JVM Library to translate GraphQL queries and mutations to Neo4j’s Cypher

This is a beta GraphQL transpiler written in Kotlin.

License: Apache 2.

How does it work

This library

1. parses a GraphQL schema and

2. uses the information of the annotated schema to translate GraphQL queries and parameters into Cypher queries and parameters.

Those Cypher queries can then executed, e.g via the Neo4j-Java-Driver (or other JVM drivers) against the graph database and the results can be returned directly to the caller.

The request, result and error handling is not part of this library, but we provide demo programs on how to use it in different languages.

Note	All the [features|supported features] are listed and explained below, more detailed docs will be added in time.

FAQ

How does this relate to the other neo4j graphql libraries?

Similar to neo4j-graphql-js this library focuses on query translation, just for the JVM instead of Node.js. It does not provide a server (except as examples) or other facilities but is meant to be used as a dependency included for a single purpose.

If this library is feature complete we plan to replace the code in the current Neo4j server plugin neo4j-graphql with a single call to this library. The server plugin would still handle request-response and error-handling, and perhaps some schema management but be slimmed down to a tiny piece.

How does this related to graphql-java

This library uses graphql-java under the hood for parsing of schema and queries, and managing the GraphQL state and context. But not for nested field resolvers or data fetching.

If you wanted, you could combine graphql-java resolvers with this library to have a part of your schema handled by Neo4j.

Thanks a lot to the maintainers of graphql-java for the awesome library.

Usage

You can use the library as dependency: org.neo4j:neo4j-graphql-java:1.0.0-M02 in any JVM program.

The basic usage should be:

val schema =
        """
        type Person {
            name: ID!
            age: Int
        }
        # Optional if you use generated queries
        type Query {
            person : [Person]
            personByName(name:ID) : Person
        }"""

val query = """ { p:personByName(name:"Joe") { age } } """

val schema = SchemaBuilder.buildSchema(idl)
val (cypher, params) = Translator(schema).translate(query, params)

// generated Cypher
cypher == "MATCH (p:Person) WHERE p.name = $pName RETURN p {.age} as p"

You find more usage examples in the:

• Movie TCK

• Filter TCK

• Cypher Directive TCK

• Property TCK

• Translator 1 TCK

• Translator 2 TCK

• Translator 3 TCK

Demo

Here is a minimalistic example in Groovy using the Neo4j-Java driver and Spark-Java as webserver. It is running against a Neo4j instance at bolt://localhost (username: neo4j, password: password) containing the :play movies graph.

(You can also use a Kotlin based server example.)

In case you wand to bind the neo4j driver directly to the graphql schema you can use the DataFetchingInterceptor to intercept the cypher queries.

// Simplistic GraphQL Server using SparkJava
@Grapes([
  @Grab('com.sparkjava:spark-core:2.7.2'),
  @Grab('org.neo4j.driver:neo4j-java-driver:1.7.2'),
  @Grab('org.neo4j:neo4j-graphql-java:1.0.0-M01'),
  @Grab('com.google.code.gson:gson:2.8.5')
])

import spark.*
import static spark.Spark.*
import com.google.gson.Gson
import org.neo4j.graphql.*
import org.neo4j.driver.v1.*

schema = """
type Person {
  name: ID!
  born: Int
  actedIn: [Movie] @relation(name:"ACTED_IN")
}
type Movie {
  title: ID!
  released: Int
  tagline: String
}
type Query {
    person : [Person]
}
"""

gson = new Gson()
render = (ResponseTransformer)gson.&toJson
def query(value) { gson.fromJson(value,Map.class)["query"] }

graphql = new Translator(SchemaBuilder.buildSchema(schema))
def translate(query) { graphql.translate(query) }

driver = GraphDatabase.driver("bolt://localhost",AuthTokens.basic("neo4j","password"))
def run(cypher) { driver.session().withCloseable { it.run(cypher.query, Values.value(cypher.params)).list{ it.asMap() }}}

post("/graphql","application/json", { req, res ->  run(translate(query(req.body())).first()) }, render);

Run the example with:

groovy docs/Server.groovy

and use http://localhost:4567/graphql as your GraphQL URL.

It uses a schema of:

type Person {
  name: ID!
  born: Int
  actedIn: [Movie] @relation(name:"ACTED_IN")
}
type Movie {
  title: ID!
  released: Int
  tagline: String
}
type Query {
    person : [Person]
}

And can run queries like:

{
  person(first:3) {
    name
    born
    actedIn(first:2) {
      title
    }
  }
}

You can also test it with curl

curl -XPOST http://localhost:4567/graphql -d'{"query":"{person {name}}"}'

This example doesn’t handle introspection queries but the one in the test directory does.

Advanced Queries

Filter, Sorting, Paging support

{
  person(filter: {name_starts_with: "L"}, orderBy: "born_asc", first: 5, offset: 2) {
    name
    born
    actedIn(first: 1) {
      title
    }
  }
}

{
  person(filter: {name_starts_with: "J", born_gte: 1970}, first:2) {
    name
    born
    actedIn(first:1) {
      title
      released
    }
  }
}

Features

Current

• parse SDL schema

• resolve query fields via result types

• handle arguments as equality comparisons for top level and nested fields

• handle relationships via @relation directive on schema fields

• @relation directive on types for rich relationships (from, to fields for start & end node)

• handle first, offset arguments

• argument types: string, int, float, array

• request parameter support

• parametrization for cypher query

• aliases

• inline and named fragments

• auto-generate query fields for all objects

• @cypher directive for fields to compute field values, support arguments

• auto-generate mutation fields for all objects to create, update, delete

• @cypher directive for top level queries and mutations, supports arguments

• date(time)

• interfaces

Next

• skip limit params

• sorting (nested)

• input types

• unions

• scalars

• spatial

Documentation

Parse SDL schema

Currently schemas with object types, enums, fragments and Query types are parsed and handled. We support @relation directives on fields and types for rich relationships We support @cypher directives on fields and top-level query and mutation fields. The configurable augmentation auto-generates queries and mutations (create,update,delete) for all types. It supports the built-in scalars for GraphQL. For arguments we support input types in many places and filters as known from GraphCool/Prisma.

Resolve query Fields via Result Types

For query fields that result in object types (even if wrapped in list/non-null), the appropriate object type is found in the schema and used to translate the query.

e.g.

type Query {
  person: [Person]
}
# query "person" is resolved to and via "Person"

type Person {
  name : String
}

Handle Arguments as Equality Comparisons for Top Level and Nested Fields

If you add a simple argument to your top-level query or nested related fields, those will be translated to direct equality comparisons.

person(name:"Joe", age:42) {
   name
}

to

MATCH (person:Person) WHERE person.name = 'Joe' AND person.age = 42 RETURN person { .name } AS person

Only that the literal values are turned into parameters.

Handle Relationships via @relation Directive on Schema Fields

If you want to represent a relationship from the graph in GraphQL you have to add an @relation directive that contains the relationship-type and the direction. Default relationship-type is 'OUT'. So you can use different domain names in your GraphQL fields that are independent of your graph model.

type Person {
  name : String
  actedIn: [Movie] @relation(name:"ACTED_IN", direction:OUT)
}

person(name:"Keanu Reeves") {
  name
  actedIn {
    title
  }
}

Note	We use Neo4j’s pattern comprehensions to represent nested graph patterns in Cypher.

Handle first, offset Arguments

To support pagination first is translated to LIMIT in Cypher and offset into SKIP For nested queries these are converted into slices for arrays.

person(offset: 5, first:10) {
  name
}

MATCH (person:Person) RETURN person { .name }  AS person SKIP 5 LIMIT 10

Argument Types: string, int, float, array

The default Neo4j types are handled both as argument types as well as field types.

Note	Datetime and spatial not yet.

Usage of ID

Each type is expect to have exactly one filed of type ID defined. If the field is named _id, it is interpreted as the database internal graph ID.

So there are 3 cases:

Case 1: Only the ID field exists

type User {
  email: ID!
  name: String!
}

Case 2: Only the ID field exists interpreted as internal ID

type User {
  _id: ID!
  email: String!
  name: String!
}

Case 3: An ID field exists but the internal ID is propagated as well

type User {
  _id: Int!
  email: ID!
  name: String!
}

Important

For the auto generated queries and mutations the ID field is used primarily.

Tip	You should create an unique index on the ID fields

Parameter Support

We handle passed in GraphQL parameters, these are resolved correctly when used within the GraphQL query.

Parametrization

As we don’t want to have literal values in our Cypher queries, all of them are translated into parameters.

person(name:"Joe", age:42, first:10) {
   name
}

to

MATCH (person:Person) WHERE person.name = $personName AND person.age = $personAge RETURN person { .name } AS person LIMIT $first

Those parameters are returned as part of the Cypher type that’s returned from the translate() method.

Aliases

We support query aliases, they are used as Cypher aliases too, so you get them back as keys in your result records.

For example:

query {
  jane: person(name:"Jane") { name, age }
  joe: person(name:"Joe") { name, age }
}

Inline and Named Fragments

This is more of a technical feature, both types of fragments are resolved internally.

Sorting (top-level)

We support sorting via an orderBy argument, which takes an Enum or String value of fieldName_asc or fieldName_desc.

query {
  person(orderBy:[name_asc, age_desc]) {
     name
     age
  }
}

MATCH (person:Person)
RETURN person { .name, .age } AS person

ORDER BY person.name ASC, person.age DESC

Note	Those enums are not yet automatically generated. And we don’t support ordering yet on nested, related fields.

@relationship on Types

To represent rich relationship types with properties, a @relation directive is supported on an object type.

In our example it would be the Role type.

type Role @relation(name:"ACTED_IN", from:"actor", to:"movie") {
   actor: Person
   movie: Movie
   roles: [String]
}
type Person {
  name: String
  born: Int
  roles: [Role]
}
type Movie {
  title: String
  released: Int
  characters: [Role]
}

person(name:"Keanu Reeves") {
   roles {
      roles
      movie {
        title
      }
   }
}

Filters

Filters are a powerful way of selecting a subset of data. Inspired by the graph.cool/Prisma filter approach, our filters work the same way.

Note	we’ll create more detailed docs, for now the prisma docs on that topic are pretty good.

We use nested input types for arbitrary filtering on query types and fields

{ Company(filter: { AND: { name_contains: "Ne", country_in ["SE"]}}) { name } }

You can also apply nested filter on relations, which use suffixes like ("",not,some, none, single, every)

{ Company(filter: {
    employees_none { name_contains: "Jan"},
    employees_some: { gender_in : [female]},
    company_not: null })
    {
      name
    }
}

Note	Those nested input types are not yet generated, we use leniency in the parser.

Inline and Named Fragments

We support inline and named fragments according to the GraphQL spec. Most of this is resolved on the parser/query side.

Named Fragment

fragment details on Person { name, email, dob }
query {
  person {
    ...details
  }
}

Inline Fragment

query {
  person {
    ... on Person { name, email, dob }
  }
}

@cypher Directives

With @cypher directives you can add the power of Cypher to your GraphQL API. It allows you, without code to compute field values using complex queries. You can also write your own, custom top-level queries and mutations using Cypher.

Arguments on the field are passed to the Cypher statement as parameters. Input types are supported, they appear as Map type in your Cypher statement.

Note	Those Cypher directive queries are only included in the generated Cypher statement if the field or query is included in the GraphQL query.

On Fields

@cypher directive on a field

type Movie {
  title: String
  released: Int
  similar(limit:Int=10): [Movie] @cypher(statement:"
        MATCH (this)-->(:Genre)<--(sim)
        WITH sim, count(*) as c ORDER BY c DESC LIMIT $limit
        RETURN sim")
}

Here the this variable is bound to the current movie and you can use it to navigate the graph and collect data. The limit variable is passed to the query as parameter.

On Queries

Similarly you can use the @cypher directive with a top level query.

@cypher directive on query

type Query {
   person(name:String) Person @cypher("MATCH (p:Person) WHERE p.name = $name RETURN p")
}

Of course you can also return arrays from your query, the statements on query fields should be read-only queries.

On Mutations

You can do the same for mutations, just with updating Cypher statements.

@cypher directive on mutation

type Mutation {
   createPerson(name:String, age:Int) Person @cypher("CREATE (p:Person) SET p.name = $name, p.age = $age RETURN p")
}

You can use more complex statements for creating these entities or even subgraphs.

Note	The common CRUD mutations and queries are auto-generated, see below.

Auto Generate Queries and Mutations

To reduce the amount of boilerplate code a user has to write we auto-generate top-level CRUD queries and mutations for all types.

This is configurable via the API, you can:

• disable auto-generation (for mutations/queries)

• disable it per type

• disable mutations per operation (create,delete,update)

For a schema like this:

type Person {
   id:ID!
   name: String
   age: Int
   movies: [Movie]
}

It would auto-generate quite a lot of things:

• a query: person(id:ID, name:String , age: Int, _id: Int, filter:_PersonFilter, orderBy:_PersonOrdering, first:Int, offset:Int) : [Person]

• a _PersonOrdering enum, for the orderBy argument with all fields for _asc and _desc sort order

• a _PersonInput for creating Person objects

• a _PersonFilter for the filter argument, which is a deeply nested input object (see Filters)

• mutations for:

  • createPerson: createPerson(id:ID!, name:String, age: Int) : Person

  • mergePerson: mergePerson(id:ID!, name:String, age:Int) : Person

  • updatePerson: updatePerson(id:ID!, name:String, age:Int) : Person

  • deletePerson: deletePerson(id:ID!) : Person

  • addPersonMovies: addPersonMovies(id:ID!,movies:[ID!]!) : Person

  • deletePersonMovies: deletePersonMovies(id:ID!,movies:[ID!]!) : Person

You can then use those in your GraphQL queries like this:

query { person(age:42, orderBy:name_asc) {
   id
   name
   age
}

or

mutation {
  createPerson(id: "34920n9qw0", name:"Jane Doe", age:42) {
    id
    name
    age
  }
}

You find more examples in the Augmentation Tests