Neo4j-GraphQL Extension

This is a GraphQL-Endpoint extension for Neo4j. It is part of the GRANDstack

This readme assumes you are somewhat familiar with GraphQL and minimally with Cypher.

Based on your GraphQL schema, it translates GraphQL Queries and Mutations into Cypher statements and executes them on Neo4j.

It offers both an HTTP API, as well as, Neo4j Cypher Procedures to execute and manage your GraphQL API.

Installation

Download and install Neo4j Desktop

Neo4j Desktop provides a quick install button for neo4j-graphql.

After creating your database you can find it under "Manage" in the "Plugins" tab for a single click install.

Use with neo4j-graphql-cli

This extension is utilized, when you use neo4j-graphql-cli

This tool

launches a Neo4j Sandbox with your GraphQL schema
provides the /graphql/ endpoint,
a Neo4j server,
an hosted GraphiQL for it.

npm install -g neo4j-graphql-cli
neo4j-graphql movies-schema.graphql

Quickstart

To generate some graph data in Neo4j just run :play movie graph in your Neo4j Browser.

GraphiQL

The best tool to use is GraphiQL the GraphQL UI. Get and install it.

Enter your GraphQL URL, like http://localhost:7474/graphql/ (note the trailing slash).

If your Neo4j Server runs with authentication enabled, add the appropriate Basic-Auth (base64 encoded) username:password header in the "Edit HTTP Headers" screen.

Command to generate the Authorization header value.

echo "Basic $(echo -n "neo4j:<password>" | base64)"

Uploading a GraphQL Schema

Here is a small example schema for the movie data. Just a Movie with actors, and a Person with movies.

Simple properties are mapped directly while the relationships are mapped to fields movies and actors

Movies Schema

type Movie  {
  title: String!
  released: Int
  actors: [Person] @relation(name:"ACTED_IN",direction:IN)
}
type Person {
  name: String!
  born: Int
  movies: [Movie] @relation(name:"ACTED_IN")
}

You can POST a GraphQL schema to the /graphql/idl/ endpoint or run the CALL graphql.idl('schema-text') procedure.

The payload is parsed and stored in Neo4j and used subsequently as the backing GraphQL schema for validating and executing queries.

CALL graphql.idl('
type Movie  {
  title: String!
  released: Int
  actors: [Person] @relation(name:"ACTED_IN",direction:IN)
}
type Person {
  name: String!
  born: Int
  movies: [Movie] @relation(name:"ACTED_IN")
}
')

You should then be able to see your schema in the Docs section of GraphiQL.

This also gives you auto-completion, validation and hints when writing queries.

To visualize your GraphQL schema in Neo4j Browser use: call graphql.schema().

Auto-Generated Query Types

From that schema, the plugin automatically generate Query Types for each of the declared types.

e.g. Movie(title,released,first,offset,_id,orderBy, filter): [User]

Each field of the entity is available as query argument, with an equality check (plural for list-contains)
We also provide a filter argument for more complex filtering with nested predicates, also for relation-fields (see graphcool docs)
For ordered results there is a orderBy argument
And first, offset allow for pagination

Now you can for instance run this query:

Simple query example

{ Person(name:"Kevin Bacon") {
    name
    born
    movies {
      title
      released
      tagline
    }
  }
}

Advanced query example

query Nineties($released: Long, $letter: String)
{ Movie(released: $released,
        filter: {title_starts_with: $letter,
                 actors_some: { name_contains: $letter}}) {
    title
    released
    actors(first: 3) {
      name
      born
      movies(first: 1, orderBy: title_desc) {
        title
        released
      }
    }
  }
}

# query variables
{ "released":1995, "letter":"A"}

This query declares query name and parameters (first line), which are passed separately ("Query Parameters box") as JSON.

And get this result:

Auto-Generated Mutations

Additionally Mutations for each type are created, which return update statistics.

e.g. for the Movie type:

createMovie(title: ID!, released: Int) : String
updateMovie(title: ID!, released: Int) : String
deleteMovie(title: ID!) : String

and for it’s relationships:

addMovieActors(title: ID!, actors:[ID]!) : String
deleteMovieActors(title: ID!, actors:[ID]!) : String

Those mutations then allow you to create and update your data with GraphQL.

Single Mutation

mutation {
    createPerson(name:"Chadwick Boseman", born: 1977)
}

Mutation Result

{ "data": {
    "createPerson": "Nodes created: 1\nProperties set: 2\nLabels added: 1\n"
  }
}

Several Mutations at once

mutation {
    pp: createMovie(title:"Black Panther", released: 2018)
    lw: createPerson(name:"Letitia Wright", born: 1993)
    cast: addMovieActors(title: "Black Panther",
          actors:["Chadwick Boseman","Letitia Wright"])
}

If the same mutations is called multiple times, you need to use alias prefixes to avoid clashes in the returned data, which is keyed on mutation names.

You can use those mutations also to load data from CSV or JSON.

Directives

Directives like @directiveName(param:value) can be used to augment the schema with additional meta-information that we use for processing.

You have already seen the @relation(name:"ACTED_IN", direction:"IN") directive to map entity references to graph relationships.

The @cypher directive is a powerful way of declaring computed fields, query types and mutations with a Cypher statement.

For instance, directors

type Movie {
  ...
  directors: [Person] @cypher(statement:"MATCH (this)<-[:DIRECTED]-(d) RETURN d")
}

schema {
   query: QueryType
   mutation: MutationType
}

A custom query

type QueryType {
  ...
  coActors(name:ID!): [Person] @cypher(statement:"MATCH (p:Person {name:$name})-[:ACTED_IN]->()<-[:ACTED_IN]-(co) RETURN distinct co")
}

A custom mutation

type MutationType {
  ...
  rateMovie(user:ID!, movie:ID!, rating:Int!): Int
  @cypher(statement:"MATCH (p:Person {name:$user}),(m:Movie {title:$movie}) MERGE (p)-[r:RATED]->(m) SET r.rating=$rating RETURN r.rating")
}

Full enhanced Schema

type Movie  {
  title: String!
  released: Int
  actors: [Person] @relation(name:"ACTED_IN",direction:IN)
  directors: [Person] @cypher(statement:"MATCH (this)<-[:DIRECTED]-(d) RETURN d")
}
type Person {
  name: String!
  born: Int
  movies: [Movie] @relation(name:"ACTED_IN")
}
schema {
   query: QueryType
   mutation: MutationType
}
type QueryType {
  coActors(name:ID!): [Person] @cypher(statement:"MATCH (p:Person {name:$name})-[:ACTED_IN]->()<-[:ACTED_IN]-(co) RETURN distinct co")
}
type MutationType {
  rateMovie(user:ID!, movie:ID!, rating:Int!): Int
  @cypher(statement:"MATCH (p:Person {name:$user}),(m:Movie {title:$movie}) MERGE (p)-[r:RATED]->(m) SET r.rating=$rating RETURN r.rating")
}

Procedures

This library also comes with Cypher Procedures to execute GraphQL from within Neo4j.

Simple Procedure Query

CALL graphql.query('{ Person(born: 1961) { name, born } }')

Advanced Procedure Query with parameters and post-processing

WITH 'query ($year:Long,$limit:Int) { Movie(released: $year, first:$limit) { title, actors {name} } }' as query

CALL graphql.query(query,{year:1995,limit:5}) YIELD result

UNWIND result.Movie as movie
RETURN movie.title, [a IN movie.actors | a.name] as actors

Update with Mutation

CALL graphql.execute('mutation { createMovie(title:"The Shape of Water", released:2018)}')

Other Information

Please leave Feedback and Issues

You can get quick answers on Neo4j-Users Slack in the #neo4j-graphql channel

License: Apache License v2.

This branch for Neo4j 3.3.x

Features

name	information	example
entities	each node label represented as entity	`{ Person {name,born} }`
multi entities	multiple entities per query turned into `UNION`	`{ Person {name,born} Movie {title,released} }`
property fields	via sampling property names and types are determined	`{ Movie {title, released} }`
field parameters	all properties can be used as filtering (exact/list) input parameters, will be turned into Cypher parameters	`{ Movie(title:"The Matrix") {released,tagline} }`
query parameters	passed through as Cypher parameters	`query MovieByParameter ($title: String!) { Person(name:$name) {name,born} }`
filter arguments	nested input types for arbitrary filtering on query types and fields	`{ Company(filter: { AND: { name_contains: "Ne", country_in ["SE"]}}) { name } }`
filter arguments for relations	filtering on relation fields, suffixes ("",not,some, none, single, every)	`{ Company(filter: { employees_none { name_contains: "Jan"}, employees_some: { gender_in : [female]}, company_not: null }) { name } }`
relationships	via a `@relationship` annotated field, optional direction	`type Person { name: String, movies : Movie @relation(name:"ACTED_IN", direction:OUT) }`
ordering	via an extra `orderBy` parameter	`query PersonSortQuery { Person(orderBy:[name_desc,born_desc]) {name,born}}`
pagination	via `first` and `offset` parameters	`query PagedPeople { Person(first:10, offset:20) {name,born}}`
schema first IDL support	define schema via IDL	`:POST /graphql/idl "type Person {name: String!, born: Int}"`
Mutations	create/delete mutations inferred from the schema	`createMovie(title:ID!, released:Int) updateMovie(title:ID!, released:Int) deleteMovie(title:ID!)` `createMoviePersons(title:ID!,persons:[ID!]) deleteMoviePersons(title:ID!,persons:[ID!])`
Cypher queries	`@cypher` directive on fields and types, parameter support	`actors : Int @cypher(statement:"RETURN size( (this)< -[:ACTED_IN]-() )")`
Cypher updates	Custom mutations by executing `@cypher` directives	`createPerson(name: String) : Person @cypher(statement:"CREATE (p:Person {name:{name}}) RETURN p")`
extensions	extra information returned	`fields are: columns, query, warnings, plan, type READ_ONLY/READ_WRITE,`

Note

@cypher directives can have a passThrough:true argument, that gives sole responsibility for the nested query result for this field to your Cypher query. You will have to provide all data/structure required by client queries. Otherwise, we assume if you return object-types that you will return the appropriate nodes from your statement.

Advanced Usage

The extension works with Neo4j 3.1 and 3.2, the code on this branch is for 3.3.

Please consult the Neo4j documentation for file locations for the other editions on the different operating systems.

Manual Installation

Download the appropriate neo4j-graphql release for your version.
Copy the jar-file into Neo4j’s plugins directory
Edit the Neo4j settings ($NEO4J_HOME/conf/neo4j.conf) to add:
dbms.unmanaged_extension_classes=org.neo4j.graphql=/graphql
You might need to add ,graphql.* if your config contains this line:
dbms.security.procedures.whitelist=
(Re)start your Neo4j server

Note	Neo4j Desktop: the configuration is available under Manage → Settings, the `plugins` folder via Open Folder.

Building manually

git clone https://github.com/neo4j-graphql/neo4j-graphql
cd neo4j-graphql
git checkout 3.2
mvn clean package
cp target/neo4j-graphql-*.jar $NEO4J_HOME/plugins
echo 'dbms.unmanaged_extension_classes=org.neo4j.graphql=/graphql' >> $NEO4J_HOME/conf/neo4j.conf
$NEO4J_HOME/bin/neo4j restart

Note	You might need to add `,graphql.*` if your config contains this line: `dbms.security.procedures.whitelist=`

Schema from Graph

If you didn’t provide a GraphQL schema, we try to derive one from the existing graph data. From sampling the data we add a type for each Node-Label with all the properties and their types found as fields.

Each relationship-type adds a reference field to the node type, named aType for A_TYPE.

Procedures

You can even visualize remote graphql schemas, e.g. here from the GitHub GraphQL API. Make sure to generate the Personal Access Token to use in your account settings.

call graphql.introspect("https://api.github.com/graphql",{Authorization:"bearer d8xxxxxxxxxxxxxxxxxxxxxxx"})

oskarhane/neo4j-graphql

Neo4j-GraphQL Extension

Installation

Use with neo4j-graphql-cli

Quickstart

GraphiQL

Uploading a GraphQL Schema

Auto-Generated Query Types

Auto-Generated Mutations

Directives

Procedures

Other Information

Features

Advanced Usage

Manual Installation

Building manually

Schema from Graph

Procedures

Resources

Neo4j-GraphQL

Libraries & Tools