Typically, your repository interface will extend 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, simply copy the ones you want to expose from CrudRepository into your domain repository.

In this first step you defined a common base interface for all your domain repositories and exposed findOne(…) as well as save(…).These methods will be routed into the base repository implementation of the store of your choice provided by Spring Data ,e.g. in the case if JPA SimpleJpaRepository, because they are matching the method signatures in CrudRepository. So the UserRepository will now be able to save users, and find single ones by id, as well as triggering a query to find Users by their email address.

Using a unique Spring Data module in your application makes things simple hence, 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 case, it’s required for a repository definition to distinguish between persistence technologies. Spring Data enters strict repository configuration mode because it detects multiple repository factories on the class path. Strict configuration requires details on the repository or the domain class to decide about Spring Data module binding for a repository definition:

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

The last way to distinguish repositories is scoping repository base packages. Base packages define the starting points for scanning for repository interface definitions which implies to have 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 mandatory.

The following strategies are available for the repository infrastructure to resolve the query. You can configure the strategy at the namespace through the query-lookup-strategy attribute in case of XML configuration or via the queryLookupStrategy attribute of the Enable${store}Repositories annotation in case of Java config. Some strategies may not be supported for particular datastores.

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 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. Assume a Person has an Address with a ZipCode. In that case a method name of

creates the property traversal x.address.zipCode. The resolution algorithm starts with 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 continue building the tree down from there, splitting the tail up in the way just described. If the first split does not match, the algorithm move 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 and essentially choose the wrong property and finally 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 end up like so:

As we treat underscore as a reserved character we strongly advise to follow standard Java naming conventions (i.e. not using underscores in property names but camel case instead).

To handle parameters in your query you simply define method parameters as already seen in the examples above. Besides that the infrastructure will recognize certain specific types like Pageable and Sort to apply pagination and sorting to your queries dynamically.

The first method allows you to 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, Slice can be used as return instead. A Slice only knows about whether there’s a next Slice available which might be just sufficient when walking thought a larger result set.

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

The results of query methods can be limited via the keywords first or top, which can be used interchangeably. An optional numeric value can be appended to top/first to specify the maximum result size to be returned. If the number is left out, a result size of 1 is assumed.

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

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

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

Repository queries can be executed asynchronously using Spring’s asynchronous method execution capability. This means the method will return immediately upon invocation and the actual query execution will occur in a task that has been submitted to a Spring TaskExecutor.

Each Spring Data module includes a repositories element that allows you to simply define a base package that Spring scans for you.

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.

The repository infrastructure can also be triggered using a store-specific @Enable${store}Repositories annotation on a JavaConfig class. For an introduction into Java-based configuration of the Spring container, see the reference documentation.^[1]

A sample configuration to enable Spring Data repositories looks something like this.

You can also use the repository infrastructure outside of a Spring container, e.g. 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.

To enrich a repository with custom functionality you first define an interface and an implementation for the custom functionality. Use the repository interface you provided to extend the custom interface.

The implementation itself does not depend on Spring Data and can be a regular Spring bean. So you can use standard dependency injection behavior to inject references to other beans like a JdbTemplate, take part in aspects, and so on.

Let your standard repository interface extend the custom one. Doing so combines the CRUD and custom functionality and makes it available to clients.

The preceding approach is not feasible when you want to add a single method to all your repository interfaces. To add custom behavior to all repositories, you first add an intermediate interface to declare the shared behavior.

Now your individual repository interfaces will extend this intermediate interface instead of the Repository interface to include the functionality declared. Next, create an implementation of the intermediate interface that extends the persistence technology-specific repository base class. This class will then act as a custom base class for the repository proxies.

The default behavior of the Spring <repositories /> namespace is to provide an implementation for all interfaces that fall under the base-package. This means that if left in its current state, an implementation instance of MyRepository will be created by Spring. This is of course not desired as it is just supposed to act as an intermediary between Repository and the actual repository interfaces you want to define for each entity. To exclude an interface that extends Repository from being instantiated as a repository instance, you can either annotate it with @NoRepositoryBean (as seen above) or move it outside of the configured base-package.

The final step is to make the Spring Data infrastructure aware of the customized repository base class. In JavaConfig this is achieved by using the repositoryBaseClass attribute of the @Enable…Repositories annotation:

A corresponding attribute is available in the XML namespace.

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

Several Spring Data modules offer integration with Querydsl via QueryDslPredicateExecutor.

To make use of Querydsl support simply extend QueryDslPredicateExecutor on your repository interface.

The above enables to write typesafe queries using Querydsl Predicate s.

Spring Data modules ships with a variety of web support if the module supports the repository programming model. The web related stuff requires Spring MVC JARs on the classpath, some of them even provide integration with Spring HATEOAS ^[2]. In general, the integration support is enabled by using the @EnableSpringDataWebSupport annotation in your JavaConfig configuration class.

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 are using XML configuration, register either SpringDataWebSupport or HateoasAwareSpringDataWebSupport as Spring beans:

If you work with the Spring JDBC module, you probably are familiar with the support to populate a DataSource using 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:

You can easily 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 , do the following:

This declaration causes the data.json file to be read and deserialized via a Jackson ObjectMapper.

The type to which the JSON object will be unmarshalled to will be determined by inspecting the _class attribute of the JSON document. The infrastructure will eventually select the appropriate repository to handle the object just deserialized.

To rather use XML to define the data the repositories shall be populated with, you can use the unmarshaller-populator element. You configure it to use one of the XML marshaller options Spring OXM provides you with. See the Spring reference documentation for details.

The JPA module of Spring Data contains a custom namespace that allows defining repository beans. It also contains certain features and element attributes that are special to JPA. Generally the JPA repositories can be set up using the repositories element:

Using this element looks up Spring Data repositories as described in Creating repository instances. Beyond that it activates persistence exception translation for all beans annotated with @Repository to let exceptions being thrown by the JPA persistence providers be converted into Spring’s DataAccessException hierarchy.

The Spring Data JPA repositories support cannot only be activated through an XML namespace but also using an annotation through JavaConfig.

The just shown configuration class sets up an embedded HSQL database using the EmbeddedDatabaseBuilder API of spring-jdbc. We then set up a EntityManagerFactory and use Hibernate as sample persistence provider. The last infrastructure component declared here is the JpaTransactionManager. We finally activate Spring Data JPA repositories using the @EnableJpaRepositories annotation which essentially carries the same attributes as the XML namespace does. If no base package is configured it will use the one the configuration class resides in.

Saving an entity can be performed via the CrudRepository.save(…)-Method. It will persist or merge the given entity using the underlying JPA EntityManager. If the entity has not been persisted yet Spring Data JPA will save the entity via a call to the entityManager.persist(…) method, otherwise the entityManager.merge(…) method will be called.

The JPA module supports defining a query manually as String or have it being derived from the method name.

Generally the query creation mechanism for JPA works as described in Query methods. Here’s a short example of what a JPA query method translates into:

Using named queries to declare queries for entities is a valid approach and works fine for a small number of queries. As the queries themselves are tied to the Java method that executes them you actually can bind them directly using the Spring Data JPA @Query annotation rather than annotating them to the domain class. This will free the domain class from persistence specific information and co-locate the query to the repository interface.

Queries annotated to the query method will take precedence over queries defined using @NamedQuery or named queries declared in orm.xml.

Using advanced LIKE expressionsThe query execution mechanism for manually defined queries using @Query allow the definition of advanced LIKE expressions inside the query definition.

In the just shown sample LIKE delimiter character % is recognized and the query transformed into a valid JPQL query (removing the %). Upon query execution the parameter handed into the method call gets augmented with the previously recognized LIKE pattern.

Native queriesThe @Query annotation allows to execute native queries by setting the nativeQuery flag to true. Note, that we currently don’t support execution of pagination or dynamic sorting for native queries as we’d have to manipulate the actual query declared and we cannot do this reliably for native SQL.

By default Spring Data JPA will use position based parameter binding as described in all the samples above. This makes query methods a little error prone to refactoring regarding the parameter position. To solve this issue you can use @Param annotation to give a method parameter a concrete name and bind the name in the query.

Note that the method parameters are switched according to the occurrence in the query defined.

As of Spring Data JPA release 1.4 we support the usage of restricted SpEL template expressions in manually defined queries via @Query. Upon query execution these expressions are evaluated against a predefined set of variables. We support the following list of variables to be used in a manual query.

The following example demonstrates one use case for the #{#entityName} expression in a query string where you want to define a repository interface with a query method with a manually defined query. In order not to have to state the actual entity name in the query string of a @Query annotation one can use the #{#entityName} Variable.

Of course you could have just used User in the query declaration directly but that would require you to change the query as well. The reference to #entityName will pick up potential future remappings of the User class to a different entity name (e.g. by using @Entity(name = "MyUser").

Another use case for the #{#entityName} expression in a query string is if you want to define a generic repository interface with specialized repository interfaces for a concrete domain type. In order not to have to repeat the definition of custom query methods on the concrete interfaces you can use the entity name expression in the query string of the @Query annotation in the generic repository interface.

In the example the interface MappedTypeRepository is the common parent interface for a few domain types extending AbstractMappedType. It also defines the generic method findAllByAttribute(…) which can be used on instances of the specialized repository interfaces. If you now invoke findByAllAttribute(…) on ConcreteRepository the query being executed will be select t from ConcreteType t where t.attribute = ?1.

All the sections above describe how to declare queries to access a given entity or collection of entities. Of course you can add custom modifying behaviour by using facilities described in Custom implementations for Spring Data repositories. As this approach is feasible for comprehensive custom functionality, you can achieve the execution of modifying queries that actually only need parameter binding by annotating the query method with @Modifying:

This will trigger the query annotated to the method as updating query instead of a selecting one. As the EntityManager might contain outdated entities after the execution of the modifying query, we do not automatically clear it (see JavaDoc of EntityManager.clear() for details) since this will effectively drop all non-flushed changes still pending in the EntityManager. If you wish the EntityManager to be cleared automatically you can set @Modifying annotation’s clearAutomatically attribute to true.

To apply JPA query hints to the queries declared in your repository interface you can use the @QueryHints annotation. It takes an array of JPA @QueryHint annotations plus a boolean flag to potentially disable the hints applied to the addtional count query triggered when applying pagination.

The just shown declaration would apply the configured @QueryHint for that actually query but omit applying it to the count query triggered to calculate the total number of pages.

The JPA 2.1 specification introduced support for specifiying Fetch- and LoadGraphs that we also support via the @EntityGraph annotation which allows to reference a @NamedEntityGraph definition, that can be annotated on an entity, to be used to configure the fetch plan of the resulting query. The type (Fetch / Load) of the fetching can be configured via the type attribute on the @EntityGraph annotation. Please have a look at the JPA 2.1 Spec 3.7.4 for further reference.

It is also possible to define ad-hoc entity graphs via @EntityGraph. The provided attributePaths will be translated into the according EntityGraph without the need of having to explicitly add @NamedEntityGraph to your domain types.

Spring Data Repositories usually return the domain model when using query methods. However, sometimes, you may need to alter the view of that model for various reasons. In this section, you will learn how to define projections to serve up simplified and reduced views of resources.

Look at the following domain model:

This Person has several attributes:

Now assume we create a corresponding repository as follows:

Spring Data will return the domain object including all of its attributes. There are two options just to retrieve the address attribute. One option is to define a repository for Address objects like this:

In this situation, using PersonRepository will still return the whole Person object. Using AddressRepository will return just the Address.

However, what if you do not want to expose address details at all? You can offer the consumer of your repository service an alternative by defining one or more projections.

The NoAddresses projection only has getters for firstName and lastName meaning that it will not serve up any address information. The query method definition returns in this case NoAdresses instead of Person.

Projections declare a contract between the underlying type and the method signatures related to the exposed properties. Hence it is required to name getter methods according to the property name of the underlying type. If the underlying property is named firstName, then the getter method must be named getFirstName otherwise Spring Data is not able to look up the source property. This type of projection is also called closed projection. Closed projections expose a subset of properties hence they can be used to optimize the query in a way to reduce the selected fields from the data store. The other type is, as you might imagine, an open projection.

This chapter will give you an introduction to Query by Example and explain how to use Examples.

Query by Example (QBE) is a user-friendly querying technique with a simple interface. It allows dynamic query creation and does not require to write queries containing field names. In fact, Query by Example does not require to write queries using store-specific query languages at all.

The Query by Example API consists of three parts:

Query by Example is suited for several use-cases but also comes with limitations:

When to use

Limitations

Before getting started with Query by Example, you need to have a domain object. To get started, simply create an interface for your repository:

This is a simple domain object. You can use it to create an Example. By default, fields having null values are ignored, and strings are matched using the store specific defaults. Examples can be built by either using the of factory method or by using ExampleMatcher. Example is immutable.

Examples are ideally be executed with repositories. To do so, let your repository interface extend QueryByExampleExecutor<T>. Here’s an excerpt from the QueryByExampleExecutor interface:

You can read more about Query by Example Execution below.

Examples are not limited to default settings. You can specify own defaults for string matching, null handling and property-specific settings using the ExampleMatcher.

You can specify behavior for individual properties (e.g. "firstname" and "lastname", "address.city" for nested properties). You can tune it with matching options and case sensitivity.

Another style to configure matcher options is by using Java 8 lambdas. This approach is a callback that asks the implementor to modify the matcher. It’s not required to return the matcher because configuration options are held within the matcher instance.

Queries created by Example use a merged view of the configuration. Default matching settings can be set at ExampleMatcher level while individual settings can be applied to particular property paths. Settings that are set on ExampleMatcher are inherited by property path settings unless they are defined explicitly. Settings on a property patch have higher precedence than default settings.

In Spring Data JPA you can use Query by Example with Repositories.

Property specifier accepts property names (e.g. "firstname" and "lastname"). You can navigate by chaining properties together with dots ("address.city"). You can tune it with matching options and case sensitivity.

To allow your query methods to be transactional simply use @Transactional at the repository interface you define.

Spring Data provides sophisticated support to transparently keep track of who created or changed an entity and the point in time this happened. To benefit from that functionality you have to equip your entity classes with auditing metadata that can be defined either using annotations or by implementing an interface.

Spring Data JPA ships with an entity listener that can be used to trigger capturing auditing information. So first you have to register the AuditingEntityListener inside your orm.xml to be used for all entities in your persistence contexts:

You can also enable the AuditingEntityListener per entity using the @EntityListeners annotation:

Note that the auditing feature requires spring-aspects.jar to be on the classpath.

With that in place, activating auditing functionality is just a matter of adding the Spring Data JPA auditing namespace element to your configuration:

As of Spring Data JPA 1.5, auditing can be enabled by annotating a configuration class with the @EnableJpaAuditing annotation.

If you expose a bean of type AuditorAware to the ApplicationContext, the auditing infrastructure will pick it up automatically and use it to determine the current user to be set on domain types. If you have multiple implementations registered in the ApplicationContext, you can select the one to be used by explicitly setting the auditorAwareRef attribute of @EnableJpaAuditing.

A plain JPA setup requires all annotation mapped entity classes listed in orm.xml. Same applies to XML mapping files. Spring Data JPA provides a ClasspathScanningPersistenceUnitPostProcessor that gets a base package configured and optionally takes a mapping filename pattern. It will then scan the given package for classes annotated with @Entity or @MappedSuperclass and also loads the configuration files matching the filename pattern and hands them to the JPA configuration. The PostProcessor has to be configured like this: