Quickstart

This is a quick start guide to run DQL queries and mutations.

This guide helps you:

• Understand how JSON data is represented as a graph
• Query the graph using DQL
• Use indexes

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Step 1: Run Dgraph

The easiest way to get Dgraph up and running is using the Dgraph on Hypermode.

1. In the Hypermode console, click Launch new backend
2. Select a region that meets your requirements
3. Type a name for your graph instance
4. Click Launch
5. Click Ratel to access the UI that provides browser-based queries, mutations and visualizations.

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Step 2: Run mutation

The create, update, and delete operations in Dgraph are called mutations.

Ratel makes it easier to run queries and mutations.

1. In the Console page, select Mutate tab.

2. Paste the following:

     {
     "set": [
       {
         "name":"Star Wars: Episode IV - A New Hope",
         "release_date": "1977-05-25",
         "director": {
           "name": "George Lucas",
           "dgraph.type": "Person"
         },
         "starring" : [
           {
             "name": "Luke Skywalker"
           },
           {
             "name": "Princess Leia"
           },
           {
             "name": "Han Solo"
           }
         ]
       },
       {
         "name":"Star Trek: The Motion Picture",
         "release_date": "1979-12-07"
       }
     ]
   }
   

   The input data is in JSON Format. Dgraph also supports RDF notation.

   The sample JSON data is an array of two movies with some attributes. These are stored as Nodes in Dgraph.

   The “Star Wars” movie has a director field which is an JSON object and a starring field which is an array of JSON objects. Each object is also stored as a Node in Dgraph . The director and starring are stored as relations.

3. Click Run to execute the mutation.

   View the Dgraph response in the JSON tab:

   {
     "data": {
       "code": "Success",
       "message": "Done",
       "queries": null,
       "uids": {
         "dg.1119451236.100": "0xfffd8d726c1de414",
         "dg.1119451236.101": "0xfffd8d726c1de40f",
         "dg.1119451236.102": "0xfffd8d726c1de410",
         "dg.1119451236.103": "0xfffd8d726c1de411",
         "dg.1119451236.104": "0xfffd8d726c1de412",
         "dg.1119451236.99": "0xfffd8d726c1de413"
       }
     }, ...
   

Dgraph displays the universal identifiers (UID) of the nodes that were created.

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Step 3: First query

1. In the Console page, select Query tab and run this query:

     {
       movies(func: has(release_date)) {
       name
       director { name }
       starring { name }
       }
       }
   

   The query lists all movies that have a release_date and for each, it looks for the director and starring relations and provides the name attribute of the related nodes if any.

2. In the response panel, select Graph, to view a Graph output:

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Step 4: Alter schema

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

1. In the Schema page, select Predicates. Dgraph creates and displays the predicates name, release-date,director and starring. A predicate is Dgraph internal representation of a node attribute or a relation.
2. Select the name predicate. Ratel displays details about the predicate type and indexes.
3. Select index and select term for name predicate.
4. Click Update to apply the index.

Set the index for the release_date:

1. Select release_date predicate.
2. Change the type to dateTime
3. Select index and choose year for the index type.
4. Click Update to apply the index on the release-date predicate.

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Step 5: Queries using indexes

Let’s get the movies having the term “Star” in their name and released before 1979.

In the Console page select Query tab and run this query:

{
  me(func: allofterms(name, "Star"), orderasc: release_date) @filter(lt(release_date, "1979")) {
    name
    release_date
    revenue
    running_time
    director {
     name
    }
    starring (orderasc: name) {
     name
    }
  }
}

Observe the JSON result and the graph result.

You can play with the release date and the search terms conditions to see Dgraph search and filtering in action.

In these five steps, you set up Dgraph, added some data, visualized it as a graph, added indexes and queried the data .

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Where to go from here

• 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.
• Go to Clients to see how to communicate with Dgraph from your app.

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Need help

• Please use discuss.dgraph.io for questions, issues, feature requests, and discussions.