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, Sort and Limit, to apply pagination, sorting and limiting 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:

The ClientConfiguration class offers the most common parameters to configure the client. In the case this is not enough, the user can add callback functions by using the withClientConfigurer(ClientConfigurationCallback<?>) method.

The following callbacks are provided:

The MappingElasticsearchConverter uses metadata to drive the mapping of objects to documents. The metadata is taken from the entity’s properties which can be annotated.

The following annotations are available:

The mapping metadata infrastructure is defined in a separate spring-data-commons project that is technology agnostic.

Looking at the Configuration from the previous section ElasticsearchCustomConversions allows registering specific rules for mapping domain and simple types.

To get started the ReactiveElasticsearchOperations needs to know about the actual client to work with. Please see Reactive Rest Client for details on the client and how to configure it.

CriteriaQuery based queries allow the creation of queries to search for data without knowing the syntax or basics of Elasticsearch queries. They allow the user to build queries by simply chaining and combining Criteria objects that specifiy the criteria the searched documents must fulfill.

Criteria and their usage are best explained by example (let’s assume we have a Book entity with a price property):

Conditions for the same field can be chained, they will be combined with a logical AND:

When chaining Criteria, by default a AND logic is used:

If you want to create nested queries, you need to use subqueries for this. Let’s assume we want to find all persons with a last name of Miller and a first name of either Jack or John:

Please refer to the API documentation of the Criteria class for a complete overview of the different available operations.

This class takes an Elasticsearch query as JSON String. The following code shows a query that searches for persons having the first name "Jack":

Using StringQuery may be appropriate if you already have an Elasticsearch query to use.

NativeQuery is the class to use when you have a complex query, or a query that cannot be expressed by using the Criteria API, for example when building queries and using aggregates. It allows to use all the different co.elastic.clients.elasticsearch._types.query_dsl.Query implementations from the Elasticsearch library therefore named "native".

The following code shows how to search for persons with a given firstname and for the found documents have a terms aggregation that counts the number of occurences of the lastnames for these persons:

[[elasticsearch.operations.searchtemplateScOp§query]] === SearchTemplateQuery

This is a special implementation of the Query interface to be used in combination with a stored search template. See Search Template support for further information.

The Elasticsearch module supports all basic query building feature as string queries, native search queries, criteria based queries or have it being derived from the method name.

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

The method name above will be translated into the following Elasticsearch json query

A list of supported keywords for Elasticsearch is shown below.

Repository methods can be defined to have the following return types for returning multiple Elements:

To access domain objects stored in a Elasticsearch using a Repository, just create an interface for it. Before you can actually go on and do that you will need an entity.

For Java configuration, use the @EnableReactiveElasticsearchRepositories annotation. If no base package is configured, the infrastructure scans the package of the annotated configuration class.

The following listing shows how to use Java configuration for a repository:

Because the repository from the previous example extends ReactiveSortingRepository, all CRUD operations are available as well as methods for sorted access to the entities. Working with the repository instance is a matter of dependency injecting it into a client, as the following example shows:

The @Highlight annotation on a repository method defines for which fields of the returned entity highlighting should be included. To search for some text in a Book 's name or summary and have the found data highlighted, the following repository method can be used:

It is possible to define multiple fields to be highlighted like above, and both the @Highlight and the @HighlightField annotation can further be customized with a @HighlightParameters annotation. Check the Javadocs for the possible configuration options.

In the search results the highlight data can be retrieved from the SearchHit class.

Sometimes the user does not need to have all the properties of an entity returned from a search but only a subset. Elasticsearch provides source filtering to reduce the amount of data that is transferred across the network to the application.

When working with Query implementations and the ElasticsearchOperations this is easily possible by setting a source filter on the query.

When using repository methods there is the @SourceFilters annotation:

In this example, all the properties of the returned Book objects would be null except the name.

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.

In order for the auditing code to be able to decide whether an entity instance is new, the entity must implement the Persistable<ID> interface which is defined as follows:

As the existence of an Id is not a sufficient criterion to determine if an enitity is new in Elasticsearch, additional information is necessary. One way is to use the creation-relevant auditing fields for this decision:

A Person entity might look as follows - omitting getter and setter methods for brevity:

After the entities have been set up and providing the AuditorAware - or ReactiveAuditorAware - the Auditing must be activated by setting the @EnableElasticsearchAuditing on a configuration class:

When using the reactive stack this must be:

If your code contains more than one AuditorAware bean for different types, you must provide the name of the bean to use as an argument to the auditorAwareRef parameter of the @EnableElasticsearchAuditing annotation.

The first way to define runtime fields is by adding the definitions to the index mappings (see https://www.elastic.co/guide/en/elasticsearch/reference/7.12/runtime-mapping-fields.html). To use this approach in Spring Data Elasticsearch the user must provide a JSON file that contains the corresponding definition, for example:

The path to this JSON file, which must be present on the classpath, must then be set in the @Mapping annotation of the entity:

The second way to define runtime fields is by adding the definitions to a search query (see https://www.elastic.co/guide/en/elasticsearch/reference/7.12/runtime-search-request.html). The following code example shows how to do this with Spring Data Elasticsearch :

The entity used is a simple object that has a price property:

The following query uses a runtime field that calculates a priceWithTax value by adding 19% to the price and uses this value in the search query to find all entities where priceWithTax is higher or equal than a given value:

This works with every implementation of the Query interface.

The enity that is used in these examples is a Person entity. This entity has a birthDate and an age property. Whereas the birthdate is fix, the age depends on the time when a query is issued and needs to be calculated dynamically.

Note that the age property is annotated with @ScriptedField. This inhibits the writing of a corresponding entry in the index mapping and marks the property as a target to put a calculated field from a search response.

The repository used in this example:

The service class has a repository injected and an ElasticsearchOperations instance to show several ways of populating and using the age property. We show the code split up in different pieces to put the explanations in

One of the changes in version 4.0.x is that Spring Data Elasticsearch does not use the Jackson Mapper anymore to map an entity to the JSON representation needed for Elasticsearch (see Elasticsearch Object Mapping). In version 3.2.x the Jackson Mapper was the default that was used. It was possible to switch to the meta-model based converter (named ElasticsearchEntityMapper) by explicitly configuring it (Meta Model Object Mapping).

In version 4.0.x the meta-model based converter is the only one that is available and does not need to be configured explicitly. If you had a custom configuration to enable the meta-model converter by providing a bean like this:

You now have to remove this bean, the ElasticsearchEntityMapper interface has been removed.

Some users had custom Jackson annotations on the entity class, for example in order to define a custom name for the mapped document in Elasticsearch or to configure date conversions. These are not taken into account anymore. The needed functionality is now provided with Spring Data Elasticsearch’s @Field annotation. Please see Mapping Annotation Overview for detailed information.

In 3.2.x the different query classes like IndexQuery or SearchQuery had properties that were taking the index name or index names that they were operating upon. If these were not set, the passed in entity was inspected to retrieve the index name that was set in the @Document annotation.
In 4.0.x the index name(s) must now be provided in an additional parameter of type IndexCoordinates. By separating this, it now is possible to use one query object against different indices.

So for example the following code:

must be changed to:

To make it easier to work with entities and use the index name that is contained in the entitie’s @Document annotation, new methods have been added like DocumentOperations.save(T entity);

In version 3.2 there was the ElasticsearchOperations interface that defined all the methods for the ElasticsearchTemplate class. In version 4 the functions have been split into different interfaces, aligning these interfaces with the Elasticsearch API:

ElasticsearchOperations now extends DocumentOperations and SearchOperations and has methods get access to an IndexOperations instance.

It is possible to define a property of en entity as the id property by naming it either id or document. This behaviour is now deprecated and will produce a warning. Please use the @Id annotation to mark a property as being the id property.

In the ReactiveElasticsearchClient.Indices interface the updateMapping methods are deprecated in favour of the putMapping methods. They do the same, but putMapping is consistent with the naming in the Elasticsearch API:

In the IndexOperations interface the methods addAlias(AliasQuery), removeAlias(AliasQuery) and queryForAlias() have been deprecated. The new methods alias(AliasAction), getAliases(String…​) and getAliasesForIndex(String…​) offer more functionality and a cleaner API.

Usage of a parent-id has been removed from Elasticsearch since version 6. We now deprecate the corresponding fields and methods.

The type mappings parameters of the @Document annotation and the IndexCoordinates object were removed. They had been deprecated in Spring Data Elasticsearch 4.0 and their values weren’t used anymore.

The @Score annotation that was used to set the score return value in an entity was deprecated in version 4.0 and has been removed. Score values are returned in the SearchHit instances that encapsulate the returned entities.

The org.springframework.data.elasticsearch.ElasticsearchException class has been removed. The remaining usages have been replaced with org.springframework.data.mapping.MappingException and org.springframework.dao.InvalidDataAccessApiUsageException.

The deprecated ScoredPage, ScrolledPage @AggregatedPage and implementations has been removed.

The deprecated GetQuery and DeleteQuery have been removed.

The deprecated find methods from ReactiveSearchOperations and ReactiveDocumentOperations have been removed.

Elasticsearch has introduced it’s new ElasticsearchClient and has deprecated the previous RestHighLevelClient. Spring Data Elasticsearch 4.4 still uses the old client as the default client for the following reasons:

Spring Data Elasticsearch now uses the new ElasticsearchClient and has deprecated the use of the previous RestHighLevelClient.

In the org.springframework.data.elasticsearch.core.index.AliasData class, which is used for alias information returned from Elasticsearch, the property filter (of type Document) is replaced by filterQuery which is of type org.springframework.data.elasticsearch.core.query.Query.

org.springframework.data.elasticsearch.annotations.Similarity was an enum class until 5.1. This enum was used in the @Field annotation to specify a similarity value. But besides the values defined by the enum, it is possible to have similarities with custom names in Elasticsearch. Therefore, the annotation property was changed from the type of the enum to a simple String. The previous enum values like Similarity.Default do still exist as String constants, so existing code will compile unmodified. Adaptions are necessary when this enum was used at other places than as a property of the @Field annotation.