This behavior has been removed and the recommended approach is now to either use query derivation (if the query parameters are simple enough) or [repositories.single-repository-behaviour].

For instance, for a view emitting user lastNames, the following:

is to be replaced by the (more flexible):

Reduce is now supported in view-based querying.

It can be triggered by prefixing the method name with count instead of find. For example: countByLastnameContains(String word) instead of findByLastnameContains(String word).

Alternatively, it can be explicitly be activated by setting reduce = true on the @View annotation.

Be sure to construct your view correctly:

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:

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.

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:

Let’s have a look at the following entity:

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

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.

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.

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 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:

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 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:

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 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.

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:

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.

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:

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:

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.

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:

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:

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:

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

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.

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

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:

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 shown in the following example:

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

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

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:

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):

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:

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 unmarshal a repository populator with JAXB:

As of version 4.0, Couchbase Server ships with a new query language called N1QL. In Spring-Data-Couchbase 2.0, N1QL is the default way of doing queries and will allow you to fully derive queries from a method name.

Prerequisite is to have a N1QL-compatible cluster and to have created a PRIMARY INDEX on the bucket where the entities will be stored. DML queries are supported from Couchbase server version 4.1.

Here is an example:

Here we see two N1QL-backed ways of querying.

The first method uses the Query annotation to provide a N1QL statement inline. SpEL (Spring Expression Language) is supported by surrounding SpEL expression blocks between #{ and }. A few N1QL-specific values are provided through SpEL:

String-based queries support parametrized queries. You can either use positional placeholders like “$1”, in which case each of the method parameters will map, in order, to $1, $2, $3…​ Alternatively, you can use named placeholders using the “$someString” syntax. Method parameters will be matched with their corresponding placeholder using the parameter’s name, which can be overridden by annotating each parameter (except a Pageable or Sort) with @Param (eg. @Param("someString")). You cannot mix the two approaches in your query and will get an IllegalArgumentException if you do.

Note that you can mix N1QL placeholders and SpEL. N1QL placeholders will still consider all method parameters, so be sure to use the correct index like in the example below:

This allows you to generate queries that would work similarly to eg. AND name = "someName" or AND age = 3, with a single method declaration.

You can also do single projections in your N1QL queries (provided it selects only one field and returns only one result, usually an aggregation like COUNT, AVG, MAX…​). Such projection would have a simple return type like long, boolean or String. This is NOT intended for projections to DTOs.

Another example:
#{#n1ql.selectEntity} WHERE #{#n1ql.filter} AND test = $1
is equivalent to
SELECT #{#n1ql.fields} FROM #{#n1ql.bucket} WHERE #{#n1ql.filter} AND test = $1

The second method uses Spring-Data’s query derivation mechanism to build a N1QL query from the method name and parameters. This will produce a query looking like this: SELECT …​ FROM …​ WHERE firstName = "valueOfFnameAtRuntime". You can combine these criteria, even do a count with a name like countByFirstname or a limit with a name like findFirst3ByLastname…​

Most Spring-Data keywords are supported: .Supported keywords inside @Query (N1QL) method names

You can use both counting queries and Limiting Query Results features with this approach.

With N1QL, another possible interface for the repository is the PagingAndSortingRepository one (which extends CRUDRepository). It adds two methods:

The second way of querying, supported also in older versions of Couchbase Server, is the View-backed one that we’ll see in the next section.

This is the historical way of secondary indexing in Couchbase. Views are much more limited in terms of querying flexibility, and each custom method may very well need its own backing view, to be prepared in the cluster beforehand.

We’ll only cover views to the extent to which they are needed, if you need in-depth information about them please refer to the official Couchbase Server manual and the Couchbase Java SDK manual.

As a rule of thumb, all repository CRUD access methods which are not "by a specific key" still require a single backing view, by default all, to find the one or more matching entities.

To cover the basic CRUD methods from the CrudRepository, one view needs to be implemented in Couchbase Server. It basically returns all documents for the specific entity and also adds the optional reduce function _count.

Since every view has a design document and view name, by convention we default to all as the view name and the uncapitalized (lowercase first letter) entity name as the design document name. So if your entity is named UserInfo, then the code expects the all view in the userInfo design document. It needs to look like this:

Note that the important part in this map function is to only include the document IDs which correspond to our entity. Because the library always adds the _class property, this is a quick and easy way to do it. If you have another property in your JSON which does the same job (like a explicit type field), then you can use that as well - you don’t have to stick to _class all the time.

Also make sure to publish your design documents into production so that they can be picked up by the library! Also, if you are curious why we use emit(meta.id, null) in the view despite the document id being always sent over to the client implicitly, it is so the view can be queried with a list of ids, eg. in the findAll(Iterable<ID> ids) CRUD method.

We’ve seen that the repositories default methods can be backed by two broad kind of features: views and N1QL (in the case of paging and sorting). In order for the CRUD operations to work, the adequate view must have been created beforehand, and this is usually left for the user to do. First because view creation (and index creation) is an expensive operation that can take quite some time if the quantity of documents is high. Second, because in production it is considered best practice to avoid administration of the cluster elements like buckets, indexes and view by an application code.

In the case where the index creation cost isn’t considered too high and you are not in a production environment, it can be triggered automatically instead, in two steps. You will first need to annotate the repositories you want managed with the relevant annotation(s):

Secondly, you’ll need to opt-in to this feature by customizing the indexManager() bean of your env-specific AbstractCouchbaseConfiguration to take certain types of annotations into account. This is done through the IndexManager(boolean processViews, boolean processN1qlPrimary, boolean processN1qlSecondary) constructor. Set the flags for the category of annotations you want processed to true, or false to deactivate the automatic creation feature.

The @Profile annotation is one possible Spring annotation to be used to differentiate configurations (or individual beans) per environment.

In 2.0, since N1QL has been introduced as a more powerful concept, view-backed queries have changed a bit outside of the CRUD methods:

Implementing your custom repository finder methods also needs backing views. The findAllAdmins guesses to use the allAdmins view in the userInfo design document, by convention. Imagine we have a field on our entity which looks like boolean isAdmin. We can write a view like this to expose them (we don’t need a reduce function for this one, unless you plan to call one by prefixing your method with count instead of find!):

By now, we’ve never actually customized our view at query time. This is where the alternative, query derivation, comes along - like in our findByFirstnameStartingWith(String fnamePrefix) method.

This view not only emits the document id, but also the firstname of every UserInfo as the key. We can now run a ViewQuery which returns us all users with a firstname of "Michael" or "Michele".

On all these derived custom finder methods, you have to use the @View annotation with at least the view name specified (and you can also override the design document name, otherwise determined by convention).

For view-based query derivation, here are the supported keywords (A and B are method parameters in this table):

Note that both reduce functions and Limiting Query Results are also supported.

Last method of querying in Couchbase (from Couchbase Server 4.0, like for N1QL) is querying for dimensional data through Spatial Views, as we’ll see in the next section.

Couchbase can accommodate multi-dimensional data and query it with the use of special views, the Spatial Views. Such views allows to perform multi-dimensional queries, not only limited to geographical data.

Integration of these views in Spring Data Couchbase repositories is done through the @Dimensional annotation. Like @View, the annotation allows to indicate usage of a Spatial View as the backing mechanism for the annotated method. The annotation requires you to give the name of the designDocument and the spatialViewName to use. Additionally, you should specify the number of dimensions the view works with (unless it is the default classical 2).

Multi-dimensionality concept is interesting, it means you can craft views that allows you to answer questions like "find all shops that are within Manhattan and open between 14:00 and 23:00" (the third dimension of the view being the opening hours).

Couchbase’s Spatial View support querying through ranges that represent "lowest" and "highest" values in each dimension, so for 2D it represents a bounding box, with the southwest-most point [x,y] as startRange and northeast-most point [x,y] as endRange.

The following query derivation keywords and parameters relative to geographical data in Spring Data are supported for Spatial Views:

Further dimensions are supported through keywords other than Within and Near and require numerical input.

One aspect that is often needed and doesn’t have a direct equivalent in the Spring Data query derivation mechanism is query consistency. In both view-based queries and N1QL, you have this concept that the secondary index can return stale data, because the latest version hasn’t been indexed yet. This gives the best performance at the expense of consistency.

Note that weaker consistencies can lead to data being returned that doesn’t match the criteria of a derived query. One trickier case is when documents are deleted from Couchbase but views have not yet caught up to the deletion. With weak consistency this can mean that a view would return IDs that are not in the database anymore, leading to null entities. The CouchbaseTemplate`s `findByView and findBySpatialView methods will remove such stale deleted entities from their result in order to avoid having nulls in the returned collections. Similarly, CouchbaseRepository’s `deleteAll method will ignore documents that the backing view provided but the SDK remove operation couldn’t find.

If one wants to have stronger consistency, there are two possibilities described in the next sections.

This follows the standard procedure for changing all repositories' base class:

Here is a complete example that you can find in RepositoryBaseTest in the integration tests:

Again following the standard procedure for custom repository methods, here is a complete example that you can find in RepositoryCustomMethodTest in the integration tests:

By storing 3 items using a MyRepository instance and calling count() then customCountItems(), we’d obtain

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.

index element on the @N1qlJoin can be used to provided the hint for the lks (current entity) index and rightIndex element can be used to provided the rks (associated entity) index.

If the join type is going to be hash join, the hash side can be specified for the rks (associated entity). If the associated entity is on the build side, it can be specified as HashSide.BUILD else HashSide.PROBE.

keys element on the @N1qlJoin annotation can be used to specify unique document keys to restrict the join key space. :leveloffset: -1

The <repositories /> element triggers the setup of the Spring Data repository infrastructure. The most important attribute is base-package, which defines the package to scan for Spring Data repository interfaces. See “XML configuration”. The following table describes the attributes of the <repositories /> element:

The <populator /> element allows to populate the a data store via the Spring Data repository infrastructure.^[1]

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.

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.