.labels.transaction}", "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "ms" }, "overrides": [] }, "gridPos": { "h": 9, "w": 24, "x": 0, "y": 22 }, "id": 19, "options": { "legend": { "calcs": [ "mean" ], "displayMode": "list", "placement": "right" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"avg\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> group(columns: [\"transaction\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: sum, createEmpty: true)", "refId": "A" } ], "title": "Avg Response time", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "displayName": "${__field.labels.transaction}", "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "ms" }, "overrides": [] }, "gridPos": { "h": 9, "w": 24, "x": 0, "y": 31 }, "id": 17, "options": { "legend": { "calcs": [ "mean" ], "displayMode": "list", "placement": "right" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"pct90.0\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> group(columns: [\"transaction\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: mean, createEmpty: true)", "refId": "A" } ], "title": "pct90 Response time", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green" } ] }, "unit": "reqps" }, "overrides": [] }, "gridPos": { "h": 11, "w": 24, "x": 0, "y": 40 }, "id": 18, "options": { "legend": { "calcs": [ "mean" ], "displayMode": "list", "placement": "right" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> group(columns: [\"transaction\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: mean, createEmpty: true)\r\n |> map(fn: (r) => ({ r with _value: float(v: r._value / float(v: ${send_interval}))}))", "refId": "A" } ], "title": "Total Throughput Per Sample", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "ms" }, "overrides": [ { "matcher": { "id": "byName", "options": "Count" }, "properties": [ { "id": "unit", "value": "none" } ] }, { "matcher": { "id": "byName", "options": "Error" }, "properties": [ { "id": "unit", "value": "none" } ] }, { "matcher": { "id": "byName", "options": "ErrorRate" }, "properties": [ { "id": "unit", "value": "percentunit" }, { "id": "decimals", "value": 2 }, { "id": "noValue", "value": "0 %" } ] } ] }, "gridPos": { "h": 11, "w": 24, "x": 0, "y": 51 }, "id": 10, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "frameIndex": 0, "showHeader": true }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"avg\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n |> group(columns: [\"transaction\"])\r\n |> mean()\r\n |> group()\r\n |> rename(columns: {\"_value\": \"Avg RT\"})", "refId": "B" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"pct50.0\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n |> group(columns: [\"transaction\"])\r\n |> median()\r\n |> group()\r\n |> rename(columns: {\"_value\": \"Median RT\"})", "refId": "H" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"min\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n |> group(columns: [\"transaction\"])\r\n |> min()\r\n |> group()\r\n |> rename(columns: {\"_value\": \"Min RT\"})", "refId": "A" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"max\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n |> group(columns: [\"transaction\"])\r\n |> max()\r\n |> group()\r\n |> rename(columns: {\"_value\": \"Max RT\"})", "refId": "C" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"pct90.0\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n |> group(columns: [\"transaction\"])\r\n |> quantile(q: 0.90, method: \"exact_mean\")\r\n |> group()\r\n |> rename(columns: {\"_value\": \"pct90\"})", "refId": "D" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"pct95.0\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n |> group(columns: [\"transaction\"])\r\n |> quantile(q: 0.95, method: \"exact_mean\")\r\n |> group()\r\n |> rename(columns: {\"_value\": \"pct95\"})", "refId": "I" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"count\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n |> group(columns: [\"transaction\"])\r\n |> sum()\r\n |> group()\r\n |> rename(columns: {\"_value\": \"Count\"})", "refId": "F" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "hide": false, "query": "countAll = from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"count\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"all\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> group(columns: [\"transaction\"])\r\n |> sum()\r\n |> group()\r\n |> toFloat()\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n\r\ncountError = from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r._measurement == \"jmeter\")\r\n |> filter(fn: (r) => r._field == \"count\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"ko\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> group(columns: [\"transaction\"])\r\n |> sum()\r\n |> group()\r\n |> toFloat()\r\n |> keep(columns: [\"_value\", \"transaction\"])\r\n\r\njoin(\r\n tables:{countAll:countAll, countError:countError},\r\n on:[\"transaction\"]\r\n )\r\n |> map(fn:(r) => ({\r\n transaction: r.transaction,\r\n ErrorRate: r._value_countError / r._value_countAll \r\n})) ", "refId": "G" } ], "title": "Metrics", "transformations": [ { "id": "merge", "options": {} } ], "type": "table" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 62 }, "id": 28, "panels": [], "title": "Errors", "type": "row" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green" } ] }, "unit": "errors" }, "overrides": [] }, "gridPos": { "h": 11, "w": 24, "x": 0, "y": 63 }, "id": 26, "options": { "legend": { "calcs": [ "sum" ], "displayMode": "list", "placement": "right" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"statut\"] == \"ko\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> group(columns: [\"transaction\"])\r\n |> aggregateWindow(every: ${send_interval}s, fn: sum, createEmpty: true)", "refId": "A" } ], "title": "Errors Per Sample", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green" } ] } }, "overrides": [] }, "gridPos": { "h": 11, "w": 24, "x": 0, "y": 74 }, "id": 29, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": true }, "showHeader": true, "sortBy": [ { "desc": true, "displayName": "Count" } ] }, "pluginVersion": "9.0.3", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "query": "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"jmeter\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"count\")\r\n |> filter(fn: (r) => r[\"application\"] == \"${application}\")\r\n |> filter(fn: (r) => r[\"transaction\"] != \"all\")\r\n |> filter(fn: (r) => r[\"responseCode\"] != \"\")\r\n |> filter(fn: (r) => r[\"responseCode\"] != \"0\")\r\n |> group(columns: [\"transaction\", \"responseCode\", \"responseMessage\"])\r\n |> keep(columns: [\"_value\", \"transaction\", \"responseCode\", \"responseMessage\"])\r\n |> sum()\r\n |> group()\r\n |> rename(columns: {\"_value\": \"Count\"})", "refId": "A" } ], "title": "Error Info", "type": "table" } ], "refresh": false, "schemaVersion": 36, "style": "dark", "tags": [], "templating": { "list": [ { "hide": 2, "name": "send_interval", "query": "${VAR_SEND_INTERVAL}", "skipUrlSync": false, "type": "constant", "current": { "value": "${VAR_SEND_INTERVAL}", "text": "${VAR_SEND_INTERVAL}", "selected": false }, "options": [ { "value": "${VAR_SEND_INTERVAL}", "text": "${VAR_SEND_INTERVAL}", "selected": false } ] }, { "current": {}, "datasource": { "type": "influxdb", "uid": "${DS_TEMPLATE-2}" }, "definition": "import \"influxdata/influxdb/schema\"\r\n\r\nschema.tagValues(\r\n bucket: v.bucket,\r\n tag: \"application\",\r\n predicate: (r) => r._measurement == \"events\",\r\n start: -1000d\r\n )", "hide": 0, "includeAll": false, "label": "application", "multi": false, "name": "application", "options": [], "query": "import \"influxdata/influxdb/schema\"\r\n\r\nschema.tagValues(\r\n bucket: v.bucket,\r\n tag: \"application\",\r\n predicate: (r) => r._measurement == \"events\",\r\n start: -1000d\r\n )", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "JMeter test results influxdb2 standart backend listener", "uid": "JMeter-test-results-influxdb2", "version": 3, "weekStart": "", "gnetId": 17472, "description": "Dashboard for visualizing JMeter metrics in influxdb2" }
2022-11-24 15:19:31 UTC
2022-11-25 08:45:51 UTC
1
5
18,151
1
801,052
27,876
0
live-agent-adapter
Live Agent Adapter
Live Agent Adapter Built-in Dashboard
2023-02-22 06:09:49 UTC
2023-02-22 06:11:34 UTC
0
null
0
0
null
null
0
0
27,876
1
18,151
1
0
null
{ "__inputs": [ { "name": "DS_PROMETHEUS", "label": "Prometheus", "description": "", "type": "datasource", "pluginId": "prometheus", "pluginName": "Prometheus" } ], "__elements": {}, "__requires": [ { "type": "panel", "id": "bargauge", "name": "Bar gauge", "version": "" }, { "type": "panel", "id": "gauge", "name": "Gauge", "version": "" }, { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.3.6" }, { "type": "datasource", "id": "prometheus", "name": "Prometheus", "version": "1.0.0" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "grafana", "uid": "-- Grafana --" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "links": [], "liveNow": false, "panels": [ { "collapsed": true, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 0 }, "id": 4, "panels": [], "title": "UpstreamWorks API", "type": "row" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 1 }, "id": 13, "options": { "displayMode": "gradient", "minVizHeight": 10, "minVizWidth": 0, "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "showUnfilled": true }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(laa_uw_chat_session_requests_total) by (job)", "legendFormat": "Requested", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(laa_uw_chat_session_requests_waiting_current) by (job)", "hide": false, "legendFormat": "Waiting", "range": true, "refId": "C" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(laa_uw_chat_session_requests_accepted_total) by (job)", "hide": false, "legendFormat": "Accepted", "range": true, "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(laa_uw_chat_session_requests_closed_total) by (job)", "hide": false, "legendFormat": "Closed", "range": true, "refId": "D" } ], "title": "Chat Sessions Status", "type": "bargauge" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 1 }, "id": 18, "options": { "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "showThresholdLabels": false, "showThresholdMarkers": true }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(laa_uw_chat_sessions_alive_current) by (job)", "legendFormat": "__auto", "range": true, "refId": "A" } ], "title": "Current", "type": "gauge" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 9 }, "id": 14, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "histogram_quantile(.90, sum by (job, le) (rate(laa_uw_http_request_duration_seconds_bucket[10m])))", "legendFormat": "__auto", "range": true, "refId": "A" } ], "title": "Request Duration P90 over 10m", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 9 }, "id": 19, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "histogram_quantile(.99, sum by (job, le) (rate(laa_uw_http_request_duration_seconds_bucket[10m])))", "legendFormat": "__auto", "range": true, "refId": "A" } ], "title": "Request Duration P99 over 10m", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 17 }, "id": 15, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(laa_uw_http_requests_total{statusCode=~\"2..\"}) by (job, statusCode)", "legendFormat": "{{job}}, {{statusCode}}", "range": true, "refId": "A" } ], "title": "2XX HTTP Requests", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 9, "w": 12, "x": 12, "y": 17 }, "id": 16, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(laa_uw_http_requests_total{statusCode!~\"2..\"}) by (job, statusCode, route)", "legendFormat": " {{statusCode}}, {{route}}", "range": true, "refId": "A" } ], "title": "3/4/5XX HTTP Requests", "type": "timeseries" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 26 }, "id": 2, "panels": [], "title": "Nuance Digital Engagement API", "type": "row" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 27 }, "id": 6, "options": { "displayMode": "gradient", "minVizHeight": 10, "minVizWidth": 0, "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "showUnfilled": true }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(laa_nuance_chat_session_requests_total) by (job)", "legendFormat": "Requested", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(laa_nuance_chat_session_requests_accepted_total) by (job)", "hide": false, "legendFormat": "Accepted", "range": true, "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(laa_nuance_chat_session_requests_closed_total) by (job)", "hide": false, "legendFormat": "Closed", "range": true, "refId": "D" } ], "title": "Chat Sessions Status", "type": "bargauge" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 27 }, "id": 20, "options": { "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "showThresholdLabels": false, "showThresholdMarkers": true }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(laa_nuance_chat_sessions_alive_current) by (job)", "legendFormat": "__auto", "range": true, "refId": "A" } ], "title": "Current", "type": "gauge" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 35 }, "id": 12, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "histogram_quantile(.90, sum by (job, le) (rate(laa_nuance_http_request_duration_seconds_bucket[10m])))", "legendFormat": "__auto", "range": true, "refId": "A" } ], "title": "Request Duration P90", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 35 }, "id": 21, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "histogram_quantile(.99, sum by (job, le) (rate(laa_nuance_http_request_duration_seconds_bucket[10m])))", "legendFormat": "__auto", "range": true, "refId": "A" } ], "title": "Request Duration P99 over 10m", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 43 }, "id": 8, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(laa_nuance_http_requests_total{statusCode=~\"2..\"}) by (job, statusCode)", "legendFormat": "{{job}}, {{statusCode}}", "range": true, "refId": "A" } ], "title": "2XX HTTP Requests", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 9, "w": 12, "x": 12, "y": 43 }, "id": 22, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "sum(laa_nuance_http_requests_total{statusCode!~\"2..\"}) by (job, statusCode, route)", "legendFormat": "{{job}}, {{statusCode}}, {{route}}", "range": true, "refId": "A" } ], "title": "3/4/5XX HTTP Requests", "type": "timeseries" } ], "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Live Agent Adapter", "uid": "wBtR5I1Vk", "version": 2, "weekStart": "", "gnetId": 18151, "description": "Live Agent Adapter Built-in Dashboard " }
2023-02-22 06:09:49 UTC
2023-02-22 06:11:35 UTC
0
0
18,236
1
810,761
28,004
0
1-windows-exporter-for-prometheus-dashboard-cn-v20201012
windows_exporter_add_process
fork 1 windows_exporter for Prometheus Dashboard CN v20201012
2023-03-08 07:58:02 UTC
2023-03-08 08:02:27 UTC
0
增加了进程监控
0
0
null
null
0
0
28,004
1
18,236
1
0
Windows的Prometheus监控看板展示,增加了资源汇总展示,优化了明细展示。更新支持windows_exporter 0.13.0。
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"barAlignment": 0, "drawStyle": "line", "fillOpacity": 5, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "log": 2, "type": "symlog" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 19 }, "id": 69, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "multi", "sort": "asc" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS_203}" }, "editorMode": "code", "expr": "irate(windows_process_io_operations_total{instance=\"$instance\", process=\"$process\"}[$__rate_interval])", "legendFormat": "{{mode}}", "range": true, "refId": "A" } ], "title": "磁盘io", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS_203}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 4, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "log": 2, "type": "symlog" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 27 }, "id": 71, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": false }, "tooltip": { "mode": "multi", "sort": "asc" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS_203}" }, "editorMode": "code", "expr": "irate(windows_process_page_faults_total{instance=\"$instance\",process=\"$process\"}[$__rate_interval])", "legendFormat": "在此进程中执行的线程产生的页错误", "range": true, "refId": "A" } ], "title": "在此进程中执行的线程产生的页错误", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS_203}" }, "fieldConfig": { "defaults": { "color": { "mode": "continuous-BlPu" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 5, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "log": 2, "type": "symlog" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "blue" }, { "color": "red", "value": 80 } ] }, "unit": "bytes" }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 27 }, "id": 73, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": false }, "tooltip": { "mode": "multi", "sort": "asc" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS_203}" }, "editorMode": "code", "expr": "windows_process_page_file_bytes{instance=\"$instance\",process=\"$process\"}", "legendFormat": "__auto", "range": true, "refId": "A" } ], "title": "当前进程在分页文件中使用的字节数", "type": "timeseries" } ], "title": "进程资源【$show_hostname:$instance】【$process】", "type": "row" } ], "refresh": false, "schemaVersion": 37, "style": "dark", "tags": [ "Prometheus", "windows_exporter" ], "templating": { "list": [ { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS_203}" }, "definition": "label_values(windows_cs_hostname, job)", "hide": 0, "includeAll": false, "label": "JOB", "multi": false, "name": "job", "options": [], "query": { "query": "label_values(windows_cs_hostname, job)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS_203}" }, "definition": "label_values(windows_cs_hostname{job=~\"$job\"}, hostname)", "hide": 0, "includeAll": true, "label": "主机名", "multi": false, "name": "hostname", "options": [], "query": { "query": "label_values(windows_cs_hostname{job=~\"$job\"}, hostname)", "refId": "Prometheus_203-hostname-Variable-Query" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 5, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS_203}" }, "definition": "label_values(windows_cs_hostname{job=~\"$job\",hostname=~\"$hostname\"}, instance)", "hide": 0, "includeAll": false, "label": "instance", "multi": false, "name": "instance", "options": [], "query": { "query": "label_values(windows_cs_hostname{job=~\"$job\",hostname=~\"$hostname\"}, instance)", "refId": "Prometheus_203-instance-Variable-Query" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 5, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS_203}" }, "definition": "label_values(windows_cs_hostname{job=~\"$job\",instance=~\"$instance\"}, hostname)", "hide": 2, "includeAll": false, "label": "展示使用的主机名", "multi": false, "name": "show_hostname", "options": [], "query": { "query": "label_values(windows_cs_hostname{job=~\"$job\",instance=~\"$instance\"}, hostname)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS_203}" }, "definition": "label_values(windows_process_handles, process)", "hide": 0, "includeAll": false, "multi": false, "name": "process", "options": [], "query": { "query": "label_values(windows_process_handles, process)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-3h", "to": "now" }, "timepicker": { "refresh_intervals": [ "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "windows_exporter_add_process", "uid": "Kdh0OoSGz", "version": 14, "weekStart": "" }
2023-03-08 07:58:02 UTC
2023-03-08 08:02:27 UTC
0
0
18,366
1
428,770
28,188
0
ra-l-s-dashboard-node-sync
Raúl's dashboard -- NODE SYNC
Dashboard to track Filecoin Lotus node API endpoints.
2023-03-27 07:16:35 UTC
2023-03-27 07:17:50 UTC
0
null
0
0
null
null
0
0
28,188
1
18,366
1
0
Dashboard to track Filecoin Lotus node API endpoints.
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2023-03-27 07:16:36 UTC
null
0
0
18,442
1
828,810
28,292
0
pint-dashboard
Pint Dashboard
Lint your Prometheus
2023-04-05 06:57:33 UTC
2023-04-05 06:58:18 UTC
0
null
0
0
null
null
0
0
28,292
1
18,442
1
0
Lint your Prometheus
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"id": "byName", "options": "name" }, "properties": [ { "id": "custom.width", "value": 217 } ] }, { "matcher": { "id": "byName", "options": "filename" }, "properties": [ { "id": "custom.width", "value": 295 } ] }, { "matcher": { "id": "byName", "options": "problem" }, "properties": [ { "id": "custom.width", "value": 921 } ] } ] }, "gridPos": { "h": 18, "w": 15, "x": 0, "y": 8 }, "id": 7, "options": { "footer": { "countRows": false, "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "9.4.3", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROM-CLOUDSCALE-THANOS-PROD}" }, "editorMode": "code", "exemplar": false, "expr": "pint_problem", "format": "table", "instant": true, "legendFormat": "__auto", "range": false, "refId": "A" } ], "title": "Problems", "transformations": [ { "id": "groupBy", "options": { "fields": { "kind": { "aggregations": [], "operation": "groupby" }, "name": { "aggregations": [], "operation": 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2023-04-05 06:57:33 UTC
2023-04-05 06:58:17 UTC
0
0
18,847
1
866,675
28,906
0
tableaux-de-bord-mongodb
Tableaux de bord MongoDB
MongoDB -> Mongodb_Exporter -> Prometheus -> Grafana.
2023-05-29 11:03:51 UTC
2023-05-29 14:20:51 UTC
0
# Environnement * Docker : Mongodb 6.0.5 * Docker : percona/mongodb_exporter 2.37.0 * Docker : prometheus 2.44.0 * Docker : grafana 9.5.2 Attention le tableau de bord ne fonctionne qu'avec la version 2.x de mongodb_exporter. ## Mongodb * Version 6.0.5 ; * Authentification désactivée pour les tests avec mongod_exporter ; * Une instance, 160 collections. ## Mongodb_exporter La configuration du docker compose : ```yml mongodb_exporter: platform: "linux" container_name: mongodb_exporter image: percona/mongodb_exporter:${mongo_exporter_version} command: - '--mongodb.global-conn-pool' - '--collector.diagnosticdata' - '--discovering-mode' - '--mongodb.uri=mongodb://mongodb:17001/admin?ssl=false' ``` Pour l'authentification : - Il faut ajouter `--auth` comme paramètre à l'image docker de MongoDB. - `--mongodb.uri=mongodb://{login}:{password}@mongodb:27001/admin?ssl=false L'utilisation de `- '--collector.diagnosticdata'` et de `--discovering-mode` remonte 5936 indicateurs, c'est déjà trop ! ## Prometheus Pour la configuration, il ne faudra pas oublier d'ajouter dans le fichier `prometheus.yml` la source pour la collecte des données. Pour une configuratyion docker, il faudra ajouter soit de façon statique l'adresse Ip du container ou utiliser la résolution DNS interne pour docker. ```yml # MONGODB - job_name: Mongodb_exporter scrape_interval: 5s tls_config: insecure_skip_verify: true dns_sd_configs: - names: - mongodb_exporter type: A port: 9216 ``` `mongodb_exporter`est le nom de l'image docker du service mongodb_exporter. ## Grafana J'utilise deux variables : `$interval`et `$instance`. ## Auteur Laurent HADJADJ - CC 4.0 BY-NC-SA
84,731
84,732
Other
null
0
0
28,906
1
18,847
1
0
MongoDB -> Mongodb_Exporter -> Prometheus -> Grafana.
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"current": { "selected": false, "text": "auto", "value": "$__auto_interval_interval" }, "hide": 0, "name": "interval", "options": [ { "selected": true, "text": "auto", "value": "$__auto_interval_interval" }, { "selected": false, "text": "1m", "value": "1m" }, { "selected": false, "text": "10m", "value": "10m" }, { "selected": false, "text": "30m", "value": "30m" }, { "selected": false, "text": "1h", "value": "1h" }, { "selected": false, "text": "6h", "value": "6h" }, { "selected": false, "text": "12h", "value": "12h" }, { "selected": false, "text": "1d", "value": "1d" }, { "selected": false, "text": "7d", "value": "7d" }, { "selected": false, "text": "14d", "value": "14d" }, { "selected": false, "text": "30d", "value": "30d" } ], "query": "1m,10m,30m,1h,6h,12h,1d,7d,14d,30d", "queryValue": "", "refresh": 2, "skipUrlSync": false, "type": "interval" } ] }, "time": { "from": "2023-05-28T06:26:53.032Z", "to": "2023-05-28T09:06:53.032Z" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "Tableaux de bord MongoDB", "uid": "AyWQt9jWk", "version": 62, "weekStart": "monday" }
2023-05-29 11:03:51 UTC
2023-05-29 14:20:51 UTC
0
0
17,308
1
299,646
26,611
0
rabbitmq-queue
RabbitMQ / Queue
A dashboard that monitors RabbitMQ. It is created using the (rabbitmq-mixin)[https://github.com/adinhodovic/rabbitmq-mixin]. Focuses on per-queue visualization based on detailed metrics.
2022-10-30 22:23:04 UTC
2022-10-30 22:55:41 UTC
0
A dashboard that monitors RabbitMQ. It is created using the (rabbitmq-mixin)[https://github.com/adinhodovic/rabbitmq-mixin]. Focuses on per-queue visualization based on detailed metrics. Combines well with the official dashboard for RabbitMQ. I've written a blog post [on RabbiqMQ per-queue monitoring](https://hodovi.cc/blog/rabbitmq-per-queue-monitoring/).
78,163
78,164
null
null
0
0
26,611
1
17,308
1
0
A dashboard that monitors RabbitMQ. It is created using the (rabbitmq-mixin)[https://github.com/adinhodovic/rabbitmq-mixin].
{ "__inputs": [], "__requires": [], "annotations": { "list": [] }, "description": "A dashboard that monitors RabbitMQ. 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2022-10-30 22:23:04 UTC
2022-10-30 22:25:58 UTC
0
0
17,386
1
604,490
26,715
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k8s-memory-namespace
k8s Memory namespace
Showing memory in namespace, showing memory in nodes, showing memory all cluster pods and memory/cpu limits/requests
2022-11-10 14:26:44 UTC
2022-11-10 14:29:27 UTC
0
Showing memory in namespace, showing memory in nodes, showing memory all cluster pods and memory/cpu limits/requests
78,449
78,450
null
null
0
0
26,715
1
17,386
1
0
Showing memory in namespace, showing memory in nodes, and showing memory all cluster pods
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2022-11-10 14:26:44 UTC
2022-11-10 14:28:25 UTC
0
0
17,426
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745,562
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besu-overview
Besu Overview
Provides an overview of Besu nodes
2022-11-17 08:26:58 UTC
2022-11-17 12:20:32 UTC
0
null
0
0
nodeExporter
null
0
0
26,769
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17,426
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0
Provides an overview of Besu nodes
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"query_result(ethereum_blockchain_height or besu_blockchain_height)", "hide": 0, "includeAll": true, "label": "System", "multi": true, "name": "system", "options": [], "query": "query_result(ethereum_blockchain_height or besu_blockchain_height)", "refresh": 2, "regex": "/instance=\"([^\"]*)\"/", "skipUrlSync": false, "sort": 5, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-12h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "Besu Overview", "uid": "5S-6O8VZk", "version": 4 }
2022-11-17 08:26:58 UTC
null
0
0
17,645
1
763,155
27,105
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tado
Tado
This dashboard uses a Prometheus exporter for the Tado thermostat to simply show the temperature, humidity and heating settings.
2022-12-18 13:28:59 UTC
2022-12-18 13:29:16 UTC
0
null
0
0
null
null
0
0
27,105
1
17,645
1
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This dashboard uses a Prometheus exporter for the Tado thermostat to simply show the temperature, humidity and heating settings.
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2022-12-18 13:28:59 UTC
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New Trade Dashboard DGRG
Monitoring for trading team
2023-02-14 14:25:54 UTC
2023-02-14 15:01:01 UTC
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2023-02-14 14:25:54 UTC
2023-02-14 15:01:01 UTC
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redpanda-ops-dashboard
Redpanda Ops Dashboard
Redpanda Operations Dashboard
2023-02-18 00:12:36 UTC
2023-03-06 13:54:54 UTC
0
A dashboard focused on Redpanda operations, with the following charts: - nodes up (count) - node uptime - topics (count) - partitions (count) - throughput (total) - leadership transfers per 5min - partition balance (percentage) - under-replicated partitions (count) - leaderless partitions (count) - storage used (percentage) - storage health - allocated memory (total, percentage) - CPU utilization (total, percentage) - Kafka RPC - cluster info
82,224
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27,841
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18,135
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}, { "selected": false, "text": "instance,shard", "value": "instance,shard" }, { "selected": false, "text": "pod,shard", "value": "pod,shard" } ], "query": "cluster, pod, instance, shard, instance\\,shard, pod\\,shard", "queryValue": "", "skipUrlSync": false, "type": "custom" }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(redpanda_cloud_data_cluster_name)", "hide": 0, "includeAll": false, "label": "Data cluster", "multi": true, "name": "data_cluster", "options": [], "query": { "query": "label_values(redpanda_cloud_data_cluster_name)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "filters": [], "hide": 0, "name": "Filters", "skipUrlSync": false, "type": "adhoc" } ] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "Redpanda Ops Dashboard", "uid": "FejE4c6nz", "version": 1, "weekStart": "", "gnetId": 18135, "description": "Redpanda Operations Dashboard" }
2023-02-18 00:12:36 UTC
2023-02-18 00:13:01 UTC
0
0
17,055
1
716,188
26,177
0
sap-netweaver
SAP NetWeaver
Shows stats of a SAP NetWeaver cluster. Brought to you by SUSE.
2022-09-26 20:19:35 UTC
2022-09-26 20:19:48 UTC
0
null
0
0
null
null
0
0
26,177
1
17,055
1
0
Shows stats of a SAP NetWeaver cluster. Brought to you by SUSE.
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2022-09-26 20:19:35 UTC
null
0
0
17,382
1
736,857
26,710
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sql-servers
SQL Servers
MSSQL Server performance metrics by Instances
2022-11-09 16:14:21 UTC
2022-11-09 16:14:27 UTC
0
null
0
0
null
null
0
0
26,710
1
17,382
1
0
MSSQL Server performance metrics by Instances
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"${DS_INFLUX-TELEGRAF}", "definition": "", "hide": 0, "includeAll": false, "label": null, "multi": false, "multiFormat": "glob", "name": "Instance", "options": [], "query": "SHOW TAG VALUES WITH KEY = \"sql_instance\"", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": { "now": true, "refresh_intervals": [ "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "browser", "title": "SQL Servers", "uid": "p1-aL7dmz", "version": 100 }
2022-11-09 16:14:21 UTC
null
0
0
17,638
1
751,517
27,097
0
podman
Podman Dashboard 中文版(zh_CN)
Podman仪表盘,由冰雪殇璃陌梦基于Prometheus-podman-exporter制作
2022-12-16 13:52:06 UTC
2023-02-05 13:58:11 UTC
0
# Grafana Podman Dashboard 通过 Grafana 监控 Podman 容器和镜像运行状态,基于`prometheus-podman-exporter`。 [English](https://grafana.com/grafana/dashboards/17639) ## 食用方法 1. 安装并运行[prometheus-podman-exporter](https://github.com/containers/prometheus-podman-exporter),记得添加参数`-i`以启用镜像信息收集 2. 导入仪表盘: - 使用`grafana.com`: > Link: [Podman Dashboard 中文版(zh_CN)](https://grafana.com/grafana/dashboards/17638) > Dashboard ID: `17638` - 或者手动导入 json 文件: [dashboard-json](https://ghproxy.com/https://raw.githubusercontent.com/DreamOfIce/grafana-podman-dashboard/main/dashboards/zh-cn.json) 3. 打开仪表盘看看吧ヾ(≧▽≦\*)o ## License MIT
79,712
79,713
Other
null
0
0
27,097
1
17,638
1
0
Podman仪表盘,由冰雪殇璃陌梦基于Prometheus-podman-exporter制作
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2022-12-16 13:52:06 UTC
2022-12-16 15:13:09 UTC
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victoriametrics-operator
VictoriaMetrics - operator
Overview for operator VictoriaMetrics v0.25.0 or higher
2023-01-12 10:34:36 UTC
2023-01-12 10:39:02 UTC
0
# VictoriaMetrics operator overview ## Requirements VictoriaMetrics operator: each revision may have different VictoriaMetrics operator version requirements. Grafana: each revision may have different Grafana version requirements. Configure metrics scraping with Prometheus text exposition format by the compatible collector, such as vmagent, single VictoriaMetrics, Prometheus or any other collector. You can also use [VMServiceScrape](https://github.com/VictoriaMetrics/helm-charts/blob/master/charts/victoria-metrics-k8s-stack/templates/victoria-metrics-operator/servicescrape.yaml) Use Prometheus datasource with Prometheus or VictoriaMetrics URL. See more details about how to configure monitoring here. ## Description Dashboard contains visualization of the most useful metrics displaying the current state and performance of the service. It covers such aspects as working queue depth, number of managed CRDs, resource utilization and others. If you have suggestions, improvements or found a bug - feel free to add an issue or add a review to the dashboard. More information about [operator](https://docs.victoriametrics.com/operator/) and [VictoriaMetrics](https://docs.victoriametrics.com/). New [releases](https://github.com/VictoriaMetrics/operator/releases).
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Overview for operator VictoriaMetrics v0.25.0 or higher
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"type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "collapsed": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 21 }, "id": 6, "panels": [], "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "refId": "A" } ], "title": "Troubleshooting", "type": "row" }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "description": "Non zero metrics indicates about error with CR object definition (typos or incorrect values) or errors with kubernetes API connection.", "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 22 }, "hiddenSeries": false, "id": 10, "legend": { "alignAsTable": true, "avg": false, "current": true, "max": true, "min": false, "show": true, "total": false, "values": true }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.3.2", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "exemplar": false, "expr": "sum(rate(controller_runtime_reconcile_errors_total{job=~\"$job\",instance=~\"$instance\"}[$__rate_interval])) by(controller) > 0 ", "instant": false, "legendFormat": "{{controller}}", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "sum(rate(controller_runtime_reconcile_total{job=~\"$job\",instance=~\"$instance\",result=\"error\"}[$__rate_interval])) by(controller) > 0", "hide": false, "legendFormat": "{{label_name}}", "range": true, "refId": "B" } ], "thresholds": [], "timeRegions": [], "title": "reconcile errors by controller", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "description": "Operator limits number of reconcilation events to 5 events per 2 seconds.\n For now, this limit is applied only for vmalert and vmagent controllers.\n It should reduce load at kubernetes cluster and increase operator performance.", "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 22 }, "hiddenSeries": false, "id": 18, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.3.2", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "sum(rate(operator_reconcile_throttled_events_total[$__rate_interval])) by(controller)", "legendFormat": "{{controller}}", "range": true, "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "throttled reconcilation events", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "description": "Number of objects waiting in the queue for reconciliation. Non-zero values indicate that operator cannot process CR objects changes with the given resources.", "fill": 1, "fillGradient": 0, "gridPos": { "h": 11, "w": 12, "x": 0, "y": 30 }, "hiddenSeries": false, "id": 20, "legend": { "alignAsTable": true, "avg": false, "current": true, "max": true, "min": false, "show": true, "total": false, "values": true }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.3.2", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "max(workqueue_depth{job=~\"$job\",instance=~\"$instance\"}) by (name)", "legendFormat": "{{label_name}}", "range": true, "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "Wokring queue depth", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "description": " For controllers with StatefulSet it's ok to see latency greater then 3 seconds. It could be vmalertmanager,vmcluster or vmagent in statefulMode.\n\n For other controllers, latency greater then 1 second may indicate issues with kubernetes cluster or operator's performance.\n ", "fieldConfig": { "defaults": { "unit": "s" }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 11, "w": 12, "x": 12, "y": 30 }, "hiddenSeries": false, "id": 26, "legend": { "alignAsTable": true, "avg": true, "current": false, "max": true, "min": false, "rightSide": false, "show": true, "total": false, "values": true }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.3.2", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "histogram_quantile(0.99,sum(rate(controller_runtime_reconcile_time_seconds_bucket[$__rate_interval])) by(le,controller) )", "legendFormat": "q.99 {{controller}}", "range": true, "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "Reconcilation latency by controller", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "s", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "collapsed": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 41 }, "id": 4, "panels": [], "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "refId": "A" } ], "title": "resources", "type": "row" }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "fieldConfig": { "defaults": { "unit": "bytes" }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 42 }, "hiddenSeries": false, "id": 28, "legend": { "alignAsTable": true, "avg": true, "current": true, "max": true, "min": false, "show": true, "total": false, "values": true }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.3.2", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "sum(go_memstats_sys_bytes{job=~\"$job\", instance=~\"$instance\"}) ", "legendFormat": "requested from system", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "sum(go_memstats_heap_inuse_bytes{job=~\"$job\", instance=~\"$instance\"}) ", "hide": false, "legendFormat": "heap inuse", "range": true, "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "sum(go_memstats_stack_inuse_bytes{job=~\"$job\", instance=~\"$instance\"})", "hide": false, "legendFormat": "stack inuse", "range": true, "refId": "C" }, { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "sum(process_resident_memory_bytes{job=~\"$job\", instance=~\"$instance\"})", "hide": false, "legendFormat": "resident", "range": true, "refId": "D" } ], "thresholds": [], "timeRegions": [], "title": "Memory usage ($instance)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "bytes", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": 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"values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 50 }, "hiddenSeries": false, "id": 32, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.3.2", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "sum(go_goroutines{job=~\"$job\", instance=~\"$instance\"})", "legendFormat": "goroutines", "range": true, "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "Goroutines ($instance)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "$ds" }, "fieldConfig": { "defaults": { "unit": "s" }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 50 }, "hiddenSeries": false, "id": 34, "legend": { "alignAsTable": true, "avg": true, "current": false, "max": true, "min": false, "show": true, "total": false, "values": true }, "lines": true, "linewidth": 1, "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "8.3.2", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "$ds" }, "editorMode": "code", "expr": "sum(rate(go_gc_duration_seconds_sum{job=~\"$job\", instance=~\"$instance\"}[$__rate_interval]))\n/\nsum(rate(go_gc_duration_seconds_count{job=~\"$job\", instance=~\"$instance\"}[$__rate_interval]))", "legendFormat": "avg gc duration", "range": true, "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "GC duration ($instance)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "s", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } } ], "refresh": "", "schemaVersion": 37, "style": "dark", "tags": [ "operator", "VictoriaMetrics" ], "templating": { "list": [ { "current": { "selected": false, "text": "cloud-c15", "value": "cloud-c15" }, "hide": 0, "includeAll": false, "multi": false, "name": "ds", "options": [], "query": "prometheus", "queryValue": "", "refresh": 1, "regex": "", "skipUrlSync": false, "type": "datasource" }, { "current": {}, "datasource": { "type": "prometheus", "uid": "$ds" }, "definition": "label_values(operator_log_messages_total,job)", "hide": 0, "includeAll": false, "multi": false, "name": "job", "options": [], "query": { "query": "label_values(operator_log_messages_total,job)", "refId": "StandardVariableQuery" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "prometheus", "uid": "$ds" }, "definition": "label_values(operator_log_messages_total{job=~\"$job\"},instance)", "hide": 0, "includeAll": true, "multi": false, "name": "instance", "options": [], "query": { "query": "label_values(operator_log_messages_total{job=~\"$job\"},instance)", "refId": "StandardVariableQuery" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "prometheus", "uid": "$ds" }, "definition": "label_values(vm_app_version{job=\"$job\", instance=\"$instance\"}, version)", "hide": 2, "includeAll": false, "multi": false, "name": "version", "options": [], "query": { "query": "label_values(vm_app_version{job=\"$job\", instance=\"$instance\"}, version)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 2, "type": "query" } ] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": {}, "timezone": "", "title": "VictoriaMetrics - operator", "uid": "1H179hunk", "version": 1, "weekStart": "", "gnetId": 17869 }
2023-01-12 10:34:36 UTC
null
0
0
18,538
1
420,992
28,425
0
blackbox-exporter
BlackBox Exporter
Prometheus BlackBox Exporter Dashboard
2023-04-16 20:09:33 UTC
2023-04-27 06:31:04 UTC
0
# Dashboard for Prometheus Blackbox Exporter uses Prometheus Blackbox-Exporter to retreive metrics.
83,753
83,754
Other
null
0
0
28,425
1
18,538
1
0
Prometheus BlackBox Exporter Dashboard
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2023-04-16 20:09:33 UTC
2023-04-21 07:42:01 UTC
0
0
17,160
1
724,272
26,375
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grafana-dashboard-for-cloudflare-analytics-and-overview
Grafana Dashboard for Cloudflare Analytics and Overview
Grafana Dashboard for Cloudflare - Pulls out all the Cloudflare Information, and Analytics from the RESTful API.
2022-10-12 03:04:24 UTC
2022-10-12 03:04:44 UTC
0
null
0
0
null
null
0
0
26,375
1
17,160
1
0
Grafana Dashboard for Cloudflare - Pulls out all the Cloudflare Information, and Analytics from the RESTful API.
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2022-10-12 03:04:24 UTC
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17,375
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k8s-resource-monitoring
K8s Resource Monitoring
The dashboard offers a simple view of the resource consumption of the deployed pods and their impact on the cluster. The outline of requests and limits for each pod and their totals can be used as a tool to determine appropriate resource quotas for your cluster.
2022-11-08 16:52:45 UTC
2022-11-08 16:58:19 UTC
0
# Kubernetes resource monitoring Grafana dashboard [![license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/instrumentisto/grafana-dashboard-kubernetes-prometheus/blob/master/LICENSE.md) [![prometheus](https://img.shields.io/badge/prometheus-%5E15.18.0-green.svg)](https://github.com/prometheus/prometheus) [![grafana](https://img.shields.io/badge/grafana-%5E9.2.3-green.svg)](https://github.com/grafana/grafana) [![GitHub Repo stars](https://img.shields.io/github/stars/yeusiukou/grafana-k8s-resource-monitoring?style=social)](https://github.com/yeusiukou/grafana-k8s-resource-monitoring) The dashboard offers a simple view of the resource consumption of the deployed pods and their impact on the cluster. The outline of requests and limits for each pod and their totals can be used as a tool to determine appropriate resource quotas for your cluster. ### Prerequisites [Kubernetes](http://kubernetes.io/) cluster with deployed [Prometheus](https://prometheus.io/). Prometheus will use metrics provided by: - [cAdvisor](https://github.com/google/cadvisor) (runs on each node by default) - node-exporter, kube-state-metrics - can be installed with Prometheus via the [helm chart](https://github.com/prometheus-community/helm-charts/tree/main/charts/prometheus). Managed Kubernetes clusters such as AKS can have them enabled [automatically](https://learn.microsoft.com/en-us/azure/azure-monitor/essentials/prometheus-metrics-scrape-default). ### Features - List of cluster nodes and their resources: CPU, memory, filesystem. ![CleanShot 2022-11-07 at 12 32 38](https://user-images.githubusercontent.com/8567013/200406399-96fbd65f-39ea-48af-a8ec-2b06bbf476d9.png) - List of the pods restarted in the selected timeframe (hover over the dots to see the exact time of the restart). - Counts of pods in each state. ![CleanShot 2022-11-07 at 11 57 00](https://user-images.githubusercontent.com/8567013/200406541-f4c98d38-ede8-44db-954a-0d016fcdf9db.png) - Resource outline for each pod. Includes requests, limits and current usage stats. These metrics and the ability to filter by namespace can help calculate appropriate resource quotas for your cluster. Click on the magnifying glass to filter the dashboard by namespace, node or pod. ![CleanShot 2022-11-07 at 12 09 09](https://user-images.githubusercontent.com/8567013/200406603-7153d3cd-6f92-4415-92b4-ffeb1cd55426.png) - Pod CPU and memory usage over time. ![CleanShot 2022-11-07 at 12 13 09](https://user-images.githubusercontent.com/8567013/200406615-2a2fd231-de0d-4a10-bbfc-94a5a4338d68.png)
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2022-11-08 16:52:45 UTC
2022-11-08 16:58:19 UTC
1
5
17,545
1
756,411
26,938
0
rabbitmq-stream
RabbitMQ-Stream
Stream protocol message rates and errors
2022-12-06 13:20:28 UTC
2022-12-06 13:20:38 UTC
0
null
0
0
null
null
0
0
26,938
1
17,545
1
0
Stream protocol message rates and errors
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"datasource": "-- Dashboard --", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 20, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": true, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [ { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?0(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(86, 166, 75, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?1(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(242, 204, 12, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?2(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(50, 116, 217, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?3(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(163, 82, 204, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?4(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 120, 10, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?5(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(150, 217, 141, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?6(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 238, 82, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?7(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(138, 184, 255, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?8(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(202, 149, 229, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?9(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 179, 87, 1)", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 19 }, "id": 7, "options": { "legend": { "calcs": [ "mean", "max", "min", "last" ], "displayMode": "table", "placement": "bottom" }, "tooltip": { "mode": "single" } }, "pluginVersion": "8.0.3", "targets": [ { "panelId": 17, "refId": "A" } ], "timeFrom": null, "timeShift": null, "title": "Stream messages received / s", "type": "timeseries" }, { "datasource": "-- Dashboard --", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 20, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": true, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [ { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?0(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(86, 166, 75, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?1(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(242, 204, 12, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?2(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(50, 116, 217, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?3(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(163, 82, 204, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?4(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 120, 10, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?5(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(150, 217, 141, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?6(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 238, 82, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?7(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(138, 184, 255, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?8(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(202, 149, 229, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?9(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 179, 87, 1)", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 9, "w": 12, "x": 12, "y": 19 }, "id": 9, "options": { "legend": { "calcs": [ "mean", "max", "min", "last" ], "displayMode": "table", "placement": "bottom" }, "tooltip": { "mode": "single" } }, "pluginVersion": "8.0.3", "targets": [ { "panelId": 20, "refId": "A" } ], "timeFrom": null, "timeShift": null, "title": "Stream messages delivered / s", "type": "timeseries" }, { "datasource": "-- Dashboard --", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 20, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": true, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [ { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?0(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(86, 166, 75, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?1(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(242, 204, 12, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?2(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(50, 116, 217, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?3(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(163, 82, 204, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?4(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 120, 10, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?5(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(150, 217, 141, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?6(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 238, 82, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?7(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(138, 184, 255, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?8(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(202, 149, 229, 1)", "mode": "fixed" } } ] }, { "matcher": { "id": "byRegexp", "options": "/^rabbit@[a-zA-Z\\.\\-]*?9(\\b|\\.)/" }, "properties": [ { "id": "color", "value": { "fixedColor": "rgba(255, 179, 87, 1)", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 28 }, "id": 11, "options": { "legend": { "calcs": [ "mean", "max", "min", "last" ], "displayMode": "table", "placement": "bottom" }, "tooltip": { "mode": "single" } }, "pluginVersion": "8.0.3", "targets": [ { "panelId": 18, "refId": "A" } ], "timeFrom": null, "timeShift": null, "title": "Stream messages confirmed to publishers / s", "type": "timeseries" }, { "datasource": "${DS_PROMETHEUS}", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 20, "gradientMode": "opacity", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "stepBefore", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": true, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "dark-red", "value": null } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 13, "w": 24, "x": 0, "y": 37 }, "id": 23, "options": { "legend": { "calcs": [ "last" ], "displayMode": "table", "placement": "right" }, "tooltip": { "mode": "single" } }, "pluginVersion": "8.0.3", "targets": [ { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_access_refused_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "interval": "", "legendFormat": "access_refused", "refId": "A" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_authentication_failure_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "error_authentication_failure", "refId": "B" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_frame_too_large_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "frame_too_large", "refId": "C" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_internal_error_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "internal_error", "refId": "D" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_precondition_failed_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "precondition_failed", "refId": "E" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_publisher_does_not_exist_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "publisher_does_not_exist", "refId": "F" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_sasl_authentication_failure_loopback_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "sasl_authentication_failure_loopback", "refId": "G" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_sasl_challenge_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "sasl_challenge", "refId": "H" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_sasl_error_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "sasl_error", "refId": "I" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_sasl_mechanism_not_supported_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "sasl_mechanism_not_supported", "refId": "J" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_stream_already_exists_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "stream_already_exists", "refId": "K" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_stream_does_not_exist_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "stream_does_not_exist", "refId": "L" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_stream_not_available_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "stream_not_available", "refId": "M" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_subscription_id_already_exists_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "subscription_id_already_exists", "refId": "N" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_subscription_id_does_not_exist_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "subscription_id_does_not_exist", "refId": "O" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_unknown_frame_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "error_unknown_frame", "refId": "P" }, { "exemplar": true, "expr": "sum by(rabbitmq_cluster) (rabbitmq_global_stream_error_vhost_access_failure_total{protocol=\"stream\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"})", "hide": false, "interval": "", "legendFormat": "vhost_access_failure", "refId": "Q" } ], "timeFrom": null, "timeShift": null, "title": "Errors since boot", "type": "timeseries" } ], "refresh": "15s", "schemaVersion": 30, "style": "dark", "tags": [ "rabbitmq-stream", "rabbitmq-prometheus" ], "templating": { "list": [ { "current": { "selected": false, "text": "default", "value": "default" }, "datasource": "${DS_PROMETHEUS}", "description": null, "error": null, "hide": 2, "includeAll": false, "label": "datasource", "multi": false, "name": "DS_PROMETHEUS", "options": [], "query": "prometheus", "refresh": 1, "regex": "", "skipUrlSync": false, "type": "datasource" }, { "allValue": null, "current": {}, "datasource": "${DS_PROMETHEUS}", "definition": "label_values(rabbitmq_identity_info, namespace)", "description": null, "error": null, "hide": 0, "includeAll": false, "label": "Namespace", "multi": false, "name": "namespace", "options": [], "query": { "query": "label_values(rabbitmq_identity_info, namespace)", "refId": "StandardVariableQuery" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "allValue": null, "current": {}, "datasource": "${DS_PROMETHEUS}", "definition": "label_values(rabbitmq_identity_info{namespace=\"$namespace\"}, rabbitmq_cluster)", "description": null, "error": null, "hide": 0, "includeAll": false, "label": "RabbitMQ Cluster", "multi": false, "name": "rabbitmq_cluster", "options": [], "query": { "query": "label_values(rabbitmq_identity_info{namespace=\"$namespace\"}, rabbitmq_cluster)", "refId": "StandardVariableQuery" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": { "refresh_intervals": [ "15s", "30s", "1m", "5m", "10m" ] }, "timezone": "", "title": "RabbitMQ-Stream", "uid": "j7MCpqZ7k", "version": 1 }
2022-12-06 13:20:28 UTC
null
0
0
17,613
1
299,646
27,061
0
django-requests-by-view
Django / Requests / By View
A dashboard that monitors Django which focuses on breaking down requests by view. It is created using the [Django-mixin](https://github.com/adinhodovic/django-mixin).
2022-12-15 00:37:21 UTC
2022-12-15 00:43:11 UTC
0
A dashboard that monitors Django which focuses on breaking down requests by view. It is created using the [Django-mixin](https://github.com/adinhodovic/django-mixin).
79,586
79,587
null
null
2
5
27,061
1
17,613
1
0
A dashboard that monitors Django which focuses on breaking down requests by view. It is created using the [Django-mixin](https://github.com/adinhodovic/django-mixin).
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2022-12-15 00:37:21 UTC
null
2
5
18,517
1
835,881
28,397
0
postgresql-aws-rds-database-overview
PostgreSQL AWS RDS Database Overview
Dashboard for PostgreSQL AWS RDS
2023-04-14 01:47:40 UTC
2023-04-14 02:12:25 UTC
0
This dashboard based on prometheus-postgres-exporter and prometheus-cloudwatch-exporter for monitoring PostgreSQL AWS RDS
83,354
83,355
Other
null
0
0
28,397
1
18,517
1
0
This dashboard works with prometheus postgres-exporte and cloudwatch exporter
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2023-04-14 01:47:40 UTC
2023-04-14 01:54:15 UTC
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celestia
CELESTIA
Get a start with this basic Celestia dashboard
2023-05-12 09:44:04 UTC
2023-05-12 09:45:18 UTC
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2023-05-12 09:44:05 UTC
2023-05-12 09:45:16 UTC
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sonarqube-system-dashboard
Sonarqube - System Dashboard
The Sonarqube - System Dashboard dashboard presents all metrics in detail of Sonarqube instance.
2022-12-17 12:40:40 UTC
2022-12-17 12:53:54 UTC
0
## Quick start with `docker` Run command below (edit sonarqube_url and token): ``` docker run -d \ -p8198:8198 \ -eSONARQUBE_SERVER='http://192.168.3.101:9001' \ -eSONARQUBE_TOKEN='squ_af1e521e19aef5c5de1cb6df89adf3cbb3a9759e' \ return200/sonarqube-exporter:latest ``` Access `localhost:8198` ![overview!](https://github.com/return200-ok/sonarqube_prometheus_exporter/blob/main/assets/sonarqube_exporter_metrisc.png?raw=true)
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2022-12-17 12:40:40 UTC
2022-12-17 12:53:50 UTC
1
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17,907
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746,033
27,451
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erlang-distribution
Erlang-Distribution
Erlang Distribution links, inet socket, port driver, dist process + tls_connection & tls_sender
2023-01-19 15:26:08 UTC
2023-01-19 15:36:05 UTC
0
null
0
0
null
null
0
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27,451
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17,907
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Erlang Distribution links, inet socket, port driver, dist process + tls_connection & tls_sender
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"seriesOverrides": [ { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?0(\\b|\\.)/", "color": "#56A64B" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?1(\\b|\\.)/", "color": "#F2CC0C" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?2(\\b|\\.)/", "color": "#3274D9" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?3(\\b|\\.)/", "color": "#A352CC" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?4(\\b|\\.)/", "color": "#FF780A" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?5(\\b|\\.)/", "color": "#96D98D" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?6(\\b|\\.)/", "color": "#FFEE52" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?7(\\b|\\.)/", "color": "#8AB8FF" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?8(\\b|\\.)/", "color": "#CA95E5" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?9(\\b|\\.)/", "color": "#FFB357" } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "(rate(erlang_vm_dist_recv_bytes[60s]) * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", 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25 }, "id": 11, "panels": [], "title": "port driver", "type": "row" }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "The total number of bytes allocated for this port by the runtime system. The port itself can have allocated memory that is not included.", "fill": 0, "fillGradient": 0, "gridPos": { "h": 5, "w": 12, "x": 0, "y": 26 }, "hiddenSeries": false, "id": 12, "legend": { "alignAsTable": true, "avg": false, "current": true, "max": true, "min": true, "show": false, "total": false, "values": true }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "dataLinks": [] }, "percentage": false, "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?0(\\b|\\.)/", "color": "#56A64B" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?1(\\b|\\.)/", "color": "#F2CC0C" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?2(\\b|\\.)/", "color": "#3274D9" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?3(\\b|\\.)/", "color": "#A352CC" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?4(\\b|\\.)/", "color": "#FF780A" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?5(\\b|\\.)/", "color": "#96D98D" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?6(\\b|\\.)/", "color": "#FFEE52" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?7(\\b|\\.)/", "color": "#8AB8FF" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?8(\\b|\\.)/", "color": "#CA95E5" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?9(\\b|\\.)/", "color": "#FFB357" } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "erlang_vm_dist_port_memory_bytes * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"}", "format": "time_series", "intervalFactor": 1, "legendFormat": "{{rabbitmq_node}} -> {{peer}}", "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Memory used by the port driver", "tooltip": { "shared": true, "sort": 2, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "decimals": 0, "format": "decbytes", "label": null, "logBase": 1, "max": null, "min": "0", "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "The total number of bytes queued by the port using the ERTS driver queue implementation\n\nAny values above a few KBs hint to an overloaded distribution", "fill": 1, "fillGradient": 0, "gridPos": { "h": 5, "w": 12, "x": 12, "y": 26 }, "hiddenSeries": false, "id": 7, "legend": { "alignAsTable": true, "avg": false, "current": true, "max": true, "min": true, "show": false, "total": false, "values": true }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "dataLinks": [] }, "percentage": false, "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/rabbit/", "color": "#C4162A" } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "erlang_vm_dist_port_queue_size_bytes * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"}", "format": "time_series", "intervalFactor": 1, "legendFormat": "{{rabbitmq_node}} -> {{peer}}", "refId": "A" } ], "thresholds": [ { "colorMode": "critical", "fill": true, "line": true, "op": "gt", "value": 0, "yaxis": "left" } ], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Data in the port driver buffer", "tooltip": { "shared": true, "sort": 2, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "decimals": 0, "format": "decbytes", "label": null, "logBase": 1, "max": null, "min": "0", "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "collapsed": false, "datasource": "${DS_PROMETHEUS}", "gridPos": { "h": 1, "w": 24, "x": 0, "y": 31 }, "id": 14, "panels": [], "repeat": "erlang_vm_dist_proc_type", "title": "$erlang_vm_dist_proc_type process", "type": "row" }, { "cacheTimeout": null, "cards": { "cardHSpacing": 2, "cardMinWidth": 5, "cardRound": null, "cardVSpacing": 2 }, "color": { "cardColor": "#b4ff00", "colorScale": "sqrt", "colorScheme": "interpolateGnYlRd", "defaultColor": "#757575", "exponent": 0.5, "mode": "discrete", "thresholds": [ { "color": "#37872D", "tooltip": "waiting", "value": "6" }, { "color": "#96D98D", "tooltip": "running", "value": "5" }, { "color": "#1F60C4", "tooltip": "garbage_collecting", "value": "4" }, { "color": "#FADE2A", "tooltip": "runnable", "value": "3" }, { "color": "#FA6400", "tooltip": "suspended", "value": "2" }, { "color": "#C4162A", "tooltip": "exiting", "value": "1" } ] }, "data": { "decimals": null, "unitFormat": "short" }, "datasource": "${DS_PROMETHEUS}", "description": "", "gridPos": { "h": 5, "w": 12, "x": 0, "y": 32 }, "highlightCards": true, "id": 18, "legend": { "show": true }, "links": [], "nullPointMode": "as empty", "targets": [ { "aggregation": "Last", "decimals": 2, "displayAliasType": "Warning / Critical", "displayType": "Regular", "displayValueWithAlias": "Never", "expr": "erlang_vm_dist_proc_status{type=\"$erlang_vm_dist_proc_type\"} * on(instance) group_left(rabbitmq_node, rabbitmq_cluster) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"} ", "format": "time_series", "intervalFactor": 1, "legendFormat": "{{rabbitmq_node}} -> {{peer}}", "refId": "A", "units": "none", "valueHandler": "Number Threshold" } ], "timeFrom": null, "timeShift": null, "title": "Process state", "tooltip": { "show": true }, "type": "flant-statusmap-panel", "useMax": true, "xAxis": { "labelFormat": "%a %m/%d", "minBucketWidthToShowWeekends": 4, "show": true, "showCrosshair": true, "showWeekends": true }, "yAxis": { "show": true, "showCrosshair": false }, "yAxisSort": "metrics" }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "The number of messages currently in the message queue of the process\n\nAny values above 0 hint to an overloaded distribution", "fill": 0, "fillGradient": 0, "gridPos": { "h": 5, "w": 12, "x": 12, "y": 32 }, "hiddenSeries": false, "id": 16, "legend": { "alignAsTable": true, "avg": false, "current": true, "max": true, "min": true, "show": false, "total": false, "values": true }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "dataLinks": [] }, "percentage": false, "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/rabbit/", "color": "#C4162A" } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "erlang_vm_dist_proc_message_queue_len{type=\"$erlang_vm_dist_proc_type\"} * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"}", "format": "time_series", "intervalFactor": 1, "legendFormat": "{{rabbitmq_node}} -> {{peer}}", "refId": "A" } ], "thresholds": [ { "colorMode": "critical", "fill": true, "line": true, "op": "gt", "value": 0, "yaxis": "left" } ], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Messages in the process queue", "tooltip": { "shared": true, "sort": 2, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "decimals": 0, "format": "short", "label": null, "logBase": 1, "max": null, "min": "0", "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "The size in bytes of process memory. This includes call stack, heap, and internal structures.", "fill": 0, "fillGradient": 0, "gridPos": { "h": 5, "w": 12, "x": 0, "y": 37 }, "hiddenSeries": false, "id": 15, "legend": { "alignAsTable": true, "avg": false, "current": true, "max": true, "min": true, "show": false, "total": false, "values": true }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "dataLinks": [] }, "percentage": false, "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?0(\\b|\\.)/", "color": "#56A64B" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?1(\\b|\\.)/", "color": "#F2CC0C" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?2(\\b|\\.)/", "color": "#3274D9" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?3(\\b|\\.)/", "color": "#A352CC" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?4(\\b|\\.)/", "color": "#FF780A" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?5(\\b|\\.)/", "color": "#96D98D" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?6(\\b|\\.)/", "color": "#FFEE52" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?7(\\b|\\.)/", "color": "#8AB8FF" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?8(\\b|\\.)/", "color": "#CA95E5" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?9(\\b|\\.)/", "color": "#FFB357" } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "erlang_vm_dist_proc_memory_bytes{type=\"$erlang_vm_dist_proc_type\"} * on(instance) group_left(rabbitmq_node, rabbitmq_cluster) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\", namespace=\"$namespace\"}", "format": "time_series", "intervalFactor": 1, "legendFormat": "{{rabbitmq_node}} -> {{peer}}", "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Memory used by the process", "tooltip": { "shared": true, "sort": 2, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "decimals": 0, "format": "decbytes", "label": null, "logBase": 1, "max": null, "min": "0", "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "The number of reductions executed by the process", "fill": 0, "fillGradient": 0, "gridPos": { "h": 5, "w": 12, "x": 12, "y": 37 }, "hiddenSeries": false, "id": 17, "legend": { "alignAsTable": true, "avg": false, "current": true, "max": true, "min": true, "show": false, "total": false, "values": true }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "dataLinks": [] }, "percentage": false, "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?0(\\b|\\.)/", "color": "#56A64B" }, { "alias": "/^rabbit@[a-zA-Z\\.\\-]*?1(\\b|\\.)/", "color": "#F2CC0C" }, { "alias": 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"current": {}, "datasource": "${DS_PROMETHEUS}", "definition": "label_values(rabbitmq_identity_info, namespace)", "hide": 0, "includeAll": false, "label": "Namespace", "multi": false, "name": "namespace", "options": [], "query": "label_values(rabbitmq_identity_info, namespace)", "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false }, { "allValue": null, "current": {}, "datasource": "${DS_PROMETHEUS}", "definition": "label_values(rabbitmq_identity_info{namespace=\"$namespace\"}, rabbitmq_cluster)", "hide": 0, "includeAll": false, "index": -1, "label": "RabbitMQ Cluster", "multi": false, "name": "rabbitmq_cluster", "options": [], "query": "label_values(rabbitmq_identity_info{namespace=\"$namespace\"}, rabbitmq_cluster)", "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false }, { "allValue": null, "current": { "text": "All", "value": [ "$__all" ] }, "datasource": "${DS_PROMETHEUS}", "definition": "label_values(erlang_vm_dist_proc_status, type)", "hide": 0, "includeAll": true, "index": -1, "label": "Process type", "multi": true, "name": "erlang_vm_dist_proc_type", "options": [], "query": "label_values(erlang_vm_dist_proc_status, type)", "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": { "refresh_intervals": [ "15s", "30s", "1m", "5m", "10m" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "Erlang-Distribution", "uid": "d-SFCCmZz", "variables": { "list": [] }, "version": 20210308, "__inputs": [ { "name": "DS_PROMETHEUS", "label": "python", "description": "", "type": "datasource", "pluginId": "prometheus", "pluginName": "Prometheus" } ] }
2023-01-19 15:26:08 UTC
2023-01-19 15:36:05 UTC
0
0
17,932
1
405,174
27,495
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strategies
strategies
strategies
2023-01-23 04:13:15 UTC
2023-01-23 04:14:46 UTC
0
null
0
0
null
null
0
0
27,495
1
17,932
1
0
null
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"type": "function" } ], "groupBy": [ { "property": { "type": "string" }, "type": "groupBy" } ], "limit": 50 }, "table": "dbo.bn_balance" } ], "title": "BTC Price", "transparent": true, "type": "timeseries" } ], "refresh": false, "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [] }, "time": { "from": "now-2d", "to": "now" }, "timepicker": {}, "timezone": "", "title": "strategies", "uid": "9M211ejMk", "version": 41, "weekStart": "", "gnetId": 17932, "description": "strategies" }
2023-01-23 04:13:15 UTC
2023-01-23 04:14:46 UTC
0
0
18,194
1
304,234
27,934
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ping
PingExporter
A simple dashboard for https://github.com/czerwonk/ping_exporter
2023-02-28 09:32:53 UTC
2023-02-28 09:34:23 UTC
0
A simple Dashboard for https://github.com/czerwonk/ping_exporter
0
0
Other
null
0
0
27,934
1
18,194
1
0
null
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2023-02-28 09:32:53 UTC
2023-02-28 09:34:21 UTC
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18,234
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1-node-exporter-dashboard-22-04-17
1 Node Exporter Dashboard 22/04/17 项目分组版
使用Grafana8新表格重建,新增健康评分概念,并新增了整体资源消耗信息的一些图表。优化重要指标展示,包含整体资源展示与资源明细图表:CPU 内存 磁盘 IO 网络等监控指标。https://github.com/starsliao/ConsulManager
2023-03-08 05:46:38 UTC
2023-03-08 05:47:48 UTC
0
1
0
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null
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28,002
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使用Grafana8新表格重建,新增健康评分概念,并新增了整体资源消耗信息的一些图表。优化重要指标展示,包含整体资源展示与资源明细图表:CPU 内存 磁盘 IO 网络等监控指标。https://github.com/starsliao/ConsulManager
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2023-03-08 05:46:38 UTC
null
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app-metrics-web-monitoring-influxdb
App Metrics - Web Monitoring - InfluxDB
Dashboard to visualize metrics captured by App Metrics ASP.NET Core Middleware -https://github.com/alhardy/AppMetrics
2023-04-14 08:17:15 UTC
2023-04-14 08:18:28 UTC
0
null
0
0
Other
null
0
0
28,404
1
18,520
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Dashboard to visualize metrics captured by App Metrics ASP.NET Core Middleware -https://github.com/alhardy/AppMetrics
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2023-04-14 08:17:15 UTC
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2023-05-23 07:45:25 UTC
2023-05-23 07:45:52 UTC
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null, "timeRegions": [], "timeShift": null, "title": "HTTP probe (by phase)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "s", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": true, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "fieldConfig": { "defaults": { "custom": {} }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 12, "y": 16 }, "hiddenSeries": false, "id": 3, "legend": { "alignAsTable": true, "avg": true, "current": false, "max": false, "min": false, "rightSide": true, "show": true, "total": false, "values": true }, "lines": false, "linewidth": 1, "nullPointMode": "null", 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null, "current": {}, "datasource": "${DS_PROMETHEUS}", "definition": "label_values(instance)", "hide": 0, "includeAll": false, "label": "target", "multi": false, "name": "target", "options": [], "query": "label_values(instance)", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false }, { "current": { "value": "${VAR_HTTP_JOB}", "text": "${VAR_HTTP_JOB}", "selected": false }, "hide": 2, "label": "http_job", "name": "http_job", "options": [ { "value": "${VAR_HTTP_JOB}", "text": "${VAR_HTTP_JOB}", "selected": false } ], "query": "${VAR_HTTP_JOB}", "skipUrlSync": false, "type": "constant" }, { "current": { "value": "${VAR_ICMP_JOB}", "text": "${VAR_ICMP_JOB}", "selected": false }, "hide": 2, "label": "icmp_job", "name": "icmp_job", "options": [ { "value": "${VAR_ICMP_JOB}", "text": "${VAR_ICMP_JOB}", "selected": false } ], "query": "${VAR_ICMP_JOB}", "skipUrlSync": false, "type": "constant" } ] }, "time": { "from": "now-30m", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Website monitoring", "uid": "EmUBHUFGk", "version": 1, "description": "Website monitoring" }
2023-05-23 07:45:25 UTC
2023-05-23 07:45:53 UTC
0
0
18,823
1
497,391
28,863
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akamai-siem
Akamai SIEM
Akamai SIEM powered by Hydrolix dashboard for security analytics
2023-05-24 13:48:02 UTC
2023-05-24 13:49:15 UTC
0
Hydrolix dashboard for Akamai security events connector monitoring
84,626
84,627
null
null
0
0
28,863
1
18,823
1
0
null
{ "__inputs": [ { "name": "DS_HYDRO", "label": "Hydrolix Cluster", "description": "", "type": "datasource", "pluginId": "grafana-clickhouse-datasource", "pluginName": "ClickHouse" }, { "name": "VAR_TABLE", "type": "constant", "label": "table", "value": "", "description": "" } ], "__elements": {}, "__requires": [ { "type": "panel", "id": "geomap", "name": "Geomap", "version": "" }, { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.5.1" }, { "type": "datasource", "id": "grafana-clickhouse-datasource", "name": "ClickHouse", "version": "3.0.1-beta" }, { "type": "panel", "id": "logs", "name": "Logs", "version": "" }, { "type": "panel", "id": "table", "name": "Table", "version": "" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "grafana", "uid": "-- Grafana --" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "links": [], "liveNow": false, "panels": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 0 }, "id": 1, "options": { "legend": { "calcs": [ "sum", "max" ], "displayMode": "table", "placement": "bottom", "showLegend": true, "sortBy": "Total", "sortDesc": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "expand": true, "format": 0, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT arrayJoin(ruleActions) as ruleAction,\ncount(),\n$__timeInterval(timestamp) as time\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(hasAll(ruleTags, arrayPopBack([${tags}])), $tags)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\n\n\nGROUP BY time, ruleAction\nORDER BY time", "refId": "A", "selectedFormat": 0 } ], "title": "SIEM Logs Count", "type": "timeseries" }, { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 0 }, "id": 2, "options": { "legend": { "calcs": [ "max", "sum" ], "displayMode": "table", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "expand": true, "format": 0, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT toString(status) as status_code, count(),\n$__timeInterval(timestamp) as time\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(hasAll(ruleTags, arrayPopBack([${tags}])), $tags)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nGROUP BY time, status_code\nORDER BY time", "refId": "A", "selectedFormat": 0 } ], "title": "SIEM Logs per Status Code", "type": "timeseries" }, { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "cellOptions": { "type": "auto" }, "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "count()" }, "properties": [ { "id": "custom.width", "value": 82 } ] }, { "matcher": { "id": "byName", "options": "message" }, "properties": [ { "id": "custom.width", "value": 456 } ] }, { "matcher": { "id": "byName", "options": "message" }, "properties": [ { "id": "links", "value": [ { "title": "Filter", "url": "/d/bd475a0d-b9b0-4577-8043-8a63aeb25e5e/akamai-siem?&var-message=${__data.fields[1]}&${__all_variables}&${__url_time_range}" } ] } ] }, { "matcher": { "id": "byName", "options": "exclude" }, "properties": [ { "id": "custom.width", "value": 71 } ] } ] }, "gridPos": { "h": 11, "w": 4, "x": 0, "y": 8 }, "id": 5, "options": { "cellHeight": "sm", "footer": { "countRows": false, "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "expand": true, "format": 0, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT count(),\narrayJoin(ruleMessages) AS message\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleTags, arrayPopBack([${tags}])), $tags)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nGROUP BY message\nORDER BY count() DESC\nLIMIT 100", "refId": "A", "selectedFormat": 0 } ], "title": "Top 100 Message", "type": "table" }, { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "cellOptions": { "type": "auto" }, "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "count()" }, "properties": [ { "id": "custom.width", "value": 82 } ] }, { "matcher": { "id": "byName", "options": "tags" }, "properties": [ { "id": "custom.width", "value": 456 }, { "id": "links", "value": [ { "title": "Filter", "url": "/d/bd475a0d-b9b0-4577-8043-8a63aeb25e5e/akamai-siem?var-tags=${__data.fields[1]}&${__all_variables}&${__url_time_range}" } ] } ] } ] }, "gridPos": { "h": 11, "w": 5, "x": 4, "y": 8 }, "id": 8, "options": { "cellHeight": "sm", "footer": { "countRows": false, "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "expand": true, "format": 0, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT count(),\narrayJoin(ruleTags) AS tags\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nGROUP BY tags\nORDER BY count() DESC\nLIMIT 100", "refId": "A", "selectedFormat": 0 } ], "title": "Top 100 Tags", "type": "table" }, { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "cellOptions": { "type": "auto" }, "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "count()" }, "properties": [ { "id": "custom.width", "value": 82 } ] }, { "matcher": { "id": "byName", "options": "path" }, "properties": [ { "id": "custom.width", "value": 731 }, { "id": "links", "value": [ { "title": "Filter", "url": "/d/bd475a0d-b9b0-4577-8043-8a63aeb25e5e/akamai-siem?var-path=${__data.fields[1]}&${__all_variables}&${__url_time_range}" } ] } ] } ] }, "gridPos": { "h": 11, "w": 6, "x": 9, "y": 8 }, "id": 11, "options": { "cellHeight": "sm", "footer": { "countRows": false, "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "expand": true, "format": 0, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT count(),\npath\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nGROUP BY path\nORDER BY count() DESC\nLIMIT 100", "refId": "A", "selectedFormat": 0 } ], "title": "Top 100 Path", "type": "table" }, { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "cellOptions": { "type": "auto" }, "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "count()" }, "properties": [ { "id": "custom.width", "value": 82 } ] }, { "matcher": { "id": "byName", "options": "clientIP" }, "properties": [ { "id": "links", "value": [ { "title": "", "url": "/d/bd475a0d-b9b0-4577-8043-8a63aeb25e5e/akamai-siem?&var-clientIP=${__data.fields[1]}&${__all_variables}&${__url_time_range}" } ] } ] } ] }, "gridPos": { "h": 11, "w": 4, "x": 15, "y": 8 }, "id": 6, "options": { "cellHeight": "sm", "footer": { "countRows": false, "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "expand": true, "format": 0, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT count(),\nclientIP\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(hasAll(ruleTags, arrayPopBack([${tags}])), $tags)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nGROUP BY clientIP\nORDER BY count() DESC\nLIMIT 100", "refId": "A", "selectedFormat": 0 } ], "title": "Top 100 Client IP", "type": "table" }, { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "cellOptions": { "type": "auto" }, "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "count()" }, "properties": [ { "id": "custom.width", "value": 82 } ] }, { "matcher": { "id": "byName", "options": "host" }, "properties": [ { "id": "links", "value": [ { "title": "", "url": "/d/bd475a0d-b9b0-4577-8043-8a63aeb25e5e/akamai-siem?&var-host=${__data.fields[1]}&${__all_variables}&${__url_time_range}" } ] } ] } ] }, "gridPos": { "h": 11, "w": 5, "x": 19, "y": 8 }, "id": 10, "options": { "cellHeight": "sm", "footer": { "countRows": false, "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "expand": true, "format": 0, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT count(),\nhost\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(hasAll(ruleTags, arrayPopBack([${tags}])), $tags)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nGROUP BY host\nORDER BY count() DESC\nLIMIT 100", "refId": "A", "selectedFormat": 0 } ], "title": "Top 100 Host", "type": "table" }, { "collapsed": true, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 19 }, "id": 3, "panels": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "gridPos": { "h": 16, "w": 24, "x": 0, "y": 20 }, "id": 4, "options": { "dedupStrategy": "none", "enableLogDetails": true, "prettifyLogMessage": true, "showCommonLabels": false, "showLabels": false, "showTime": false, "sortOrder": "Descending", "wrapLogMessage": true }, "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "format": 2, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT ruleActions, * EXCEPT(ruleActions)\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(hasAll(ruleTags, arrayPopBack([${tags}])), $tags)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nLIMIT 100", "refId": "A", "selectedFormat": 2 } ], "title": "Raw Logs", "type": "logs" } ], "title": "Raw Logs", "type": "row" }, { "collapsed": true, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 20 }, "id": 9, "panels": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "hideFrom": { "legend": false, "tooltip": false, "viz": false } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green" } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 19, "w": 24, "x": 0, "y": 21 }, "id": 7, "options": { "basemap": { "config": {}, "name": "Layer 0", "type": "default" }, "controls": { "mouseWheelZoom": true, "showAttribution": true, "showDebug": false, "showMeasure": false, "showScale": false, "showZoom": true }, "layers": [ { "config": { "blur": 15, "radius": 10, "weight": { "fixed": 1, "max": 1, "min": 0 } }, "filterData": { "id": "byRefId", "options": "A" }, "location": { "gazetteer": "public/gazetteer/countries.json", "lookup": "country", "mode": "lookup" }, "name": "Layer 1", "opacity": 1, "tooltip": true, "type": "heatmap" } ], "tooltip": { "mode": "details" }, "view": { "allLayers": true, "id": "zero", "lat": 0, "lon": 0, "zoom": 1 } }, "pluginVersion": "9.5.1", "targets": [ { "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "format": 1, "meta": { "builderOptions": { "fields": [], "limit": 100, "mode": "list" } }, "queryType": "sql", "rawSql": "SELECT country, count()\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nAND $__conditionalAll(host IN (${host}), $host)\nAND $__conditionalAll(method IN (${method}), $method)\nAND $__conditionalAll(status IN (${status}), $status)\nAND $__conditionalAll(country IN (${country}), $country)\nAND $__conditionalAll(clientIP IN (${clientIP}), $clientIP)\nAND $__conditionalAll(path IN (${path}), $path)\nAND $__conditionalAll(hasAll(ruleMessages, arrayPopBack([${message}])), $message)\nAND $__conditionalAll(hasAll(ruleTags, arrayPopBack([${tags}])), $tags)\nAND $__conditionalAll(has(ruleActions, '${ruleActions}'), $ruleActions)\nAND if ('${requestHeadersName:text}' = '', true, mapContainsKeyLike(requestHeaders, '%${requestHeadersName:text}%'))\nAND if ('${responseHeadersName:text}' = '', true, mapContainsKeyLike(responseHeaders, '%${responseHeadersName:text}%'))\nGROUP BY country", "refId": "A", "selectedFormat": 1 } ], "title": "Geo Map", "type": "geomap" } ], "title": "Geo Map", "type": "row" } ], "refresh": "30s", "schemaVersion": 38, "style": "dark", "tags": [], "templating": { "list": [ { "hide": 2, "name": "table", "query": "${VAR_TABLE}", "skipUrlSync": false, "type": "constant", "current": { "value": "${VAR_TABLE}", "text": "${VAR_TABLE}", "selected": false }, "options": [ { "value": "${VAR_TABLE}", "text": "${VAR_TABLE}", "selected": false } ] }, { "current": {}, "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "definition": "SELECT host\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY host\nLIMIT 100", "hide": 0, "includeAll": true, "label": "Host", "multi": true, "name": "host", "options": [], "query": "SELECT host\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY host\nLIMIT 100", "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": { "selected": true, "text": "All", "value": "$__all" }, "hide": 0, "includeAll": true, "label": "Rule Action", "multi": false, "name": "ruleActions", "options": [ { "selected": true, "text": "All", "value": "$__all" }, { "selected": false, "text": "monitor", "value": "monitor" }, { "selected": false, "text": "alert", "value": "alert" }, { "selected": false, "text": "allow", "value": "allow" }, { "selected": false, "text": "tarpit", "value": "tarpit" } ], "query": "monitor, alert, allow, tarpit", "queryValue": "", "skipUrlSync": false, "type": "custom" }, { "current": {}, "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "definition": "SELECT method\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY method\nLIMIT 100", "hide": 0, "includeAll": true, "label": "Method", "multi": true, "name": "method", "options": [], "query": "SELECT method\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY method\nLIMIT 100", "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "definition": "SELECT status\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY status\nLIMIT 100", "hide": 0, "includeAll": true, "label": "Status Code", "multi": true, "name": "status", "options": [], "query": "SELECT status\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY status\nLIMIT 100", "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "grafana-clickhouse-datasource", "uid": "${DS_HYDRO}" }, "definition": "SELECT country\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY country\nLIMIT 100", "hide": 0, "includeAll": true, "label": "Country", "multi": true, "name": "country", "options": [], "query": "SELECT country\nFROM ${table}\nWHERE $__timeFilter(timestamp)\nGROUP BY country\nLIMIT 100", "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": { "selected": false, "text": "", "value": "" }, "hide": 0, "label": "Rule Message", "name": "message", "options": [ { "selected": true, "text": "", "value": "" } ], "query": "", "skipUrlSync": false, "type": "textbox" }, { "current": { "selected": false, "text": "", "value": "" }, "hide": 0, "label": "Rule Tags", "name": "tags", "options": [ { "selected": true, "text": "", "value": "" } ], "query": "", "skipUrlSync": false, "type": "textbox" }, { "current": { "selected": false, "text": "", "value": "" }, "hide": 0, "label": "Client IP", "name": "clientIP", "options": [ { "selected": true, "text": "", "value": "" } ], "query": "", "skipUrlSync": false, "type": "textbox" }, { "current": { "selected": false, "text": "", "value": "" }, "hide": 0, "label": "Path", "name": "path", "options": [ { "selected": true, "text": "", "value": "" } ], "query": "", "skipUrlSync": false, "type": "textbox" }, { "current": { "selected": false, "text": "", "value": "" }, "description": "For example look * request header name *", "hide": 0, "label": "requestHeaders Name", "name": "requestHeadersName", "options": [ { "selected": true, "text": "", "value": "" } ], "query": "", "skipUrlSync": false, "type": "textbox" }, { "current": { "selected": false, "text": "", "value": "" }, "description": "For example look * response header name *", "hide": 0, "label": "responseHeaders Name", "name": "responseHeadersName", "options": [ { "selected": true, "text": "", "value": "" } ], "query": "", "skipUrlSync": false, "type": "textbox" } ] }, "time": { "from": "now-24h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Akamai SIEM", "uid": "bd475a0d-b9b0-4577-8043-8a63aeb25e5e", "version": 63, "weekStart": "", "gnetId": 18823, "description": "Akamai SIEM powered by Hydrolix dashboard for security analytics\r\n" }
2023-05-24 13:48:02 UTC
2023-05-24 13:49:07 UTC
0
0
18,833
1
850,340
28,879
0
frontend
Frontend
first test for grafana faro
2023-05-25 15:46:33 UTC
2023-05-25 15:47:01 UTC
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"datasource": { "type": "loki", "uid": "${DS_LOGS}" }, "editorMode": "code", "expr": "topk(10, count by (browser_name, browser_version) (count_over_time({kind=\"exception\", app=\"$app\"} | logfmt [$__range])))", "queryType": "instant", "refId": "A" } ], "title": "Top Browsers by Exception Count", "transformations": [ { "id": "organize", "options": { "excludeByName": { "Time": true }, "indexByName": {}, "renameByName": { "Value #A": "Count", "browser_name": "Browser", "browser_version": "Version", "page_url": "URL", "value": "Error" } } } ], "type": "table" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 30 }, "id": 27, "panels": [], "title": "Meta", "type": "row" }, { "datasource": { "type": "loki", "uid": "${DS_LOGS}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { 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2023-05-25 15:46:33 UTC
2023-05-25 15:47:01 UTC
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changex-dashboard-latest
ChangeX DASHBOARD For Users
For Users
2022-09-12 13:33:42 UTC
2022-09-12 13:34:00 UTC
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{ "__inputs": [ { "name": "DS_INFINITY", "label": "Infinity", "description": "", "type": "datasource", "pluginId": "yesoreyeram-infinity-datasource", "pluginName": "Infinity" }, { "name": "DS_GRAPHQL_DATA SOURCE", "label": "GraphQL Data Source", "description": "", "type": "datasource", "pluginId": "fifemon-graphql-datasource", "pluginName": "GraphQL Data Source" } ], "__requires": [ { "type": "datasource", "id": "fifemon-graphql-datasource", "name": "GraphQL Data Source", "version": "1.3.0" }, { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.1.3-e1f2f3c" }, { "type": "panel", "id": "stat", "name": "Stat", "version": "" }, { "type": "panel", "id": "table", "name": "Table", "version": "" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" }, { "type": "datasource", "id": "yesoreyeram-infinity-datasource", "name": "Infinity", "version": "0.8.8" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "grafana", "uid": "-- 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"green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 0, "y": 1 }, "id": 31, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "message", "text": " STATUS", "type": "string" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://txnotifications.changex.io/", "url_options": { "data": "", "method": "GET" } } ], "title": "NOTIFICATIONS SERVICE", "transformations": [ { "id": "convertFieldType", "options": { "conversions": [ { "destinationType": "boolean", "targetField": " STATUS" } ], "fields": {} } } ], "type": "table" }, { "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "mDe4CYmVz" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "center", "displayMode": "color-background", "filterable": false, "inspect": true, "minWidth": 50 }, "mappings": [ { "options": { "false": { "color": "red", "index": 0, "text": "DATABASE NOT CONNECTED" }, "true": { "color": "green", "index": 1, "text": "DATABASE CONNECTED" } }, "type": "value" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "#EAB839", "value": 60 }, { "color": "red", "value": 85 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 6, "y": 1 }, "id": 16, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [ { "desc": true, "displayName": "STATUS" } ] }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "db", "text": " STATUS", "type": "string" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "json_options": { "root_is_not_array": true }, "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://txnotifications.changex.io/status/db", "url_options": { "data": "", "method": "GET" } } ], "title": "NOTIFICATIONS SERVICE DATABASE", "transformations": [ { "id": "convertFieldType", "options": { "conversions": [ { "destinationType": "boolean", "targetField": " STATUS" } ], "fields": {} } } ], "type": "table" }, { "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "mDe4CYmVz" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "center", "displayMode": "color-background", "inspect": false }, "mappings": [ { "options": { "false": { "color": "red", "index": 1, "text": "SERVICE IS PROBABLY DOWN" }, "true": { "color": "green", "index": 0, "text": "SERVICE IS RUNNING" } }, "type": "value" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 12, "y": 1 }, "id": 33, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "message", "text": " STATUS", "type": "string" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://bridge.changex.io/status/db", "url_options": { "data": "", "method": "GET" } } ], "title": "BRIDGE SERVICE", "transformations": [ { "id": "convertFieldType", "options": { "conversions": [ { "destinationType": "boolean", "targetField": " STATUS" } ], "fields": {} } } ], "type": "table" }, { "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "mDe4CYmVz" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "center", "displayMode": "color-background", "inspect": false }, "mappings": [ { "options": { "false": { "color": "red", "index": 1, "text": "SERVICE IS PROBABLY DOWN" }, "true": { "color": "green", "index": 0, "text": "SERVICE IS RUNNING" } }, "type": "value" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 18, "y": 1 }, "id": 34, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "message", "text": " STATUS", "type": "string" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://bridge.changex.io/status/ws", "url_options": { "data": "", "method": "GET" } } ], "title": "BRIDGE WEBSOCKETS", "transformations": [ { "id": "convertFieldType", "options": { "conversions": [ { "destinationType": "boolean", "targetField": " STATUS" } ], "fields": {} } } ], "type": "table" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 4 }, "id": 23, "panels": [], "title": "MARKETS", "type": "row" }, { "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "mDe4CYmVz" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "blue", "value": null }, { "color": "semi-dark-green", "value": 80 } ] }, "unit": "currencyUSD" }, "overrides": [] }, "gridPos": { "h": 4, "w": 6, "x": 0, "y": 5 }, "id": 2, "interval": "1", "links": [], "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "titleSize": 16, "valueSize": 27 }, "textMode": "auto" }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "current_price", "text": "Current Price", "type": "number" }, { "selector": "total_volume", "text": "24h Volume", "type": "number" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://changex-price-fetcher-xcl5j.ondigitalocean.app/coins/markets?ids=changex&vs_currency=usd", "url_options": { "data": "", "method": "GET" } } ], "title": "COINGECKO", "transformations": [ { "id": "configFromData", "options": { "applyTo": { "id": "byType", "options": "string" }, "configRefId": "A", "mappings": [] } } ], "type": "stat" }, { "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "mDe4CYmVz" }, "fieldConfig": { "defaults": { "color": { "mode": "continuous-GrYlRd" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 20, "gradientMode": "scheme", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 3, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 9, "w": 18, "x": 6, "y": 5 }, "id": 10, "interval": "60", "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "pluginVersion": "9.1.0-76166", "targets": [ { "columns": [], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "dataframe", "global_query_id": "", "refId": "A", "root_selector": "prices", "source": "url", "type": "json", "url": "https://changex-price-fetcher-xcl5j.ondigitalocean.app/coins/hydra/market_chart?days=30&vs_currency=usd", "url_options": { "data": "", "method": "GET" } } ], "title": "CHANGE PRICE HISTORY", "transformations": [ { "id": "convertFieldType", "options": { "conversions": [ { "dateFormat": "YYYY-MM-DD", "destinationType": "time", "targetField": "0" } ], "fields": {} } }, { "id": "sortBy", "options": { "fields": {}, "sort": [ { "desc": true, "field": "timestamp" } ] } }, { "id": "renameByRegex", "options": { "regex": "([0]*)", "renamePattern": "Date" } }, { "id": "renameByRegex", "options": { "regex": "([1]*)", "renamePattern": "Price" } } ], "type": "timeseries" }, { "datasource": { "type": "fifemon-graphql-datasource", "uid": "YC_Sc6WVk" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "noValue": "....", "thresholds": { "mode": "absolute", "steps": [ { "color": "blue", "value": null }, { "color": "green", "value": 10 }, { "color": "green", "value": 80 } ] }, "unit": "currencyUSD" }, "overrides": [] }, "gridPos": { "h": 4, "w": 6, "x": 0, "y": 9 }, "id": 19, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": false }, "text": { "titleSize": 13, "valueSize": 27 }, "textMode": "value_and_name" }, "pluginVersion": "9.0.6", "targets": [ { "aliasBy": "", "annotationTags": "", "annotationText": "", "annotationTitle": "", "columns": [], "constant": 6.5, "dataPath": "data", "datasource": { "type": "fifemon-graphql-datasource", "uid": "YC_Sc6WVk" }, "endTimePath": "endTime", "filters": [], "format": "table", "global_query_id": "", "groupBy": "", "queryText": "query {\n data:tokenDayDatas(\n orderDirection: \"asc\"\n \torderBy: \"date\"\n \tskip: 0\n \tfirst: 0\n where: {date_gt: \"${__from:date:seconds}\" token: \"bd3c617d271b3467bd9b83dda73c9288de2fb0c9\"}) {\n priceUSD\n dailyVolumeUSD\n }\n}", "refId": "A", "root_selector": "", "source": "url", "timePath": "", "type": "json", "url": "https://jsonplaceholder.typicode.com/users", "url_options": { "data": "", "method": "GET" } } ], "title": "HYDRA DEX", "transformations": [ { "id": "organize", "options": { "excludeByName": {}, "indexByName": {}, "renameByName": { "dailyVolumeUSD": "Today's Volume", "priceUSD": "Current Price" } } }, { "id": "convertFieldType", "options": { "conversions": [ { "destinationType": "number", "targetField": "priceUSD" } ], "fields": {} } } ], "type": "stat" }, { "datasource": { "type": "fifemon-graphql-datasource", "uid": "YC_Sc6WVk" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null }, { "color": "#EAB839", "value": 10 }, { "color": "green", "value": 20 } ] } }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 0, "y": 13 }, "id": 29, "interval": "60", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "titleSize": 17, "valueSize": 27 }, "textMode": "value" }, "pluginVersion": "9.0.6", "targets": [ { "aliasBy": "", "annotationTags": "", "annotationText": "", "annotationTitle": "", "columns": [], "constant": 6.5, "dataPath": "data", "datasource": { "type": "fifemon-graphql-datasource", "uid": "YC_Sc6WVk" }, "endTimePath": "endTime", "filters": [], "format": "table", "global_query_id": "", "groupBy": "", "queryText": "query {\n data:tokenDayDatas(\n orderDirection: \"asc\"\n \torderBy: \"date\"\n \tskip: 0\n \tfirst: 0\n where: {date_gt: \"${__from:date:seconds}\" token: \"bd3c617d271b3467bd9b83dda73c9288de2fb0c9\"}) {\n \tdailyTxns\n \n }\n}", "refId": "A", "root_selector": "", "source": "url", "timePath": "", "type": "json", "url": "https://jsonplaceholder.typicode.com/users", "url_options": { "data": "", "method": "GET" } } ], "title": "Today's Transactions ", "transformations": [ { "id": "convertFieldType", "options": { "conversions": [ { "destinationType": "number", "targetField": "dailyTxns" } ], "fields": {} } } ], "type": "stat" }, { "datasource": {}, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 2, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "yellow", "value": null }, { "color": "green", "value": 10 }, { "color": "semi-dark-blue", "value": 20 } ] }, "unit": "percent" }, "overrides": [] }, "gridPos": { "h": 2, "w": 9, "x": 6, "y": 14 }, "id": 27, "interval": "3600", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "titleSize": 15, "valueSize": 27 }, "textMode": "auto" }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "price_change_percentage_24h", "text": "", "type": "number" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://changex-price-fetcher-xcl5j.ondigitalocean.app/coins/markets?ids=changex&vs_currency=usd", "url_options": { "data": "", "method": "GET" } } ], "title": "PRICE CHANGE FOR THE LAST 24 HOURS", "type": "stat" }, { "datasource": {}, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null }, { "color": "yellow", "value": 70 }, { "color": "orange", "value": 85 } ] }, "unit": "percent" }, "overrides": [] }, "gridPos": { "h": 2, "w": 9, "x": 15, "y": 14 }, "id": 12, "interval": "600", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "titleSize": 25, "valueSize": 30 }, "textMode": "auto" }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "inPercent", "text": "APY ≈ ", "type": "number" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://hydra-dex-backend.changex.io/api/staking/apy?amount=1", "url_options": { "data": "", "method": "GET" } } ], "type": "stat" }, { "datasource": {}, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null }, { "color": "yellow", "value": 70 }, { "color": "orange", "value": 85 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 0, "y": 16 }, "id": 37, "interval": "600", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "titleSize": 25, "valueSize": 30 }, "textMode": "auto" }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "activeStakers", "text": " ", "type": "number" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://hydra-dex-backend.changex.io/api/staking/active-stakers", "url_options": { "data": "", "method": "GET" } } ], "title": "ACTIVE STAKERS", "type": "stat" }, { "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "mDe4CYmVz" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null }, { "color": "yellow", "value": 70 }, { "color": "orange", "value": 85 } ] }, "unit": "currencyUSD" }, "overrides": [] }, "gridPos": { "h": 3, "w": 9, "x": 6, "y": 16 }, "id": 38, "interval": "600", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "titleSize": 25, "valueSize": 30 }, "textMode": "auto" }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "totalRewardsDistributed", "text": " ", "type": "number" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://hydra-dex-backend.changex.io/api/staking/total-rewards-distributed", "url_options": { "data": "", "method": "GET" } } ], "title": "TOTAL REWARDS PAID OUT", "type": "stat" }, { "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "mDe4CYmVz" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null }, { "color": "yellow", "value": 70 }, { "color": "orange", "value": 85 } ] }, "unit": "currencyUSD" }, "overrides": [] }, "gridPos": { "h": 3, "w": 9, "x": 15, "y": 16 }, "id": 39, "interval": "600", "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": { "titleSize": 25, "valueSize": 30 }, "textMode": "auto" }, "pluginVersion": "9.0.6", "targets": [ { "columns": [ { "selector": "totalStakedBalance", "text": " ", "type": "number" } ], "datasource": { "type": "yesoreyeram-infinity-datasource", "uid": "ki060Bz4z" }, "filters": [], "format": "table", "global_query_id": "", "refId": "A", "root_selector": "", "source": "url", "type": "json", "url": "https://hydra-dex-backend.changex.io/api/staking/total-staked-balance", "url_options": { "data": "", "method": "GET" } } ], "title": "TOTAL STAKED", "type": "stat" } ], "refresh": "1m", "schemaVersion": 36, "style": "dark", "tags": [], "templating": { "list": [] }, "time": { "from": "now-30d", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ] }, "timezone": "", "title": "ChangeX DASHBOARD For Users", "uid": "m-3Y5Bz4b", "version": 6, "weekStart": "", "gnetId": 16943, "description": "For Users" }
2022-09-12 13:33:42 UTC
2022-09-12 13:34:00 UTC
0
0
17,001
1
498,884
26,065
0
pixie-pod-overview
Pixie Pod Overview
Dashboard shows an overview of the specified pod, including high-level HTTP application metrics, and resource usage.
2022-09-16 15:32:13 UTC
2022-09-16 15:32:56 UTC
0
To learn how to monitor infrastructure health using Pixie, see the [tutorial](https://docs.px.dev/tutorials/pixie-101/infra-health/).
76,976
76,977
Other
null
0
0
26,065
1
17,001
1
0
Dashboard shows an overview of the specified pod, including high-level HTTP application metrics, and resource usage. To learn how to monitor infrastructure health using Pixie, see https://docs.px.dev/tutorials/pixie-101/infra-health/
{ "__inputs": [ { "name": "DS_PIXIE_GRAFANA DATASOURCE PLUGIN", "label": "Pixie Grafana Datasource Plugin", "description": "", "type": "datasource", "pluginId": "pixie-pixie-datasource", "pluginName": "Pixie Grafana Datasource Plugin" } ], "__requires": [ { "type": "datasource", "id": "pixie-pixie-datasource", "name": "Pixie Grafana Datasource Plugin", "version": "0.0.9" }, { "type": "panel", "id": "table", "name": "Table", "version": "" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": "-- Grafana --", "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "iteration": 1657153831003, "links": [], "liveNow": false, "panels": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "request throughput", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "/s" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 0, "y": 0 }, "id": 2, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable changes\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = inbound_request_timeseries_by_container(start_time, pod)\noutput.request_throughput = output.request_throughput * px.pow(10,9)\npx.display(output[['time_', 'request_throughput', 'container']])" }, "queryType": "run-script", "refId": "A" } ], "title": "HTTP Requests", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "error rate", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "/s" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 8, "y": 0 }, "id": 4, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = inbound_request_timeseries_by_container(start_time, pod)\noutput.errors_per_ns = output.errors_per_ns * px.pow(10,9)\npx.display(output[['time_', 'errors_per_ns', 'container']])" }, "queryType": "run-script", "refId": "A" } ], "title": "HTTP Errors", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisGridShow": true, "axisLabel": "milliseconds", "axisPlacement": "auto", "axisSoftMax": 6, "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "ms" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 16, "y": 0 }, "id": 10, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = inbound_latency_timeseries(start_time, pod)\noutput.latency_p50 = output.latency_p50 / px.pow(10,6)\noutput.latency_p90 = output.latency_p90 / px.pow(10,6)\noutput.latency_p99 = output.latency_p99 / px.pow(10,6)\n\npx.display(output[['time_', 'latency_p50', 'latency_p90', 'latency_p99']])" }, "queryType": "run-script", "refId": "A" } ], "title": "HTTP Latency", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "CPU Usage", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "percent" }, "overrides": [] }, "gridPos": { "h": 12, "w": 8, "x": 0, "y": 13 }, "id": 6, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = resource_timeseries(start_time, pod)\noutput.cpu_usage_percent = output.cpu_usage * 100\npx.display(output[['time_', 'cpu_usage_percent', 'container']])" }, "queryType": "run-script", "refId": "A" } ], "title": "CPU Usage", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "state" }, "properties": [ { "id": "custom.width", "value": 131 }, { "id": "custom.displayMode", "value": "color-text" }, { "id": "mappings", "value": [ { "options": { "Failed": { "color": "red", "index": 5 }, "Pending": { "color": "yellow", "index": 6 }, "Running": { "color": "green", "index": 0 }, "Succeeded": { "color": "green", "index": 4 }, "Terminated": { "color": "red", "index": 3 }, "Unknown": { "color": "red", "index": 7 }, "Waiting": { "color": "yellow", "index": 8 }, "false": { "color": "red", "index": 2 }, "true": { "color": "green", "index": 1 } }, "type": "value" } ] } ] }, { "matcher": { "id": "byName", "options": "ready" }, "properties": [ { "id": "custom.width", "value": 97 } ] }, { "matcher": { "id": "byName", "options": "name" }, "properties": [ { "id": "custom.width", "value": 102 } ] }, { "matcher": { "id": "byName", "options": "message" }, "properties": [ { "id": "custom.width", "value": 84 } ] } ] }, "gridPos": { "h": 12, "w": 8, "x": 8, "y": 13 }, "id": 8, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "7.5.1", "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = containers(start_time, pod)\n\noutput.state = px.pluck(output.status, \"state\") \noutput.message = px.pluck(output.status, \"ready\") \n\noutput = output[['name', 'state', 'message', 'id']]\n\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Container List", "type": "table" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "pid" }, "properties": [ { "id": "custom.width", "value": 118 } ] }, { "matcher": { "id": "byName", "options": "cmd" }, "properties": [ { "id": "custom.width", "value": 321 } ] } ] }, "gridPos": { "h": 12, "w": 8, "x": 16, "y": 13 }, "id": 12, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "7.5.1", "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = processes(start_time, pod)\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Process List", "type": "table" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "Network Throughput", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "KBs" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 0, "y": 25 }, "id": 15, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = network_timeseries(start_time, pod)\noutput.rx_bytes_per_ns = output.rx_bytes_per_ns * px.pow(10, 9) / px.pow(2,10)\noutput.tx_bytes_per_ns = output.tx_bytes_per_ns * px.pow(10, 9) / px.pow(2,10)\noutput = output[['time_', 'rx_bytes_per_ns', 'tx_bytes_per_ns']]\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Network Sent And Received", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "Disk Read Throughput", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "KBs" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 8, "y": 25 }, "id": 16, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": ".05# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = resource_timeseries(start_time, pod)\noutput.total_disk_read_throughput = output.total_disk_read_throughput * px.pow(10,9) / px.pow(2,10)\noutput = output[['time_', 'total_disk_read_throughput', 'container']]\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Bytes Read", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "Disk Write Throughput", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "binBps" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 16, "y": 25 }, "id": 17, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = resource_timeseries(start_time, pod)\noutput.total_disk_write_throughput = output.total_disk_write_throughput * px.pow(10,9)\noutput = output[['time_', 'total_disk_write_throughput', 'container']]\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Bytes Written", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "RSS", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "decmbytes" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 0, "y": 38 }, "id": 18, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = resource_timeseries(start_time, pod)\noutput.rss = output.rss / px.pow(2,20)\noutput = output[['time_', 'rss', 'container']]\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Resident Set Size", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisLabel": "vsize", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "decmbytes" }, "overrides": [] }, "gridPos": { "h": 13, "w": 8, "x": 8, "y": 38 }, "id": 19, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = resource_timeseries(start_time, pod)\noutput.vsize = output.vsize / px.pow(2,20)\noutput = output[['time_', 'vsize', 'container']]\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Virtual Memory Size", "type": "timeseries" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "latency_p99" }, "properties": [ { "id": "custom.displayMode", "value": "color-text" }, { "id": "thresholds", "value": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "light-orange", "value": 150 }, { "color": "red", "value": 300 } ] } }, { "id": "decimals", "value": 1 }, { "id": "unit", "value": "ms" } ] }, { "matcher": { "id": "byName", "options": "error_rate" }, "properties": [ { "id": "unit", "value": "percentunit" } ] }, { "matcher": { "id": "byName", "options": "request_throughput" }, "properties": [ { "id": "unit", "value": "/s" }, { "id": "decimals", "value": 1 } ] }, { "matcher": { "id": "byName", "options": "requesting_pod" }, "properties": [ { "id": "links", "value": [ { "title": "", "url": "/d/_t3foxCnz/px-pod?orgId=1&${pixieCluster:queryparam}&var-pixiePod=${__data.fields.requesting_pod}" } ] } ] }, { "matcher": { "id": "byName", "options": "requesting_svc" }, "properties": [ { "id": "links", "value": [ { "title": "", "url": "/d/qhHtFyCnk/px-service?orgId=1&${pixieCluster:queryparam}&var-pixieService=${__data.fields.requesting_svc}" } ] } ] } ] }, "gridPos": { "h": 10, "w": 24, "x": 0, "y": 51 }, "id": 20, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [ { "desc": true, "displayName": "error_rate" } ] }, "pluginVersion": "7.5.1", "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = inbound_let_summary(start_time, pod)\noutput.latency_p99 = px.pluck_float64(output.latency, \"p99\") / px.pow(10,6)\noutput.request_throughput = output.request_throughput * px.pow(10,9)\noutput = output[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency_p99', 'error_rate', 'request_throughput']]\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Inbound HTTP Traffic To Pod", "type": "table" }, { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "phase" }, "properties": [ { "id": "custom.displayMode", "value": "color-text" }, { "id": "mappings", "value": [ { "options": { "Running": { "color": "green", "index": 0 } }, "type": "value" } ] } ] }, { "matcher": { "id": "byName", "options": "ready" }, "properties": [ { "id": "custom.displayMode", "value": "color-text" }, { "id": "mappings", "value": [ { "options": { "false": { "color": "red", "index": 1 }, "true": { "color": "green", "index": 0 } }, "type": "value" } ] } ] }, { "matcher": { "id": "byName", "options": "node" }, "properties": [ { "id": "links", "value": [ { "title": "", "url": "/d/tcpoz_jnz/px-node?orgId=1&${pixieCluster:queryparam}&var-node=${__data.fields.node}&var-groupby=node" } ] } ] }, { "matcher": { "id": "byName", "options": "service" }, "properties": [ { "id": "links", "value": [ { "title": "", "url": "/d/qhHtFyCnk/px-service?orgId=1&${pixieCluster:queryparam}&var-pixieService=${__data.fields.service}" } ] } ] } ] }, "gridPos": { "h": 8, "w": 24, "x": 0, "y": 61 }, "id": 21, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true }, "pluginVersion": "7.5.1", "targets": [ { "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "queryBody": { "pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''Pod Overview\n\nOverview of a specific Pod monitored by Pixie with its high level application metrics\n(latency, error-rate & rps) and resource usage (cpu, writes, reads).\n\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\npod = \"$pixiePod\"\n\ndef containers(start_time: int, pod: str):\n ''' A list of containers in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.name = df.ctx['container_name']\n df.id = df.ctx['container_id']\n df = df.groupby(['name', 'id']).agg()\n df.status = px.container_id_to_status(df.id)\n return df\n\n\ndef node(start_time: int, pod: str):\n ''' A list containing the node the `pod` is running on.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.node = df.ctx['node_name']\n df.service = df.ctx['service']\n df.pod_id = df.ctx['pod_id']\n df.pod_name = df.ctx['pod']\n df = df.groupby(['node', 'service', 'pod_id', 'pod_name']).agg()\n df.pod_start_time = px.pod_name_to_start_time(df.pod_name)\n df.status = px.pod_name_to_status(df.pod_name)\n return df.drop('pod_name')\n\n\ndef processes(start_time: int, pod: str):\n ''' A list of processes in `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.cmd = df.ctx['cmdline']\n df.pid = df.ctx['pid']\n df = df.groupby(['pid', 'cmd', 'upid']).agg()\n return df\n\n\ndef resource_timeseries(start_time: int, pod: str):\n ''' Compute the resource usage as a timeseries for `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n df.container = df.ctx['container_name']\n\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby(['upid', 'container', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.actual_disk_read_throughput = (df.read_bytes_max - df.read_bytes_min) / window_ns\n df.actual_disk_write_throughput = (df.write_bytes_max - df.write_bytes_min) / window_ns\n df.total_disk_read_throughput = (df.rchar_bytes_max - df.rchar_bytes_min) / window_ns\n df.total_disk_write_throughput = (df.wchar_bytes_max - df.wchar_bytes_min) / window_ns\n\n # Then aggregate process individual process metrics.\n df = df.groupby(['timestamp', 'container']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.sum),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.sum),\n total_disk_read_throughput=('total_disk_read_throughput', px.sum),\n total_disk_write_throughput=('total_disk_write_throughput', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n df['time_'] = df['timestamp']\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns', 'timestamp'])\n\n\ndef network_timeseries(start_time: int, pod: str):\n ''' Gets the network stats (transmitted/received traffic) for the input node.\n\n Args:\n @start_time Starting time of the data to examine.\n @node: The full name of the node to filter on.\n '''\n df = px.DataFrame(table='network_stats', start_time=start_time)\n df = df[df.ctx['pod'] == pod]\n df.timestamp = px.bin(df.time_, window_ns)\n\n # First calculate network usage by node over all windows.\n # Data is sharded by Pod in network_stats.\n df = df.groupby(['timestamp', 'pod_id']).agg(\n rx_bytes_end=('rx_bytes', px.max),\n rx_bytes_start=('rx_bytes', px.min),\n tx_bytes_end=('tx_bytes', px.max),\n tx_bytes_start=('tx_bytes', px.min),\n tx_errors_end=('tx_errors', px.max),\n tx_errors_start=('tx_errors', px.min),\n rx_errors_end=('rx_errors', px.max),\n rx_errors_start=('rx_errors', px.min),\n tx_drops_end=('tx_drops', px.max),\n tx_drops_start=('tx_drops', px.min),\n rx_drops_end=('rx_drops', px.max),\n rx_drops_start=('rx_drops', px.min),\n )\n\n # Calculate the network statistics rate over the window.\n # We subtract the counter value at the beginning ('_start')\n # from the value at the end ('_end').\n df.rx_bytes_per_ns = (df.rx_bytes_end - df.rx_bytes_start) / window_ns\n df.tx_bytes_per_ns = (df.tx_bytes_end - df.tx_bytes_start) / window_ns\n df.rx_drops_per_ns = (df.rx_drops_end - df.rx_drops_start) / window_ns\n df.tx_drops_per_ns = (df.tx_drops_end - df.tx_drops_start) / window_ns\n df.rx_errors_per_ns = (df.rx_errors_end - df.rx_errors_start) / window_ns\n df.tx_errors_per_ns = (df.tx_errors_end - df.tx_errors_start) / window_ns\n\n # Add up the network values per node.\n df = df.groupby(['timestamp']).agg(\n rx_bytes_per_ns=('rx_bytes_per_ns', px.sum),\n tx_bytes_per_ns=('tx_bytes_per_ns', px.sum),\n rx_drop_per_ns=('rx_drops_per_ns', px.sum),\n tx_drops_per_ns=('tx_drops_per_ns', px.sum),\n rx_errors_per_ns=('rx_errors_per_ns', px.sum),\n tx_errors_per_ns=('tx_errors_per_ns', px.sum),\n )\n df['time_'] = df['timestamp']\n return df\n\n\ndef inbound_latency_timeseries(start_time: int, pod: str):\n ''' Compute the latency as a timeseries for requests received by `pod`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.time_ = df.timestamp\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\n\ndef inbound_request_timeseries_by_container(start_time: int, pod: str):\n ''' Compute the request statistics as a timeseries for requests received\n by `pod` by container.\n\n Args:\n @start_time: The timestamp of data to start at.\n @pod: The name of the pod to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n df.container = df.ctx['container']\n\n df = df.groupby(['timestamp', 'container']).agg(\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.time_ = df.timestamp\n\n return df[['time_', 'container', 'request_throughput', 'errors_per_ns', 'error_rate']]\n\n\ndef inbound_let_summary(start_time: int, pod: str):\n ''' Gets a summary of requests inbound to `pod`.\n\n Args:\n @start_time Starting time of the data to examine.\n @pod: The pod to filter on.\n '''\n df = let_helper(start_time)\n df = df[df.pod == pod]\n\n quantiles_agg = df.groupby(['pod', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n )\n\n quantiles_table = quantiles_agg[['pod', 'remote_addr', 'latency',\n 'total_request_count']]\n\n range_agg = df.groupby(['pod', 'remote_addr', 'timestamp']).agg(\n requests_per_window=('time_', px.count),\n error_rate=('failure', px.mean)\n )\n\n rps_table = range_agg.groupby(['pod', 'remote_addr']).agg(\n requests_per_window=('requests_per_window', px.mean),\n error_rate=('error_rate', px.mean)\n )\n\n joined_table = quantiles_table.merge(rps_table,\n how='inner',\n left_on=['pod', 'remote_addr'],\n right_on=['pod', 'remote_addr'],\n suffixes=['', '_x'])\n\n joined_table.error_rate = px.Percent(joined_table.error_rate)\n joined_table.request_throughput = joined_table.requests_per_window / window_ns\n\n joined_table.responder = df.pod\n joined_table.requesting_ip = df.remote_addr\n joined_table.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n joined_table.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return joined_table[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency',\n 'error_rate', 'request_throughput']]\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by pod is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n df.pod = df.ctx['pod']\n df.timestamp = px.bin(df.time_, window_ns)\n df.failure = df.resp_status >= 400\n\n # Filter only to inbound pod traffic (server-side).\n # Don't include traffic initiated by this pod to an external location.\n df = df[df.trace_role == 2]\n\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n df = df[filter_out_conds]\n\n return df\n\n\ndef stacktraces(start_time: int, pod: str):\n df = px.DataFrame(table='stack_traces.beta', start_time=start_time)\n\n df.namespace = df.ctx['namespace']\n df.pod = df.ctx['pod']\n df.container = df.ctx['container']\n df.cmdline = df.ctx['cmdline']\n\n # Filter on the pod.\n df = df[df.pod == pod]\n\n # Get stack trace totals for the pod.\n # This must be done before any additional filtering to avoid skewing percentages.\n grouping_agg = df.groupby([\"pod\"]).agg(\n count=('count', px.sum)\n )\n\n # Combine flamegraphs from different intervals into one larger framegraph.\n df = df.groupby(['namespace', 'pod', 'container', 'cmdline', 'stack_trace_id']).agg(\n stack_trace=('stack_trace', px.any),\n count=('count', px.sum)\n )\n\n # Compute percentages.\n df = df.merge(\n grouping_agg,\n how='inner',\n left_on=\"pod\",\n right_on=\"pod\",\n suffixes=['', '_x']\n )\n df.percent = 100.0 * df.count / df.count_x\n df.drop('pod_x')\n\n return df\n\noutput = node(start_time, pod)\noutput.phase = px.pluck(output.status, \"phase\")\noutput.ready = px.pluck(output.status, \"ready\")\noutput = output[['node', 'phase', 'ready', 'service', 'pod_id', 'pod_start_time']]\npx.display(output)" }, "queryType": "run-script", "refId": "A" } ], "title": "Pod Metadata", "type": "table" } ], "schemaVersion": 27, "style": "dark", "tags": [], "templating": { "list": [ { "current": {}, "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "definition": "Clusters", "hide": 0, "includeAll": false, "multi": false, "name": "pixieCluster", "options": [], "query": { "queryType": "get-clusters" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}", "definition": "Pods", "hide": 0, "includeAll": false, "multi": false, "name": "pixiePod", "options": [], "query": { "queryBody": { "clusterID": "$pixieCluster" }, "queryType": "get-pods" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Pixie Pod Overview", "description": "Dashboard shows an overview of the specified pod, including high-level HTTP application metrics, and resource usage. ", "uid": "_t3foxCnz", "version": 1, "weekStart": "", "gnetId": 17001 }
2022-09-16 15:32:13 UTC
2022-09-16 15:32:55 UTC
0
0
17,084
1
546,928
26,215
0
pi-hole-exporter
PI-Hole Exporter
A dashboard for PI-Hole Exporter from https://github.com/eko/pihole-exporter
2022-09-29 15:28:14 UTC
2022-09-29 15:29:05 UTC
0
null
0
0
nodeExporter
null
0
0
26,215
1
17,084
1
0
A dashboard for PI-Hole Exporter from https://github.com/eko/pihole-exporter
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false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "pihole_unique_domains", "format": "time_series", "intervalFactor": 1, "legendFormat": "", "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Unique domains", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "transparent": true, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } } ], "refresh": false, "schemaVersion": 18, "style": "dark", "tags": [], "templating": { "list": [] }, "time": { "from": "now-5m", "to": "now" }, "timepicker": { "hidden": false, "nowDelay": "", "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "PI-Hole Exporter", "uid": "MIBVglomg", "version": 122, "description": "A dashboard for PI-Hole Exporter from https://github.com/eko/pihole-exporter" }
2022-09-29 15:28:14 UTC
null
0
0
17,723
1
568,677
27,194
0
opentelemetry-collector
OpenTelemetry Collector v20221228
Provides information about the status of the OpenTelemetry Collector
2022-12-28 11:30:31 UTC
2022-12-28 11:36:50 UTC
0
Include Span, Metrics and Logs information for Reseive/Export
80,137
80,138
Other
null
0
0
27,194
1
17,723
1
0
Provides information about the status of the OpenTelemetry Collector
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"expr": "sum(rate(otelcol_processor_spans_received{processor=~\"$processor\"}[1m])) by (processor)", "format": "time_series", "interval": "", "intervalFactor": 1, "legendFormat": "{{processor}} | received", "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(otelcol_processor_spans_dropped{processor=~\"$processor\"}[1m])) by (processor)", "interval": "", "legendFormat": "{{processor}} | dropped", "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(otelcol_processor_accepted_spans{processor=~\"$processor\"}[1m])) by (processor)", "interval": "", "legendFormat": "{{processor}} | accepted", "refId": "C" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(otelcol_processor_refused_spans{processor=~\"$processor\"}[1m])) by (processor)", "interval": "", "legendFormat": "{{processor}} | refused", "refId": "E" } ], "thresholds": [], "timeRegions": [], "title": "Processor Spans Received, Dropped, Accepted, Refused/Second", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "min": "0", "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 12, "y": 30 }, "hiddenSeries": false, "id": 14, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.2.4", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(otelcol_processor_queued_retry_success_send{processor=~\"$processor\"}[1m])) by (processor)", "format": "time_series", "hide": false, "interval": "", "intervalFactor": 1, "legendFormat": "{{processor}}", "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(otelcol_processor_queued_retry_fail_send{processor=~\"$processor\"}[1m])) by (processor)", "interval": "", "legendFormat": "{{processor}}", "refId": "B" } ], "thresholds": [], "timeRegions": [], "title": "QueuedRetry Successful vs Failed Sent/Second", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 39 }, "hiddenSeries": false, "id": 2, "interval": "", "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.2.4", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(otelcol_processor_spans_received{processor=~\"$processor\"}[1m]) / rate(otelcol_processor_batches_received{processor=~\"$processor\"}[1m])) by (processor)", "format": "time_series", "hide": false, "interval": "", "intervalFactor": 1, "legendFormat": "{{processor}}", "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "Spans per Batch (avg from rates)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 12, "y": 39 }, "hiddenSeries": false, "id": 12, "interval": "", "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.2.4", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(otelcol_processor_batches_received{processor=~\"$processor\"}[1m])) by (processor)", "format": "time_series", "interval": "", "intervalFactor": 1, "legendFormat": "{{processor}} | received", "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(rate(otelcol_processor_trace_batches_dropped{processor=~\"$processor\"}[1m])) by (processor)", "interval": "", "legendFormat": "{{processor}} | trace dropped", "refId": "B" } ], "thresholds": [], "timeRegions": [], "title": "Batches Received vs Dropped /Second", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 48 }, "hiddenSeries": false, "id": 6, "interval": "", "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.2.4", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(otelcol_processor_queued_retry_success_send{processor=~\"$processor\"}) by (processor)", "format": "time_series", "hide": false, "interval": "", "intervalFactor": 1, "legendFormat": "{{processor}}", "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "Successful Batches Sent Cumulative", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "min": "0", "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 12, "y": 48 }, "hiddenSeries": false, "id": 3, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "percentage": false, "pluginVersion": "9.2.4", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "sum(otelcol_processor_spans_received{exporter=~\"$exporter\"}) by (exporter)", "format": "time_series", "hide": false, "interval": "", "intervalFactor": 1, "legendFormat": "{{exporter}}", "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "Enqueued Spans Cumulative", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "short", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } } ], "refresh": false, "schemaVersion": 37, "style": "dark", "tags": [ "opentelemetry" ], "templating": { "list": [ { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(otelcol_process_cpu_seconds,namespace)", "hide": 0, "includeAll": false, "multi": true, "name": "namespace", "options": [], "query": { "query": "label_values(otelcol_process_cpu_seconds,namespace)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(otelcol_process_cpu_seconds{namespace=~\"$namespace\"},deployment_environment)", "hide": 0, "includeAll": true, "multi": true, "name": "env", "options": [], "query": { "query": "label_values(otelcol_process_cpu_seconds{namespace=~\"$namespace\"},deployment_environment)", "refId": "StandardVariableQuery" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" }, { "allValue": ".*", "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(otelcol_receiver_accepted_metric_points, receiver)", "hide": 0, "includeAll": true, "label": "receiver", "multi": true, "name": "receiver", "options": [], "query": { "query": "label_values(otelcol_receiver_accepted_metric_points, receiver)", "refId": "StandardVariableQuery" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "allValue": ".*", "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(otelsvc_exporter)", "hide": 0, "includeAll": true, "label": "exporter", "multi": true, "name": "exporter", "options": [], "query": { "query": "label_values(otelsvc_exporter)", "refId": "Mimir-exporter-Variable-Query" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(processor)", "hide": 0, "includeAll": true, "label": "processor", "multi": true, "name": "processor", "options": [], "query": { "query": "label_values(processor)", "refId": "Mimir-processor-Variable-Query" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": { "refresh_intervals": [ "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "OpenTelemetry Collector v20221228", "uid": "BKf2sowmj", "version": 9, "weekStart": "" }
2022-12-28 11:30:31 UTC
2022-12-28 11:34:45 UTC
0
0
18,148
1
800,178
27,859
0
readsb-prometheus
readsb
Using Prometheus as a datasource to visualise readsb data
2023-02-21 02:54:31 UTC
2023-02-21 02:56:37 UTC
0
Used to visualise metrics collected from the mikenye/readsb-protobuf docker image. This dashboard aims to emulate the majority of the functionality of graphs1090, but using Prometheus and Grafana.
0
0
null
null
0
0
27,859
1
18,148
1
0
null
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SNR", "transformations": [ { "id": "calculateField", "options": { "alias": "Signal to Noise Ratio", "binary": { "left": "avg(readsb_local_signal{host=~\"readsb\"})", "operator": "-", "reducer": "sum", "right": "avg(readsb_local_noise{host=~\"readsb\"})" }, "mode": "binary", "reduce": { "reducer": "sum" }, "replaceFields": true } } ], "type": "stat" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "decimals": 1, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "red", "value": null }, { "color": "green", "value": 0.01 }, { "color": "red", "value": 0.05 } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 3, "w": 6, "x": 18, "y": 0 }, "id": 8, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": 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Graphs", "type": "row" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "stepBefore", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "short" }, "overrides": [ { "matcher": { "id": "byName", "options": "Messages > 3dbFS" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } }, { "id": "custom.spanNulls", "value": true } ] }, { 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false, "stacking": { "group": "A", "mode": "normal" }, "thresholdsStyle": { "mode": "off" } }, "decimals": 2, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 20 }, "id": 16, "options": { "legend": { "calcs": [ "mean", "max" ], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "max(readsb_cpu_reader{host=~\"$hostname\"}) / 60000", "legendFormat": "SDR Reader", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "max(readsb_cpu_demod{host=~\"$hostname\"}) / 60000", "hide": false, "legendFormat": "Demodulator", "range": true, "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "builder", "expr": "max(readsb_cpu_background{host=~\"$hostname\"}) / 60000", "hide": false, "legendFormat": "Background", "range": true, "refId": "C" } ], "title": "readsb CPU", "type": "timeseries" } ], "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [ { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(host)", "hide": 0, "includeAll": true, "label": "Hostname", "multi": true, "name": "hostname", "options": [], "query": { "query": "label_values(host)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "type": "query" } ] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "readsb", "uid": "xxPTRy1Vk", "version": 11, "weekStart": "", "gnetId": 18148, "description": "Using Prometheus as a datasource to visualise readsb data" }
2023-02-21 02:54:31 UTC
2023-02-21 02:56:37 UTC
0
0
18,363
1
428,770
28,185
0
ra-l-s-dashboard-grouped-by-op-filtered-by-nodes-canaries
Raúl's dashboard (grouped by op, filtered by nodes) (CANARIES)
Dashboard to track Filecoin Lotus node API endpoints.
2023-03-27 07:01:24 UTC
2023-03-27 07:03:06 UTC
0
null
0
0
null
null
0
0
28,185
1
18,363
1
0
Dashboard to track Filecoin Lotus node API endpoints.
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true, "multi": true, "name": "endpoint", "options": [], "query": { "query": "lotus_api_request_duration_ms_bucket", "refId": "Prometheus-endpoint-Variable-Query" }, "refresh": 2, "regex": "/.*endpoint=\"([a-zA-Z]*)\"/", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "prometheus" }, "definition": "lotus_pubsub_send_rpc", "hide": 0, "includeAll": true, "multi": true, "name": "pod", "options": [], "query": { "query": "lotus_pubsub_send_rpc", "refId": "Prometheus-pod-Variable-Query" }, "refresh": 2, "regex": "/.*pod=\"(.*?)\"/", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ] }, "timezone": "", "title": "Raúl's dashboard (grouped by op, filtered by nodes) 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2023-03-27 07:01:24 UTC
null
0
0
18,394
1
826,362
28,220
0
used-wasted-resources
Used | Wasted Resources
Dashboard to show the resource requests vs allocatable in the cluster
2023-03-30 09:48:00 UTC
2023-03-30 09:51:17 UTC
0
Uses kube-prometheus-stack with node-exporter and kube-state-metrics
0
0
nodeExporter
null
0
0
28,220
1
18,394
1
0
Dashboard to show the resource requests vs allocatable in the cluster
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2023-03-30 09:48:00 UTC
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pgbackrest
Crunchy/pgBackRest
pgBackRest monitoring dashboard for PGO Crunchy Operator
2023-05-05 14:46:43 UTC
2023-05-05 14:50:24 UTC
0
null
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0
null
null
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18,677
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2023-05-05 14:46:43 UTC
2023-05-05 14:50:08 UTC
0
0
17,853
1
773,880
27,382
0
ruckus-msp-cloud-dashboard-venue
Ruckus MSP Cloud Dashboard - Venue
This Dashboard queries all APs from all the End Customers (tenants) of MSP and show a single windows to monitor
2023-01-11 08:53:13 UTC
2023-01-11 08:53:48 UTC
0
null
80,573
80,574
Other
null
0
0
27,382
1
17,853
1
0
This Dashboard queries all APs from all the End Customers (tenants) of MSP and show a single windows to monitor
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"matcher": { "id": "byName", "options": "Needs Attention" }, "properties": [ { "id": "links", "value": [ { "title": "Needs attention", "url": "d/x1d4sjWRkdsf/ruckus-cloud-tenant-dashboard?orgId=1&from=now-15m&to=now&viewPanel=27" } ] } ] } ] }, "gridPos": { "h": 4, "w": 3, "x": 6, "y": 16 }, "id": 47, "options": { "colorMode": "background", "graphMode": "area", "justifyMode": "center", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "value_and_name" }, "pluginVersion": "9.3.2", "targets": [ { "alias": "Out of (total)", "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "hide": false, "measurement": "/^$series_name$/", "orderByTime": "ASC", "policy": "default", "refId": "B", "resultFormat": "time_series", "select": [ [ { "params": [ "value" ], "type": "field" }, { "params": [], "type": "sum" } ] ], "tags": [ { "key": "Venue_Name", "operator": "=~", "value": "/^$venue$/" }, { "condition": "AND", "key": "Device_type", "operator": "=", "value": "Switch" } ] } ], "timeFrom": "15m", "transformations": [], "transparent": true, "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "description": "", "fieldConfig": { "defaults": { "custom": { "align": "auto", "displayMode": "auto", "filterable": true, "inspect": false }, "mappings": [ { "options": { "Error": { "color": "dark-red", "index": 0 }, "Not Saved": { "color": "dark-yellow", "index": 3 }, "Off": { "color": "dark-red", "index": 1 }, "Warning": { "color": "dark-yellow", "index": 2 }, "unsecure": { "color": "dark-yellow", "index": 4 } }, "type": "value" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "rgb(255, 255, 255)", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Uptime" }, "properties": [ { "id": "custom.displayMode", "value": "auto" }, { "id": "unit", "value": "s" } ] }, { "matcher": { "id": "byName", "options": "Status" }, "properties": [ { "id": "mappings", "value": [ { "options": { "1_01_NeverContactedCloud": { "color": "blue", "index": 1, "text": "Never Contacted Cloud" }, "2_00_Operational": { "color": "green", "index": 0, "text": "Operational" }, "2_02_ApplyingConfiguration": { "color": "super-light-green", "index": 4, "text": "Applying Configuration" }, "3_04_DisconnectedFromCloud": { "color": "red", "index": 2, "text": "Disconnected From Cloud" }, "4_04_HeartbeatLost": { "color": "orange", "index": 3, "text": "Heartbeat Lost" } }, "type": "value" } ] }, { "id": "custom.displayMode", "value": "color-text" } ] }, { "matcher": { "id": "byName", "options": "Last seen on " }, "properties": [ { "id": "unit", "value": "dateTimeAsLocalNoDateIfToday" }, { "id": "custom.align", "value": "right" } ] }, { "matcher": { "id": "byName", "options": "Device Name" }, "properties": [ { "id": "links", "value": [ { "title": "Show Device details", "url": "d/MHK5JWhVk/devices?orgId=1&var-device=${__data.fields[\"Device Name\"]}&var-series_name=$series_name&from=now-15m&to=now" } ] } ] } ] }, "gridPos": { "h": 12, "w": 24, "x": 0, "y": 20 }, "id": 12, "options": { "footer": { "fields": [ "Serial_No" ], "reducer": [ "distinctCount" ], "show": true }, "showHeader": true, "sortBy": [ { "desc": true, "displayName": "Status" } ] }, "pluginVersion": "9.3.2", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "groupBy": [ { "params": [ "Device_Name" ], "type": "tag" }, { "params": [ "Device_IPaddr" ], "type": "tag" }, { "params": [ "Device_MAC" ], "type": "tag" }, { "params": [ "Customer_Name" ], "type": "tag" }, { "params": [ "Last_Seen" ], "type": "tag" }, { "params": [ "Serial_No" ], "type": "tag" }, { "params": [ "Venue_Name" ], "type": "tag" }, { "params": [ "Uptime" ], "type": "tag" }, { "params": [ "Status" ], "type": "tag" }, { "params": [ "Dev_fw" ], "type": "tag" }, { "params": [ 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"Last_Seen": 6, "Serial_No": 8, "Stat": 13, "Status": 9, "Time": 0, "Uptime": 7, "Venue_Name": 5 }, "renameByName": { "AP_IPaddr": "IP Address", "AP_MAC": "MAC Address", "AP_Name": "Access Point Name", "Customer_Name": "End Customer name", "Dev_fw": "Firmware", "Device": "", "Device_IPaddr": "Device IP", "Device_MAC": "Device MAC", "Device_Model": "Device Model", "Device_Name": "Device Name", "Device_type": "Type", "Last_Seen": "Last seen on ", "Serial_No": "Serial Number", "Status": "Status", "Uptime": "Last known Uptime", "Venue_Name": "Venue Name", "valueStr": "HiVision Status" } } } ], "type": "table" } ], "refresh": false, "schemaVersion": 37, "style": "dark", "tags": [ "ruckus", "msp", "cloud" ], "templating": { "list": [ { "current": { "selected": true, "text": "sltaps", "value": "sltaps" }, "hide": 2, "name": "series_name", "options": [ { "selected": true, "text": "sltaps", "value": "sltaps" } ], "query": "sltaps", "skipUrlSync": false, "type": "textbox" }, { "current": {}, "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "definition": "select Venue_Name, value from $series_name WHERE time >= now() - 15m and time <= now()", "description": "Enter your Venue", "hide": 0, "includeAll": false, "label": "Venue", "multi": false, "name": "venue", "options": [], "query": "select Venue_Name, value from $series_name WHERE time >= now() - 15m and time <= now()", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "type": "query" } ] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": { "hidden": false, "refresh_intervals": [ "15m" ] }, "timezone": "browser", "title": "Ruckus MSP Cloud Dashboard - Venue", "uid": "09chXZ24k", "version": 35, "weekStart": "", "gnetId": 17853 }
2023-01-11 08:53:13 UTC
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0
dnsdist
dnsdist
dnsdist is a highly DNS-, DoS- and abuse-aware loadbalancer. Its goal in life is to route traffic to the best server, delivering top performance to legitimate users while shunting or blocking abusive traffic.
2023-02-06 08:34:16 UTC
2023-02-06 08:40:18 UTC
0
Shows all available metrics for PowerDNS dnsdist
81,384
81,385
null
null
0
0
27,708
1
18,044
1
0
dnsdist is a highly DNS-, DoS- and abuse-aware loadbalancer. Its goal in life is to route traffic to the best server, delivering top performance to legitimate users while shunting or blocking abusive traffic.
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}
2023-02-06 08:34:16 UTC
2023-02-06 08:36:29 UTC
0
0
18,276
1
683,756
28,050
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kafka-dashboard
Kafka Dashboard
Kafka resource usage and consumer lag overview
2023-03-14 05:37:07 UTC
2023-04-10 07:59:04 UTC
0
## 使用jmx_exporter对kafka进行监控 ## Use jmx_exporter to collect Kafka metrics 导入jmx_prometheus_javaagent-0.18.0.jar和config.yml Use jmx_prometheus_javaagent-0.18.0.jar and edit config.yml windows启动kafka-javaagent startwithagent.bat的启动脚本 Edit kafka-javaagent startwithagent.bat for Windows system to start kafka, Such as: ```powershell SET KAFKA_HOME=E:\kafka_2.12-2.8.1 SET KAFKA_OPTS=-javaagent:%KAFKA_HOME%\jmx_prometheus_javaagent-0.18.0.jar=6660:%KAFKA_HOME%\config.yml cd %KAFKA_HOME%\bin\windows kafka-server-start.bat %KAFKA_HOME%\config\server.properties ``` config.yml内容(如果不添加任何指定监控项为默认值): ```yaml lowercaseOutputName: true rules: # Special cases and very specific rules - pattern : kafka.server<type=(.+), name=(.+), clientId=(.+), topic=(.+), partition=(.*)><>Value name: kafka_server_$1_$2 type: GAUGE labels: clientId: "$3" topic: "$4" partition: "$5" - pattern : kafka.server<type=(.+), name=(.+), clientId=(.+), brokerHost=(.+), brokerPort=(.+)><>Value name: kafka_server_$1_$2 type: GAUGE labels: clientId: "$3" broker: "$4:$5" - pattern : kafka.coordinator.(\w+)<type=(.+), name=(.+)><>Value name: kafka_coordinator_$1_$2_$3 type: GAUGE # Generic per-second counters with 0-2 key/value pairs - pattern: kafka.(\w+)<type=(.+), name=(.+)PerSec\w*, (.+)=(.+), (.+)=(.+)><>Count name: kafka_$1_$2_$3_total type: COUNTER labels: "$4": "$5" "$6": "$7" - pattern: kafka.(\w+)<type=(.+), name=(.+)PerSec\w*, (.+)=(.+)><>Count name: kafka_$1_$2_$3_total type: COUNTER labels: "$4": "$5" - pattern: kafka.(\w+)<type=(.+), name=(.+)PerSec\w*><>Count name: kafka_$1_$2_$3_total type: COUNTER - pattern: kafka.server<type=(.+), client-id=(.+)><>([a-z-]+) name: kafka_server_quota_$3 type: GAUGE labels: resource: "$1" clientId: "$2" - pattern: kafka.server<type=(.+), user=(.+), client-id=(.+)><>([a-z-]+) name: kafka_server_quota_$4 type: GAUGE labels: resource: "$1" user: "$2" clientId: "$3" # Generic gauges with 0-2 key/value pairs - pattern: kafka.(\w+)<type=(.+), name=(.+), (.+)=(.+), (.+)=(.+)><>Value name: kafka_$1_$2_$3 type: GAUGE labels: "$4": "$5" "$6": "$7" - pattern: kafka.(\w+)<type=(.+), name=(.+), (.+)=(.+)><>Value name: kafka_$1_$2_$3 type: GAUGE labels: "$4": "$5" - pattern: kafka.(\w+)<type=(.+), name=(.+)><>Value name: kafka_$1_$2_$3 type: GAUGE # Emulate Prometheus 'Summary' metrics for the exported 'Histogram's. # # Note that these are missing the '_sum' metric! - pattern: kafka.(\w+)<type=(.+), name=(.+), (.+)=(.+), (.+)=(.+)><>Count name: kafka_$1_$2_$3_count type: COUNTER labels: "$4": "$5" "$6": "$7" - pattern: kafka.(\w+)<type=(.+), name=(.+), (.+)=(.*), (.+)=(.+)><>(\d+)thPercentile name: kafka_$1_$2_$3 type: GAUGE labels: "$4": "$5" "$6": "$7" quantile: "0.$8" - pattern: kafka.(\w+)<type=(.+), name=(.+), (.+)=(.+)><>Count name: kafka_$1_$2_$3_count type: COUNTER labels: "$4": "$5" - pattern: kafka.(\w+)<type=(.+), name=(.+), (.+)=(.*)><>(\d+)thPercentile name: kafka_$1_$2_$3 type: GAUGE labels: "$4": "$5" quantile: "0.$6" - pattern: kafka.(\w+)<type=(.+), name=(.+)><>Count name: kafka_$1_$2_$3_count type: COUNTER - pattern: kafka.(\w+)<type=(.+), name=(.+)><>(\d+)thPercentile name: kafka_$1_$2_$3 type: GAUGE labels: quantile: "0.$4" ``` linux启动添加类似脚本或者改动启动文件: Edit `start.sh`: ```shell KAFKA_HOME=/xxx/kafka_2.12-2.8.1 export KAFKA_OPTS=-javaagent:$KAFKA_HOME/jmx_prometheus_javaagent-0.18.0.jar=6660:$KAFKA_HOME/config.yml $KAFKA_HOME/bin/kafka-server-start.sh -daemon $KAFKA_HOME/config/server.properties ``` ## 添加配置至prometheus ## Edit prometheus.yml ​ 在prometheus.yml文件中添加: ```yaml - job_name: "kafka-jmx" # metrics_path defaults to '/metrics' # scheme defaults to 'http'. static_configs: - targets: ["{Prometheus Server}:6660"] ``` 访问{Prometheus Server}6660/metrics查看指标 View http://{Prometheus Server}6660/metrics to checkout Kafka metrics
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Other
null
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0
28,050
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18,276
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Kafka resource usage and consumer lag overview
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"options": [], "query": { "query": "label_values(kafka_cluster_partition_replicascount{job=\"$kafka_job_name\"}, topic)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-12h", "to": "now" }, "timepicker": { "refresh_intervals": [ "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "browser", "title": "Kafka Dashboard", "uid": "i8HLvrkiz", "version": 3, "weekStart": "" }
2023-03-14 05:37:07 UTC
2023-04-10 07:59:04 UTC
0
0
17,091
1
718,484
26,227
0
tuya-fe-visitor-monitor
tuya FE visitor monitor
nginx 日志监控
2022-09-30 08:47:21 UTC
2022-09-30 08:47:32 UTC
0
null
0
0
null
null
0
0
26,227
1
17,091
1
0
nginx 日志监控
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true, "name": "path", "options": [ { "selected": true, "text": "All", "value": "$__all" }, { "selected": false, "text": "/mnt/logs/iot.developer.log", "value": "/mnt/logs/iot.developer.log" }, { "selected": false, "text": "/mnt/logs/developer.log", "value": "/mnt/logs/developer.log" } ], "query": "/mnt/logs/iot.developer.log,/mnt/logs/developer.log", "type": "custom" }, { "allValue": null, "current": {}, "datasource": "${DS_INFLUXDB_NGINX}", "hide": 0, "includeAll": true, "label": null, "multi": true, "name": "code", "options": [], "query": "SHOW TAG VALUES WITH KEY = resp_code", "refresh": 1, "regex": "", "sort": 0, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-24h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "tuya FE visitor monitor", "uid": "K8xRgnvik", "version": 26 }
2022-09-30 08:47:21 UTC
null
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fortigate-fgt200f
FORTIGATE FGT200F
Dashboard baseado em informações coletadas via SNMP no fortigate.
2023-02-14 13:47:18 UTC
2023-02-14 13:53:03 UTC
0
null
81,607
81,608
null
null
0
0
27,788
1
18,098
1
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6s0xz6TTIpRQ7GD
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2023-02-14 13:47:18 UTC
2023-02-14 13:53:02 UTC
0
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pm2-dashboard
PM2 Dashboard
pm2 metrics dashboard
2023-04-18 05:24:48 UTC
2023-04-18 05:25:33 UTC
0
null
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pm2 metrics dashboard
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"label_values(job)", "hide": 0, "includeAll": false, "label": "Job", "multi": false, "name": "job", "options": [], "query": "label_values(job)", "refresh": 1, "regex": "/.*pm2/", "skipUrlSync": false, "sort": 5, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false }, { "allValue": null, "current": {}, "datasource": "${DS_PROMETHEUS}", "definition": "label_values({job=~\"$job\"},instance) ", "hide": 0, "includeAll": false, "label": "Hostname", "multi": true, "name": "host", "options": [], "query": "label_values({job=~\"$job\"},instance) ", "refresh": 1, "regex": "/([^]+)/", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-5m", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "PM2 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2023-04-18 05:24:48 UTC
null
0
0
18,658
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59,972
28,597
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windows-servers-status
Windows Servers Status Summary
Windows Server Status using Prometheus and windows_exporter
2023-05-03 10:22:10 UTC
2023-05-03 10:25:17 UTC
0
This dashboard for Windows Server Summary using Prometheus and windows_exporter
83,992
83,993
null
null
0
0
28,597
1
18,658
1
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null
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2023-05-03 10:22:10 UTC
2023-05-03 10:25:17 UTC
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2023-05-04 20:04:51 UTC
2023-05-04 20:05:44 UTC
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true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "datasource": null, "fieldConfig": { "defaults": { "custom": {} }, "overrides": [] }, "gridPos": { "h": 2, "w": 24, "x": 0, "y": 58 }, "id": 109, "options": { "content": "### Gossipsub Metrics", "mode": "markdown" }, "pluginVersion": "7.4.3", "timeFrom": null, "timeShift": null, "title": "", "transparent": true, "type": "text" }, { "datasource": "${DS_PROMETHEUS}", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": {}, "mappings": [], "max": 100, "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 60 }, "id": 111, "options": { "displayMode": "gradient", "orientation": "auto", "reduceOptions": { "calcs": [ "mean" ], "fields": "", "values": false }, "showUnfilled": true, "text": {} }, "pluginVersion": "7.4.3", "targets": [ { "expr": "gossipsub_mesh_peers_per_main_topic", "instant": true, "interval": "", "legendFormat": "{{topic_hash}}", "refId": "A" } ], "timeFrom": null, "timeShift": null, "title": "Mesh peers per topic", "type": "bargauge" }, { "datasource": "${DS_PROMETHEUS}", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": {}, "mappings": [], "max": 100, "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 12, "y": 60 }, "id": 114, "options": { "displayMode": "gradient", "orientation": "auto", "reduceOptions": { "calcs": [ "mean" ], "fields": "", "values": false }, "showUnfilled": true, "text": {} }, "pluginVersion": "7.4.3", "targets": [ { "expr": "gossipsub_avg_peer_score_per_topic", "interval": "", "legendFormat": "{{topic_hash}}", "refId": "A" } ], "timeFrom": null, "timeShift": null, "title": "Average Peer Score per Topic", "type": "bargauge" }, { "datasource": "${DS_PROMETHEUS}", "description": "", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": {}, "mappings": [], "max": 100, "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 7, "w": 24, "x": 0, "y": 68 }, "id": 110, "options": { "displayMode": "gradient", "orientation": "vertical", "reduceOptions": { "calcs": [ "mean" ], "fields": "", "values": false }, "showUnfilled": true, "text": {} }, "pluginVersion": "7.4.3", "targets": [ { "expr": "gossipsub_mesh_peers_per_subnet_topic", "instant": true, "interval": "", "legendFormat": "{{subnet}}", "refId": "A" } ], "timeFrom": null, "timeShift": null, "title": "Mesh peers per subnet", "type": "bargauge" }, { "datasource": null, "fieldConfig": { "defaults": { "custom": {} }, "overrides": [] }, "gridPos": { "h": 2, "w": 24, "x": 0, "y": 75 }, "id": 67, "links": [], "options": { "content": "\n### Logging\n\n\n\n", "mode": "markdown" }, "pluginVersion": "7.4.3", "timeFrom": null, "timeShift": null, "title": "", "transparent": true, "type": "text" }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "", "fieldConfig": { "defaults": { "custom": {}, "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 6, "w": 6, "x": 0, "y": 77 }, "hiddenSeries": false, "id": 71, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": false, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pluginVersion": "7.4.3", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "crit_total", "format": "time_series", "interval": "", "intervalFactor": 1, "legendFormat": "", "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Crit Logs Total", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "none", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "", "fieldConfig": { "defaults": { "custom": {}, "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 6, "w": 6, "x": 6, "y": 77 }, "hiddenSeries": false, "id": 70, "legend": { "alignAsTable": false, "avg": false, "current": false, "max": false, "min": false, "rightSide": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pluginVersion": "7.4.3", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "rate(error_total[1m])", "format": "time_series", "intervalFactor": 1, "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Error Logs per Minute", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "none", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "", "fieldConfig": { "defaults": { "custom": {}, "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 6, "w": 6, "x": 12, "y": 77 }, "hiddenSeries": false, "id": 69, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": false, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pluginVersion": "7.4.3", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "rate(warn_total[1m])", "format": "time_series", "intervalFactor": 1, "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Warn Logs per Minute", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "none", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "", "fieldConfig": { "defaults": { "custom": {}, "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 6, "w": 6, "x": 18, "y": 77 }, "hiddenSeries": false, "id": 68, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": false, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pluginVersion": "7.4.3", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "rate(info_total[1m])", "format": "time_series", "intervalFactor": 1, "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Info Logs per Minute", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "none", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "datasource": null, "fieldConfig": { "defaults": { "custom": {} }, "overrides": [] }, "gridPos": { "h": 2, "w": 24, "x": 0, "y": 83 }, "id": 51, "links": [], "options": { "content": "\n### Core BeaconChain Functions\n\nTiming of core `BeaconChain` functions and syncing.\n\n\n", "mode": "markdown" }, "pluginVersion": "7.4.3", "timeFrom": null, "timeShift": null, "title": "", "transparent": true, "type": "text" }, { "aliasColors": {}, "bars": false, "cacheTimeout": null, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "fieldConfig": { "defaults": { "custom": {}, "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 5, "w": 8, "x": 0, "y": 85 }, "hiddenSeries": false, "id": 10, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": false, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pluginVersion": "7.4.3", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "rate(beacon_block_processing_seconds_sum[30s])\n/\nrate(beacon_block_processing_seconds_count[30s])", "format": "time_series", "instant": false, "interval": "", "intervalFactor": 1, "legendFormat": "", "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Block Processing Times (24s moving avg)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "s", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "cacheTimeout": null, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "fieldConfig": { "defaults": { "custom": {}, "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 5, "w": 8, "x": 8, "y": 85 }, "hiddenSeries": false, "id": 11, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": false, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pluginVersion": "7.4.3", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "rate(beacon_fork_choice_seconds_sum[1m])\n/\nrate(beacon_fork_choice_seconds_count[1m])", "format": "time_series", "intervalFactor": 1, "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Find & Update Head Routine (1m avg)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "s", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "cacheTimeout": null, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "fieldConfig": { "defaults": { "custom": {}, "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 5, "w": 8, "x": 16, "y": 85 }, "hiddenSeries": false, "id": 54, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": false, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pluginVersion": "7.4.3", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "sync_slots_per_second", "format": "time_series", "intervalFactor": 1, "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Syncing Slots per Second", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "none", "label": "Slots", "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "cacheTimeout": null, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "Time taken to calculate the tree hash root of a BeaconState during block processing.", "fieldConfig": { "defaults": { "custom": {}, "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 5, "w": 12, "x": 0, "y": 90 }, "hiddenSeries": false, "id": 37, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": false, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pluginVersion": "7.4.3", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "rate(beacon_block_processing_block_root_seconds_sum[5m])\n/\nrate(beacon_block_processing_block_root_seconds_count[5m])", "format": "time_series", "intervalFactor": 1, "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Block Tree Hash Times (5m avg)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "s", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "cacheTimeout": null, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "description": "Time taken to calculate the tree hash root of a BeaconState during block processing.", "fieldConfig": { "defaults": { "custom": {}, "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 5, "w": 12, "x": 12, "y": 90 }, "hiddenSeries": false, "id": 20, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": false, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pluginVersion": "7.4.3", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "rate(beacon_block_processing_state_root_seconds_sum[5m])\n/\nrate(beacon_block_processing_state_root_seconds_count[5m])", "format": "time_series", "intervalFactor": 1, "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "State Tree Hash Times (5m avg)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "s", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "datasource": null, "fieldConfig": { "defaults": { "custom": {} }, "overrides": [] }, "gridPos": { "h": 2, "w": 24, "x": 0, "y": 95 }, "id": 56, "links": [], "options": { "content": "\n### Database\n\nStats about the on-disk database (LevelDB)\n\n\n\n", "mode": "markdown" }, "pluginVersion": "7.4.3", "timeFrom": null, "timeShift": null, "title": "", "transparent": true, "type": "text" }, { "aliasColors": {}, "bars": false, "cacheTimeout": null, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "fieldConfig": { "defaults": { "custom": {}, "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 5, "w": 12, "x": 0, "y": 97 }, "hiddenSeries": false, "id": 42, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": false, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pluginVersion": "7.4.3", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "store_disk_db_size", "format": "time_series", "intervalFactor": 1, "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "On-Disk Database Size (Hot DB Only)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "decbytes", "label": "", "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "cacheTimeout": null, "dashLength": 10, "dashes": false, "datasource": "${DS_PROMETHEUS}", "fieldConfig": { "defaults": { "custom": {}, "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 5, "w": 6, "x": 12, "y": 97 }, "hiddenSeries": false, "id": 19, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": false, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, 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"defaults": { "custom": {}, "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 5, "w": 6, "x": 18, "y": 97 }, "hiddenSeries": false, "id": 22, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": false, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pluginVersion": "7.4.3", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "increase(store_disk_db_write_bytes_total [1m])", "format": "time_series", "intervalFactor": 1, "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "DB Throughput (Write) (1m avg)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": 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"stack": false, "steppedLine": false, "targets": [ { "expr": "rate(beacon_persist_chain_sum[1m])\n/\nrate(beacon_persist_chain_count[1m])", "format": "time_series", "intervalFactor": 1, "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "Persist Beacon Chain Times (1m avg)", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "s", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } } ], "refresh": "10s", "schemaVersion": 27, "style": "dark", "tags": [], "templating": { "list": [] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": { "refresh_intervals": [ "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "HEXAR VAL", "uid": "yY7PIGdZd", "version": 3, "description": "venezuela val " }
2023-05-04 20:04:51 UTC
2023-05-04 20:05:40 UTC
0
0
17,188
1
724,295
26,409
0
cloudflare-zone-analytics
CloudFlare Zone Analytics
Dashboard for Prometheus cloudflare-exporter https://github.com/lablabs/cloudflare-exporter
2022-10-14 13:56:37 UTC
2022-10-14 13:58:10 UTC
0
null
0
0
null
null
0
0
26,409
1
17,188
1
0
Dashboard for Prometheus cloudflare-exporter https://github.com/lablabs/cloudflare-exporter
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[], "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-30m", "to": "now" }, "timepicker": { "refresh_intervals": [ "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ] }, "timezone": "", "title": "CloudFlare Zone Analytics", "uid": "mWxtnz5Mz", "version": 43, "description": "Dashboard for Prometheus cloudflare-exporter\r\nhttps://github.com/lablabs/cloudflare-exporter" }
2022-10-14 13:56:37 UTC
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kubernetes-logs
Kubernetes Logs in ClickHouse
Simple dashboard for visualizing Kubernetes logs in ClickHouse
2022-10-27 13:26:53 UTC
2022-10-27 13:45:56 UTC
0
Dashboard to accompany blog on loading [Sending Kubernetes logs To ClickHouse with Fluent Bit](https://clickhouse.com/blog/kubernetes-logs-to-clickhouse-fluent-bit).
78,032
78,033
null
null
0
0
26,562
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17,282
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null
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2022-10-27 13:26:53 UTC
2022-10-27 13:31:56 UTC
0
0
17,389
1
737,359
26,718
0
bind-dns
Bind DNS
Bind DNS Service Statistics.
2022-11-11 08:46:25 UTC
2022-11-11 08:46:40 UTC
0
null
0
0
null
null
0
0
26,718
1
17,389
1
0
Bind DNS Service Statistics.
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2022-11-11 08:46:25 UTC
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mikrotik-snmp
Mikrotik snmp
Mikrotik monitoring (8.2.1) Edited By Soroush Kaviani
2022-12-06 13:12:04 UTC
2022-12-06 13:14:07 UTC
0
null
0
0
nodeExporter
null
0
0
26,937
1
17,544
1
0
Mikrotik monitoring (8.2.1) Edited By Soroush Kaviani
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(mtxrQueueTreeName) group_left(mtxrQueueTreeIndex) mtxrQueueTreeIndex{instance='$instance',}))", "description": null, "error": null, "hide": 1, "includeAll": true, "label": "", "multi": false, "name": "queuetree_flow", "options": [], "query": { "query": "query_result((mtxrQueueTreeParentIndex * on (mtxrQueueTreeName) group_left(mtxrQueueTreeIndex) mtxrQueueTreeIndex{instance='$instance',}))", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "/mtxrQueueTreeIndex=\"(.*)\",instance/", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-5m", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "Mikrotik snmp", "uid": "EIl_irh7k", "version": 16 }
2022-12-06 13:12:04 UTC
null
0
0
17,670
1
764,425
27,133
0
discord-bot
Discord Bot
This is a dashboard for the discord-ext-prometheus Python library.
2022-12-20 16:56:28 UTC
2022-12-20 18:52:51 UTC
0
discord-ext-prometheus is available on [PyPi](https://pypi.org/project/discord-ext-prometheus/).
0
0
Other
null
0
0
27,133
1
17,670
1
0
null
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"axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 2, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "decimals": 0, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 5, "w": 8, "x": 8, "y": 10 }, "id": 48, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": false }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "discord_stat_total_channels{job=~\"$job\"}", "legendFormat": "{{job}}", "range": true, "refId": "A" } ], "title": "Channels Over Time", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 5, "w": 8, "x": 16, "y": 10 }, "id": 22, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": false }, "tooltip": { "mode": "multi", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "rate(process_cpu_seconds_total{job=~\"$job\"}[1m])", "legendFormat": "{{job}}", "range": true, "refId": "A" } ], "title": "CPU Activity", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "ERROR" }, "properties": [ { "id": "color", "value": { "fixedColor": "red", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "WARNING" }, "properties": [ { "id": "color", "value": { "fixedColor": "orange", "mode": "fixed" } } ] }, { "matcher": { "id": "byName", "options": "INFO" }, "properties": [ { "id": "color", "value": { "fixedColor": "green", "mode": "fixed" } } ] } ] }, "gridPos": { "h": 5, "w": 8, "x": 0, "y": 15 }, "id": 28, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": false }, "tooltip": { "mode": "multi", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "round(max(rate(logging_total{job=~\"$job\"}[1m])) by (level) * 45)", "interval": "", "legendFormat": "__auto", "range": true, "refId": "A" } ], "title": "Logs", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 2, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "decimals": 0, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 5, "w": 8, "x": 8, "y": 15 }, "id": 46, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": false }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "discord_stat_total_users{job=~\"$job\"}", "legendFormat": "{{job}}", "range": true, "refId": "A" } ], "title": "Users Over Time", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic", "seriesBy": "last" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "hue", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "decimals": 1, "mappings": [], "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "s" }, "overrides": [] }, "gridPos": { "h": 5, "w": 8, "x": 16, "y": 15 }, "id": 24, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": false }, "tooltip": { "mode": "multi", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "expr": "scrape_duration_seconds{job=~\"$job\"}", "legendFormat": "{{job}}", "range": true, "refId": "A" } ], "title": "Scrape Duration", "type": "timeseries" } ], "refresh": "5s", "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [ { "allValue": "", "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(discord_connected, job)", "description": "The job", "hide": 0, "includeAll": true, "label": "Job", "multi": false, "name": "job", "options": [], "query": { "query": "label_values(discord_connected, job)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 5, "type": "query" } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Discord Bot", "uid": "TGkTuzD4z", "version": 90, "weekStart": "", "gnetId": 17670 }
2022-12-20 16:56:28 UTC
2022-12-20 17:02:06 UTC
0
0
18,125
1
797,650
27,831
0
evidently-data-drift-dashboard
Evidently Data Drift Dashboard
trial dashboard
2023-02-17 12:45:11 UTC
2023-02-17 12:46:09 UTC
0
null
0
0
null
null
0
0
27,831
1
18,125
1
0
null
{ "__inputs": [ { "name": "DS_PROMETHEUS", "label": "Prometheus", "description": "", "type": "datasource", "pluginId": "prometheus", "pluginName": "Prometheus" } ], "__elements": {}, "__requires": [ { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.3.6" }, { "type": "datasource", "id": "prometheus", "name": "Prometheus", "version": "1.0.0" }, { "type": "panel", "id": "stat", "name": "Stat", "version": "" }, { "type": "panel", "id": "state-timeline", "name": "State timeline", "version": "" }, { "type": "panel", "id": "text", "name": "Text", "version": "" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "datasource", "uid": "grafana" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "editable": true, 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"color": { "mode": "fixed" }, "custom": { "fillOpacity": 70, "lineWidth": 0, "spanNulls": false }, "mappings": [ { "options": { "0": { "color": "green", "index": 1, "text": "No Data Drift" }, "1": { "color": "red", "index": 0, "text": "Data Drift Detected" } }, "type": "value" } ], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 1 } ] } }, "overrides": [] }, "gridPos": { "h": 7, "w": 24, "x": 0, "y": 4 }, "id": 2, "options": { "alignValue": "center", "legend": { "displayMode": "list", "placement": "bottom", "showLegend": false }, "mergeValues": true, "rowHeight": 0.9, "showValue": "auto", "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": true, "expr": "evidently:data_drift:dataset_drift{dataset_name=\"${dataset_name:raw}\"}", "instant": false, "interval": "", "legendFormat": "dataset drift", "refId": "A" } ], "title": "Dataset Drift", "type": "state-timeline" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "max": 1, "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 0.8 } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 7, "w": 12, "x": 0, "y": 11 }, "id": 6, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": false }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": true, "expr": "evidently:data_drift:share_drifted_features{dataset_name=\"${dataset_name:raw}\"}", "interval": "", "legendFormat": "share", "refId": "A" } ], "title": "Share of drifted features", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 0 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 7, "w": 6, "x": 12, "y": 11 }, "id": 8, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "center", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/^Drifted$/", "values": false }, "text": {}, "textMode": "auto" }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": true, "expr": "evidently:data_drift:n_drifted_features{dataset_name=\"${dataset_name:raw}\"}", "format": "time_series", "hide": false, "instant": false, "interval": "", "legendFormat": "Drifted", "refId": "B" } ], "title": "# of drifted features", "transformations": [], "type": "stat" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 7, "w": 6, "x": 18, "y": 11 }, "id": 15, "options": { "colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "text": {}, "textMode": "auto" }, "pluginVersion": "9.3.6", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": true, "expr": "evidently:data_drift:n_drifted_features{dataset_name=\"${dataset_name:raw}\"} / evidently:data_drift:share_drifted_features{dataset_name=\"${dataset_name:raw}\"}", "format": "time_series", "hide": false, "instant": false, "interval": "", "legendFormat": "Total", "refId": "A" } ], "title": "# of features", "transformations": [], "type": "stat" }, { "collapsed": false, "datasource": { "type": "datasource", "uid": "grafana" }, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 18 }, "id": 10, "panels": [], "targets": [ { "datasource": { "type": "datasource", "uid": "grafana" }, "refId": "A" } ], "title": "Detailed information", "type": "row" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", 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"right", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": true, "expr": "evidently:data_drift:p_value{dataset_name=\"${dataset_name:raw}\"}", "interval": "", "legendFormat": "{{feature}}", "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": true, "expr": "evidently:reference_dataset_hash", "hide": false, "interval": "", "intervalFactor": 1, "legendFormat": "reference_hash", "refId": "B" } ], "title": "P-value of features", "type": "timeseries" } ], "refresh": "10s", "schemaVersion": 37, "style": "dark", "tags": [], "templating": { "list": [ { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(dataset_name)", "hide": 0, "includeAll": false, "multi": false, "name": "dataset_name", "options": [], "query": { "query": "label_values(dataset_name)", "refId": "StandardVariableQuery" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "type": "query" } ] }, "time": { "from": "now-5m", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Evidently Data Drift Dashboard", "uid": "U54hsxv7k", "version": 1, "weekStart": "", "gnetId": 18125, "description": "trial dashboard" }
2023-02-17 12:45:11 UTC
2023-02-17 12:46:09 UTC
0
0
18,788
1
854,524
28,811
0
grafana-faro-frontend-monitoring
Grafana Faro - Frontend Monitoring
Frontend monitoring dashboard using metrics collected with Grafana Faro SDK.
2023-05-19 14:42:26 UTC
2023-05-19 14:43:28 UTC
0
null
0
0
null
null
0
0
28,811
1
18,788
1
0
Frontend monitoring dashboard using metrics collected with Grafana Faro SDK.
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"multi": false, "name": "env", "options": [], "query": { "label": "Environment", "refId": "LokiVariableQueryEditor-VariableQuery", "stream": "", "type": 1 }, "refresh": 1, "regex": "K8S.*", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": {}, "datasource": { "type": "loki", "uid": "${DS_ACCUV_APPLICATION LOGS}" }, "definition": "label_values(app)", "hide": 0, "includeAll": false, "label": "Application", "multi": false, "name": "app", "options": [], "query": { "label": "Application", "refId": "LokiVariableQueryEditor-VariableQuery", "stream": "", "type": 1 }, "refresh": 1, "regex": "MainSpa", "skipUrlSync": false, "sort": 0, "type": "query" }, { "current": { "selected": false, "text": "AccuV Application Logs", "value": "AccuV Application Logs" }, "hide": 2, "includeAll": false, "label": "datasource", "multi": false, "name": "datasource", "options": [], "query": "loki", "refresh": 1, "regex": "/.*(Logs).*/", "skipUrlSync": false, "type": "datasource" } ] }, "time": { 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2023-05-19 14:42:26 UTC
null
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16,812
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582,917
25,777
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grafanacloud-billing
GrafanaCloud Billing
GrafanaCloud User dashboard for viewing metrics related to billing.
2022-08-30 01:11:00 UTC
2022-08-30 01:11:12 UTC
0
null
0
0
null
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25,777
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GrafanaCloud User dashboard for viewing metrics related to billing.
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2022-08-30 01:11:00 UTC
null
0
0
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750,145
26,841
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loki-stack-monitoring-promtail-loki
Loki stack monitoring (Promtail, Loki)
This dashboard can be used to detect issues on the Loki stack, when deployed in Kubernetes. Shows: some error metrics published by Promtail/Loki. Error and warning logs emitted by Promtail/Loki. Memory and CPU usage of Promtail/Loki compared against the Kubernetes memory/cpu limits and requests.
2022-11-24 22:59:39 UTC
2022-11-24 23:02:00 UTC
0
null
0
0
collectd
null
0
0
26,841
1
17,479
1
0
This dashboard can be used to detect issues on the Loki stack, when deployed in Kubernetes. Shows: some error metrics published by Promtail/Loki. Error and warning logs emitted by Promtail/Loki. Memory and CPU usage of Promtail/Loki compared against the Kubernetes memory/cpu limits and requests.
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2022-11-24 22:59:39 UTC
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kubernetes-monitoring-sit
KubeMonitor
Dashboard for Kubernetes cluster with Prometheus.
2022-12-15 09:36:11 UTC
2022-12-15 09:37:51 UTC
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Dashboard for Kubernetes cluster with Prometheus.
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"allValue": ".*", "current": {}, "datasource": { "uid": "$cluster" }, "definition": "label_values(node)", "hide": 0, "includeAll": true, "multi": false, "name": "Node", "options": [], "query": { "query": "label_values(node)", "refId": "Kafka-Node-Variable-Query" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "uid": "$cluster" }, "definition": "label_values(namespace)", "hide": 0, "includeAll": false, "multi": false, "name": "namespace", "options": [], "query": { "query": "label_values(namespace)", "refId": "Kafka-namespace-Variable-Query" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "uid": "$cluster" }, "definition": "label_values(container_cpu_usage_seconds_total{namespace=\"$namespace\",container!=\"POD\"},container)", "hide": 0, "includeAll": true, "multi": true, "name": "application", "options": [], "query": { "query": "label_values(container_cpu_usage_seconds_total{namespace=\"$namespace\",container!=\"POD\"},container)", "refId": "Kafka-application-Variable-Query" }, "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-3h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "2m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "browser", "title": "KubeMonitor", "uid": "f8W_uzz7z", "version": 3, "weekStart": "" }
2022-12-15 09:36:11 UTC
2022-12-15 09:37:46 UTC
0
0
18,364
1
428,770
28,186
0
ra-l-s-dashboard-grouped-by-op-filtered-by-nodes-mirror
Raúl's dashboard (grouped by op, filtered by nodes) (MIRROR)
Dashboard to track Filecoin Lotus node API endpoints.
2023-03-27 07:04:55 UTC
2023-03-27 07:05:51 UTC
0
null
0
0
null
null
0
0
28,186
1
18,364
1
0
Dashboard to track Filecoin Lotus node API endpoints.
{ "__inputs": [ { "name": "DS_PROMETHEUS", "label": "Prometheus", "description": "", "type": "datasource", "pluginId": "prometheus", "pluginName": "Prometheus" } ], "__elements": [], "__requires": [ { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.0.1" }, { "type": "datasource", "id": "prometheus", "name": "Prometheus", "version": "1.0.0" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "datasource", "uid": "grafana" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "description": "Dashboard to track Filecoin Lotus node API endpoints.", "editable": true, "fiscalYearStartMonth": 0, "gnetId": 18364, "graphTooltip": 1, "id": null, "iteration": 1679900428433, "links": [], "liveNow": false, "panels": [ { "collapsed": true, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 0 }, "id": 13, "panels": [ { "datasource": { "type": "prometheus", "uid": "prometheus" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic", "seriesBy": "last" }, "custom": { "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 20, "gradientMode": "hue", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineStyle": { "fill": "solid" }, "lineWidth": 1, "pointSize": 4, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green" }, { "color": "red", "value": 80 } ] }, "unit": "ms" }, "overrides": [ { "matcher": { "id": "byRegexp", "options": "request count.*" }, "properties": [ { "id": "custom.axisPlacement", "value": "left" }, { "id": "custom.fillOpacity", "value": 0 }, { "id": "custom.lineStyle", "value": { "fill": "solid" } }, { "id": "unit", "value": "reqps" }, { "id": "custom.lineWidth", "value": 1 }, { "id": "custom.drawStyle", "value": "bars" }, { "id": "custom.pointSize", "value": 2 }, { "id": "color", "value": { "fixedColor": "#adadad4d", "mode": "fixed" } }, { "id": "custom.gradientMode", "value": "none" }, { "id": "custom.fillOpacity", "value": 0 } ] } ] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 1 }, "id": 17, "maxPerRow": 2, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" }, "tooltip": { "mode": "multi", "sort": "desc" } }, "repeat": "pod", "repeatDirection": "h", "targets": [ { "datasource": { "type": "prometheus", "uid": "prometheus" }, "editorMode": "builder", "exemplar": false, "expr": "rate(lotus_api_request_duration_ms_count{exported_endpoint=~\"$endpoint\", pod=~\"$pod\"}[$__rate_interval])", "hide": false, "interval": "", "legendFormat": "request count", "range": true, "refId": "E" }, { "datasource": { "type": "prometheus", "uid": "prometheus" }, "editorMode": "code", "exemplar": false, "expr": "histogram_quantile(0.95, sum by(le) (rate(lotus_api_request_duration_ms_bucket{exported_endpoint=~\"$endpoint\", pod=~\"$pod\"}[$__rate_interval])))", "instant": false, "interval": "", "legendFormat": "p95", "range": true, "refId": "A" }, { "datasource": { "type": "prometheus", "uid": "prometheus" }, "editorMode": "code", "exemplar": false, "expr": "histogram_quantile(0.9, sum by(le) (rate(lotus_api_request_duration_ms_bucket{exported_endpoint=~\"$endpoint\", pod=~\"$pod\"}[$__rate_interval])))", "hide": false, "instant": false, "interval": "", "legendFormat": "p90", "range": true, "refId": "B" }, { "datasource": { "type": "prometheus", "uid": "prometheus" }, "editorMode": "code", "exemplar": false, "expr": "histogram_quantile(0.8, sum by(le) (rate(lotus_api_request_duration_ms_bucket{exported_endpoint=~\"$endpoint\", pod=~\"$pod\"}[$__rate_interval])))", "hide": false, "instant": false, "interval": "", "legendFormat": "p80", "range": true, "refId": "C" }, { "datasource": { "type": "prometheus", "uid": "prometheus" }, "editorMode": "code", "exemplar": false, "expr": "histogram_quantile(0.5, sum by(le) (rate(lotus_api_request_duration_ms_bucket{exported_endpoint=~\"$endpoint\", pod=~\"$pod\"}[$__rate_interval])))", "hide": false, "instant": false, "interval": "", "legendFormat": "p50", "range": true, "refId": "D" } ], "title": "$endpoint @ $pod", "transparent": true, "type": "timeseries" } ], "repeat": "endpoint", "repeatDirection": "h", "title": "$endpoint", "type": "row" } ], "refresh": false, "schemaVersion": 36, "style": "dark", "tags": [], "templating": { "list": [ { "current": {}, "datasource": { "type": "prometheus", "uid": "prometheus" }, "definition": "lotus_api_request_duration_ms_bucket", "hide": 0, "includeAll": true, "multi": true, "name": "endpoint", "options": [], "query": { "query": "lotus_api_request_duration_ms_bucket", "refId": "Prometheus-endpoint-Variable-Query" }, "refresh": 2, "regex": "/.*endpoint=\"([a-zA-Z]*)\"/", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "prometheus" }, "definition": "lotus_pubsub_send_rpc", "hide": 0, "includeAll": true, "multi": true, "name": "pod", "options": [], "query": { "query": "lotus_pubsub_send_rpc", "refId": "Prometheus-pod-Variable-Query" }, "refresh": 2, "regex": "/.*pod=\"(.*?)\"/", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ] }, "timezone": "", "title": "Raúl's dashboard (grouped by op, filtered by nodes) (MIRROR)", "uid": "M7rdOTfVz", "version": 2, "weekStart": "" }
2023-03-27 07:04:56 UTC
null
0
0
18,458
1
643,783
28,319
0
prometheus-alerts
Prometheus Alerts
Dashboard showing Prometheus Alerts (both pending and firing) for alerts adjustment and debugging
2023-04-07 03:50:44 UTC
2023-04-07 03:50:55 UTC
0
based on 11098
0
0
null
null
0
0
28,319
1
18,458
1
0
Dashboard showing Prometheus Alerts (both pending and firing) for alerts adjustment and debugging
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For filling this table `Alerts annotations` have to be switched on.", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "decimals": 2, "displayName": "", "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] }, "unit": "short" }, "overrides": [ { "matcher": { "id": "byName", "options": "Value" }, "properties": [ { "id": "displayName", "value": "time" }, { "id": "unit", "value": "time: YYYY-MM-DD HH:mm:ss" }, { "id": "custom.align", "value": "left" } ] }, { "matcher": { "id": "byRegexp", "options": "^((?!^alertname|^instance|^severity|^count|^time).)*$" }, "properties": [ { "id": "custom.hidden", "value": true } ] }, { "matcher": { "id": "byName", "options": "alertname" }, "properties": [ { "id": "custom.width", "value": 161 } ] } ] }, "gridPos": { "h": 10, "w": 7, "x": 17, "y": 0 }, "id": 442, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [] }, "pluginVersion": "8.5.0", "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "editorMode": "code", "exemplar": false, "expr": "ALERTS_FOR_STATE{instance=~\"$instance\",alertname=~\"$alertname\"}*1000", "format": "table", "instant": true, "range": false, "refId": "A" } ], "title": "Alerts Start List", "transformations": [ { "id": "organize", "options": { "excludeByName": {}, "indexByName": { "Time": 1, "Value": 0, "alertname": 2, "instance": 3, "job": 4, "severity": 5 }, "renameByName": {} } } ], "type": "table" }, { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "description": "Shows how many times was particular alert started in a defined time range. Alert can be started either directly as `firing` or as a `pending`. Pending alerts wait for a defined time before it flips to a `firing` alert. This is specified with the `FOR` clause in a Prometheus `rules` file.", "fieldConfig": { "defaults": { "color": { "mode": "thresholds" }, "custom": { "align": "auto", "displayMode": "auto", "inspect": false }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null } ] } }, "overrides": [ { "matcher": { "id": "byName", "options": "Time" }, "properties": [ { "id": "displayName", "value": "Time" }, { "id": "custom.align" } ] }, { "matcher": { "id": "byName", "options": "instance" }, "properties": [ { "id": "unit", "value": "short" }, { "id": "decimals", "value": 2 }, { "id": "custom.align" } ] }, { "matcher": { "id": "byName", "options": "Value" }, "properties": [ { "id": "displayName", "value": "count" }, { "id": "unit", "value": "short" }, { "id": "decimals", "value": 0 }, { "id": "custom.align" } ] }, { "matcher": { "id": "byRegexp", "options": "^((?!^alertname|^instance|^severity|^count).)*$" }, "properties": [ { "id": "custom.hidden", "value": true } ] } ] }, "gridPos": { "h": 13, "w": 24, "x": 0, "y": 10 }, "id": 414, "options": { "footer": { "fields": "", "reducer": [ "sum" ], "show": false }, "showHeader": true, "sortBy": [ { "desc": true, "displayName": "count" } ] }, "pluginVersion": "8.5.0", "targets": [ { "expr": "changes( ALERTS_FOR_STATE{instance=~\"$instance\",alertname=~\"$alertname\"}[$__range] ) + 1", "format": "table", "hide": false, "instant": true, "refId": "B", "datasource": "${DS_PROMETHEUS}" } ], "title": "Total Alert Counts", "transformations": [ { "id": "merge", "options": { "reducers": [] } } ], "type": "table" } ], "refresh": false, "schemaVersion": 36, "style": "dark", "tags": [ "prometheus", "alerts" ], "templating": { "list": [ { "current": { "selected": false, "text": "default", "value": "default" }, "hide": 2, "includeAll": false, "label": "Prometheus datasource", "multi": false, "name": "datasource", "options": [], "query": "prometheus", "refresh": 1, "regex": "", "skipUrlSync": false, "type": "datasource" }, { "current": {}, "datasource": { "type": "prometheus", "uid": "$datasource" }, "definition": "label_values(ALERTS_FOR_STATE,instance)", "hide": 0, "includeAll": true, "label": "Instance:", "multi": true, "name": "instance", "options": [], "query": { "query": "label_values(ALERTS_FOR_STATE,instance)", "refId": "Prometheus-instance-Variable-Query" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(ALERTS_FOR_STATE,alertname)", "hide": 0, "includeAll": true, "label": "Alert:", "multi": true, "name": "alertname", "options": [], "query": { "query": "label_values(ALERTS_FOR_STATE,alertname)", "refId": "Prometheus-alertname-Variable-Query" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "current": {}, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "definition": "label_values(ALERTS, alertstate)", "hide": 0, "includeAll": true, "label": "State:", "multi": true, "name": "alertstate", "options": [], "query": { "query": "label_values(ALERTS, alertstate)", "refId": "Prometheus-alertstate-Variable-Query" }, "refresh": 2, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tagsQuery": "", "type": "query", "useTags": false }, { "datasource": { "uid": " Thanos Trickster" }, "filters": [], "hide": 0, "label": "", "name": "Filters", "skipUrlSync": false, "type": "adhoc" } ] }, "time": { "from": "now-2d", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "Prometheus Alerts", "uid": "eea-9_sik", "version": 4, "weekStart": "" }
2023-04-07 03:50:44 UTC
null
0
0
18,599
1
345,879
28,514
0
open-liberty
Open Liberty - mpMetrics-5.0
Created by the Open Liberty observability team
2023-04-24 20:39:35 UTC
2023-04-27 20:58:21 UTC
0
This dashboard visualizes CPU, Heap, REST, Servlet, Connection Pool, Sessions, Threadpool, GC, and other JVM metrics. This dashboard works with Open Liberty servers running with `mpMetrics-5.x` feature. For prior mpMetrics versions, use the [Open Liberty - mpMetrics-2.x-4.x](https://grafana.com/grafana/dashboards/11706) dashboard View our Open Liberty MicroProfile 3.3 blog post for detailed [setup instructions](https://openliberty.io/blog/2020/04/09/microprofile-3-3-open-liberty-20004.html#gra). ### Open Liberty - Enable the **/metrics** endpoint by enabling the desired **`mpMetrics`** feature (e.g. `mpMetrics-5.0`) in the `server.xml`. - Learn about [Monitoring with MicroProfile metrics](https://openliberty.io/docs/latest/introduction-monitoring-metrics.html) - Check out our Open Liberty Guide on [Providing metrics from a Microservice](https://openliberty.io/guides/microprofile-metrics.html) ### Prometheus Configure the `scrape_configs` section of *prometheus.yml* in one of the following ways. For metrics on an **insecure** endpoint: ``` - job_name: 'liberty' scrape_interval: 5s static_configs: - targets: ['localhost:9080'] ``` For metrics on an **secure** endpoint using **Basic Authentication**: ``` - job_name: 'liberty-secure' scrape_interval: 5s static_configs: - targets: ['localhost:9443'] basic_auth: username: "<your-username>" password: "<your-password>" tls_config: insecure_skip_verify: true scheme: "https" ``` ###
83,705
83,706
Other
null
0
0
28,514
1
18,599
1
0
Created by the Open Liberty observability team
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"refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "Open Liberty - mpMetrics-5.0", "uid": "websphere-liberty-m2", "version": 1, "weekStart": "" }
2023-04-24 20:39:35 UTC
2023-04-24 20:50:09 UTC
0
0
18,619
1
419,705
28,546
0
idrac-host-stats
iDRAC - Host Stats
SNMP Based Dashboard to Monitor Dell Hosts via iDRAC - by ilovepancakes95
2023-04-27 17:59:51 UTC
2023-04-27 18:14:53 UTC
0
null
83,777
83,778
Telegraf
null
0
0
28,546
1
18,619
1
0
SNMP Based Dashboard to Monitor Dell Hosts via iDRAC - by ilovepancakes95
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2023-04-27 17:59:51 UTC
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opentelemetry-collector-v20221228
OpenTelemetry Collector v20221228
Provides information about the status of the OpenTelemetry Collector
2022-12-28 20:29:59 UTC
2022-12-28 20:30:41 UTC
0
null
0
0
null
null
0
0
27,197
1
17,726
1
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Provides information about the status of the OpenTelemetry Collector
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2022-12-28 20:29:59 UTC
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myDashboard1
2023-02-15 18:30:31 UTC
2023-02-15 18:34:10 UTC
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Telegraf
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{ "__inputs": [ { "name": "DS_INFLUXDB", "label": "InfluxDB", "description": "rpi_influxDB", "type": "datasource", "pluginId": "influxdb", "pluginName": "InfluxDB" } ], "__elements": { "5Vh1_HJVk": { "name": "Temperature", "uid": "5Vh1_HJVk", "kind": 1, "model": { "datasource": { "type": "influxdb", "uid": "Ol9gpO0Vz" }, "description": "", "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "celsius" }, "overrides": [ { "__systemRef": "hideSeriesFrom", "matcher": { "id": "byNames", "options": { "mode": "exclude", "names": [ "Temperature_m value" ], "prefix": "All except:", "readOnly": true } }, "properties": [ { "id": "custom.hideFrom", "value": { "legend": false, "tooltip": false, "viz": true } } ] } ] }, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "Ol9gpO0Vz" }, "key": "Q-f6a8ac21-f975-428b-872b-a870b1cc6c87-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"Temperature_1\" or r[\"_measurement\"] == \"Temperature_2\" or r[\"_measurement\"] == \"Temperature_3\" or r[\"_measurement\"] == \"Temperature_4\" or r[\"_measurement\"] == \"Temperature_m\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Temperature", "type": "timeseries" } } }, "__requires": [ { "type": "panel", "id": "bargauge", "name": "Bar gauge", "version": "" }, { "type": "panel", "id": "gauge", "name": "Gauge", "version": "" }, { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "9.4.0-beta1" }, { "type": "datasource", "id": "influxdb", "name": "InfluxDB", "version": "1.0.0" }, { "type": "panel", "id": "stat", "name": "Stat", "version": "" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": { "type": "grafana", "uid": "-- Grafana --" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, 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v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"Temperature_m\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"value\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Mean Temperature", "type": "gauge" }, { "gridPos": { "h": 8, "w": 8, "x": 3, "y": 1 }, "id": 2, "libraryPanel": { "uid": "5Vh1_HJVk", "name": "Temperature" } }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "fieldConfig": { "defaults": { "mappings": [], "max": 100, "min": 0, "thresholds": { "mode": "percentage", "steps": [ { "color": "red", "value": null }, { "color": "orange", "value": 12 }, { "color": "yellow", "value": 25 }, { "color": "green", "value": 35 }, { "color": "yellow", "value": 65 }, { "color": "orange", "value": 75 }, { "color": "red", "value": 85 } ] }, "unit": "celsius" }, "overrides": [] }, "gridPos": { "h": 8, "w": 3, "x": 13, "y": 1 }, "id": 24, "options": { "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "showThresholdLabels": false, "showThresholdMarkers": true }, "pluginVersion": "9.4.0-beta1", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "key": "Q-93cd19fb-f2c7-4b26-aa5a-4fb3413c2ad6-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"Humidity_Mean\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"value\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Humidity", "type": "gauge" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 8, "x": 16, "y": 1 }, "id": 1, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "key": "Q-0a7b2d33-c79a-4754-8838-ab14ad06650f-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"Humidity_Mean\" or r[\"_measurement\"] == \"Humidity_2\" or r[\"_measurement\"] == \"Humidity_1\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"value\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Humidity", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "fieldConfig": { "defaults": { "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "red", "value": null }, { "color": "#EAB839", "value": 100 }, { "color": "yellow", "value": 200 }, { "color": "green", "value": 350 }, { "color": "yellow", "value": 5000 }, { "color": "orange", "value": 6000 }, { "color": "red", "value": 7000 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 8, "w": 3, "x": 0, "y": 9 }, "id": 22, "options": { "colorMode": "background", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.0-beta1", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "key": "Q-95f2af68-d565-4bfa-bf80-70b63c967fbf-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"Light_1\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"value\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Light", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 8, "x": 3, "y": 9 }, "id": 3, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "key": "Q-23a2e30e-f952-4cf1-aab6-c1ffc25965ba-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"Light_1\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"value\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Light", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "fieldConfig": { "defaults": { "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "yellow", "value": 100 }, { "color": "orange", "value": 200 }, { "color": "red", "value": 300 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 8, "w": 3, "x": 13, "y": 9 }, "id": 23, "options": { "colorMode": "background", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "textMode": "auto" }, "pluginVersion": "9.4.0-beta1", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "key": "Q-e05eefed-8611-4178-aeed-b714c63d121f-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"PPM_1\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"value\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Gas", "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 8, "x": 16, "y": 9 }, "id": 4, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "key": "Q-fa8a92b6-3ebb-4e6c-bd64-291949bc8684-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"PPM_1\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"value\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Gas", "type": "timeseries" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 17 }, "id": 18, "panels": [], "title": "Soil Sensors", "type": "row" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "fieldConfig": { "defaults": { "mappings": [], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null }, { "color": "orange", "value": 70 }, { "color": "red", "value": 85 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 3, "x": 0, "y": 18 }, "id": 20, "options": { "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "showThresholdLabels": false, "showThresholdMarkers": true }, "pluginVersion": "9.4.0-beta1", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "key": "Q-0381f2af-4f72-40fc-8d38-6ce06a36c5e6-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"SoilTemp_mean\" )\r\n |> filter(fn: (r) => r[\"_field\"] == \"value\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Temperature", "type": "gauge" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 8, "x": 3, "y": 18 }, "id": 25, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "key": "Q-3db19e99-4d66-4b39-8da0-f4ece56a5a9c-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"SoilTemp_1\" or r[\"_measurement\"] == \"SoilTemp_2\" or r[\"_measurement\"] == \"SoilTemp_3\" or r[\"_measurement\"] == \"SoilTemp_4\" or r[\"_measurement\"] == \"SoilTemp_mean\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"value\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Temperature", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "continuous-GrYlRd" }, "mappings": [], "max": 100, "min": 0, "thresholds": { "mode": "absolute", "steps": [ { "color": "red", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 3, "x": 13, "y": 18 }, "id": 19, "options": { "displayMode": "lcd", "minVizHeight": 10, "minVizWidth": 0, "orientation": "horizontal", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "showUnfilled": true }, "pluginVersion": "9.4.0-beta1", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "key": "Q-e1ba9ae3-5556-454c-8a0b-d75c62aa32f6-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"SoilMoist_Mean\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Moisture", "type": "bargauge" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 8, "x": 16, "y": 18 }, "id": 26, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "key": "Q-d7b71e7e-3836-40ce-a95b-74f5a6c64230-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"SoilMoist_1\" or r[\"_measurement\"] == \"SoilMoist_2\" or r[\"_measurement\"] == \"SoilMoist_3\" or r[\"_measurement\"] == \"SoilMoist_4\" or r[\"_measurement\"] == \"SoilMoist_Mean\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"value\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Moisture", "type": "timeseries" }, { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 26 }, "id": 16, "panels": [], "title": "Water Sensors", "type": "row" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "fieldConfig": { "defaults": { "mappings": [], "thresholds": { "mode": "percentage", "steps": [ { "color": "green", "value": null }, { "color": "orange", "value": 70 }, { "color": "red", "value": 85 } ] }, "unit": "celsius" }, "overrides": [] }, "gridPos": { "h": 8, "w": 3, "x": 0, "y": 27 }, "id": 12, "options": { "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "showThresholdLabels": false, "showThresholdMarkers": true }, "pluginVersion": "9.4.0-beta1", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "key": "Q-6912027c-28c4-4267-8eb1-15894deb7004-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"waterTemp_1\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"value\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Water Temperature", "type": "gauge" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 8, "x": 3, "y": 27 }, "id": 8, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "key": "Q-6912027c-28c4-4267-8eb1-15894deb7004-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"waterTemp_1\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"value\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Temperature", "type": "timeseries" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "continuous-YlBl" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "red", "value": null }, { "color": "yellow", "value": 500 }, { "color": "#EAB839", "value": 1000 }, { "color": "green", "value": 1500 }, { "color": "yellow", "value": 4500 }, { "color": "orange", "value": 4700 }, { "color": "red", "value": 4900 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 3, "x": 13, "y": 27 }, "id": 21, "options": { "displayMode": "basic", "minVizHeight": 10, "minVizWidth": 0, "orientation": "vertical", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "", "values": false }, "showUnfilled": true }, "pluginVersion": "9.4.0-beta1", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "key": "Q-4c40904e-e8a0-4f54-9e28-a8c265921eb2-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"waterLevel_1\" )\r\n |> filter(fn: (r) => r[\"_field\"] == \"value\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Level", "type": "bargauge" }, { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 0, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "auto", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 8, "w": 8, "x": 16, "y": 27 }, "id": 7, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_INFLUXDB}" }, "key": "Q-7033f499-a7ae-47a0-a3cd-6cd433b05bb2-0", "query": "from(bucket: \"Bucket1\")\r\n |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"waterLevel_1\" or r[\"_measurement\"] == \"waterLevel_2\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"value\")\r\n |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)\r\n |> yield(name: \"last\")", "refId": "A" } ], "title": "Level", "type": "timeseries" } ], "refresh": "", "revision": 1, "schemaVersion": 38, "style": "dark", "tags": [], "templating": { "list": [] }, "time": { "from": "now-6h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Summary", "uid": "7ZIgs3AVz", "version": 28, "weekStart": "", "gnetId": 18112, "description": "myDashboard1" }
2023-02-15 18:30:31 UTC
2023-02-15 18:34:10 UTC
0
0
18,842
1
518,670
28,898
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qnap-nas-storage
QNAP SNMP
A dashboard for visualizing collected QNAP NAS Storage data from the target's SNMP port using Telegraf.
2023-05-26 19:15:36 UTC
2023-05-26 19:31:54 UTC
0
Telegraf configuration file available on GitHub: https://github.com/marjan-mesgarani/Telegraf-Config-Files/tree/main/QNAP
0
0
Telegraf
null
0
0
28,898
1
18,842
1
0
null
{ "__inputs": [ { "name": "DS_TELEGRAF_QNAP", "label": "Telegraf QNAP", "description": "", "type": "datasource", "pluginId": "influxdb", "pluginName": "InfluxDB" } ], "__elements": [], "__requires": [ { "type": "grafana", "id": "grafana", "name": "Grafana", "version": "8.4.4" }, { "type": "datasource", "id": "influxdb", "name": "InfluxDB", "version": "1.0.0" }, { "type": "panel", "id": "stat", "name": "Stat", "version": "" }, { "type": "panel", "id": "table", "name": "Table", "version": "" }, { "type": "panel", "id": "text", "name": "Text", "version": "" }, { "type": "panel", "id": "timeseries", "name": "Time series", "version": "" } ], "annotations": { "list": [ { "builtIn": 1, "datasource": "-- Grafana --", "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "target": { "limit": 100, "matchAny": false, "tags": [], "type": "dashboard" }, "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": 5664, "links": [], "liveNow": false, "panels": [ { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_QNAP}" }, "fieldConfig": { "defaults": { "color": { "fixedColor": "#133f81", "mode": "fixed" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 3, "w": 4, "x": 0, "y": 0 }, "id": 2, "options": { "colorMode": "background", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": false }, "text": { "valueSize": 25 }, "textMode": "auto" }, "pluginVersion": "8.3.6", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_QNAP}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "measurement": "qnap_snmp", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "host_name" ], "type": "field" }, { "params": [], "type": "last" } ] ], "tags": [ { "key": "agent_host", "operator": "=", "value": "192.168.1.40" } ] } ], "title": "Host Name", "transformations": [ { "id": "filterFieldsByName", "options": { "include": { "names": [ "qnap_snmp.last" ] } } } ], "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_QNAP}" }, "fieldConfig": { "defaults": { "color": { "fixedColor": "#330b46", "mode": "fixed" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] } }, "overrides": [] }, "gridPos": { "h": 3, "w": 4, "x": 4, "y": 0 }, "id": 3, "options": { "colorMode": "background", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": false }, "text": { "valueSize": 25 }, "textMode": "auto" }, "pluginVersion": "8.3.6", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_QNAP}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "measurement": "qnap_snmp", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "model_name" ], "type": "field" }, { "params": [], "type": "last" } ] ], "tags": [ { "key": "agent_host", "operator": "=", "value": "192.168.1.40" } ] } ], "title": "Model", "transformations": [ { "id": "filterFieldsByName", "options": { "include": { "names": [ "qnap_snmp.last" ] } } } ], "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_QNAP}" }, "fieldConfig": { "defaults": { "color": { "fixedColor": "#091a4e", "mode": "fixed" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "timeticks" }, "overrides": [] }, "gridPos": { "h": 3, "w": 4, "x": 8, "y": 0 }, "id": 4, "options": { "colorMode": "background", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": false }, "text": { "valueSize": 25 }, "textMode": "auto" }, "pluginVersion": "8.3.6", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_QNAP}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "measurement": "qnap_snmp", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "system_uptime" ], "type": "field" }, { "params": [], "type": "last" } ] ], "tags": [ { "key": "agent_host", "operator": "=", "value": "192.168.1.40" } ] } ], "title": "Uptime", "transformations": [ { "id": "filterFieldsByName", "options": { "include": { "names": [ "qnap_snmp.last" ] } } } ], "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_QNAP}" }, "fieldConfig": { "defaults": { "color": { "fixedColor": "super-light-yellow", "mode": "fixed" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 3, "w": 3, "x": 12, "y": 0 }, "id": 12, "options": { "colorMode": "background", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": false }, "text": {}, "textMode": "auto" }, "pluginVersion": "8.3.6", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_QNAP}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, { "params": [ "null" ], "type": "fill" } ], "measurement": "qnap_snmp", "orderByTime": "ASC", "policy": "default", "refId": "A", "resultFormat": "time_series", "select": [ [ { "params": [ "hd_number" ], "type": "field" }, { "params": [], "type": "last" } ] ], "tags": [ { "key": "agent_host", "operator": "=", "value": "192.168.1.40" } ] } ], "title": "Hard Disk", "transformations": [ { "id": "filterFieldsByName", "options": { "include": { "names": [ "qnap_snmp.last" ] } } } ], "type": "stat" }, { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_QNAP}" }, "fieldConfig": { "defaults": { "color": { "fixedColor": "super-light-yellow", "mode": "fixed" }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "none" }, "overrides": [] }, "gridPos": { "h": 3, "w": 3, "x": 15, "y": 0 }, "id": 13, "options": { "colorMode": "background", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": { "calcs": [ "lastNotNull" ], "fields": "/.*/", "values": false }, "text": {}, "textMode": "auto" }, "pluginVersion": "8.3.6", "targets": [ { "datasource": { "type": "influxdb", "uid": "${DS_TELEGRAF_QNAP}" }, "groupBy": [ { "params": [ "$__interval" ], "type": "time" }, 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2023-05-26 19:15:36 UTC
2023-05-26 19:31:50 UTC
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kubernetes-persistentvolume-overview
Kubernetes - PersistentVolume Overview
Kubernetes PersistentVolume Overview
2022-12-22 12:08:56 UTC
2022-12-22 12:09:59 UTC
0
Kubernetes PersistentVolume Overview
79,946
79,947
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27,146
1
17,682
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Kubernetes PersistentVolume Overview
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Used Warning Threshold", "name": "warning_threshold", "options": [ { "selected": true, "text": "80", "value": "80" } ], "query": "80", "skipUrlSync": false, "type": "textbox" } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Kubernetes - PersistentVolume Overview", "uid": "peR80gTGk", "version": 3, "weekStart": "" }
2022-12-22 12:08:56 UTC
2022-12-22 12:09:58 UTC
0
0
17,806
1
17,382
27,299
0
asonix-relay
AodeRelay ActivityPub Relay
Insights into the AodeRelay ActivityPub Relay software.
2023-01-05 10:25:20 UTC
2023-01-06 08:11:05 UTC
0
The AodeRelay ActivityPub Relay built by @[email protected] can export Prometheus metrics since version v0.3.78. See their repo for more information on how to enable its scraping endpoint.
80,368
80,369
Other
null
0
0
27,299
1
17,806
1
0
Insights in the running Asonix Relay
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2023-01-05 10:25:20 UTC
2023-01-06 08:11:05 UTC
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18,689
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839,423
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home-energy-usage-v2
Home Energy Usage v2
A dashboard that shows your home energy usage. Using Prometheus, Home Assistant and the Emporia Vue 2.
2023-05-07 21:18:34 UTC
2023-05-07 21:18:41 UTC
0
null
0
0
null
null
0
0
28,670
1
18,689
1
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A dashboard that shows your home energy usage. Using Prometheus, Home Assistant and the Emporia Vue 2.
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2023-05-07 21:18:34 UTC
null
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kong-official
Kong (official)
Dashboard that graphs metrics exported via Prometheus plugin in Kong (http://github.com/kong/kong)
2022-10-20 21:33:42 UTC
2022-10-20 21:33:59 UTC
0
null
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0
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null
0
0
26,474
1
17,236
1
0
Dashboard that graphs metrics exported via Prometheus plugin in Kong (http://github.com/kong/kong)
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\"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (route,le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p90-{{route}}", "refId": "A" }, { "expr": "histogram_quantile(0.95, sum(rate(kong_kong_latency_ms_bucket{service =~ \"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (route,le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p95-{{route}}", "refId": "B" }, { "expr": "histogram_quantile(0.99, sum(rate(kong_kong_latency_ms_bucket{service =~ \"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (route,le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p99-{{route}}", "refId": "C" } ], "thresholds": [], "timeRegions": [], "title": "Kong Proxy Latency per Route", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "ms", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 7, "w": 8, "x": 0, "y": 58 }, "hiddenSeries": false, "id": 12, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "paceLength": 10, "percentage": false, "pluginVersion": "8.4.5", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "histogram_quantile(0.90, sum(rate(kong_request_latency_ms_bucket{}[1m])) by (le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p90", "refId": "A" }, { "expr": "histogram_quantile(0.95, sum(rate(kong_request_latency_ms_bucket{}[1m])) by (le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p95", "refId": "B" }, { "expr": "histogram_quantile(0.99, sum(rate(kong_request_latency_ms_bucket{}[1m])) by (le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p99", "refId": "C" } ], "thresholds": [], "timeRegions": [], "title": "Request Time across all services", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "ms", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 7, "w": 8, "x": 8, "y": 58 }, "hiddenSeries": false, "id": 13, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "paceLength": 10, "percentage": false, "pluginVersion": "8.4.5", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "histogram_quantile(0.90, sum(rate(kong_request_latency_ms_bucket{service =~ \"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (service,le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p90-{{service}}", "refId": "A" }, { "expr": "histogram_quantile(0.95, sum(rate(kong_request_latency_ms_bucket{service =~ \"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (service,le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p95-{{service}}", "refId": "B" }, { "expr": "histogram_quantile(0.99, sum(rate(kong_request_latency_ms_bucket{service =~ \"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (service,le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p99-{{service}}", "refId": "C" } ], "thresholds": [], "timeRegions": [], "title": "Request Time per service", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "ms", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 7, "w": 8, "x": 16, "y": 58 }, "hiddenSeries": false, "id": 41, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "paceLength": 10, "percentage": false, "pluginVersion": "8.4.5", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "histogram_quantile(0.90, sum(rate(kong_request_latency_ms_bucket{service =~ \"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (route,le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p90-{{route}}", "refId": "A" }, { "expr": "histogram_quantile(0.95, sum(rate(kong_request_latency_ms_bucket{service =~ \"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (route,le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p95-{{route}}", "refId": "B" }, { "expr": "histogram_quantile(0.99, sum(rate(kong_request_latency_ms_bucket{service =~ \"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (route,le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p99-{{route}}", "refId": "C" } ], "thresholds": [], "timeRegions": [], "title": "Request Time per Route", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "ms", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 7, "w": 8, "x": 0, "y": 65 }, "height": "250", "hiddenSeries": false, "id": 14, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "paceLength": 10, "percentage": false, "pluginVersion": "8.4.5", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "histogram_quantile(0.90, sum(rate(kong_upstream_latency_ms_bucket{}[1m])) by (le))", "format": "time_series", "interval": "", "intervalFactor": 2, "legendFormat": "p90", "refId": "A" }, { "expr": "histogram_quantile(0.95, sum(rate(kong_upstream_latency_ms_bucket{}[1m])) by (le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p95", "refId": "B" }, { "expr": "histogram_quantile(0.99, sum(rate(kong_upstream_latency_ms_bucket{}[1m])) by (le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p99", "refId": "C" } ], "thresholds": [], "timeRegions": [], "title": "Upstream time across all services", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "ms", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 7, "w": 8, "x": 8, "y": 65 }, "height": "250", "hiddenSeries": false, "id": 15, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "paceLength": 10, "percentage": false, "pluginVersion": "8.4.5", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "histogram_quantile(0.90, sum(rate(kong_upstream_latency_ms_bucket{service =~ \"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (service,le))", "format": "time_series", "interval": "", "intervalFactor": 2, "legendFormat": "p90-{{service}}", "refId": "A" }, { "expr": "histogram_quantile(0.95, sum(rate(kong_upstream_latency_ms_bucket{service =~ \"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (service,le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p95-{{service}}", "refId": "B" }, { "expr": "histogram_quantile(0.99, sum(rate(kong_upstream_latency_ms_bucket{service =~ \"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (service,le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p99-{{service}}", "refId": "C" } ], "thresholds": [], "timeRegions": [], "title": "Upstream Time across per service", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "ms", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 7, "w": 8, "x": 16, "y": 65 }, "height": "250", "hiddenSeries": false, "id": 40, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "paceLength": 10, "percentage": false, "pluginVersion": "8.4.5", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "histogram_quantile(0.90, sum(rate(kong_upstream_latency_ms_bucket{service =~ \"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (route,le))", "format": "time_series", "interval": "", "intervalFactor": 2, "legendFormat": "p90-{{route}}", "refId": "A" }, { "expr": "histogram_quantile(0.95, sum(rate(kong_upstream_latency_ms_bucket{service =~ \"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (route,le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p95-{{route}}", "refId": "B" }, { "expr": "histogram_quantile(0.99, sum(rate(kong_upstream_latency_ms_bucket{service =~ \"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (route,le))", "format": "time_series", "intervalFactor": 2, "legendFormat": "p99-{{route}}", "refId": "C" } ], "thresholds": [], "timeRegions": [], "title": "Upstream Time across per Route", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "ms", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } } ], "title": "Latencies (Need to set config.latency_metrics, it will cause performance impact)", "type": "row" }, { "collapsed": true, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 4 }, "id": 34, "panels": [ { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 7, "w": 24, "x": 0, "y": 73 }, "hiddenSeries": false, "id": 3, "legend": { "alignAsTable": true, "avg": true, "current": true, "max": true, "min": true, "rightSide": true, "show": true, "total": false, "values": true }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "paceLength": 10, "percentage": false, "pluginVersion": "8.4.5", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": true, "expr": "sum(irate(kong_bandwidth_bytes{instance=~\"$instance\"}[1m])) by (type)", "format": "time_series", "interval": "", "intervalFactor": 2, "legendFormat": "{{type}}", "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "Total Bandwidth", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "Bps", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": false } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 7, "w": 12, "x": 0, "y": 80 }, "hiddenSeries": false, "id": 2, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "paceLength": 10, "percentage": false, "pluginVersion": "8.4.5", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "sum(irate(kong_bandwidth_bytes{direction=\"egress\", service =~\"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (service)", "format": "time_series", "intervalFactor": 2, "legendFormat": "service:{{service}}", "refId": "A" }, { "expr": "sum(irate(kong_bandwidth_bytes{direction=\"egress\", service =~\"$service\",route=~\"$route\",instance=~\"$instance\"}[1m])) by (route)", "format": "time_series", "intervalFactor": 2, "legendFormat": "route:{{route}}", "refId": "B" } ], "thresholds": [], "timeRegions": [], "title": "Egress per service/route", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "Bps", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "fieldConfig": { "defaults": { "links": [] }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 7, "w": 12, "x": 12, "y": 80 }, "hiddenSeries": false, "id": 9, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "options": { "alertThreshold": true }, "paceLength": 10, "percentage": false, "pluginVersion": "8.4.5", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "sum(irate(kong_bandwidth_bytes{direction=\"ingress\", service =~\"$service\"}[1m])) by (service)", "format": "time_series", "intervalFactor": 2, "legendFormat": "{{service}}", "refId": "A" } ], "thresholds": [], "timeRegions": [], "title": "Ingress per service/route", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "mode": "time", "show": true, "values": [] }, "yaxes": [ { "format": "Bps", "logBase": 1, "show": true }, { "format": "short", "logBase": 1, "show": true } ], "yaxis": { "align": false } } ], "title": "Bandwidth (Need to set config.bandwidth_metrics, it will cause performance impact)", "type": "row" }, { "collapsed": true, "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 5 }, "id": 45, "panels": [ { "cards": {}, "color": { "cardColor": "#b4ff00", "colorScale": "sqrt", "colorScheme": "interpolateRdYlGn", "exponent": 0.5, "mode": "spectrum" }, "dataFormat": "tsbuckets", "gridPos": { "h": 8, "w": 12, "x": 0, "y": 88 }, "heatmap": {}, "hideZeroBuckets": false, "highlightCards": true, "id": 49, "legend": { "show": false }, "pluginVersion": "7.5.4", "reverseYBuckets": false, "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": true, "expr": "sum(kong_upstream_target_health{state=\"healthy\",upstream=~\"$upstream\"}) by (upstream,target,address) * -1 + sum(kong_upstream_target_health{state=~\"(unhealthy|dns_error)\",upstream=~\"$upstream\"}) by (upstream,target,address)", "format": "heatmap", "hide": false, "instant": false, "interval": "", "legendFormat": "{{upstream}}:{{target}}::{{address}}", "refId": "A" } ], "title": "Healthy status", "tooltip": { "show": true, "showHistogram": false }, "transformations": [ { "id": "seriesToColumns", "options": {} } ], "type": "heatmap", "xAxis": { "show": true }, "yAxis": { "format": "short", "logBase": 1, "show": true }, "yBucketBound": "auto" }, { "columns": [], "fontSize": "100%", "gridPos": { "h": 8, "w": 12, "x": 12, "y": 88 }, "id": 47, "options": { "frameIndex": 0, "showHeader": true }, "pluginVersion": "7.5.4", "showHeader": true, "sort": { "col": 0, "desc": true }, "styles": [ { "alias": "", "align": "auto", "colors": [ "rgba(245, 54, 54, 0.9)", "rgba(237, 129, 40, 0.89)", "rgba(50, 172, 45, 0.97)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "mappingType": 1, "pattern": "Time", "thresholds": [], "type": "hidden", "unit": "short" }, { "alias": "", "align": "auto", "colors": [ "rgba(245, 54, 54, 0.9)", "rgba(237, 129, 40, 0.89)", "rgba(50, 172, 45, 0.97)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "mappingType": 1, "pattern": "state", "thresholds": [], "type": "hidden", "unit": "short" }, { "alias": "state", "align": "auto", "colorMode": "cell", "colors": [ "rgba(245, 54, 54, 0.9)", "#FADE2A", "rgba(50, 172, 45, 0.97)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "mappingType": 1, "pattern": "state_value", "thresholds": [ "0", "1" ], "type": "string", "unit": "short", "valueMaps": [ { "text": "healthy", "value": "1" }, { "text": "healthchecks_off", "value": "0" }, { "text": "unhealthy", "value": "-1" } ] }, { "alias": "", "align": "auto", "colors": [ "rgba(245, 54, 54, 0.9)", "rgba(237, 129, 40, 0.89)", "rgba(50, 172, 45, 0.97)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "mappingType": 1, "pattern": "Value", "thresholds": [], "type": "hidden", "unit": "short" } ], "targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": false, "expr": "sum(\n# map state to a numeric value\n# since grafana doesn't support value mapping yet\n label_replace(\n label_replace(\n label_replace(\n 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2022-10-20 21:33:42 UTC
null
0
0
17,629
1
761,884
27,081
0
prometheus-flask-exporter-dashboard
Prometheus Flask exporter dashboard
Dashboard
2022-12-15 19:15:02 UTC
2022-12-15 19:16:55 UTC
0
null
0
0
Other
null
0
0
27,081
1
17,629
1
0
Dashboard
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2022-12-15 19:15:02 UTC
null
0
0
17,686
1
764,262
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0
kubernetes-cluster-overview
Kubernetes - Cluster Overview
Kubernetes Cluster Overview
2022-12-22 12:14:12 UTC
2022-12-22 12:14:57 UTC
0
Kubernetes Cluster Overview
79,962
79,963
Other
null
0
0
27,150
1
17,686
1
0
Overview of Kubernetes cluster-level metrics
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2022-12-22 12:14:12 UTC
2022-12-22 12:14:56 UTC
0
0
18,505
1
835,147
28,383
0
autometrics-overview
Autometrics Overview
Shows the functions with the highest request and error rates
2023-04-12 17:55:40 UTC
2023-04-12 17:58:09 UTC
0
This is a dashboard that will work for any project instrumented with [Autometrics](https://autometrics.dev).
83,302
83,303
Other
null
0
0
28,383
1
18,505
1
0
null
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2023-04-12 17:55:40 UTC
2023-04-12 17:58:09 UTC
0
0
18,753
1
857,441
28,771
0
solar-pv-system
Solar PV System
Solar PV System, You can monitor the energy flow of the solar pv system.
2023-05-15 16:24:19 UTC
2023-05-15 16:28:13 UTC
0
null
84,344
84,345
null
null
0
0
28,771
1
18,753
1
0
Iammeter Solar PV System, You can monitor the energy flow of the solar pv system.
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2023-05-15 16:24:19 UTC
2023-05-15 16:28:13 UTC
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