ClickHouse as an alternative to Elasticsearch for log storage and analysis
I think it&#x27;s an unfair comparison, notably because:<p>1) Clickhouse is rigid-schema + append-only - you can&#x27;t simply dump semi-structured data (csv&#x2F;json&#x2F;documents) into it and worry about schema (index definition) + querying later.  The only clickhouse integration I&#x27;ve seen up close had a lot of &quot;json&quot; blobs in it as a workaround, which cannot be queried with the same ease as in ES.<p>2) Clickhouse scalability is not as simple&#x2F;documented as elasticsearch.  You can set up a 200-node ES cluster with a relatively simple helm config or readily-available cloudformation recipe.<p>3) Elastic is more than elasticsearch - kibana and the &quot;on top of elasticsearch&quot; featureset is pretty substantial.<p>4) Every language&#x2F;platform under the sun (except powerbi... god damnit) has native + mature client drivers for elasticsearch, and you can fall back to bog-standard http calls for querying if you need&#x2F;want.  ClickHouse supports some very elementary SQL primitives (&quot;ANSI&quot;) and even those have some gotchas and are far from drop-in.<p>In this manner, I think that clickhouse is better compared as a self-hosted alternative to Aurora and other cloud-native scalable SQL databases, and less a replacement for elasticsearch.  If you&#x27;re using Elasticsearch for OLAP, you&#x27;re probably better to ETL the semi-structured&#x2F;raw data out of ES that you specifically wan to a more suitable database which is meant for that.