Spring Data JPA - Reference Documentation

Spring Data 3.0 upgrades its Hibernate baseline to Hibernate 6. As quite a few things have changed in that version, a couple of things that have worked before might need some tweaks.

There are a few variants how you can get started with your repository interface.

The typical approach is to extend CrudRepository, which gives you methods for CRUD functionality. CRUD stands for Create, Read, Update, Delete. With version 3.0 we also introduced ListCrudRepository which is very similar to the CrudRepository but for those methods that return multiple entities it returns a List instead of an Iterable which you might find easier to use.

If you are using a reactive store you might choose ReactiveCrudRepository, or RxJava3CrudRepository depending on which reactive framework you are using.

If you are using Kotlin you might pick CoroutineCrudRepository which utilizes Kotlin’s coroutines.

Additional you can extend PagingAndSortingRepository, ReactiveSortingRepository, RxJava3SortingRepository, or CoroutineSortingRepository if you need methods that allow to specify a Sort abstraction or in the first case a Pageable abstraction. Note that the various sorting repositories no longer extended their respective CRUD repository as they did in Spring Data Versions pre 3.0. Therefore, you need to extend both interfaces if you want functionality of both.

If you do not want to extend Spring Data interfaces, you can also annotate your repository interface with @RepositoryDefinition. Extending one of the CRUD repository interfaces 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 the CRUD repository into your domain repository. When doing so, you may change the return type of methods. Spring Data will honor the return type if possible. For example, for methods returning multiple entities you may choose Iterable<T>, List<T>, Collection<T> or a VAVR list.

If many repositories in your application should have the same set of methods you can define your own base interface to inherit from. Such an interface must be annotated with @NoRepositoryBean. This prevents Spring Data to try to create an instance of it directly and failing because it can’t determine the entity for that repository, since it still contains a generic type variable.

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

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.

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:

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

The following example shows a repository that uses generic interfaces:

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

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

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:

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.

The query builder mechanism built into the Spring Data repository infrastructure is useful for building constraining queries over entities of the repository.

The following example shows how to create a number of queries:

Parsing query method names is divided into subject and predicate. The first part (find…By, exists…By) defines the subject of the query, the second part forms the predicate. The introducing clause (subject) can contain further expressions. Any text between find (or other introducing keywords) and By is considered to be descriptive unless using one of the result-limiting keywords such as a Distinct to set a distinct flag on the query to be created or Top/First to limit query results.

The appendix contains the full list of query method subject keywords and query method predicate keywords including sorting and letter-casing modifiers. However, the first By acts as a delimiter to indicate the start of the actual criteria predicate. At a very basic level, you can define conditions on entity properties and concatenate them with And and Or.

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:

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:

Assume a Person has an Address with a ZipCode. In that case, the method creates the x.address.zipCode property traversal. 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:

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).

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:

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 knows only 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 need only 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.

You can limit the results of query methods by using the first or top keywords, which you can use interchangeably. You can append an optional numeric value 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:

The limiting expressions also support the Distinct keyword for datastores that support distinct queries. Also, for the queries that limit 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 available pages), it is applied within the limited result.

Query methods that return multiple results can use standard Java Iterable, List, and Set. Beyond that, we support returning Spring Data’s Streamable, a custom extension of Iterable, as well as collection types provided by Vavr. Refer to the appendix explaining all possible query method return types.

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:

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.

You can process the results of query methods incrementally by using a Java 8 Stream<T> as the 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:

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

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:

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. Bean names for nested repository interfaces are prefixed with their enclosing type name. The base-package attribute allows wildcards so that you can define a pattern of scanned packages.

You can also trigger the repository infrastructure by using a store-specific @Enable${store}Repositories annotation on a Java configuration class. For an introduction to 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:

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 with a persistence technology-specific RepositoryFactory that you can use, as follows:

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

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 follows:

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 implementations. 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:

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

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:

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

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:

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:

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

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 the following example shows:

To use the Querydsl support, extend QuerydslPredicateExecutor on your repository interface, as the following example shows:

The preceding example lets you write type-safe queries by using Querydsl Predicate instances, as the following example shows:

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 the following example shows:

The @EnableSpringDataWebSupport annotation registers a few components. We discuss those later in this section. It also detects Spring HATEOAS on the classpath and registers integration components (if present) for it as well.

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

If you work with the Spring JDBC module, you are probably familiar with the support for populating 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 called data.json with the following content:

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:

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 unmarshall a repository populator with JAXB:

This section describes the basics of configuring Spring Data JPA through either:

This section describes how to persist (save) entities with Spring Data JPA.

This section describes the various ways to create a query with Spring Data JPA.

The JPA 2.1 specification introduced support for calling stored procedures by using the JPA criteria query API. We Introduced the @Procedure annotation for declaring stored procedure metadata on a repository method.

The examples to follow use the following stored procedure:

Metadata for stored procedures can be configured by using the NamedStoredProcedureQuery annotation on an entity type.

Note that @NamedStoredProcedureQuery has two different names for the stored procedure. name is the name JPA uses. procedureName is the name the stored procedure has in the database.

You can reference stored procedures from a repository method in multiple ways. The stored procedure to be called can either be defined directly by using the value or procedureName attribute of the @Procedure annotation. This refers directly to the stored procedure in the database and ignores any configuration via @NamedStoredProcedureQuery.

Alternatively you may specify the @NamedStoredProcedureQuery.name attribute as the @Procedure.name attribute. If neither value, procedureName nor name is configured, the name of the repository method is used as the name attribute.

The following example shows how to reference an explicitly mapped procedure:

The following example is equivalent to the previous one but uses the procedureName alias:

The following is again equivalent to the previous two but using the method name instead of an explicite annotation attribute.

The following example shows how to reference a stored procedure by referencing the @NamedStoredProcedureQuery.name attribute.

If the stored procedure getting called has a single out parameter that parameter may be returned as the return value of the method. If there are multiple out parameters specified in a @NamedStoredProcedureQuery annotation those can be returned as a Map with the key being the parameter name given in the @NamedStoredProcedureQuery annotation.

JPA 2 introduces a criteria API that you can use to build queries programmatically. By writing a criteria, you define the where clause of a query for a domain class. Taking another step back, these criteria can be regarded as a predicate over the entity that is described by the JPA criteria API constraints.

Spring Data JPA takes the concept of a specification from Eric Evans' book, “Domain Driven Design”, following the same semantics and providing an API to define such specifications with the JPA criteria API. To support specifications, you can extend your repository interface with the JpaSpecificationExecutor interface, as follows:

The additional interface has methods that let you run specifications in a variety of ways. For example, the findAll method returns all entities that match the specification, as shown in the following example:

The Specification interface is defined as follows:

Specifications can easily be used to build an extensible set of predicates on top of an entity that then can be combined and used with JpaRepository without the need to declare a query (method) for every needed combination, as shown in the following example:

The Customer_ type is a metamodel type generated using the JPA Metamodel generator (see the Hibernate implementation’s documentation for an example). So the expression, Customer_.createdAt, assumes the Customer has a createdAt attribute of type Date. Besides that, we have expressed some criteria on a business requirement abstraction level and created executable Specifications. So a client might use a Specification as follows:

Why not create a query for this kind of data access? Using a single Specification does not gain a lot of benefit over a plain query declaration. The power of specifications really shines when you combine them to create new Specification objects. You can achieve this through the default methods of Specification we provide to build expressions similar to the following:

And with JPA 2.1, the CriteriaBuilder API introduced CriteriaDelete. This is provided through JpaSpecificationExecutor’s `delete(Specification) API.

By default, CRUD methods on repository instances inherited from SimpleJpaRepository are transactional. For read operations, the transaction configuration readOnly flag is set to true. All others are configured with a plain @Transactional so that default transaction configuration applies. Repository methods that are backed by transactional repository fragments inherit the transactional attributes from the actual fragment method.

If you need to tweak transaction configuration for one of the methods declared in a repository, redeclare the method in your repository interface, as follows:

Another way to alter transactional behaviour is to use a facade or service implementation that (typically) covers more than one repository. Its purpose is to define transactional boundaries for non-CRUD operations. The following example shows how to use such a facade for more than one repository:

To specify the lock mode to be used, you can use the @Lock annotation on query methods, as shown in the following example:

This method declaration causes the query being triggered to be equipped with a LockModeType of READ. You can also define locking for CRUD methods by redeclaring them in your repository interface and adding the @Lock annotation, as shown in the following example:

When working with multiple EntityManager instances and custom repository implementations, you need to wire the correct EntityManager into the repository implementation class. You can do so by explicitly naming the EntityManager in the @PersistenceContext annotation or, if the EntityManager is @Autowired, by using @Qualifier.

As of Spring Data JPA 1.9, Spring Data JPA includes a class called JpaContext that lets you obtain the EntityManager by managed domain class, assuming it is managed by only one of the EntityManager instances in the application. The following example shows how to use JpaContext in a custom repository:

The advantage of this approach is that, if the domain type gets assigned to a different persistence unit, the repository does not have to be touched to alter the reference to the persistence unit.

Spring supports having multiple persistence units. Sometimes, however, you might want to modularize your application but still make sure that all these modules run inside a single persistence unit. To enable that behavior, Spring Data JPA offers a PersistenceUnitManager implementation that automatically merges persistence units based on their name, as shown in the following example:

Instances of the repository interfaces are usually created by a container, for which Spring is the most natural choice when working with Spring Data. Spring offers sophisticated support for creating bean instances, as documented in Creating Repository Instances. As of version 1.1.0, Spring Data JPA ships with a custom CDI extension that allows using the repository abstraction in CDI environments. The extension is part of the JAR. To activate it, include the Spring Data JPA JAR on your classpath.

You can now set up the infrastructure by implementing a CDI Producer for the EntityManagerFactory and EntityManager, as shown in the following example:

The necessary setup can vary depending on the JavaEE environment. You may need to do nothing more than redeclare a EntityManager as a CDI bean, as follows:

In the preceding example, the container has to be capable of creating JPA EntityManagers itself. All the configuration does is re-export the JPA EntityManager as a CDI bean.

The Spring Data JPA CDI extension picks up all available EntityManager instances as CDI beans and creates a proxy for a Spring Data repository whenever a bean of a repository type is requested by the container. Thus, obtaining an instance of a Spring Data repository is a matter of declaring an @Injected property, as shown in the following example:

Spring Data Envers makes typical Envers queries available in repositories for Spring Data JPA. It differs from other Spring Data modules in that it is always used in combination with another Spring Data Module: Spring Data JPA.

Envers is a Hibernate module that adds auditing capabilities to JPA entities. This documentation assumes you are familiar with Envers, just as Spring Data Envers relies on Envers being properly configured.

As a starting point for using Spring Data Envers, you need a project with Spring Data JPA on the classpath and an additional spring-data-envers dependency:

This also brings hibernate-envers into the project as a transient dependency.

To enable Spring Data Envers and Spring Data JPA, we need to configure two beans and a special repositoryFactoryBeanClass:

To actually use Spring Data Envers, make one or more repositories into a {spring-data-commons-javadoc-base}/org/springframework/data/repository/history/RevisionRepository.html[RevisionRepository] by adding it as an extended interface:

The entity for that repository must be an entity with Envers auditing enabled (that is, it must have an @Audited annotation):

You can now use the methods from RevisionRepository to query the revisions of the entity, as the following test case shows:

You can download the Spring Data Envers example in the Spring Data Examples repository and play around with to get a feel for how the library works.

You should also check out the {spring-data-commons-javadoc-base}/org/springframework/data/repository/history/RevisionRepository.html[Javadoc for RevisionRepository] and related classes.

You can ask questions at Stackoverflow by using the spring-data-envers tag.

The source code and issue tracker for Spring Data Envers is hosted at GitHub.