This library
-
parses a GraphQL schema and
-
uses the information of the annotated schema to translate GraphQL queries and parameters into Cypher queries and parameters.
Those Cypher queries can then be 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 supported features are listed and explained below, more detailed docs will be added in time. |
For complex examples take a look at our example projects
Since the javascript pendant of this library (neo4j-graphql-js) has majored into a neo4j product, we want to migrate our augmented schema, to match as much as possible to the one of the @neo4j/graphql.
Therefore, we created a list of issues to track progress.
We will try to make the migration as smooth as possible. For this purpose we will support the old, and the new way of schema augmentation until the next major release.
To already test the new features, you can enable them via some setting in the SchemaConfig
The GRANDstack is a full-stack package that integrates React frontends via GraphQL through neo4j-graphql-js with Neo4j.
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.
We plan to replace the code in the current Neo4j server plugin neo4j-graphql with a single call to this library.
The server plugin could still exist as an example that shows how to handle request-response and error-handling, and perhaps some minimal schema management but be slimmed down to a tiny piece of code.
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.
|
Note
|
We also use neo4j-opencypher-dsl provided graciously by the spring-data-neo4j-rx project to generate our cypher queries.
|
You can use the library as dependency: org.neo4j:neo4j-graphql-java:1.8.0 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 ctx = QueryContext()
val (cypher, params) = Translator(schema).translate(query, params, ctx)
// generated Cypher
cypher == "MATCH (p:Person) WHERE p.name = $pName RETURN p {.age} as p"You find more usage examples in the:
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: s3cr3t) 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('com.google.code.gson:gson:2.8.5'),
@Grab('org.neo4j:neo4j-graphql-java:1.8.0')
])
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","s3cr3t"))
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.
{
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
}
}
}
-
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
-
@cypher directive for top level queries and mutations, supports arguments
-
@cypher directives can have a
passThrough:trueargument, 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. -
auto-generate mutation fields for all objects to create, update, delete
-
date(time)
-
interfaces
-
complex filter parameters, with optional query optimization strategy
-
scalars
-
spatial
-
skip limit params
-
sorting (nested)
-
ignoring fields
-
Currently, schemas with object types, enums, fragments and Query types are parsed and handled.
-
@relationdirectives on fields and types for rich relationships -
@cypherdirectives on fields and top-level query and mutation fields. -
The configurable augmentation auto-generates queries and mutations (create,update,delete) for all types.
-
Supports the built-in scalars for GraphQL.
-
For arguments input types in many places and filters from GraphCool/Prisma.
For query fields that result in object types (even if wrapped in list/non-null), the appropriate object type is determined via 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
}This library supports queries for both neo4j 4.x and 5.x. By default, the neo4j 5 dialect is enabled. The dialect can be changed via QueryContext.
var query
val ctx = QueryContext(neo4jDialect = Dialect.DEFAULT) // Dialect.DEFAULT matches to neo4j version < 5.x
query = translator.translate(query, params, ctx)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 an equivalent of
MATCH (person:Person) WHERE person.name = 'Joe' AND person.age = 42 RETURN person { .name } AS personThe literal values are turned into Cypher query parameters.
If you want to represent a relationship from the graph in GraphQL you have to add a @relation directive which contains the relationship-type and the direction.
The default direction for a relationship is 'OUT'. Other values are 'IN' and 'BOTH'.
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. This will be updated to subqueries from 4.1 |
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 10The default Neo4j Cypher types are handled both as argument types as well as field types.
|
Note
|
Spatial is not yet covered. |
Each type is expected 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:
type User {
email: ID!
name: String!
}type User {
_id: ID!
email: String!
name: String!
}type User {
_id: Int!
email: ID!
name: String!
}|
Important
|
For the auto generated queries and mutations the ID field is used as primary key.
|
|
Tip
|
You should create a unique constraint on the ID fields
|
GraphQL parameter’s are passed onto Cypher, these are resolved correctly when used within the GraphQL query.
For query injection prevention and caching purposes, literal values 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 $firstThose parameters are returned as part of the Cypher type that’s returned from the translate()-method.
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 }
}This is more of a technical feature, both types of fragments are resolved internally.
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
|
We don’t yet support ordering on nested relationship fields. |
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 are a powerful way of selecting a subset of data. Inspired by the graph.cool/Prisma filter approach, our filters work the same way.
These filters are documented in detail in the https://grandstack.io/docs/graphql-filtering [GRANDstack docs^].
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
}
}If you encounter performance problems with the cypher queries generated for the filter, you can activate an alternative algorithm using:
var query
try {
val ctx = QueryContext(optimizedQuery = setOf(QueryContext.OptimizationStrategy.FILTER_AS_MATCH))
query = translator.translate(query, params, ctx)
} catch (e: OptimizedQueryException) {
query = translator.translate(query, params)
}If no query can be generated by the alternative algorithm, an OptimizedQueryException is thrown,
so that a fallback to the actual algorithm can be used.
Examples of the alternative algorithm can be seen in the tests.
We support inline and named fragments according to the GraphQL spec. Most of this is resolved on the parser/query side.
fragment details on Person { name, email, dob }
query {
person {
...details
}
}query {
person {
... on Person { name, email, dob }
}
}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 and can be used by name. They must not be prefixed by $ since they are no longer parameters. Just use the same name as the field’s argument.
The current node is passed to the statement as this.
The statement should contain exactly one return expression without any alias.
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. |
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.
You can use it to navigate the graph and collect data.
The limit variable is passed to the query as parameter.
Similarly, you can use the @cypher directive with a top-level query.
type Query {
person(name:String) Person @cypher("MATCH (p:Person) WHERE p.name = name RETURN p")
}You can also return arrays from your query, the statements on query fields should be read-only queries.
You can do the same for mutations, just with updating Cypher statements.
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. |
To reduce the amount of boilerplate code you have to write, we auto-generate 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 per type
-
disable mutations per operation (create,delete,update)
-
configure capitalization of top level generated fields
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
_PersonOrderingenum, for theorderByargument with all fields for_ascand_descsort order -
a
_PersonInputfor creating Person objects -
a
_PersonFilterfor thefilterargument, 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 and the Custom queries and mutations Tests
Sometimes you need the possibility to generate the augmented schema at compile time. To achieve this, we provide a maven plugin which can be used as follows:
<plugin>
<groupId>org.neo4j</groupId>
<artifactId>neo4j-graphql-augmented-schema-generator-maven-plugin</artifactId>
<version>1.8.0</version>
<executions>
<execution>
<goals>
<goal>generate-schema</goal>
</goals>
<configuration>
<schemaConfig> <!--(1)-->
<pluralizeFields>true</pluralizeFields>
<useWhereFilter>true</useWhereFilter>
<queryOptionStyle>INPUT_TYPE</queryOptionStyle>
<mutation>
<enabled>false</enabled>
</mutation>
</schemaConfig>
<outputDirectory>${project.build.directory}/augmented-schema</outputDirectory>
<fileset> <!--(2)-->
<directory>${project.basedir}/src/main/resources</directory>
<include>*.graphql</include>
</fileset>
</configuration>
</execution>
</executions>
</plugin>-
Use the same configuration as for your SchemaBuilder
-
Define the source schema for which you want to have an augmented schema generated
Take a look at the spring boot dsg example for a use case of this plugin, where it is used in combination with a code generator to have a type save graphql API