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The Spring Data R2DBC project applies core Spring concepts to the development of solutions that use the R2DBC drivers for relational databases. We provide a DatabaseClient as a high-level abstraction for storing and querying rows.

This document is the reference guide for Spring Data - R2DBC Support. It explains R2DBC module concepts and semantics and.

This section provides some basic introduction to Spring and databases.

1. Learning Spring

Spring Data uses Spring framework’s core functionality, including:

While you need not know the Spring APIs, understanding the concepts behind them is important. At a minimum, the idea behind Inversion of Control (IoC) should be familiar, and you should be familiar with whatever IoC container you choose to use.

You can use the core functionality of the R2DBC support directly, with no need to invoke the IoC services of the Spring Container. This is much like JdbcTemplate, which can be used “standalone” without any other services of the Spring container. To use all the features of Spring Data R2DBC, such as the repository support, you need to configure some parts of the library to use Spring.

To learn more about Spring, refer to the comprehensive documentation that explains the Spring Framework in detail. There are a lot of articles, blog entries, and books on the subject. See the Spring framework home page for more information.

2. What is R2DBC?

R2DBC is the acronym for Reactive Relational Database Connectivity. R2DBC is an API specification initiative that declares a reactive API to be implemented by driver vendors to access their relational databases.

Part of the answer as to why R2DBC was created is the need for a non-blocking application stack to handle concurrency with a small number of threads and scale with fewer hardware resources. This need cannot be satisfied by reusing standardized relational database access APIs — namely JDBC –- as JDBC is a fully blocking API. Attempts to compensate for blocking behavior with a ThreadPool are of limited use.

The other part of the answer is that most applications use a relational database to store their data. While several NoSQL database vendors provide reactive database clients for their databases, migration to NoSQL is not an option for most projects. This was the motivation for a new common API to serve as a foundation for any non-blocking database driver. While the open source ecosystem hosts various non-blocking relational database driver implementations, each client comes with a vendor-specific API, so a generic layer on top of these libraries is not possible.

3. What is Reactive?

The term, “reactive”, refers to programming models that are built around reacting to change, availability, and processability -— network components reacting to I/O events, UI controllers reacting to mouse events, resources being made available, and others. In that sense, non-blocking is reactive, because, instead of being blocked, we are now in the mode of reacting to notifications as operations complete or data becomes available.

There is also another important mechanism that we on the Spring team associate with reactive, and that is non-blocking back pressure. In synchronous, imperative code, blocking calls serve as a natural form of back pressure that forces the caller to wait. In non-blocking code, it becomes essential to control the rate of events so that a fast producer does not overwhelm its destination.

Reactive Streams is a small spec (also adopted in Java 9) that defines the interaction between asynchronous components with back pressure. For example, a data repository (acting as a Publisher) can produce data that an HTTP server (acting as a Subscriber) can then write to the response. The main purpose of Reactive Streams is to let the subscriber control how quickly or how slowly the publisher produces data.

4. Reactive API

Reactive Streams plays an important role for interoperability. It is of interest to libraries and infrastructure components but less useful as an application API, because it is too low-level. Applications need a higher-level and richer, functional API to compose async logic —- similar to the Java 8 Stream API but not only for tables. This is the role that reactive libraries play.

Project Reactor is the reactive library of choice for Spring Data R2DBC. It provides the Mono and Flux API types to work on data sequences of 0..1 (Mono) and 0..N (Flux) through a rich set of operators aligned with the ReactiveX vocabulary of operators. Reactor is a Reactive Streams library, and, therefore, all of its operators support non-blocking back pressure. Reactor has a strong focus on server-side Java. It is developed in close collaboration with Spring.

Spring Data R2DBC requires Project Reactor as a core dependency, but it is interoperable with other reactive libraries through the Reactive Streams specification. As a general rule, a Spring Data R2DBC repository accepts a plain Publisher as input, adapts it to a Reactor type internally, uses that, and returns either a Mono or a Flux as output. So, you can pass any Publisher as input and apply operations on the output, but you need to adapt the output for use with another reactive library. Whenever feasible, Spring Data adapts transparently to the use of RxJava or another reactive library.

5. Requirements

The Spring Data R2DBC 1.x binaries require:

6. Additional Help Resources

Learning a new framework is not always straightforward. In this section, we try to provide what we think is an easy-to-follow guide for starting with the Spring Data R2DBC module. However, if you encounter issues or you need advice, use one of the following links:

Community Forum

Spring Data on Stack Overflow is a tag for all Spring Data (not just R2DBC) users to share information and help each other. Note that registration is needed only for posting.

Professional Support

Professional, from-the-source support, with guaranteed response time, is available from Pivotal Sofware, Inc., the company behind Spring Data and Spring.

7. Following Development

  • For information on the Spring Data R2DBC source code repository, nightly builds, and snapshot artifacts, see the Spring Data R2DBC home page.

  • You can help make Spring Data best serve the needs of the Spring community by interacting with developers through the community on Stack Overflow.

  • If you encounter a bug or want to suggest an improvement, please create a ticket on the Spring Data R2DBC issue tracker.

  • To stay up to date with the latest news and announcements in the Spring ecosystem, subscribe to the Spring Community Portal.

  • You can also follow the Spring blog or the Spring Data project team on Twitter (SpringData).

8. Project Metadata

9. New & Noteworthy

9.1. What’s New in Spring Data R2DBC 1.1.0 RELEASE

9.2. What’s New in Spring Data R2DBC 1.0.0 RELEASE

  • Upgrade to R2DBC 0.8.0.RELEASE.

  • @Modifying annotation for query methods to consume affected row count.

  • Repository save(…) with an associated Id terminates with TransientDataAccessException if the row does not exist in the database.

  • Added SingleConnectionConnectionFactory for testing using connection singletons.

  • Support for SpEL expressions in @Query.

9.3. What’s New in Spring Data R2DBC 1.0.0 RC1

  • ConnectionFactory routing through AbstractRoutingConnectionFactory.

  • Utilities for schema initialization through ResourceDatabasePopulator and ScriptUtils.

  • Propagation and reset of Auto-Commit and Isolation Level control through TransactionDefinition.

  • Support for Entity-level converters.

  • Kotlin extensions for reified generics and Coroutines.

  • Add pluggable mechanism to register dialects.

9.4. What’s New in Spring Data R2DBC 1.0.0 M2

  • Support for named parameters.

9.5. What’s New in Spring Data R2DBC 1.0.0 M1

  • Initial R2DBC support through DatabaseClient.

  • Initial Transaction support through TransactionalDatabaseClient.

  • Initial R2DBC Repository Support through R2dbcRepository.

  • Initial Dialect support for Postgres and Microsoft SQL Server.

10. Dependencies

Due to the different inception dates of individual Spring Data modules, most of them carry different major and minor version numbers. The easiest way to find compatible ones is to rely on the Spring Data Release Train BOM that we ship with the compatible versions defined. In a Maven project, you would declare this dependency in the <dependencyManagement /> section of your POM, as follows:

Example 1. Using the Spring Data release train BOM

The current release train version is Neumann-SR3. The train names ascend alphabetically and the currently available trains are listed here. The version name follows the following pattern: ${name}-${release}, where release can be one of the following:

  • BUILD-SNAPSHOT: Current snapshots

  • M1, M2, and so on: Milestones

  • RC1, RC2, and so on: Release candidates

  • RELEASE: GA release

  • SR1, SR2, and so on: Service releases

A working example of using the BOMs can be found in our Spring Data examples repository. With that in place, you can declare the Spring Data modules you would like to use without a version in the <dependencies /> block, as follows:

Example 2. Declaring a dependency to a Spring Data module

10.1. Dependency Management with Spring Boot

Spring Boot selects a recent version of Spring Data modules for you. If you still want to upgrade to a newer version, configure the property spring-data-releasetrain.version to the train name and iteration you would like to use.

10.2. Spring Framework

The current version of Spring Data modules require Spring Framework in version 5.2.8.RELEASE or better. The modules might also work with an older bugfix version of that minor version. However, using the most recent version within that generation is highly recommended.

11. Working with Spring Data Repositories

The goal of the Spring Data repository abstraction is to significantly reduce the amount of boilerplate code required to implement data access layers for various persistence stores.

Spring Data repository documentation and your module

This chapter explains the core concepts and interfaces of Spring Data repositories. The information in this chapter is pulled from the Spring Data Commons module. It uses the configuration and code samples for the Java Persistence API (JPA) module. You should adapt the XML namespace declaration and the types to be extended to the equivalents of the particular module that you use. “[repositories.namespace-reference]” covers XML configuration, which is supported across all Spring Data modules supporting the repository API. “Repository query keywords” covers the query method keywords supported by the repository abstraction in general. For detailed information on the specific features of your module, see the chapter on that module of this document.

11.1. Core concepts

The central interface in the Spring Data repository abstraction is Repository. It takes the domain class to manage as well as the ID type of the domain class as type arguments. This interface acts primarily as a marker interface to capture the types to work with and to help you to discover interfaces that extend this one. The CrudRepository provides sophisticated CRUD functionality for the entity class that is being managed.

Example 3. CrudRepository interface
public interface CrudRepository<T, ID> extends Repository<T, ID> {

  <S extends T> S save(S entity);      (1)

  Optional<T> findById(ID primaryKey); (2)

  Iterable<T> findAll();               (3)

  long count();                        (4)

  void delete(T entity);               (5)

  boolean existsById(ID primaryKey);   (6)

  // … more functionality omitted.
1 Saves the given entity.
2 Returns the entity identified by the given ID.
3 Returns all entities.
4 Returns the number of entities.
5 Deletes the given entity.
6 Indicates whether an entity with the given ID exists.
We also provide persistence technology-specific abstractions, such as JpaRepository or MongoRepository. Those interfaces extend CrudRepository and expose the capabilities of the underlying persistence technology in addition to the rather generic persistence technology-agnostic interfaces such as CrudRepository.

On top of the CrudRepository, there is a PagingAndSortingRepository abstraction that adds additional methods to ease paginated access to entities:

Example 4. PagingAndSortingRepository interface
public interface PagingAndSortingRepository<T, ID> extends CrudRepository<T, ID> {

  Iterable<T> findAll(Sort sort);

  Page<T> findAll(Pageable pageable);

To access the second page of User by a page size of 20, you could do something like the following:

PagingAndSortingRepository<User, Long> repository = // … get access to a bean
Page<User> users = repository.findAll(PageRequest.of(1, 20));

In addition to query methods, query derivation for both count and delete queries is available. The following list shows the interface definition for a derived count query:

Example 5. Derived Count Query
interface UserRepository extends CrudRepository<User, Long> {

  long countByLastname(String lastname);

The following list shows the interface definition for a derived delete query:

Example 6. Derived Delete Query
interface UserRepository extends CrudRepository<User, Long> {

  long deleteByLastname(String lastname);

  List<User> removeByLastname(String lastname);

11.2. Query methods

Standard CRUD functionality repositories usually have queries on the underlying datastore. With Spring Data, declaring those queries becomes a four-step process:

  1. Declare an interface extending Repository or one of its subinterfaces and type it to the domain class and ID type that it should handle, as shown in the following example:

    interface PersonRepository extends Repository<Person, Long> { … }
  2. Declare query methods on the interface.

    interface PersonRepository extends Repository<Person, Long> {
      List<Person> findByLastname(String lastname);
  3. Set up Spring to create proxy instances for those interfaces, either with JavaConfig or with XML configuration.

    1. To use Java configuration, create a class similar to the following:

      import org.springframework.data.jpa.repository.config.EnableJpaRepositories;
      class Config { … }
    2. To use XML configuration, define a bean similar to the following:

      <?xml version="1.0" encoding="UTF-8"?>
      <beans xmlns="http://www.springframework.org/schema/beans"
         <jpa:repositories base-package="com.acme.repositories"/>

    The JPA namespace is used in this example. If you use the repository abstraction for any other store, you need to change this to the appropriate namespace declaration of your store module. In other words, you should exchange jpa in favor of, for example, mongodb.

    + Also, note that the JavaConfig variant does not configure a package explicitly, because the package of the annotated class is used by default. To customize the package to scan, use one of the basePackage… attributes of the data-store-specific repository’s @Enable${store}Repositories-annotation.

  4. Inject the repository instance and use it, as shown in the following example:

    class SomeClient {
      private final PersonRepository repository;
      SomeClient(PersonRepository repository) {
        this.repository = repository;
      void doSomething() {
        List<Person> persons = repository.findByLastname("Matthews");

The sections that follow explain each step in detail:

11.3. Defining Repository Interfaces

First, define a domain class-specific repository interface. The interface must extend Repository and be typed to the domain class and an ID type. If you want to expose CRUD methods for that domain type, extend CrudRepository instead of Repository.

11.3.1. Fine-tuning Repository Definition

Typically, your repository interface extends Repository, CrudRepository, or PagingAndSortingRepository. Alternatively, if you do not want to extend Spring Data interfaces, you can also annotate your repository interface with @RepositoryDefinition. Extending CrudRepository exposes a complete set of methods to manipulate your entities. If you prefer to be selective about the methods being exposed, copy the methods you want to expose from CrudRepository into your domain repository.

Doing so lets you define your own abstractions on top of the provided Spring Data Repositories functionality.

The following example shows how to selectively expose CRUD methods (findById and save, in this case):

Example 7. Selectively exposing CRUD methods
interface MyBaseRepository<T, ID> extends Repository<T, ID> {

  Optional<T> findById(ID id);

  <S extends T> S save(S entity);

interface UserRepository extends MyBaseRepository<User, Long> {
  User findByEmailAddress(EmailAddress emailAddress);

In the prior example, you defined a common base interface for all your domain repositories and exposed findById(…) as well as save(…).These methods are routed into the base repository implementation of the store of your choice provided by Spring Data (for example, if you use JPA, the implementation is SimpleJpaRepository), because they match the method signatures in CrudRepository. So the UserRepository can now save users, find individual users by ID, and trigger a query to find Users by email address.

The intermediate repository interface is annotated with @NoRepositoryBean. Make sure you add that annotation to all repository interfaces for which Spring Data should not create instances at runtime.

11.3.2. Using Repositories with Multiple Spring Data Modules

Using a unique Spring Data module in your application makes things simple, because all repository interfaces in the defined scope are bound to the Spring Data module. Sometimes, applications require using more than one Spring Data module. In such cases, a repository definition must distinguish between persistence technologies. When it detects multiple repository factories on the class path, Spring Data enters strict repository configuration mode. Strict configuration uses details on the repository or the domain class to decide about Spring Data module binding for a repository definition:

  1. If the repository definition extends the module-specific repository, then it is a valid candidate for the particular Spring Data module.

  2. If the domain class is annotated with the module-specific type annotation, then it is a valid candidate for the particular Spring Data module. Spring Data modules accept either third-party annotations (such as JPA’s @Entity) or provide their own annotations (such as @Document for Spring Data MongoDB and Spring Data Elasticsearch).

The following example shows a repository that uses module-specific interfaces (JPA in this case):

Example 8. Repository definitions using module-specific interfaces
interface MyRepository extends JpaRepository<User, Long> { }

interface MyBaseRepository<T, ID> extends JpaRepository<T, ID> { … }

interface UserRepository extends MyBaseRepository<User, Long> { … }

MyRepository and UserRepository extend JpaRepository in their type hierarchy. They are valid candidates for the Spring Data JPA module.

The following example shows a repository that uses generic interfaces:

Example 9. Repository definitions using generic interfaces
interface AmbiguousRepository extends Repository<User, Long> { … }

interface MyBaseRepository<T, ID> extends CrudRepository<T, ID> { … }

interface AmbiguousUserRepository extends MyBaseRepository<User, Long> { … }

AmbiguousRepository and AmbiguousUserRepository extend only Repository and CrudRepository in their type hierarchy. While this is perfectly fine when using a unique Spring Data module, multiple modules cannot distinguish to which particular Spring Data these repositories should be bound.

The following example shows a repository that uses domain classes with annotations:

Example 10. Repository definitions using domain classes with annotations
interface PersonRepository extends Repository<Person, Long> { … }

class Person { … }

interface UserRepository extends Repository<User, Long> { … }

class User { … }

PersonRepository references Person, which is annotated with the JPA @Entity annotation, so this repository clearly belongs to Spring Data JPA. UserRepository references User, which is annotated with Spring Data MongoDB’s @Document annotation.

The following bad example shows a repository that uses domain classes with mixed annotations:

Example 11. Repository definitions using domain classes with mixed annotations
interface JpaPersonRepository extends Repository<Person, Long> { … }

interface MongoDBPersonRepository extends Repository<Person, Long> { … }

class Person { … }

This example shows a domain class using both JPA and Spring Data MongoDB annotations. It defines two repositories, JpaPersonRepository and MongoDBPersonRepository. One is intended for JPA and the other for MongoDB usage. Spring Data is no longer able to tell the repositories apart, which leads to undefined behavior.

Repository type details and distinguishing domain class annotations are used for strict repository configuration to identify repository candidates for a particular Spring Data module. Using multiple persistence technology-specific annotations on the same domain type is possible and enables reuse of domain types across multiple persistence technologies. However, Spring Data can then no longer determine a unique module with which to bind the repository.

The last way to distinguish repositories is by scoping repository base packages. Base packages define the starting points for scanning for repository interface definitions, which implies having repository definitions located in the appropriate packages. By default, annotation-driven configuration uses the package of the configuration class. The base package in XML-based configuration is mandatory.

The following example shows annotation-driven configuration of base packages:

Example 12. Annotation-driven configuration of base packages
@EnableJpaRepositories(basePackages = "com.acme.repositories.jpa")
@EnableMongoRepositories(basePackages = "com.acme.repositories.mongo")
class Configuration { … }

11.4. Defining Query Methods

The repository proxy has two ways to derive a store-specific query from the method name:

  • By deriving the query from the method name directly.

  • By using a manually defined query.

Available options depend on the actual store. However, there must be a strategy that decides what actual query is created. The next section describes the available options.

11.4.1. Query Lookup Strategies

The following strategies are available for the repository infrastructure to resolve the query. With XML configuration, you can configure the strategy at the namespace through the query-lookup-strategy attribute. For Java configuration, you can use the queryLookupStrategy attribute of the Enable${store}Repositories annotation. Some strategies may not be supported for particular datastores.

  • CREATE attempts to construct a store-specific query from the query method name. The general approach is to remove a given set of well known prefixes from the method name and parse the rest of the method. You can read more about query construction in “Query Creation”.

  • USE_DECLARED_QUERY tries to find a declared query and throws an exception if cannot find one. The query can be defined by an annotation somewhere or declared by other means. Consult the documentation of the specific store to find available options for that store. If the repository infrastructure does not find a declared query for the method at bootstrap time, it fails.

  • CREATE_IF_NOT_FOUND (default) combines CREATE and USE_DECLARED_QUERY. It looks up a declared query first, and, if no declared query is found, it creates a custom method name-based query. This is the default lookup strategy and, thus, is used if you do not configure anything explicitly. It allows quick query definition by method names but also custom-tuning of these queries by introducing declared queries as needed.

11.4.2. Query Creation

The query builder mechanism built into Spring Data repository infrastructure is useful for building constraining queries over entities of the repository. The mechanism strips the prefixes find…By, read…By, query…By, count…By, and get…By from the method and starts parsing the rest of it. The introducing clause can contain further expressions, such as a Distinct to set a distinct flag on the query to be created. However, the first By acts as delimiter to indicate the start of the actual criteria. At a very basic level, you can define conditions on entity properties and concatenate them with And and Or. The following example shows how to create a number of queries:

Example 13. Query creation from method names
interface PersonRepository extends Repository<Person, Long> {

  List<Person> findByEmailAddressAndLastname(EmailAddress emailAddress, String lastname);

  // Enables the distinct flag for the query
  List<Person> findDistinctPeopleByLastnameOrFirstname(String lastname, String firstname);
  List<Person> findPeopleDistinctByLastnameOrFirstname(String lastname, String firstname);

  // Enabling ignoring case for an individual property
  List<Person> findByLastnameIgnoreCase(String lastname);
  // Enabling ignoring case for all suitable properties
  List<Person> findByLastnameAndFirstnameAllIgnoreCase(String lastname, String firstname);

  // Enabling static ORDER BY for a query
  List<Person> findByLastnameOrderByFirstnameAsc(String lastname);
  List<Person> findByLastnameOrderByFirstnameDesc(String lastname);

The actual result of parsing the method depends on the persistence store for which you create the query. However, there are some general things to notice:

  • The expressions are usually property traversals combined with operators that can be concatenated. You can combine property expressions with AND and OR. You also get support for operators such as Between, LessThan, GreaterThan, and Like for the property expressions. The supported operators can vary by datastore, so consult the appropriate part of your reference documentation.

  • The method parser supports setting an IgnoreCase flag for individual properties (for example, findByLastnameIgnoreCase(…)) or for all properties of a type that supports ignoring case (usually String instances — for example, findByLastnameAndFirstnameAllIgnoreCase(…)). Whether ignoring cases is supported may vary by store, so consult the relevant sections in the reference documentation for the store-specific query method.

  • You can apply static ordering by appending an OrderBy clause to the query method that references a property and by providing a sorting direction (Asc or Desc). To create a query method that supports dynamic sorting, see “Special parameter handling”.

11.4.3. Property Expressions

Property expressions can refer only to a direct property of the managed entity, as shown in the preceding example. At query creation time, you already make sure that the parsed property is a property of the managed domain class. However, you can also define constraints by traversing nested properties. Consider the following method signature:

List<Person> findByAddressZipCode(ZipCode zipCode);

Assume a Person has an Address with a ZipCode. In that case, the method creates the property traversal x.address.zipCode. The resolution algorithm starts by interpreting the entire part (AddressZipCode) as the property and checks the domain class for a property with that name (uncapitalized). If the algorithm succeeds, it uses that property. If not, the algorithm splits up the source at the camel case parts from the right side into a head and a tail and tries to find the corresponding property — in our example, AddressZip and Code. If the algorithm finds a property with that head, it takes the tail and continues building the tree down from there, splitting the tail up in the way just described. If the first split does not match, the algorithm moves the split point to the left (Address, ZipCode) and continues.

Although this should work for most cases, it is possible for the algorithm to select the wrong property. Suppose the Person class has an addressZip property as well. The algorithm would match in the first split round already, choose the wrong property, and fail (as the type of addressZip probably has no code property).

To resolve this ambiguity you can use _ inside your method name to manually define traversal points. So our method name would be as follows:

List<Person> findByAddress_ZipCode(ZipCode zipCode);

Because we treat the underscore character as a reserved character, we strongly advise following standard Java naming conventions (that is, not using underscores in property names but using camel case instead).

11.4.4. Special parameter handling

To handle parameters in your query, define method parameters as already seen in the preceding examples. Besides that, the infrastructure recognizes certain specific types like Pageable and Sort, to apply pagination and sorting to your queries dynamically. The following example demonstrates these features:

Example 14. Using Pageable, Slice, and Sort in query methods
Page<User> findByLastname(String lastname, Pageable pageable);

Slice<User> findByLastname(String lastname, Pageable pageable);

List<User> findByLastname(String lastname, Sort sort);

List<User> findByLastname(String lastname, Pageable pageable);
APIs taking Sort and Pageable expect non-null values to be handed into methods. If you don’t want to apply any sorting or pagination use Sort.unsorted() and Pageable.unpaged().

The first method lets you pass an org.springframework.data.domain.Pageable instance to the query method to dynamically add paging to your statically defined query. A Page knows about the total number of elements and pages available. It does so by the infrastructure triggering a count query to calculate the overall number. As this might be expensive (depending on the store used), you can instead return a Slice. A Slice only knows about whether a next Slice is available, which might be sufficient when walking through a larger result set.

Sorting options are handled through the Pageable instance, too. If you only need sorting, add an org.springframework.data.domain.Sort parameter to your method. As you can see, returning a List is also possible. In this case, the additional metadata required to build the actual Page instance is not created (which, in turn, means that the additional count query that would have been necessary is not issued). Rather, it restricts the query to look up only the given range of entities.

To find out how many pages you get for an entire query, you have to trigger an additional count query. By default, this query is derived from the query you actually trigger.
Paging and Sorting

Simple sorting expressions can be defined by using property names. Expressions can be concatenated to collect multiple criterias into one expression.

Example 15. Defining sort expressions
Sort sort = Sort.by("firstname").ascending()

For a more type-safe way of defining sort expressions, start with the type to define the sort expression for and use method references to define the properties to sort on.

Example 16. Defining sort expressions using the type-safe API
TypedSort<Person> person = Sort.sort(Person.class);

Sort sort = person.by(Person::getFirstname).ascending()
TypedSort.by(…) makes use of runtime proxies by typically using CGlib which may interfere with native image compilation when using tools like Graal VM Native.

If your store implementation supports Querydsl, you can also use the metamodel types generated to define sort expressions:

Example 17. Defining sort expressions using the Querydsl API
QSort sort = QSort.by(QPerson.firstname.asc())

11.4.5. Limiting Query Results

The results of query methods can be limited by using the first or top keywords, which can be used interchangeably. An optional numeric value can be appended to top or first to specify the maximum result size to be returned. If the number is left out, a result size of 1 is assumed. The following example shows how to limit the query size:

Example 18. Limiting the result size of a query with Top and First
User findFirstByOrderByLastnameAsc();

User findTopByOrderByAgeDesc();

Page<User> queryFirst10ByLastname(String lastname, Pageable pageable);

Slice<User> findTop3ByLastname(String lastname, Pageable pageable);

List<User> findFirst10ByLastname(String lastname, Sort sort);

List<User> findTop10ByLastname(String lastname, Pageable pageable);

The limiting expressions also support the Distinct keyword. Also, for the queries limiting the result set to one instance, wrapping the result into with the Optional keyword is supported.

If pagination or slicing is applied to a limiting query pagination (and the calculation of the number of pages available), it is applied within the limited result.

Limiting the results in combination with dynamic sorting by using a Sort parameter lets you express query methods for the 'K' smallest as well as for the 'K' biggest elements.

11.4.6. Repository Methods Returning Collections or Iterables

Query methods that return multiple results can use standard Java Iterable, List, Set. Beyond that we support returning Spring Data’s Streamable, a custom extension of Iterable, as well as collection types provided by Vavr.

Using Streamable as Query Method Return Type

Streamable can be used as alternative to Iterable or any collection type. It provides convenience methods to access a non-parallel Stream (missing from Iterable), the ability to directly ….filter(…) and ….map(…) over the elements and concatenate the Streamable to others:

Example 19. Using Streamable to combine query method results
interface PersonRepository extends Repository<Person, Long> {
  Streamable<Person> findByFirstnameContaining(String firstname);
  Streamable<Person> findByLastnameContaining(String lastname);

Streamable<Person> result = repository.findByFirstnameContaining("av")
Returning Custom Streamable Wrapper Types

Providing dedicated wrapper types for collections is a commonly used pattern to provide API on a query execution result that returns multiple elements. Usually these types are used by invoking a repository method returning a collection-like type and creating an instance of the wrapper type manually. That additional step can be avoided as Spring Data allows to use these wrapper types as query method return types if they meet the following criterias:

  1. The type implements Streamable.

  2. The type exposes either a constructor or a static factory method named of(…) or valueOf(…) taking Streamable as argument.

A sample use case looks as follows:

class Product { (1)
  MonetaryAmount getPrice() { … }

@RequiredArgConstructor(staticName = "of")
class Products implements Streamable<Product> { (2)

  private Streamable<Product> streamable;

  public MonetaryAmount getTotal() { (3)
    return streamable.stream() //
      .reduce(Money.of(0), MonetaryAmount::add);

interface ProductRepository implements Repository<Product, Long> {
  Products findAllByDescriptionContaining(String text); (4)
1 A Product entity that exposes API to access the product’s price.
2 A wrapper type for a Streamable<Product> that can be constructed via Products.of(…) (factory method created via the Lombok annotation).
3 The wrapper type exposes additional API calculating new values on the Streamable<Product>.
4 That wrapper type can be used as query method return type directly. No need to return Stremable<Product> and manually wrap it in the repository client.
Support for Vavr Collections

Vavr is a library to embrace functional programming concepts in Java. It ships with a custom set of collection types that can be used as query method return types.

Vavr collection type Used Vavr implementation type Valid Java source types










The types in the first column (or subtypes thereof) can be used as quer method return types and will get the types in the second column used as implementation type depending on the Java type of the actual query result (thrid column). Alternatively, Traversable (Vavr the Iterable equivalent) can be declared and we derive the implementation class from the actual return value, i.e. a java.util.List will be turned into a Vavr List/Seq, a java.util.Set becomes a Vavr LinkedHashSet/Set etc.

11.4.7. Null Handling of Repository Methods

As of Spring Data 2.0, repository CRUD methods that return an individual aggregate instance use Java 8’s Optional to indicate the potential absence of a value. Besides that, Spring Data supports returning the following wrapper types on query methods:

  • com.google.common.base.Optional

  • scala.Option

  • io.vavr.control.Option

Alternatively, query methods can choose not to use a wrapper type at all. The absence of a query result is then indicated by returning null. Repository methods returning collections, collection alternatives, wrappers, and streams are guaranteed never to return null but rather the corresponding empty representation. See “Repository query return types” for details.

Nullability Annotations

You can express nullability constraints for repository methods by using Spring Framework’s nullability annotations. They provide a tooling-friendly approach and opt-in null checks during runtime, as follows:

  • @NonNullApi: Used on the package level to declare that the default behavior for parameters and return values is to not accept or produce null values.

  • @NonNull: Used on a parameter or return value that must not be null (not needed on a parameter and return value where @NonNullApi applies).

  • @Nullable: Used on a parameter or return value that can be null.

Spring annotations are meta-annotated with JSR 305 annotations (a dormant but widely spread JSR). JSR 305 meta-annotations let tooling vendors such as IDEA, Eclipse, and Kotlin provide null-safety support in a generic way, without having to hard-code support for Spring annotations. To enable runtime checking of nullability constraints for query methods, you need to activate non-nullability on the package level by using Spring’s @NonNullApi in package-info.java, as shown in the following example:

Example 20. Declaring Non-nullability in package-info.java
package com.acme;

Once non-null defaulting is in place, repository query method invocations get validated at runtime for nullability constraints. If a query execution result violates the defined constraint, an exception is thrown. This happens when the method would return null but is declared as non-nullable (the default with the annotation defined on the package the repository resides in). If you want to opt-in to nullable results again, selectively use @Nullable on individual methods. Using the result wrapper types mentioned at the start of this section continues to work as expected: An empty result is translated into the value that represents absence.

The following example shows a number of the techniques just described:

Example 21. Using different nullability constraints
package com.acme;                                                       (1)

import org.springframework.lang.Nullable;

interface UserRepository extends Repository<User, Long> {

  User getByEmailAddress(EmailAddress emailAddress);                    (2)

  User findByEmailAddress(@Nullable EmailAddress emailAdress);          (3)

  Optional<User> findOptionalByEmailAddress(EmailAddress emailAddress); (4)
1 The repository resides in a package (or sub-package) for which we have defined non-null behavior.
2 Throws an EmptyResultDataAccessException when the query executed does not produce a result. Throws an IllegalArgumentException when the emailAddress handed to the method is null.
3 Returns null when the query executed does not produce a result. Also accepts null as the value for emailAddress.
4 Returns Optional.empty() when the query executed does not produce a result. Throws an IllegalArgumentException when the emailAddress handed to the method is null.
Nullability in Kotlin-based Repositories

Kotlin has the definition of nullability constraints baked into the language. Kotlin code compiles to bytecode, which does not express nullability constraints through method signatures but rather through compiled-in metadata. Make sure to include the kotlin-reflect JAR in your project to enable introspection of Kotlin’s nullability constraints. Spring Data repositories use the language mechanism to define those constraints to apply the same runtime checks, as follows:

Example 22. Using nullability constraints on Kotlin repositories
interface UserRepository : Repository<User, String> {

  fun findByUsername(username: String): User     (1)

  fun findByFirstname(firstname: String?): User? (2)
1 The method defines both the parameter and the result as non-nullable (the Kotlin default). The Kotlin compiler rejects method invocations that pass null to the method. If the query execution yields an empty result, an EmptyResultDataAccessException is thrown.
2 This method accepts null for the firstname parameter and returns null if the query execution does not produce a result.

11.4.8. Streaming query results

The results of query methods can be processed incrementally by using a Java 8 Stream<T> as return type. Instead of wrapping the query results in a Stream data store-specific methods are used to perform the streaming, as shown in the following example:

Example 23. Stream the result of a query with Java 8 Stream<T>
@Query("select u from User u")
Stream<User> findAllByCustomQueryAndStream();

Stream<User> readAllByFirstnameNotNull();

@Query("select u from User u")
Stream<User> streamAllPaged(Pageable pageable);
A Stream potentially wraps underlying data store-specific resources and must, therefore, be closed after usage. You can either manually close the Stream by using the close() method or by using a Java 7 try-with-resources block, as shown in the following example:
Example 24. Working with a Stream<T> result in a try-with-resources block
try (Stream<User> stream = repository.findAllByCustomQueryAndStream()) {
Not all Spring Data modules currently support Stream<T> as a return type.

11.4.9. Async query results

Repository queries can be run asynchronously by using Spring’s asynchronous method execution capability. This means the method returns immediately upon invocation while the actual query execution occurs in a task that has been submitted to a Spring TaskExecutor. Asynchronous query execution is different from reactive query execution and should not be mixed. Refer to store-specific documentation for more details on reactive support. The following example shows a number of asynchronous queries:

Future<User> findByFirstname(String firstname);               (1)

CompletableFuture<User> findOneByFirstname(String firstname); (2)

ListenableFuture<User> findOneByLastname(String lastname);    (3)
1 Use java.util.concurrent.Future as the return type.
2 Use a Java 8 java.util.concurrent.CompletableFuture as the return type.
3 Use a org.springframework.util.concurrent.ListenableFuture as the return type.

11.5. Creating Repository Instances

In this section, you create instances and bean definitions for the defined repository interfaces. One way to do so is by using the Spring namespace that is shipped with each Spring Data module that supports the repository mechanism, although we generally recommend using Java configuration.

11.5.1. XML configuration

Each Spring Data module includes a repositories element that lets you define a base package that Spring scans for you, as shown in the following example:

Example 25. Enabling Spring Data repositories via XML
<?xml version="1.0" encoding="UTF-8"?>
<beans:beans xmlns:beans="http://www.springframework.org/schema/beans"

  <repositories base-package="com.acme.repositories" />


In the preceding example, Spring is instructed to scan com.acme.repositories and all its sub-packages for interfaces extending Repository or one of its sub-interfaces. For each interface found, the infrastructure registers the persistence technology-specific FactoryBean to create the appropriate proxies that handle invocations of the query methods. Each bean is registered under a bean name that is derived from the interface name, so an interface of UserRepository would be registered under userRepository. The base-package attribute allows wildcards so that you can define a pattern of scanned packages.

Using filters

By default, the infrastructure picks up every interface extending the persistence technology-specific Repository sub-interface located under the configured base package and creates a bean instance for it. However, you might want more fine-grained control over which interfaces have bean instances created for them. To do so, use <include-filter /> and <exclude-filter /> elements inside the <repositories /> element. The semantics are exactly equivalent to the elements in Spring’s context namespace. For details, see the Spring reference documentation for these elements.

For example, to exclude certain interfaces from instantiation as repository beans, you could use the following configuration:

Example 26. Using exclude-filter element
<repositories base-package="com.acme.repositories">
  <context:exclude-filter type="regex" expression=".*SomeRepository" />

The preceding example excludes all interfaces ending in SomeRepository from being instantiated.

11.5.2. JavaConfig

The repository infrastructure can also be triggered by using a store-specific @Enable${store}Repositories annotation on a JavaConfig class. For an introduction into Java-based configuration of the Spring container, see JavaConfig in the Spring reference documentation.

A sample configuration to enable Spring Data repositories resembles the following:

Example 27. Sample annotation based repository configuration
class ApplicationConfiguration {

  EntityManagerFactory entityManagerFactory() {
    // …
The preceding example uses the JPA-specific annotation, which you would change according to the store module you actually use. The same applies to the definition of the EntityManagerFactory bean. See the sections covering the store-specific configuration.

11.5.3. Standalone usage

You can also use the repository infrastructure outside of a Spring container — for example, in CDI environments. You still need some Spring libraries in your classpath, but, generally, you can set up repositories programmatically as well. The Spring Data modules that provide repository support ship a persistence technology-specific RepositoryFactory that you can use as follows:

Example 28. Standalone usage of repository factory
RepositoryFactorySupport factory = … // Instantiate factory here
UserRepository repository = factory.getRepository(UserRepository.class);

11.6. Custom Implementations for Spring Data Repositories

This section covers repository customization and how fragments form a composite repository.

When a query method requires a different behavior or cannot be implemented by query derivation, then it is necessary to provide a custom implementation. Spring Data repositories let you provide custom repository code and integrate it with generic CRUD abstraction and query method functionality.

11.6.1. Customizing Individual Repositories

To enrich a repository with custom functionality, you must first define a fragment interface and an implementation for the custom functionality, as shown in the following example:

Example 29. Interface for custom repository functionality
interface CustomizedUserRepository {
  void someCustomMethod(User user);
Example 30. Implementation of custom repository functionality
class CustomizedUserRepositoryImpl implements CustomizedUserRepository {

  public void someCustomMethod(User user) {
    // Your custom implementation
The most important part of the class name that corresponds to the fragment interface is the Impl postfix.

The implementation itself does not depend on Spring Data and can be a regular Spring bean. Consequently, you can use standard dependency injection behavior to inject references to other beans (such as a JdbcTemplate), take part in aspects, and so on.

Then you can let your repository interface extend the fragment interface, as shown in the following example:

Example 31. Changes to your repository interface
interface UserRepository extends CrudRepository<User, Long>, CustomizedUserRepository {

  // Declare query methods here

Extending the fragment interface with your repository interface combines the CRUD and custom functionality and makes it available to clients.

Spring Data repositories are implemented by using fragments that form a repository composition. Fragments are the base repository, functional aspects (such as QueryDsl), and custom interfaces along with their implementation. Each time you add an interface to your repository interface, you enhance the composition by adding a fragment. The base repository and repository aspect implementations are provided by each Spring Data module.

The following example shows custom interfaces and their implementations:

Example 32. Fragments with their implementations
interface HumanRepository {
  void someHumanMethod(User user);

class HumanRepositoryImpl implements HumanRepository {

  public void someHumanMethod(User user) {
    // Your custom implementation

interface ContactRepository {

  void someContactMethod(User user);

  User anotherContactMethod(User user);

class ContactRepositoryImpl implements ContactRepository {

  public void someContactMethod(User user) {
    // Your custom implementation

  public User anotherContactMethod(User user) {
    // Your custom implementation

The following example shows the interface for a custom repository that extends CrudRepository:

Example 33. Changes to your repository interface
interface UserRepository extends CrudRepository<User, Long>, HumanRepository, ContactRepository {

  // Declare query methods here

Repositories may be composed of multiple custom implementations that are imported in the order of their declaration. Custom implementations have a higher priority than the base implementation and repository aspects. This ordering lets you override base repository and aspect methods and resolves ambiguity if two fragments contribute the same method signature. Repository fragments are not limited to use in a single repository interface. Multiple repositories may use a fragment interface, letting you reuse customizations across different repositories.

The following example shows a repository fragment and its implementation:

Example 34. Fragments overriding save(…)
interface CustomizedSave<T> {
  <S extends T> S save(S entity);

class CustomizedSaveImpl<T> implements CustomizedSave<T> {

  public <S extends T> S save(S entity) {
    // Your custom implementation

The following example shows a repository that uses the preceding repository fragment:

Example 35. Customized repository interfaces
interface UserRepository extends CrudRepository<User, Long>, CustomizedSave<User> {

interface PersonRepository extends CrudRepository<Person, Long>, CustomizedSave<Person> {

If you use namespace configuration, the repository infrastructure tries to autodetect custom implementation fragments by scanning for classes below the package in which it found a repository. These classes need to follow the naming convention of appending the namespace element’s repository-impl-postfix attribute to the fragment interface name. This postfix defaults to Impl. The following example shows a repository that uses the default postfix and a repository that sets a custom value for the postfix:

Example 36. Configuration example
<repositories base-package="com.acme.repository" />

<repositories base-package="com.acme.repository" repository-impl-postfix="MyPostfix" />

The first configuration in the preceding example tries to look up a class called com.acme.repository.CustomizedUserRepositoryImpl to act as a custom repository implementation. The second example tries to lookup com.acme.repository.CustomizedUserRepositoryMyPostfix.

Resolution of Ambiguity

If multiple implementations with matching class names are found in different packages, Spring Data uses the bean names to identify which one to use.

Given the following two custom implementations for the CustomizedUserRepository shown earlier, the first implementation is used. Its bean name is customizedUserRepositoryImpl, which matches that of the fragment interface (CustomizedUserRepository) plus the postfix Impl.

Example 37. Resolution of amibiguous implementations
package com.acme.impl.one;

class CustomizedUserRepositoryImpl implements CustomizedUserRepository {

  // Your custom implementation
package com.acme.impl.two;

class CustomizedUserRepositoryImpl implements CustomizedUserRepository {

  // Your custom implementation

If you annotate the UserRepository interface with @Component("specialCustom"), the bean name plus Impl then matches the one defined for the repository implementation in com.acme.impl.two, and it is used instead of the first one.

Manual Wiring

If your custom implementation uses annotation-based configuration and autowiring only, the preceding approach shown works well, because it is treated as any other Spring bean. If your implementation fragment bean needs special wiring, you can declare the bean and name it according to the conventions described in the preceding section. The infrastructure then refers to the manually defined bean definition by name instead of creating one itself. The following example shows how to manually wire a custom implementation:

Example 38. Manual wiring of custom implementations
<repositories base-package="com.acme.repository" />

<beans:bean id="userRepositoryImpl" class="…">
  <!-- further configuration -->

11.6.2. Customize the Base Repository

The approach described in the preceding section requires customization of each repository interfaces when you want to customize the base repository behavior so that all repositories are affected. To instead change behavior for all repositories, you can create an implementation that extends the persistence technology-specific repository base class. This class then acts as a custom base class for the repository proxies, as shown in the following example:

Example 39. Custom repository base class
class MyRepositoryImpl<T, ID>
  extends SimpleJpaRepository<T, ID> {

  private final EntityManager entityManager;

  MyRepositoryImpl(JpaEntityInformation entityInformation,
                          EntityManager entityManager) {
    super(entityInformation, entityManager);

    // Keep the EntityManager around to used from the newly introduced methods.
    this.entityManager = entityManager;

  public <S extends T> S save(S entity) {
    // implementation goes here
The class needs to have a constructor of the super class which the store-specific repository factory implementation uses. If the repository base class has multiple constructors, override the one taking an EntityInformation plus a store specific infrastructure object (such as an EntityManager or a template class).

The final step is to make the Spring Data infrastructure aware of the customized repository base class. In Java configuration, you can do so by using the repositoryBaseClass attribute of the @Enable${store}Repositories annotation, as shown in the following example:

Example 40. Configuring a custom repository base class using JavaConfig
@EnableJpaRepositories(repositoryBaseClass = MyRepositoryImpl.class)
class ApplicationConfiguration { … }

A corresponding attribute is available in the XML namespace, as shown in the following example:

Example 41. Configuring a custom repository base class using XML
<repositories base-package="com.acme.repository"
     base-class="….MyRepositoryImpl" />

11.7. Publishing Events from Aggregate Roots

Entities managed by repositories are aggregate roots. In a Domain-Driven Design application, these aggregate roots usually publish domain events. Spring Data provides an annotation called @DomainEvents that you can use on a method of your aggregate root to make that publication as easy as possible, as shown in the following example:

Example 42. Exposing domain events from an aggregate root
class AnAggregateRoot {

    @DomainEvents (1)
    Collection<Object> domainEvents() {
        // … return events you want to get published here

    @AfterDomainEventPublication (2)
    void callbackMethod() {
       // … potentially clean up domain events list
1 The method using @DomainEvents can return either a single event instance or a collection of events. It must not take any arguments.
2 After all events have been published, we have a method annotated with @AfterDomainEventPublication. It can be used to potentially clean the list of events to be published (among other uses).

The methods are called every time one of a Spring Data repository’s save(…) methods is called.

11.8. Spring Data Extensions

This section documents a set of Spring Data extensions that enable Spring Data usage in a variety of contexts. Currently, most of the integration is targeted towards Spring MVC.

11.8.1. Querydsl Extension

Querydsl is a framework that enables the construction of statically typed SQL-like queries through its fluent API.

Several Spring Data modules offer integration with Querydsl through QuerydslPredicateExecutor, as shown in the following example:

Example 43. QuerydslPredicateExecutor interface
public interface QuerydslPredicateExecutor<T> {

  Optional<T> findById(Predicate predicate);  (1)

  Iterable<T> findAll(Predicate predicate);   (2)

  long count(Predicate predicate);            (3)

  boolean exists(Predicate predicate);        (4)

  // … more functionality omitted.
1 Finds and returns a single entity matching the Predicate.
2 Finds and returns all entities matching the Predicate.
3 Returns the number of entities matching the Predicate.
4 Returns whether an entity that matches the Predicate exists.

To make use of Querydsl support, extend QuerydslPredicateExecutor on your repository interface, as shown in the following example

Example 44. Querydsl integration on repositories
interface UserRepository extends CrudRepository<User, Long>, QuerydslPredicateExecutor<User> {

The preceding example lets you write typesafe queries using Querydsl Predicate instances, as shown in the following example:

Predicate predicate = user.firstname.equalsIgnoreCase("dave")


11.8.2. Web support

This section contains the documentation for the Spring Data web support as it is implemented in the current (and later) versions of Spring Data Commons. As the newly introduced support changes many things, we kept the documentation of the former behavior in [web.legacy].

Spring Data modules that support the repository programming model ship with a variety of web support. The web related components require Spring MVC JARs to be on the classpath. Some of them even provide integration with Spring HATEOAS. In general, the integration support is enabled by using the @EnableSpringDataWebSupport annotation in your JavaConfig configuration class, as shown in the following example:

Example 45. Enabling Spring Data web support
class WebConfiguration {}

The @EnableSpringDataWebSupport annotation registers a few components we will discuss in a bit. It will also detect Spring HATEOAS on the classpath and register integration components for it as well if present.

Alternatively, if you use XML configuration, register either SpringDataWebConfiguration or HateoasAwareSpringDataWebConfiguration as Spring beans, as shown in the following example (for SpringDataWebConfiguration):

Example 46. Enabling Spring Data web support in XML
<bean class="org.springframework.data.web.config.SpringDataWebConfiguration" />

<!-- If you use Spring HATEOAS, register this one *instead* of the former -->
<bean class="org.springframework.data.web.config.HateoasAwareSpringDataWebConfiguration" />
Basic Web Support

The configuration shown in the previous section registers a few basic components:

  • A DomainClassConverter to let Spring MVC resolve instances of repository-managed domain classes from request parameters or path variables.

  • HandlerMethodArgumentResolver implementations to let Spring MVC resolve Pageable and Sort instances from request parameters.


The DomainClassConverter lets you use domain types in your Spring MVC controller method signatures directly, so that you need not manually lookup the instances through the repository, as shown in the following example:

Example 47. A Spring MVC controller using domain types in method signatures
class UserController {

  String showUserForm(@PathVariable("id") User user, Model model) {

    model.addAttribute("user", user);
    return "userForm";

As you can see, the method receives a User instance directly, and no further lookup is necessary. The instance can be resolved by letting Spring MVC convert the path variable into the id type of the domain class first and eventually access the instance through calling findById(…) on the repository instance registered for the domain type.

Currently, the repository has to implement CrudRepository to be eligible to be discovered for conversion.
HandlerMethodArgumentResolvers for Pageable and Sort

The configuration snippet shown in the previous section also registers a PageableHandlerMethodArgumentResolver as well as an instance of SortHandlerMethodArgumentResolver. The registration enables Pageable and Sort as valid controller method arguments, as shown in the following example:

Example 48. Using Pageable as controller method argument
class UserController {

  private final UserRepository repository;

  UserController(UserRepository repository) {
    this.repository = repository;

  String showUsers(Model model, Pageable pageable) {

    model.addAttribute("users", repository.findAll(pageable));
    return "users";

The preceding method signature causes Spring MVC try to derive a Pageable instance from the request parameters by using the following default configuration:

Table 1. Request parameters evaluated for Pageable instances


Page you want to retrieve. 0-indexed and defaults to 0.


Size of the page you want to retrieve. Defaults to 20.


Properties that should be sorted by in the format property,property(,ASC|DESC)(,IgnoreCase). Default sort direction is case-sensitive ascending. Use multiple sort parameters if you want to switch direction or case sensitivity — for example, ?sort=firstname&sort=lastname,asc&sort=city,ignorecase.

To customize this behavior, register a bean implementing the PageableHandlerMethodArgumentResolverCustomizer interface or the SortHandlerMethodArgumentResolverCustomizer interface, respectively. Its customize() method gets called, letting you change settings, as shown in the following example:

@Bean SortHandlerMethodArgumentResolverCustomizer sortCustomizer() {
    return s -> s.setPropertyDelimiter("<-->");

If setting the properties of an existing MethodArgumentResolver is not sufficient for your purpose, extend either SpringDataWebConfiguration or the HATEOAS-enabled equivalent, override the pageableResolver() or sortResolver() methods, and import your customized configuration file instead of using the @Enable annotation.

If you need multiple Pageable or Sort instances to be resolved from the request (for multiple tables, for example), you can use Spring’s @Qualifier annotation to distinguish one from another. The request parameters then have to be prefixed with ${qualifier}_. The followig example shows the resulting method signature:

String showUsers(Model model,
      @Qualifier("thing1") Pageable first,
      @Qualifier("thing2") Pageable second) { … }

you have to populate thing1_page and thing2_page and so on.

The default Pageable passed into the method is equivalent to a PageRequest.of(0, 20) but can be customized by using the @PageableDefault annotation on the Pageable parameter.

Hypermedia Support for Pageables

Spring HATEOAS ships with a representation model class (PagedResources) that allows enriching the content of a Page instance with the necessary Page metadata as well as links to let the clients easily navigate the pages. The conversion of a Page to a PagedResources is done by an implementation of the Spring HATEOAS ResourceAssembler interface, called the PagedResourcesAssembler. The following example shows how to use a PagedResourcesAssembler as a controller method argument:

Example 49. Using a PagedResourcesAssembler as controller method argument
class PersonController {

  @Autowired PersonRepository repository;

  @RequestMapping(value = "/persons", method = RequestMethod.GET)
  HttpEntity<PagedResources<Person>> persons(Pageable pageable,
    PagedResourcesAssembler assembler) {

    Page<Person> persons = repository.findAll(pageable);
    return new ResponseEntity<>(assembler.toResources(persons), HttpStatus.OK);

Enabling the configuration as shown in the preceding example lets the PagedResourcesAssembler be used as a controller method argument. Calling toResources(…) on it has the following effects:

  • The content of the Page becomes the content of the PagedResources instance.

  • The PagedResources object gets a PageMetadata instance attached, and it is populated with information from the Page and the underlying PageRequest.

  • The PagedResources may get prev and next links attached, depending on the page’s state. The links point to the URI to which the method maps. The pagination parameters added to the method match the setup of the PageableHandlerMethodArgumentResolver to make sure the links can be resolved later.

Assume we have 30 Person instances in the database. You can now trigger a request (GET http://localhost:8080/persons) and see output similar to the following:

{ "links" : [ { "rel" : "next",
                "href" : "http://localhost:8080/persons?page=1&size=20 }
  "content" : [
     … // 20 Person instances rendered here
  "pageMetadata" : {
    "size" : 20,
    "totalElements" : 30,
    "totalPages" : 2,
    "number" : 0

You see that the assembler produced the correct URI and also picked up the default configuration to resolve the parameters into a Pageable for an upcoming request. This means that, if you change that configuration, the links automatically adhere to the change. By default, the assembler points to the controller method it was invoked in, but that can be customized by handing in a custom Link to be used as base to build the pagination links, which overloads the PagedResourcesAssembler.toResource(…) method.

Web Databinding Support

Spring Data projections (described in Projections) can be used to bind incoming request payloads by either using JSONPath expressions (requires Jayway JsonPath or XPath expressions (requires XmlBeam), as shown in the following example:

Example 50. HTTP payload binding using JSONPath or XPath expressions
public interface UserPayload {

  String getFirstname();

  @JsonPath({ "$.lastname", "$.user.lastname" })
  String getLastname();

The type shown in the preceding example can be used as a Spring MVC handler method argument or by using ParameterizedTypeReference on one of RestTemplate's methods. The preceding method declarations would try to find firstname anywhere in the given document. The lastname XML lookup is performed on the top-level of the incoming document. The JSON variant of that tries a top-level lastname first but also tries lastname nested in a user sub-document if the former does not return a value. That way, changes in the structure of the source document can be mitigated easily without having clients calling the exposed methods (usually a drawback of class-based payload binding).

Nested projections are supported as described in Projections. If the method returns a complex, non-interface type, a Jackson ObjectMapper is used to map the final value.

For Spring MVC, the necessary converters are registered automatically as soon as @EnableSpringDataWebSupport is active and the required dependencies are available on the classpath. For usage with RestTemplate, register a ProjectingJackson2HttpMessageConverter (JSON) or XmlBeamHttpMessageConverter manually.

For more information, see the web projection example in the canonical Spring Data Examples repository.

Querydsl Web Support

For those stores having QueryDSL integration, it is possible to derive queries from the attributes contained in a Request query string.

Consider the following query string:


Given the User object from previous examples, a query string can be resolved to the following value by using the QuerydslPredicateArgumentResolver.

The feature is automatically enabled, along with @EnableSpringDataWebSupport, when Querydsl is found on the classpath.

Adding a @QuerydslPredicate to the method signature provides a ready-to-use Predicate, which can be run by using the QuerydslPredicateExecutor.

Type information is typically resolved from the method’s return type. Since that information does not necessarily match the domain type, it might be a good idea to use the root attribute of QuerydslPredicate.

The following exampe shows how to use @QuerydslPredicate in a method signature:

class UserController {

  @Autowired UserRepository repository;

  @RequestMapping(value = "/", method = RequestMethod.GET)
  String index(Model model, @QuerydslPredicate(root = User.class) Predicate predicate,    (1)
          Pageable pageable, @RequestParam MultiValueMap<String, String> parameters) {

    model.addAttribute("users", repository.findAll(predicate, pageable));

    return "index";
1 Resolve query string arguments to matching Predicate for User.

The default binding is as follows:

  • Object on simple properties as eq.

  • Object on collection like properties as contains.

  • Collection on simple properties as in.

Those bindings can be customized through the bindings attribute of @QuerydslPredicate or by making use of Java 8 default methods and adding the QuerydslBinderCustomizer method to the repository interface.

interface UserRepository extends CrudRepository<User, String>,
                                 QuerydslPredicateExecutor<User>,                (1)
                                 QuerydslBinderCustomizer<QUser> {               (2)

  default void customize(QuerydslBindings bindings, QUser user) {

    bindings.bind(user.username).first((path, value) -> path.contains(value))    (3)
      .first((StringPath path, String value) -> path.containsIgnoreCase(value)); (4)
    bindings.excluding(user.password);                                           (5)
1 QuerydslPredicateExecutor provides access to specific finder methods for Predicate.
2 QuerydslBinderCustomizer defined on the repository interface is automatically picked up and shortcuts @QuerydslPredicate(bindings=…​).
3 Define the binding for the username property to be a simple contains binding.
4 Define the default binding for String properties to be a case-insensitive contains match.
5 Exclude the password property from Predicate resolution.

11.8.3. Repository Populators

If you work with the Spring JDBC module, you are probably familiar with the support to populate a DataSource with SQL scripts. A similar abstraction is available on the repositories level, although it does not use SQL as the data definition language because it must be store-independent. Thus, the populators support XML (through Spring’s OXM abstraction) and JSON (through Jackson) to define data with which to populate the repositories.

Assume you have a file data.json with the following content:

Example 51. Data defined in JSON
[ { "_class" : "com.acme.Person",
 "firstname" : "Dave",
  "lastname" : "Matthews" },
  { "_class" : "com.acme.Person",
 "firstname" : "Carter",
  "lastname" : "Beauford" } ]

You can populate your repositories by using the populator elements of the repository namespace provided in Spring Data Commons. To populate the preceding data to your PersonRepository, declare a populator similar to the following:

Example 52. Declaring a Jackson repository populator
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"

  <repository:jackson2-populator locations="classpath:data.json" />


The preceding declaration causes the data.json file to be read and deserialized by a Jackson ObjectMapper.

The type to which the JSON object is unmarshalled is determined by inspecting the _class attribute of the JSON document. The infrastructure eventually selects the appropriate repository to handle the object that was deserialized.

To instead use XML to define the data the repositories should be populated with, you can use the unmarshaller-populator element. You configure it to use one of the XML marshaller options available in Spring OXM. See the Spring reference documentation for details. The following example shows how to unmarshal a repository populator with JAXB:

Example 53. Declaring an unmarshalling repository populator (using JAXB)
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"

  <repository:unmarshaller-populator locations="classpath:data.json"
    unmarshaller-ref="unmarshaller" />

  <oxm:jaxb2-marshaller contextPath="com.acme" />


Reference Documentation

12. Introduction

12.1. Document Structure

This part of the reference documentation explains the core functionality offered by Spring Data R2DBC.

R2DBC support” introduces the R2DBC module feature set.

R2DBC Repositories” introduces the repository support for R2DBC.

13. R2DBC support

R2DBC contains a wide range of features:

  • Spring configuration support with Java-based @Configuration classes for an R2DBC driver instance.

  • A DatabaseClient helper class that increases productivity when performing common R2DBC operations with integrated object mapping between rows and POJOs.

  • Exception translation into Spring’s portable Data Access Exception hierarchy.

  • Feature-rich object mapping integrated with Spring’s Conversion Service.

  • Annotation-based mapping metadata that is extensible to support other metadata formats.

  • Automatic implementation of Repository interfaces, including support for custom query methods.

For most tasks, you should use DatabaseClient or the repository support, which both use the rich mapping functionality. DatabaseClient is the place to look for accessing functionality such as ad-hoc CRUD operations.

13.1. Getting Started

An easy way to set up a working environment is to create a Spring-based project through start.spring.io. To do so:

  1. Add the following to the pom.xml files dependencies element:

      <!-- other dependency elements omitted -->
      <!-- a R2DBC driver -->
  2. Change the version of Spring in the pom.xml to be

  3. Add the following location of the Spring Milestone repository for Maven to your pom.xml such that it is at the same level as your <dependencies/> element:

        <name>Spring Maven MILESTONE Repository</name>

The repository is also browseable here.

You may also want to set the logging level to DEBUG to see some additional information. To do so, edit the application.properties file to have the following content:


Then you can, for example, create a Person class to persist, as follows:

package org.spring.r2dbc.example;

public class Person {

  private String id;
  private String name;
  private int age;

  public Person(String id, String name, int age) {
    this.id = id;
    this.name = name;
    this.age = age;

  public String getId() {
    return id;
  public String getName() {
    return name;
  public int getAge() {
    return age;

  public String toString() {
    return "Person [id=" + id + ", name=" + name + ", age=" + age + "]";

Next, you need to create a table structure in your database, as follows:

   name VARCHAR(255),
   age INT);

You also need a main application to run, as follows:

package org.spring.r2dbc.example;

public class R2dbcApp {

  private static final Log log = LogFactory.getLog(R2dbcApp.class);

  public static void main(String[] args) throws Exception {

    ConnectionFactory connectionFactory = ConnectionFactories.get("r2dbc:h2:mem:///test?options=DB_CLOSE_DELAY=-1;DB_CLOSE_ON_EXIT=FALSE");

    DatabaseClient client = DatabaseClient.create(connectionFactory);

    client.execute("CREATE TABLE person" +
        "(id VARCHAR(255) PRIMARY KEY," +
        "name VARCHAR(255)," +
        "age INT)")

      .using(new Person("joe", "Joe", 34))

      .doOnNext(it -> log.info(it))

When you run the main program, the preceding examples produce output similar to the following:

2018-11-28 10:47:03,893 DEBUG ata.r2dbc.function.DefaultDatabaseClient: 310 - Executing SQL statement [CREATE TABLE person
   name VARCHAR(255),
   age INT)]
2018-11-28 10:47:04,074 DEBUG ata.r2dbc.function.DefaultDatabaseClient: 908 - Executing SQL statement [INSERT INTO person (id, name, age) VALUES($1, $2, $3)]
2018-11-28 10:47:04,092 DEBUG ata.r2dbc.function.DefaultDatabaseClient: 575 - Executing SQL statement [SELECT id, name, age FROM person]
2018-11-28 10:47:04,436  INFO        org.spring.r2dbc.example.R2dbcApp:  43 - Person [id='joe', name='Joe', age=34]

Even in this simple example, there are few things to notice:

  • You can create an instance of the central helper class in Spring Data R2DBC (DatabaseClient) by using a standard io.r2dbc.spi.ConnectionFactory object.

  • The mapper works against standard POJO objects without the need for any additional metadata (though you can, optionally, provide that information — see here.).

  • Mapping conventions can use field access. Notice that the Person class has only getters.

  • If the constructor argument names match the column names of the stored row, they are used to instantiate the object.

13.2. Examples Repository

There is a GitHub repository with several examples that you can download and play around with to get a feel for how the library works.

13.3. Connecting to a Relational Database with Spring

One of the first tasks when using relational databases and Spring is to create a io.r2dbc.spi.ConnectionFactory object by using the IoC container. Make sure to use a supported database and driver.

13.3.1. Registering a ConnectionFactory Instance using Java-based Metadata

The following example shows an example of using Java-based bean metadata to register an instance of io.r2dbc.spi.ConnectionFactory:

Example 54. Registering a io.r2dbc.spi.ConnectionFactory object using Java-based bean metadata
public class ApplicationConfiguration extends AbstractR2dbcConfiguration {

  public ConnectionFactory connectionFactory() {
    return …;

This approach lets you use the standard io.r2dbc.spi.ConnectionFactory instance, with the container using Spring’s AbstractR2dbcConfiguration. As compared to registering a ConnectionFactory instance directly, the configuration support has the added advantage of also providing the container with an ExceptionTranslator implementation that translates R2DBC exceptions to exceptions in Spring’s portable DataAccessException hierarchy for data access classes annotated with the @Repository annotation. This hierarchy and the use of @Repository is described in Spring’s DAO support features.

AbstractR2dbcConfiguration also registers DatabaseClient, which is required for database interaction and for Repository implementation.

13.3.2. R2DBC Drivers

Spring Data R2DBC supports drivers through R2DBC’s pluggable SPI mechanism. You can use any driver that implements the R2DBC spec with Spring Data R2DBC. Since Spring Data R2DBC reacts to specific features of each database, it requires a Dialect implementation otherwise your application won’t start up. Spring Data R2DBC ships with dialect impelemtations for the following drivers:

Spring Data R2DBC reacts to database specifics by inspecting the ConnectionFactory and selects the appropriate database dialect accordingly. You need to configure your own R2dbcDialect if the driver you use is not yet known to Spring Data R2DBC.

Dialects are resolved by DialectResolver from a ConnectionFactory, typically by inspecting ConnectionFactoryMetadata.
You can let Spring auto-discover your R2dbcDialect by registering a class that implements org.springframework.data.r2dbc.dialect.DialectResolver$R2dbcDialectProvider through META-INF/spring.factories. DialectResolver discovers dialect provider implementations from the class path using Spring’s SpringFactoriesLoader.

13.4. Introduction to DatabaseClient

Spring Data R2DBC includes a reactive, non-blocking DatabaseClient for database interaction. The client has a functional, fluent API with reactive types for declarative composition. DatabaseClient encapsulates resource handling (such as opening and closing connections) so that your application code can run SQL queries or call higher-level functionality (such as inserting or selecting data).

DatabaseClient is a recently developed application component that provides a minimal set of convenience methods that is likely to be extended through time.
Once configured, DatabaseClient is thread-safe and can be reused across multiple instances.

Another central feature of DatabaseClient is the translation of exceptions thrown by R2DBC drivers into Spring’s portable Data Access Exception hierarchy. See “Exception Translation” for more information.

The next section contains an example of how to work with the DatabaseClient in the context of the Spring container.

13.4.1. Creating a DatabaseClient Object

The simplest way to create a DatabaseClient object is through a static factory method, as follows:

DatabaseClient.create(ConnectionFactory connectionFactory)

The preceding method creates a DatabaseClient with default settings.

You can also obtain a Builder instance from DatabaseClient.builder(). You can customize the client by calling the following methods:

  • ….exceptionTranslator(…): Supply a specific R2dbcExceptionTranslator to customize how R2DBC exceptions are translated into Spring’s portable Data Access Exception hierarchy. See “Exception Translation” for more information.

  • ….dataAccessStrategy(…): Set the strategy how SQL queries are generated and how objects are mapped.

Once built, a DatabaseClient instance is immutable. However, you can clone it and build a modified copy without affecting the original instance, as the following example shows:

DatabaseClient client1 = DatabaseClient.builder()

DatabaseClient client2 = client1.mutate()

13.4.2. Controlling Database Connections

Spring Data R2DBC obtains a connection to the database through a ConnectionFactory. A ConnectionFactory is part of the R2DBC specification and is a generalized connection factory. It lets a container or a framework hide connection pooling and transaction management issues from the application code.

When you use Spring Data R2DBC, you can create a ConnectionFactory by using your R2DBC driver. ConnectionFactory implementations can either return the same connection or different connections or provide connection pooling. DatabaseClient uses ConnectionFactory to create and release connections for each operation without affinity to a particular connection across multiple operations.

Assuming you use H2 as a database, a typical programmatic setup looks something like the following listing:

H2ConnectionConfiguration config = … (1)
ConnectionFactory factory = new H2ConnectionFactory(config); (2)

DatabaseClient client = DatabaseClient.create(factory); (3)
1 Prepare the database specific configuration (host, port, credentials etc.)
2 Create a connection factory using that configuration.
3 Create a DatabaseClient to use that connection factory.

13.5. Exception Translation

The Spring framework provides exception translation for a wide variety of database and mapping technologies. The Spring support for R2DBC extends this feature by providing implementations of the R2dbcExceptionTranslator interface.

R2dbcExceptionTranslator is an interface to be implemented by classes that can translate between R2dbcException and Spring’s own org.springframework.dao.DataAccessException, which is agnostic in regard to data access strategy. Implementations can be generic (for example, using SQLState codes) or proprietary (for example, using Postgres error codes) for greater precision.

R2dbcExceptionSubclassTranslator is the implementation of R2dbcExceptionTranslator that is used by default. It considers R2DBC’s categorized exception hierarchy to translate these into Spring’s consistent exception hierarchy. R2dbcExceptionSubclassTranslator uses SqlStateR2dbcExceptionTranslator as its fallback if it is not able to translate an exception.

SqlErrorCodeR2dbcExceptionTranslator uses specific vendor codes by using Spring JDBC’s SQLErrorCodes. It is more precise than the SQLState implementation. The error code translations are based on codes held in a JavaBean type class called SQLErrorCodes. Instances of this class are created and populated by an SQLErrorCodesFactory, which (as the name suggests) is a factory for creating SQLErrorCodes based on the contents of a configuration file named sql-error-codes.xml from Spring’s Data Access module. This file is populated with vendor codes and based on the ConnectionFactoryName taken from ConnectionFactoryMetadata. The codes for the actual database you are using are used.

The SqlErrorCodeR2dbcExceptionTranslator applies matching rules in the following sequence:

  1. Any custom translation implemented by a subclass. Normally, the provided concrete SqlErrorCodeR2dbcExceptionTranslator is used, so this rule does not apply. It applies only if you have actually provided a subclass implementation.

  2. Any custom implementation of the SQLExceptionTranslator interface that is provided as the customSqlExceptionTranslator property of the SQLErrorCodes class.

  3. Error code matching is applied.

  4. Use a fallback translator.

By default, the SQLErrorCodesFactory is used to define error codes and custom exception translations. They are looked up from a file named sql-error-codes.xml (which must be on the classpath), and the matching SQLErrorCodes instance is located based on the database name from the database metadata of the database in use. SQLErrorCodesFactory requires Spring JDBC.

You can extend SqlErrorCodeR2dbcExceptionTranslator, as the following example shows:

public class CustomSqlErrorCodeR2dbcExceptionTranslator extends SqlErrorCodeR2dbcExceptionTranslator {

  protected DataAccessException customTranslate(String task, String sql, R2dbcException r2dbcex) {

    if (sqlex.getErrorCode() == -12345) {
      return new DeadlockLoserDataAccessException(task, r2dbcex);

    return null;

In the preceding example, the specific error code (-12345) is translated, while other errors are left to be translated by the default translator implementation. To use this custom translator, you must configure DatabaseClient through the exceptionTranslator builder method, and you must use this DatabaseClient for all of the data access processing where this translator is needed. The following example shows how you can use this custom translator:

ConnectionFactory connectionFactory = …;

CustomSqlErrorCodeR2dbcExceptionTranslator exceptionTranslator =
  new CustomSqlErrorCodeR2dbcExceptionTranslator();

DatabaseClient client = DatabaseClient.builder()

13.6. Executing Statements

DatabaseClient provides the basic functionality of running a statement. The following example shows what you need to include for minimal but fully functional code that creates a new table:

Mono<Void> completion = client.execute("CREATE TABLE person (id VARCHAR(255) PRIMARY KEY, name VARCHAR(255), age INTEGER);")

DatabaseClient is designed for convenient, fluent usage. It exposes intermediate, continuation, and terminal methods at each stage of the execution specification. The preceding example above uses then() to return a completion Publisher that completes as soon as the query (or queries, if the SQL query contains multiple statements) completes.

execute(…) accepts either the SQL query string or a query Supplier<String> to defer the actual query creation until execution.

13.6.1. Running Queries

SQL queries can return values or the number of affected rows. DatabaseClient can return the number of updated rows or the rows themselves, depending on the issued query.

The following example shows an UPDATE statement that returns the number of updated rows:

Mono<Integer> affectedRows = client.execute("UPDATE person SET name = 'Joe'")

Running a SELECT query returns a different type of result, in particular tabular results. Tabular data is typically consumed by streaming each Row. You might have noticed the use of fetch() in the previous example. fetch() is a continuation operator that lets you specify how much data you want to consume.

Mono<Map<String, Object>> first = client.execute("SELECT id, name FROM person")

Calling first() returns the first row from the result and discards remaining rows. You can consume data with the following operators:

  • first() return the first row of the entire result.

  • one() returns exactly one result and fails if the result contains more rows.

  • all() returns all rows of the result.

  • rowsUpdated() returns the number of affected rows (INSERT count, UPDATE count).

By default, DatabaseClient queries return their results as Map of column name to value. You can customize type mapping by applying an as(Class<T>) operator, as follows:

Flux<Person> all = client.execute("SELECT id, name FROM mytable")

as(…) applies Convention-based Object Mapping and maps the resulting columns to your POJO.

13.6.2. Mapping Results

You can customize result extraction beyond Map and POJO result extraction by providing an extractor BiFunction<Row, RowMetadata, T>. The extractor function interacts directly with R2DBC’s Row and RowMetadata objects and can return arbitrary values (singular values, collections and maps, and objects).

The following example extracts the id column and emits its value:

Flux<String> names = client.execute("SELECT name FROM person")
        .map((row, rowMetadata) -> row.get("id", String.class))
What about null?

Relational database results can contain null values. The Reactive Streams specification forbids the emission of null values. That requirement mandates proper null handling in the extractor function. While you can obtain null values from a Row, you must not emit a null value. You must wrap any null values in an object (for example, Optional for singular values) to make sure a null value is never returned directly by your extractor function.

13.6.3. Binding Values to Queries

A typical application requires parameterized SQL statements to select or update rows according to some input. These are typically SELECT statements constrained by a WHERE clause or INSERT and UPDATE statements that accept input parameters. Parameterized statements bear the risk of SQL injection if parameters are not escaped properly. DatabaseClient leverages R2DBC’s bind API to eliminate the risk of SQL injection for query parameters. You can provide a parameterized SQL statement with the execute(…) operator and bind parameters to the actual Statement. Your R2DBC driver then executes the statement by using prepared statements and parameter substitution.

Parameter binding supports two binding strategies:

  • By Index, using zero-based parameter indexes.

  • By Name, using the placeholder name.

The following example shows parameter binding for a query:

db.execute("INSERT INTO person (id, name, age) VALUES(:id, :name, :age)")
    .bind("id", "joe")
    .bind("name", "Joe")
    .bind("age", 34);
R2DBC Native Bind Markers

R2DBC uses database-native bind markers that depend on the actual database vendor. As an example, Postgres uses indexed markers, such as $1, $2, $n. Another example is SQL Server, which uses named bind markers prefixed with @.

This is different from JDBC, which requires ? as bind markers. In JDBC, the actual drivers translate ? bind markers to database-native markers as part of their statement execution.

Spring Data R2DBC lets you use native bind markers or named bind markers with the :name syntax.

Named parameter support leverages a R2dbcDialect instance to expand named parameters to native bind markers at the time of query execution, which gives you a certain degree of query portability across various database vendors.

The query-preprocessor unrolls named Collection parameters into a series of bind markers to remove the need of dynamic query creation based on the number of arguments. Nested object arrays are expanded to allow usage of (for example) select lists.

Consider the following query:

SELECT id, name, state FROM table WHERE (name, age) IN (('John', 35), ('Ann', 50))

The preceding query can be parametrized and executed as follows:

List<Object[]> tuples = new ArrayList<>();
tuples.add(new Object[] {"John", 35});
tuples.add(new Object[] {"Ann",  50});

db.execute("SELECT id, name, state FROM table WHERE (name, age) IN (:tuples)")
    .bind("tuples", tuples)
Usage of select lists is vendor-dependent.

The following example shows a simpler variant using IN predicates:

db.execute("SELECT id, name, state FROM table WHERE age IN (:ages)")
    .bind("ages", Arrays.asList(35, 50))

13.6.4. Statement Filters

You can register a Statement filter (StatementFilterFunction) through DatabaseClient to intercept and modify statements in their execution, as the following example shows:

db.execute("INSERT INTO table (name, state) VALUES(:name, :state)")
    .filter((s, next) -> next.execute(s.returnGeneratedValues("id")))
    .bind("name", …)
    .bind("state", …)

DatabaseClient exposes also simplified filter(…) overload accepting UnaryOperator<Statement>:

db.execute("INSERT INTO table (name, state) VALUES(:name, :state)")
    .filter(s -> s.returnGeneratedValues("id"))
    .bind("name", …)
    .bind("state", …)

db.execute("SELECT id, name, state FROM table")
    .filter(s -> s.fetchSize(25))

StatementFilterFunction allow filtering of the executed Statement and filtering of Result objects.

13.7. Fluent Data Access API

The SQL API of DatabaseClient offers you maximum flexibility to run any type of SQL. DatabaseClient provides a more narrow interface for typical ad-hoc use-cases, such as querying, inserting, updating, and deleting data.

The entry points (insert(), select(), update(), and others) follow a natural naming schema based on the operation to be run. Moving on from the entry point, the API is designed to offer only context-dependent methods that lead to a terminating method that creates and runs a SQL statement. Spring Data R2DBC uses a R2dbcDialect abstraction to determine bind markers, pagination support and the data types natively supported by the underlying driver.

Consider the following simple query:

Flux<Person> people = databaseClient.select()
  .from(Person.class)                         (1)
  .all();                                     (2)
1 Using Person with the from(…) method sets the FROM table based on mapping metadata. It also maps tabular results on Person result objects.
2 Fetching all() rows returns a Flux<Person> without limiting results.

The following example declares a more complex query that specifies the table name by name, a WHERE condition, and an ORDER BY clause:

Mono<Person> first = databaseClient.select()
  .from("legoset")                           (1)
  .matching(where("firstname").is("John")    (2)
    .and("lastname").in("Doe", "White"))
  .orderBy(desc("id"))                       (3)
  .one();                                    (4)
1 Selecting from a table by name returns row results as Map<String, Object> with case-insensitive column name matching.
2 The issued query declares a WHERE condition on firstname and lastname columns to filter results.
3 Results can be ordered by individual column names, resulting in an ORDER BY clause.
4 Selecting the one result fetches only a single row. This way of consuming rows expects the query to return exactly a single result. Mono emits a IncorrectResultSizeDataAccessException if the query yields more than a single result.
You can directly apply Projections to result documents by providing the target type via as(Class<?>).

You can consume Query results in three ways:

  • Through object mapping (for example, as(Class<T>)) by using Spring Data’s mapping-metadata.

  • As Map<String, Object> where column names are mapped to their value. Column names are looked up in a case-insensitive way.

  • By supplying a mapping BiFunction for direct access to R2DBC Row and RowMetadata.

You can switch between retrieving a single entity and retrieving multiple entities through the following terminating methods:

  • first(): Consume only the first row, returning a Mono. The returned Mono completes without emitting an object if the query returns no results.

  • one(): Consume exactly one row, returning a Mono. The returned Mono completes without emitting an object if the query returns no results. If the query returns more than one row, Mono completes exceptionally emitting IncorrectResultSizeDataAccessException.

  • all(): Consume all returned rows returning a Flux.

  • rowsUpdated: Consume the number of affected rows. It is typically used with INSERT,UPDATE, and DELETE statements.

13.7.1. Selecting Data

You can use the select() entry point to express your SELECT queries. The resulting SELECT queries support the commonly used clauses (WHERE and ORDER BY) and support pagination. The fluent API style let you chain together multiple methods while having easy-to-understand code. To improve readability, you can use static imports that let you avoid using the 'new' keyword for creating Criteria instances.

Methods for the Criteria Class

The Criteria class provides the following methods, all of which correspond to SQL operators:

  • Criteria and (String column): Adds a chained Criteria with the specified property to the current Criteria and returns the newly created one.

  • Criteria or (String column): Adds a chained Criteria with the specified property to the current Criteria and returns the newly created one.

  • Criteria greaterThan (Object o): Creates a criterion by using the > operator.

  • Criteria greaterThanOrEquals (Object o): Creates a criterion by using the >= operator.

  • Criteria in (Object…​ o): Creates a criterion by using the IN operator for a varargs argument.

  • Criteria in (Collection<?> collection): Creates a criterion by using the IN operator using a collection.

  • Criteria is (Object o): Creates a criterion by using column matching (property = value).

  • Criteria isNull (): Creates a criterion by using the IS NULL operator.

  • Criteria isNotNull (): Creates a criterion by using the IS NOT NULL operator.

  • Criteria lessThan (Object o): Creates a criterion by using the < operator.

  • Criteria lessThanOrEquals (Object o): Creates a criterion by using the operator.

  • Criteria like (Object o): Creates a criterion by using the LIKE operator without escape character processing.

  • Criteria not (Object o): Creates a criterion by using the != operator.

  • Criteria notIn (Object…​ o): Creates a criterion by using the NOT IN operator for a varargs argument.

  • Criteria notIn (Collection<?> collection): Creates a criterion by using the NOT IN operator using a collection.

You can use Criteria with SELECT, UPDATE, and DELETE queries.

Methods for SELECT operations

The select() entry point exposes some additional methods that provide options for the query:

  • from (Class<T>): Specifies the source table by using a mapped object. By default, it returns results as T.

  • from (String): Specifies the source table name. By default, it returns results as Map<String, Object>.

  • as (Class<T>): Maps results to T.

  • map (BiFunction<Row, RowMetadata, T>): Supplies a mapping function to extract results.

  • project (String…​ columns): Specifies which columns to return.

  • matching (Criteria): Declares a WHERE condition to filter results.

  • orderBy (Order): Declares an ORDER BY clause to sort results.

  • page (Page pageable): Retrieves a particular page within the result. It limits the size of the returned results and reads from an offset.

  • fetch (): Transition call declaration to the fetch stage to declare result consumption multiplicity.

13.7.2. Inserting Data

You can use the insert() entry point to insert data. Similar to select(), insert() allows free-form and mapped object inserts.

Consider the following simple typed insert operation:

Mono<Void> insert = databaseClient.insert()
        .into(Person.class)                       (1)
        .using(new Person(…))                     (2)
        .then();                                  (3)
1 Using Person with the into(…) method sets the INTO table, based on mapping metadata. It also prepares the insert statement to accept Person objects for inserting.
2 Provide a scalar Person object. Alternatively, you can supply a Publisher to execute a stream of INSERT statements. This method extracts all non-null values and inserts them.
3 Use then() to insert an object without consuming further details. Modifying statements allow consumption of the number of affected rows or tabular results for consuming generated keys.

Inserts also support untyped operations, as the following example shows:

Mono<Void> insert = databaseClient.insert()
        .into("person")                           (1)
        .value("firstname", "John")               (2)
        .nullValue("lastname")                    (3)
        .then();                                  (4)
1 Start an insert into the person table.
2 Provide a non-null value for firstname.
3 Set lastname to null.
4 Use then() to insert an object without consuming further details. Modifying statements allow consumption of the number of affected rows or tabular results for consuming generated keys.
Methods for INSERT operations

The insert() entry point exposes the following additional methods to provide options for the operation:

  • into (Class<T>): Specifies the target table using a mapped object. By default, it returns results as T.

  • into (String): Specifies the target table name. By default, it returns results as Map<String, Object>.

  • using (T): Specifies the object to insert.

  • using (Publisher<T>): Accepts a stream of objects to insert.

  • table (String): Overrides the target table name.

  • value (String, Object): Provides a column value to insert.

  • nullValue (String): Provides a null value to insert.

  • map (BiFunction<Row, RowMetadata, T>): Supplies a mapping function to extract results.

  • then (): Executes INSERT without consuming any results.

  • fetch (): Transition call declaration to the fetch stage to declare result consumption multiplicity.

13.7.3. Updating Data

You can use the update() entry point to update rows. Updating data starts by specifying the table to update by accepting Update specifying assignments. It also accepts Criteria to create a WHERE clause.

Consider the following simple typed update operation:

Person modified = …

Mono<Void> update = databaseClient.update()
  .table(Person.class)                      (1)
  .using(modified)                          (2)
  .then();                                  (3)
1 Using Person with the table(…) method sets the table to update based on mapping metadata.
2 Provide a scalar Person object value. using(…) accepts the modified object and derives primary keys and updates all column values.
3 Use then() to update the rows of an object without consuming further details. Modifying statements also allow consumption of the number of affected rows.

Update also supports untyped operations, as the following example shows:

Mono<Void> update = databaseClient.update()
  .table("person")                           (1)
  .using(Update.update("firstname", "Jane")) (2)
  .matching(where("firstname").is("John"))   (3)
  .then();                                   (4)
1 Update the person table.
2 Provide a, Update definition of which columns to update.
3 The issued query declares a WHERE condition on firstname columns to filter the rows to update.
4 Use then() to update the rows of an object without consuming further details. Modifying statements also allow consumption of the number of affected rows.
Methods for UPDATE operations

The update() entry point exposes the following additional methods to provide options for the operation:

  • table (Class<T>): Specifies the target table byusing a mapped object. Returns results by default as T.

  • table (String): Specifies the target table name. By default, it returns results as Map<String, Object>.

  • using `(T)`Specifies the object to update. It derives criteria itself.

  • using (Update): Specifies the update definition.

  • matching (Criteria): Declares a WHERE condition to indicate which rows to update.

  • then (): Runs the UPDATE without consuming any results.

  • fetch (): Transition call declaration to the fetch stage to fetch the number of updated rows.

13.7.4. Deleting Data

You can use the delete() entry point to delete rows. Removing data starts with a specification of the table to delete from and, optionally, accepts a Criteria to create a WHERE clause.

Consider the following simple insert operation:

Mono<Void> delete = databaseClient.delete()
  .from(Person.class)                       (1)
  .matching(where("firstname").is("John")   (2)
    .and("lastname").in("Doe", "White"))
  .then();                                  (3)
1 Using Person with the from(…) method sets the FROM table, based on mapping metadata.
2 The issued query declares a WHERE condition on firstname and lastname columns to filter rows to delete.
3 Use then() to delete rows from an object without consuming further details. Modifying statements also allow consumption of the number of affected rows.
Methods for DELETE operations

The delete() entry point exposes the following additional methods to provide options for the operation:

  • from (Class<T>): Specifies the target table by using a mapped object. By default, it returns results as T.

  • from (String): Specifies the target table name. By default, it returns results as Map<String, Object>.

  • matching (Criteria): Declares a WHERE condition to define the rows to delete.

  • then (): Runs the DELETE without consuming any results.

  • fetch (): Transition call declaration to the fetch stage to fetch the number of deleted rows.

13.8. Transactions

A common pattern when using relational databases is grouping multiple queries within a unit of work that is guarded by a transaction. Relational databases typically associate a transaction with a single transport connection. Consequently, using different connections results in using different transactions. Spring Data R2DBC includes transaction-awareness in DatabaseClient that lets you group multiple statements within the same transaction by using Spring’s Transaction Management. Spring Data R2DBC provides an implementation for ReactiveTransactionManager with R2dbcTransactionManager.

The following example shows how to programmatically manage a transaction

Example 55. Programmatic Transaction Management
ReactiveTransactionManager tm = new R2dbcTransactionManager(connectionFactory);
TransactionalOperator operator = TransactionalOperator.create(tm); (1)

DatabaseClient client = DatabaseClient.create(connectionFactory);

Mono<Void> atomicOperation = client.execute("INSERT INTO person (id, name, age) VALUES(:id, :name, :age)")
  .bind("id", "joe")
  .bind("name", "Joe")
  .bind("age", 34)
  .then(client.execute("INSERT INTO contacts (id, name) VALUES(:id, :name)")
    .bind("id", "joe")
    .bind("name", "Joe")
  .as(operator::transactional); (2)
1 Associate the TransactionalOperator with the ReactiveTransactionManager.
2 Bind the operation to the TransactionalOperator.

Spring’s declarative Transaction Management is a less invasive, annotation-based approach to transaction demarcation, as the following example shows:

Example 56. Declarative Transaction Management
@EnableTransactionManagement                                                           (1)
class Config extends AbstractR2dbcConfiguration {

  public ConnectionFactory connectionFactory() {
    return // ...

  ReactiveTransactionManager transactionManager(ConnectionFactory connectionFactory) { (2)
    return new R2dbcTransactionManager(connectionFactory);

class MyService {

  private final DatabaseClient client;

  MyService(DatabaseClient client) {
    this.client = client;

  public Mono<Void> insertPerson() {

    return client.execute("INSERT INTO person (id, name, age) VALUES(:id, :name, :age)")
      .bind("id", "joe")
      .bind("name", "Joe")
      .bind("age", 34)
      .then(client.execute("INSERT INTO contacts (id, name) VALUES(:id, :name)")
        .bind("id", "joe")
        .bind("name", "Joe")
1 Enable declarative transaction management.
2 Provide a ReactiveTransactionManager implementation to back reactive transaction features.

14. R2DBC Repositories

This chapter points out the specialties for repository support for R2DBC. This chapter builds on the core repository support explained in Working with Spring Data Repositories. Before reading this chapter, you should have a sound understanding of the basic concepts explained there.

14.1. Usage

To access domain entities stored in a relational database, you can use our sophisticated repository support that eases implementation quite significantly. To do so, create an interface for your repository. Consider the following Person class:

Example 57. Sample Person entity
public class Person {

  private Long id;
  private String firstname;
  private String lastname;

  // … getters and setters omitted

The following example shows a repository interface for the preceding Person class:

Example 58. Basic repository interface to persist Person entities
public interface PersonRepository extends ReactiveCrudRepository<Person, Long> {

  // additional custom query methods go here

To configure R2DBC repositories, you can use the @EnableR2dbcRepositories annotation. If no base package is configured, the infrastructure scans the package of the annotated configuration class. The following example shows how to use Java configuration for a repository:

Example 59. Java configuration for repositories
class ApplicationConfig extends AbstractR2dbcConfiguration {

  public ConnectionFactory connectionFactory() {
    return …;

Because our domain repository extends ReactiveCrudRepository, it provides you with reactive CRUD operations to access the entities. On top of ReactiveCrudRepository, there is also ReactiveSortingRepository, which adds additional sorting functionality similar to that of PagingAndSortingRepository. Working with the repository instance is merely a matter of dependency injecting it into a client. Consequently, you can retrieve all Person objects with the following code:

Example 60. Paging access to Person entities
public class PersonRepositoryTests {

  @Autowired PersonRepository repository;

  public void readsAllEntitiesCorrectly() {


  public void readsEntitiesByNameCorrectly() {

    repository.findByFirstname("Hello World")

The preceding example creates an application context with Spring’s unit test support, which performs annotation-based dependency injection into test cases. Inside the test method, we use the repository to query the database. We use StepVerifier as a test aid to verify our expectations against the results.

14.2. Query Methods

Most of the data access operations you usually trigger on a repository result in a query being run against the databases. Defining such a query is a matter of declaring a method on the repository interface, as the following example shows:

Example 61. PersonRepository with query methods
interface ReactivePersonRepository extends ReactiveSortingRepository<Person, Long> {

  Flux<Person> findByFirstname(String firstname);                                   (1)

  Flux<Person> findByFirstname(Publisher<String> firstname);                        (2)

  Flux<Person> findByFirstnameOrderByLastname(String firstname, Pageable pageable); (3)

  Mono<Person> findByFirstnameAndLastname(String firstname, String lastname);       (4)

  Mono<Person> findFirstByLastname(String lastname);                                (5)

  @Query("SELECT * FROM person WHERE lastname = :lastname")
  Flux<Person> findByLastname(String lastname);                                     (6)

  @Query("SELECT firstname, lastname FROM person WHERE lastname = $1")
  Mono<Person> findFirstByLastname(String lastname);                                (7)
1 The method shows a query for all people with the given lastname. The query is derived by parsing the method name for constraints that can be concatenated with And and Or. Thus, the method name results in a query expression of SELECT … FROM person WHERE firstname = :firstname.
2 The method shows a query for all people with the given firstname once the firstname is emitted by the given Publisher.
3 Use Pageable to pass offset and sorting parameters to the database.
4 Find a single entity for the given criteria. It completes with IncorrectResultSizeDataAccessException on non-unique results.
5 Unless <4>, the first entity is always emitted even if the query yields more result documents.
6 The findByLastname method shows a query for all people with the given last name.
7 A query for a single Person entity projecting only firstname and lastname columns. The annotated query uses native bind markers, which are Postgres bind markers in this example.

The following table shows the keywords that are supported for query methods:

Table 2. Supported keywords for query methods
Keyword Sample Logical result


findByBirthdateAfter(Date date)

birthdate > date


findByAgeGreaterThan(int age)

age > age


findByAgeGreaterThanEqual(int age)

age >= age


findByBirthdateBefore(Date date)

birthdate < date


findByAgeLessThan(int age)

age < age


findByAgeLessThanEqual(int age)

age ⇐ age


findByAgeBetween(int from, int to)

age BETWEEN from AND to


findByAgeNotBetween(int from, int to)

age NOT BETWEEN from AND to


findByAgeIn(Collection<Integer> ages)

age IN (age1, age2, ageN)


findByAgeNotIn(Collection ages)

age NOT IN (age1, age2, ageN)

IsNotNull, NotNull


firstname IS NOT NULL

IsNull, Null


firstname IS NULL

Like, StartingWith, EndingWith

findByFirstnameLike(String name)

firstname LIKE name

NotLike, IsNotLike

findByFirstnameNotLike(String name)

firstname NOT LIKE name

Containing on String

findByFirstnameContaining(String name)

firstname LIKE '%' + name +'%'

NotContaining on String

findByFirstnameNotContaining(String name)

firstname NOT LIKE '%' + name +'%'

(No keyword)

findByFirstname(String name)

firstname = name


findByFirstnameNot(String name)

firstname != name

IsTrue, True


active IS TRUE

IsFalse, False


active IS FALSE

14.2.1. Modifying Queries

The previous sections describe how to declare queries to access a given entity or collection of entities. Using keywords from the preceding table can be used in conjunction with delete…By or remove…By to create derived queries that delete matching rows.

Example 62. Delete…By Query
interface ReactivePersonRepository extends ReactiveSortingRepository<Person, String> {

  Mono<Integer> deleteByLastname(String lastname);            (1)

  Mono<Void> deletePersonByLastname(String lastname);         (2)

  Mono<Boolean> deletePersonByLastname(String lastname);      (3)
1 Using a return type of Mono<Integer> returns the number of affected rows.
2 Using Void just reports whether the rows were successfully deleted without emitting a result value.
3 Using Boolean reports whether at least one row was removed.

As this approach is feasible for comprehensive custom functionality, you can modify queries that only need parameter binding by annotating the query method with @Modifying, as shown in the following example:

@Query("UPDATE person SET firstname = :firstname where lastname = :lastname")
Mono<Integer> setFixedFirstnameFor(String firstname, String lastname);

The result of a modifying query can be:

  • Void (or Kotlin Unit) to discard update count and await completion.

  • Integer or another numeric type emitting the affected rows count.

  • Boolean to emit whether at least one row was updated.

The @Modifying annotation is only relevant in combination with the @Query annotation. Derived custom methods do not require this annotation.

Alternatively, you can add custom modifying behavior by using the facilities described in Custom Implementations for Spring Data Repositories.

14.2.2. Queries with SpEL Expressions

Query string definitions can be used together with SpEL expressions to create dynamic queries at runtime. SpEL expressions can provide predicate values which are evaluated right before executing the query.

Expressions expose method arguments through an array that contains all the arguments. The following query uses [0] to declare the predicate value for lastname (which is equivalent to the :lastname parameter binding):

public interface PersonRepository extends ReactiveCrudRepository<Person, String> {

  @Query("SELECT * FROM person WHERE lastname = :#{[0]} }")
  List<Person> findByQueryWithExpression(String lastname);

SpEL in query strings can be a powerful way to enhance queries. However, they can also accept a broad range of unwanted arguments. You should make sure to sanitize strings before passing them to the query to avoid unwanted changes to your query.

Expression support is extensible through the Query SPI: org.springframework.data.spel.spi.EvaluationContextExtension. The Query SPI can contribute properties and functions and can customize the root object. Extensions are retrieved from the application context at the time of SpEL evaluation when the query is built.

When using SpEL expressions in combination with plain parameters, use named parameter notation instead of native bind markers to ensure a proper binding order.

14.2.3. Entity State Detection Strategies

The following table describes the strategies that Spring Data R2DBC offers for detecting whether an entity is new:

Table 3. Options for detection whether an entity is new in Spring Data R2DBC

Id-Property inspection (the default)

By default, the save() method inspects the identifier property of the given entity. If the identifier property is null, then the entity is assumed to be new. Otherwise, it is assumed exist in the datbase.

Implementing Persistable

If an entity implements Persistable, Spring Data R2DBC delegates the new detection to the isNew(…) method of the entity. See the Javadoc for details.

Optimistic Locking through @Version

If an entity uses Optimistic Locking by (version property annotated with @Version), Spring Data R2DBC checks if the entity is new by inspecting the version property whether its value corresponds with Java’s default initialization value. That is 0 for primitive types and null for wrapper types.

Implementing EntityInformation

You can customize the EntityInformation abstraction used in SimpleR2dbcRepository by creating a subclass of R2dbcRepositoryFactory and overriding getEntityInformation(…). You then have to register the custom implementation of R2dbcRepositoryFactory as a Spring bean. Note that this should rarely be necessary. See the Javadoc for details.

14.2.4. ID Generation

Spring Data R2DBC uses the ID to identify entities. The ID of an entity must be annotated with Spring Data’s @Id annotation.

When your database has an auto-increment column for the ID column, the generated value gets set in the entity after inserting it into the database.

One important constraint is that, after saving an entity, the entity must not be new anymore. Note that whether an entity is new is part of the entity’s state. With auto-increment columns, this happens automatically, because the ID gets set by Spring Data with the value from the ID column.

14.2.5. Optimistic Locking

The @Version annotation provides syntax similar to that of JPA in the context of R2DBC and makes sure updates are only applied to documents with a matching version. Therefore, the actual value of the version property is added to the update query in such a way that the update does not have any effect if another operation altered the document in the meantime. In that case, an OptimisticLockingFailureException is thrown. The following example shows these features:

class Person {

  @Id Long id;
  String firstname;
  String lastname;
  @Version Long version;

R2dbcEntityTemplate template = …;

Mono<Person> daenerys = template.insert(new Person("Daenerys"));                      (1)

Person other = template.select(Person.class)
				.first().block();                                                     (2)

template.save(daenerys);                                                              (3)

template.save(other).subscribe(); // emits OptimisticLockingFailureException          (4)
1 Initially insert row. version is set to 0.
2 Load the just inserted row. version is still 0.
3 Update the row with version = 0. Set the lastname and bump version to 1.
4 Try to update the previously loaded document that still has version = 0. The operation fails with an OptimisticLockingFailureException, as the current version is 1.

14.2.6. Projections

Spring Data query methods usually return one or multiple instances of the aggregate root managed by the repository. However, it might sometimes be desirable to create projections based on certain attributes of those types. Spring Data allows modeling dedicated return types, to more selectively retrieve partial views of the managed aggregates.

Imagine a repository and aggregate root type such as the following example:

Example 63. A sample aggregate and repository
class Person {

  @Id UUID id;
  String firstname, lastname;
  Address address;

  static class Address {
    String zipCode, city, street;

interface PersonRepository extends Repository<Person, UUID> {

  Flux<Person> findByLastname(String lastname);

Now imagine that we want to retrieve the person’s name attributes only. What means does Spring Data offer to achieve this? The rest of this chapter answers that question.

Interface-based Projections

The easiest way to limit the result of the queries to only the name attributes is by declaring an interface that exposes accessor methods for the properties to be read, as shown in the following example:

Example 64. A projection interface to retrieve a subset of attributes
interface NamesOnly {

  String getFirstname();
  String getLastname();

The important bit here is that the properties defined here exactly match properties in the aggregate root. Doing so lets a query method be added as follows:

Example 65. A repository using an interface based projection with a query method
interface PersonRepository extends Repository<Person, UUID> {

  Flux<NamesOnly> findByLastname(String lastname);

The query execution engine creates proxy instances of that interface at runtime for each element returned and forwards calls to the exposed methods to the target object.

Projections can be used recursively. If you want to include some of the Address information as well, create a projection interface for that and return that interface from the declaration of getAddress(), as shown in the following example:

Example 66. A projection interface to retrieve a subset of attributes
interface PersonSummary {

  String getFirstname();
  String getLastname();
  AddressSummary getAddress();

  interface AddressSummary {
    String getCity();

On method invocation, the address property of the target instance is obtained and wrapped into a projecting proxy in turn.

Closed Projections

A projection interface whose accessor methods all match properties of the target aggregate is considered to be a closed projection. The following example (which we used earlier in this chapter, too) is a closed projection:

Example 67. A closed projection
interface NamesOnly {

  String getFirstname();
  String getLastname();

If you use a closed projection, Spring Data can optimize the query execution, because we know about all the attributes that are needed to back the projection proxy. For more details on that, see the module-specific part of the reference documentation.

Open Projections

Accessor methods in projection interfaces can also be used to compute new values by using the @Value annotation, as shown in the following example:

Example 68. An Open Projection
interface NamesOnly {

  @Value("#{target.firstname + ' ' + target.lastname}")
  String getFullName();

The aggregate root backing the projection is available in the target variable. A projection interface using @Value is an open projection. Spring Data cannot apply query execution optimizations in this case, because the SpEL expression could use any attribute of the aggregate root.

The expressions used in @Value should not be too complex — you want to avoid programming in String variables. For very simple expressions, one option might be to resort to default methods (introduced in Java 8), as shown in the following example:

Example 69. A projection interface using a default method for custom logic
interface NamesOnly {

  String getFirstname();
  String getLastname();

  default String getFullName() {
    return getFirstname().concat(" ").concat(getLastname());

This approach requires you to be able to implement logic purely based on the other accessor methods exposed on the projection interface. A second, more flexible, option is to implement the custom logic in a Spring bean and then invoke that from the SpEL expression, as shown in the following example:

Example 70. Sample Person object
class MyBean {

  String getFullName(Person person) {

interface NamesOnly {

  String getFullName();

Notice how the SpEL expression refers to myBean and invokes the getFullName(…) method and forwards the projection target as a method parameter. Methods backed by SpEL expression evaluation can also use method parameters, which can then be referred to from the expression. The method parameters are available through an Object array named args. The following example shows how to get a method parameter from the args array:

Example 71. Sample Person object
interface NamesOnly {

  @Value("#{args[0] + ' ' + target.firstname + '!'}")
  String getSalutation(String prefix);

Again, for more complex expressions, you should use a Spring bean and let the expression invoke a method, as described earlier.

Class-based Projections (DTOs)

Another way of defining projections is by using value type DTOs (Data Transfer Objects) that hold properties for the fields that are supposed to be retrieved. These DTO types can be used in exactly the same way projection interfaces are used, except that no proxying happens and no nested projections can be applied.

If the store optimizes the query execution by limiting the fields to be loaded, the fields to be loaded are determined from the parameter names of the constructor that is exposed.

The following example shows a projecting DTO:

Example 72. A projecting DTO
class NamesOnly {

  private final String firstname, lastname;

  NamesOnly(String firstname, String lastname) {

    this.firstname = firstname;
    this.lastname = lastname;

  String getFirstname() {
    return this.firstname;

  String getLastname() {
    return this.lastname;

  // equals(…) and hashCode() implementations
Avoid boilerplate code for projection DTOs

You can dramatically simplify the code for a DTO by using Project Lombok, which provides an @Value annotation (not to be confused with Spring’s @Value annotation shown in the earlier interface examples). If you use Project Lombok’s @Value annotation, the sample DTO shown earlier would become the following:

class NamesOnly {
	String firstname, lastname;

Fields are private final by default, and the class exposes a constructor that takes all fields and automatically gets equals(…) and hashCode() methods implemented.

Dynamic Projections

So far, we have used the projection type as the return type or element type of a collection. However, you might want to select the type to be used at invocation time (which makes it dynamic). To apply dynamic projections, use a query method such as the one shown in the following example:

Example 73. A repository using a dynamic projection parameter
interface PersonRepository extends Repository<Person, UUID> {

  <T> Flux<T> findByLastname(String lastname, Class<T> type);

This way, the method can be used to obtain the aggregates as is or with a projection applied, as shown in the following example:

Example 74. Using a repository with dynamic projections
void someMethod(PersonRepository people) {

  Flux<Person> aggregates =
    people.findByLastname("Matthews", Person.class);

  Flux<NamesOnly> aggregates =
    people.findByLastname("Matthews", NamesOnly.class);

14.3. Working with multiple Databases

When working with multiple, potentially different databases, your application will require a different approach to configuration. The provided AbstractR2dbcConfiguration support class assumes a single ConnectionFactory from which the Dialect gets derived. That being said, you need to define a few beans yourself to configure Spring Data R2DBC to work with multiple databases.

R2DBC repositories require either a DatabaseClient and ReactiveDataAccessStrategy or R2dbcEntityOperations to implement repositories. A simple configuration to scan for repositories without using AbstractR2dbcConfiguration looks like:

@EnableR2dbcRepositories(basePackages = "com.acme.mysql", entityOperationsRef = "mysqlR2dbcEntityOperations")
static class MySQLConfiguration {

    public ConnectionFactory mysqlConnectionFactory() {
        return …;

    public R2dbcEntityOperations mysqlR2dbcEntityOperations(@Qualifier("mysql") ConnectionFactory connectionFactory) {

        DefaultReactiveDataAccessStrategy strategy = new DefaultReactiveDataAccessStrategy(MySqlDialect.INSTANCE);
        DatabaseClient databaseClient = DatabaseClient.builder()

        return new R2dbcEntityTemplate(databaseClient, strategy);

Note that @EnableR2dbcRepositories allows configuration either through databaseClientRef or entityOperationsRef. Using various DatabaseClient beans is useful when connecting to multiple databases of the same type. When using different database systems that differ in their dialect, use @EnableR2dbcRepositories(entityOperationsRef = …)` instead.

15. Controlling Database Connections

This section covers:

15.1. Using ConnectionFactory

Spring obtains an R2DBC connection to the database through a ConnectionFactory. A ConnectionFactory is part of the R2DBC specification and is a generalized connection factory. It lets a container or a framework hide connection pooling and transaction management issues from the application code. As a developer, you need not know details about how to connect to the database. That is the responsibility of the administrator who sets up the ConnectionFactory. You most likely fill both roles as you develop and test code, but you do not necessarily have to know how the production data source is configured.

When you use Spring’s R2DBC layer, you can configure your own with a connection pool implementation provided by a third party. A popular implementation is R2DBC Pool. Implementations in the Spring distribution are meant only for testing purposes and do not provide pooling.

To configure a ConnectionFactory:

  1. Obtain a connection with ConnectionFactory as you typically obtain an R2DBC ConnectionFactory.

  2. Provide an R2DBC URL. (See the documentation for your driver for the correct value.)

The following example shows how to configure a ConnectionFactory in Java:

ConnectionFactory factory = ConnectionFactories.get("r2dbc:h2:mem:///test?options=DB_CLOSE_DELAY=-1;DB_CLOSE_ON_EXIT=FALSE");

15.2. Using ConnectionFactoryUtils

The ConnectionFactoryUtils class is a convenient and powerful helper class that provides static methods to obtain connections from ConnectionFactory and close connections (if necessary). It supports subscriber Context-bound connections with, for example ConnectionFactoryTransactionManager.

15.3. Implementing SmartConnectionFactory

The SmartConnectionFactory interface should be implemented by classes that can provide a connection to a relational database. It extends the ConnectionFactory interface to let classes that use it query whether the connection should be closed after a given operation. This usage is efficient when you know that you need to reuse a connection.

15.4. Using TransactionAwareConnectionFactoryProxy

TransactionAwareConnectionFactoryProxy is a proxy for a target ConnectionFactory. The proxy wraps that target ConnectionFactory to add awareness of Spring-managed transactions.

15.5. Using ConnectionFactoryTransactionManager

The ConnectionFactoryTransactionManager class is a ReactiveTransactionManager implementation for single R2DBC datasources. It binds an R2DBC connection from the specified data source to the subscriber Context, potentially allowing for one subscriber connection for each data source.

Application code is required to retrieve the R2DBC connection through ConnectionFactoryUtils.getConnection(ConnectionFactory), instead of R2DBC’s standard ConnectionFactory.create(). All framework classes (such as DatabaseClient) use this strategy implicitly. If not used with this transaction manager, the lookup strategy behaves exactly like the common one. Thus, it can be used in any case.

The ConnectionFactoryTransactionManager class supports custom isolation levels that get applied to the connection.

16. Initializing a ConnectionFactory

The org.springframework.data.r2dbc.connectionfactory.init package provides support for initializing an existing ConnectionFactory. You may sometimes need to initialize an instance that runs on a server somewhere or an embedded database.

16.1. Initializing a Database by Using @Bean methods

If you want to initialize a database and you can provide a reference to a ConnectionFactory bean, you can use the

Example 75. Using ConnectionFactoryInitializer to initialize a ConnectionFactory
public class InitializerConfiguration {

	public ConnectionFactoryInitializer initializer(ConnectionFactory connectionFactory) {

		ConnectionFactoryInitializer initializer = new ConnectionFactoryInitializer();

		CompositeDatabasePopulator populator = new CompositeDatabasePopulator();
		populator.addPopulators(new ResourceDatabasePopulator(new ClassPathResource("com/foo/sql/db-schema.sql")));
		populator.addPopulators(new ResourceDatabasePopulator(new ClassPathResource("com/foo/sql/test-data1.sql")));

		return initializer;

The preceding example runs the two specified scripts against the database. The first script creates a schema, and the second populates tables with a test data set.

The default behavior of the database initializer is to unconditionally run the provided scripts. This may not always be what you want — for instance, if you run the scripts against a database that already has test data in it. The likelihood of accidentally deleting data is reduced by following the common pattern (shown earlier) of creating the tables first and then inserting the data. The first step fails if the tables already exist.

However, to gain more control over the creation and deletion of existing data, ConnectionFactoryInitializer and ResourceDatabasePopulator support various switches such as switching the initialization on and off.

Each statement should be separated by ; or a new line if the ; character is not present at all in the script. You can control that globally or script by script, as the following example shows:

Example 76. Customizing statement separators
public class InitializerConfiguration {

	public ConnectionFactoryInitializer initializer(ConnectionFactory connectionFactory) {

		ConnectionFactoryInitializer initializer = new ConnectionFactoryInitializer();


		ResourceDatabasePopulator populator = new ResourceDatabasePopulator(new ClassPathResource("com/foo/sql/db-schema.sql"));
		populator.setSeparator("@@");                                                (1)

		return initializer;
1 Set the separator scripts to @@.

In this example, the schema scripts uses @@ as statement separator.

16.1.1. Initialization of Other Components that Depend on the Database

A large class of applications (those that do not use the database until after the Spring context has started) can use the database initializer with no further complications. If your application is not one of those, you might need to read the rest of this section.

The database initializer depends on a ConnectionFactory instance and runs the scripts provided in its initialization callback (analogous to an init-method in an XML bean definition, a @PostConstruct method in a component, or the afterPropertiesSet() method in a component that implements InitializingBean). If other beans depend on the same data source and use the data source in an initialization callback, there might be a problem because the data has not yet been initialized. A common example of this is a cache that initializes eagerly and loads data from the database on application startup.

To get around this issue, you have two options:

  1. change your cache initialization strategy to a later phase or

  2. ensure that the database initializer is initialized first

Changing your cache initialization strategy might be easy if the application is in your control and not otherwise. Some suggestions for how to implement this include:

  • Make the cache initialize lazily on first usage, which improves application startup time.

  • Have your cache or a separate component that initializes the cache implement Lifecycle or SmartLifecycle. When the application context starts, you can automatically start a SmartLifecycle by setting its autoStartup flag, and you can manually start a Lifecycle by calling ConfigurableApplicationContext.start() on the enclosing context.

  • Use a Spring ApplicationEvent or similar custom observer mechanism to trigger the cache initialization. ContextRefreshedEvent is always published by the context when it is ready for use (after all beans have been initialized), so that is often a useful hook (this is how the SmartLifecycle works by default).

Ensuring that the database initializer is initialized first can also be easy. Some suggestions on how to implement this include:

  • Rely on the default behavior of the Spring BeanFactory, which is that beans are initialized in registration order. You can easily arrange that by adopting the common practice of a set of @Import configuration that order your application modules and ensuring that the database and database initialization are listed first.

  • Separate the ConnectionFactory and the business components that use it and control their startup order by putting them in separate ApplicationContext instances (for example, the parent context contains the ConnectionFactory, and the child context contains the business components).

17. Mapping

Rich mapping support is provided by the MappingR2dbcConverter. MappingR2dbcConverter has a rich metadata model that allows mapping domain objects to a data row. The mapping metadata model is populated by using annotations on your domain objects. However, the infrastructure is not limited to using annotations as the only source of metadata information. The MappingR2dbcConverter also lets you map objects to rows without providing any additional metadata, by following a set of conventions.

This section describes the features of the MappingR2dbcConverter, including how to use conventions for mapping objects to rows and how to override those conventions with annotation-based mapping metadata.

17.1. Object Mapping Fundamentals

This section covers the fundamentals of Spring Data object mapping, object creation, field and property access, mutability and immutability. Note, that this section only applies to Spring Data modules that do not use the object mapping of the underlying data store (like JPA). Also be sure to consult the store-specific sections for store-specific object mapping, like indexes, customizing column or field names or the like.

Core responsibility of the Spring Data object mapping is to create instances of domain objects and map the store-native data structures onto those. This means we need two fundamental steps:

  1. Instance creation by using one of the constructors exposed.

  2. Instance population to materialize all exposed properties.

17.1.1. Object creation

Spring Data automatically tries to detect a persistent entity’s constructor to be used to materialize objects of that type. The resolution algorithm works as follows:

  1. If there’s a no-argument constructor, it will be used. Other constructors will be ignored.

  2. If there’s a single constructor taking arguments, it will be used.

  3. If there are multiple constructors taking arguments, the one to be used by Spring Data will have to be annotated with @PersistenceConstructor.

The value resolution assumes constructor argument names to match the property names of the entity, i.e. the resolution will be performed as if the property was to be populated, including all customizations in mapping (different datastore column or field name etc.). This also requires either parameter names information available in the class file or an @ConstructorProperties annotation being present on the constructor.

The value resolution can be customized by using Spring Framework’s @Value value annotation using a store-specific SpEL expression. Please consult the section on store specific mappings for further details.

Object creation internals

To avoid the overhead of reflection, Spring Data object creation uses a factory class generated at runtime by default, which will call the domain classes constructor directly. I.e. for this example type:

class Person {
  Person(String firstname, String lastname) { … }

we will create a factory class semantically equivalent to this one at runtime:

class PersonObjectInstantiator implements ObjectInstantiator {

  Object newInstance(Object... args) {
    return new Person((String) args[0], (String) args[1]);

This gives us a roundabout 10% performance boost over reflection. For the domain class to be eligible for such optimization, it needs to adhere to a set of constraints:

  • it must not be a private class

  • it must not be a non-static inner class

  • it must not be a CGLib proxy class

  • the constructor to be used by Spring Data must not be private

If any of these criteria match, Spring Data will fall back to entity instantiation via reflection.

17.1.2. Property population

Once an instance of the entity has been created, Spring Data populates all remaining persistent properties of that class. Unless already populated by the entity’s constructor (i.e. consumed through its constructor argument list), the identifier property will be populated first to allow the resolution of cyclic object references. After that, all non-transient properties that have not already been populated by the constructor are set on the entity instance. For that we use the following algorithm:

  1. If the property is immutable but exposes a with… method (see below), we use the with… method to create a new entity instance with the new property value.

  2. If property access (i.e. access through getters and setters) is defined, we’re invoking the setter method.

  3. If the property is mutable we set the field directly.

  4. If the property is immutable we’re using the constructor to be used by persistence operations (see Object creation) to create a copy of the instance.

  5. By default, we set the field value directly.

Property population internals

Similarly to our optimizations in object construction we also use Spring Data runtime generated accessor classes to interact with the entity instance.

class Person {

  private final Long id;
  private String firstname;
  private @AccessType(Type.PROPERTY) String lastname;

  Person() {
    this.id = null;

  Person(Long id, String firstname, String lastname) {
    // Field assignments

  Person withId(Long id) {
    return new Person(id, this.firstname, this.lastame);

  void setLastname(String lastname) {
    this.lastname = lastname;
Example 77. A generated Property Accessor
class PersonPropertyAccessor implements PersistentPropertyAccessor {

  private static final MethodHandle firstname;              (2)

  private Person person;                                    (1)

  public void setProperty(PersistentProperty property, Object value) {

    String name = property.getName();

    if ("firstname".equals(name)) {
      firstname.invoke(person, (String) value);             (2)
    } else if ("id".equals(name)) {
      this.person = person.withId((Long) value);            (3)
    } else if ("lastname".equals(name)) {
      this.person.setLastname((String) value);              (4)
1 PropertyAccessor’s hold a mutable instance of the underlying object. This is, to enable mutations of otherwise immutable properties.
2 By default, Spring Data uses field-access to read and write property values. As per visibility rules of private fields, MethodHandles are used to interact with fields.
3 The class exposes a withId(…) method that’s used to set the identifier, e.g. when an instance is inserted into the datastore and an identifier has been generated. Calling withId(…) creates a new Person object. All subsequent mutations will take place in the new instance leaving the previous untouched.
4 Using property-access allows direct method invocations without using MethodHandles.

This gives us a roundabout 25% performance boost over reflection. For the domain class to be eligible for such optimization, it needs to adhere to a set of constraints:

  • Types must not reside in the default or under the java package.

  • Types and their constructors must be public

  • Types that are inner classes must be static.

  • The used Java Runtime must allow for declaring classes in the originating ClassLoader. Java 9 and newer impose certain limitations.

By default, Spring Data attempts to use generated property accessors and falls back to reflection-based ones if a limitation is detected.

Let’s have a look at the following entity:

Example 78. A sample entity
class Person {

  private final @Id Long id;                                                (1)
  private final String firstname, lastname;                                 (2)
  private final LocalDate birthday;
  private final int age;                                                    (3)

  private String comment;                                                   (4)
  private @AccessType(Type.PROPERTY) String remarks;                        (5)

  static Person of(String firstname, String lastname, LocalDate birthday) { (6)

    return new Person(null, firstname, lastname, birthday,
      Period.between(birthday, LocalDate.now()).getYears());

  Person(Long id, String firstname, String lastname, LocalDate birthday, int age) { (6)

    this.id = id;
    this.firstname = firstname;
    this.lastname = lastname;
    this.birthday = birthday;
    this.age = age;

  Person withId(Long id) {                                                  (1)
    return new Person(id, this.firstname, this.lastname, this.birthday, this.age);

  void setRemarks(String remarks) {                                         (5)
    this.remarks = remarks;
1 The identifier property is final but set to null in the constructor. The class exposes a withId(…) method that’s used to set the identifier, e.g. when an instance is inserted into the datastore and an identifier has been generated. The original Person instance stays unchanged as a new one is created. The same pattern is usually applied for other properties that are store managed but might have to be changed for persistence operations. The wither method is optional as the persistence constructor (see 6) is effectively a copy constructor and setting the property will be translated into creating a fresh instance with the new identifier value applied.
2 The firstname and lastname properties are ordinary immutable properties potentially exposed through getters.
3 The age property is an immutable but derived one from the birthday property. With the design shown, the database value will trump the defaulting as Spring Data uses the only declared constructor. Even if the intent is that the calculation should be preferred, it’s important that this constructor also takes age as parameter (to potentially ignore it) as otherwise the property population step will attempt to set the age field and fail due to it being immutable and no with… method being present.
4 The comment property is mutable is populated by setting its field directly.
5 The remarks properties are mutable and populated by setting the comment field directly or by invoking the setter method for
6 The class exposes a factory method and a constructor for object creation. The core idea here is to use factory methods instead of additional constructors to avoid the need for constructor disambiguation through @PersistenceConstructor. Instead, defaulting of properties is handled within the factory method.

17.1.3. General recommendations

  • Try to stick to immutable objects — Immutable objects are straightforward to create as materializing an object is then a matter of calling its constructor only. Also, this avoids your domain objects to be littered with setter methods that allow client code to manipulate the objects state. If you need those, prefer to make them package protected so that they can only be invoked by a limited amount of co-located types. Constructor-only materialization is up to 30% faster than properties population.

  • Provide an all-args constructor — Even if you cannot or don’t want to model your entities as immutable values, there’s still value in providing a constructor that takes all properties of the entity as arguments, including the mutable ones, as this allows the object mapping to skip the property population for optimal performance.

  • Use factory methods instead of overloaded constructors to avoid @PersistenceConstructor — With an all-argument constructor needed for optimal performance, we usually want to expose more application use case specific constructors that omit things like auto-generated identifiers etc. It’s an established pattern to rather use static factory methods to expose these variants of the all-args constructor.

  • Make sure you adhere to the constraints that allow the generated instantiator and property accessor classes to be used — 

  • For identifiers to be generated, still use a final field in combination with an all-arguments persistence constructor (preferred) or a with… method — 

  • Use Lombok to avoid boilerplate code — As persistence operations usually require a constructor taking all arguments, their declaration becomes a tedious repetition of boilerplate parameter to field assignments that can best be avoided by using Lombok’s @AllArgsConstructor.

17.1.4. Kotlin support

Spring Data adapts specifics of Kotlin to allow object creation and mutation.

Kotlin object creation

Kotlin classes are supported to be instantiated , all classes are immutable by default and require explicit property declarations to define mutable properties. Consider the following data class Person:

data class Person(val id: String, val name: String)

The class above compiles to a typical class with an explicit constructor. We can customize this class by adding another constructor and annotate it with @PersistenceConstructor to indicate a constructor preference:

data class Person(var id: String, val name: String) {

    constructor(id: String) : this(id, "unknown")

Kotlin supports parameter optionality by allowing default values to be used if a parameter is not provided. When Spring Data detects a constructor with parameter defaulting, then it leaves these parameters absent if the data store does not provide a value (or simply returns null) so Kotlin can apply parameter defaulting. Consider the following class that applies parameter defaulting for name

data class Person(var id: String, val name: String = "unknown")

Every time the name parameter is either not part of the result or its value is null, then the name defaults to unknown.

Property population of Kotlin data classes

In Kotlin, all classes are immutable by default and require explicit property declarations to define mutable properties. Consider the following data class Person:

data class Person(val id: String, val name: String)

This class is effectively immutable. It allows to create new instances as Kotlin generates a copy(…) method that creates new object instances copying all property values from the existing object and applying property values provided as arguments to the method.

17.2. Convention-based Mapping

MappingR2dbcConverter has a few conventions for mapping objects to rows when no additional mapping metadata is provided. The conventions are:

  • The short Java class name is mapped to the table name in the following manner. The class com.bigbank.SavingsAccount maps to the savings_account table name.

  • Nested objects are not supported.

  • The converter uses any Spring Converters registered with it to override the default mapping of object properties to row columns and values.

  • The fields of an object are used to convert to and from columns in the row. Public JavaBean properties are not used.

  • If you have a single non-zero-argument constructor whose constructor argument names match top-level column names of the row, that constructor is used. Otherwise, the zero-argument constructor is used. If there is more than one non-zero-argument constructor, an exception is thrown.

17.3. Mapping Configuration

By default (unless explicitly configured) an instance of MappingR2dbcConverter is created when you create a DatabaseClient. You can create your own instance of the MappingR2dbcConverter. By creating your own instance, you can register Spring converters to map specific classes to and from the database.

You can configure the MappingR2dbcConverter as well as DatabaseClient and ConnectionFactory by using Java-based metadata. The following example uses Spring’s Java-based configuration:

Example 79. @Configuration class to configure R2DBC mapping support
public class MyAppConfig extends AbstractR2dbcConfiguration {

  public ConnectionFactory connectionFactory() {
    return ConnectionFactories.get("r2dbc:…");

  // the following are optional

  protected List<Object> getCustomConverters() {

    List<Converter<?, ?>> converterList = new ArrayList<Converter<?, ?>>();
    converterList.add(new org.springframework.data.r2dbc.test.PersonReadConverter());
    converterList.add(new org.springframework.data.r2dbc.test.PersonWriteConverter());
    return converterList;

AbstractR2dbcConfiguration requires you to implement a method that defines a ConnectionFactory.

You can add additional converters to the converter by overriding the r2dbcCustomConversions method.

AbstractR2dbcConfiguration creates a DatabaseClient instance and registers it with the container under the name of databaseClient.

17.4. Metadata-based Mapping

To take full advantage of the object mapping functionality inside the Spring Data R2DBC support, you should annotate your mapped objects with the @Table annotation. Although it is not necessary for the mapping framework to have this annotation (your POJOs are mapped correctly, even without any annotations), it lets the classpath scanner find and pre-process your domain objects to extract the necessary metadata. If you do not use this annotation, your application takes a slight performance hit the first time you store a domain object, because the mapping framework needs to build up its internal metadata model so that it knows about the properties of your domain object and how to persist them. The following example shows a domain object:

Example 80. Example domain object
package com.mycompany.domain;

public class Person {

  private Long id;

  private Integer ssn;

  private String firstName;

  private String lastName;
The @Id annotation tells the mapper which property you want to use as the primary key.

17.4.1. Default Type Mapping

The following table explains how property types of an entity affect mapping:

Source Type Target Type Remarks

Primitive types and wrapper types


Can be customized using Explicit Converters.

JSR-310 Date/Time types


Can be customized using Explicit Converters.

String, BigInteger, BigDecimal, and UUID


Can be customized using Explicit Converters.

Blob and Clob


Can be customized using Explicit Converters.

byte[], ByteBuffer


Considered a binary payload.


Array of T

Conversion to Array type if supported by the configured driver, not supported otherwise.

Arrays of primitive types, wrapper types and String

Array of wrapper type (e.g. int[]Integer[])

Conversion to Array type if supported by the configured driver, not supported otherwise.

Complex objects

Target type depends on registered Converter.

Requires a Explicit Converters, not supported otherwise.

The native data type for a column depends on the R2DBC driver type mapping. Drivers can contribute additional simple types such as Geometry types.

17.4.2. Mapping Annotation Overview

The MappingR2dbcConverter can use metadata to drive the mapping of objects to rows. The following annotations are available:

  • @Id: Applied at the field level to mark the primary key.

  • @Table: Applied at the class level to indicate this class is a candidate for mapping to the database. You can specify the name of the table where the database is stored.

  • @Transient: By default, all fields are mapped to the row. This annotation excludes the field where it is applied from being stored in the database. Transient properties cannot be used within a persistence constructor as the converter cannot materialize a value for the constructor argument.

  • @PersistenceConstructor: Marks a given constructor — even a package protected one — to use when instantiating the object from the database. Constructor arguments are mapped by name to the values in the retrieved row.

  • @Column: Applied at the field level to describe the name of the column as it is represented in the row, allowing the name to be different than the field name of the class.

The mapping metadata infrastructure is defined in the separate spring-data-commons project that is technology-agnostic. Specific subclasses are used in the R2DBC support to support annotation based metadata. Other strategies can also be put in place (if there is demand).

17.4.3. Customized Object Construction

The mapping subsystem allows the customization of the object construction by annotating a constructor with the @PersistenceConstructor annotation. The values to be used for the constructor parameters are resolved in the following way:

  • If a parameter is annotated with the @Value annotation, the given expression is evaluated, and the result is used as the parameter value.

  • If the Java type has a property whose name matches the given field of the input row, then its property information is used to select the appropriate constructor parameter to which to pass the input field value. This works only if the parameter name information is present in the Java .class files, which you can achieve by compiling the source with debug information or using the -parameters command-line switch for javac in Java 8.

  • Otherwise, a MappingException is thrown to indicate that the given constructor parameter could not be bound.

class OrderItem {

  private @Id String id;
  private int quantity;
  private double unitPrice;

  OrderItem(String id, int quantity, double unitPrice) {
    this.id = id;
    this.quantity = quantity;
    this.unitPrice = unitPrice;

  // getters/setters ommitted

17.4.4. Overriding Mapping with Explicit Converters

When storing and querying your objects, it is often convenient to have a R2dbcConverter instance to handle the mapping of all Java types to OutboundRow instances. However, you may sometimes want the R2dbcConverter instances to do most of the work but let you selectively handle the conversion for a particular type — perhaps to optimize performance.

To selectively handle the conversion yourself, register one or more one or more org.springframework.core.convert.converter.Converter instances with the R2dbcConverter.

You can use the r2dbcCustomConversions method in AbstractR2dbcConfiguration to configure converters. The examples at the beginning of this chapter show how to perform the configuration with Java.

Custom top-level entity conversion requires asymmetric types for conversion. Inbound data is extracted from R2DBC’s Row. Outbound data (to be used with INSERT/UPDATE statements) is represented as OutboundRow and later assembled to a statement.

The following example of a Spring Converter implementation converts from a Row to a Person POJO:

 public class PersonReadConverter implements Converter<Row, Person> {

  public Person convert(Row source) {
    Person p = new Person(source.get("id", String.class),source.get("name", String.class));
    p.setAge(source.get("age", Integer.class));
    return p;

Please note that converters get applied on singular properties. Collection properties (e.g. Collection<Person>) are iterated and converted element-wise. Collection converters (e.g. Converter<List<Person>>, OutboundRow) are not supported.

R2DBC uses boxed primitives (Integer.class instead of int.class) to return primitive values.

The following example converts from a Person to a OutboundRow:

public class PersonWriteConverter implements Converter<Person, OutboundRow> {

  public OutboundRow convert(Person source) {
    OutboundRow row = new OutboundRow();
    row.put("id", SettableValue.from(source.getId()));
    row.put("name", SettableValue.from(source.getFirstName()));
    row.put("age", SettableValue.from(source.getAge()));
    return row;

18. Kotlin Support

Kotlin is a statically typed language that targets the JVM (and other platforms) which allows writing concise and elegant code while providing excellent interoperability with existing libraries written in Java.

Spring Data provides first-class support for Kotlin and lets developers write Kotlin applications almost as if Spring Data was a Kotlin native framework.

The easiest way to build a Spring application with Kotlin is to leverage Spring Boot and its dedicated Kotlin support. This comprehensive tutorial will teach you how to build Spring Boot applications with Kotlin using start.spring.io.

18.1. Requirements

Spring Data supports Kotlin 1.3 and requires kotlin-stdlib (or one of its variants, such as kotlin-stdlib-jdk8) and kotlin-reflect to be present on the classpath. Those are provided by default if you bootstrap a Kotlin project via start.spring.io.

18.2. Null Safety

One of Kotlin’s key features is null safety, which cleanly deals with null values at compile time. This makes applications safer through nullability declarations and the expression of “value or no value” semantics without paying the cost of wrappers, such as Optional. (Kotlin allows using functional constructs with nullable values. See this comprehensive guide to Kotlin null safety.)

Although Java does not let you express null safety in its type system, Spring Data API is annotated with JSR-305 tooling friendly annotations declared in the org.springframework.lang package. By default, types from Java APIs used in Kotlin are recognized as platform types, for which null checks are relaxed. Kotlin support for JSR-305 annotations and Spring nullability annotations provide null safety for the whole Spring Data API to Kotlin developers, with the advantage of dealing with null related issues at compile time.

See Null Handling of Repository Methods how null safety applies to Spring Data Repositories.

You can configure JSR-305 checks by adding the -Xjsr305 compiler flag with the following options: -Xjsr305={strict|warn|ignore}.

For Kotlin versions 1.1+, the default behavior is the same as -Xjsr305=warn. The strict value is required take Spring Data API null-safety into account. Kotlin types inferred from Spring API but should be used with the knowledge that Spring API nullability declaration could evolve, even between minor releases and that more checks may be added in the future.

Generic type arguments, varargs, and array elements nullability are not supported yet, but should be in an upcoming release.

18.3. Object Mapping

See Kotlin support for details on how Kotlin objects are materialized.

18.4. Extensions

Kotlin extensions provide the ability to extend existing classes with additional functionality. Spring Data Kotlin APIs use these extensions to add new Kotlin-specific conveniences to existing Spring APIs.

Keep in mind that Kotlin extensions need to be imported to be used. Similar to static imports, an IDE should automatically suggest the import in most cases.

For example, Kotlin reified type parameters provide a workaround for JVM generics type erasure, and Spring Data provides some extensions to take advantage of this feature. This allows for a better Kotlin API.

To retrieve a list of SWCharacter objects in Java, you would normally write the following:

Flux<SWCharacter> characters = client.select().from(SWCharacter.class).fetch().all();

With Kotlin and the Spring Data extensions, you can instead write the following:

val characters =  client.select().from<SWCharacter>().fetch().all()
// or (both are equivalent)
val characters : Flux<SWCharacter> = client.select().from().fetch().all()

As in Java, characters in Kotlin is strongly typed, but Kotlin’s clever type inference allows for shorter syntax.

Spring Data R2DBC provides the following extensions:

  • Reified generics support for DatabaseClient and Criteria.

  • Coroutines extensions for DatabaseClient.

18.5. Coroutines

Kotlin Coroutines are lightweight threads allowing to write non-blocking code imperatively. On language side, suspend functions provides an abstraction for asynchronous operations while on library side kotlinx.coroutines provides functions like async { } and types like Flow.

Spring Data modules provide support for Coroutines on the following scope:

  • Deferred and Flow return values support in Kotlin extensions

18.5.1. Dependencies

Coroutines support is enabled when kotlinx-coroutines-core, kotlinx-coroutines-reactive and kotlinx-coroutines-reactor dependencies are in the classpath:

Example 81. Dependencies to add in Maven pom.xml


Supported versions 1.3.0 and above.

18.5.2. How Reactive translates to Coroutines?

For return values, the translation from Reactive to Coroutines APIs is the following:

  • fun handler(): Mono<Void> becomes suspend fun handler()

  • fun handler(): Mono<T> becomes suspend fun handler(): T or suspend fun handler(): T? depending on if the Mono can be empty or not (with the advantage of being more statically typed)

  • fun handler(): Flux<T> becomes fun handler(): Flow<T>

Flow is Flux equivalent in Coroutines world, suitable for hot or cold stream, finite or infinite streams, with the following main differences:

  • Flow is push-based while Flux is push-pull hybrid

  • Backpressure is implemented via suspending functions

  • Flow has only a single suspending collect method and operators are implemented as extensions

  • Operators are easy to implement thanks to Coroutines

  • Extensions allow adding custom operators to Flow

  • Collect operations are suspending functions

  • map operator supports asynchronous operation (no need for flatMap) since it takes a suspending function parameter

Read this blog post about Going Reactive with Spring, Coroutines and Kotlin Flow for more details, including how to run code concurrently with Coroutines.

18.5.3. Repositories

Here is an example of a Coroutines repository:

interface CoroutineRepository : CoroutineCrudRepository<User, String> {

    suspend fun findOne(id: String): User

    fun findByFirstname(firstname: String): Flow<User>

Coroutines repositories are built on reactive repositories to expose the non-blocking nature of data access through Kotlin’s Coroutines. Methods on a Coroutines repository can be backed either by a query method or a custom implementation. Invoking a custom implementation method propagates the Coroutines invocation to the actual implementation method if the custom method is suspend`able without requiring the implementation method to return a reactive type such as `Mono or Flux.

Coroutines repositories are only discovered when the repository extends the CoroutineCrudRepository interface.


Appendix A: Repository query keywords

Supported query keywords

The following table lists the keywords generally supported by the Spring Data repository query derivation mechanism. However, consult the store-specific documentation for the exact list of supported keywords, because some keywords listed here might not be supported in a particular store.

Table 4. Query keywords
Logical keyword Keyword expressions






After, IsAfter


Before, IsBefore


Containing, IsContaining, Contains


Between, IsBetween


EndingWith, IsEndingWith, EndsWith




False, IsFalse


GreaterThan, IsGreaterThan


GreaterThanEqual, IsGreaterThanEqual


In, IsIn


Is, Equals, (or no keyword)


IsEmpty, Empty


IsNotEmpty, NotEmpty


NotNull, IsNotNull


Null, IsNull


LessThan, IsLessThan


LessThanEqual, IsLessThanEqual


Like, IsLike


Near, IsNear


Not, IsNot


NotIn, IsNotIn


NotLike, IsNotLike


Regex, MatchesRegex, Matches


StartingWith, IsStartingWith, StartsWith


True, IsTrue


Within, IsWithin

Appendix B: Repository query return types

Supported Query Return Types

The following table lists the return types generally supported by Spring Data repositories. However, consult the store-specific documentation for the exact list of supported return types, because some types listed here might not be supported in a particular store.

Geospatial types (such as GeoResult, GeoResults, and GeoPage) are available only for data stores that support geospatial queries.
Table 5. Query return types
Return type Description


Denotes no return value.


Java primitives.

Wrapper types

Java wrapper types.


A unique entity. Expects the query method to return one result at most. If no result is found, null is returned. More than one result triggers an IncorrectResultSizeDataAccessException.


An Iterator.


A Collection.


A List.


A Java 8 or Guava Optional. Expects the query method to return one result at most. If no result is found, Optional.empty() or Optional.absent() is returned. More than one result triggers an IncorrectResultSizeDataAccessException.


Either a Scala or Vavr Option type. Semantically the same behavior as Java 8’s Optional, described earlier.


A Java 8 Stream.


A convenience extension of Iterable that directy exposes methods to stream, map and filter results, concatenate them etc.

Types that implement Streamable and take a Streamable constructor or factory method argument

Types that expose a constructor or ….of(…)/….valueOf(…) factory method taking a Streamable as argument. See Returning Custom Streamable Wrapper Types for details.

Vavr Seq, List, Map, Set

Vavr collection types. See Support for Vavr Collections for details.


A Future. Expects a method to be annotated with @Async and requires Spring’s asynchronous method execution capability to be enabled.


A Java 8 CompletableFuture. Expects a method to be annotated with @Async and requires Spring’s asynchronous method execution capability to be enabled.


A org.springframework.util.concurrent.ListenableFuture. Expects a method to be annotated with @Async and requires Spring’s asynchronous method execution capability to be enabled.


A sized chunk of data with an indication of whether there is more data available. Requires a Pageable method parameter.


A Slice with additional information, such as the total number of results. Requires a Pageable method parameter.


A result entry with additional information, such as the distance to a reference location.


A list of GeoResult<T> with additional information, such as the average distance to a reference location.


A Page with GeoResult<T>, such as the average distance to a reference location.


A Project Reactor Mono emitting zero or one element using reactive repositories. Expects the query method to return one result at most. If no result is found, Mono.empty() is returned. More than one result triggers an IncorrectResultSizeDataAccessException.


A Project Reactor Flux emitting zero, one, or many elements using reactive repositories. Queries returning Flux can emit also an infinite number of elements.


A RxJava Single emitting a single element using reactive repositories. Expects the query method to return one result at most. If no result is found, Mono.empty() is returned. More than one result triggers an IncorrectResultSizeDataAccessException.


A RxJava Maybe emitting zero or one element using reactive repositories. Expects the query method to return one result at most. If no result is found, Mono.empty() is returned. More than one result triggers an IncorrectResultSizeDataAccessException.


A RxJava Flowable emitting zero, one, or many elements using reactive repositories. Queries returning Flowable can emit also an infinite number of elements.