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.

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. 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:

Spring Data JDBC uses implementations of the interface Dialect to encapsulate behavior that is specific to a database or its JDBC driver. By default, the AbstractJdbcConfiguration tries to determine the database in use and register the correct Dialect. This behavior can be changed by overwriting jdbcDialect(NamedParameterJdbcOperations).

If you use a database for which no dialect is available, then your application won’t startup. In that case, you’ll have to ask your vendor to provide a Dialect implementation. Alternatively, you can:

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:

The properties of the following types are currently supported:

The handling of referenced entities is limited. This is based on the idea of aggregate roots as described above. If you reference another entity, that entity is, by definition, part of your aggregate. So, if you remove the reference, the previously referenced entity gets deleted. This also means references are 1-1 or 1-n, but not n-1 or n-m.

If you have n-1 or n-m references, you are, by definition, dealing with two separate aggregates. References between those should be encoded as simple id values, which should map properly with Spring Data JDBC.

Custom converters can be registered, for types that are not supported by default, by inheriting your configuration from AbstractJdbcConfiguration and overwriting the method jdbcCustomConversions().

The constructor of JdbcCustomConversions accepts a list of org.springframework.core.convert.converter.Converter.

Converters should be annotated with @ReadingConverter or @WritingConverter in order to control their applicability to only reading from or to writing to the database.

TIMESTAMPTZ in the example is a database specific data type that needs conversion into something more suitable for a domain model.

When you use the standard implementations of CrudRepository that Spring Data JDBC provides, they expect a certain table structure. You can tweak that by providing a NamingStrategy in your application context.

When the NamingStrategy does not matching on your database table names, you can customize the names with the @Table annotation. The element value of this annotation provides the custom table name. The following example maps the MyEntity class to the CUSTOM_TABLE_NAME table in the database:

When the NamingStrategy does not matching on your database column names, you can customize the names with the @Column annotation. The element value of this annotation provides the custom column name. The following example maps the name property of the MyEntity class to the CUSTOM_COLUMN_NAME column in the database:

The @MappedCollection annotation can be used on a reference type (one-to-one relationship) or on Sets, Lists, and Maps (one-to-many relationship). idColumn element of the annotation provides a custom name for the foreign key column referencing the id column in the other table. In the following example the corresponding table for the MySubEntity class has a NAME column, and the CUSTOM_MY_ENTITY_ID_COLUMN_NAME column of the MyEntity id for relationship reasons:

When using List and Map you must have an additional column for the position of a dataset in the List or the key value of the entity in the Map. This additional column name may be customized with the keyColumn Element of the @MappedCollection annotation:

Embedded entities are used to have value objects in your java data model, even if there is only one table in your database. In the following example you see, that MyEntity is mapped with the @Embedded annotation. The consequence of this is, that in the database a table my_entity with the two columns id and name (from the EmbeddedEntity class) is expected.

However, if the name column is actually null within the result set, the entire property embeddedEntity will be set to null according to the onEmpty of @Embedded, which nulls objects when all nested properties are null.
Opposite to this behavior USE_EMPTY tries to create a new instance using either a default constructor or one that accepts nullable parameter values from the result set.

If you need a value object multiple times in an entity, this can be achieved with the optional prefix element of the @Embedded annotation. This element represents a prefix and is prepend for each column name in the embedded object.

Embedded entities containing a Collection or a Map will always be considered non empty since they will at least contain the empty collection or map. Such an entity will therefore never be null even when using @Embedded(onEmpty = USE_NULL).

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

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

When your data base 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 any more. 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. If you are not using auto-increment columns, you can use a BeforeSave listener, which sets the ID of the entity (covered later in this document).

Spring Data JDBC supports optimistic locking by means of a numeric attribute that is annotated with @Version on the aggregate root. Whenever Spring Data JDBC saves an aggregate with such a version attribute two things happen: The update statement for the aggregate root will contain a where clause checking that the version stored in the database is actually unchanged. If this isn’t the case an OptimisticLockingFailureException will be thrown. Also the version attribute gets increased both in the entity and in the database so a concurrent action will notice the change and throw an OptimisticLockingFailureException if applicable as described above.

This process also applies to inserting new aggregates, where a null or 0 version indicates a new instance and the increased instance afterwards marks the instance as not new anymore, making this work rather nicely with cases where the id is generated during object construction for example when UUIDs are used.

During deletes the version check also applies but no version is increased.

The JDBC module supports defining a query manually as a String in a @Query annotation or as named query in a property file.

Deriving a query from the name of the method is is currently limited to simple properties, that means properties present in the aggregate root directly. Also, only select queries are supported by this approach.

The following example shows how to use @Query to declare a query method:

For converting the query result into entities the same RowMapper is used by default as for the queries Spring Data JDBC generates itself. The query you provide must match the format the RowMapper expects. Columns for all properties that are used in the constructor of an entity must be provided. Columns for properties that get set via setter, wither or field access are optional. Properties that don’t have a matching column in the result will not be set. The query is used for populating the aggregate root, embedded entities and one-to-one relationships including arrays of primitive types which get stored and loaded as SQL-array-types. Separate queries are generated for maps, lists, sets and arrays of entities.

If no query is given in an annotation as described in the previous section Spring Data JDBC will try to locate a named query. There are two ways how the name of the query can be determined. The default is to take the domain class of the query, i.e. the aggregate root of the repository, take its simple name and append the name of the method separated by a .. Alternatively the @Query annotation has a name attribute which can be used to specify the name of a query to be looked up.

Named queries are expected to be provided in the property file META-INF/jdbc-named-queries.properties on the classpath.

The location of that file may be changed by setting a value to @EnableJdbcRepositories.namedQueriesLocation.

The easiest way to properly plug MyBatis into Spring Data JDBC is by importing MyBatisJdbcConfiguration into you application configuration:

As you can see, all you need to declare is a SqlSessionFactoryBean as MyBatisJdbcConfiguration relies on a SqlSession bean to be available in the ApplicationContext eventually.

For each operation in CrudRepository, Spring Data JDBC runs multiple statements. If there is a SqlSessionFactory in the application context, Spring Data checks, for each step, whether the SessionFactory offers a statement. If one is found, that statement (including its configured mapping to an entity) is used.

The name of the statement is constructed by concatenating the fully qualified name of the entity type with Mapper. and a String determining the kind of statement. For example, if an instance of org.example.User is to be inserted, Spring Data JDBC looks for a statement named org.example.UserMapper.insert.

When the statement is run, an instance of [MyBatisContext] gets passed as an argument, which makes various arguments available to the statement.

The following table describes the available MyBatis statements:

An EntityCallback is directly associated with its domain type through its generic type argument. Each Spring Data module typically ships with a set of predefined EntityCallback interfaces covering the entity lifecycle.

Implement the interface suiting your application needs like shown in the example below:

EntityCallback beans are picked up by the store specific implementations in case they are registered in the ApplicationContext. Most template APIs already implement ApplicationContextAware and therefore have access to the ApplicationContext

The following example explains a collection of valid entity callback registrations:

Spring Data JDBC uses the EntityCallback API for its auditing support and reacts on the following callbacks:

The following example shows an implementation of a Converter that converts from a Boolean object to a String value:

There are a couple of things to notice here: Boolean and String are both simple types hence Spring Data requires a hint in which direction this converter should apply (reading or writing). By annotating this converter with @WritingConverter you instruct Spring Data to write every Boolean property as String in the database.

The following example shows an implementation of a Converter that converts from a String to a Boolean value:

There are a couple of things to notice here: String and Boolean are both simple types hence Spring Data requires a hint in which direction this converter should apply (reading or writing). By annotating this converter with @ReadingConverter you instruct Spring Data to convert every String value from the database that should be assigned to a Boolean property.

The following example of a Spring Converter implementation converts from a String to a custom Email value object:

If you write a Converter whose source and target type are native types, we cannot determine whether we should consider it as a reading or a writing converter. Registering the converter instance as both might lead to unwanted results. For example, a Converter<String, Long> is ambiguous, although it probably does not make sense to try to convert all String instances into Long instances when writing. To let you force the infrastructure to register a converter for only one way, we provide @ReadingConverter and @WritingConverter annotations to be used in the converter implementation.

Converters are subject to explicit registration as instances are not picked up from a classpath or container scan to avoid unwanted registration with a conversion service and the side effects resulting from such a registration. Converters are registered with CustomConversions as the central facility that allows registration and querying for registered converters based on source- and target type.

CustomConversions ships with a pre-defined set of converter registrations:

To let your query methods be transactional, use @Transactional at the repository interface you define, as the following example shows:

Typically, you want the readOnly flag to be set to true, because most of the query methods only read data. In contrast to that, deleteInactiveUsers() uses the @Modifying annotation and overrides the transaction configuration. Thus, the method is with the readOnly flag set to false.

Spring Data provides sophisticated support to transparently keep track of who created or changed an entity and when the change 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. Additionally, auditing has to be enabled either through Annotation configuration or XML configuration to register the required infrastructure components. Please refer to the store-specific section for configuration samples.