Learn the fundamentals of Dgraph - an open-source Graph Database that implements GraphQL as its query language 🚀 https://cloud.dgraph.io

#database #graphql #100SecondsOfCode

References:

Try Dgraph for free https://cloud.dgraph.io
Dgraph on Github https://github.com/dgraph-io/dgraph
What is a Graph Database? https://en.wikipedia.org/wiki/Graph_database
2024-02-04T05:56:38.657Z
nova-2
2-general-nova
2024-01-09.29447
30089e05-99d1-4376-b32e-c263170674af
ac9f260f-007b-400c-9d34-281ea1a70286
ba18de4016ad11557b43cc9cfef5b1513a733b9cd44301a3fe53b5cceb530ca1
67875a7f-c9c4-48a0-aa55-5bdb8a91c34a
deprecated
D graph, a graph database that natively implements GraphQL as its query language.
When we build apps, our data is often highly relational, requiring deep joins and complex traversals, while needing to scale around the universe with high reliability and low latency.
Instead of managing data in tables, columns, and rows, a graph database is structured as nodes and edges.
An entity, like an astronaut or rocket, is a node, while a relationship between 2 nodes is an edge.
Not only is this more intuitive to developers, but it's also a more scalable approach when dealing with deep relational data, like a social graph or recommendation engine.
D graph is not the first graph database, but the first to implement GraphQL natively as its query language, which means you can use it in its full capacity with a language that you already know and love.
On top of that, it's open source and is the most starred graph based on GitHub.
It's written entirely in Go, is ACID compliant, runs fast at scale, can be self hosted, or hosted in the cloud as a fully managed service.
You can get started by pulling the Docker container or by using a fully managed cloud service.
What you see here is the dashboard for a free slash GraphQL account, which is ideal for an early stage startup.
First, we need a schema, which is defined in GraphQL.
We have types for astronaut, mission and rocket.
An astronaut can ride on many rockets through missions.
Now that we have a schema, we can go to the API Explorer, which is automatically generated for us.
We can use GraphQL mutations to create, update, and delete data.
When we add a new node to the graph, it returns a unique identifier, which can then be used to create relationships or edges between other nodes.
And now that we populated data, we can view it in the visualizer or make queries against it with GraphQL.
Let's grab all the astronauts that have been on a rocket by writing a query that traverses through the mission's node.
If we're happy with the result, We can use the code exporter to convert it to JavaScript to use in a front end web application.
However, we may want to implement more advanced queries, like a full text search, for example.
We can do that by going back to our schema and making that field searchable.
That will automatically add an index annotation to our GraphQL schema, and that's all it takes to build a full text search engine.
This has been d graph in 100 seconds.
If you wanna see more short videos like this, make sure to like and subscribe, and let me know what you think about Dgraph in the comments below.
Thanks for watching, and I will see you in the next one.

Speaker 0: D graph, a graph database that natively implements GraphQL as its query language. When we build apps, our data is often highly relational, requiring deep joins and complex traversals, while needing to scale around the universe with high reliability and low latency. Instead of managing data in tables, columns, and rows, a graph database is structured as nodes and edges. An entity, like an astronaut or rocket, is a node, while a relationship between 2 nodes is an edge. Not only is this more intuitive to developers, but it's also a more scalable approach when dealing with deep relational data, like a social graph or recommendation engine.

D graph is not the first graph database, but the first to implement GraphQL natively as its query language, which means you can use it in its full capacity with a language that you already know and love. On top of that, it's open source and is the most starred graph based on GitHub. It's written entirely in Go, is ACID compliant, runs fast at scale, can be self hosted, or hosted in the cloud as a fully managed service. You can get started by pulling the Docker container or by using a fully managed cloud service. What you see here is the dashboard for a free slash GraphQL account, which is ideal for an early stage startup.

First, we need a schema, which is defined in GraphQL. We have types for astronaut, mission and rocket. An astronaut can ride on many rockets through missions. Now that we have a schema, we can go to the API Explorer, which is automatically generated for us. We can use GraphQL mutations to create, update, and delete data.

When we add a new node to the graph, it returns a unique identifier, which can then be used to create relationships or edges between other nodes. And now that we populated data, we can view it in the visualizer or make queries against it with GraphQL. Let's grab all the astronauts that have been on a rocket by writing a query that traverses through the mission's node. If we're happy with the result, We can use the code exporter to convert it to JavaScript to use in a front end web application. However, we may want to implement more advanced queries, like a full text search, for example.

We can do that by going back to our schema and making that field searchable. That will automatically add an index annotation to our GraphQL schema, and that's all it takes to build a full text search engine. This has been d graph in 100 seconds. If you wanna see more short videos like this, make sure to like and subscribe, and let me know what you think about Dgraph in the comments below. Thanks for watching, and I will see you in the next one.
D graph, a graph database that natively implements GraphQL as its query language. When we build apps, our data is often highly relational, requiring deep joins and complex traversals, while needing to scale around the universe with high reliability and low latency. Instead of managing data in tables, columns, and rows, a graph database is structured as nodes and edges. An entity, like an astronaut or rocket, is a node, while a relationship between 2 nodes is an edge. Not only is this more intuitive to developers, but it's also a more scalable approach when dealing with deep relational data, like a social graph or recommendation engine. D graph is not the first graph database, but the first to implement GraphQL natively as its query language, which means you can use it in its full capacity with a language that you already know and love. On top of that, it's open source and is the most starred graph based on GitHub. It's written entirely in Go, is ACID compliant, runs fast at scale, can be self hosted, or hosted in the cloud as a fully managed service. You can get started by pulling the Docker container or by using a fully managed cloud service. What you see here is the dashboard for a free slash GraphQL account, which is ideal for an early stage startup. First, we need a schema, which is defined in GraphQL. We have types for astronaut, mission and rocket. An astronaut can ride on many rockets through missions. Now that we have a schema, we can go to the API Explorer, which is automatically generated for us. We can use GraphQL mutations to create, update, and delete data.
databases
software
data
data management
When we add a new node to the graph, it returns a unique identifier, which can then be used to create relationships or edges between other nodes. And now that we populated data, we can view it in the visualizer or make queries against it with GraphQL. Let's grab all the astronauts that have been on a rocket by writing a query that traverses through the mission's node. If we're happy with the result, We can use the code exporter to convert it to JavaScript to use in a front end web application.
space exploration
databases
However, we may want to implement more advanced queries, like a full text search, for example. We can do that by going back to our schema and making that field searchable. That will automatically add an index annotation to our GraphQL schema, and that's all it takes to build a full text search engine.
databases
This has been d graph in 100 seconds. If you wanna see more short videos like this, make sure to like and subscribe, and let me know what you think about Dgraph in the comments below. Thanks for watching, and I will see you in the next one.
D graph, a graph database that natively implements GraphQL as its query language. When we build apps, our data is often highly relational, requiring deep joins and complex traversals, while needing to scale around the universe with high reliability and low latency. Instead of managing data in tables, columns, and rows, a graph database is structured as nodes and edges. An entity, like an astronaut or rocket, is a node, while a relationship between 2 nodes is an edge. Not only is this more intuitive to developers, but it's also a more scalable approach when dealing with deep relational data, like a social graph or recommendation engine. D graph is not the first graph database, but the first to implement GraphQL natively as its query language, which means you can use it in its full capacity with a language that you already know and love. On top of that, it's open source and is the most starred graph based on GitHub. It's written entirely in Go, is ACID compliant, runs fast at scale, can be self hosted, or hosted in the cloud as a fully managed service. You can get started by pulling the Docker container or by using a fully managed cloud service. What you see here is the dashboard for a free slash GraphQL account, which is ideal for an early stage startup. First, we need a schema, which is defined in GraphQL. We have types for astronaut, mission and rocket. An astronaut can ride on many rockets through missions. Now that we have a schema, we can go to the API Explorer, which is automatically generated for us. We can use GraphQL mutations to create, update, and delete data. When we add a new node to the graph, it returns a unique identifier, which can then be used to create relationships or edges between other nodes. And now that we populated data, we can view it in the visualizer or make queries against it with GraphQL. Let's grab all the astronauts that have been on a rocket by writing a query that traverses through the mission's node. If we're happy with the result, We can use the code exporter to convert it to JavaScript to use in a front end web application. However, we may want to implement more advanced queries, like a full text search, for example. We can do that by going back to our schema and making that field searchable. That will automatically add an index annotation to our GraphQL schema, and that's all it takes to build a full text search engine. This has been d graph in 100 seconds. If you wanna see more short videos like this, make sure to like and subscribe, and let me know what you think about Dgraph in the comments below. Thanks for watching, and I will see you in the next one.
D
d
graph,
graph
a
a
graph
graph
database
database
that
that
natively
natively
implements
implements
GraphQL
graphql
as
as
its
its
query
query
language.
language
When
when
we
we
build
build
apps,
apps
our
our
data
data
is
is
often
often
highly
highly
relational,
relational
requiring
requiring
deep
deep
joins
joins
and
and
complex
complex
traversals,
traversals
while
while
needing
needing
to
to
scale
scale
around
around
the
the
universe
universe
with
with
high
high
reliability
reliability
and
and
low
low
latency.
latency
Instead
instead
of
of
managing
managing
data
data
in
in
tables,
tables
columns,
columns
and
and
rows,
rows
a
a
graph
graph
database
database
is
is
structured
structured
as
as
nodes
nodes
and
and
edges.
edges
An
an
entity,
entity
like
like
an
an
astronaut
astronaut
or
or
rocket,
rocket
is
is
a
a
node,
node
while
while
a
a
relationship
relationship
between
between
2
2
nodes
nodes
is
is
an
an
edge.
edge
Not
not
only
only
is
is
this
this
more
more
intuitive
intuitive
to
to
developers,
developers
but
but
it's
it's
also
also
a
a
more
more
scalable
scalable
approach
approach
when
when
dealing
dealing
with
with
deep
deep
relational
relational
data,
data
like
like
a
a
social
social
graph
graph
or
or
recommendation
recommendation
engine.
engine
D
d
graph
graph
is
is
not
not
the
the
first
first
graph
graph
database,
database
but
but
the
the
first
first
to
to
implement
implement
GraphQL
graphql
natively
natively
as
as
its
its
query
query
language,
language
which
which
means
means
you
you
can
can
use
use
it
it
in
in
its
its
full
full
capacity
capacity
with
with
a
a
language
language
that
that
you
you
already
already
know
know
and
and
love.
love
On
on
top
top
of
of
that,
that
it's
it's
open
open
source
source
and
and
is
is
the
the
most
most
starred
starred
graph
graph
based
based
on
on
GitHub.
github
It's
it's
written
written
entirely
entirely
in
in
Go,
go
is
is
ACID
acid
compliant,
compliant
runs
runs
fast
fast
at
at
scale,
scale
can
can
be
be
self
self
hosted,
hosted
or
or
hosted
hosted
in
in
the
the
cloud
cloud
as
as
a
a
fully
fully
managed
managed
service.
service
You
you
can
can
get
get
started
started
by
by
pulling
pulling
the
the
Docker
docker
container
container
or
or
by
by
using
using
a
a
fully
fully
managed
managed
cloud
cloud
service.
service
What
what
you
you
see
see
here
here
is
is
the
the
dashboard
dashboard
for
for
a
a
free
free
slash
slash
GraphQL
graphql
account,
account
which
which
is
is
ideal
ideal
for
for
an
an
early
early
stage
stage
startup.
startup
First,
first
we
we
need
need
a
a
schema,
schema
which
which
is
is
defined
defined
in
in
GraphQL.
graphql
We
we
have
have
types
types
for
for
astronaut,
astronaut
mission
mission
and
and
rocket.
rocket
An
an
astronaut
astronaut
can
can
ride
ride
on
on
many
many
rockets
rockets
through
through
missions.
missions
Now
now
that
that
we
we
have
have
a
a
schema,
schema
we
we
can
can
go
go
to
to
the
the
API
api
Explorer,
explorer
which
which
is
is
automatically
automatically
generated
generated
for
for
us.
us
We
we
can
can
use
use
GraphQL
graphql
mutations
mutations
to
to
create,
create
update,
update
and
and
delete
delete
data.
data
When
when
we
we
add
add
a
a
new
new
node
node
to
to
the
the
graph,
graph
it
it
returns
returns
a
a
unique
unique
identifier,
identifier
which
which
can
can
then
then
be
be
used
used
to
to
create
create
relationships
relationships
or
or
edges
edges
between
between
other
other
nodes.
nodes
And
and
now
now
that
that
we
we
populated
populated
data,
data
we
we
can
can
view
view
it
it
in
in
the
the
visualizer
visualizer
or
or
make
make
queries
queries
against
against
it
it
with
with
GraphQL.
graphql
Let's
let's
grab
grab
all
all
the
the
astronauts
astronauts
that
that
have
have
been
been
on
on
a
a
rocket
rocket
by
by
writing
writing
a
a
query
query
that
that
traverses
traverses
through
through
the
the
mission's
mission's
node.
node
If
if
we're
we're
happy
happy
with
with
the
the
result,
result
We
we
can
can
use
use
the
the
code
code
exporter
exporter
to
to
convert
convert
it
it
to
to
JavaScript
javascript
to
to
use
use
in
in
a
a
front
front
end
end
web
web
application.
application
However,
however
we
we
may
may
want
want
to
to
implement
implement
more
more
advanced
advanced
queries,
queries
like
like
a
a
full
full
text
text
search,
search
for
for
example.
example
We
we
can
can
do
do
that
that
by
by
going
going
back
back
to
to
our
our
schema
schema
and
and
making
making
that
that
field
field
searchable.
searchable
That
that
will
will
automatically
automatically
add
add
an
an
index
index
annotation
annotation
to
to
our
our
GraphQL
graphql
schema,
schema
and
and
that's
that's
all
all
it
it
takes
takes
to
to
build
build
a
a
full
full
text
text
search
search
engine.
engine
This
this
has
has
been
been
d
d
graph
graph
in
in
100
100
seconds.
seconds
If
if
you
you
wanna
wanna
see
see
more
more
short
short
videos
videos
like
like
this,
this
make
make
sure
sure
to
to
like
like
and
and
subscribe,
subscribe
and
and
let
let
me
me
know
know
what
what
you
you
think
think
about
about
Dgraph
dgraph
in
in
the
the
comments
comments
below.
below
Thanks
thanks
for
for
watching,
watching
and
and
I
i
will
will
see
see
you
you
in
in
the
the
next
next
one.
one
success
The speaker explains that a graph database is a more efficient way to manage data in a graph, as it is not the first graph database to implement GraphQL. The dashboard for a free account is ideal for early-stage startups, and the schema is created in Go, is ACID compliant, and runs fast at scale. The search engine can be done by creating a full text search and adding an index annotation to the schema.
Dgraph Graph Database in 100 Seconds
D graph, a graph database that natively implements GraphQL as its query language. When we build apps, our data is often highly relational, requiring deep joins and complex traversals, while needing to scale around the universe with high reliability and low latency. Instead of managing data in tables, columns, and rows, a graph database is structured as nodes and edges. An entity, like an astronaut or rocket, is a node, while a relationship between 2 nodes is an edge. Not only is this more intuitive to developers, but it's also a more scalable approach when dealing with deep relational data, like a social graph or recommendation engine. D graph is not the first graph database, but the first to implement GraphQL natively as its query language, which means you can use it in its full capacity with a language that you already know and love. On top of that, it's open source and is the most starred graph based on GitHub. It's written entirely in Go, is ACID compliant, runs fast at scale, can be self hosted, or hosted in the cloud as a fully managed service. You can get started by pulling the Docker container or by using a fully managed cloud service. What you see here is the dashboard for a free slash GraphQL account, which is ideal for an early stage startup. First, we need a schema, which is defined in GraphQL. We have types for astronaut, mission and rocket. An astronaut can ride on many rockets through missions. Now that we have a schema, we can go to the API Explorer, which is automatically generated for us. We can use GraphQL mutations to create, update, and delete data. When we add a new node to the graph, it returns a unique identifier, which can then be used to create relationships or edges between other nodes. And now that we populated data, we can view it in the visualizer or make queries against it with GraphQL. Let's grab all the astronauts that have been on a rocket by writing a query that traverses through the mission's node. If we're happy with the result, We can use the code exporter to convert it to JavaScript to use in a front end web application. However, we may want to implement more advanced queries, like a full text search, for example. We can do that by going back to our schema and making that field searchable. That will automatically add an index annotation to our GraphQL schema, and that's all it takes to build a full text search engine. This has been d graph in 100 seconds. If you wanna see more short videos like this, make sure to like and subscribe, and let me know what you think about Dgraph in the comments below. Thanks for watching, and I will see you in the next one.
The speaker explains that a graph database is a more efficient way to manage data in a graph, as it is not the first graph database to implement GraphQL. The dashboard for a free account is ideal for early-stage startups, and the schema is created in Go, is ACID compliant, and runs fast at scale. The search engine can be done by creating a full text search and adding an index annotation to the schema.

        [INST]
        You are youtuber called Fireship you make engaging high-intensity and entertaining coding tutorials and tech news. 
        you covers a wide range of topics relevant to programmers, aiming to help them learn and improve their skills quickly.
        
        Given the title of the video : Dgraph Graph Database in 100 Seconds 
        and a small summary : The speaker explains that a graph database is a more efficient way to manage data in a graph, as it is not the first graph database to implement GraphQL. The dashboard for a free account is ideal for early-stage startups, and the schema is created in Go, is ACID compliant, and runs fast at scale. The search engine can be done by creating a full text search and adding an index annotation to the schema.
        [/INST]
        
        Generate the video : D graph, a graph database that natively implements GraphQL as its query language. When we build apps, our data is often highly relational, requiring deep joins and complex traversals, while needing to scale around the universe with high reliability and low latency. Instead of managing data in tables, columns, and rows, a graph database is structured as nodes and edges. An entity, like an astronaut or rocket, is a node, while a relationship between 2 nodes is an edge. Not only is this more intuitive to developers, but it's also a more scalable approach when dealing with deep relational data, like a social graph or recommendation engine. D graph is not the first graph database, but the first to implement GraphQL natively as its query language, which means you can use it in its full capacity with a language that you already know and love. On top of that, it's open source and is the most starred graph based on GitHub. It's written entirely in Go, is ACID compliant, runs fast at scale, can be self hosted, or hosted in the cloud as a fully managed service. You can get started by pulling the Docker container or by using a fully managed cloud service. What you see here is the dashboard for a free slash GraphQL account, which is ideal for an early stage startup. First, we need a schema, which is defined in GraphQL. We have types for astronaut, mission and rocket. An astronaut can ride on many rockets through missions. Now that we have a schema, we can go to the API Explorer, which is automatically generated for us. We can use GraphQL mutations to create, update, and delete data. When we add a new node to the graph, it returns a unique identifier, which can then be used to create relationships or edges between other nodes. And now that we populated data, we can view it in the visualizer or make queries against it with GraphQL. Let's grab all the astronauts that have been on a rocket by writing a query that traverses through the mission's node. If we're happy with the result, We can use the code exporter to convert it to JavaScript to use in a front end web application. However, we may want to implement more advanced queries, like a full text search, for example. We can do that by going back to our schema and making that field searchable. That will automatically add an index annotation to our GraphQL schema, and that's all it takes to build a full text search engine. This has been d graph in 100 seconds. If you wanna see more short videos like this, make sure to like and subscribe, and let me know what you think about Dgraph in the comments below. Thanks for watching, and I will see you in the next one.
        