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Get Started - Quickstart Guide

Note This is a quick start guide. You can find the getting started tutorial series here.

Dgraph

Designed from the ground up to be run in production, Dgraph is the native GraphQL database with a graph backend. It is open-source, scalable, distributed, highly available and lightning fast.

Dgraph cluster consists of different nodes (Zero, Alpha & Ratel), and each node serves a different purpose.

  • Dgraph Zero controls the Dgraph cluster, assigns servers to a group, and re-balances data between server groups.

  • Dgraph Alpha hosts predicates and indexes. Predicates are either the properties associated with a node or the relationship between two nodes. Indexes are the tokenizers that can be associated with the predicates to enable filtering using appropriate functions.

  • Ratel serves the UI to run queries, mutations & altering schema.

You need at least one Dgraph Zero and one Dgraph Alpha to get started.

Here’s a four-step tutorial to get you up and running.

This is a quick-start guide to running Dgraph. For an interactive walkthrough, take the tour.

Tip

This guide is for the powerful query language of Dgraph, GraphQL+- which is a variation of a query language created by Facebook, GraphQL.

You can find the instructions to get started with GraphQL from dgraph.io/graphql.

Step 1: Run Dgraph

There are several ways to install and run Dgraph, all of which you can find in the Download page

The easiest way to get Dgraph up and running is using the dgraph/standalone docker image. Follow the instructions here to install Docker if you don’t have it already.

This standalone image is meant for quickstart purposes only. It is not recommended for production environments.

docker run --rm -it -p 8080:8080 -p 9080:9080 -p 8000:8000 -v ~/dgraph:/dgraph dgraph/standalone:master

This would start a single container with Dgraph Alpha, Dgraph Zero and Ratel running in it. You would find the Dgraph data stored in a folder named dgraph of your home directory.

Tip Usually, you need to set the estimated memory Dgraph alpha can take through lru_mb flag. This is just a hint to the Dgraph alpha, and actual usage would be higher than this. It is recommended to set lru_mb to the one-third of the available RAM. For the standalone setup, it is set to that by default.

Step 2: Run Mutation

Tip

Once Dgraph is running, you can access Ratel at http://localhost:8000. It allows browser-based queries, mutations and visualizations.

You can run the mutations and queries below from either curl in the command line or by pasting the mutation data in the Ratel.

Dataset

The dataset is a movie graph, where and the graph nodes are entities of the type directors, actors, genres, or movies.

Storing data in the graph

Changing the data stored in Dgraph is a mutation. Dgraph as of now supports mutation for two kinds of data: RDF and JSON. The following RDF mutation stores information about the first three releases of the the ‘‘Star Wars’’ series and one of the ‘‘Star Trek’’ movies. Running the RDF mutation, either through the curl or Ratel UI’s mutate tab will store the data in Dgraph.

curl -H "Content-Type: application/rdf" "localhost:8080/mutate?commitNow=true" -XPOST -d $'
{
  set {
   _:luke <name> "Luke Skywalker" .
   _:luke <dgraph.type> "Person" .
   _:leia <name> "Princess Leia" .
   _:leia <dgraph.type> "Person" .
   _:han <name> "Han Solo" .
   _:han <dgraph.type> "Person" .
   _:lucas <name> "George Lucas" .
   _:lucas <dgraph.type> "Person" .
   _:irvin <name> "Irvin Kernshner" .
   _:irvin <dgraph.type> "Person" .
   _:richard <name> "Richard Marquand" .
   _:richard <dgraph.type> "Person" .

   _:sw1 <name> "Star Wars: Episode IV - A New Hope" .
   _:sw1 <release_date> "1977-05-25" .
   _:sw1 <revenue> "775000000" .
   _:sw1 <running_time> "121" .
   _:sw1 <starring> _:luke .
   _:sw1 <starring> _:leia .
   _:sw1 <starring> _:han .
   _:sw1 <director> _:lucas .
   _:sw1 <dgraph.type> "Film" .

   _:sw2 <name> "Star Wars: Episode V - The Empire Strikes Back" .
   _:sw2 <release_date> "1980-05-21" .
   _:sw2 <revenue> "534000000" .
   _:sw2 <running_time> "124" .
   _:sw2 <starring> _:luke .
   _:sw2 <starring> _:leia .
   _:sw2 <starring> _:han .
   _:sw2 <director> _:irvin .
   _:sw2 <dgraph.type> "Film" .

   _:sw3 <name> "Star Wars: Episode VI - Return of the Jedi" .
   _:sw3 <release_date> "1983-05-25" .
   _:sw3 <revenue> "572000000" .
   _:sw3 <running_time> "131" .
   _:sw3 <starring> _:luke .
   _:sw3 <starring> _:leia .
   _:sw3 <starring> _:han .
   _:sw3 <director> _:richard .
   _:sw3 <dgraph.type> "Film" .

   _:st1 <name> "Star Trek: The Motion Picture" .
   _:st1 <release_date> "1979-12-07" .
   _:st1 <revenue> "139000000" .
   _:st1 <running_time> "132" .
   _:st1 <dgraph.type> "Film" .
  }
}
' | python -m json.tool | less
Tip To run an RDF/JSON mutation using a file via curl, you can use the curl option --data-binary @/path/to/mutation.rdf instead of -d $''. The --data-binary option skips curl’s default URL-encoding.

Step 3: Alter Schema

Alter the schema to add indexes on some of the data so queries can use term matching, filtering and sorting.

curl "localhost:8080/alter" -XPOST -d $'
  name: string @index(term) .
  release_date: datetime @index(year) .
  revenue: float .
  running_time: int .
  starring: uid .
  director: uid .

  type Person {
    name
  }

  type Film {
    name
    release_date
    revenue
    running_time
    starring
    director
  }
' | python -m json.tool | less
Tip To submit the schema from the Ratel UI, go to Schema page, click on Bulk Edit, and paste the schema.

Step 4: Run Queries

Get all movies

Run this query to get all the movies. The query lists all the movies that have a starring edge.

curl -H "Content-Type: application/graphql+-" "localhost:8080/query" -XPOST -d $'
{
 me(func: has(starring)) {
   name
  }
}
' | python -m json.tool | less
Tip You can also run the GraphQL+- query from the Ratel UI’s query tab.

Get all movies released after “1980”

Run this query to get “Star Wars” movies released after “1980”. Try it in the user interface to see the result as a graph.

curl -H "Content-Type: application/graphql+-" "localhost:8080/query" -XPOST -d $'
{
  me(func:allofterms(name, "Star Wars")) @filter(ge(release_date, "1980")) {
    name
    release_date
    revenue
    running_time
    director {
     name
    }
    starring {
     name
    }
  }
}
' | python -m json.tool | less

Output:

{
  "data":{
    "me":[
      {
        "name":"Star Wars: Episode V - The Empire Strikes Back",
        "release_date":"1980-05-21T00:00:00Z",
        "revenue":534000000.0,
        "running_time":124,
        "director":[
          {
            "name":"Irvin Kernshner"
          }
        ],
        "starring":[
          {
            "name":"Han Solo"
          },
          {
            "name":"Luke Skywalker"
          },
          {
            "name":"Princess Leia"
          }
        ]
      },
      {
        "name":"Star Wars: Episode VI - Return of the Jedi",
        "release_date":"1983-05-25T00:00:00Z",
        "revenue":572000000.0,
        "running_time":131,
        "director":[
          {
            "name":"Richard Marquand"
          }
        ],
        "starring":[
          {
            "name":"Han Solo"
          },
          {
            "name":"Luke Skywalker"
          },
          {
            "name":"Princess Leia"
          }
        ]
      }
    ]
  }
}

That’s it! In these four steps, we set up Dgraph, added some data, set a schema and queried that data back.

Where to go from here

  • Go to Clients to see how to communicate with Dgraph from your application.
  • Take the Tour for a guided tour of how to write queries in Dgraph.
  • A wider range of queries can also be found in the Query Language reference.
  • See Deploy if you wish to run Dgraph in a cluster.

Need Help

  • Please use discuss.dgraph.io for questions, feature requests and discussions.
  • Please use Github Issues if you encounter bugs or have feature requests.