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Version: v22

Fetching data

How graphql fetches data

Each field in graphql has a graphql.schema.DataFetcher associated with it.

Some fields will use specialised data fetcher code that knows how to go to a database say to get field information while most simply take data from the returned in memory objects using the field name and Plain Old Java Object (POJO) patterns to get the data.

Note : Data fetchers are some times called "resolvers" in other graphql implementations.

So imagine a type declaration like the one below :

type Query {
products(match : String) : [Product] # a list of products

type Product {
id : ID
name : String
description : String
cost : Float
tax : Float
launchDate(dateFormat : String = "dd, MMM, yyyy') : String

The Query.products field has a data fetcher, as does each field in the type Product.

The data fetcher on the Query.products field is likely to be a more complex data fetcher, containing code that goes to a database say to get a list of Product objects. It takes an optional match argument and hence can filter these product results if the client specified it.

It might look like the following :

DataFetcher productsDataFetcher = new DataFetcher<List<ProductDTO>>() {
public List<ProductDTO> get(DataFetchingEnvironment environment) {
DatabaseSecurityCtx ctx = environment.getContext();

List<ProductDTO> products;
String match = environment.getArgument("match");
if (match != null) {
products = fetchProductsFromDatabaseWithMatching(ctx, match);
} else {
products = fetchAllProductsFromDatabase(ctx);
return products;

Each DataFetcher is passed a graphql.schema.DataFetchingEnvironment object which contains what field is being fetched, what arguments have been supplied to the field and other information such as the field's type, its parent type, the query root object or the query context object.

Note how the data fetcher code above uses the context object as an application specific security handle to get access to the database. This is a common technique to provide lower layer calling context.

Once we have a list of ProductDTO objects we typically don't need specialised data fetchers on each field. graphql-java ships with a smart graphql.schema.PropertyDataFetcher that knows how to follow POJO patterns based on the field name. In the example above there is a name field and hence it will try to look for a public String getName() POJO method to get the data.

graphql.schema.PropertyDataFetcher is the data fetcher that is automatically associated with each field by default.

You can however still get access to the graphql.schema.DataFetchingEnvironment in your DTO methods. This allows you to tweak values before sending them out. For example above we have a launchDate field that takes an optional dateFormat argument. We can have the ProductDTO have logic that applies this date formatting to the desired format.

class ProductDTO {

private ID id;
private String name;
private String description;
private Double cost;
private Double tax;
private LocalDateTime launchDate;

// ...

public String getName() {
return name;

// ...

public String getLaunchDate(DataFetchingEnvironment environment) {
String dateFormat = environment.getArgument("dateFormat");
return yodaTimeFormatter(launchDate,dateFormat);

Customising PropertyDataFetcher

As mentioned above graphql.schema.PropertyDataFetcher is the default data fetcher for fields in graphql-java and it will use standard patterns for fetching object field values.

It supports a POJO approach and a Map approach in a Java idiomatic way. By default it assumes that for a graphql field fieldX it can find a POJO property called fieldX or a map key called fieldX if the backing object is a Map.

However you may have small differences between your graphql schema naming and runtime object naming. For example imagine that Product.description is actually represented as getDesc() in the runtime backing Java object.

You can specify it directly by wiring in a field data fetcher. This will tell the graphql.schema.PropertyDataFetcher to use the property name desc when fetching data for the graphql field named description.

GraphQLFieldDefinition descriptionField = GraphQLFieldDefinition.newFieldDefinition()

GraphQLCodeRegistry codeRegistry = GraphQLCodeRegistry.newCodeRegistry()
coordinates("ObjectType", "description"),

The interesting parts of the DataFetchingEnvironment

Every data fetcher is passed a graphql.schema.DataFetchingEnvironment object which allows it to know more about what is being fetched and what arguments have been provided. Here are some of the more interesting parts of DataFetchingEnvironment.

  • <T> T getSource() - the source object is used to get information for a field. Its the object that is the result of the parent field fetch. In the common case it is an in memory DTO object and hence simple POJO getters will be used for fields values. In more complex cases, you may examine it to know how to get the specific information for the current field. As the graphql field tree is executed, each returned field value becomes the source object for child fields.

  • <T> T getRoot() - this special object is used to seed the graphql query. The root and the source is the same thing for the top level fields. The root object never changes during the query and it may be null and hence no used.

  • Map<String, Object> getArguments() - this represents the arguments that have been provided on a field and the values of those arguments that have been resolved from passed in variables, AST literals and default argument values. You use the arguments of a field to control what values it returns.

  • <T> T getContext() - the context object is set up when the query is first executed and stays the same over the lifetime of the query. The context can be any value and is typically used to give each data fetcher some calling context needed when trying to get field data. For example the current user credentials or the database connection parameters could be contained with a context object so that data fetchers can make business layer calls. One of the key design decisions you have as a graphql system designer is how you will use context in your fetchers if at all. Some people use a dependency framework that injects context into data fetchers automatically and hence don't need to use this.

  • ExecutionStepInfo getExecutionStepInfo() - the field type information is a catch all bucket of field type information that is built up as the query is executed. The following section explains more on this.

  • DataFetchingFieldSelectionSet getSelectionSet() - the selection set represents the child fields that have been "selected" under neath the currently executing field. This can be useful to help look ahead to see what sub field information a client wants. The following section explains more on this.

  • ExecutionId getExecutionId() - each query execution is given a unique id. You can use this perhaps on logs to tag each individual query.

The interesting parts of ExecutionStepInfo

The execution of a graphql query creates a call tree of fields and their types. graphql.execution.ExecutionStepInfo.getParentTypeInfo allows you to navigate upwards and see what types and fields led to the current field execution.

Since this forms a tree path during execution, the graphql.execution.ExecutionStepInfo.getPath method returns the representation of that path. This can be useful for logging and debugging queries.

There are also helper methods there to help you get the underlying type name of non null and list wrapped types.

The interesting parts of DataFetchingFieldSelectionSet

Imagine a query such as the following

query {
products {
# the fields below represent the selection set
sellingLocations {

The sub fields here of the products field represent the selection set of that field. It can be useful to know what sub selection has been asked for so the data fetcher can optimise the data access queries. For example an SQL backed system may be able to use the field sub selection to only retrieve the columns that have been asked for.

In the example above we have asked for sellingLocations information and hence we may be able to make an more efficient data access query where we ask for product information and selling location information at the same time.