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

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

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

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

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

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

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

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

In the prior example, you defined a common base interface for all your domain repositories and exposed findById(…) as well as save(…).These methods are routed into the base repository implementation of the store of your choice provided by Spring Data (for example, if you use JPA, the implementation is SimpleJpaRepository), because they match the method signatures in CrudRepository. So the UserRepository can now save users, find individual users by ID, and trigger a query to find Users by email address.

Using a unique Spring Data module in your application makes things simple, because all repository interfaces in the defined scope are bound to the Spring Data module. Sometimes, applications require using more than one Spring Data module. In such cases, a repository definition must distinguish between persistence technologies. When it detects multiple repository factories on the class path, Spring Data enters strict repository configuration mode. Strict configuration uses details on the repository or the domain class to decide about Spring Data module binding for a repository definition:

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

The following example shows a repository that uses generic interfaces:

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

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

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

The last way to distinguish repositories is by scoping repository base packages. Base packages define the starting points for scanning for repository interface definitions, which implies having repository definitions located in the appropriate packages. By default, annotation-driven configuration uses the package of the configuration class. The base package in XML-based configuration is mandatory.

The following example shows annotation-driven configuration of base packages:

The following strategies are available for the repository infrastructure to resolve the query. With XML configuration, you can configure the strategy at the namespace through the query-lookup-strategy attribute. For Java configuration, you can use the queryLookupStrategy attribute of the EnableJpaRepositories 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.

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:

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 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:

Use the store-specific @EnableJpaRepositories annotation on a Java configuration class to define a configuration for repository activation. 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:

Each Spring Data module includes a repositories element that lets you define a base package that Spring scans for you, as shown in the following example:

In the preceding example, Spring is instructed to scan com.acme.repositories and all its sub-packages for interfaces extending Repository or one of its sub-interfaces. For each interface found, the infrastructure registers the persistence technology-specific FactoryBean to create the appropriate proxies that handle invocations of the query methods. Each bean is registered under a bean name that is derived from the interface name, so an interface of UserRepository would be registered under userRepository. Bean names for nested repository interfaces are prefixed with their enclosing type name. The base package attribute allows wildcards so that you can define a pattern of scanned packages.

By default, the infrastructure picks up every interface that extends the persistence technology-specific Repository sub-interface located under the configured base package and creates a bean instance for it. However, you might want more fine-grained control over which interfaces have bean instances created for them. To do so, use filter elements inside the repository declaration. The semantics are exactly equivalent to the elements in Spring’s component filters. For details, see the Spring reference documentation for these elements.

For example, to exclude certain interfaces from instantiation as repository beans, you could use the following configuration:

The preceding example excludes all interfaces ending in SomeRepository from being instantiated and includes those ending with SomeOtherRepository.

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 configuration, you can do so by using the repositoryBaseClass, 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.

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:

You can choose among several approaches to use as a basis for your Cassandra database access. Spring’s support for Apache Cassandra comes in different flavors. Once you start using one of these approaches, you can still mix and match to include a feature from a different approach. The following approaches work well:

The following example shows how to use Java-based bean metadata to register an instance of a com.datastax.oss.driver.api.core.CqlSession:

This approach lets you use the standard com.datastax.oss.driver.api.core.CqlSession API that you may already know.

An alternative is to register an instance of com.datastax.oss.driver.api.core.CqlSession with the container by using Spring’s CqlSessionFactoryBean. As compared to instantiating a com.datastax.oss.driver.api.core.CqlSession instance directly, the FactoryBean approach has the added advantage of also providing the container with an ExceptionTranslator implementation that translates Cassandra exceptions to exceptions in Spring’s portable DataAccessException hierarchy. This hierarchy and the use of @Repository is described in Spring’s DAO support features.

The following example shows Java-based factory class usage:

Using CassandraTemplate with object mapping and repository support requires a CassandraTemplate, CassandraMappingContext, CassandraConverter, and enabling repository support.

The following example shows how to register components to configure object mapping and repository support:

Creating configuration classes that register Spring Data for Apache Cassandra components can be an exhausting challenge, so Spring Data for Apache Cassandra comes with a pre-built configuration support class. Classes that extend from AbstractCassandraConfiguration register beans for Spring Data for Apache Cassandra use. AbstractCassandraConfiguration lets you provide various configuration options, such as initial entities, default query options, pooling options, socket options, and many more. AbstractCassandraConfiguration also supports you with schema generation based on initial entities, if any are provided. Extending from AbstractCassandraConfiguration requires you to at least provide the keyspace name by implementing the getKeyspaceName method. The following example shows how to register beans by using AbstractCassandraConfiguration:

This section describes how to configure Spring Data Cassandra with XML.

The first thing to start with is a Cassandra keyspace. A keyspace is a logical grouping of tables that share the same replication factor and replication strategy. Keyspace management is located in the CqlSession configuration, which has the KeyspaceSpecification and startup and shutdown CQL script execution.

Declaring a keyspace with a specification allows creating and dropping of the Keyspace. It derives CQL from the specification so that you need not write CQL yourself. The following example specifies a Cassadra keyspace by using XML:

The org.springframework.data.cassandra.core.cql.session.init package provides support for initializing an existing SessionFactory. You may sometimes need to initialize a keyspace that runs on a server somewhere.

Spring Data for Apache Cassandra approaches data access with mapped entity classes that fit your data model. You can use these entity classes to create Cassandra table specifications and user type definitions.

Schema creation is tied to CqlSession initialization by SchemaAction. The following actions are supported:

This section provides some examples of the CqlTemplate class in action. These examples are not an exhaustive list of all of the functionality exposed by the CqlTemplate. See the Javadoc for that.

CassandraTemplate should always be configured as a Spring bean, although we show an example earlier where you can instantiate it directly. However, because we are assuming the context of making a Spring module, we assume the presence of the Spring container.

There are two ways to get a CassandraTemplate, depending on how you load you Spring ApplicationContext:

You can autowire a CassandraOperations into your project, as the following example shows:

As with all Spring autowiring, this assumes there is only one bean of type CassandraOperations in the ApplicationContext. If you have multiple CassandraTemplate beans (which is the case if you work with multiple keyspaces in the same project), then you can use the @Qualifier annotation to designate the bean you want to autowire.

You can also look up the CassandraTemplate bean from the ApplicationContext, as shown in the following example:

Spring Data for Apache Cassandra relies on the DataStax Java driver’s CodecRegistry to ensure type support. As types are added or changed, the Spring Data for Apache Cassandra module continues to function without requiring changes. See CQL data types and “Data Mapping and Type Conversion” for the current type mapping matrix.

CassandraTemplate has several convenient methods for saving and inserting your objects. To have more fine-grained control over the conversion process, you can register Spring Converter instances with the MappingCassandraConverter (for example, Converter<Row, Person>).

The simple case of using the INSERT operation is to save a POJO. In this case, the table name is determined by the simple class name (not the fully qualified class name). The table to store the object can be overridden by using mapping metadata.

When inserting or updating, the id property must be set. Apache Cassandra has no means to generate an ID.

The following example uses the save operation and retrieves its contents:

You can use the following operations to insert and save:

You can use the following update operations:

You can also use the old fashioned way and write your own CQL statements, as the following example shows:

You can also configure additional options such as TTL, consistency level, and lightweight transactions when using InsertOptions and UpdateOptions.

For updates, you can select to update a number of rows.

The following example shows updating a single account object by adding a one-time $50.00 bonus to the balance with the + assignment:

In addition to the Query discussed earlier, we provide the update definition by using an Update object. The Update class has methods that match the update assignments available for Apache Cassandra.

Most methods return the Update object to provide a fluent API for code styling purposes.

You can use the following overloaded methods to remove an object from the database:

The @Version annotation provides syntax similar to that of JPA in the context of Cassandra and makes sure updates are only applied to rows with a matching version. Optimistic Locking leverages Cassandra’s lightweight transactions to conditionally insert, update and delete rows. Therefore, INSERT statements are executed with the IF NOT EXISTS condition. For updates and deletes, the actual value of the version property is added to the UPDATE condition in such a way that the modification does not have any effect if another operation altered the row in the meantime. In that case, an OptimisticLockingFailureException is thrown. The following example shows these features:

In earlier sections, we saw how to retrieve a single object by using the selectOneById method on CassandraTemplate. Doing so returns a single domain object. We can also query for a collection of rows to be returned as a list of domain objects. Assuming we have a number of Person objects with name and age values stored as rows in a table and that each person has an account balance, we can now run a query by using the following code:

The select, selectOne, and stream methods take a Query object as a parameter. This object defines the criteria and options used to perform the query. The criteria is specified by using a Criteria object that has a static factory method named where that instantiates a new Criteria object. We recommend using a static import for org.springframework.data.cassandra.core.query.Criteria.where and Query.query, to make the query more readable.

This query should return a list of Person objects that meet the specified criteria. The Criteria class has the following methods that correspond to the operators provided in Apache Cassandra:

The Query class has the following methods that return rows:

The query methods must specify the target type T that is returned.

The CassandraOperations interface is one of the central components when it comes to more low-level interaction with Apache Cassandra. It offers a wide range of methods. You can find multiple overloads for every method. Most of them cover optional (nullable) parts of the API.

FluentCassandraOperations provide a more narrow interface for common methods of CassandraOperations providing a more readable, fluent API. The entry points (query(…), insert(…), update(…), and delete(…)) follow a natural naming scheme based on the operation to execute. Moving on from the entry point, the API is designed to offer only context-dependent methods that guide the developer towards a terminating method that invokes the actual CassandraOperation. The following example shows the fluent API:

If a table in Cassandra holds entities of different types, such as a Jedi within a Table of SWCharacters, you can use different types to map the query result. You can use as(Class<?> targetType) to map results to a different target type, while query(Class<?> entityType) still applies to the query and table name. The following example uses the query and as methods:

The terminating methods (first(), one(), all(), and stream()) handle switching between retrieving a single entity and retrieving multiple entities as List or Stream and similar operations.

The CqlTemplate class (and its asynchronous and reactive variants) offers various methods accepting static CQL, Statement objects and PreparedStatementCreator. Methods accepting static CQL without additional arguments typically run the CQL statement as-is without further processing. Methods accepting static CQL in combination with an arguments array (such as execute(String cql, Object…​ args) and queryForRows(String cql, Object…​ args)) use prepared statements. Internally, these methods create a PreparedStatementCreator and PreparedStatementBinder objects to prepare the statement and later on to bind values to the statement to run it. Spring Data Cassandra generally uses index-based parameter bindings for prepared statements.

Since Cassandra Driver version 4, prepared statements are cached on the driver level which removes the need to keep track of prepared statements in the application.

The following example shows how to issue a query with a parametrized prepared statement:

In cases where you require more control over statement preparation and parameter binding (for example, using named binding parameters), you can fully control prepared statement creation and parameter binding by calling query methods with PreparedStatementCreator and PreparedStatementBinder arguments:

Spring Data Cassandra ships with classes supporting that pattern in the cql package:

The CassandraTemplate class is built on top of CqlTemplate to provide a higher level of abstraction. The use of prepared statements can be controlled directly on CassandraTemplate (and its asynchronous and reactive variants) by calling setUsePreparedStatements(false) respective setUsePreparedStatements(true). Note that the use of prepared statements by CassandraTemplate is enabled by default.

The following example shows the use of methods that generate and that accept CQL:

Calling entity-bound methods such as select(Query, Class<T>) or update(Query, Update, Class<T>) build CQL statements themselves to perform the intended operations. Some CassandraTemplate methods (such as select(Statement<?>, Class<T>)) also accepts CQL Statement objects as part of their API.

It’s possible to participate in prepared statements when calling methods accepting a Statement with a SimpleStatement object. The template API extracts the query string and parameters (positional and named parameters) and uses these to prepare, bind, and run the statement. Non-SimpleStatement objects cannot be used with prepared statements.

Since Cassandra driver 4.0, prepared statements are cached by the CqlSession cache so it is okay to prepare the same string twice. Previous versions required caching of prepared statements outside of the driver. See also the Driver documentation on Prepared Statements for further reference.

Below you can find a list of all GlobalObservabilityConventions and ObservabilityConventions declared by this project.

Below you can find a list of all metrics declared by this project.

Below you can find a list of all spans declared by this project.

You can configure Reactive Cassandra support by using Java Configuration classes. Reactive Cassandra support adapts a CqlSession to provide a reactive processing model on top of an asynchronous driver.

A reactive CqlSession is configured similarly to an imperative CqlSession. We provide supporting configuration classes that come with predefined defaults and require only environment-specific information to configure Spring Data for Apache Cassandra. The base class for reactive support is AbstractReactiveCassandraConfiguration. This configuration class extends the imperative AbstractCassandraConfiguration, so the reactive support also configures the imperative API support. The following example shows how to register Apache Cassandra beans in a configuration class: ReactiveAppCassandraConfiguration .Registering Spring Data for Apache Cassandra beans using AbstractReactiveCassandraConfiguration

The configuration class in the preceding example is schema-management-enabled to create CQL objects during startup. See Schema Management for further details.

This section provides some examples of ReactiveCqlTemplate class usage. These examples are not an exhaustive list of all of the functionality exposed by the ReactiveCqlTemplate. See the attendant Javadocs for that.

ReactiveCassandraTemplate should always be configured as a Spring bean, although an earlier example showed how to instantiate it directly. However, this section assumes that the template is used in a Spring module, so it also assumes that the Spring container is being used.

There are two ways to get a ReactiveCassandraTemplate, depending on how you load you Spring ApplicationContext:

You can autowire a ReactiveCassandraTemplate into your project, as the following example shows:

Like all Spring autowiring, this assumes there is only one bean of type ReactiveCassandraOperations in the ApplicationContext. If you have multiple ReactiveCassandraTemplate beans (which can be the case if you are working with multiple keyspaces in the same project), then you can use the @Qualifier annotation to designate which bean you want to autowire.

You can also look up the ReactiveCassandraTemplate bean from the ApplicationContext, as shown in the following example:

CassandraTemplate has several convenient methods for saving and inserting your objects. To have more fine-grained control over the conversion process, you can register Spring Converter instances with the MappingCassandraConverter (for example, Converter<Row, Person>).

The simple case of using the INSERT operation is to save a POJO. In this case, the table name is determined by the simple class name (not the fully qualified class name). The table to store the object can be overridden by using mapping metadata.

When inserting or updating, the id property must be set. Apache Cassandra has no means to generate an ID.

The following example uses the save operation and retrieves its contents:

You can use the following operations to insert and save:

You can use the following update operations:

You can also use the old fashioned way and write your own CQL statements, as the following example shows:

You can also configure additional options such as TTL, consistency level, and lightweight transactions when using InsertOptions and UpdateOptions.

For updates, you can select to update a number of rows.

The following example shows updating a single account object by adding a one-time $50.00 bonus to the balance with the + assignment:

In addition to the Query discussed earlier, we provide the update definition by using an Update object. The Update class has methods that match the update assignments available for Apache Cassandra.

Most methods return the Update object to provide a fluent API for code styling purposes.

For more detail, see “Methods for Executing Updates for Rows”.

Spring Data query methods usually return one or multiple instances of the aggregate root managed by the repository. However, it might sometimes be desirable to create projections based on certain attributes of those types. Spring Data allows modeling dedicated return types, to more selectively retrieve partial views of the managed aggregates.

Imagine a repository and aggregate root type such as the following example:

Now imagine that we want to retrieve the person’s name attributes only. What means does Spring Data offer to achieve this? The rest of this chapter answers that question.

You can specify query options for query methods by passing a QueryOptions object. The options apply to the query before the actual query execution. QueryOptions is treated as a non-query parameter and is not considered to be a query parameter value. Query options apply to derived and string @Query repository methods.

To statically set the consistency level, use the @Consistency annotation on query methods. The declared consistency level is applied to the query each time it is executed. The following example sets the consistency level to ConsistencyLevel.LOCAL_ONE:

The DataStax Cassandra documentation includes a good discussion of the available consistency levels.

Instances of the repository interfaces are usually created by a container, and the Spring container is the most natural choice when working with Spring Data. Spring Data for Apache Cassandra ships with a custom CDI extension that allows using the repository abstraction in CDI environments. The extension is part of the JAR.To activate it, drop the Spring Data for Apache Cassandra JAR into your classpath. You can now set up the infrastructure by implementing a CDI Producer for the CassandraTemplate, as the following examlpe shows:

The Spring Data for Apache Cassandra CDI extension picks up CassandraOperations as a CDI bean and creates a proxy for a Spring Data repository whenever a bean of a repository type is requested by the container. Thus, obtaining an instance of a Spring Data repository is a matter of declaring an injected property, as the following example shows:

We provide @CreatedBy and @LastModifiedBy to capture the user who created or modified the entity as well as @CreatedDate and @LastModifiedDate to capture when the change happened.

As you can see, the annotations can be applied selectively, depending on which information you want to capture. The annotations, indicating to capture when changes are made, can be used on properties of type JDK8 date and time types, long, Long, and legacy Java Date and Calendar.

Auditing metadata does not necessarily need to live in the root level entity but can be added to an embedded one (depending on the actual store in use), as shown in the snippet below.

In case you do not want to use annotations to define auditing metadata, you can let your domain class implement the Auditable interface. It exposes setter methods for all of the auditing properties.

In case you use either @CreatedBy or @LastModifiedBy, the auditing infrastructure somehow needs to become aware of the current principal. To do so, we provide an AuditorAware<T> SPI interface that you have to implement to tell the infrastructure who the current user or system interacting with the application is. The generic type T defines what type the properties annotated with @CreatedBy or @LastModifiedBy have to be.

The following example shows an implementation of the interface that uses Spring Security’s Authentication object:

The implementation accesses the Authentication object provided by Spring Security and looks up the custom UserDetails instance that you have created in your UserDetailsService implementation. We assume here that you are exposing the domain user through the UserDetails implementation but that, based on the Authentication found, you could also look it up from anywhere.

When using reactive infrastructure you might want to make use of contextual information to provide @CreatedBy or @LastModifiedBy information. We provide an ReactiveAuditorAware<T> SPI interface that you have to implement to tell the infrastructure who the current user or system interacting with the application is. The generic type T defines what type the properties annotated with @CreatedBy or @LastModifiedBy have to be.

The following example shows an implementation of the interface that uses reactive Spring Security’s Authentication object:

The implementation accesses the Authentication object provided by Spring Security and looks up the custom UserDetails instance that you have created in your UserDetailsService implementation. We assume here that you are exposing the domain user through the UserDetails implementation but that, based on the Authentication found, you could also look it up from anywhere.

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/factory method 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.

Unless explicitly configured, an instance of MappingCassandraConverter is created by default when creating a CassandraTemplate. You can create your own instance of the MappingCassandraConverter to tell it where to scan the classpath at startup for your domain classes to extract metadata and construct indexes.

Also, by creating your own instance, you can register Spring Converter instances to use for mapping specific classes to and from the database. The following example configuration class sets up Cassandra mapping support:

AbstractCassandraConfiguration requires you to implement methods that define a keyspace. AbstractCassandraConfiguration also has a method named getEntityBasePackages(…). You can override it to tell the converter where to scan for classes annotated with the @Table annotation.

You can add additional converters to the MappingCassandraConverter by overriding the customConversions method.

Cassandra requires at least one partition key field for a CQL table. A table can additionally declare one or more clustering key fields. When your CQL table has a composite primary key, you must create a @PrimaryKeyClass to define the structure of the composite primary key. In this context, “composite primary key” means one or more partition columns optionally combined with one or more clustering columns.

Primary keys can make use of any singular simple Cassandra type or mapped user-defined Type. Collection-typed primary keys are not supported.

Embedded entities are used to design value objects in your Java domain model whose properties are flattened out into the table. In the following example you see, that User.name is annotated with @Embedded. The consequence of this is that all properties of UserName are folded into the user table which consists of 3 columns (user_id, firstname, lastname).

However, if the firstname and lastname column values are actually null within the result set, the entire property name 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.

You can embed a value object multiple times in an entity by using the optional prefix element of the @Embedded annotation. This element represents a prefix and is prepended to each column name in the embedded object. Note that properties will overwrite each other if multiple properties render to the same column name.

The MappingCassandraConverter can use metadata to drive the mapping of objects to rows in a Cassandra table. An overview of the annotations follows:

The mapping metadata infrastructure is defined in the separate, spring-data-commons project that is both technology- and data store-agnostic.

The following example shows a more complex mapping:

The following example shows how to map a UDT Address:

The following example shows how map a tuple:

You can combine converting and saving in a single process, basically using the converter to do the saving.

The following example uses a Converter to convert a Person object to a java.lang.String with Jackson 2:

Similar to how you can combine saving and converting, you can also combine reading and converting.

The following example uses a Converter that converts a java.lang.String into a Person object with Jackson 2:

Spring Data for Apache Cassandra Java configuration provides a convenient way to register Spring Converter instances: MappingCassandraConverter. The following configuration snippet shows how to manually register converters as well as configure CustomConversions:

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:

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: