This behavior has been removed and the recommended approach is now to either use query derivation (if the query parameters are simple enough) or Adding custom behavior to single repositories.

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:

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.

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.

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.

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.

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.

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”, which 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