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Preface

The Spring Data MongoDB project applies core Spring concepts to the development of solutions that use the MongoDB document style data store. We provide a “template” as a high-level abstraction for storing and querying documents. You may notice similarities to the JDBC support provided by the Spring Framework.

This document is the reference guide for Spring Data - MongoDB Support. It explains MongoDB module concepts and semantics and syntax for various store namespaces.

This section provides some basic introduction to Spring and Document databases. The rest of the document refers only to Spring Data MongoDB features and assumes the user is familiar with MongoDB and Spring concepts.

1. Learning Spring

Spring Data uses Spring framework’s core functionality, including:

While you need not know the Spring APIs, understanding the concepts behind them is important. At a minimum, the idea behind Inversion of Control (IoC) should be familiar, and you should be familiar with whatever IoC container you choose to use.

The core functionality of the MongoDB support can be used directly, with no need to invoke the IoC services of the Spring Container. This is much like JdbcTemplate, which can be used "'standalone'" without any other services of the Spring container. To leverage all the features of Spring Data MongoDB, such as the repository support, you need to configure some parts of the library to use Spring.

To learn more about Spring, you can refer to the comprehensive documentation that explains the Spring Framework in detail. There are a lot of articles, blog entries, and books on the subject. See the Spring framework home page for more information.

2. Learning NoSQL and Document databases

NoSQL stores have taken the storage world by storm. It is a vast domain with a plethora of solutions, terms, and patterns (to make things worse, even the term itself has multiple meanings). While some of the principles are common, you must be familiar with MongoDB to some degree. The best way to get acquainted is to read the documentation and follow the examples. It usually does not take more then 5-10 minutes to go through them and, especially if you are coming from an RDMBS-only background, these exercises can be an eye opener.

The starting point for learning about MongoDB is www.mongodb.org. Here is a list of other useful resources:

3. Requirements

The Spring Data MongoDB 4.x binaries require JDK level 17 and above and Spring Framework 6.0.10 and above.

In terms of document stores, you need at least version 3.6 of MongoDB, though we recommend a more recent version.

3.1. Compatibility Matrix

The following compatibility matrix summarizes Spring Data versions to MongoDB driver/database versions. Database versions show the highest supported server version that pass the Spring Data test suite. You can use newer server versions unless your application uses functionality that is affected by changes in the MongoDB server. See also the official MongoDB driver compatibility matrix for driver- and server version compatibility.

Spring Data Release Train Spring Data MongoDB Driver Version Server Version

2023.0

4.1.x

4.9.x

6.0.x

2022.0

4.0.x

4.7.x

6.0.x

2021.2

3.4.x

4.6.x

5.0.x

2021.1

3.3.x

4.4.x

5.0.x

2021.0

3.2.x

4.1.x

4.4.x

2020.0

3.1.x

4.1.x

4.4.x

Neumann

3.0.x

4.0.x

4.4.x

Moore

2.2.x

3.11.x/Reactive Streams 1.12.x

4.2.x

Lovelace

2.1.x

3.8.x/Reactive Streams 1.9.x

4.0.x

3.1.1. Relevant Changes in MongoDB 4.4

  • Fields list must not contain text search score property when no $text criteria present. See also $text operator

  • Sort must not be an empty document when running map reduce.

3.1.2. Relevant Changes in MongoDB 4.2

4. Additional Help Resources

Learning a new framework is not always straightforward. In this section, we try to provide what we think is an easy-to-follow guide for starting with the Spring Data MongoDB module. However, if you encounter issues or you need advice, feel free to use one of the following links:

Community Forum

Spring Data on Stack Overflow is a tag for all Spring Data (not just Document) users to share information and help each other. Note that registration is needed only for posting.

Professional Support

Professional, from-the-source support, with guaranteed response time, is available from Pivotal Software, Inc., the company behind Spring Data and Spring.

5. Following Development

For information on the Spring Data Mongo source code repository, nightly builds, and snapshot artifacts, see the Spring Data Mongo homepage. You can help make Spring Data best serve the needs of the Spring community by interacting with developers through the Community on Stack Overflow. To follow developer activity, look for the mailing list information on the Spring Data Mongo homepage. If you encounter a bug or want to suggest an improvement, please create a ticket on the Spring Data issue tracker. To stay up to date with the latest news and announcements in the Spring eco system, subscribe to the Spring Community Portal. You can also follow the Spring blog or the project team on Twitter (SpringData).

6. Upgrading

6.1. Upgrading Spring Data

Instructions for how to upgrade from earlier versions of Spring Data are provided on the project wiki. Follow the links in the release notes section to find the version that you want to upgrade to.

Upgrading instructions are always the first item in the release notes. If you are more than one release behind, please make sure that you also review the release notes of the versions that you jumped.

6.2. Upgrading MongoDB Drivers

Spring Data MongoDB 4.x requires the MongoDB Java Driver 4.8.x
To learn more about driver versions please visit the MongoDB Documentation.

7. Dependencies

Due to the different inception dates of individual Spring Data modules, most of them carry different major and minor version numbers. The easiest way to find compatible ones is to rely on the Spring Data Release Train BOM that we ship with the compatible versions defined. In a Maven project, you would declare this dependency in the <dependencyManagement /> section of your POM as follows:

Example 1. Using the Spring Data release train BOM
<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>org.springframework.data</groupId>
      <artifactId>spring-data-bom</artifactId>
      <version>2023.0.1</version>
      <scope>import</scope>
      <type>pom</type>
    </dependency>
  </dependencies>
</dependencyManagement>

The current release train version is 2023.0.1. The train version uses calver with the pattern YYYY.MINOR.MICRO. The version name follows ${calver} for GA releases and service releases and the following pattern for all other versions: ${calver}-${modifier}, where modifier can be one of the following:

  • SNAPSHOT: Current snapshots

  • M1, M2, and so on: Milestones

  • RC1, RC2, and so on: Release candidates

You can find a working example of using the BOMs in our Spring Data examples repository. With that in place, you can declare the Spring Data modules you would like to use without a version in the <dependencies /> block, as follows:

Example 2. Declaring a dependency to a Spring Data module
<dependencies>
  <dependency>
    <groupId>org.springframework.data</groupId>
    <artifactId>spring-data-jpa</artifactId>
  </dependency>
<dependencies>

7.1. Dependency Management with Spring Boot

Spring Boot selects a recent version of the Spring Data modules for you. If you still want to upgrade to a newer version, set the spring-data-bom.version property to the train version and iteration you would like to use.

See Spring Boot’s documentation (search for "Spring Data Bom") for more details.

7.2. Spring Framework

The current version of Spring Data modules require Spring Framework 6.0.10 or better. The modules might also work with an older bugfix version of that minor version. However, using the most recent version within that generation is highly recommended.

8. Working with Spring Data Repositories

The goal of the Spring Data repository abstraction is to significantly reduce the amount of boilerplate code required to implement data access layers for various persistence stores.

Spring Data repository documentation and your module

This chapter explains the core concepts and interfaces of Spring Data repositories. The information in this chapter is pulled from the Spring Data Commons module. It uses the configuration and code samples for the Jakarta Persistence API (JPA) module. If you want to use XML configuration you should adapt the XML namespace declaration and the types to be extended to the equivalents of the particular module that you use. “Namespace reference” covers XML configuration, which is supported across all Spring Data modules that support the repository API. “Repository query keywords” covers the query method keywords supported by the repository abstraction in general. For detailed information on the specific features of your module, see the chapter on that module of this document.

8.1. Core concepts

The central interface in the Spring Data repository abstraction is Repository. It takes the domain class to manage as well as the identifier type of the domain class as type arguments. This interface acts primarily as a marker interface to capture the types to work with and to help you to discover interfaces that extend this one. The CrudRepository and ListCrudRepository interfaces provide sophisticated CRUD functionality for the entity class that is being managed.

Example 3. CrudRepository Interface
public interface CrudRepository<T, ID> extends Repository<T, ID> {

  <S extends T> S save(S entity);      (1)

  Optional<T> findById(ID primaryKey); (2)

  Iterable<T> findAll();               (3)

  long count();                        (4)

  void delete(T entity);               (5)

  boolean existsById(ID primaryKey);   (6)

  // … more functionality omitted.
}
1 Saves the given entity.
2 Returns the entity identified by the given ID.
3 Returns all entities.
4 Returns the number of entities.
5 Deletes the given entity.
6 Indicates whether an entity with the given ID exists.

The methods declared in this interface are commonly referred to as CRUD methods. ListCrudRepository offers equivalent methods, but they return List where the CrudRepository methods return an Iterable.

We also provide persistence technology-specific abstractions, such as JpaRepository or MongoRepository. Those interfaces extend CrudRepository and expose the capabilities of the underlying persistence technology in addition to the rather generic persistence technology-agnostic interfaces such as CrudRepository.

Additional to the CrudRepository, there is a PagingAndSortingRepository abstraction that adds additional methods to ease paginated access to entities:

Example 4. PagingAndSortingRepository interface
public interface PagingAndSortingRepository<T, ID>  {

  Iterable<T> findAll(Sort sort);

  Page<T> findAll(Pageable pageable);
}

To access the second page of User by a page size of 20, you could do something like the following:

PagingAndSortingRepository<User, Long> repository = // … get access to a bean
Page<User> users = repository.findAll(PageRequest.of(1, 20));

In addition to pagination, scrolling provides a more fine-grained access to iterate through chunks of larger result sets.

In addition to query methods, query derivation for both count and delete queries is available. The following list shows the interface definition for a derived count query:

Example 5. Derived Count Query
interface UserRepository extends CrudRepository<User, Long> {

  long countByLastname(String lastname);
}

The following listing shows the interface definition for a derived delete query:

Example 6. Derived Delete Query
interface UserRepository extends CrudRepository<User, Long> {

  long deleteByLastname(String lastname);

  List<User> removeByLastname(String lastname);
}

8.2. Query Methods

Standard CRUD functionality repositories usually have queries on the underlying datastore. With Spring Data, declaring those queries becomes a four-step process:

  1. Declare an interface extending Repository or one of its subinterfaces and type it to the domain class and ID type that it should handle, as shown in the following example:

    interface PersonRepository extends Repository<Person, Long> { … }
    
  2. Declare query methods on the interface.

    interface PersonRepository extends Repository<Person, Long> {
      List<Person> findByLastname(String lastname);
    }
    
  3. Set up Spring to create proxy instances for those interfaces, either with JavaConfig or with XML configuration.

    Java
    import org.springframework.data.….repository.config.EnableMongoRepositories;
    
    @EnableMongoRepositories
    class Config { … }
    
    XML
    <?xml version="1.0" encoding="UTF-8"?>
    <beans xmlns="http://www.springframework.org/schema/beans"
       xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
       xmlns:jpa="http://www.springframework.org/schema/data/jpa"
       xsi:schemaLocation="http://www.springframework.org/schema/beans
         https://www.springframework.org/schema/beans/spring-beans.xsd
         http://www.springframework.org/schema/data/jpa
         https://www.springframework.org/schema/data/jpa/spring-jpa.xsd">
    
       <repositories base-package="com.acme.repositories"/>
    
    </beans>

    The JPA namespace is used in this example. If you use the repository abstraction for any other store, you need to change this to the appropriate namespace declaration of your store module. In other words, you should exchange jpa in favor of, for example, mongodb.

    Note that the JavaConfig variant does not configure a package explicitly, because the package of the annotated class is used by default. To customize the package to scan, use one of the basePackage… attributes of the data-store-specific repository’s @EnableMongoRepositories-annotation.

  4. Inject the repository instance and use it, as shown in the following example:

    class SomeClient {
    
      private final PersonRepository repository;
    
      SomeClient(PersonRepository repository) {
        this.repository = repository;
      }
    
      void doSomething() {
        List<Person> persons = repository.findByLastname("Matthews");
      }
    }
    

The sections that follow explain each step in detail:

8.3. Defining Repository Interfaces

To define a repository interface, you first need to define a domain class-specific repository interface. The interface must extend Repository and be typed to the domain class and an ID type. If you want to expose CRUD methods for that domain type, you may extend CrudRepository, or one of its variants instead of Repository.

8.3.1. Fine-tuning Repository Definition

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):

Example 7. Selectively exposing CRUD methods
@NoRepositoryBean
interface MyBaseRepository<T, ID> extends Repository<T, ID> {

  Optional<T> findById(ID id);

  <S extends T> S save(S entity);
}

interface UserRepository extends MyBaseRepository<User, Long> {
  User findByEmailAddress(EmailAddress emailAddress);
}

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.

The intermediate repository interface is annotated with @NoRepositoryBean. Make sure you add that annotation to all repository interfaces for which Spring Data should not create instances at runtime.

8.3.2. Using Repositories with Multiple Spring Data Modules

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:

  1. If the repository definition extends the module-specific repository, it is a valid candidate for the particular Spring Data module.

  2. If the domain class is annotated with the module-specific type annotation, it is a valid candidate for the particular Spring Data module. Spring Data modules accept either third-party annotations (such as JPA’s @Entity) or provide their own annotations (such as @Document for Spring Data MongoDB and Spring Data Elasticsearch).

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

Example 8. Repository definitions using module-specific interfaces
interface MyRepository extends JpaRepository<User, Long> { }

@NoRepositoryBean
interface MyBaseRepository<T, ID> extends JpaRepository<T, ID> { … }

interface UserRepository extends MyBaseRepository<User, Long> { … }

MyRepository and UserRepository extend JpaRepository in their type hierarchy. They are valid candidates for the Spring Data JPA module.

The following example shows a repository that uses generic interfaces:

Example 9. Repository definitions using generic interfaces
interface AmbiguousRepository extends Repository<User, Long> { … }

@NoRepositoryBean
interface MyBaseRepository<T, ID> extends CrudRepository<T, ID> { … }

interface AmbiguousUserRepository extends MyBaseRepository<User, Long> { … }

AmbiguousRepository and AmbiguousUserRepository extend only Repository and CrudRepository in their type hierarchy. While this is fine when using a unique Spring Data module, multiple modules cannot distinguish to which particular Spring Data these repositories should be bound.

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

Example 10. Repository definitions using domain classes with annotations
interface PersonRepository extends Repository<Person, Long> { … }

@Entity
class Person { … }

interface UserRepository extends Repository<User, Long> { … }

@Document
class User { … }

PersonRepository references Person, which is annotated with the JPA @Entity annotation, so this repository clearly belongs to Spring Data JPA. UserRepository references User, which is annotated with Spring Data MongoDB’s @Document annotation.

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

Example 11. Repository definitions using domain classes with mixed annotations
interface JpaPersonRepository extends Repository<Person, Long> { … }

interface MongoDBPersonRepository extends Repository<Person, Long> { … }

@Entity
@Document
class Person { … }

This example shows a domain class using both JPA and Spring Data MongoDB annotations. It defines two repositories, JpaPersonRepository and MongoDBPersonRepository. One is intended for JPA and the other for MongoDB usage. Spring Data is no longer able to tell the repositories apart, which leads to undefined behavior.

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:

Example 12. Annotation-driven configuration of base packages
@EnableJpaRepositories(basePackages = "com.acme.repositories.jpa")
@EnableMongoRepositories(basePackages = "com.acme.repositories.mongo")
class Configuration { … }

8.4. Defining Query Methods

The repository proxy has two ways to derive a store-specific query from the method name:

  • By deriving the query from the method name directly.

  • By using a manually defined query.

Available options depend on the actual store. However, there must be a strategy that decides what actual query is created. The next section describes the available options.

8.4.1. Query Lookup Strategies

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 EnableMongoRepositories annotation. Some strategies may not be supported for particular datastores.

  • CREATE attempts to construct a store-specific query from the query method name. The general approach is to remove a given set of well known prefixes from the method name and parse the rest of the method. You can read more about query construction in “Query Creation”.

  • USE_DECLARED_QUERY tries to find a declared query and throws an exception if it cannot find one. The query can be defined by an annotation somewhere or declared by other means. See the documentation of the specific store to find available options for that store. If the repository infrastructure does not find a declared query for the method at bootstrap time, it fails.

  • CREATE_IF_NOT_FOUND (the default) combines CREATE and USE_DECLARED_QUERY. It looks up a declared query first, and, if no declared query is found, it creates a custom method name-based query. This is the default lookup strategy and, thus, is used if you do not configure anything explicitly. It allows quick query definition by method names but also custom-tuning of these queries by introducing declared queries as needed.

8.4.2. Query Creation

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:

Example 13. Query creation from method names
interface PersonRepository extends Repository<Person, Long> {

  List<Person> findByEmailAddressAndLastname(EmailAddress emailAddress, String lastname);

  // Enables the distinct flag for the query
  List<Person> findDistinctPeopleByLastnameOrFirstname(String lastname, String firstname);
  List<Person> findPeopleDistinctByLastnameOrFirstname(String lastname, String firstname);

  // Enabling ignoring case for an individual property
  List<Person> findByLastnameIgnoreCase(String lastname);
  // Enabling ignoring case for all suitable properties
  List<Person> findByLastnameAndFirstnameAllIgnoreCase(String lastname, String firstname);

  // Enabling static ORDER BY for a query
  List<Person> findByLastnameOrderByFirstnameAsc(String lastname);
  List<Person> findByLastnameOrderByFirstnameDesc(String lastname);
}

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:

  • The expressions are usually property traversals combined with operators that can be concatenated. You can combine property expressions with AND and OR. You also get support for operators such as Between, LessThan, GreaterThan, and Like for the property expressions. The supported operators can vary by datastore, so consult the appropriate part of your reference documentation.

  • The method parser supports setting an IgnoreCase flag for individual properties (for example, findByLastnameIgnoreCase(…)) or for all properties of a type that supports ignoring case (usually String instances — for example, findByLastnameAndFirstnameAllIgnoreCase(…)). Whether ignoring cases is supported may vary by store, so consult the relevant sections in the reference documentation for the store-specific query method.

  • You can apply static ordering by appending an OrderBy clause to the query method that references a property and by providing a sorting direction (Asc or Desc). To create a query method that supports dynamic sorting, see “Paging, Iterating Large Results, Sorting”.

8.4.3. Property Expressions

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:

List<Person> findByAddressZipCode(ZipCode zipCode);

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:

List<Person> findByAddress_ZipCode(ZipCode zipCode);

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).

8.4.4. Paging, Iterating Large Results, Sorting

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:

Example 14. Using Pageable, Slice, ScrollPosition, and Sort in query methods
Page<User> findByLastname(String lastname, Pageable pageable);

Slice<User> findByLastname(String lastname, Pageable pageable);

Window<User> findTop10ByLastname(String lastname, ScrollPosition position, Sort sort);

List<User> findByLastname(String lastname, Sort sort);

List<User> findByLastname(String lastname, Pageable pageable);
APIs taking Sort and Pageable expect non-null values to be handed into methods. If you do not want to apply any sorting or pagination, use Sort.unsorted() and Pageable.unpaged().

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.

To find out how many pages you get for an entire query, you have to trigger an additional count query. By default, this query is derived from the query you actually trigger.
Which Method is Appropriate?

The value provided by the Spring Data abstractions is perhaps best shown by the possible query method return types outlined in the following table below. The table shows which types you can return from a query method

Table 1. Consuming Large Query Results
Method Amount of Data Fetched Query Structure Constraints

List<T>

All results.

Single query.

Query results can exhaust all memory. Fetching all data can be time-intensive.

Streamable<T>

All results.

Single query.

Query results can exhaust all memory. Fetching all data can be time-intensive.

Stream<T>

Chunked (one-by-one or in batches) depending on Stream consumption.

Single query using typically cursors.

Streams must be closed after usage to avoid resource leaks.

Flux<T>

Chunked (one-by-one or in batches) depending on Flux consumption.

Single query using typically cursors.

Store module must provide reactive infrastructure.

Slice<T>

Pageable.getPageSize() + 1 at Pageable.getOffset()

One to many queries fetching data starting at Pageable.getOffset() applying limiting.

A Slice can only navigate to the next Slice.

  • Slice provides details whether there is more data to fetch.

  • Offset-based queries becomes inefficient when the offset is too large because the database still has to materialize the full result.

Offset-based Window<T>

limit + 1 at OffsetScrollPosition.getOffset()

One to many queries fetching data starting at OffsetScrollPosition.getOffset() applying limiting.

A Window can only navigate to the next Window.

  • Window provides details whether there is more data to fetch.

  • Offset-based queries becomes inefficient when the offset is too large because the database still has to materialize the full result.

Page<T>

Pageable.getPageSize() at Pageable.getOffset()

One to many queries starting at Pageable.getOffset() applying limiting. Additionally, COUNT(…) query to determine the total number of elements can be required.

Often times, COUNT(…) queries are required that are costly.

  • Offset-based queries becomes inefficient when the offset is too large because the database still has to materialize the full result.

Keyset-based Window<T>

limit + 1 using a rewritten WHERE condition

One to many queries fetching data starting at KeysetScrollPosition.getKeys() applying limiting.

A Window can only navigate to the next Window.

  • Window provides details whether there is more data to fetch.

  • Keyset-based queries require a proper index structure for efficient querying.

  • Most data stores do not work well when Keyset-based query results contain null values.

  • Results must expose all sorting keys in their results requiring projections to select potentially more properties than required for the actual projection.

Paging and Sorting

You can define simple sorting expressions by using property names. You can concatenate expressions to collect multiple criteria into one expression.

Example 15. Defining sort expressions
Sort sort = Sort.by("firstname").ascending()
  .and(Sort.by("lastname").descending());

For a more type-safe way to define sort expressions, start with the type for which to define the sort expression and use method references to define the properties on which to sort.

Example 16. Defining sort expressions by using the type-safe API
TypedSort<Person> person = Sort.sort(Person.class);

Sort sort = person.by(Person::getFirstname).ascending()
  .and(person.by(Person::getLastname).descending());
TypedSort.by(…) makes use of runtime proxies by (typically) using CGlib, which may interfere with native image compilation when using tools such as Graal VM Native.

If your store implementation supports Querydsl, you can also use the generated metamodel types to define sort expressions:

Example 17. Defining sort expressions by using the Querydsl API
QSort sort = QSort.by(QPerson.firstname.asc())
  .and(QSort.by(QPerson.lastname.desc()));
Scrolling

Scrolling is a more fine-grained approach to iterate through larger results set chunks. Scrolling consists of a stable sort, a scroll type (Offset- or Keyset-based scrolling) and result limiting. You can define simple sorting expressions by using property names and define static result limiting using the Top or First keyword through query derivation. You can concatenate expressions to collect multiple criteria into one expression.

Scroll queries return a Window<T> that allows obtaining the scroll position to resume to obtain the next Window<T> until your application has consumed the entire query result. Similar to consuming a Java Iterator<List<…>> by obtaining the next batch of results, query result scrolling lets you access the a ScrollPosition through Window.positionAt(…​).

Window<User> users = repository.findFirst10ByLastnameOrderByFirstname("Doe", OffsetScrollPosition.initial());
do {

  for (User u : users) {
    // consume the user
  }

  // obtain the next Scroll
  users = repository.findFirst10ByLastnameOrderByFirstname("Doe", users.positionAt(users.size() - 1));
} while (!users.isEmpty() && users.hasNext());

WindowIterator provides a utility to simplify scrolling across Windows by removing the need to check for the presence of a next Window and applying the ScrollPosition.

WindowIterator<User> users = WindowIterator.of(position -> repository.findFirst10ByLastnameOrderByFirstname("Doe", position))
  .startingAt(OffsetScrollPosition.initial());

while (users.hasNext()) {
  User u = users.next();
  // consume the user
}
Scrolling using Offset

Offset scrolling uses similar to pagination, an Offset counter to skip a number of results and let the data source only return results beginning at the given Offset. This simple mechanism avoids large results being sent to the client application. However, most databases require materializing the full query result before your server can return the results.

Example 18. Using OffsetScrollPosition with Repository Query Methods
interface UserRepository extends Repository<User, Long> {

  Window<User> findFirst10ByLastnameOrderByFirstname(String lastname, OffsetScrollPosition position);
}

WindowIterator<User> users = WindowIterator.of(position -> repository.findFirst10ByLastnameOrderByFirstname("Doe", position))
  .startingAt(OffsetScrollPosition.initial()); (1)
1 Start from the initial offset at position 0.
Scrolling using Keyset-Filtering

Offset-based requires most databases require materializing the entire result before your server can return the results. So while the client only sees the portion of the requested results, your server needs to build the full result, which causes additional load.

Keyset-Filtering approaches result subset retrieval by leveraging built-in capabilities of your database aiming to reduce the computation and I/O requirements for individual queries. This approach maintains a set of keys to resume scrolling by passing keys into the query, effectively amending your filter criteria.

The core idea of Keyset-Filtering is to start retrieving results using a stable sorting order. Once you want to scroll to the next chunk, you obtain a ScrollPosition that is used to reconstruct the position within the sorted result. The ScrollPosition captures the keyset of the last entity within the current Window. To run the query, reconstruction rewrites the criteria clause to include all sort fields and the primary key so that the database can leverage potential indexes to run the query. The database needs only constructing a much smaller result from the given keyset position without the need to fully materialize a large result and then skipping results until reaching a particular offset.

Keyset-Filtering requires the keyset properties (those used for sorting) to be non-nullable. This limitation applies due to the store specific null value handling of comparison operators as well as the need to run queries against an indexed source. Keyset-Filtering on nullable properties will lead to unexpected results.

Example 19. Using KeysetScrollPosition with Repository Query Methods
interface UserRepository extends Repository<User, Long> {

  Window<User> findFirst10ByLastnameOrderByFirstname(String lastname, KeysetScrollPosition position);
}

WindowIterator<User> users = WindowIterator.of(position -> repository.findFirst10ByLastnameOrderByFirstname("Doe", position))
  .startingAt(KeysetScrollPosition.initial()); (1)
1 Start at the very beginning and do not apply additional filtering.

Keyset-Filtering works best when your database contains an index that matches the sort fields, hence a static sort works well. Scroll queries applying Keyset-Filtering require to the properties used in the sort order to be returned by the query, and these must be mapped in the returned entity.

You can use interface and DTO projections, however make sure to include all properties that you’ve sorted by to avoid keyset extraction failures.

When specifying your Sort order, it is sufficient to include sort properties relevant to your query; You do not need to ensure unique query results if you do not want to. The keyset query mechanism amends your sort order by including the primary key (or any remainder of composite primary keys) to ensure each query result is unique.

8.4.5. Limiting Query Results

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:

Example 20. Limiting the result size of a query with Top and First
User findFirstByOrderByLastnameAsc();

User findTopByOrderByAgeDesc();

Page<User> queryFirst10ByLastname(String lastname, Pageable pageable);

Slice<User> findTop3ByLastname(String lastname, Pageable pageable);

List<User> findFirst10ByLastname(String lastname, Sort sort);

List<User> findTop10ByLastname(String lastname, Pageable pageable);

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.

Limiting the results in combination with dynamic sorting by using a Sort parameter lets you express query methods for the 'K' smallest as well as for the 'K' biggest elements.

8.4.6. Repository Methods Returning Collections or Iterables

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.

Using Streamable as Query Method Return Type

You can use Streamable as alternative to Iterable or any collection type. It provides convenience methods to access a non-parallel Stream (missing from Iterable) and the ability to directly ….filter(…) and ….map(…) over the elements and concatenate the Streamable to others:

Example 21. Using Streamable to combine query method results
interface PersonRepository extends Repository<Person, Long> {
  Streamable<Person> findByFirstnameContaining(String firstname);
  Streamable<Person> findByLastnameContaining(String lastname);
}

Streamable<Person> result = repository.findByFirstnameContaining("av")
  .and(repository.findByLastnameContaining("ea"));
Returning Custom Streamable Wrapper Types

Providing dedicated wrapper types for collections is a commonly used pattern to provide an API for a query result that returns multiple elements. Usually, these types are used by invoking a repository method returning a collection-like type and creating an instance of the wrapper type manually. You can avoid that additional step as Spring Data lets you use these wrapper types as query method return types if they meet the following criteria:

  1. The type implements Streamable.

  2. The type exposes either a constructor or a static factory method named of(…) or valueOf(…) that takes Streamable as an argument.

The following listing shows an example:

class Product {                                         (1)
  MonetaryAmount getPrice() { … }
}

@RequiredArgsConstructor(staticName = "of")
class Products implements Streamable<Product> {         (2)

  private final Streamable<Product> streamable;

  public MonetaryAmount getTotal() {                    (3)
    return streamable.stream()
      .map(Priced::getPrice)
      .reduce(Money.of(0), MonetaryAmount::add);
  }


  @Override
  public Iterator<Product> iterator() {                 (4)
    return streamable.iterator();
  }
}

interface ProductRepository implements Repository<Product, Long> {
  Products findAllByDescriptionContaining(String text); (5)
}
1 A Product entity that exposes API to access the product’s price.
2 A wrapper type for a Streamable<Product> that can be constructed by using Products.of(…) (factory method created with the Lombok annotation). A standard constructor taking the Streamable<Product> will do as well.
3 The wrapper type exposes an additional API, calculating new values on the Streamable<Product>.
4 Implement the Streamable interface and delegate to the actual result.
5 That wrapper type Products can be used directly as a query method return type. You do not need to return Streamable<Product> and manually wrap it after the query in the repository client.
Support for Vavr Collections

Vavr is a library that embraces functional programming concepts in Java. It ships with a custom set of collection types that you can use as query method return types, as the following table shows:

Vavr collection type Used Vavr implementation type Valid Java source types

io.vavr.collection.Seq

io.vavr.collection.List

java.util.Iterable

io.vavr.collection.Set

io.vavr.collection.LinkedHashSet

java.util.Iterable

io.vavr.collection.Map

io.vavr.collection.LinkedHashMap

java.util.Map

You can use the types in the first column (or subtypes thereof) as query method return types and get the types in the second column used as implementation type, depending on the Java type of the actual query result (third column). Alternatively, you can declare Traversable (the Vavr Iterable equivalent), and we then derive the implementation class from the actual return value. That is, a java.util.List is turned into a Vavr List or Seq, a java.util.Set becomes a Vavr LinkedHashSet Set, and so on.

8.4.7. Streaming Query Results

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:

Example 22. Stream the result of a query with Java 8 Stream<T>
@Query("select u from User u")
Stream<User> findAllByCustomQueryAndStream();

Stream<User> readAllByFirstnameNotNull();

@Query("select u from User u")
Stream<User> streamAllPaged(Pageable pageable);
A Stream potentially wraps underlying data store-specific resources and must, therefore, be closed after usage. You can either manually close the Stream by using the close() method or by using a Java 7 try-with-resources block, as shown in the following example:
Example 23. Working with a Stream<T> result in a try-with-resources block
try (Stream<User> stream = repository.findAllByCustomQueryAndStream()) {
  stream.forEach(…);
}
Not all Spring Data modules currently support Stream<T> as a return type.

8.4.8. Null Handling of Repository Methods

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:

  • com.google.common.base.Optional

  • scala.Option

  • io.vavr.control.Option

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.

Nullability Annotations

You can express nullability constraints for repository methods by using Spring Framework’s nullability annotations. They provide a tooling-friendly approach and opt-in null checks during runtime, as follows:

  • @NonNullApi: Used on the package level to declare that the default behavior for parameters and return values is, respectively, neither to accept nor to produce null values.

  • @NonNull: Used on a parameter or return value that must not be null (not needed on a parameter and return value where @NonNullApi applies).

  • @Nullable: Used on a parameter or return value that can be null.

Spring annotations are meta-annotated with JSR 305 annotations (a dormant but widely used JSR). JSR 305 meta-annotations let tooling vendors (such as IDEA, Eclipse, and Kotlin) provide null-safety support in a generic way, without having to hard-code support for Spring annotations. To enable runtime checking of nullability constraints for query methods, you need to activate non-nullability on the package level by using Spring’s @NonNullApi in package-info.java, as shown in the following example:

Example 24. Declaring Non-nullability in package-info.java
@org.springframework.lang.NonNullApi
package com.acme;

Once non-null defaulting is in place, repository query method invocations get validated at runtime for nullability constraints. If a query result violates the defined constraint, an exception is thrown. This happens when the method would return null but is declared as non-nullable (the default with the annotation defined on the package in which the repository resides). If you want to opt-in to nullable results again, selectively use @Nullable on individual methods. Using the result wrapper types mentioned at the start of this section continues to work as expected: an empty result is translated into the value that represents absence.

The following example shows a number of the techniques just described:

Example 25. Using different nullability constraints
package com.acme;                                                       (1)

import org.springframework.lang.Nullable;

interface UserRepository extends Repository<User, Long> {

  User getByEmailAddress(EmailAddress emailAddress);                    (2)

  @Nullable
  User findByEmailAddress(@Nullable EmailAddress emailAdress);          (3)

  Optional<User> findOptionalByEmailAddress(EmailAddress emailAddress); (4)
}
1 The repository resides in a package (or sub-package) for which we have defined non-null behavior.
2 Throws an EmptyResultDataAccessException when the query does not produce a result. Throws an IllegalArgumentException when the emailAddress handed to the method is null.
3 Returns null when the query does not produce a result. Also accepts null as the value for emailAddress.
4 Returns Optional.empty() when the query does not produce a result. Throws an IllegalArgumentException when the emailAddress handed to the method is null.
Nullability in Kotlin-based Repositories

Kotlin has the definition of nullability constraints baked into the language. Kotlin code compiles to bytecode, which does not express nullability constraints through method signatures but rather through compiled-in metadata. Make sure to include the kotlin-reflect JAR in your project to enable introspection of Kotlin’s nullability constraints. Spring Data repositories use the language mechanism to define those constraints to apply the same runtime checks, as follows:

Example 26. Using nullability constraints on Kotlin repositories
interface UserRepository : Repository<User, String> {

  fun findByUsername(username: String): User     (1)

  fun findByFirstname(firstname: String?): User? (2)
}
1 The method defines both the parameter and the result as non-nullable (the Kotlin default). The Kotlin compiler rejects method invocations that pass null to the method. If the query yields an empty result, an EmptyResultDataAccessException is thrown.
2 This method accepts null for the firstname parameter and returns null if the query does not produce a result.

8.4.9. Asynchronous Query Results

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:

@Async
Future<User> findByFirstname(String firstname);               (1)

@Async
CompletableFuture<User> findOneByFirstname(String firstname); (2)
1 Use java.util.concurrent.Future as the return type.
2 Use a Java 8 java.util.concurrent.CompletableFuture as the return type.

8.5. Creating Repository Instances

This section covers how to create instances and bean definitions for the defined repository interfaces.

8.5.1. Java Configuration

Use the store-specific @EnableMongoRepositories 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:

Example 27. Sample annotation-based repository configuration
@Configuration
@EnableJpaRepositories("com.acme.repositories")
class ApplicationConfiguration {

  @Bean
  EntityManagerFactory entityManagerFactory() {
    // …
  }
}
The preceding example uses the JPA-specific annotation, which you would change according to the store module you actually use. The same applies to the definition of the EntityManagerFactory bean. See the sections covering the store-specific configuration.

8.5.2. XML Configuration

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:

Example 28. Enabling Spring Data repositories via XML
<?xml version="1.0" encoding="UTF-8"?>
<beans:beans xmlns:beans="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns="http://www.springframework.org/schema/data/jpa"
  xsi:schemaLocation="http://www.springframework.org/schema/beans
    https://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/data/jpa
    https://www.springframework.org/schema/data/jpa/spring-jpa.xsd">

  <jpa:repositories base-package="com.acme.repositories" />

</beans:beans>

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.

8.5.3. Using Filters

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:

Example 29. Using filters
Java
@Configuration
@EnableMongoRepositories(basePackages = "com.acme.repositories",
    includeFilters = { @Filter(type = FilterType.REGEX, pattern = ".*SomeRepository") },
    excludeFilters = { @Filter(type = FilterType.REGEX, pattern = ".*SomeOtherRepository") })
class ApplicationConfiguration {

  @Bean
  EntityManagerFactory entityManagerFactory() {
    // …
  }
}
XML
<repositories base-package="com.acme.repositories">
  <context:exclude-filter type="regex" expression=".*SomeRepository" />
  <context:include-filter type="regex" expression=".*SomeOtherRepository" />
</repositories>

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

8.5.4. Standalone Usage

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:

Example 30. Standalone usage of the repository factory
RepositoryFactorySupport factory = … // Instantiate factory here
UserRepository repository = factory.getRepository(UserRepository.class);

8.6. Custom Implementations for Spring Data Repositories

Spring Data provides various options to create query methods with little coding. But when those options don’t fit your needs you can also provide your own custom implementation for repository methods. This section describes how to do that.

8.6.1. Customizing Individual Repositories

To enrich a repository with custom functionality, you must first define a fragment interface and an implementation for the custom functionality, as follows:

Example 31. Interface for custom repository functionality
interface CustomizedUserRepository {
  void someCustomMethod(User user);
}
Example 32. Implementation of custom repository functionality
class CustomizedUserRepositoryImpl implements CustomizedUserRepository {

  public void someCustomMethod(User user) {
    // Your custom implementation
  }
}
The most important part of the class name that corresponds to the fragment interface is the Impl postfix.

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:

Example 33. Changes to your repository interface
interface UserRepository extends CrudRepository<User, Long>, CustomizedUserRepository {

  // Declare query methods here
}

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:

Example 34. Fragments with their implementations
interface HumanRepository {
  void someHumanMethod(User user);
}

class HumanRepositoryImpl implements HumanRepository {

  public void someHumanMethod(User user) {
    // Your custom implementation
  }
}

interface ContactRepository {

  void someContactMethod(User user);

  User anotherContactMethod(User user);
}

class ContactRepositoryImpl implements ContactRepository {

  public void someContactMethod(User user) {
    // Your custom implementation
  }

  public User anotherContactMethod(User user) {
    // Your custom implementation
  }
}

The following example shows the interface for a custom repository that extends CrudRepository:

Example 35. Changes to your repository interface
interface UserRepository extends CrudRepository<User, Long>, HumanRepository, ContactRepository {

  // Declare query methods here
}

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:

Example 36. Fragments overriding save(…)
interface CustomizedSave<T> {
  <S extends T> S save(S entity);
}

class CustomizedSaveImpl<T> implements CustomizedSave<T> {

  public <S extends T> S save(S entity) {
    // Your custom implementation
  }
}

The following example shows a repository that uses the preceding repository fragment:

Example 37. Customized repository interfaces
interface UserRepository extends CrudRepository<User, Long>, CustomizedSave<User> {
}

interface PersonRepository extends CrudRepository<Person, Long>, CustomizedSave<Person> {
}
Configuration

The repository infrastructure tries to autodetect custom implementation fragments by scanning for classes below the package in which it found a repository. These classes need to follow the naming convention of appending a postfix defaulting to Impl.

The following example shows a repository that uses the default postfix and a repository that sets a custom value for the postfix:

Example 38. Configuration example
Java
@EnableMongoRepositories(repositoryImplementationPostfix = "MyPostfix")
class Configuration { … }
XML
<repositories base-package="com.acme.repository" />

<repositories base-package="com.acme.repository" repository-impl-postfix="MyPostfix" />

The first configuration in the preceding example tries to look up a class called com.acme.repository.CustomizedUserRepositoryImpl to act as a custom repository implementation. The second example tries to look up com.acme.repository.CustomizedUserRepositoryMyPostfix.

Resolution of Ambiguity

If multiple implementations with matching class names are found in different packages, Spring Data uses the bean names to identify which one to use.

Given the following two custom implementations for the CustomizedUserRepository shown earlier, the first implementation is used. Its bean name is customizedUserRepositoryImpl, which matches that of the fragment interface (CustomizedUserRepository) plus the postfix Impl.

Example 39. Resolution of ambiguous implementations
package com.acme.impl.one;

class CustomizedUserRepositoryImpl implements CustomizedUserRepository {

  // Your custom implementation
}
package com.acme.impl.two;

@Component("specialCustomImpl")
class CustomizedUserRepositoryImpl implements CustomizedUserRepository {

  // Your custom implementation
}

If you annotate the UserRepository interface with @Component("specialCustom"), the bean name plus Impl then matches the one defined for the repository implementation in com.acme.impl.two, and it is used instead of the first one.

Manual Wiring

If your custom implementation uses annotation-based configuration and autowiring only, the preceding approach shown works well, because it is treated as any other Spring bean. If your implementation fragment bean needs special wiring, you can declare the bean and name it according to the conventions described in the preceding section. The infrastructure then refers to the manually defined bean definition by name instead of creating one itself. The following example shows how to manually wire a custom implementation:

Example 40. Manual wiring of custom implementations
Java
class MyClass {
  MyClass(@Qualifier("userRepositoryImpl") UserRepository userRepository) {
    …
  }
}
XML
<repositories base-package="com.acme.repository" />

<beans:bean id="userRepositoryImpl" class="…">
  <!-- further configuration -->
</beans:bean>

8.6.2. Customize the Base Repository

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:

Example 41. Custom repository base class
class MyRepositoryImpl<T, ID>
  extends SimpleJpaRepository<T, ID> {

  private final EntityManager entityManager;

  MyRepositoryImpl(JpaEntityInformation entityInformation,
                          EntityManager entityManager) {
    super(entityInformation, entityManager);

    // Keep the EntityManager around to used from the newly introduced methods.
    this.entityManager = entityManager;
  }

  @Transactional
  public <S extends T> S save(S entity) {
    // implementation goes here
  }
}
The class needs to have a constructor of the super class which the store-specific repository factory implementation uses. If the repository base class has multiple constructors, override the one taking an EntityInformation plus a store specific infrastructure object (such as an EntityManager or a template class).

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:

Example 42. Configuring a custom repository base class
Java
@Configuration
@EnableMongoRepositories(repositoryBaseClass = MyRepositoryImpl.class)
class ApplicationConfiguration { … }
XML
<repositories base-package="com.acme.repository"
     base-class="….MyRepositoryImpl" />

8.7. Publishing Events from Aggregate Roots

Entities managed by repositories are aggregate roots. In a Domain-Driven Design application, these aggregate roots usually publish domain events. Spring Data provides an annotation called @DomainEvents that you can use on a method of your aggregate root to make that publication as easy as possible, as shown in the following example:

Example 43. Exposing domain events from an aggregate root
class AnAggregateRoot {

    @DomainEvents (1)
    Collection<Object> domainEvents() {
        // … return events you want to get published here
    }

    @AfterDomainEventPublication (2)
    void callbackMethod() {
       // … potentially clean up domain events list
    }
}
1 The method that uses @DomainEvents can return either a single event instance or a collection of events. It must not take any arguments.
2 After all events have been published, we have a method annotated with @AfterDomainEventPublication. You can use it to potentially clean the list of events to be published (among other uses).

The methods are called every time one of the following a Spring Data repository methods are called:

  • save(…), saveAll(…)

  • delete(…), deleteAll(…), deleteAllInBatch(…), deleteInBatch(…)

Note, that these methods take the aggregate root instances as arguments. This is why deleteById(…) is notably absent, as the implementations might choose to issue a query deleting the instance and thus we would never have access to the aggregate instance in the first place.

8.8. Spring Data Extensions

This section documents a set of Spring Data extensions that enable Spring Data usage in a variety of contexts. Currently, most of the integration is targeted towards Spring MVC.

8.8.1. Querydsl Extension

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:

Example 44. QuerydslPredicateExecutor interface
public interface QuerydslPredicateExecutor<T> {

  Optional<T> findById(Predicate predicate);  (1)

  Iterable<T> findAll(Predicate predicate);   (2)

  long count(Predicate predicate);            (3)

  boolean exists(Predicate predicate);        (4)

  // … more functionality omitted.
}
1 Finds and returns a single entity matching the Predicate.
2 Finds and returns all entities matching the Predicate.
3 Returns the number of entities matching the Predicate.
4 Returns whether an entity that matches the Predicate exists.

To use the Querydsl support, extend QuerydslPredicateExecutor on your repository interface, as the following example shows:

Example 45. Querydsl integration on repositories
interface UserRepository extends CrudRepository<User, Long>, QuerydslPredicateExecutor<User> {
}

The preceding example lets you write type-safe queries by using Querydsl Predicate instances, as the following example shows:

Predicate predicate = user.firstname.equalsIgnoreCase("dave")
	.and(user.lastname.startsWithIgnoreCase("mathews"));

userRepository.findAll(predicate);

8.8.2. Web support

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:

Example 46. Enabling Spring Data web support
Java
@Configuration
@EnableWebMvc
@EnableSpringDataWebSupport
class WebConfiguration {}
XML
<bean class="org.springframework.data.web.config.SpringDataWebConfiguration" />

<!-- If you use Spring HATEOAS, register this one *instead* of the former -->
<bean class="org.springframework.data.web.config.HateoasAwareSpringDataWebConfiguration" />

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.

Basic Web Support
Enabling Spring Data web support in XML

The configuration shown in the previous section registers a few basic components:

  • A Using the DomainClassConverter Class to let Spring MVC resolve instances of repository-managed domain classes from request parameters or path variables.

  • HandlerMethodArgumentResolver implementations to let Spring MVC resolve Pageable and Sort instances from request parameters.

  • Jackson Modules to de-/serialize types like Point and Distance, or store specific ones, depending on the Spring Data Module used.

Using the DomainClassConverter Class

The DomainClassConverter class lets you use domain types in your Spring MVC controller method signatures directly so that you need not manually lookup the instances through the repository, as the following example shows:

Example 47. A Spring MVC controller using domain types in method signatures
@Controller
@RequestMapping("/users")
class UserController {

  @RequestMapping("/{id}")
  String showUserForm(@PathVariable("id") User user, Model model) {

    model.addAttribute("user", user);
    return "userForm";
  }
}

The method receives a User instance directly, and no further lookup is necessary. The instance can be resolved by letting Spring MVC convert the path variable into the id type of the domain class first and eventually access the instance through calling findById(…) on the repository instance registered for the domain type.

Currently, the repository has to implement CrudRepository to be eligible to be discovered for conversion.
HandlerMethodArgumentResolvers for Pageable and Sort

The configuration snippet shown in the previous section also registers a PageableHandlerMethodArgumentResolver as well as an instance of SortHandlerMethodArgumentResolver. The registration enables Pageable and Sort as valid controller method arguments, as the following example shows:

Example 48. Using Pageable as a controller method argument
@Controller
@RequestMapping("/users")
class UserController {

  private final UserRepository repository;

  UserController(UserRepository repository) {
    this.repository = repository;
  }

  @RequestMapping
  String showUsers(Model model, Pageable pageable) {

    model.addAttribute("users", repository.findAll(pageable));
    return "users";
  }
}

The preceding method signature causes Spring MVC try to derive a Pageable instance from the request parameters by using the following default configuration:

Table 2. Request parameters evaluated for Pageable instances

page

Page you want to retrieve. 0-indexed and defaults to 0.

size

Size of the page you want to retrieve. Defaults to 20.

sort

Properties that should be sorted by in the format property,property(,ASC|DESC)(,IgnoreCase). The default sort direction is case-sensitive ascending. Use multiple sort parameters if you want to switch direction or case sensitivity — for example, ?sort=firstname&sort=lastname,asc&sort=city,ignorecase.

To customize this behavior, register a bean that implements the PageableHandlerMethodArgumentResolverCustomizer interface or the SortHandlerMethodArgumentResolverCustomizer interface, respectively. Its customize() method gets called, letting you change settings, as the following example shows:

@Bean SortHandlerMethodArgumentResolverCustomizer sortCustomizer() {
    return s -> s.setPropertyDelimiter("<-->");
}

If setting the properties of an existing MethodArgumentResolver is not sufficient for your purpose, extend either SpringDataWebConfiguration or the HATEOAS-enabled equivalent, override the pageableResolver() or sortResolver() methods, and import your customized configuration file instead of using the @Enable annotation.

If you need multiple Pageable or Sort instances to be resolved from the request (for multiple tables, for example), you can use Spring’s @Qualifier annotation to distinguish one from another. The request parameters then have to be prefixed with ${qualifier}_. The following example shows the resulting method signature:

String showUsers(Model model,
      @Qualifier("thing1") Pageable first,
      @Qualifier("thing2") Pageable second) { … }

You have to populate thing1_page, thing2_page, and so on.

The default Pageable passed into the method is equivalent to a PageRequest.of(0, 20), but you can customize it by using the @PageableDefault annotation on the Pageable parameter.

Hypermedia Support for Page and Slice

Spring HATEOAS ships with a representation model class (PagedModel/SlicedModel) that allows enriching the content of a Page or Slice instance with the necessary Page/Slice metadata as well as links to let the clients easily navigate the pages. The conversion of a Page to a PagedModel is done by an implementation of the Spring HATEOAS RepresentationModelAssembler interface, called the PagedResourcesAssembler. Similarly Slice instances can be converted to a SlicedModel using a SlicedResourcesAssembler. The following example shows how to use a PagedResourcesAssembler as a controller method argument, as the SlicedResourcesAssembler works exactly the same:

Example 49. Using a PagedResourcesAssembler as controller method argument
@Controller
class PersonController {

  private final PersonRepository repository;

  // Constructor omitted

  @GetMapping("/people")
  HttpEntity<PagedModel<Person>> people(Pageable pageable,
    PagedResourcesAssembler assembler) {

    Page<Person> people = repository.findAll(pageable);
    return ResponseEntity.ok(assembler.toModel(people));
  }
}

Enabling the configuration, as shown in the preceding example, lets the PagedResourcesAssembler be used as a controller method argument. Calling toModel(…) on it has the following effects:

  • The content of the Page becomes the content of the PagedModel instance.

  • The PagedModel object gets a PageMetadata instance attached, and it is populated with information from the Page and the underlying Pageable.

  • The PagedModel may get prev and next links attached, depending on the page’s state. The links point to the URI to which the method maps. The pagination parameters added to the method match the setup of the PageableHandlerMethodArgumentResolver to make sure the links can be resolved later.

Assume we have 30 Person instances in the database. You can now trigger a request (GET http://localhost:8080/people) and see output similar to the following:

{ "links" : [
    { "rel" : "next", "href" : "http://localhost:8080/persons?page=1&size=20" }
  ],
  "content" : [
     … // 20 Person instances rendered here
  ],
  "pageMetadata" : {
    "size" : 20,
    "totalElements" : 30,
    "totalPages" : 2,
    "number" : 0
  }
}
The JSON envelope format shown here doesn’t follow any formally specified structure and it’s not guaranteed stable and we might change it at any time. It’s highly recommended to enable the rendering as a hypermedia-enabled, official media type, supported by Spring HATEOAS, like HAL. Those can be activated by using its @EnableHypermediaSupport annotation. Find more information in the Spring HATEOAS reference documentation.

The assembler produced the correct URI and also picked up the default configuration to resolve the parameters into a Pageable for an upcoming request. This means that, if you change that configuration, the links automatically adhere to the change. By default, the assembler points to the controller method it was invoked in, but you can customize that by passing a custom Link to be used as base to build the pagination links, which overloads the PagedResourcesAssembler.toModel(…) method.

Spring Data Jackson Modules

The core module, and some of the store specific ones, ship with a set of Jackson Modules for types, like org.springframework.data.geo.Distance and org.springframework.data.geo.Point, used by the Spring Data domain.
Those Modules are imported once web support is enabled and com.fasterxml.jackson.databind.ObjectMapper is available.

During initialization SpringDataJacksonModules, like the SpringDataJacksonConfiguration, get picked up by the infrastructure, so that the declared com.fasterxml.jackson.databind.Modules are made available to the Jackson ObjectMapper.

Data binding mixins for the following domain types are registered by the common infrastructure.

org.springframework.data.geo.Distance
org.springframework.data.geo.Point
org.springframework.data.geo.Box
org.springframework.data.geo.Circle
org.springframework.data.geo.Polygon

The individual module may provide additional SpringDataJacksonModules.
Please refer to the store specific section for more details.

Web Databinding Support

You can use Spring Data projections (described in Projections) to bind incoming request payloads by using either JSONPath expressions (requires Jayway JsonPath) or XPath expressions (requires XmlBeam), as the following example shows:

Example 50. HTTP payload binding using JSONPath or XPath expressions
@ProjectedPayload
public interface UserPayload {

  @XBRead("//firstname")
  @JsonPath("$..firstname")
  String getFirstname();

  @XBRead("/lastname")
  @JsonPath({ "$.lastname", "$.user.lastname" })
  String getLastname();
}

You can use the type shown in the preceding example as a Spring MVC handler method argument or by using ParameterizedTypeReference on one of methods of the RestTemplate. The preceding method declarations would try to find firstname anywhere in the given document. The lastname XML lookup is performed on the top-level of the incoming document. The JSON variant of that tries a top-level lastname first but also tries lastname nested in a user sub-document if the former does not return a value. That way, changes in the structure of the source document can be mitigated easily without having clients calling the exposed methods (usually a drawback of class-based payload binding).

Nested projections are supported as described in Projections. If the method returns a complex, non-interface type, a Jackson ObjectMapper is used to map the final value.

For Spring MVC, the necessary converters are registered automatically as soon as @EnableSpringDataWebSupport is active and the required dependencies are available on the classpath. For usage with RestTemplate, register a ProjectingJackson2HttpMessageConverter (JSON) or XmlBeamHttpMessageConverter manually.

For more information, see the web projection example in the canonical Spring Data Examples repository.

Querydsl Web Support

For those stores that have QueryDSL integration, you can derive queries from the attributes contained in a Request query string.

Consider the following query string:

?firstname=Dave&lastname=Matthews

Given the User object from the previous examples, you can resolve a query string to the following value by using the QuerydslPredicateArgumentResolver, as follows:

QUser.user.firstname.eq("Dave").and(QUser.user.lastname.eq("Matthews"))
The feature is automatically enabled, along with @EnableSpringDataWebSupport, when Querydsl is found on the classpath.

Adding a @QuerydslPredicate to the method signature provides a ready-to-use Predicate, which you can run by using the QuerydslPredicateExecutor.

Type information is typically resolved from the method’s return type. Since that information does not necessarily match the domain type, it might be a good idea to use the root attribute of QuerydslPredicate.

The following example shows how to use @QuerydslPredicate in a method signature:

@Controller
class UserController {

  @Autowired UserRepository repository;

  @RequestMapping(value = "/", method = RequestMethod.GET)
  String index(Model model, @QuerydslPredicate(root = User.class) Predicate predicate,    (1)
          Pageable pageable, @RequestParam MultiValueMap<String, String> parameters) {

    model.addAttribute("users", repository.findAll(predicate, pageable));

    return "index";
  }
}
1 Resolve query string arguments to matching Predicate for User.

The default binding is as follows:

  • Object on simple properties as eq.

  • Object on collection like properties as contains.

  • Collection on simple properties as in.

You can customize those bindings through the bindings attribute of @QuerydslPredicate or by making use of Java 8 default methods and adding the QuerydslBinderCustomizer method to the repository interface, as follows:

interface UserRepository extends CrudRepository<User, String>,
                                 QuerydslPredicateExecutor<User>,                (1)
                                 QuerydslBinderCustomizer<QUser> {               (2)

  @Override
  default void customize(QuerydslBindings bindings, QUser user) {

    bindings.bind(user.username).first((path, value) -> path.contains(value))    (3)
    bindings.bind(String.class)
      .first((StringPath path, String value) -> path.containsIgnoreCase(value)); (4)
    bindings.excluding(user.password);                                           (5)
  }
}
1 QuerydslPredicateExecutor provides access to specific finder methods for Predicate.
2 QuerydslBinderCustomizer defined on the repository interface is automatically picked up and shortcuts @QuerydslPredicate(bindings=…​).
3 Define the binding for the username property to be a simple contains binding.
4 Define the default binding for String properties to be a case-insensitive contains match.
5 Exclude the password property from Predicate resolution.
You can register a QuerydslBinderCustomizerDefaults bean holding default Querydsl bindings before applying specific bindings from the repository or @QuerydslPredicate.

8.8.3. Repository Populators

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:

Example 51. Data defined in JSON
[ { "_class" : "com.acme.Person",
 "firstname" : "Dave",
  "lastname" : "Matthews" },
  { "_class" : "com.acme.Person",
 "firstname" : "Carter",
  "lastname" : "Beauford" } ]

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:

Example 52. Declaring a Jackson repository populator
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:repository="http://www.springframework.org/schema/data/repository"
  xsi:schemaLocation="http://www.springframework.org/schema/beans
    https://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/data/repository
    https://www.springframework.org/schema/data/repository/spring-repository.xsd">

  <repository:jackson2-populator locations="classpath:data.json" />

</beans>

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:

Example 53. Declaring an unmarshalling repository populator (using JAXB)
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:repository="http://www.springframework.org/schema/data/repository"
  xmlns:oxm="http://www.springframework.org/schema/oxm"
  xsi:schemaLocation="http://www.springframework.org/schema/beans
    https://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/data/repository
    https://www.springframework.org/schema/data/repository/spring-repository.xsd
    http://www.springframework.org/schema/oxm
    https://www.springframework.org/schema/oxm/spring-oxm.xsd">

  <repository:unmarshaller-populator locations="classpath:data.json"
    unmarshaller-ref="unmarshaller" />

  <oxm:jaxb2-marshaller contextPath="com.acme" />

</beans>

Reference Documentation

9. Introduction

9.1. Document Structure

This part of the reference documentation explains the core functionality offered by Spring Data MongoDB.

MongoDB support” introduces the MongoDB module feature set.

MongoDB Repositories” introduces the repository support for MongoDB.

10. MongoDB support

The MongoDB support contains a wide range of features:

  • Spring configuration support with Java-based @Configuration classes or an XML namespace for a Mongo driver instance and replica sets.

  • MongoTemplate helper class that increases productivity when performing common Mongo operations.Includes integrated object mapping between documents and POJOs.

  • Exception translation into Spring’s portable Data Access Exception hierarchy.

  • Feature-rich Object Mapping integrated with Spring’s Conversion Service.

  • Annotation-based mapping metadata that is extensible to support other metadata formats.

  • Persistence and mapping lifecycle events.

  • Java-based Query, Criteria, and Update DSLs.

  • Automatic implementation of Repository interfaces, including support for custom finder methods.

  • QueryDSL integration to support type-safe queries.

  • Cross-store persistence support for JPA Entities with fields transparently persisted and retrieved with MongoDB (deprecated - to be removed without replacement).

  • GeoSpatial integration.

For most tasks, you should use MongoTemplate or the Repository support, which both leverage the rich mapping functionality. MongoTemplate is the place to look for accessing functionality such as incrementing counters or ad-hoc CRUD operations. MongoTemplate also provides callback methods so that it is easy for you to get the low-level API artifacts, such as com.mongodb.client.MongoDatabase, to communicate directly with MongoDB. The goal with naming conventions on various API artifacts is to copy those in the base MongoDB Java driver so you can easily map your existing knowledge onto the Spring APIs.

10.1. Getting Started

An easy way to bootstrap setting up a working environment is to create a Spring-based project in STS.

First, you need to set up a running MongoDB server. Refer to the MongoDB Quick Start guide for an explanation on how to startup a MongoDB instance. Once installed, starting MongoDB is typically a matter of running the following command: ${MONGO_HOME}/bin/mongod

To create a Spring project in STS:

  1. Go to File → New → Spring Template Project → Simple Spring Utility Project, and press Yes when prompted. Then enter a project and a package name, such as org.spring.mongodb.example.

  2. Add the following to the pom.xml files dependencies element:

    <dependencies>
    
      <!-- other dependency elements omitted -->
    
      <dependency>
        <groupId>org.springframework.data</groupId>
        <artifactId>spring-data-mongodb</artifactId>
        <version>4.1.1</version>
      </dependency>
    
    </dependencies>
  3. Change the version of Spring in the pom.xml to be

    <spring.framework.version>6.0.10</spring.framework.version>
  4. Add the following location of the Spring Milestone repository for Maven to your pom.xml such that it is at the same level of your <dependencies/> element:

    <repositories>
      <repository>
        <id>spring-milestone</id>
        <name>Spring Maven MILESTONE Repository</name>
        <url>https://repo.spring.io/milestone</url>
      </repository>
    </repositories>

The repository is also browseable here.

You may also want to set the logging level to DEBUG to see some additional information. To do so, edit the log4j.properties file to have the following content:

log4j.category.org.springframework.data.mongodb=DEBUG
log4j.appender.stdout.layout.ConversionPattern=%d{ABSOLUTE} %5p %40.40c:%4L - %m%n

Then you can create a Person class to persist:

package org.spring.mongodb.example;

public class Person {

  private String id;
  private String name;
  private int age;

  public Person(String name, int age) {
    this.name = name;
    this.age = age;
  }

  public String getId() {
    return id;
  }
  public String getName() {
    return name;
  }
  public int getAge() {
    return age;
  }

  @Override
  public String toString() {
    return "Person [id=" + id + ", name=" + name + ", age=" + age + "]";
  }
}

You also need a main application to run:

package org.spring.mongodb.example;

import static org.springframework.data.mongodb.core.query.Criteria.where;

import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.springframework.data.mongodb.core.MongoOperations;
import org.springframework.data.mongodb.core.MongoTemplate;
import org.springframework.data.mongodb.core.query.Query;

import com.mongodb.client.MongoClients;

public class MongoApp {

  private static final Log log = LogFactory.getLog(MongoApp.class);

  public static void main(String[] args) throws Exception {

    MongoOperations mongoOps = new MongoTemplate(MongoClients.create(), "database");
    mongoOps.insert(new Person("Joe", 34));

    log.info(mongoOps.findOne(new Query(where("name").is("Joe")), Person.class));

    mongoOps.dropCollection("person");
  }
}

When you run the main program, the preceding examples produce the following output:

10:01:32,062 DEBUG apping.MongoPersistentEntityIndexCreator:  80 - Analyzing class class org.spring.example.Person for index information.
10:01:32,265 DEBUG ramework.data.mongodb.core.MongoTemplate: 631 - insert Document containing fields: [_class, age, name] in collection: Person
10:01:32,765 DEBUG ramework.data.mongodb.core.MongoTemplate:1243 - findOne using query: { "name" : "Joe"} in db.collection: database.Person
10:01:32,953  INFO      org.spring.mongodb.example.MongoApp:  25 - Person [id=4ddbba3c0be56b7e1b210166, name=Joe, age=34]
10:01:32,984 DEBUG ramework.data.mongodb.core.MongoTemplate: 375 - Dropped collection [database.person]

Even in this simple example, there are few things to notice:

  • You can instantiate the central helper class of Spring Mongo, MongoTemplate, by using the standard com.mongodb.client.MongoClient object and the name of the database to use.

  • The mapper works against standard POJO objects without the need for any additional metadata (though you can optionally provide that information. See here.).

  • Conventions are used for handling the id field, converting it to be an ObjectId when stored in the database.

  • Mapping conventions can use field access. Notice that the Person class has only getters.

  • If the constructor argument names match the field names of the stored document, they are used to instantiate the object

10.2. Examples Repository

There is a GitHub repository with several examples that you can download and play around with to get a feel for how the library works.

10.3. Connecting to MongoDB with Spring

One of the first tasks when using MongoDB and Spring is to create a com.mongodb.client.MongoClient object using the IoC container. There are two main ways to do this, either by using Java-based bean metadata or by using XML-based bean metadata. Both are discussed in the following sections.

For those not familiar with how to configure the Spring container using Java-based bean metadata instead of XML-based metadata, see the high-level introduction in the reference docs here as well as the detailed documentation here.

10.3.1. Registering a Mongo Instance by using Java-based Metadata

The following example shows an example of using Java-based bean metadata to register an instance of a com.mongodb.client.MongoClient:

Example 54. Registering a com.mongodb.client.MongoClient object using Java-based bean metadata
@Configuration
public class AppConfig {

  /*
   * Use the standard Mongo driver API to create a com.mongodb.client.MongoClient instance.
   */
   public @Bean MongoClient mongoClient() {
       return MongoClients.create("mongodb://localhost:27017");
   }
}

This approach lets you use the standard com.mongodb.client.MongoClient instance, with the container using Spring’s MongoClientFactoryBean. As compared to instantiating a com.mongodb.client.MongoClient instance directly, the FactoryBean has the added advantage of also providing the container with an ExceptionTranslator implementation that translates MongoDB exceptions to exceptions in Spring’s portable DataAccessException hierarchy for data access classes annotated with the @Repository annotation. This hierarchy and the use of @Repository is described in Spring’s DAO support features.

The following example shows an example of a Java-based bean metadata that supports exception translation on @Repository annotated classes:

Example 55. Registering a com.mongodb.client.MongoClient object by using Spring’s MongoClientFactoryBean and enabling Spring’s exception translation support
@Configuration
public class AppConfig {

    /*
     * Factory bean that creates the com.mongodb.client.MongoClient instance
     */
     public @Bean MongoClientFactoryBean mongo() {
          MongoClientFactoryBean mongo = new MongoClientFactoryBean();
          mongo.setHost("localhost");
          return mongo;
     }
}

To access the com.mongodb.client.MongoClient object created by the MongoClientFactoryBean in other @Configuration classes or your own classes, use a private @Autowired MongoClient mongoClient; field.

10.3.2. Registering a Mongo Instance by Using XML-based Metadata

While you can use Spring’s traditional <beans/> XML namespace to register an instance of com.mongodb.client.MongoClient with the container, the XML can be quite verbose, as it is general-purpose. XML namespaces are a better alternative to configuring commonly used objects, such as the Mongo instance. The mongo namespace lets you create a Mongo instance server location, replica-sets, and options.

To use the Mongo namespace elements, you need to reference the Mongo schema, as follows:

Example 56. XML schema to configure MongoDB
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
          xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
          xmlns:mongo="http://www.springframework.org/schema/data/mongo"
          xsi:schemaLocation=
          "
          http://www.springframework.org/schema/data/mongo https://www.springframework.org/schema/data/mongo/spring-mongo.xsd
          http://www.springframework.org/schema/beans
          https://www.springframework.org/schema/beans/spring-beans.xsd">

    <!-- Default bean name is 'mongo' -->
    <mongo:mongo-client host="localhost" port="27017"/>

</beans>

The following example shows a more advanced configuration with MongoClientSettings (note that these are not recommended values):

Example 57. XML schema to configure a com.mongodb.client.MongoClient object with MongoClientSettings
<beans>

  <mongo:mongo-client host="localhost" port="27017">
    <mongo:client-settings connection-pool-max-connection-life-time="10"
        connection-pool-min-size="10"
		connection-pool-max-size="20"
		connection-pool-maintenance-frequency="10"
		connection-pool-maintenance-initial-delay="11"
		connection-pool-max-connection-idle-time="30"
		connection-pool-max-wait-time="15" />
  </mongo:mongo-client>

</beans>

The following example shows a configuration using replica sets:

Example 58. XML schema to configure a com.mongodb.client.MongoClient object with Replica Sets
<mongo:mongo-client id="replicaSetMongo" replica-set="rs0">
    <mongo:client-settings cluster-hosts="127.0.0.1:27017,localhost:27018" />
</mongo:mongo-client>

10.3.3. The MongoDatabaseFactory Interface

While com.mongodb.client.MongoClient is the entry point to the MongoDB driver API, connecting to a specific MongoDB database instance requires additional information, such as the database name and an optional username and password. With that information, you can obtain a com.mongodb.client.MongoDatabase object and access all the functionality of a specific MongoDB database instance. Spring provides the org.springframework.data.mongodb.core.MongoDatabaseFactory interface, shown in the following listing, to bootstrap connectivity to the database:

public interface MongoDatabaseFactory {

  MongoDatabase getDatabase() throws DataAccessException;

  MongoDatabase getDatabase(String dbName) throws DataAccessException;
}

The following sections show how you can use the container with either Java-based or XML-based metadata to configure an instance of the MongoDatabaseFactory interface. In turn, you can use the MongoDatabaseFactory instance to configure MongoTemplate.

Instead of using the IoC container to create an instance of MongoTemplate, you can use them in standard Java code, as follows:

public class MongoApp {

  private static final Log log = LogFactory.getLog(MongoApp.class);

  public static void main(String[] args) throws Exception {

    MongoOperations mongoOps = new MongoTemplate(new SimpleMongoClientDatabaseFactory(MongoClients.create(), "database"));

    mongoOps.insert(new Person("Joe", 34));

    log.info(mongoOps.findOne(new Query(where("name").is("Joe")), Person.class));

    mongoOps.dropCollection("person");
  }
}

The code in bold highlights the use of SimpleMongoClientDbFactory and is the only difference between the listing shown in the getting started section.

Use SimpleMongoClientDbFactory when choosing com.mongodb.client.MongoClient as the entrypoint of choice.

10.3.4. Registering a MongoDatabaseFactory

To register a MongoDatabaseFactory instance with the container, you write code much like what was highlighted in the previous code listing. The following listing shows a simple example:

@Configuration
public class MongoConfiguration {

  @Bean
  public MongoDatabaseFactory mongoDatabaseFactory() {
    return new SimpleMongoClientDatabaseFactory(MongoClients.create(), "database");
  }
}

MongoDB Server generation 3 changed the authentication model when connecting to the DB. Therefore, some of the configuration options available for authentication are no longer valid. You should use the MongoClient-specific options for setting credentials through MongoCredential to provide authentication data, as shown in the following example:

Java
@Configuration
public class ApplicationContextEventTestsAppConfig extends AbstractMongoClientConfiguration {

  @Override
  public String getDatabaseName() {
    return "database";
  }

  @Override
  protected void configureClientSettings(Builder builder) {

    builder
        .credential(MongoCredential.createCredential("name", "db", "pwd".toCharArray()))
        .applyToClusterSettings(settings  -> {
          settings.hosts(singletonList(new ServerAddress("127.0.0.1", 27017)));
        });
  }
}
XML
<mongo:db-factory dbname="database" />
Username and password credentials used in XML-based configuration must be URL-encoded when these contain reserved characters, such as :, %, @, or ,. The following example shows encoded credentials: m0ng0@dmin:mo_res:bw6},Qsdxx@admin@databasem0ng0%40dmin:mo_res%3Abw6%7D%2CQsdxx%40admin@database See section 2.2 of RFC 3986 for further details.

If you need to configure additional options on the com.mongodb.client.MongoClient instance that is used to create a SimpleMongoClientDbFactory, you can refer to an existing bean as shown in the following example. To show another common usage pattern, the following listing shows the use of a property placeholder, which lets you parametrize the configuration and the creation of a MongoTemplate:

Java
@Configuration
@PropertySource("classpath:/com/myapp/mongodb/config/mongo.properties")
public class ApplicationContextEventTestsAppConfig extends AbstractMongoClientConfiguration {

  @Autowired
  Environment env;

  @Override
  public String getDatabaseName() {
    return "database";
  }

  @Override
  protected void configureClientSettings(Builder builder) {

    builder.applyToClusterSettings(settings -> {
    settings.hosts(singletonList(
          new ServerAddress(env.getProperty("mongo.host"), env.getProperty("mongo.port", Integer.class))));
    });

    builder.applyToConnectionPoolSettings(settings -> {

      settings.maxConnectionLifeTime(env.getProperty("mongo.pool-max-life-time", Integer.class), TimeUnit.MILLISECONDS)
          .minSize(env.getProperty("mongo.pool-min-size", Integer.class))
          .maxSize(env.getProperty("mongo.pool-max-size", Integer.class))
          .maintenanceFrequency(10, TimeUnit.MILLISECONDS)
          .maintenanceInitialDelay(11, TimeUnit.MILLISECONDS)
          .maxConnectionIdleTime(30, TimeUnit.SECONDS)
          .maxWaitTime(15, TimeUnit.MILLISECONDS);
    });
  }
}
XML
<context:property-placeholder location="classpath:/com/myapp/mongodb/config/mongo.properties"/>

<mongo:mongo-client host="${mongo.host}" port="${mongo.port}">
  <mongo:client-settings connection-pool-max-connection-life-time="${mongo.pool-max-life-time}"
    connection-pool-min-size="${mongo.pool-min-size}"
    connection-pool-max-size="${mongo.pool-max-size}"
    connection-pool-maintenance-frequency="10"
    connection-pool-maintenance-initial-delay="11"
    connection-pool-max-connection-idle-time="30"
    connection-pool-max-wait-time="15" />
</mongo:mongo-client>

<mongo:db-factory dbname="database" mongo-ref="mongoClient"/>

<bean id="anotherMongoTemplate" class="org.springframework.data.mongodb.core.MongoTemplate">
  <constructor-arg name="mongoDbFactory" ref="mongoDbFactory"/>
</bean>

10.4. Introduction to MongoTemplate

The MongoTemplate class, located in the org.springframework.data.mongodb.core package, is the central class of Spring’s MongoDB support and provides a rich feature set for interacting with the database. The template offers convenience operations to create, update, delete, and query MongoDB documents and provides a mapping between your domain objects and MongoDB documents.

Once configured, MongoTemplate is thread-safe and can be reused across multiple instances.

The mapping between MongoDB documents and domain classes is done by delegating to an implementation of the MongoConverter interface. Spring provides MappingMongoConverter, but you can also write your own converter. See “Custom Conversions - Overriding Default Mapping” for more detailed information.

The MongoTemplate class implements the interface MongoOperations. In as much as possible, the methods on MongoOperations are named after methods available on the MongoDB driver Collection object, to make the API familiar to existing MongoDB developers who are used to the driver API. For example, you can find methods such as find, findAndModify, findAndReplace, findOne, insert, remove, save, update, and updateMulti. The design goal was to make it as easy as possible to transition between the use of the base MongoDB driver and MongoOperations. A major difference between the two APIs is that MongoOperations can be passed domain objects instead of Document. Also, MongoOperations has fluent APIs for Query, Criteria, and Update operations instead of populating a Document to specify the parameters for those operations.

The preferred way to reference the operations on MongoTemplate instance is through its interface, MongoOperations.

The default converter implementation used by MongoTemplate is MappingMongoConverter. While the MappingMongoConverter can use additional metadata to specify the mapping of objects to documents, it can also convert objects that contain no additional metadata by using some conventions for the mapping of IDs and collection names. These conventions, as well as the use of mapping annotations, are explained in the “Mapping” chapter.

Another central feature of MongoTemplate is translation of exceptions thrown by the MongoDB Java driver into Spring’s portable Data Access Exception hierarchy. See “Exception Translation” for more information.

MongoTemplate offers many convenience methods to help you easily perform common tasks. However, if you need to directly access the MongoDB driver API, you can use one of several Execute callback methods. The execute callbacks gives you a reference to either a com.mongodb.client.MongoCollection or a com.mongodb.client.MongoDatabase object. See the “Execution Callbacks” section for more information.

The next section contains an example of how to work with the MongoTemplate in the context of the Spring container.

10.4.1. Instantiating MongoTemplate

You can use the following configuration to create and register an instance of MongoTemplate, as the following example shows:

Example 59. Registering a com.mongodb.client.MongoClient object and enabling Spring’s exception translation support
Java
@Configuration
class AppConfig {

  @Bean
  MongoClient mongoClient() {
      return MongoClients.create("mongodb://localhost:27017");
  }

  @Bean
  MongoTemplate mongoTemplate(MongoClient mongoClient) {
      return new MongoTemplate(mongoClient, "geospatial");
  }
}
XML
<mongo:mongo-client host="localhost" port="27017"/>

<bean id="mongoTemplate" class="org.springframework.data.mongodb.core.MongoTemplate">
  <constructor-arg ref="mongoClient"/>
  <constructor-arg name="databaseName" value="geospatial"/>
</bean>

There are several overloaded constructors of MongoTemplate:

  • MongoTemplate(MongoClient mongo, String databaseName): Takes the MongoClient object and the default database name to operate against.

  • MongoTemplate(MongoDatabaseFactory mongoDbFactory): Takes a MongoDbFactory object that encapsulated the MongoClient object, database name, and username and password.

  • MongoTemplate(MongoDatabaseFactory mongoDbFactory, MongoConverter mongoConverter): Adds a MongoConverter to use for mapping.

Other optional properties that you might like to set when creating a MongoTemplate are the default WriteResultCheckingPolicy, WriteConcern, and ReadPreference properties.

The preferred way to reference the operations on MongoTemplate instance is through its interface, MongoOperations.

10.4.2. WriteResultChecking Policy

When in development, it is handy to either log or throw an exception if the com.mongodb.WriteResult returned from any MongoDB operation contains an error. It is quite common to forget to do this during development and then end up with an application that looks like it runs successfully when, in fact, the database was not modified according to your expectations. You can set the WriteResultChecking property of MongoTemplate to one of the following values: EXCEPTION or NONE, to either throw an Exception or do nothing, respectively. The default is to use a WriteResultChecking value of NONE.

10.4.3. WriteConcern

If it has not yet been specified through the driver at a higher level (such as com.mongodb.client.MongoClient), you can set the com.mongodb.WriteConcern property that the MongoTemplate uses for write operations. If the WriteConcern property is not set, it defaults to the one set in the MongoDB driver’s DB or Collection setting.

10.4.4. WriteConcernResolver

For more advanced cases where you want to set different WriteConcern values on a per-operation basis (for remove, update, insert, and save operations), a strategy interface called WriteConcernResolver can be configured on MongoTemplate. Since MongoTemplate is used to persist POJOs, the WriteConcernResolver lets you create a policy that can map a specific POJO class to a WriteConcern value. The following listing shows the WriteConcernResolver interface:

public interface WriteConcernResolver {
  WriteConcern resolve(MongoAction action);
}

You can use the MongoAction argument to determine the WriteConcern value or use the value of the Template itself as a default. MongoAction contains the collection name being written to, the java.lang.Class of the POJO, the converted Document, the operation (REMOVE, UPDATE, INSERT, INSERT_LIST, or SAVE), and a few other pieces of contextual information. The following example shows two sets of classes getting different WriteConcern settings:

private class MyAppWriteConcernResolver implements WriteConcernResolver {

  public WriteConcern resolve(MongoAction action) {
    if (action.getEntityClass().getSimpleName().contains("Audit")) {
      return WriteConcern.NONE;
    } else if (action.getEntityClass().getSimpleName().contains("Metadata")) {
      return WriteConcern.JOURNAL_SAFE;
    }
    return action.getDefaultWriteConcern();
  }
}

10.5. Saving, Updating, and Removing Documents

MongoTemplate lets you save, update, and delete your domain objects and map those objects to documents stored in MongoDB.

Consider the following class:

public class Person {

  private String id;
  private String name;
  private int age;

  public Person(String name, int age) {
    this.name = name;
    this.age = age;
  }

  public String getId() {
    return id;
  }
  public String getName() {
    return name;
  }
  public int getAge() {
    return age;
  }

  @Override
  public String toString() {
    return "Person [id=" + id + ", name=" + name + ", age=" + age + "]";
  }

}

Given the Person class in the preceding example, you can save, update and delete the object, as the following example shows:

MongoOperations is the interface that MongoTemplate implements.
package org.spring.example;

import static org.springframework.data.mongodb.core.query.Criteria.where;
import static org.springframework.data.mongodb.core.query.Update.update;
import static org.springframework.data.mongodb.core.query.Query.query;

import java.util.List;

import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.springframework.data.mongodb.core.MongoOperations;
import org.springframework.data.mongodb.core.MongoTemplate;
import org.springframework.data.mongodb.core.SimpleMongoClientDbFactory;

import com.mongodb.client.MongoClients;

public class MongoApp {

  private static final Log log = LogFactory.getLog(MongoApp.class);

  public static void main(String[] args) {

    MongoOperations mongoOps = new MongoTemplate(new SimpleMongoClientDbFactory(MongoClients.create(), "database"));

    Person p = new Person("Joe", 34);

    // Insert is used to initially store the object into the database.
    mongoOps.insert(p);
    log.info("Insert: " + p);

    // Find
    p = mongoOps.findById(p.getId(), Person.class);
    log.info("Found: " + p);

    // Update
    mongoOps.updateFirst(query(where("name").is("Joe")), update("age", 35), Person.class);
    p = mongoOps.findOne(query(where("name").is("Joe")), Person.class);
    log.info("Updated: " + p);

    // Delete
    mongoOps.remove(p);

    // Check that deletion worked
    List<Person> people =  mongoOps.findAll(Person.class);
    log.info("Number of people = : " + people.size());


    mongoOps.dropCollection(Person.class);
  }
}

The preceding example would produce the following log output (including debug messages from MongoTemplate):

DEBUG apping.MongoPersistentEntityIndexCreator:  80 - Analyzing class class org.spring.example.Person for index information.
DEBUG work.data.mongodb.core.MongoTemplate: 632 - insert Document containing fields: [_class, age, name] in collection: person
INFO               org.spring.example.MongoApp:  30 - Insert: Person [id=4ddc6e784ce5b1eba3ceaf5c, name=Joe, age=34]
DEBUG work.data.mongodb.core.MongoTemplate:1246 - findOne using query: { "_id" : { "$oid" : "4ddc6e784ce5b1eba3ceaf5c"}} in db.collection: database.person
INFO               org.spring.example.MongoApp:  34 - Found: Person [id=4ddc6e784ce5b1eba3ceaf5c, name=Joe, age=34]
DEBUG work.data.mongodb.core.MongoTemplate: 778 - calling update using query: { "name" : "Joe"} and update: { "$set" : { "age" : 35}} in collection: person
DEBUG work.data.mongodb.core.MongoTemplate:1246 - findOne using query: { "name" : "Joe"} in db.collection: database.person
INFO               org.spring.example.MongoApp:  39 - Updated: Person [id=4ddc6e784ce5b1eba3ceaf5c, name=Joe, age=35]
DEBUG work.data.mongodb.core.MongoTemplate: 823 - remove using query: { "id" : "4ddc6e784ce5b1eba3ceaf5c"} in collection: person
INFO               org.spring.example.MongoApp:  46 - Number of people = : 0
DEBUG work.data.mongodb.core.MongoTemplate: 376 - Dropped collection [database.person]

MongoConverter caused implicit conversion between a String and an ObjectId stored in the database by recognizing (through convention) the Id property name.

The preceding example is meant to show the use of save, update, and remove operations on MongoTemplate and not to show complex mapping functionality.

The query syntax used in the preceding example is explained in more detail in the section “Querying Documents”.

10.5.1. How the _id Field is Handled in the Mapping Layer

MongoDB requires that you have an _id field for all documents. If you do not provide one, the driver assigns an ObjectId with a generated value. When you use the MappingMongoConverter, certain rules govern how properties from the Java class are mapped to this _id field:

  1. A property or field annotated with @Id (org.springframework.data.annotation.Id) maps to the _id field.

  2. A property or field without an annotation but named id maps to the _id field.

The following outlines what type conversion, if any, is done on the property mapped to the _id document field when using the MappingMongoConverter (the default for MongoTemplate).

  1. If possible, an id property or field declared as a String in the Java class is converted to and stored as an ObjectId by using a Spring Converter<String, ObjectId>. Valid conversion rules are delegated to the MongoDB Java driver. If it cannot be converted to an ObjectId, then the value is stored as a string in the database.

  2. An id property or field declared as BigInteger in the Java class is converted to and stored as an ObjectId by using a Spring Converter<BigInteger, ObjectId>.

If no field or property specified in the previous sets of rules is present in the Java class, an implicit _id file is generated by the driver but not mapped to a property or field of the Java class.

When querying and updating, MongoTemplate uses the converter that corresponds to the preceding rules for saving documents so that field names and types used in your queries can match what is in your domain classes.

Some environments require a customized approach to map Id values such as data stored in MongoDB that did not run through the Spring Data mapping layer. Documents can contain _id values that can be represented either as ObjectId or as String. Reading documents from the store back to the domain type works just fine. Querying for documents via their id can be cumbersome due to the implicit ObjectId conversion. Therefore documents cannot be retrieved that way. For those cases @MongoId provides more control over the actual id mapping attempts.

Example 60. @MongoId mapping
public class PlainStringId {
  @MongoId String id; (1)
}

public class PlainObjectId {
  @MongoId ObjectId id; (2)
}

public class StringToObjectId {
  @MongoId(FieldType.OBJECT_ID) String id; (3)
}
1 The id is treated as String without further conversion.
2 The id is treated as ObjectId.
3 The id is treated as ObjectId if the given String is a valid ObjectId hex, otherwise as String. Corresponds to @Id usage.

10.5.2. Type Mapping

MongoDB collections can contain documents that represent instances of a variety of types.This feature can be useful if you store a hierarchy of classes or have a class with a property of type Object.In the latter case, the values held inside that property have to be read in correctly when retrieving the object.Thus, we need a mechanism to store type information alongside the actual document.

To achieve that, the MappingMongoConverter uses a MongoTypeMapper abstraction with DefaultMongoTypeMapper as its main implementation.Its default behavior to store the fully qualified classname under _class inside the document.Type hints are written for top-level documents as well as for every value (if it is a complex type and a subtype of the declared property type).The following example (with a JSON representation at the end) shows how the mapping works:

Example 61. Type mapping
class Sample {
  Contact value;
}

abstract class Contact { … }

class Person extends Contact { … }

Sample sample = new Sample();
sample.value = new Person();

mongoTemplate.save(sample);

{
  "value" : { "_class" : "com.acme.Person" },
  "_class" : "com.acme.Sample"
}

Spring Data MongoDB stores the type information as the last field for the actual root class as well as for the nested type (because it is complex and a subtype of Contact).So, if you now use mongoTemplate.findAll(Object.class, "sample"), you can find out that the document stored is a Sample instance.You can also find out that the value property is actually a Person.

Customizing Type Mapping

If you want to avoid writing the entire Java class name as type information but would rather like to use a key, you can use the @TypeAlias annotation on the entity class.If you need to customize the mapping even more, have a look at the TypeInformationMapper interface.An instance of that interface can be configured at the DefaultMongoTypeMapper, which can, in turn, be configured on MappingMongoConverter.The following example shows how to define a type alias for an entity:

Example 62. Defining a type alias for an Entity
@TypeAlias("pers")
class Person {

}

Note that the resulting document contains pers as the value in the _class Field.

Type aliases only work if the mapping context is aware of the actual type. The required entity metadata is determined either on first save or has to be provided via the configurations initial entity set. By default, the configuration class scans the base package for potential candidates.

@Configuration
class AppConfig extends AbstractMongoClientConfiguration {

  @Override
  protected Set<Class<?>> getInitialEntitySet() {
    return Collections.singleton(Person.class);
  }

  // ...
}
Configuring Custom Type Mapping

The following example shows how to configure a custom MongoTypeMapper in MappingMongoConverter:

class CustomMongoTypeMapper extends DefaultMongoTypeMapper {
  //implement custom type mapping here
}
Example 63. Configuring a custom MongoTypeMapper
Java
@Configuration
class SampleMongoConfiguration extends AbstractMongoClientConfiguration {

  @Override
  protected String getDatabaseName() {
    return "database";
  }

  @Bean
  @Override
  public MappingMongoConverter mappingMongoConverter(MongoDatabaseFactory databaseFactory,
			MongoCustomConversions customConversions, MongoMappingContext mappingContext) {
    MappingMongoConverter mmc = super.mappingMongoConverter();
    mmc.setTypeMapper(customTypeMapper());
    return mmc;
  }

  @Bean
  public MongoTypeMapper customTypeMapper() {
    return new CustomMongoTypeMapper();
  }
}
XML
<mongo:mapping-converter type-mapper-ref="customMongoTypeMapper"/>

<bean name="customMongoTypeMapper" class="com.acme.CustomMongoTypeMapper"/>

Note that the preceding example extends the AbstractMongoClientConfiguration class and overrides the bean definition of the MappingMongoConverter where we configured our custom MongoTypeMapper.

10.5.3. Methods for Saving and Inserting Documents

There are several convenient methods on MongoTemplate for saving and inserting your objects. To have more fine-grained control over the conversion process, you can register Spring converters with the MappingMongoConverter — for example Converter<Person, Document> and Converter<Document, Person>.

The difference between insert and save operations is that a save operation performs an insert if the object is not already present.

The simple case of using the save operation is to save a POJO. In this case, the collection name is determined by name (not fully qualified) of the class. You may also call the save operation with a specific collection name. You can use mapping metadata to override the collection in which to store the object.

When inserting or saving, if the Id property is not set, the assumption is that its value will be auto-generated by the database. Consequently, for auto-generation of an ObjectId to succeed, the type of the Id property or field in your class must be a String, an ObjectId, or a BigInteger.

The following example shows how to save a document and retrieving its contents:

Example 64. Inserting and retrieving documents using the MongoTemplate
import static org.springframework.data.mongodb.core.query.Criteria.where;
import static org.springframework.data.mongodb.core.query.Criteria.query;

…

Person p = new Person("Bob", 33);
mongoTemplate.insert(p);

Person qp = mongoTemplate.findOne(query(where("age").is(33)), Person.class);

The following insert and save operations are available:

  • void save (Object objectToSave): Save the object to the default collection.

  • void save (Object objectToSave, String collectionName): Save the object to the specified collection.

A similar set of insert operations is also available:

  • void insert (Object objectToSave): Insert the object to the default collection.

  • void insert (Object objectToSave, String collectionName): Insert the object to the specified collection.

Into Which Collection Are My Documents Saved?

There are two ways to manage the collection name that is used for the documents. The default collection name that is used is the class name changed to start with a lower-case letter. So a com.test.Person class is stored in the person collection. You can customize this by providing a different collection name with the @Document annotation. You can also override the collection name by providing your own collection name as the last parameter for the selected MongoTemplate method calls.

Inserting or Saving Individual Objects

The MongoDB driver supports inserting a collection of documents in a single operation. The following methods in the MongoOperations interface support this functionality:

  • insert: Inserts an object. If there is an existing document with the same id, an error is generated.

  • insertAll: Takes a Collection of objects as the first parameter. This method inspects each object and inserts it into the appropriate collection, based on the rules specified earlier.

  • save: Saves the object, overwriting any object that might have the same id.

Inserting Several Objects in a Batch

The MongoDB driver supports inserting a collection of documents in one operation. The following methods in the MongoOperations interface support this functionality:

  • insert methods: Take a Collection as the first argument. They insert a list of objects in a single batch write to the database.

10.5.4. Updating Documents in a Collection

For updates, you can update the first document found by using MongoOperation.updateFirst or you can update all documents that were found to match the query by using the MongoOperation.updateMulti method. The following example shows an update of all SAVINGS accounts where we are adding a one-time $50.00 bonus to the balance by using the $inc operator:

Example 65. Updating documents by using the MongoTemplate
import static org.springframework.data.mongodb.core.query.Criteria.where;
import static org.springframework.data.mongodb.core.query.Query;
import static org.springframework.data.mongodb.core.query.Update;

...

WriteResult wr = mongoTemplate.updateMulti(new Query(where("accounts.accountType").is(Account.Type.SAVINGS)),
  new Update().inc("accounts.$.balance", 50.00), Account.class);

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 modifiers available for MongoDB.

Most methods return the Update object to provide a fluent style for the API.

Methods for Running Updates for Documents
  • updateFirst: Updates the first document that matches the query document criteria with the updated document.

  • updateMulti: Updates all objects that match the query document criteria with the updated document.

updateFirst does not support ordering. Please use findAndModify to apply Sort. NOTE: Index hints for the update operation can be provided via Query.withHint(…​).
Methods in the Update Class

You can use a little "'syntax sugar'" with the Update class, as its methods are meant to be chained together. Also, you can kick-start the creation of a new Update instance by using public static Update update(String key, Object value) and using static imports.

The Update class contains the following methods:

  • Update addToSet (String key, Object value) Update using the $addToSet update modifier

  • Update currentDate (String key) Update using the $currentDate update modifier

  • Update currentTimestamp (String key) Update using the $currentDate update modifier with $type timestamp

  • Update inc (String key, Number inc) Update using the $inc update modifier

  • Update max (String key, Object max) Update using the $max update modifier

  • Update min (String key, Object min) Update using the $min update modifier

  • Update multiply (String key, Number multiplier) Update using the $mul update modifier

  • Update pop (String key, Update.Position pos) Update using the $pop update modifier

  • Update pull (String key, Object value) Update using the $pull update modifier

  • Update pullAll (String key, Object[] values) Update using the $pullAll update modifier

  • Update push (String key, Object value) Update using the $push update modifier

  • Update pushAll (String key, Object[] values) Update using the $pushAll update modifier

  • Update rename (String oldName, String newName) Update using the $rename update modifier

  • Update set (String key, Object value) Update using the $set update modifier

  • Update setOnInsert (String key, Object value) Update using the $setOnInsert update modifier

  • Update unset (String key) Update using the $unset update modifier

Some update modifiers, such as $push and $addToSet, allow nesting of additional operators.

// { $push : { "category" : { "$each" : [ "spring" , "data" ] } } }
new Update().push("category").each("spring", "data")

// { $push : { "key" : { "$position" : 0 , "$each" : [ "Arya" , "Arry" , "Weasel" ] } } }
new Update().push("key").atPosition(Position.FIRST).each(Arrays.asList("Arya", "Arry", "Weasel"));

// { $push : { "key" : { "$slice" : 5 , "$each" : [ "Arya" , "Arry" , "Weasel" ] } } }
new Update().push("key").slice(5).each(Arrays.asList("Arya", "Arry", "Weasel"));

// { $addToSet : { "values" : { "$each" : [ "spring" , "data" , "mongodb" ] } } }
new Update().addToSet("values").each("spring", "data", "mongodb");

10.5.5. “Upserting” Documents in a Collection

Related to performing an updateFirst operation, you can also perform an “upsert” operation, which will perform an insert if no document is found that matches the query. The document that is inserted is a combination of the query document and the update document. The following example shows how to use the upsert method:

template.update(Person.class)
  .matching(query(where("ssn").is(1111).and("firstName").is("Joe").and("Fraizer").is("Update"))
  .apply(update("address", addr))
  .upsert();
upsert does not support ordering. Please use findAndModify to apply Sort.

10.5.6. Finding and Upserting Documents in a Collection

The findAndModify(…) method on MongoCollection can update a document and return either the old or newly updated document in a single operation. MongoTemplate provides four findAndModify overloaded methods that take Query and Update classes and converts from Document to your POJOs:

<T> T findAndModify(Query query, Update update, Class<T> entityClass);

<T> T findAndModify(Query query, Update update, Class<T> entityClass, String collectionName);

<T> T findAndModify(Query query, Update update, FindAndModifyOptions options, Class<T> entityClass);

<T> T findAndModify(Query query, Update update, FindAndModifyOptions options, Class<T> entityClass, String collectionName);

The following example inserts a few Person objects into the container and performs a findAndUpdate operation:

template.insert(new Person("Tom", 21));
template.insert(new Person("Dick", 22));
template.insert(new Person("Harry", 23));

Query query = new Query(Criteria.where("firstName").is("Harry"));
Update update = new Update().inc("age", 1);

Person oldValue = template.update(Person.class)
  .matching(query)
  .apply(update)
  .findAndModifyValue(); // return's old person object

assertThat(oldValue.getFirstName()).isEqualTo("Harry");
assertThat(oldValue.getAge()).isEqualTo(23);

Person newValue = template.query(Person.class)
  .matching(query)
  .findOneValue();

assertThat(newValue.getAge()).isEqualTo(24);

Person newestValue = template.update(Person.class)
  .matching(query)
  .apply(update)
  .withOptions(FindAndModifyOptions.options().returnNew(true)) // Now return the newly updated document when updating
  .findAndModifyValue();

assertThat(newestValue.getAge()).isEqualTo(25);

The FindAndModifyOptions method lets you set the options of returnNew, upsert, and remove.An example extending from the previous code snippet follows:

Person upserted = template.update(Person.class)
  .matching(new Query(Criteria.where("firstName").is("Mary")))
  .apply(update)
  .withOptions(FindAndModifyOptions.options().upsert(true).returnNew(true))
  .findAndModifyValue()

assertThat(upserted.getFirstName()).isEqualTo("Mary");
assertThat(upserted.getAge()).isOne();

10.5.7. Aggregation Pipeline Updates

Update methods exposed by MongoOperations and ReactiveMongoOperations also accept an Aggregation Pipeline via AggregationUpdate. Using AggregationUpdate allows leveraging MongoDB 4.2 aggregations in an update operation. Using aggregations in an update allows updating one or more fields by expressing multiple stages and multiple conditions with a single operation.

The update can consist of the following stages:

  • AggregationUpdate.set(…​).toValue(…​)$set : { …​ }

  • AggregationUpdate.unset(…​)$unset : [ …​ ]

  • AggregationUpdate.replaceWith(…​)$replaceWith : { …​ }

Example 66. Update Aggregation
AggregationUpdate update = Aggregation.newUpdate()
    .set("average").toValue(ArithmeticOperators.valueOf("tests").avg())     (1)
    .set("grade").toValue(ConditionalOperators.switchCases(                 (2)
        when(valueOf("average").greaterThanEqualToValue(90)).then("A"),
        when(valueOf("average").greaterThanEqualToValue(80)).then("B"),
        when(valueOf("average").greaterThanEqualToValue(70)).then("C"),
        when(valueOf("average").greaterThanEqualToValue(60)).then("D"))
        .defaultTo("F")
    );

template.update(Student.class)                                              (3)
    .apply(update)
    .all();                                                                 (4)
db.students.update(                                                         (3)
   { },
   [
     { $set: { average : { $avg: "$tests" } } },                            (1)
     { $set: { grade: { $switch: {                                          (2)
                           branches: [
                               { case: { $gte: [ "$average", 90 ] }, then: "A" },
                               { case: { $gte: [ "$average", 80 ] }, then: "B" },
                               { case: { $gte: [ "$average", 70 ] }, then: "C" },
                               { case: { $gte: [ "$average", 60 ] }, then: "D" }
                           ],
                           default: "F"
     } } } }
   ],
   { multi: true }                                                          (4)
)
1 The 1st $set stage calculates a new field average based on the average of the tests field.
2 The 2nd $set stage calculates a new field grade based on the average field calculated by the first aggregation stage.
3 The pipeline is run on the students collection and uses Student for the aggregation field mapping.
4 Apply the update to all matching documents in the collection.

10.5.8. Finding and Replacing Documents

The most straight forward method of replacing an entire Document is via its id using the save method. However this might not always be feasible. findAndReplace offers an alternative that allows to identify the document to replace via a simple query.

Example 67. Find and Replace Documents
Optional<User> result = template.update(Person.class)      (1)
    .matching(query(where("firstame").is("Tom")))          (2)
    .replaceWith(new Person("Dick"))
    .withOptions(FindAndReplaceOptions.options().upsert()) (3)
    .as(User.class)                                        (4)
    .findAndReplace();                                     (5)
1 Use the fluent update API with the domain type given for mapping the query and deriving the collection name or just use MongoOperations#findAndReplace.
2 The actual match query mapped against the given domain type. Provide sort, fields and collation settings via the query.
3 Additional optional hook to provide options other than the defaults, like upsert.
4 An optional projection type used for mapping the operation result. If none given the initial domain type is used.
5 Trigger the actual processing. Use findAndReplaceValue to obtain the nullable result instead of an Optional.
Please note that the replacement must not hold an id itself as the id of the existing Document will be carried over to the replacement by the store itself. Also keep in mind that findAndReplace will only replace the first document matching the query criteria depending on a potentially given sort order.

10.5.9. Methods for Removing Documents

You can use one of five overloaded methods to remove an object from the database:

template.remove(tywin, "GOT");                                              (1)

template.remove(query(where("lastname").is("lannister")), "GOT");           (2)

template.remove(new Query().limit(3), "GOT");                               (3)

template.findAllAndRemove(query(where("lastname").is("lannister"), "GOT");  (4)

template.findAllAndRemove(new Query().limit(3), "GOT");                     (5)
1 Remove a single entity specified by its _id from the associated collection.
2 Remove all documents that match the criteria of the query from the GOT collection.
3 Remove the first three documents in the GOT collection. Unlike <2>, the documents to remove are identified by their _id, running the given query, applying sort, limit, and skip options first, and then removing all at once in a separate step.
4 Remove all documents matching the criteria of the query from the GOT collection. Unlike <3>, documents do not get deleted in a batch but one by one.
5 Remove the first three documents in the GOT collection. Unlike <3>, documents do not get deleted in a batch but one by one.

10.5.10. Optimistic Locking

The @Version annotation provides syntax similar to that of JPA in the context of MongoDB and makes sure updates are only applied to documents with a matching version. Therefore, the actual value of the version property is added to the update query in such a way that the update does not have any effect if another operation altered the document in the meantime. In that case, an OptimisticLockingFailureException is thrown. The following example shows these features:

@Document
class Person {

  @Id String id;
  String firstname;
  String lastname;
  @Version Long version;
}

Person daenerys = template.insert(new Person("Daenerys"));                            (1)

Person tmp = template.findOne(query(where("id").is(daenerys.getId())), Person.class); (2)

daenerys.setLastname("Targaryen");
template.save(daenerys);                                                              (3)

template.save(tmp); // throws OptimisticLockingFailureException                       (4)
1 Intially insert document. version is set to 0.
2 Load the just inserted document. version is still 0.
3 Update the document with version = 0. Set the lastname and bump version to 1.
4 Try to update the previously loaded document that still has version = 0. The operation fails with an OptimisticLockingFailureException, as the current version is 1.
Optimistic Locking requires to set the WriteConcern to ACKNOWLEDGED. Otherwise OptimisticLockingFailureException can be silently swallowed.
As of Version 2.2 MongoOperations also includes the @Version property when removing an entity from the database. To remove a Document without version check use MongoOperations#remove(Query,…​) instead of MongoOperations#remove(Object).
As of Version 2.2 repositories check for the outcome of acknowledged deletes when removing versioned entities. An OptimisticLockingFailureException is raised if a versioned entity cannot be deleted through CrudRepository.delete(Object). In such case, the version was changed or the object was deleted in the meantime. Use CrudRepository.deleteById(ID) to bypass optimistic locking functionality and delete objects regardless of their version.

10.6. Querying Documents

You can use the Query and Criteria classes to express your queries.They have method names that mirror the native MongoDB operator names, such as lt, lte, is, and others.The Query and Criteria classes follow a fluent API style so that you can chain together multiple method criteria and queries while having easy-to-understand code.To improve readability, static imports let you avoid using the 'new' keyword for creating Query and Criteria instances.You can also use BasicQuery to create Query instances from plain JSON Strings, as shown in the following example:

Example 68. Creating a Query instance from a plain JSON String
BasicQuery query = new BasicQuery("{ age : { $lt : 50 }, accounts.balance : { $gt : 1000.00 }}");
List<Person> result = mongoTemplate.find(query, Person.class);

Spring MongoDB also supports GeoSpatial queries (see the GeoSpatial Queries section) and Map-Reduce operations (see the Map-Reduce section.).

10.6.1. Querying Documents in a Collection

Earlier, we saw how to retrieve a single document by using the findOne and findById methods on MongoTemplate. These methods return a single domain object. We can also query for a collection of documents to be returned as a list of domain objects. Assuming that we have a number of Person objects with name and age stored as documents in a collection and that each person has an embedded account document with a balance, we can now run a query using the following code:

Example 69. Querying for documents using the MongoTemplate
import static org.springframework.data.mongodb.core.query.Criteria.where;
import static org.springframework.data.mongodb.core.query.Query.query;

// ...

List<Person> result = template.query(Person.class)
  .matching(query(where("age").lt(50).and("accounts.balance").gt(1000.00d)))
  .all();

All find methods take a Query object as a parameter. This object defines the criteria and options used to perform the query. The criteria are specified by using a Criteria object that has a static factory method named where to instantiate a new Criteria object. We recommend using static imports for org.springframework.data.mongodb.core.query.Criteria.where and Query.query to make the query more readable.

The query should return a list of Person objects that meet the specified criteria. The rest of this section lists the methods of the Criteria and Query classes that correspond to the operators provided in MongoDB. Most methods return the Criteria object, to provide a fluent style for the API.

Methods for the Criteria Class

The Criteria class provides the following methods, all of which correspond to operators in MongoDB:

  • Criteria all (Object o) Creates a criterion using the $all operator

  • Criteria and (String key) Adds a chained Criteria with the specified key to the current Criteria and returns the newly created one

  • Criteria andOperator (Criteria…​ criteria) Creates an and query using the $and operator for all of the provided criteria (requires MongoDB 2.0 or later)

  • Criteria andOperator (Collection<Criteria> criteria) Creates an and query using the $and operator for all of the provided criteria (requires MongoDB 2.0 or later)

  • Criteria elemMatch (Criteria c) Creates a criterion using the $elemMatch operator

  • Criteria exists (boolean b) Creates a criterion using the $exists operator

  • Criteria gt (Object o) Creates a criterion using the $gt operator

  • Criteria gte (Object o) Creates a criterion using the $gte operator

  • Criteria in (Object…​ o) Creates a criterion using the $in operator for a varargs argument.

  • Criteria in (Collection<?> collection) Creates a criterion using the $in operator using a collection

  • Criteria is (Object o) Creates a criterion using field matching ({ key:value }). If the specified value is a document, the order of the fields and exact equality in the document matters.

  • Criteria lt (Object o) Creates a criterion using the $lt operator

  • Criteria lte (Object o) Creates a criterion using the $lte operator

  • Criteria mod (Number value, Number remainder) Creates a criterion using the $mod operator

  • Criteria ne (Object o) Creates a criterion using the $ne operator

  • Criteria nin (Object…​ o) Creates a criterion using the $nin operator

  • Criteria norOperator (Criteria…​ criteria) Creates an nor query using the $nor operator for all of the provided criteria

  • Criteria norOperator (Collection<Criteria> criteria) Creates an nor query using the $nor operator for all of the provided criteria

  • Criteria not () Creates a criterion using the $not meta operator which affects the clause directly following

  • Criteria orOperator (Criteria…​ criteria) Creates an or query using the $or operator for all of the provided criteria

  • Criteria orOperator (Collection<Criteria> criteria) Creates an or query using the $or operator for all of the provided criteria

  • Criteria regex (String re) Creates a criterion using a $regex

  • Criteria sampleRate (double sampleRate) Creates a criterion using the $sampleRate operator

  • Criteria size (int s) Creates a criterion using the $size operator

  • Criteria type (int t) Creates a criterion using the $type operator

  • Criteria matchingDocumentStructure (MongoJsonSchema schema) Creates a criterion using the $jsonSchema operator for JSON schema criteria. $jsonSchema can only be applied on the top level of a query and not property specific. Use the properties attribute of the schema to match against nested fields.

  • Criteria bits() is the gateway to MongoDB bitwise query operators like $bitsAllClear.

The Criteria class also provides the following methods for geospatial queries (see the GeoSpatial Queries section to see them in action):

  • Criteria within (Circle circle) Creates a geospatial criterion using $geoWithin $center operators.

  • Criteria within (Box box) Creates a geospatial criterion using a $geoWithin $box operation.

  • Criteria withinSphere (Circle circle) Creates a geospatial criterion using $geoWithin $center operators.

  • Criteria near (Point point) Creates a geospatial criterion using a $near operation

  • Criteria nearSphere (Point point) Creates a geospatial criterion using $nearSphere$center operations. This is only available for MongoDB 1.7 and higher.

  • Criteria minDistance (double minDistance) Creates a geospatial criterion using the $minDistance operation, for use with $near.

  • Criteria maxDistance (double maxDistance) Creates a geospatial criterion using the $maxDistance operation, for use with $near.

Methods for the Query class

The Query class has some additional methods that provide options for the query:

  • Query addCriteria (Criteria criteria) used to add additional criteria to the query

  • Field fields () used to define fields to be included in the query results

  • Query limit (int limit) used to limit the size of the returned results to the provided limit (used for paging)

  • Query skip (int skip) used to skip the provided number of documents in the results (used for paging)

  • Query with (Sort sort) used to provide sort definition for the results

  • Query with (ScrollPosition position) used to provide a scroll position (Offset- or Keyset-based pagination) to start or resume a Scroll

Selecting fields

MongoDB supports projecting fields returned by a query. A projection can include and exclude fields (the _id field is always included unless explicitly excluded) based on their name.

Example 70. Selecting result fields
public class Person {

    @Id String id;
    String firstname;

    @Field("last_name")
    String lastname;

    Address address;
}

query.fields().include("lastname");              (1)

query.fields().exclude("id").include("lastname") (2)

query.fields().include("address")                (3)

query.fields().include("address.city")           (4)
1 Result will contain both _id and last_name via { "last_name" : 1 }.
2 Result will only contain the last_name via { "_id" : 0, "last_name" : 1 }.
3 Result will contain the _id and entire address object via { "address" : 1 }.
4 Result will contain the _id and and address object that only contains the city field via { "address.city" : 1 }.

Starting with MongoDB 4.4 you can use aggregation expressions for field projections as shown below:

Example 71. Computing result fields using expressions
query.fields()
  .project(MongoExpression.create("'$toUpper' : '$last_name'"))         (1)
  .as("last_name");                                                     (2)

query.fields()
  .project(StringOperators.valueOf("lastname").toUpper())               (3)
  .as("last_name");

query.fields()
  .project(AggregationSpELExpression.expressionOf("toUpper(lastname)")) (4)
  .as("last_name");
1 Use a native expression. The used field name must refer to field names within the database document.
2 Assign the field name to which the expression result is projected. The resulting field name is not mapped against the domain model.
3 Use an AggregationExpression. Other than native MongoExpression, field names are mapped to the ones used in the domain model.
4 Use SpEL along with an AggregationExpression to invoke expression functions. Field names are mapped to the ones used in the domain model.

@Query(fields="…") allows usage of expression field projections at Repository level as described in MongoDB JSON-based Query Methods and Field Restriction.

10.6.2. Methods for Querying for Documents

The query methods need to specify the target type T that is returned, and they are overloaded with an explicit collection name for queries that should operate on a collection other than the one indicated by the return type. The following query methods let you find one or more documents:

  • findAll: Query for a list of objects of type T from the collection.

  • findOne: Map the results of an ad-hoc query on the collection to a single instance of an object of the specified type.

  • findById: Return an object of the given ID and target class.

  • find: Map the results of an ad-hoc query on the collection to a List of the specified type.

  • findAndRemove: Map the results of an ad-hoc query on the collection to a single instance of an object of the specified type. The first document that matches the query is returned and removed from the collection in the database.

10.6.3. Query Distinct Values

MongoDB provides an operation to obtain distinct values for a single field by using a query from the resulting documents. Resulting values are not required to have the same data type, nor is the feature limited to simple types. For retrieval, the actual result type does matter for the sake of conversion and typing. The following example shows how to query for distinct values:

Example 72. Retrieving distinct values
template.query(Person.class)  (1)
  .distinct("lastname")       (2)
  .all();                     (3)
1 Query the Person collection.
2 Select distinct values of the lastname field. The field name is mapped according to the domain types property declaration, taking potential @Field annotations into account.
3 Retrieve all distinct values as a List of Object (due to no explicit result type being specified).

Retrieving distinct values into a Collection of Object is the most flexible way, as it tries to determine the property value of the domain type and convert results to the desired type or mapping Document structures.

Sometimes, when all values of the desired field are fixed to a certain type, it is more convenient to directly obtain a correctly typed Collection, as shown in the following example:

Example 73. Retrieving strongly typed distinct values
template.query(Person.class)  (1)
  .distinct("lastname")       (2)
  .as(String.class)           (3)
  .all();                     (4)
1 Query the collection of Person.
2 Select distinct values of the lastname field. The fieldname is mapped according to the domain types property declaration, taking potential @Field annotations into account.
3 Retrieved values are converted into the desired target type — in this case, String. It is also possible to map the values to a more complex type if the stored field contains a document.
4 Retrieve all distinct values as a List of String. If the type cannot be converted into the desired target type, this method throws a DataAccessException.

10.6.4. GeoSpatial Queries

MongoDB supports GeoSpatial queries through the use of operators such as $near, $within, geoWithin, and $nearSphere. Methods specific to geospatial queries are available on the Criteria class. There are also a few shape classes (Box, Circle, and Point) that are used in conjunction with geospatial related Criteria methods.

Using GeoSpatial queries requires attention when used within MongoDB transactions, see Special behavior inside transactions.

To understand how to perform GeoSpatial queries, consider the following Venue class (taken from the integration tests and relying on the rich MappingMongoConverter):

@Document(collection="newyork")
public class Venue {

  @Id
  private String id;
  private String name;
  private double[] location;

  @PersistenceConstructor
  Venue(String name, double[] location) {
    super();
    this.name = name;
    this.location = location;
  }

  public Venue(String name, double x, double y) {
    super();
    this.name = name;
    this.location = new double[] { x, y };
  }

  public String getName() {
    return name;
  }

  public double[] getLocation() {
    return location;
  }

  @Override
  public String toString() {
    return "Venue [id=" + id + ", name=" + name + ", location="
        + Arrays.toString(location) + "]";
  }
}

To find locations within a Circle, you can use the following query:

Circle circle = new Circle(-73.99171, 40.738868, 0.01);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").within(circle)), Venue.class);

To find venues within a Circle using spherical coordinates, you can use the following query:

Circle circle = new Circle(-73.99171, 40.738868, 0.003712240453784);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").withinSphere(circle)), Venue.class);

To find venues within a Box, you can use the following query:

//lower-left then upper-right
Box box = new Box(new Point(-73.99756, 40.73083), new Point(-73.988135, 40.741404));
List<Venue> venues =
    template.find(new Query(Criteria.where("location").within(box)), Venue.class);

To find venues near a Point, you can use the following queries:

Point point = new Point(-73.99171, 40.738868);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").near(point).maxDistance(0.01)), Venue.class);
Point point = new Point(-73.99171, 40.738868);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").near(point).minDistance(0.01).maxDistance(100)), Venue.class);

To find venues near a Point using spherical coordinates, you can use the following query:

Point point = new Point(-73.99171, 40.738868);
List<Venue> venues =
    template.find(new Query(
        Criteria.where("location").nearSphere(point).maxDistance(0.003712240453784)),
        Venue.class);
Geo-near Queries

Changed in 2.2!
MongoDB 4.2 removed support for the geoNear command which had been previously used to run the NearQuery.

Spring Data MongoDB 2.2 MongoOperations#geoNear uses the $geoNear aggregation instead of the geoNear command to run a NearQuery.

The calculated distance (the dis when using a geoNear command) previously returned within a wrapper type now is embedded into the resulting document. If the given domain type already contains a property with that name, the calculated distance is named calculated-distance with a potentially random postfix.

Target types may contain a property named after the returned distance to (additionally) read it back directly into the domain type as shown below.

GeoResults<VenueWithDisField> = template.query(Venue.class) (1)
    .as(VenueWithDisField.class)                            (2)
    .near(NearQuery.near(new GeoJsonPoint(-73.99, 40.73), KILOMETERS))
    .all();
1 Domain type used to identify the target collection and potential query mapping.
2 Target type containing a dis field of type Number.

MongoDB supports querying the database for geo locations and calculating the distance from a given origin at the same time. With geo-near queries, you can express queries such as "find all restaurants in the surrounding 10 miles". To let you do so, MongoOperations provides geoNear(…) methods that take a NearQuery as an argument (as well as the already familiar entity type and collection), as shown in the following example:

Point location = new Point(-73.99171, 40.738868);
NearQuery query = NearQuery.near(location).maxDistance(new Distance(10, Metrics.MILES));

GeoResults<Restaurant> = operations.geoNear(query, Restaurant.class);

We use the NearQuery builder API to set up a query to return all Restaurant instances surrounding the given Point out to 10 miles. The Metrics enum used here actually implements an interface so that other metrics could be plugged into a distance as well. A Metric is backed by a multiplier to transform the distance value of the given metric into native distances. The sample shown here would consider the 10 to be miles. Using one of the built-in metrics (miles and kilometers) automatically triggers the spherical flag to be set on the query. If you want to avoid that, pass plain double values into maxDistance(…). For more information, see the JavaDoc of NearQuery and Distance.

The geo-near operations return a GeoResults wrapper object that encapsulates GeoResult instances. Wrapping GeoResults allows accessing the average distance of all results. A single GeoResult object carries the entity found plus its distance from the origin.

10.6.5. GeoJSON Support

MongoDB supports GeoJSON and simple (legacy) coordinate pairs for geospatial data. Those formats can both be used for storing as well as querying data. See the MongoDB manual on GeoJSON support to learn about requirements and restrictions.

GeoJSON Types in Domain Classes

Usage of GeoJSON types in domain classes is straightforward. The org.springframework.data.mongodb.core.geo package contains types such as GeoJsonPoint, GeoJsonPolygon, and others. These types are extend the existing org.springframework.data.geo types. The following example uses a GeoJsonPoint:

public class Store {

	String id;

	/**
	 * location is stored in GeoJSON format.
	 * {
	 *   "type" : "Point",
	 *   "coordinates" : [ x, y ]
	 * }
	 */
	GeoJsonPoint location;
}

If the coordinates of a GeoJSON object represent latitude and longitude pairs, the longitude goes first followed by latitude.
GeoJsonPoint therefore treats getX() as longitude and getY() as latitude.

GeoJSON Types in Repository Query Methods

Using GeoJSON types as repository query parameters forces usage of the $geometry operator when creating the query, as the following example shows:

public interface StoreRepository extends CrudRepository<Store, String> {

	List<Store> findByLocationWithin(Polygon polygon);  (1)

}

/*
 * {
 *   "location": {
 *     "$geoWithin": {
 *       "$geometry": {
 *         "type": "Polygon",
 *         "coordinates": [
 *           [
 *             [-73.992514,40.758934],
 *             [-73.961138,40.760348],
 *             [-73.991658,40.730006],
 *             [-73.992514,40.758934]
 *           ]
 *         ]
 *       }
 *     }
 *   }
 * }
 */
repo.findByLocationWithin(                              (2)
  new GeoJsonPolygon(
    new Point(-73.992514, 40.758934),
    new Point(-73.961138, 40.760348),
    new Point(-73.991658, 40.730006),
    new Point(-73.992514, 40.758934)));                 (3)

/*
 * {
 *   "location" : {
 *     "$geoWithin" : {
 *        "$polygon" : [ [-73.992514,40.758934] , [-73.961138,40.760348] , [-73.991658,40.730006] ]
 *     }
 *   }
 * }
 */
repo.findByLocationWithin(                              (4)
  new Polygon(
    new Point(-73.992514, 40.758934),
    new Point(-73.961138, 40.760348),
    new Point(-73.991658, 40.730006)));
1 Repository method definition using the commons type allows calling it with both the GeoJSON and the legacy format.
2 Use GeoJSON type to make use of $geometry operator.
3 Note that GeoJSON polygons need to define a closed ring.
4 Use the legacy format $polygon operator.
Metrics and Distance calculation

Then MongoDB $geoNear operator allows usage of a GeoJSON Point or legacy coordinate pairs.

NearQuery.near(new Point(-73.99171, 40.738868))
{
  "$geoNear": {
    //...
    "near": [-73.99171, 40.738868]
  }
}
NearQuery.near(new GeoJsonPoint(-73.99171, 40.738868))
{
  "$geoNear": {
    //...
    "near": { "type": "Point", "coordinates": [-73.99171, 40.738868] }
  }
}

Though syntactically different the server is fine accepting both no matter what format the target Document within the collection is using.

There is a huge difference in the distance calculation. Using the legacy format operates upon Radians on an Earth like sphere, whereas the GeoJSON format uses Meters.

To avoid a serious headache make sure to set the Metric to the desired unit of measure which ensures the distance to be calculated correctly.

In other words:

Assume you’ve got 5 Documents like the ones below:

{
    "_id" : ObjectId("5c10f3735d38908db52796a5"),
    "name" : "Penn Station",
    "location" : { "type" : "Point", "coordinates" : [  -73.99408, 40.75057 ] }
}
{
    "_id" : ObjectId("5c10f3735d38908db52796a6"),
    "name" : "10gen Office",
    "location" : { "type" : "Point", "coordinates" : [ -73.99171, 40.738868 ] }
}
{
    "_id" : ObjectId("5c10f3735d38908db52796a9"),
    "name" : "City Bakery ",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
}
{
    "_id" : ObjectId("5c10f3735d38908db52796aa"),
    "name" : "Splash Bar",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
}
{
    "_id" : ObjectId("5c10f3735d38908db52796ab"),
    "name" : "Momofuku Milk Bar",
    "location" : { "type" : "Point", "coordinates" : [ -73.985839, 40.731698 ] }
}

Fetching all Documents within a 400 Meter radius from [-73.99171, 40.738868] would look like this using GeoJSON:

Example 74. GeoNear with GeoJSON
{
    "$geoNear": {
        "maxDistance": 400, (1)
        "num": 10,
        "near": { type: "Point", coordinates: [-73.99171, 40.738868] },
        "spherical":true, (2)
        "key": "location",
        "distanceField": "distance"
    }
}

Returning the following 3 Documents:

{
    "_id" : ObjectId("5c10f3735d38908db52796a6"),
    "name" : "10gen Office",
    "location" : { "type" : "Point", "coordinates" : [ -73.99171, 40.738868 ] }
    "distance" : 0.0 (3)
}
{
    "_id" : ObjectId("5c10f3735d38908db52796a9"),
    "name" : "City Bakery ",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
    "distance" : 69.3582262492474 (3)
}
{
    "_id" : ObjectId("5c10f3735d38908db52796aa"),
    "name" : "Splash Bar",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
    "distance" : 69.3582262492474 (3)
}
1 Maximum distance from center point in Meters.
2 GeoJSON always operates upon a sphere.
3 Distance from center point in Meters.

Now, when using legacy coordinate pairs one operates upon Radians as discussed before. So we use Metrics#KILOMETERS when constructing the `$geoNear command. The Metric makes sure the distance multiplier is set correctly.

Example 75. GeoNear with Legacy Coordinate Pairs
{
    "$geoNear": {
        "maxDistance": 0.0000627142377, (1)
        "distanceMultiplier": 6378.137, (2)
        "num": 10,
        "near": [-73.99171, 40.738868],
        "spherical":true, (3)
        "key": "location",
        "distanceField": "distance"
    }
}

Returning the 3 Documents just like the GeoJSON variant:

{
    "_id" : ObjectId("5c10f3735d38908db52796a6"),
    "name" : "10gen Office",
    "location" : { "type" : "Point", "coordinates" : [ -73.99171, 40.738868 ] }
    "distance" : 0.0 (4)
}
{
    "_id" : ObjectId("5c10f3735d38908db52796a9"),
    "name" : "City Bakery ",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
    "distance" : 0.0693586286032982 (4)
}
{
    "_id" : ObjectId("5c10f3735d38908db52796aa"),
    "name" : "Splash Bar",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
    "distance" : 0.0693586286032982 (4)
}
1 Maximum distance from center point in Radians.
2 The distance multiplier so we get Kilometers as resulting distance.
3 Make sure we operate on a 2d_sphere index.
4 Distance from center point in Kilometers - take it times 1000 to match Meters of the GeoJSON variant.
GeoJSON Jackson Modules

By using the Web support, Spring Data registers additional Jackson Moduless to the ObjectMapper for de-/serializing common Spring Data domain types. Please refer to the Spring Data Jackson Modules section to learn more about the infrastructure setup of this feature.

The MongoDB module additionally registers JsonDeserializers for the following GeoJSON types via its GeoJsonConfiguration exposing the GeoJsonModule.

org.springframework.data.mongodb.core.geo.GeoJsonPoint
org.springframework.data.mongodb.core.geo.GeoJsonMultiPoint
org.springframework.data.mongodb.core.geo.GeoJsonLineString
org.springframework.data.mongodb.core.geo.GeoJsonMultiLineString
org.springframework.data.mongodb.core.geo.GeoJsonPolygon
org.springframework.data.mongodb.core.geo.GeoJsonMultiPolygon

The GeoJsonModule only registers JsonDeserializers!
To equip the ObjectMapper with a symmetric set of JsonSerializers you need to either manually configure those for the ObjectMapper or provide a custom SpringDataJacksonModules configuration exposing GeoJsonModule.serializers() as a Spring Bean.

class GeoJsonConfiguration implements SpringDataJacksonModules {

	@Bean
	public Module geoJsonSerializers() {
		return GeoJsonModule.serializers();
	}
}

The next major version (4.0) will register both, JsonDeserializers and JsonSerializers for GeoJSON types by default.

10.6.6. Full-text Queries

Since version 2.6 of MongoDB, you can run full-text queries by using the $text operator. Methods and operations specific to full-text queries are available in TextQuery and TextCriteria. When doing full text search, see the MongoDB reference for its behavior and limitations.

Full-text Search

Before you can actually use full-text search, you must set up the search index correctly. See Text Index for more detail on how to create index structures. The following example shows how to set up a full-text search:

db.foo.createIndex(
{
  title : "text",
  content : "text"
},
{
  weights : {
              title : 3
            }
}
)

A query searching for coffee cake can be defined and run as follows:

Example 76. Full Text Query
Query query = TextQuery
  .queryText(new TextCriteria().matchingAny("coffee", "cake"));

List<Document> page = template.find(query, Document.class);

To sort results by relevance according to the weights use TextQuery.sortByScore.

Example 77. Full Text Query - Sort by Score
Query query = TextQuery
  .queryText(new TextCriteria().matchingAny("coffee", "cake"))
  .sortByScore() (1)
  .includeScore(); (2)

List<Document> page = template.find(query, Document.class);
1 Use the score property for sorting results by relevance which triggers .sort({'score': {'$meta': 'textScore'}}).
2 Use TextQuery.includeScore() to include the calculated relevance in the resulting Document.

You can exclude search terms by prefixing the term with - or by using notMatching, as shown in the following example (note that the two lines have the same effect and are thus redundant):

// search for 'coffee' and not 'cake'
TextQuery.queryText(new TextCriteria().matching("coffee").matching("-cake"));
TextQuery.queryText(new TextCriteria().matching("coffee").notMatching("cake"));

TextCriteria.matching takes the provided term as is. Therefore, you can define phrases by putting them between double quotation marks (for example, \"coffee cake\") or using by TextCriteria.phrase. The following example shows both ways of defining a phrase:

// search for phrase 'coffee cake'
TextQuery.queryText(new TextCriteria().matching("\"coffee cake\""));
TextQuery.queryText(new TextCriteria().phrase("coffee cake"));

You can set flags for $caseSensitive and $diacriticSensitive by using the corresponding methods on TextCriteria. Note that these two optional flags have been introduced in MongoDB 3.2 and are not included in the query unless explicitly set.

10.6.7. Collations

Since version 3.4, MongoDB supports collations for collection and index creation and various query operations. Collations define string comparison rules based on the ICU collations. A collation document consists of various properties that are encapsulated in Collation, as the following listing shows:

Collation collation = Collation.of("fr")         (1)

  .strength(ComparisonLevel.secondary()          (2)
    .includeCase())

  .numericOrderingEnabled()                      (3)

  .alternate(Alternate.shifted().punct())        (4)

  .forwardDiacriticSort()                        (5)

  .normalizationEnabled();                       (6)
1 Collation requires a locale for creation. This can be either a string representation of the locale, a Locale (considering language, country, and variant) or a CollationLocale. The locale is mandatory for creation.
2 Collation strength defines comparison levels that denote differences between characters. You can configure various options (case-sensitivity, case-ordering, and others), depending on the selected strength.
3 Specify whether to compare numeric strings as numbers or as strings.
4 Specify whether the collation should consider whitespace and punctuation as base characters for purposes of comparison.
5 Specify whether strings with diacritics sort from back of the string, such as with some French dictionary ordering.
6 Specify whether to check whether text requires normalization and whether to perform normalization.

Collations can be used to create collections and indexes. If you create a collection that specifies a collation, the collation is applied to index creation and queries unless you specify a different collation. A collation is valid for a whole operation and cannot be specified on a per-field basis.

Like other metadata, collations can be be derived from the domain type via the collation attribute of the @Document annotation and will be applied directly when running queries, creating collections or indexes.

Annotated collations will not be used when a collection is auto created by MongoDB on first interaction. This would require additional store interaction delaying the entire process. Please use MongoOperations.createCollection for those cases.
Collation french = Collation.of("fr");
Collation german = Collation.of("de");

template.createCollection(Person.class, CollectionOptions.just(collation));

template.indexOps(Person.class).ensureIndex(new Index("name", Direction.ASC).collation(german));
MongoDB uses simple binary comparison if no collation is specified (Collation.simple()).

Using collations with collection operations is a matter of specifying a Collation instance in your query or operation options, as the following two examples show:

Example 78. Using collation with find
Collation collation = Collation.of("de");

Query query = new Query(Criteria.where("firstName").is("Amél")).collation(collation);

List<Person> results = template.find(query, Person.class);
Example 79. Using collation with aggregate
Collation collation = Collation.of("de");

AggregationOptions options = AggregationOptions.builder().collation(collation).build();

Aggregation aggregation = newAggregation(
  project("tags"),
  unwind("tags"),
  group("tags")
    .count().as("count")
).withOptions(options);

AggregationResults<TagCount> results = template.aggregate(aggregation, "tags", TagCount.class);
Indexes are only used if the collation used for the operation matches the index collation.

MongoDB Repositories support Collations via the collation attribute of the @Query annotation.

Example 80. Collation support for Repositories
public interface PersonRepository extends MongoRepository<Person, String> {

  @Query(collation = "en_US")  (1)
  List<Person> findByFirstname(String firstname);

  @Query(collation = "{ 'locale' : 'en_US' }") (2)
  List<Person> findPersonByFirstname(String firstname);

  @Query(collation = "?1") (3)
  List<Person> findByFirstname(String firstname, Object collation);

  @Query(collation = "{ 'locale' : '?1' }") (4)
  List<Person> findByFirstname(String firstname, String collation);

  List<Person> findByFirstname(String firstname, Collation collation); (5)

  @Query(collation = "{ 'locale' : 'en_US' }")
  List<Person> findByFirstname(String firstname, @Nullable Collation collation); (6)
}
1 Static collation definition resulting in { 'locale' : 'en_US' }.
2 Static collation definition resulting in { 'locale' : 'en_US' }.
3 Dynamic collation depending on 2nd method argument. Allowed types include String (eg. 'en_US'), Locacle (eg. Locacle.US) and Document (eg. new Document("locale", "en_US"))
4 Dynamic collation depending on 2nd method argument.
5 Apply the Collation method parameter to the query.
6 The Collation method parameter overrides the default collation from @Query if not null.
In case you enabled the automatic index creation for repository finder methods a potential static collation definition, as shown in (1) and (2), will be included when creating the index.
The most specifc Collation outrules potentially defined others. Which means Method argument over query method annotation over domain type annotation.

To streamline usage of collation attributes throughout the codebase it is also possible to use the @Collation annotation, which serves as a meta annotation for the ones mentioned above. The same rules and locations apply, plus, direct usage of @Collation supersedes any collation values defined on @Query and other annotations. Which means, if a collation is declared via @Query and additionally via @Collation, then the one from @Collation is picked.

Example 81. Using @Collation
@Collation("en_US") (1)
class Game {
  // ...
}

interface GameRepository extends Repository<Game, String> {

  @Collation("en_GB")  (2)
  List<Game> findByTitle(String title);

  @Collation("de_AT")  (3)
  @Query(collation="en_GB")
  List<Game> findByDescriptionContaining(String keyword);
}
1 Instead of @Document(collation=…​).
2 Instead of @Query(collation=…​).
3 Favors @Collation over meta usage.
JSON Schema

As of version 3.6, MongoDB supports collections that validate documents against a provided JSON Schema. The schema itself and both validation action and level can be defined when creating the collection, as the following example shows:

Example 82. Sample JSON schema
{
  "type": "object",                                                        (1)

  "required": [ "firstname", "lastname" ],                                 (2)

  "properties": {                                                          (3)

    "firstname": {                                                         (4)
      "type": "string",
      "enum": [ "luke", "han" ]
    },
    "address": {                                                           (5)
      "type": "object",
      "properties": {
        "postCode": { "type": "string", "minLength": 4, "maxLength": 5 }
      }
    }
  }
}
1 JSON schema documents always describe a whole document from its root. A schema is a schema object itself that can contain embedded schema objects that describe properties and subdocuments.
2 required is a property that describes which properties are required in a document. It can be specified optionally, along with other schema constraints. See MongoDB’s documentation on available keywords.
3 properties is related to a schema object that describes an object type. It contains property-specific schema constraints.
4 firstname specifies constraints for the firsname field inside the document. Here, it is a string-based properties element declaring possible field values.
5 address is a subdocument defining a schema for values in its postCode field.

You can provide a schema either by specifying a schema document (that is, by using the Document API to parse or build a document object) or by building it with Spring Data’s JSON schema utilities in org.springframework.data.mongodb.core.schema. MongoJsonSchema is the entry point for all JSON schema-related operations. The following example shows how use MongoJsonSchema.builder() to create a JSON schema:

Example 83. Creating a JSON schema
MongoJsonSchema.builder()                                                    (1)
    .required("lastname")                                                    (2)

    .properties(
                required(string("firstname").possibleValues("luke", "han")), (3)

                object("address")
                     .properties(string("postCode").minLength(4).maxLength(5)))

    .build();                                                                (4)
1 Obtain a schema builder to configure the schema with a fluent API.
2 Configure required properties either directly as shown here or with more details as in 3.
3 Configure the required String-typed firstname field, allowing only luke and han values. Properties can be typed or untyped. Use a static import of JsonSchemaProperty to make the syntax slightly more compact and to get entry points such as string(…).
4 Build the schema object. Use the schema to create either a collection or query documents.

There are already some predefined and strongly typed schema objects (JsonSchemaObject and JsonSchemaProperty) available through static methods on the gateway interfaces. However, you may need to build custom property validation rules, which can be created through the builder API, as the following example shows:

// "birthdate" : { "bsonType": "date" }
JsonSchemaProperty.named("birthdate").ofType(Type.dateType());

// "birthdate" : { "bsonType": "date", "description", "Must be a date" }
JsonSchemaProperty.named("birthdate").with(JsonSchemaObject.of(Type.dateType()).description("Must be a date"));

CollectionOptions provides the entry point to schema support for collections, as the following example shows:

Example 84. Create collection with $jsonSchema
MongoJsonSchema schema = MongoJsonSchema.builder().required("firstname", "lastname").build();

template.createCollection(Person.class, CollectionOptions.empty().schema(schema));
Generating a Schema

Setting up a schema can be a time consuming task and we encourage everyone who decides to do so, to really take the time it takes. It’s important, schema changes can be hard. However, there might be times when one does not want to balked with it, and that is where JsonSchemaCreator comes into play.

JsonSchemaCreator and its default implementation generates a MongoJsonSchema out of domain types metadata provided by the mapping infrastructure. This means, that annotated properties as well as potential custom conversions are considered.

Example 85. Generate Json Schema from domain type
public class Person {

    private final String firstname;                   (1)
    private final int age;                            (2)
    private Species species;                          (3)
    private Address address;                          (4)
    private @Field(fieldType=SCRIPT) String theForce; (5)
    private @Transient Boolean useTheForce;           (6)

    public Person(String firstname, int age) {        (1) (2)

        this.firstname = firstname;
        this.age = age;
    }

    // gettter / setter omitted
}

MongoJsonSchema schema = MongoJsonSchemaCreator.create(mongoOperations.getConverter())
    .createSchemaFor(Person.class);

template.createCollection(Person.class, CollectionOptions.empty().schema(schema));
{
    'type' : 'object',
    'required' : ['age'],                     (2)
    'properties' : {
        'firstname' : { 'type' : 'string' },  (1)
        'age' : { 'bsonType' : 'int' }        (2)
        'species' : {                         (3)
            'type' : 'string',
            'enum' : ['HUMAN', 'WOOKIE', 'UNKNOWN']
        }
        'address' : {                         (4)
            'type' : 'object'
            'properties' : {
                'postCode' : { 'type': 'string' }
            }
        },
        'theForce' : { 'type' : 'javascript'} (5)
     }
}
1 Simple object properties are consideres regular properties.
2 Primitive types are considered required properties
3 Enums are restricted to possible values.
4 Object type properties are inspected and represented as nested documents.
5 String type property that is converted to Code by the converter.
6 @Transient properties are omitted when generating the schema.
_id properties using types that can be converted into ObjectId like String are mapped to { type : 'object' } unless there is more specific information available via the @MongoId annotation.
Table 3. Sepcial Schema Generation rules
Java Schema Type Notes

Object

type : object

with properties if metadata available.

Collection

type : array

-

Map

type : object

-

Enum

type : string

with enum property holding the possible enumeration values.

array

type : array

simple type array unless it’s a byte[]

byte[]

bsonType : binData

-

The above example demonstrated how to derive the schema from a very precise typed source. Using polymorphic elements within the domain model can lead to inaccurate schema representation for Object and generic <T> types, which are likely to represented as { type : 'object' } without further specification. MongoJsonSchemaCreator.property(…) allows defining additional details such as nested document types that should be considered when rendering the schema.

Example 86. Specify additional types for properties
class Root {
	Object value;
}

class A {
	String aValue;
}

class B {
	String bValue;
}
MongoJsonSchemaCreator.create()
    .property("value").withTypes(A.class, B.class) (1)
{
    'type' : 'object',
    'properties' : {
        'value' : {
            'type' : 'object',
            'properties' : {                       (1)
                'aValue' : { 'type' : 'string' },
                'bValue' : { 'type' : 'string' }
            }
        }
    }
}
1 Properties of the given types are merged into one element.

MongoDBs schema-free approach allows storing documents of different structure in one collection. Those may be modeled having a common base class. Regardless of the chosen approach, MongoJsonSchemaCreator.merge(…) can help circumvent the need of merging multiple schema into one.

Example 87. Merging multiple Schemas into a single Schema definition
abstract class Root {
	String rootValue;
}

class A extends Root {
	String aValue;
}

class B extends Root {
	String bValue;
}

MongoJsonSchemaCreator.mergedSchemaFor(A.class, B.class) (1)
{
    'type' : 'object',
       'properties' : { (1)
           'rootValue' : { 'type' : 'string' },
           'aValue' : { 'type' : 'string' },
           'bValue' : { 'type' : 'string' }
       }
    }
}
1 Properties (and their inherited ones) of the given types are combined into one schema.

Properties with the same name need to refer to the same JSON schema in order to be combined. The following example shows a definition that cannot be merged automatically because of a data type mismatch. In this case a ConflictResolutionFunction must be provided to MongoJsonSchemaCreator.

class A extends Root {
	String value;
}

class B extends Root {
	Integer value;
}
Query a collection for matching JSON Schema

You can use a schema to query any collection for documents that match a given structure defined by a JSON schema, as the following example shows:

Example 88. Query for Documents matching a $jsonSchema
MongoJsonSchema schema = MongoJsonSchema.builder().required("firstname", "lastname").build();

template.find(query(matchingDocumentStructure(schema)), Person.class);
Encrypted Fields

MongoDB 4.2 Field Level Encryption allows to directly encrypt individual properties.

Properties can be wrapped within an encrypted property when setting up the JSON Schema as shown in the example below.

Example 89. Client-Side Field Level Encryption via Json Schema
MongoJsonSchema schema = MongoJsonSchema.builder()
    .properties(
        encrypted(string("ssn"))
            .algorithm("AEAD_AES_256_CBC_HMAC_SHA_512-Deterministic")
            .keyId("*key0_id")
	).build();

Instead of defining encrypted fields manually it is possible leverage the @Encrypted annotation as shown in the snippet below.

Example 90. Client-Side Field Level Encryption via Json Schema
@Document
@Encrypted(keyId = "xKVup8B1Q+CkHaVRx+qa+g==", algorithm = "AEAD_AES_256_CBC_HMAC_SHA_512-Random") (1)
static class Patient {

    @Id String id;
    String name;

    @Encrypted (2)
    String bloodType;

    @Encrypted(algorithm = "AEAD_AES_256_CBC_HMAC_SHA_512-Deterministic") (3)
    Integer ssn;
}
1 Default encryption settings that will be set for encryptMetadata.
2 Encrypted field using default encryption settings.
3 Encrypted field overriding the default encryption algorithm.

The @Encrypted Annotation supports resolving keyIds via SpEL Expressions. To do so additional environment metadata (via the MappingContext) is required and must be provided.

@Document
@Encrypted(keyId = "#{mongocrypt.keyId(#target)}")
static class Patient {

    @Id String id;
    String name;

    @Encrypted(algorithm = "AEAD_AES_256_CBC_HMAC_SHA_512-Random")
    String bloodType;

    @Encrypted(algorithm = "AEAD_AES_256_CBC_HMAC_SHA_512-Deterministic")
    Integer ssn;
}

MongoJsonSchemaCreator schemaCreator = MongoJsonSchemaCreator.create(mappingContext);
MongoJsonSchema patientSchema = schemaCreator
    .filter(MongoJsonSchemaCreator.encryptedOnly())
    .createSchemaFor(Patient.class);

The mongocrypt.keyId function is defined via an EvaluationContextExtension as shown in the snippet below. Providing a custom extension provides the most flexible way of computing keyIds.

public class EncryptionExtension implements EvaluationContextExtension {

    @Override
    public String getExtensionId() {
        return "mongocrypt";
    }

    @Override
    public Map<String, Function> getFunctions() {
        return Collections.singletonMap("keyId", new Function(getMethod("computeKeyId", String.class), this));
    }

    public String computeKeyId(String target) {
        // ... lookup via target element name
    }
}
JSON Schema Types

The following table shows the supported JSON schema types:

Table 4. Supported JSON schema types
Schema Type Java Type Schema Properties

untyped

-

description, generated description, enum, allOf, anyOf, oneOf, not

object

Object

required, additionalProperties, properties, minProperties, maxProperties, patternProperties

array

any array except byte[]

uniqueItems, additionalItems, items, minItems, maxItems

string

String

minLength, maxLentgth, pattern

int

int, Integer

multipleOf, minimum, exclusiveMinimum, maximum, exclusiveMaximum

long

long, Long

multipleOf, minimum, exclusiveMinimum, maximum, exclusiveMaximum

double

float, Float, double, Double

multipleOf, minimum, exclusiveMinimum, maximum, exclusiveMaximum

decimal

BigDecimal

multipleOf, minimum, exclusiveMinimum, maximum, exclusiveMaximum

number

Number

multipleOf, minimum, exclusiveMinimum, maximum, exclusiveMaximum

binData

byte[]

(none)

boolean

boolean, Boolean

(none)

null

null

(none)

objectId

ObjectId

(none)

date

java.util.Date

(none)

timestamp

BsonTimestamp

(none)

regex

java.util.regex.Pattern

(none)

untyped is a generic type that is inherited by all typed schema types. It provides all untyped schema properties to typed schema types.

For more information, see $jsonSchema.

10.6.8. Fluent Template API

The MongoOperations interface is one of the central components when it comes to more low-level interaction with MongoDB. It offers a wide range of methods covering needs from collection creation, index creation, and CRUD operations to more advanced functionality, such as Map-Reduce and aggregations. You can find multiple overloads for each method. Most of them cover optional or nullable parts of the API.

FluentMongoOperations provides a more narrow interface for the common methods of MongoOperations and provides a more readable, fluent API. The entry points (insert(…), find(…), update(…), and others) follow a natural naming schema based on the operation to be run. Moving on from the entry point, the API is designed to offer only context-dependent methods that lead to a terminating method that invokes the actual MongoOperations counterpart — the all method in the case of the following example:

List<SWCharacter> all = ops.find(SWCharacter.class)
  .inCollection("star-wars")                        (1)
  .all();
1 Skip this step if SWCharacter defines the collection with @Document or if you use the class name as the collection name, which is fine.

Sometimes, a collection in MongoDB holds entities of different types, such as a Jedi within a collection of SWCharacters. To use different types for Query and return value mapping, you can use as(Class<?> targetType) to map results differently, as the following example shows:

List<Jedi> all = ops.find(SWCharacter.class)    (1)
  .as(Jedi.class)                               (2)
  .matching(query(where("jedi").is(true)))
  .all();
1 The query fields are mapped against the SWCharacter type.
2 Resulting documents are mapped into Jedi.
You can directly apply Projections to result documents by providing the target type via as(Class<?>).
Using projections allows MongoTemplate to optimize result mapping by limiting the actual response to fields required by the projection target type. This applies as long as the Query itself does not contain any field restriction and the target type is a closed interface or DTO projection.
Projections must not be applied to DBRefs.

You can switch between retrieving a single entity and retrieving multiple entities as a List or a Stream through the terminating methods: first(), one(), all(), or stream().

When writing a geo-spatial query with near(NearQuery), the number of terminating methods is altered to include only the methods that are valid for running a geoNear command in MongoDB (fetching entities as a GeoResult within GeoResults), as the following example shows:

GeoResults<Jedi> results = mongoOps.query(SWCharacter.class)
  .as(Jedi.class)
  .near(alderaan) // NearQuery.near(-73.9667, 40.78).maxDis…
  .all();

10.6.9. Type-safe Queries for Kotlin

Kotlin embraces domain-specific language creation through its language syntax and its extension system. Spring Data MongoDB ships with a Kotlin Extension for Criteria using Kotlin property references to build type-safe queries. Queries using this extension are typically benefit from improved readability. Most keywords on Criteria have a matching Kotlin extension, such as inValues and regex.

Consider the following example explaining Type-safe Queries:

import org.springframework.data.mongodb.core.query.*

mongoOperations.find<Book>(
  Query(Book::title isEqualTo "Moby-Dick")               (1)
)

mongoOperations.find<Book>(
  Query(titlePredicate = Book::title exists true)
)

mongoOperations.find<Book>(
  Query(
    Criteria().andOperator(
      Book::price gt 5,
      Book::price lt 10
    ))
)

// Binary operators
mongoOperations.find<BinaryMessage>(
  Query(BinaryMessage::payload bits { allClear(0b101) }) (2)
)

// Nested Properties (i.e. refer to "book.author")
mongoOperations.find<Book>(
  Query(Book::author / Author::name regex "^H")          (3)
)
1 isEqualTo() is an infix extension function with receiver type KProperty<T> that returns Criteria.
2 For bitwise operators, pass a lambda argument where you call one of the methods of Criteria.BitwiseCriteriaOperators.
3 To construct nested properties, use the / character (overloaded operator div).

10.6.10. Additional Query Options

MongoDB offers various ways of applying meta information, like a comment or a batch size, to a query.Using the Query API directly there are several methods for those options.

Query query = query(where("firstname").is("luke"))
    .comment("find luke")         (1)
    .cursorBatchSize(100)                                 (2)
1 The comment propagated to the MongoDB profile log.
2 The number of documents to return in each response batch.

On the repository level the @Meta annotation provides means to add query options in a declarative way.

@Meta(comment = "find luke", cursorBatchSize = 100, flags = { SLAVE_OK })
List<Person> findByFirstname(String firstname);

10.7. Query by Example

10.7.1. Introduction

This chapter provides an introduction to Query by Example and explains how to use it.

Query by Example (QBE) is a user-friendly querying technique with a simple interface. It allows dynamic query creation and does not require you to write queries that contain field names. In fact, Query by Example does not require you to write queries by using store-specific query languages at all.

10.7.2. Usage

The Query by Example API consists of four parts:

  • Probe: The actual example of a domain object with populated fields.

  • ExampleMatcher: The ExampleMatcher carries details on how to match particular fields. It can be reused across multiple Examples.

  • Example: An Example consists of the probe and the ExampleMatcher. It is used to create the query.

  • FetchableFluentQuery: A FetchableFluentQuery offers a fluent API, that allows further customization of a query derived from an Example. Using the fluent API lets you to specify ordering projection and result processing for your query.

Query by Example is well suited for several use cases:

  • Querying your data store with a set of static or dynamic constraints.

  • Frequent refactoring of the domain objects without worrying about breaking existing queries.

  • Working independently from the underlying data store API.

Query by Example also has several limitations:

  • No support for nested or grouped property constraints, such as firstname = ?0 or (firstname = ?1 and lastname = ?2).

  • Only supports starts/contains/ends/regex matching for strings and exact matching for other property types.

Before getting started with Query by Example, you need to have a domain object. To get started, create an interface for your repository, as shown in the following example:

Example 91. Sample Person object
public class Person {

  @Id
  private String id;
  private String firstname;
  private String lastname;
  private Address address;

  // … getters and setters omitted
}

The preceding example shows a simple domain object. You can use it to create an Example. By default, fields having null values are ignored, and strings are matched by using the store specific defaults.

Inclusion of properties into a Query by Example criteria is based on nullability. Properties using primitive types (int, double, …) are always included unless the ExampleMatcher ignores the property path.

Examples can be built by either using the of factory method or by using ExampleMatcher. Example is immutable. The following listing shows a simple Example:

Example 92. Simple Example
Person person = new Person();                         (1)
person.setFirstname("Dave");                          (2)

Example<Person> example = Example.of(person);         (3)
1 Create a new instance of the domain object.
2 Set the properties to query.
3 Create the Example.

You can run the example queries by using repositories. To do so, let your repository interface extend QueryByExampleExecutor<T>. The following listing shows an excerpt from the QueryByExampleExecutor interface:

Example 93. The QueryByExampleExecutor
public interface QueryByExampleExecutor<T> {

  <S extends T> S findOne(Example<S> example);

  <S extends T> Iterable<S> findAll(Example<S> example);

  // … more functionality omitted.
}

10.7.3. Example Matchers

Examples are not limited to default settings. You can specify your own defaults for string matching, null handling, and property-specific settings by using the ExampleMatcher, as shown in the following example:

Example 94. Example matcher with customized matching
Person person = new Person();                          (1)
person.setFirstname("Dave");                           (2)

ExampleMatcher matcher = ExampleMatcher.matching()     (3)
  .withIgnorePaths("lastname")                         (4)
  .withIncludeNullValues()                             (5)
  .withStringMatcher(StringMatcher.ENDING);            (6)

Example<Person> example = Example.of(person, matcher); (7)
1 Create a new instance of the domain object.
2 Set properties.
3 Create an ExampleMatcher to expect all values to match. It is usable at this stage even without further configuration.
4 Construct a new ExampleMatcher to ignore the lastname property path.
5 Construct a new ExampleMatcher to ignore the lastname property path and to include null values.
6 Construct a new ExampleMatcher to ignore the lastname property path, to include null values, and to perform suffix string matching.
7 Create a new Example based on the domain object and the configured ExampleMatcher.

By default, the ExampleMatcher expects all values set on the probe to match. If you want to get results matching any of the predicates defined implicitly, use ExampleMatcher.matchingAny().

You can specify behavior for individual properties (such as "firstname" and "lastname" or, for nested properties, "address.city"). You can tune it with matching options and case sensitivity, as shown in the following example:

Example 95. Configuring matcher options
ExampleMatcher matcher = ExampleMatcher.matching()
  .withMatcher("firstname", endsWith())
  .withMatcher("lastname", startsWith().ignoreCase());
}

Another way to configure matcher options is to use lambdas (introduced in Java 8). This approach creates a callback that asks the implementor to modify the matcher. You need not return the matcher, because configuration options are held within the matcher instance. The following example shows a matcher that uses lambdas:

Example 96. Configuring matcher options with lambdas
ExampleMatcher matcher = ExampleMatcher.matching()
  .withMatcher("firstname", match -> match.endsWith())
  .withMatcher("firstname", match -> match.startsWith());
}

Queries created by Example use a merged view of the configuration. Default matching settings can be set at the ExampleMatcher level, while individual settings can be applied to particular property paths. Settings that are set on ExampleMatcher are inherited by property path settings unless they are defined explicitly. Settings on a property patch have higher precedence than default settings. The following table describes the scope of the various ExampleMatcher settings:

Table 5. Scope of ExampleMatcher settings
Setting Scope

Null-handling

ExampleMatcher

String matching

ExampleMatcher and property path

Ignoring properties

Property path

Case sensitivity

ExampleMatcher and property path

Value transformation

Property path

10.7.4. Fluent API

QueryByExampleExecutor offers one more method, which we did not mention so far: <S extends T, R> R findBy(Example<S> example, Function<FluentQuery.FetchableFluentQuery<S>, R> queryFunction). As with other methods, it executes a query derived from an Example. However, with the second argument, you can control aspects of that execution that you cannot dynamically control otherwise. You do so by invoking the various methods of the FetchableFluentQuery in the second argument. sortBy lets you specify an ordering for your result. as lets you specify the type to which you want the result to be transformed. project limits the queried attributes. first, firstValue, one, oneValue, all, page, stream, count, and exists define what kind of result you get and how the query behaves when more than the expected number of results are available.

Example 97. Use the fluent API to get the last of potentially many results, ordered by lastname.
Optional<Person> match = repository.findBy(example,
    q -> q
        .sortBy(Sort.by("lastname").descending())
        .first()
);

10.7.5. Running an Example

The following example shows how to query by example when using a repository (of Person objects, in this case):

Example 98. Query by Example using a repository
public interface PersonRepository extends QueryByExampleExecutor<Person> {

}

public class PersonService {

  @Autowired PersonRepository personRepository;

  public List<Person> findPeople(Person probe) {
    return personRepository.findAll(Example.of(probe));
  }
}

An Example containing an untyped ExampleSpec uses the Repository type and its collection name. Typed ExampleSpec instances use their type as the result type and the collection name from the Repository instance.

When including null values in the ExampleSpec, Spring Data Mongo uses embedded document matching instead of dot notation property matching. Doing so forces exact document matching for all property values and the property order in the embedded document.

Spring Data MongoDB provides support for the following matching options:

Table 6. StringMatcher options
Matching Logical result

DEFAULT (case-sensitive)

{"firstname" : firstname}

DEFAULT (case-insensitive)

{"firstname" : { $regex: firstname, $options: 'i'}}

EXACT (case-sensitive)

{"firstname" : { $regex: /^firstname$/}}

EXACT (case-insensitive)

{"firstname" : { $regex: /^firstname$/, $options: 'i'}}

STARTING (case-sensitive)

{"firstname" : { $regex: /^firstname/}}

STARTING (case-insensitive)

{"firstname" : { $regex: /^firstname/, $options: 'i'}}

ENDING (case-sensitive)

{"firstname" : { $regex: /firstname$/}}

ENDING (case-insensitive)

{"firstname" : { $regex: /firstname$/, $options: 'i'}}

CONTAINING (case-sensitive)

{"firstname" : { $regex: /.*firstname.*/}}

CONTAINING (case-insensitive)

{"firstname" : { $regex: /.*firstname.*/, $options: 'i'}}

REGEX (case-sensitive)

{"firstname" : { $regex: /firstname/}}

REGEX (case-insensitive)

{"firstname" : { $regex: /firstname/, $options: 'i'}}

10.7.6. Untyped Example

By default Example is strictly typed. This means that the mapped query has an included type match, restricting it to probe assignable types. For example, when sticking with the default type key (_class), the query has restrictions such as (_class : { $in : [ com.acme.Person] }).

By using the UntypedExampleMatcher, it is possible to bypass the default behavior and skip the type restriction. So, as long as field names match, nearly any domain type can be used as the probe for creating the reference, as the following example shows:

Example 99. Untyped Example Query
class JustAnArbitraryClassWithMatchingFieldName {
  @Field("lastname") String value;
}

JustAnArbitraryClassWithMatchingFieldNames probe = new JustAnArbitraryClassWithMatchingFieldNames();
probe.value = "stark";

Example example = Example.of(probe, UntypedExampleMatcher.matching());

Query query = new Query(new Criteria().alike(example));
List<Person> result = template.find(query, Person.class);

UntypedExampleMatcher is likely the right choice for you if you are storing different entities within a single collection or opted out of writing type hints.

Also, keep in mind that using @TypeAlias requires eager initialization of the MappingContext. To do so, configure initialEntitySet to to ensure proper alias resolution for read operations.

10.8. Counting Documents

In pre-3.x versions of SpringData MongoDB the count operation used MongoDBs internal collection statistics. With the introduction of MongoDB Transactions this was no longer possible because statistics would not correctly reflect potential changes during a transaction requiring an aggregation-based count approach. So in version 2.x MongoOperations.count() would use the collection statistics if no transaction was in progress, and the aggregation variant if so.

As of Spring Data MongoDB 3.x any count operation uses regardless the existence of filter criteria the aggregation-based count approach via MongoDBs countDocuments. If the application is fine with the limitations of working upon collection statistics MongoOperations.estimatedCount() offers an alternative.

By setting MongoTemplate#useEstimatedCount(…​) to true MongoTemplate#count(…​) operations, that use an empty filter query, will be delegated to estimatedCount, as long as there is no transaction active and the template is not bound to a session. It will still be possible to obtain exact numbers via MongoTemplate#exactCount, but may speed up things.

MongoDBs native countDocuments method and the $match aggregation, do not support $near and $nearSphere but require $geoWithin along with $center or $centerSphere which does not support $minDistance (see https://jira.mongodb.org/browse/SERVER-37043).

Therefore a given Query will be rewritten for count operations using Reactive-/MongoTemplate to bypass the issue like shown below.

{ location : { $near : [-73.99171, 40.738868], $maxDistance : 1.1 } } (1)
{ location : { $geoWithin : { $center: [ [-73.99171, 40.738868], 1.1] } } } (2)

{ location : { $near : [-73.99171, 40.738868], $minDistance : 0.1, $maxDistance : 1.1 } } (3)
{$and :[ { $nor :[ { location :{ $geoWithin :{ $center :[ [-73.99171, 40.738868 ], 0.01] } } } ]}, { location :{ $geoWithin :{ $center :[ [-73.99171, 40.738868 ], 1.1] } } } ] } (4)
1 Count source query using $near.
2 Rewritten query now using $geoWithin with $center.
3 Count source query using $near with $minDistance and $maxDistance.
4 Rewritten query now a combination of $nor $geowithin critierias to work around unsupported $minDistance.

10.9. Map-Reduce Operations

You can query MongoDB by using Map-Reduce, which is useful for batch processing, for data aggregation, and for when the query language does not fulfill your needs.

Spring provides integration with MongoDB’s Map-Reduce by providing methods on MongoOperations to simplify the creation and running of Map-Reduce operations.It can convert the results of a Map-Reduce operation to a POJO and integrates with Spring’s Resource abstraction.This lets you place your JavaScript files on the file system, classpath, HTTP server, or any other Spring Resource implementation and then reference the JavaScript resources through an easy URI style syntax — for example, classpath:reduce.js;.Externalizing JavaScript code in files is often preferable to embedding them as Java strings in your code.Note that you can still pass JavaScript code as Java strings if you prefer.

10.9.1. Example Usage

To understand how to perform Map-Reduce operations, we use an example from the book, MongoDB - The Definitive Guide [1].In this example, we create three documents that have the values [a,b], [b,c], and [c,d], respectively.The values in each document are associated with the key, 'x', as the following example shows (assume these documents are in a collection named jmr1):

{ "_id" : ObjectId("4e5ff893c0277826074ec533"), "x" : [ "a", "b" ] }
{ "_id" : ObjectId("4e5ff893c0277826074ec534"), "x" : [ "b", "c" ] }
{ "_id" : ObjectId("4e5ff893c0277826074ec535"), "x" : [ "c", "d" ] }

The following map function counts the occurrence of each letter in the array for each document:

function () {
    for (var i = 0; i < this.x.length; i++) {
        emit(this.x[i], 1);
    }
}

The follwing reduce function sums up the occurrence of each letter across all the documents:

function (key, values) {
    var sum = 0;
    for (var i = 0; i < values.length; i++)
        sum += values[i];
    return sum;
}

Running the preceding functions result in the following collection:

{ "_id" : "a", "value" : 1 }
{ "_id" : "b", "value" : 2 }
{ "_id" : "c", "value" : 2 }
{ "_id" : "d", "value" : 1 }

Assuming that the map and reduce functions are located in map.js and reduce.js and bundled in your jar so they are available on the classpath, you can run a Map-Reduce operation as follows:

MapReduceResults<ValueObject> results = mongoOperations.mapReduce("jmr1", "classpath:map.js", "classpath:reduce.js", ValueObject.class);
for (ValueObject valueObject : results) {
  System.out.println(valueObject);
}

The preceding exmaple produces the following output:

ValueObject [id=a, value=1.0]
ValueObject [id=b, value=2.0]
ValueObject [id=c, value=2.0]
ValueObject [id=d, value=1.0]

The MapReduceResults class implements Iterable and provides access to the raw output and timing and count statistics.The following listing shows the ValueObject class:

public class ValueObject {

  private String id;
  private float value;

  public String getId() {
    return id;
  }

  public float getValue() {
    return value;
  }

  public void setValue(float value) {
    this.value = value;
  }

  @Override
  public String toString() {
    return "ValueObject [id=" + id + ", value=" + value + "]";
  }
}

By default, the output type of INLINE is used so that you need not specify an output collection.To specify additional Map-Reduce options, use an overloaded method that takes an additional MapReduceOptions argument.The class MapReduceOptions has a fluent API, so adding additional options can be done in a compact syntax.The following example sets the output collection to jmr1_out (note that setting only the output collection assumes a default output type of REPLACE):

MapReduceResults<ValueObject> results = mongoOperations.mapReduce("jmr1", "classpath:map.js", "classpath:reduce.js",
                                                                     new MapReduceOptions().outputCollection("jmr1_out"), ValueObject.class);

There is also a static import (import static org.springframework.data.mongodb.core.mapreduce.MapReduceOptions.options;) that can be used to make the syntax slightly more compact, as the following example shows:

MapReduceResults<ValueObject> results = mongoOperations.mapReduce("jmr1", "classpath:map.js", "classpath:reduce.js",
                                                                     options().outputCollection("jmr1_out"), ValueObject.class);

You can also specify a query to reduce the set of data that is fed into the Map-Reduce operation.The following example removes the document that contains [a,b] from consideration for Map-Reduce operations:

Query query = new Query(where("x").ne(new String[] { "a", "b" }));
MapReduceResults<ValueObject> results = mongoOperations.mapReduce(query, "jmr1", "classpath:map.js", "classpath:reduce.js",
                                                                     options().outputCollection("jmr1_out"), ValueObject.class);

Note that you can specify additional limit and sort values on the query, but you cannot skip values.

10.10. Script Operations

MongoDB 4.2 removed support for the eval command used by ScriptOperations.
There is no replacement for the removed functionality.

MongoDB allows running JavaScript functions on the server by either directly sending the script or calling a stored one. ScriptOperations can be accessed through MongoTemplate and provides basic abstraction for JavaScript usage. The following example shows how to us the ScriptOperations class:

ScriptOperations scriptOps = template.scriptOps();

ExecutableMongoScript echoScript = new ExecutableMongoScript("function(x) { return x; }");
scriptOps.execute(echoScript, "directly execute script");     (1)

scriptOps.register(new NamedMongoScript("echo", echoScript)); (2)
scriptOps.call("echo", "execute script via name");            (3)
1 Run the script directly without storing the function on server side.
2 Store the script using 'echo' as its name. The given name identifies the script and allows calling it later.
3 Run the script with name 'echo' using the provided parameters.

10.11. Group Operations

As an alternative to using Map-Reduce to perform data aggregation, you can use the group operation which feels similar to using SQL’s group by query style, so it may feel more approachable vs. using Map-Reduce. Using the group operations does have some limitations, for example it is not supported in a shared environment and it returns the full result set in a single BSON object, so the result should be small, less than 10,000 keys.

Spring provides integration with MongoDB’s group operation by providing methods on MongoOperations to simplify the creation and running of group operations. It can convert the results of the group operation to a POJO and also integrates with Spring’s Resource abstraction abstraction. This will let you place your JavaScript files on the file system, classpath, http server or any other Spring Resource implementation and then reference the JavaScript resources via an easy URI style syntax, e.g. 'classpath:reduce.js;. Externalizing JavaScript code in files if often preferable to embedding them as Java strings in your code. Note that you can still pass JavaScript code as Java strings if you prefer.

10.11.1. Example Usage

In order to understand how group operations work the following example is used, which is somewhat artificial. For a more realistic example consult the book 'MongoDB - The definitive guide'. A collection named group_test_collection created with the following rows.

{ "_id" : ObjectId("4ec1d25d41421e2015da64f1"), "x" : 1 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f2"), "x" : 1 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f3"), "x" : 2 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f4"), "x" : 3 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f5"), "x" : 3 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f6"), "x" : 3 }

We would like to group by the only field in each row, the x field and aggregate the number of times each specific value of x occurs. To do this we need to create an initial document that contains our count variable and also a reduce function which will increment it each time it is encountered. The Java code to run the group operation is shown below

GroupByResults<XObject> results = mongoTemplate.group("group_test_collection",
                                                      GroupBy.key("x").initialDocument("{ count: 0 }").reduceFunction("function(doc, prev) { prev.count += 1 }"),
                                                      XObject.class);

The first argument is the name of the collection to run the group operation over, the second is a fluent API that specifies properties of the group operation via a GroupBy class. In this example we are using just the intialDocument and reduceFunction methods. You can also specify a key-function, as well as a finalizer as part of the fluent API. If you have multiple keys to group by, you can pass in a comma separated list of keys.

The raw results of the group operation is a JSON document that looks like this

{
  "retval" : [ { "x" : 1.0 , "count" : 2.0} ,
               { "x" : 2.0 , "count" : 1.0} ,
               { "x" : 3.0 , "count" : 3.0} ] ,
  "count" : 6.0 ,
  "keys" : 3 ,
  "ok" : 1.0
}

The document under the "retval" field is mapped onto the third argument in the group method, in this case XObject which is shown below.

public class XObject {

  private float x;

  private float count;


  public float getX() {
    return x;
  }

  public void setX(float x) {
    this.x = x;
  }

  public float getCount() {
    return count;
  }

  public void setCount(float count) {
    this.count = count;
  }

  @Override
  public String toString() {
    return "XObject [x=" + x + " count = " + count + "]";
  }
}

You can also obtain the raw result as a Document by calling the method getRawResults on the GroupByResults class.

There is an additional method overload of the group method on MongoOperations which lets you specify a Criteria object for selecting a subset of the rows. An example which uses a Criteria object, with some syntax sugar using static imports, as well as referencing a key-function and reduce function javascript files via a Spring Resource string is shown below.

import static org.springframework.data.mongodb.core.mapreduce.GroupBy.keyFunction;
import static org.springframework.data.mongodb.core.query.Criteria.where;

GroupByResults<XObject> results = mongoTemplate.group(where("x").gt(0),
                                        "group_test_collection",
                                        keyFunction("classpath:keyFunction.js").initialDocument("{ count: 0 }").reduceFunction("classpath:groupReduce.js"), XObject.class);

10.12. Aggregation Framework Support

Spring Data MongoDB provides support for the Aggregation Framework introduced to MongoDB in version 2.2.

For further information, see the full reference documentation of the aggregation framework and other data aggregation tools for MongoDB.

10.12.1. Basic Concepts

The Aggregation Framework support in Spring Data MongoDB is based on the following key abstractions: Aggregation, AggregationDefinition, and AggregationResults.

  • Aggregation

    An Aggregation represents a MongoDB aggregate operation and holds the description of the aggregation pipeline instructions. Aggregations are created by invoking the appropriate newAggregation(…) static factory method of the Aggregation class, which takes a list of AggregateOperation and an optional input class.

    The actual aggregate operation is run by the aggregate method of the MongoTemplate, which takes the desired output class as a parameter.

  • TypedAggregation

    A TypedAggregation, just like an Aggregation, holds the instructions of the aggregation pipeline and a reference to the input type, that is used for mapping domain properties to actual document fields.

    At runtime, field references get checked against the given input type, considering potential @Field annotations.

Changed in 3.2 referencing non-existent properties does no longer raise errors. To restore the previous behaviour use the strictMapping option of AggregationOptions.

  • AggregationDefinition

    An AggregationDefinition represents a MongoDB aggregation pipeline operation and describes the processing that should be performed in this aggregation step. Although you could manually create an AggregationDefinition, we recommend using the static factory methods provided by the Aggregate class to construct an AggregateOperation.

  • AggregationResults

    AggregationResults is the container for the result of an aggregate operation. It provides access to the raw aggregation result, in the form of a Document to the mapped objects and other information about the aggregation.

    The following listing shows the canonical example for using the Spring Data MongoDB support for the MongoDB Aggregation Framework:

    import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;
    
    Aggregation agg = newAggregation(
        pipelineOP1(),
        pipelineOP2(),
        pipelineOPn()
    );
    
    AggregationResults<OutputType> results = mongoTemplate.aggregate(agg, "INPUT_COLLECTION_NAME", OutputType.class);
    List<OutputType> mappedResult = results.getMappedResults();
    

Note that, if you provide an input class as the first parameter to the newAggregation method, the MongoTemplate derives the name of the input collection from this class. Otherwise, if you do not not specify an input class, you must provide the name of the input collection explicitly. If both an input class and an input collection are provided, the latter takes precedence.

10.12.2. Supported Aggregation Operations & Stages

The MongoDB Aggregation Framework provides the following types of aggregation stages and operations:

Aggregation Stages
  • addFields - AddFieldsOperation

  • bucket / bucketAuto - BucketOperation / BucketAutoOperation

  • count - CountOperation

  • densify - DensifyOperation

  • facet - FacetOperation

  • geoNear - GeoNearOperation

  • graphLookup - GraphLookupOperation

  • group - GroupOperation

  • limit - LimitOperation

  • lookup - LookupOperation

  • match - MatchOperation

  • merge - MergeOperation

  • project - ProjectionOperation

  • redact - RedactOperation

  • replaceRoot - ReplaceRootOperation

  • sample - SampleOperation

  • set - SetOperation

  • setWindowFields - SetWindowFieldsOperation

  • skip - SkipOperation

  • sort / sortByCount - SortOperation / SortByCountOperation

  • unionWith - UnionWithOperation

  • unset - UnsetOperation

  • unwind - UnwindOperation

Unsupported aggregation stages (like $search for MongoDB Atlas) can be provided by implementing either AggregationOperation. Aggregation.stage is a shortcut for registering a pipeline stage by providing its JSON or Bson representation.

Aggregation.stage("""
    { $search : {
        "near": {
          "path": "released",
          "origin": { "$date": { "$numberLong": "..." } } ,
          "pivot": 7
        }
      }
    }
""");
Aggregation Operators
  • Group/Accumulator Aggregation Operators

  • Boolean Aggregation Operators

  • Comparison Aggregation Operators

  • Arithmetic Aggregation Operators

  • String Aggregation Operators

  • Date Aggregation Operators

  • Array Aggregation Operators

  • Conditional Aggregation Operators

  • Lookup Aggregation Operators

  • Convert Aggregation Operators

  • Object Aggregation Operators

  • Script Aggregation Operators

At the time of this writing, we provide support for the following Aggregation Operators in Spring Data MongoDB:

Table 7. Aggregation Operators currently supported by Spring Data MongoDB

Set Aggregation Operators

setEquals, setIntersection, setUnion, setDifference, setIsSubset, anyElementTrue, allElementsTrue

Group/Accumulator Aggregation Operators

addToSet, bottom, bottomN, covariancePop, covarianceSamp, expMovingAvg, first, firstN, last, lastN max, maxN, min, minN, avg, push, sum, top, topN, count (*), stdDevPop, stdDevSamp

Arithmetic Aggregation Operators

abs, acos, acosh, add (* via plus), asin, asin, atan, atan2, atanh, ceil, cos, cosh, derivative, divide, exp, floor, integral, ln, log, log10, mod, multiply, pow, round, sqrt, subtract (* via minus), sin, sinh, tan, tanh, trunc

String Aggregation Operators

concat, substr, toLower, toUpper, strcasecmp, indexOfBytes, indexOfCP, regexFind, regexFindAll, regexMatch, replaceAll, replaceOne, split`, strLenBytes, strLenCP, substrCP, trim, ltrim, rtim

Comparison Aggregation Operators

eq (* via is), gt, gte, lt, lte, ne

Array Aggregation Operators

arrayElementAt, arrayToObject, concatArrays, filter, first, in, indexOfArray, isArray, last, range`, reverseArray, reduce, size, sortArray, slice, zip

Literal Operators

literal

Date Aggregation Operators

dateSubstract, dateTrunc, dayOfYear, dayOfMonth, dayOfWeek, year, month, week, hour, minute, second, millisecond, dateAdd, dateDiff, dateToString, dateFromString, dateFromParts, dateToParts, isoDayOfWeek, isoWeek, isoWeekYear, tsIncrement, tsSecond

Variable Operators

map

Conditional Aggregation Operators

cond, ifNull, switch

Type Aggregation Operators

type

Convert Aggregation Operators

convert, degreesToRadians, toBool, toDate, toDecimal, toDouble, toInt, toLong, toObjectId, toString

Object Aggregation Operators

objectToArray, mergeObjects, getField, setField

Script Aggregation Operators

function, accumulator

* The operation is mapped or added by Spring Data MongoDB.

Note that the aggregation operations not listed here are currently not supported by Spring Data MongoDB. Comparison aggregation operators are expressed as Criteria expressions.

10.12.3. Projection Expressions

Projection expressions are used to define the fields that are the outcome of a particular aggregation step. Projection expressions can be defined through the project method of the Aggregation class, either by passing a list of String objects or an aggregation framework Fields object. The projection can be extended with additional fields through a fluent API by using the and(String) method and aliased by using the as(String) method. Note that you can also define fields with aliases by using the Fields.field static factory method of the aggregation framework, which you can then use to construct a new Fields instance. References to projected fields in later aggregation stages are valid only for the field names of included fields or their aliases (including newly defined fields and their aliases). Fields not included in the projection cannot be referenced in later aggregation stages. The following listings show examples of projection expression:

Example 100. Projection expression examples
// generates {$project: {name: 1, netPrice: 1}}
project("name", "netPrice")

// generates {$project: {thing1: $thing2}}
project().and("thing1").as("thing2")

// generates {$project: {a: 1, b: 1, thing2: $thing1}}
project("a","b").and("thing1").as("thing2")
Example 101. Multi-Stage Aggregation using Projection and Sorting
// generates {$project: {name: 1, netPrice: 1}}, {$sort: {name: 1}}
project("name", "netPrice"), sort(ASC, "name")

// generates {$project: {name: $firstname}}, {$sort: {name: 1}}
project().and("firstname").as("name"), sort(ASC, "name")

// does not work
project().and("firstname").as("name"), sort(ASC, "firstname")

More examples for project operations can be found in the AggregationTests class. Note that further details regarding the projection expressions can be found in the corresponding section of the MongoDB Aggregation Framework reference documentation.

10.12.4. Faceted Classification

As of Version 3.4, MongoDB supports faceted classification by using the Aggregation Framework. A faceted classification uses semantic categories (either general or subject-specific) that are combined to create the full classification entry. Documents flowing through the aggregation pipeline are classified into buckets. A multi-faceted classification enables various aggregations on the same set of input documents, without needing to retrieve the input documents multiple times.

Buckets

Bucket operations categorize incoming documents into groups, called buckets, based on a specified expression and bucket boundaries. Bucket operations require a grouping field or a grouping expression. You can define them by using the bucket() and bucketAuto() methods of the Aggregate class. BucketOperation and BucketAutoOperation can expose accumulations based on aggregation expressions for input documents. You can extend the bucket operation with additional parameters through a fluent API by using the with…() methods and the andOutput(String) method. You can alias the operation by using the as(String) method. Each bucket is represented as a document in the output.

BucketOperation takes a defined set of boundaries to group incoming documents into these categories. Boundaries are required to be sorted. The following listing shows some examples of bucket operations:

Example 102. Bucket operation examples
// generates {$bucket: {groupBy: $price, boundaries: [0, 100, 400]}}
bucket("price").withBoundaries(0, 100, 400);

// generates {$bucket: {groupBy: $price, default: "Other" boundaries: [0, 100]}}
bucket("price").withBoundaries(0, 100).withDefault("Other");

// generates {$bucket: {groupBy: $price, boundaries: [0, 100], output: { count: { $sum: 1}}}}
bucket("price").withBoundaries(0, 100).andOutputCount().as("count");

// generates {$bucket: {groupBy: $price, boundaries: [0, 100], 5, output: { titles: { $push: "$title"}}}
bucket("price").withBoundaries(0, 100).andOutput("title").push().as("titles");

BucketAutoOperation determines boundaries in an attempt to evenly distribute documents into a specified number of buckets. BucketAutoOperation optionally takes a granularity value that specifies the preferred number series to use to ensure that the calculated boundary edges end on preferred round numbers or on powers of 10. The following listing shows examples of bucket operations:

Example 103. Bucket operation examples
// generates {$bucketAuto: {groupBy: $price, buckets: 5}}
bucketAuto("price", 5)

// generates {$bucketAuto: {groupBy: $price, buckets: 5, granularity: "E24"}}
bucketAuto("price", 5).withGranularity(Granularities.E24).withDefault("Other");

// generates {$bucketAuto: {groupBy: $price, buckets: 5, output: { titles: { $push: "$title"}}}
bucketAuto("price", 5).andOutput("title").push().as("titles");

To create output fields in buckets, bucket operations can use AggregationExpression through andOutput() and SpEL expressions through andOutputExpression().

Note that further details regarding bucket expressions can be found in the $bucket section and $bucketAuto section of the MongoDB Aggregation Framework reference documentation.

Multi-faceted Aggregation

Multiple aggregation pipelines can be used to create multi-faceted aggregations that characterize data across multiple dimensions (or facets) within a single aggregation stage. Multi-faceted aggregations provide multiple filters and categorizations to guide data browsing and analysis. A common implementation of faceting is how many online retailers provide ways to narrow down search results by applying filters on product price, manufacturer, size, and other factors.

You can define a FacetOperation by using the facet() method of the Aggregation class. You can customize it with multiple aggregation pipelines by using the and() method. Each sub-pipeline has its own field in the output document where its results are stored as an array of documents.

Sub-pipelines can project and filter input documents prior to grouping. Common use cases include extraction of date parts or calculations before categorization. The following listing shows facet operation examples:

Example 104. Facet operation examples
// generates {$facet: {categorizedByPrice: [ { $match: { price: {$exists : true}}}, { $bucketAuto: {groupBy: $price, buckets: 5}}]}}
facet(match(Criteria.where("price").exists(true)), bucketAuto("price", 5)).as("categorizedByPrice"))

// generates {$facet: {categorizedByCountry: [ { $match: { country: {$exists : true}}}, { $sortByCount: "$country"}]}}
facet(match(Criteria.where("country").exists(true)), sortByCount("country")).as("categorizedByCountry"))

// generates {$facet: {categorizedByYear: [
//     { $project: { title: 1, publicationYear: { $year: "publicationDate"}}},
//     { $bucketAuto: {groupBy: $price, buckets: 5, output: { titles: {$push:"$title"}}}
// ]}}
facet(project("title").and("publicationDate").extractYear().as("publicationYear"),
      bucketAuto("publicationYear", 5).andOutput("title").push().as("titles"))
  .as("categorizedByYear"))

Note that further details regarding facet operation can be found in the $facet section of the MongoDB Aggregation Framework reference documentation.

Sort By Count

Sort by count operations group incoming documents based on the value of a specified expression, compute the count of documents in each distinct group, and sort the results by count. It offers a handy shortcut to apply sorting when using Faceted Classification. Sort by count operations require a grouping field or grouping expression. The following listing shows a sort by count example:

Example 105. Sort by count example
// generates { $sortByCount: "$country" }
sortByCount("country");

A sort by count operation is equivalent to the following BSON (Binary JSON):

{ $group: { _id: <expression>, count: { $sum: 1 } } },
{ $sort: { count: -1 } }
Spring Expression Support in Projection Expressions

We support the use of SpEL expressions in projection expressions through the andExpression method of the ProjectionOperation and BucketOperation classes. This feature lets you define the desired expression as a SpEL expression. On running a query, the SpEL expression is translated into a corresponding MongoDB projection expression part. This arrangement makes it much easier to express complex calculations.

Complex Calculations with SpEL expressions

Consider the following SpEL expression:

1 + (q + 1) / (q - 1)

The preceding expression is translated into the following projection expression part:

{ "$add" : [ 1, {
    "$divide" : [ {
        "$add":["$q", 1]}, {
        "$subtract":[ "$q", 1]}
    ]
}]}

You can see examples in more context in Aggregation Framework Example 5 and Aggregation Framework Example 6. You can find more usage examples for supported SpEL expression constructs in SpelExpressionTransformerUnitTests. The following table shows the SpEL transformations supported by Spring Data MongoDB:

Table 8. Supported SpEL transformations
SpEL Expression Mongo Expression Part

a == b

{ $eq : [$a, $b] }

a != b

{ $ne : [$a , $b] }

a > b

{ $gt : [$a, $b] }

a >= b

{ $gte : [$a, $b] }

a < b

{ $lt : [$a, $b] }

a ⇐ b

{ $lte : [$a, $b] }

a + b

{ $add : [$a, $b] }

a - b

{ $subtract : [$a, $b] }

a * b

{ $multiply : [$a, $b] }

a / b

{ $divide : [$a, $b] }

a^b

{ $pow : [$a, $b] }

a % b

{ $mod : [$a, $b] }

a && b

{ $and : [$a, $b] }

a || b

{ $or : [$a, $b] }

!a

{ $not : [$a] }

In addition to the transformations shown in the preceding table, you can use standard SpEL operations such as new to (for example) create arrays and reference expressions through their names (followed by the arguments to use in brackets). The following example shows how to create an array in this fashion:

// { $setEquals : [$a, [5, 8, 13] ] }
.andExpression("setEquals(a, new int[]{5, 8, 13})");
Aggregation Framework Examples

The examples in this section demonstrate the usage patterns for the MongoDB Aggregation Framework with Spring Data MongoDB.

Aggregation Framework Example 1

In this introductory example, we want to aggregate a list of tags to get the occurrence count of a particular tag from a MongoDB collection (called tags) sorted by the occurrence count in descending order. This example demonstrates the usage of grouping, sorting, projections (selection), and unwinding (result splitting).

class TagCount {
 String tag;
 int n;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

Aggregation agg = newAggregation(
    project("tags"),
    unwind("tags"),
    group("tags").count().as("n"),
    project("n").and("tag").previousOperation(),
    sort(DESC, "n")
);

AggregationResults<TagCount> results = mongoTemplate.aggregate(agg, "tags", TagCount.class);
List<TagCount> tagCount = results.getMappedResults();

The preceding listing uses the following algorithm:

  1. Create a new aggregation by using the newAggregation static factory method, to which we pass a list of aggregation operations. These aggregate operations define the aggregation pipeline of our Aggregation.

  2. Use the project operation to select the tags field (which is an array of strings) from the input collection.

  3. Use the unwind operation to generate a new document for each tag within the tags array.

  4. Use the group operation to define a group for each tags value for which we aggregate the occurrence count (by using the count aggregation operator and collecting the result in a new field called n).

  5. Select the n field and create an alias for the ID field generated from the previous group operation (hence the call to previousOperation()) with a name of tag.

  6. Use the sort operation to sort the resulting list of tags by their occurrence count in descending order.

  7. Call the aggregate method on MongoTemplate to let MongoDB perform the actual aggregation operation, with the created Aggregation as an argument.

Note that the input collection is explicitly specified as the tags parameter to the aggregate Method. If the name of the input collection is not specified explicitly, it is derived from the input class passed as the first parameter to the newAggreation method.

Aggregation Framework Example 2

This example is based on the Largest and Smallest Cities by State example from the MongoDB Aggregation Framework documentation. We added additional sorting to produce stable results with different MongoDB versions. Here we want to return the smallest and largest cities by population for each state by using the aggregation framework. This example demonstrates grouping, sorting, and projections (selection).

class ZipInfo {
   String id;
   String city;
   String state;
   @Field("pop") int population;
   @Field("loc") double[] location;
}

class City {
   String name;
   int population;
}

class ZipInfoStats {
   String id;
   String state;
   City biggestCity;
   City smallestCity;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<ZipInfo> aggregation = newAggregation(ZipInfo.class,
    group("state", "city")
       .sum("population").as("pop"),
    sort(ASC, "pop", "state", "city"),
    group("state")
       .last("city").as("biggestCity")
       .last("pop").as("biggestPop")
       .first("city").as("smallestCity")
       .first("pop").as("smallestPop"),
    project()
       .and("state").previousOperation()
       .and("biggestCity")
          .nested(bind("name", "biggestCity").and("population", "biggestPop"))
       .and("smallestCity")
          .nested(bind("name", "smallestCity").and("population", "smallestPop")),
    sort(ASC, "state")
);

AggregationResults<ZipInfoStats> result = mongoTemplate.aggregate(aggregation, ZipInfoStats.class);
ZipInfoStats firstZipInfoStats = result.getMappedResults().get(0);

Note that the ZipInfo class maps the structure of the given input-collection. The ZipInfoStats class defines the structure in the desired output format.

The preceding listings use the following algorithm:

  1. Use the group operation to define a group from the input-collection. The grouping criteria is the combination of the state and city fields, which forms the ID structure of the group. We aggregate the value of the population property from the grouped elements by using the sum operator and save the result in the pop field.

  2. Use the sort operation to sort the intermediate-result by the pop, state and city fields, in ascending order, such that the smallest city is at the top and the biggest city is at the bottom of the result. Note that the sorting on state and city is implicitly performed against the group ID fields (which Spring Data MongoDB handled).

  3. Use a group operation again to group the intermediate result by state. Note that state again implicitly references a group ID field. We select the name and the population count of the biggest and smallest city with calls to the last(…) and first(…​) operators, respectively, in the project operation.

  4. Select the state field from the previous group operation. Note that state again implicitly references a group ID field. Because we do not want an implicitly generated ID to appear, we exclude the ID from the previous operation by using and(previousOperation()).exclude(). Because we want to populate the nested City structures in our output class, we have to emit appropriate sub-documents by using the nested method.

  5. Sort the resulting list of StateStats by their state name in ascending order in the sort operation.

Note that we derive the name of the input collection from the ZipInfo class passed as the first parameter to the newAggregation method.

Aggregation Framework Example 3

This example is based on the States with Populations Over 10 Million example from the MongoDB Aggregation Framework documentation. We added additional sorting to produce stable results with different MongoDB versions. Here we want to return all states with a population greater than 10 million, using the aggregation framework. This example demonstrates grouping, sorting, and matching (filtering).

class StateStats {
   @Id String id;
   String state;
   @Field("totalPop") int totalPopulation;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<ZipInfo> agg = newAggregation(ZipInfo.class,
    group("state").sum("population").as("totalPop"),
    sort(ASC, previousOperation(), "totalPop"),
    match(where("totalPop").gte(10 * 1000 * 1000))
);

AggregationResults<StateStats> result = mongoTemplate.aggregate(agg, StateStats.class);
List<StateStats> stateStatsList = result.getMappedResults();

The preceding listings use the following algorithm:

  1. Group the input collection by the state field and calculate the sum of the population field and store the result in the new field "totalPop".

  2. Sort the intermediate result by the id-reference of the previous group operation in addition to the "totalPop" field in ascending order.

  3. Filter the intermediate result by using a match operation which accepts a Criteria query as an argument.

Note that we derive the name of the input collection from the ZipInfo class passed as first parameter to the newAggregation method.

Aggregation Framework Example 4

This example demonstrates the use of simple arithmetic operations in the projection operation.

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<Product> agg = newAggregation(Product.class,
    project("name", "netPrice")
        .and("netPrice").plus(1).as("netPricePlus1")
        .and("netPrice").minus(1).as("netPriceMinus1")
        .and("netPrice").multiply(1.19).as("grossPrice")
        .and("netPrice").divide(2).as("netPriceDiv2")
        .and("spaceUnits").mod(2).as("spaceUnitsMod2")
);

AggregationResults<Document> result = mongoTemplate.aggregate(agg, Document.class);
List<Document> resultList = result.getMappedResults();

Note that we derive the name of the input collection from the Product class passed as first parameter to the newAggregation method.

Aggregation Framework Example 5

This example demonstrates the use of simple arithmetic operations derived from SpEL Expressions in the projection operation.

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<Product> agg = newAggregation(Product.class,
    project("name", "netPrice")
        .andExpression("netPrice + 1").as("netPricePlus1")
        .andExpression("netPrice - 1").as("netPriceMinus1")
        .andExpression("netPrice / 2").as("netPriceDiv2")
        .andExpression("netPrice * 1.19").as("grossPrice")
        .andExpression("spaceUnits % 2").as("spaceUnitsMod2")
        .andExpression("(netPrice * 0.8  + 1.2) * 1.19").as("grossPriceIncludingDiscountAndCharge")

);

AggregationResults<Document> result = mongoTemplate.aggregate(agg, Document.class);
List<Document> resultList = result.getMappedResults();
Aggregation Framework Example 6

This example demonstrates the use of complex arithmetic operations derived from SpEL Expressions in the projection operation.

Note: The additional parameters passed to the addExpression method can be referenced with indexer expressions according to their position. In this example, we reference the first parameter of the parameters array with [0]. When the SpEL expression is transformed into a MongoDB aggregation framework expression, external parameter expressions are replaced with their respective values.

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

double shippingCosts = 1.2;

TypedAggregation<Product> agg = newAggregation(Product.class,
    project("name", "netPrice")
        .andExpression("(netPrice * (1-discountRate)  + [0]) * (1+taxRate)", shippingCosts).as("salesPrice")
);

AggregationResults<Document> result = mongoTemplate.aggregate(agg, Document.class);
List<Document> resultList = result.getMappedResults();

Note that we can also refer to other fields of the document within the SpEL expression.

Aggregation Framework Example 7

This example uses conditional projection. It is derived from the $cond reference documentation.

public class InventoryItem {

  @Id int id;
  String item;
  String description;
  int qty;
}

public class InventoryItemProjection {

  @Id int id;
  String item;
  String description;
  int qty;
  int discount
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<InventoryItem> agg = newAggregation(InventoryItem.class,
  project("item").and("discount")
    .applyCondition(ConditionalOperator.newBuilder().when(Criteria.where("qty").gte(250))
      .then(30)
      .otherwise(20))
    .and(ifNull("description", "Unspecified")).as("description")
);

AggregationResults<InventoryItemProjection> result = mongoTemplate.aggregate(agg, "inventory", InventoryItemProjection.class);
List<InventoryItemProjection> stateStatsList = result.getMappedResults();

This one-step aggregation uses a projection operation with the inventory collection. We project the discount field by using a conditional operation for all inventory items that have a qty greater than or equal to 250. A second conditional projection is performed for the description field. We apply the Unspecified description to all items that either do not have a description field or items that have a null description.

As of MongoDB 3.6, it is possible to exclude fields from the projection by using a conditional expression.

Example 106. Conditional aggregation projection
TypedAggregation<Book> agg = Aggregation.newAggregation(Book.class,
  project("title")
    .and(ConditionalOperators.when(ComparisonOperators.valueOf("author.middle")     (1)
        .equalToValue(""))                                                          (2)
        .then("$$REMOVE")                                                           (3)
        .otherwiseValueOf("author.middle")                                          (4)
    )
	.as("author.middle"));
1 If the value of the field author.middle
2 does not contain a value,
3 then use $$REMOVE to exclude the field.
4 Otherwise, add the field value of author.middle.

10.13. Index and Collection Management

MongoTemplate provides a few methods for managing indexes and collections. These methods are collected into a helper interface called IndexOperations. You can access these operations by calling the indexOps method and passing in either the collection name or the java.lang.Class of your entity (the collection name is derived from the .class, either by name or from annotation metadata).

The following listing shows the IndexOperations interface:

public interface IndexOperations {

  void ensureIndex(IndexDefinition indexDefinition);

  void dropIndex(String name);

  void dropAllIndexes();

  void resetIndexCache();

  List<IndexInfo> getIndexInfo();
}

10.13.1. Methods for Creating an Index

You can create an index on a collection to improve query performance by using the MongoTemplate class, as the following example shows:

mongoTemplate.indexOps(Person.class).ensureIndex(new Index().on("name",Order.ASCENDING));

ensureIndex makes sure that an index for the provided IndexDefinition exists for the collection.

You can create standard, geospatial, and text indexes by using the IndexDefinition, GeoSpatialIndex and TextIndexDefinition classes. For example, given the Venue class defined in a previous section, you could declare a geospatial query, as the following example shows:

mongoTemplate.indexOps(Venue.class).ensureIndex(new GeospatialIndex("location"));
Index and GeospatialIndex support configuration of collations.

10.13.2. Accessing Index Information

The IndexOperations interface has the getIndexInfo method that returns a list of IndexInfo objects. This list contains all the indexes defined on the collection. The following example defines an index on the Person class that has an age property:

template.indexOps(Person.class).ensureIndex(new Index().on("age", Order.DESCENDING).unique());

List<IndexInfo> indexInfoList = template.indexOps(Person.class).getIndexInfo();

// Contains
// [IndexInfo [fieldSpec={_id=ASCENDING}, name=_id_, unique=false, sparse=false],
//  IndexInfo [fieldSpec={age=DESCENDING}, name=age_-1, unique=true, sparse=false]]

10.13.3. Methods for Working with a Collection

The following example shows how to create a collection:

Example 107. Working with collections by using MongoTemplate
MongoCollection<Document> collection = null;
if (!mongoTemplate.getCollectionNames().contains("MyNewCollection")) {
    collection = mongoTemplate.createCollection("MyNewCollection");
}

mongoTemplate.dropCollection("MyNewCollection");
  • getCollectionNames: Returns a set of collection names.

  • collectionExists: Checks to see if a collection with a given name exists.

  • createCollection: Creates an uncapped collection.

  • dropCollection: Drops the collection.

  • getCollection: Gets a collection by name, creating it if it does not exist.

Collection creation allows customization with CollectionOptions and supports collations.

10.14. Running Commands

You can get at the MongoDB driver’s MongoDatabase.runCommand( ) method by using the executeCommand(…) methods on MongoTemplate. These methods also perform exception translation into Spring’s DataAccessException hierarchy.

10.14.1. Methods for running commands

  • Document executeCommand (Document command): Run a MongoDB command.

  • Document executeCommand (Document command, ReadPreference readPreference): Run a MongoDB command with the given nullable MongoDB ReadPreference.

  • Document executeCommand (String jsonCommand): Run a MongoDB command expressed as a JSON string.

10.15. Lifecycle Events

The MongoDB mapping framework includes several org.springframework.context.ApplicationEvent events that your application can respond to by registering special beans in the ApplicationContext. Being based on Spring’s ApplicationContext event infrastructure enables other products, such as Spring Integration, to easily receive these events, as they are a well known eventing mechanism in Spring-based applications.

Entity lifecycle events can be costly and you may notice a change in the performance profile when loading large result sets. You can disable lifecycle events on the Template API.

To intercept an object before it goes through the conversion process (which turns your domain object into a org.bson.Document), you can register a subclass of AbstractMongoEventListener that overrides the onBeforeConvert method. When the event is dispatched, your listener is called and passed the domain object before it goes into the converter. The following example shows how to do so:

public class BeforeConvertListener extends AbstractMongoEventListener<Person> {
  @Override
  public void onBeforeConvert(BeforeConvertEvent<Person> event) {
    ... does some auditing manipulation, set timestamps, whatever ...
  }
}

To intercept an object before it goes into the database, you can register a subclass of org.springframework.data.mongodb.core.mapping.event.AbstractMongoEventListener that overrides the onBeforeSave method. When the event is dispatched, your listener is called and passed the domain object and the converted com.mongodb.Document. The following example shows how to do so:

public class BeforeSaveListener extends AbstractMongoEventListener<Person> {
  @Override
  public void onBeforeSave(BeforeSaveEvent<Person> event) {
    … change values, delete them, whatever …
  }
}

Declaring these beans in your Spring ApplicationContext causes them to be invoked whenever the event is dispatched.

The following callback methods are present in AbstractMappingEventListener:

  • onBeforeConvert: Called in MongoTemplate insert, insertList, and save operations before the object is converted to a Document by a MongoConverter.

  • onBeforeSave: Called in MongoTemplate insert, insertList, and save operations before inserting or saving the Document in the database.

  • onAfterSave: Called in MongoTemplate insert, insertList, and save operations after inserting or saving the Document in the database.

  • onAfterLoad: Called in MongoTemplate find, findAndRemove, findOne, and getCollection methods after the Document has been retrieved from the database.

  • onAfterConvert: Called in MongoTemplate find, findAndRemove, findOne, and getCollection methods after the Document has been retrieved from the database was converted to a POJO.

Lifecycle events are only emitted for root level types. Complex types used as properties within a document root are not subject to event publication unless they are document references annotated with @DBRef.
Lifecycle events depend on an ApplicationEventMulticaster, which in case of the SimpleApplicationEventMulticaster can be configured with a TaskExecutor, and therefore gives no guarantees when an Event is processed.

10.16. Entity Callbacks

The Spring Data infrastructure provides hooks for modifying an entity before and after certain methods are invoked. Those so called EntityCallback instances provide a convenient way to check and potentially modify an entity in a callback fashioned style.
An EntityCallback looks pretty much like a specialized ApplicationListener. Some Spring Data modules publish store specific events (such as BeforeSaveEvent) that allow modifying the given entity. In some cases, such as when working with immutable types, these events can cause trouble. Also, event publishing relies on ApplicationEventMulticaster. If configuring that with an asynchronous TaskExecutor it can lead to unpredictable outcomes, as event processing can be forked onto a Thread.

Entity callbacks provide integration points with both synchronous and reactive APIs to guarantee in-order execution at well-defined checkpoints within the processing chain, returning a potentially modified entity or an reactive wrapper type.

Entity callbacks are typically separated by API type. This separation means that a synchronous API considers only synchronous entity callbacks and a reactive implementation considers only reactive entity callbacks.

The Entity Callback API has been introduced with Spring Data Commons 2.2. It is the recommended way of applying entity modifications. Existing store specific ApplicationEvents are still published before the invoking potentially registered EntityCallback instances.

10.16.1. Implementing Entity Callbacks

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.

Example 108. Anatomy of an EntityCallback
@FunctionalInterface
public interface BeforeSaveCallback<T> extends EntityCallback<T> {

	/**
	 * Entity callback method invoked before a domain object is saved.
	 * Can return either the same or a modified instance.
	 *
	 * @return the domain object to be persisted.
	 */
	T onBeforeSave(T entity <2>, String collection <3>); (1)
}
1 BeforeSaveCallback specific method to be called before an entity is saved. Returns a potentially modifed instance.
2 The entity right before persisting.
3 A number of store specific arguments like the collection the entity is persisted to.
Example 109. Anatomy of a reactive EntityCallback
@FunctionalInterface
public interface ReactiveBeforeSaveCallback<T> extends EntityCallback<T> {

	/**
	 * Entity callback method invoked on subscription, before a domain object is saved.
	 * The returned Publisher can emit either the same or a modified instance.
	 *
	 * @return Publisher emitting the domain object to be persisted.
	 */
	Publisher<T> onBeforeSave(T entity <2>, String collection <3>); (1)
}
1 BeforeSaveCallback specific method to be called on subscription, before an entity is saved. Emits a potentially modifed instance.
2 The entity right before persisting.
3 A number of store specific arguments like the collection the entity is persisted to.
Optional entity callback parameters are defined by the implementing Spring Data module and inferred from call site of EntityCallback.callback().

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

Example 110. Example BeforeSaveCallback
class DefaultingEntityCallback implements BeforeSaveCallback<Person>, Ordered {      (2)

	@Override
	public Object onBeforeSave(Person entity, String collection) {                   (1)

		if(collection == "user") {
		    return // ...
		}

		return // ...
	}

	@Override
	public int getOrder() {
		return 100;                                                                  (2)
	}
}
1 Callback implementation according to your requirements.
2 Potentially order the entity callback if multiple ones for the same domain type exist. Ordering follows lowest precedence.

10.16.2. Registering Entity Callbacks

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

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

Example 111. Example EntityCallback Bean registration
@Order(1)                                                           (1)
@Component
class First implements BeforeSaveCallback<Person> {

	@Override
	public Person onBeforeSave(Person person) {
		return // ...
	}
}

@Component
class DefaultingEntityCallback implements BeforeSaveCallback<Person>,
                                                           Ordered { (2)

	@Override
	public Object onBeforeSave(Person entity, String collection) {
		// ...
	}

	@Override
	public int getOrder() {
		return 100;                                                  (2)
	}
}

@Configuration
public class EntityCallbackConfiguration {

    @Bean
    BeforeSaveCallback<Person> unorderedLambdaReceiverCallback() {   (3)
        return (BeforeSaveCallback<Person>) it -> // ...
    }
}

@Component
class UserCallbacks implements BeforeConvertCallback<User>,
                                        BeforeSaveCallback<User> {   (4)

	@Override
	public Person onBeforeConvert(User user) {
		return // ...
	}

	@Override
	public Person onBeforeSave(User user) {
		return // ...
	}
}
1 BeforeSaveCallback receiving its order from the @Order annotation.
2 BeforeSaveCallback receiving its order via the Ordered interface implementation.
3 BeforeSaveCallback using a lambda expression. Unordered by default and invoked last. Note that callbacks implemented by a lambda expression do not expose typing information hence invoking these with a non-assignable entity affects the callback throughput. Use a class or enum to enable type filtering for the callback bean.
4 Combine multiple entity callback interfaces in a single implementation class.

10.16.3. Store specific EntityCallbacks

Spring Data MongoDB uses the EntityCallback API for its auditing support and reacts on the following callbacks.

Table 9. Supported Entity Callbacks
Callback Method Description Order

Reactive/BeforeConvertCallback

onBeforeConvert(T entity, String collection)

Invoked before a domain object is converted to org.bson.Document.

Ordered.LOWEST_PRECEDENCE

Reactive/AfterConvertCallback

onAfterConvert(T entity, org.bson.Document target, String collection)

Invoked after a domain object is loaded.
Can modify the domain object after reading it from a org.bson.Document.

Ordered.LOWEST_PRECEDENCE

Reactive/AuditingEntityCallback

onBeforeConvert(Object entity, String collection)

Marks an auditable entity created or modified

100

Reactive/BeforeSaveCallback

onBeforeSave(T entity, org.bson.Document target, String collection)

Invoked before a domain object is saved.
Can modify the target, to be persisted, Document containing all mapped entity information.

Ordered.LOWEST_PRECEDENCE

Reactive/AfterSaveCallback

onAfterSave(T entity, org.bson.Document target, String collection)

Invoked before a domain object is saved.
Can modify the domain object, to be returned after save, Document containing all mapped entity information.

Ordered.LOWEST_PRECEDENCE

10.17. Exception Translation

The Spring framework provides exception translation for a wide variety of database and mapping technologies. This has traditionally been for JDBC and JPA. The Spring support for MongoDB extends this feature to the MongoDB Database by providing an implementation of the org.springframework.dao.support.PersistenceExceptionTranslator interface.

The motivation behind mapping to Spring’s consistent data access exception hierarchy is that you are then able to write portable and descriptive exception handling code without resorting to coding against MongoDB error codes. All of Spring’s data access exceptions are inherited from the root DataAccessException class so that you can be sure to catch all database related exception within a single try-catch block. Note that not all exceptions thrown by the MongoDB driver inherit from the MongoException class. The inner exception and message are preserved so that no information is lost.

Some of the mappings performed by the MongoExceptionTranslator are com.mongodb.Network to DataAccessResourceFailureException and MongoException error codes 1003, 12001, 12010, 12011, and 12012 to InvalidDataAccessApiUsageException. Look into the implementation for more details on the mapping.

10.18. Execution Callbacks

One common design feature of all Spring template classes is that all functionality is routed into one of the template’s execute callback methods. Doing so helps to ensure that exceptions and any resource management that may be required are performed consistently. While JDBC and JMS need this feature much more than MongoDB does, it still offers a single spot for exception translation and logging to occur. Consequently, using these execute callbacks is the preferred way to access the MongoDB driver’s MongoDatabase and MongoCollection objects to perform uncommon operations that were not exposed as methods on MongoTemplate.

The following list describes the execute callback methods.

  • <T> T execute (Class<?> entityClass, CollectionCallback<T> action): Runs the given CollectionCallback for the entity collection of the specified class.

  • <T> T execute (String collectionName, CollectionCallback<T> action): Runs the given CollectionCallback on the collection of the given name.

  • <T> T execute (DbCallback<T> action): Runs a DbCallback, translating any exceptions as necessary. Spring Data MongoDB provides support for the Aggregation Framework introduced to MongoDB in version 2.2.

  • <T> T execute (String collectionName, DbCallback<T> action): Runs a DbCallback on the collection of the given name translating any exceptions as necessary.

  • <T> T executeInSession (DbCallback<T> action): Runs the given DbCallback within the same connection to the database so as to ensure consistency in a write-heavy environment where you may read the data that you wrote.

The following example uses the CollectionCallback to return information about an index:

boolean hasIndex = template.execute("geolocation", new CollectionCallbackBoolean>() {
  public Boolean doInCollection(Venue.class, DBCollection collection) throws MongoException, DataAccessException {
    List<Document> indexes = collection.getIndexInfo();
    for (Document document : indexes) {
      if ("location_2d".equals(document.get("name"))) {
        return true;
      }
    }
    return false;
  }
});

10.19. GridFS Support

MongoDB supports storing binary files inside its filesystem, GridFS. Spring Data MongoDB provides a GridFsOperations interface as well as the corresponding implementation, GridFsTemplate, to let you interact with the filesystem. You can set up a GridFsTemplate instance by handing it a MongoDatabaseFactory as well as a MongoConverter, as the following example shows:

Java
class GridFsConfiguration extends AbstractMongoClientConfiguration {

  // … further configuration omitted

  @Bean
  public GridFsTemplate gridFsTemplate() {
    return new GridFsTemplate(mongoDbFactory(), mappingMongoConverter());
  }
}
XML
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:mongo="http://www.springframework.org/schema/data/mongo"
  xsi:schemaLocation="http://www.springframework.org/schema/data/mongo
                      https://www.springframework.org/schema/data/mongo/spring-mongo.xsd
                      http://www.springframework.org/schema/beans
                      https://www.springframework.org/schema/beans/spring-beans.xsd">

  <mongo:db-factory id="mongoDbFactory" dbname="database" />
  <mongo:mapping-converter id="converter" />

  <bean class="org.springframework.data.mongodb.gridfs.GridFsTemplate">
    <constructor-arg ref="mongoDbFactory" />
    <constructor-arg ref="converter" />
  </bean>

</beans>

The template can now be injected and used to perform storage and retrieval operations, as the following example shows:

Example 112. Using GridFsTemplate to store files
class GridFsClient {

  @Autowired
  GridFsOperations operations;

  @Test
  public void storeFileToGridFs() {

    FileMetadata metadata = new FileMetadata();
    // populate metadata
    Resource file = … // lookup File or Resource

    operations.store(file.getInputStream(), "filename.txt", metadata);
  }
}

The store(…) operations take an InputStream, a filename, and (optionally) metadata information about the file to store. The metadata can be an arbitrary object, which will be marshaled by the MongoConverter configured with the GridFsTemplate. Alternatively, you can also provide a Document.

You can read files from the filesystem through either the find(…) or the getResources(…) methods. Let’s have a look at the find(…) methods first. You can either find a single file or multiple files that match a Query. You can use the GridFsCriteria helper class to define queries. It provides static factory methods to encapsulate default metadata fields (such as whereFilename() and whereContentType()) or a custom one through whereMetaData(). The following example shows how to use GridFsTemplate to query for files:

Example 113. Using GridFsTemplate to query for files
class GridFsClient {

  @Autowired
  GridFsOperations operations;

  @Test
  public void findFilesInGridFs() {
    GridFSFindIterable result = operations.find(query(whereFilename().is("filename.txt")))
  }
}
Currently, MongoDB does not support defining sort criteria when retrieving files from GridFS. For this reason, any sort criteria defined on the Query instance handed into the find(…) method are disregarded.

The other option to read files from the GridFs is to use the methods introduced by the ResourcePatternResolver interface. They allow handing an Ant path into the method and can thus retrieve files matching the given pattern. The following example shows how to use GridFsTemplate to read files:

Example 114. Using GridFsTemplate to read files
class GridFsClient {

  @Autowired
  GridFsOperations operations;

  @Test
  public void readFilesFromGridFs() {
    GridFsResources[] txtFiles = operations.getResources("*.txt");
  }
}

GridFsOperations extends ResourcePatternResolver and lets the GridFsTemplate (for example) to be plugged into an ApplicationContext to read Spring Config files from MongoDB database.

10.20. Infinite Streams with Tailable Cursors

By default, MongoDB automatically closes a cursor when the client exhausts all results supplied by the cursor. Closing a cursor on exhaustion turns a stream into a finite stream. For capped collections, you can use a Tailable Cursor that remains open after the client consumed all initially returned data.

Capped collections can be created with MongoOperations.createCollection. To do so, provide the required CollectionOptions.empty().capped()…​.

Tailable cursors can be consumed with both, the imperative and the reactive MongoDB API. It is highly recommended to use the reactive variant, as it is less resource-intensive. However, if you cannot use the reactive API, you can still use a messaging concept that is already prevalent in the Spring ecosystem.

10.20.1. Tailable Cursors with MessageListener

Listening to a capped collection using a Sync Driver creates a long running, blocking task that needs to be delegated to a separate component. In this case, we need to first create a MessageListenerContainer, which will be the main entry point for running the specific SubscriptionRequest. Spring Data MongoDB already ships with a default implementation that operates on MongoTemplate and is capable of creating and running Task instances for a TailableCursorRequest.

The following example shows how to use tailable cursors with MessageListener instances:

Example 115. Tailable Cursors with MessageListener instances
MessageListenerContainer container = new DefaultMessageListenerContainer(template);
container.start();                                                                  (1)

MessageListener<Document, User> listener = System.out::println;                     (2)

TailableCursorRequest request = TailableCursorRequest.builder()
  .collection("orders")                                                             (3)
  .filter(query(where("value").lt(100)))                                            (4)
  .publishTo(listener)                                                              (5)
  .build();

container.register(request, User.class);                                            (6)

// ...

container.stop();                                                                   (7)
1 Starting the container intializes the resources and starts Task instances for already registered SubscriptionRequest instances. Requests added after startup are ran immediately.
2 Define the listener called when a Message is received. The Message#getBody() is converted to the requested domain type. Use Document to receive raw results without conversion.
3 Set the collection to listen to.
4 Provide an optional filter for documents to receive.
5 Set the message listener to publish incoming Messages to.
6 Register the request. The returned Subscription can be used to check the current Task state and cancel it to free resources.
7 Do not forget to stop the container once you are sure you no longer need it. Doing so stops all running Task instances within the container.

10.20.2. Reactive Tailable Cursors

Using tailable cursors with a reactive data types allows construction of infinite streams. A tailable cursor remains open until it is closed externally. It emits data as new documents arrive in a capped collection.

Tailable cursors may become dead, or invalid, if either the query returns no match or the cursor returns the document at the “end” of the collection and the application then deletes that document. The following example shows how to create and use an infinite stream query:

Example 116. Infinite Stream queries with ReactiveMongoOperations
Flux<Person> stream = template.tail(query(where("name").is("Joe")), Person.class);

Disposable subscription = stream.doOnNext(person -> System.out.println(person)).subscribe();

// …

// Later: Dispose the subscription to close the stream
subscription.dispose();

Spring Data MongoDB Reactive repositories support infinite streams by annotating a query method with @Tailable. This works for methods that return Flux and other reactive types capable of emitting multiple elements, as the following example shows:

Example 117. Infinite Stream queries with ReactiveMongoRepository
public interface PersonRepository extends ReactiveMongoRepository<Person, String> {

  @Tailable
  Flux<Person> findByFirstname(String firstname);

}

Flux<Person> stream = repository.findByFirstname("Joe");

Disposable subscription = stream.doOnNext(System.out::println).subscribe();

// …

// Later: Dispose the subscription to close the stream
subscription.dispose();

10.21. Change Streams

As of MongoDB 3.6, Change Streams let applications get notified about changes without having to tail the oplog.

Change Stream support is only possible for replica sets or for a sharded cluster.

Change Streams can be consumed with both, the imperative and the reactive MongoDB Java driver. It is highly recommended to use the reactive variant, as it is less resource-intensive. However, if you cannot use the reactive API, you can still obtain change events by using the messaging concept that is already prevalent in the Spring ecosystem.

It is possible to watch both on a collection as well as database level, whereas the database level variant publishes changes from all collections within the database. When subscribing to a database change stream, make sure to use a suitable type for the event type as conversion might not apply correctly across different entity types. In doubt, use Document.

10.21.1. Change Streams with MessageListener

Listening to a Change Stream by using a Sync Driver creates a long running, blocking task that needs to be delegated to a separate component. In this case, we need to first create a MessageListenerContainer, which will be the main entry point for running the specific SubscriptionRequest tasks. Spring Data MongoDB already ships with a default implementation that operates on MongoTemplate and is capable of creating and running Task instances for a ChangeStreamRequest.

The following example shows how to use Change Streams with MessageListener instances:

Example 118. Change Streams with MessageListener instances
MessageListenerContainer container = new DefaultMessageListenerContainer(template);
container.start();                                                                                        (1)

MessageListener<ChangeStreamDocument<Document>, User> listener = System.out::println;                     (2)
ChangeStreamRequestOptions options = new ChangeStreamRequestOptions("user", ChangeStreamOptions.empty()); (3)

Subscription subscription = container.register(new ChangeStreamRequest<>(listener, options), User.class); (4)

// ...

container.stop();                                                                                         (5)
1 Starting the container initializes the resources and starts Task instances for already registered SubscriptionRequest instances. Requests added after startup are ran immediately.
2 Define the listener called when a Message is received. The Message#getBody() is converted to the requested domain type. Use Document to receive raw results without conversion.
3 Set the collection to listen to and provide additional options through ChangeStreamOptions.
4 Register the request. The returned Subscription can be used to check the current Task state and cancel it to free resources.
5 Do not forget to stop the container once you are sure you no longer need it. Doing so stops all running Task instances within the container.

Errors while processing are passed on to an org.springframework.util.ErrorHandler. If not stated otherwise a log appending ErrorHandler gets applied by default.
Please use register(request, body, errorHandler) to provide additional functionality.

10.21.2. Reactive Change Streams

Subscribing to Change Streams with the reactive API is a more natural approach to work with streams. Still, the essential building blocks, such as ChangeStreamOptions, remain the same. The following example shows how to use Change Streams emitting ChangeStreamEvents:

Example 119. Change Streams emitting ChangeStreamEvent
Flux<ChangeStreamEvent<User>> flux = reactiveTemplate.changeStream(User.class) (1)
    .watchCollection("people")
    .filter(where("age").gte(38))                                              (2)
    .listen();                                                                 (3)
1 The event target type the underlying document should be converted to. Leave this out to receive raw results without conversion.
2 Use an aggregation pipeline or just a query Criteria to filter events.
3 Obtain a Flux of change stream events. The ChangeStreamEvent#getBody() is converted to the requested domain type from (2).

10.21.3. Resuming Change Streams

Change Streams can be resumed and resume emitting events where you left. To resume the stream, you need to supply either a resume token or the last known server time (in UTC). Use ChangeStreamOptions to set the value accordingly.

The following example shows how to set the resume offset using server time:

Example 120. Resume a Change Stream
Flux<ChangeStreamEvent<User>> resumed = template.changeStream(User.class)
    .watchCollection("people")
    .resumeAt(Instant.now().minusSeconds(1)) (1)
    .listen();
1 You may obtain the server time of an ChangeStreamEvent through the getTimestamp method or use the resumeToken exposed through getResumeToken.
In some cases an Instant might not be a precise enough measure when resuming a Change Stream. Use a MongoDB native BsonTimestamp for that purpose.

10.22. Time Series

MongoDB 5.0 introduced Time Series collections that are optimized to efficiently store documents over time such as measurements or events. Those collections need to be created as such before inserting any data. Collections can be created by either running the createCollection command, defining time series collection options or extracting options from a @TimeSeries annotation as shown in the examples below.

Example 121. Create a Time Series Collection
Create a Time Series via the MongoDB Driver
template.execute(db -> {

    com.mongodb.client.model.CreateCollectionOptions options = new CreateCollectionOptions();
    options.timeSeriesOptions(new TimeSeriesOptions("timestamp"));

    db.createCollection("weather", options);
    return "OK";
});
Create a Time Series Collection with CollectionOptions
template.createCollection("weather", CollectionOptions.timeSeries("timestamp"));
Create a Time Series Collection derived from an Annotation
@TimeSeries(collection="weather", timeField = "timestamp")
public class Measurement {

    String id;
    Instant timestamp;
    // ...
}

template.createCollection(Measurement.class);

The snippets above can easily be transferred to the reactive API offering the very same methods. Make sure to properly subscribe to the returned publishers.

10.23. Observability

Spring Data MongoDB currently has the most up-to-date code to support Observability in your MongoDB application. These changes, however, haven’t been picked up by Spring Boot (yet). Until those changes are applied, if you wish to use Spring Data MongoDB’s flavor of Observability, you must carry out the following steps.

  1. First of all, you must opt into Spring Data MongoDB’s configuration settings by customizing MongoClientSettings through either your @SpringBootApplication class or one of your configuration classes.

    Example 122. Registering MongoDB Micrometer customizer setup
    @Bean
    MongoClientSettingsBuilderCustomizer mongoMetricsSynchronousContextProvider(ObservationRegistry registry) {
        return (clientSettingsBuilder) -> {
            clientSettingsBuilder.contextProvider(ContextProviderFactory.create(registry))
                                 .addCommandListener(new MongoObservationCommandListener(registry));
        };
    }
    
  2. Your project must include Spring Boot Actuator.

  3. Disable Spring Boot’s autoconfigured MongoDB command listener and enable tracing manually by adding the following properties to your application.properties

    Example 123. Custom settings to apply
    # Disable Spring Boot's autoconfigured tracing
    management.metrics.mongo.command.enabled=false
    # Enable it manually
    management.tracing.enabled=true

    Be sure to add any other relevant settings needed to configure the tracer you are using based upon Micrometer’s reference documentation.

This should do it! You are now running with Spring Data MongoDB’s usage of Spring Observability’s Observation API.

10.23.1. Observability - Conventions

Below you can find a list of all GlobalObservationConvention and ObservationConvention declared by this project.

Table 10. ObservationConvention implementations

ObservationConvention Class Name

Applicable ObservationContext Class Name

org.springframework.data.mongodb.observability.DefaultMongoHandlerObservationConvention

MongoHandlerContext

org.springframework.data.mongodb.observability.MongoHandlerObservationConvention

MongoHandlerContext

10.23.2. Observability - Metrics

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

Mongodb Command Observation

Timer created around a MongoDB command execution.

Metric name spring.data.mongodb.command. Type timer.

Metric name spring.data.mongodb.command.active. Type long task timer.

KeyValues that are added after starting the Observation might be missing from the *.active metrics.
Micrometer internally uses nanoseconds for the baseunit. However, each backend determines the actual baseunit. (i.e. Prometheus uses seconds)

Fully qualified name of the enclosing class org.springframework.data.mongodb.observability.MongoObservation.

Table 11. Low cardinality Keys

Name

Description

db.connection_string (required)

MongoDB connection string.

db.mongodb.collection (required)

MongoDB collection name.

db.name (required)

MongoDB database name.

db.operation (required)

MongoDB command value.

db.system (required)

MongoDB database system.

db.user (required)

MongoDB user.

net.peer.name (required)

Name of the database host.

net.peer.port (required)

Logical remote port number.

net.sock.peer.addr (required)

Mongo peer address.

net.sock.peer.port (required)

Mongo peer port.

net.transport (required)

Network transport.

spring.data.mongodb.cluster_id (required)

MongoDB cluster identifier.

10.23.3. Observability - Spans

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

Mongodb Command Observation Span

Timer created around a MongoDB command execution.

Span name spring.data.mongodb.command.

Fully qualified name of the enclosing class org.springframework.data.mongodb.observability.MongoObservation.

Table 12. Tag Keys

Name

Description

db.connection_string (required)

MongoDB connection string.

db.mongodb.collection (required)

MongoDB collection name.

db.name (required)

MongoDB database name.

db.operation (required)

MongoDB command value.

db.system (required)

MongoDB database system.

db.user (required)

MongoDB user.

net.peer.name (required)

Name of the database host.

net.peer.port (required)

Logical remote port number.

net.sock.peer.addr (required)

Mongo peer address.

net.sock.peer.port (required)

Mongo peer port.

net.transport (required)

Network transport.

spring.data.mongodb.cluster_id (required)

MongoDB cluster identifier.

See also OpenTelemetry Semantic Conventions for further reference.

11. MongoDB Sessions

As of version 3.6, MongoDB supports the concept of sessions. The use of sessions enables MongoDB’s Causal Consistency model, which guarantees running operations in an order that respects their causal relationships. Those are split into ServerSession instances and ClientSession instances. In this section, when we speak of a session, we refer to ClientSession.

Operations within a client session are not isolated from operations outside the session.

Both MongoOperations and ReactiveMongoOperations provide gateway methods for tying a ClientSession to the operations. MongoCollection and MongoDatabase use session proxy objects that implement MongoDB’s collection and database interfaces, so you need not add a session on each call. This means that a potential call to MongoCollection#find() is delegated to MongoCollection#find(ClientSession).

Methods such as (Reactive)MongoOperations#getCollection return native MongoDB Java Driver gateway objects (such as MongoCollection) that themselves offer dedicated methods for ClientSession. These methods are NOT session-proxied. You should provide the ClientSession where needed when interacting directly with a MongoCollection or MongoDatabase and not through one of the #execute callbacks on MongoOperations.

11.1. Synchronous ClientSession support.

The following example shows the usage of a session:

Example 124. ClientSession with MongoOperations
ClientSessionOptions sessionOptions = ClientSessionOptions.builder()
    .causallyConsistent(true)
    .build();

ClientSession session = client.startSession(sessionOptions); (1)

template.withSession(() -> session)
    .execute(action -> {

        Query query = query(where("name").is("Durzo Blint"));
        Person durzo = action.findOne(query, Person.class);  (2)

        Person azoth = new Person("Kylar Stern");
        azoth.setMaster(durzo);

        action.insert(azoth);                                (3)

        return azoth;
    });

session.close()                                              (4)
1 Obtain a new session from the server.
2 Use MongoOperation methods as before. The ClientSession gets applied automatically.
3 Make sure to close the ClientSession.
4 Close the session.
When dealing with DBRef instances, especially lazily loaded ones, it is essential to not close the ClientSession before all data is loaded. Otherwise, lazy fetch fails.

11.2. Reactive ClientSession support

The reactive counterpart uses the same building blocks as the imperative one, as the following example shows:

Example 125. ClientSession with ReactiveMongoOperations
ClientSessionOptions sessionOptions = ClientSessionOptions.builder()
    .causallyConsistent(true)
    .build();

Publisher<ClientSession> session = client.startSession(sessionOptions); (1)

template.withSession(session)
    .execute(action -> {

        Query query = query(where("name").is("Durzo Blint"));
        return action.findOne(query, Person.class)
            .flatMap(durzo -> {

                Person azoth = new Person("Kylar Stern");
                azoth.setMaster(durzo);

                return action.insert(azoth);                            (2)
            });
    }, ClientSession::close)                                            (3)
    .subscribe();                                                       (4)
1 Obtain a Publisher for new session retrieval.
2 Use ReactiveMongoOperation methods as before. The ClientSession is obtained and applied automatically.
3 Make sure to close the ClientSession.
4 Nothing happens until you subscribe. See the Project Reactor Reference Guide for details.

By using a Publisher that provides the actual session, you can defer session acquisition to the point of actual subscription. Still, you need to close the session when done, so as to not pollute the server with stale sessions. Use the doFinally hook on execute to call ClientSession#close() when you no longer need the session. If you prefer having more control over the session itself, you can obtain the ClientSession through the driver and provide it through a Supplier.

Reactive use of ClientSession is limited to Template API usage. There’s currently no session integration with reactive repositories.

12. MongoDB Transactions

As of version 4, MongoDB supports Transactions. Transactions are built on top of Sessions and, consequently, require an active ClientSession.

Unless you specify a MongoTransactionManager within your application context, transaction support is DISABLED. You can use setSessionSynchronization(ALWAYS) to participate in ongoing non-native MongoDB transactions.

To get full programmatic control over transactions, you may want to use the session callback on MongoOperations.

The following example shows programmatic transaction control within a SessionCallback:

Example 126. Programmatic transactions
ClientSession session = client.startSession(options);                   (1)

template.withSession(session)
    .execute(action -> {

        session.startTransaction();                                     (2)

        try {

            Step step = // ...;
            action.insert(step);

            process(step);

            action.update(Step.class).apply(Update.set("state", // ...

            session.commitTransaction();                                (3)

        } catch (RuntimeException e) {
            session.abortTransaction();                                 (4)
        }
    }, ClientSession::close)                                            (5)
1 Obtain a new ClientSession.
2 Start the transaction.
3 If everything works out as expected, commit the changes.
4 Something broke, so roll back everything.
5 Do not forget to close the session when done.

The preceding example lets you have full control over transactional behavior while using the session scoped MongoOperations instance within the callback to ensure the session is passed on to every server call. To avoid some of the overhead that comes with this approach, you can use a TransactionTemplate to take away some of the noise of manual transaction flow.

12.1. Transactions with TransactionTemplate

Spring Data MongoDB transactions support a TransactionTemplate. The following example shows how to create and use a TransactionTemplate:

Example 127. Transactions with TransactionTemplate
template.setSessionSynchronization(ALWAYS);                                     (1)

// ...

TransactionTemplate txTemplate = new TransactionTemplate(anyTxManager);         (2)

txTemplate.execute(new TransactionCallbackWithoutResult() {

    @Override
    protected void doInTransactionWithoutResult(TransactionStatus status) {     (3)

        Step step = // ...;
        template.insert(step);

        process(step);

        template.update(Step.class).apply(Update.set("state", // ...
    };
});
1 Enable transaction synchronization during Template API configuration.
2 Create the TransactionTemplate using the provided PlatformTransactionManager.
3 Within the callback the ClientSession and transaction are already registered.
Changing state of MongoTemplate during runtime (as you might think would be possible in item 1 of the preceding listing) can cause threading and visibility issues.

12.2. Transactions with MongoTransactionManager

MongoTransactionManager is the gateway to the well known Spring transaction support. It lets applications use the managed transaction features of Spring. The MongoTransactionManager binds a ClientSession to the thread. MongoTemplate detects the session and operates on these resources which are associated with the transaction accordingly. MongoTemplate can also participate in other, ongoing transactions. The following example shows how to create and use transactions with a MongoTransactionManager:

Example 128. Transactions with MongoTransactionManager
@Configuration
static class Config extends AbstractMongoClientConfiguration {

    @Bean
    MongoTransactionManager transactionManager(MongoDatabaseFactory dbFactory) {  (1)
        return new MongoTransactionManager(dbFactory);
    }

    // ...
}

@Component
public class StateService {

    @Transactional
    void someBusinessFunction(Step step) {                                        (2)

        template.insert(step);

        process(step);

        template.update(Step.class).apply(Update.set("state", // ...
    };
});
1 Register MongoTransactionManager in the application context.
2 Mark methods as transactional.
@Transactional(readOnly = true) advises MongoTransactionManager to also start a transaction that adds the ClientSession to outgoing requests.

12.3. Reactive Transactions

Same as with the reactive ClientSession support, the ReactiveMongoTemplate offers dedicated methods for operating within a transaction without having to worry about the committing or stopping actions depending on the operations outcome.

Unless you specify a ReactiveMongoTransactionManager within your application context, transaction support is DISABLED. You can use setSessionSynchronization(ALWAYS) to participate in ongoing non-native MongoDB transactions.

Using the plain MongoDB reactive driver API a delete within a transactional flow may look like this.

Example 129. Native driver support
Mono<DeleteResult> result = Mono
    .from(client.startSession())                                                             (1)

    .flatMap(session -> {
        session.startTransaction();                                                          (2)

        return Mono.from(collection.deleteMany(session, ...))                                (3)

            .onErrorResume(e -> Mono.from(session.abortTransaction()).then(Mono.error(e)))   (4)

            .flatMap(val -> Mono.from(session.commitTransaction()).then(Mono.just(val)))     (5)

            .doFinally(signal -> session.close());                                           (6)
      });
1 First we obviously need to initiate the session.
2 Once we have the ClientSession at hand, start the transaction.
3 Operate within the transaction by passing on the ClientSession to the operation.
4 If the operations completes exceptionally, we need to stop the transaction and preserve the error.
5 Or of course, commit the changes in case of success. Still preserving the operations result.
6 Lastly, we need to make sure to close the session.

The culprit of the above operation is in keeping the main flows DeleteResult instead of the transaction outcome published via either commitTransaction() or abortTransaction(), which leads to a rather complicated setup.

12.4. Transactions with TransactionalOperator

Spring Data MongoDB transactions support a TransactionalOperator. The following example shows how to create and use a TransactionalOperator:

Example 130. Transactions with TransactionalOperator
template.setSessionSynchronization(ALWAYS);                                          (1)

// ...

TransactionalOperator rxtx = TransactionalOperator.create(anyTxManager,
                                   new DefaultTransactionDefinition());              (2)


Step step = // ...;
template.insert(step);

Mono<Void> process(step)
    .then(template.update(Step.class).apply(Update.set("state", …))
    .as(rxtx::transactional)                                                         (3)
    .then();
1 Enable transaction synchronization for Transactional participation.
2 Create the TransactionalOperator using the provided ReactiveTransactionManager.
3 TransactionalOperator.transactional(…) provides transaction management for all upstream operations.

12.5. Transactions with ReactiveMongoTransactionManager

ReactiveMongoTransactionManager is the gateway to the well known Spring transaction support. It allows applications to leverage the managed transaction features of Spring. The ReactiveMongoTransactionManager binds a ClientSession to the subscriber Context. ReactiveMongoTemplate detects the session and operates on these resources which are associated with the transaction accordingly. ReactiveMongoTemplate can also participate in other, ongoing transactions. The following example shows how to create and use transactions with a ReactiveMongoTransactionManager:

Example 131. Transactions with ReactiveMongoTransactionManager
@Configuration
public class Config extends AbstractReactiveMongoConfiguration {

    @Bean
    ReactiveMongoTransactionManager transactionManager(ReactiveMongoDatabaseFactory factory) {  (1)
        return new ReactiveMongoTransactionManager(factory);
    }

    // ...
}

@Service
public class StateService {

    @Transactional
    Mono<UpdateResult> someBusinessFunction(Step step) {                                  (2)

        return template.insert(step)
            .then(process(step))
            .then(template.update(Step.class).apply(Update.set("state", …));
    };
});
1 Register ReactiveMongoTransactionManager in the application context.
2 Mark methods as transactional.
@Transactional(readOnly = true) advises ReactiveMongoTransactionManager to also start a transaction that adds the ClientSession to outgoing requests.

12.6. Special behavior inside transactions

Inside transactions, MongoDB server has a slightly different behavior.

Connection Settings

The MongoDB drivers offer a dedicated replica set name configuration option turing the driver into auto detection mode. This option helps identifying the primary replica set nodes and command routing during a transaction.

Make sure to add replicaSet to the MongoDB URI. Please refer to connection string options for further details.

Collection Operations

MongoDB does not support collection operations, such as collection creation, within a transaction. This also affects the on the fly collection creation that happens on first usage. Therefore make sure to have all required structures in place.

Transient Errors

MongoDB can add special labels to errors raised during transactional operations. Those may indicate transient failures that might vanish by merely retrying the operation. We highly recommend Spring Retry for those purposes. Nevertheless one may override MongoTransactionManager#doCommit(MongoTransactionObject) to implement a Retry Commit Operation behavior as outlined in the MongoDB reference manual.

Count

MongoDB count operates upon collection statistics which may not reflect the actual situation within a transaction. The server responds with error 50851 when issuing a count command inside of a multi-document transaction. Once MongoTemplate detects an active transaction, all exposed count() methods are converted and delegated to the aggregation framework using $match and $count operators, preserving Query settings, such as collation.

Restrictions apply when using geo commands inside of the aggregation count helper. The following operators cannot be used and must be replaced with a different operator:

  • $where$expr

  • $near$geoWithin with $center

  • $nearSphere$geoWithin with $centerSphere

Queries using Criteria.near(…) and Criteria.nearSphere(…) must be rewritten to Criteria.within(…) respective Criteria.withinSphere(…). Same applies for the near query keyword in repository query methods that must be changed to within. See also MongoDB JIRA ticket DRIVERS-518 for further reference.

The following snippet shows count usage inside the session-bound closure:

session.startTransaction();

template.withSession(session)
    .execute(action -> {
        action.count(query(where("state").is("active")), Step.class)
        ...

The snippet above materializes in the following command:

db.collection.aggregate(
   [
      { $match: { state: "active" } },
      { $count: "totalEntityCount" }
   ]
)

instead of:

db.collection.find( { state: "active" } ).count()

13. Reactive MongoDB support

The reactive MongoDB support contains the following basic set of features:

  • Spring configuration support that uses Java-based @Configuration classes, a MongoClient instance, and replica sets.

  • ReactiveMongoTemplate, which is a helper class that increases productivity by using MongoOperations in a reactive manner. It includes integrated object mapping between Document instances and POJOs.

  • Exception translation into Spring’s portable Data Access Exception hierarchy.

  • Feature-rich Object Mapping integrated with Spring’s ConversionService.

  • Annotation-based mapping metadata that is extensible to support other metadata formats.

  • Persistence and mapping lifecycle events.

  • Java based Query, Criteria, and Update DSLs.

  • Automatic implementation of reactive repository interfaces including support for custom query methods.

For most tasks, you should use ReactiveMongoTemplate or the repository support, both of which use the rich mapping functionality. ReactiveMongoTemplate is the place to look for accessing functionality such as incrementing counters or ad-hoc CRUD operations. ReactiveMongoTemplate also provides callback methods so that you can use the low-level API artifacts (such as MongoDatabase) to communicate directly with MongoDB. The goal with naming conventions on various API artifacts is to copy those in the base MongoDB Java driver so that you can map your existing knowledge onto the Spring APIs.

13.1. Getting Started

Spring MongoDB support requires MongoDB 2.6 or higher and Java SE 8 or higher.

First, you need to set up a running MongoDB server. Refer to the MongoDB Quick Start guide for an explanation on how to startup a MongoDB instance. Once installed, starting MongoDB is typically a matter of running the following command: ${MONGO_HOME}/bin/mongod

To create a Spring project in STS, go to File → New → Spring Template Project → Simple Spring Utility Project and press Yes when prompted. Then enter a project and a package name, such as org.spring.mongodb.example.

Then add the following to the pom.xml dependencies section.

<dependencies>

  <!-- other dependency elements omitted -->

  <dependency>
    <groupId>org.springframework.data</groupId>
    <artifactId>spring-data-mongodb</artifactId>
    <version>4.1.1</version>
  </dependency>

  <dependency>
    <groupId>org.mongodb</groupId>
    <artifactId>mongodb-driver-reactivestreams</artifactId>
    <version>4.9.1</version>
  </dependency>

  <dependency>
    <groupId>io.projectreactor</groupId>
    <artifactId>reactor-core</artifactId>
    <version>2022.0.8</version>
  </dependency>

</dependencies>
MongoDB uses two different drivers for blocking and reactive (non-blocking) data access. While blocking operations are provided by default, you can opt-in for reactive usage.

To get started with a working example, create a simple Person class to persist, as follows:

@Document
public class Person {

  private String id;
  private String name;
  private int age;

  public Person(String name, int age) {
    this.name = name;
    this.age = age;
  }

  public String getId() {
    return id;
  }
  public String getName() {
    return name;
  }
  public int getAge() {
    return age;
  }

  @Override
  public String toString() {
    return "Person [id=" + id + ", name=" + name + ", age=" + age + "]";
  }
}

Then create an application to run, as follows:

public class ReactiveMongoApp {

  private static final Logger log = LoggerFactory.getLogger(ReactiveMongoApp.class);

  public static void main(String[] args) throws Exception {

    CountDownLatch latch = new CountDownLatch(1);

    ReactiveMongoTemplate mongoOps = new ReactiveMongoTemplate(MongoClients.create(), "database");

    mongoOps.insert(new Person("Joe", 34))
          .flatMap(p -> mongoOps.findOne(new Query(where("name").is("Joe")), Person.class))
          .doOnNext(person -> log.info(person.toString()))
          .flatMap(person -> mongoOps.dropCollection("person"))
          .doOnComplete(latch::countDown)
          .subscribe();

    latch.await();
  }
}

Running the preceding class produces the following output:

2016-09-20 14:56:57,373 DEBUG .index.MongoPersistentEntityIndexCreator: 124 - Analyzing class class example.ReactiveMongoApp$Person for index information.
2016-09-20 14:56:57,452 DEBUG .data.mongodb.core.ReactiveMongoTemplate: 975 - Inserting Document containing fields: [_class, name, age] in collection: person
2016-09-20 14:56:57,541 DEBUG .data.mongodb.core.ReactiveMongoTemplate:1503 - findOne using query: { "name" : "Joe"} fields: null for class: class example.ReactiveMongoApp$Person in collection: person
2016-09-20 14:56:57,545 DEBUG .data.mongodb.core.ReactiveMongoTemplate:1979 - findOne using query: { "name" : "Joe"} in db.collection: database.person
2016-09-20 14:56:57,567  INFO                 example.ReactiveMongoApp:  43 - Person [id=57e1321977ac501c68d73104, name=Joe, age=34]
2016-09-20 14:56:57,573 DEBUG .data.mongodb.core.ReactiveMongoTemplate: 528 - Dropped collection [person]

Even in this simple example, there are a few things to take notice of:

  • You can instantiate the central helper class of Spring Mongo (ReactiveMongoTemplate) by using the standard com.mongodb.reactivestreams.client.MongoClient object and the name of the database to use.

  • The mapper works against standard POJO objects without the need for any additional metadata (though you can optionally provide that information. See here.).

  • Conventions are used for handling the ID field, converting it to be an ObjectId when stored in the database.

  • Mapping conventions can use field access. Notice that the Person class has only getters.

  • If the constructor argument names match the field names of the stored document, they are used to instantiate the object

There is a GitHub repository with several examples that you can download and play around with to get a feel for how the library works.

13.2. Connecting to MongoDB with Spring and the Reactive Streams Driver

One of the first tasks when using MongoDB and Spring is to create a com.mongodb.reactivestreams.client.MongoClient object by using the IoC container.

13.2.1. Registering a MongoClient Instance Using Java-based Metadata

The following example shows how to use Java-based bean metadata to register an instance of a com.mongodb.reactivestreams.client.MongoClient:

Example 132. Registering a com.mongodb.reactivestreams.client.MongoClient object using Java based bean metadata
@Configuration
public class AppConfig {

  /*
   * Use the Reactive Streams Mongo Client API to create a com.mongodb.reactivestreams.client.MongoClient instance.
   */
   public @Bean MongoClient reactiveMongoClient()  {
       return MongoClients.create("mongodb://localhost");
   }
}

This approach lets you use the standard com.mongodb.reactivestreams.client.MongoClient API (which you may already know).

An alternative is to register an instance of com.mongodb.reactivestreams.client.MongoClient instance with the container by using Spring’s ReactiveMongoClientFactoryBean. As compared to instantiating a com.mongodb.reactivestreams.client.MongoClient instance directly, the FactoryBean approach has the added advantage of also providing the container with an ExceptionTranslator implementation that translates MongoDB exceptions to exceptions in Spring’s portable DataAccessException hierarchy for data access classes annotated with the @Repository annotation. This hierarchy and use of @Repository is described in Spring’s DAO support features.

The following example shows Java-based bean metadata that supports exception translation on @Repository annotated classes:

Example 133. Registering a com.mongodb.reactivestreams.client.MongoClient object using Spring’s MongoClientFactoryBean and enabling Spring’s exception translation support
@Configuration
public class AppConfig {

    /*
     * Factory bean that creates the com.mongodb.reactivestreams.client.MongoClient instance
     */
     public @Bean ReactiveMongoClientFactoryBean mongoClient() {

          ReactiveMongoClientFactoryBean clientFactory = new ReactiveMongoClientFactoryBean();
          clientFactory.setHost("localhost");

          return clientFactory;
     }
}

To access the com.mongodb.reactivestreams.client.MongoClient object created by the ReactiveMongoClientFactoryBean in other @Configuration or your own classes, get the MongoClient from the context.

13.2.2. The ReactiveMongoDatabaseFactory Interface

While com.mongodb.reactivestreams.client.MongoClient is the entry point to the reactive MongoDB driver API, connecting to a specific MongoDB database instance requires additional information, such as the database name. With that information, you can obtain a com.mongodb.reactivestreams.client.MongoDatabase object and access all the functionality of a specific MongoDB database instance. Spring provides the org.springframework.data.mongodb.core.ReactiveMongoDatabaseFactory interface to bootstrap connectivity to the database. The following listing shows the ReactiveMongoDatabaseFactory interface:

public interface ReactiveMongoDatabaseFactory {

  /**
   * Creates a default {@link MongoDatabase} instance.
   *
   * @return
   * @throws DataAccessException
   */
  MongoDatabase getMongoDatabase() throws DataAccessException;

  /**
   * Creates a {@link MongoDatabase} instance to access the database with the given name.
   *
   * @param dbName must not be {@literal null} or empty.
   * @return
   * @throws DataAccessException
   */
  MongoDatabase getMongoDatabase(String dbName) throws DataAccessException;

  /**
   * Exposes a shared {@link MongoExceptionTranslator}.
   *
   * @return will never be {@literal null}.
   */
  PersistenceExceptionTranslator getExceptionTranslator();
}

The org.springframework.data.mongodb.core.SimpleReactiveMongoDatabaseFactory class implements the ReactiveMongoDatabaseFactory interface and is created with a standard com.mongodb.reactivestreams.client.MongoClient instance and the database name.

Instead of using the IoC container to create an instance of ReactiveMongoTemplate, you can use them in standard Java code, as follows:

public class MongoApp {

  private static final Log log = LogFactory.getLog(MongoApp.class);

  public static void main(String[] args) throws Exception {

    ReactiveMongoOperations mongoOps = new ReactiveMongoOperations(new SimpleReactiveMongoDatabaseFactory(MongoClient.create(), "database"));

    mongoOps.insert(new Person("Joe", 34))
        .flatMap(p -> mongoOps.findOne(new Query(where("name").is("Joe")), Person.class))
        .doOnNext(person -> log.info(person.toString()))
        .flatMap(person -> mongoOps.dropCollection("person"))
        .subscribe();
  }
}

The use of SimpleReactiveMongoDatabaseFactory is the only difference between the listing shown in the getting started section.

13.2.3. Registering a ReactiveMongoDatabaseFactory Instance by Using Java-based Metadata

To register a ReactiveMongoDatabaseFactory instance with the container, you can write code much like what was highlighted in the previous code listing, as the following example shows:

@Configuration
public class MongoConfiguration {

  public @Bean ReactiveMongoDatabaseFactory reactiveMongoDatabaseFactory() {
    return new SimpleReactiveMongoDatabaseFactory(MongoClients.create(), "database");
  }
}

To define the username and password, create a MongoDB connection string and pass it into the factory method, as the next listing shows. The following listing also shows how to use ReactiveMongoDatabaseFactory to register an instance of ReactiveMongoTemplate with the container:

@Configuration
public class MongoConfiguration {

  public @Bean ReactiveMongoDatabaseFactory reactiveMongoDatabaseFactory() {
    return new SimpleReactiveMongoDatabaseFactory(MongoClients.create("mongodb://joe:secret@localhost"), "database");
  }

  public @Bean ReactiveMongoTemplate reactiveMongoTemplate() {
    return new ReactiveMongoTemplate(reactiveMongoDatabaseFactory());
  }
}

13.3. Introduction to ReactiveMongoTemplate

The ReactiveMongoTemplate class, located in the org.springframework.data.mongodb package, is the central class of the Spring’s Reactive MongoDB support and provides a rich feature set to interact with the database. The template offers convenience operations to create, update, delete, and query for MongoDB documents and provides a mapping between your domain objects and MongoDB documents.

Once configured, ReactiveMongoTemplate is thread-safe and can be reused across multiple instances.

The mapping between MongoDB documents and domain classes is done by delegating to an implementation of the MongoConverter interface. Spring provides a default implementation with MongoMappingConverter, but you can also write your own converter. See the section on MongoConverter instances for more detailed information.

The ReactiveMongoTemplate class implements the ReactiveMongoOperations interface. As much as possible, the methods on ReactiveMongoOperations mirror methods available on the MongoDB driver Collection object, to make the API familiar to existing MongoDB developers who are used to the driver API. For example, you can find methods such as find, findAndModify, findOne, insert, remove, save, update, and updateMulti. The design goal is to make it as easy as possible to transition between the use of the base MongoDB driver and ReactiveMongoOperations. A major difference between the two APIs is that ReactiveMongoOperations can be passed domain objects instead of Document, and there are fluent APIs for Query, Criteria, and Update operations instead of populating a Document to specify the parameters for those operations.

The preferred way to reference the operations on ReactiveMongoTemplate instance is through its ReactiveMongoOperations interface.

The default converter implementation used by ReactiveMongoTemplate is MappingMongoConverter. While the MappingMongoConverter can use additional metadata to specify the mapping of objects to documents, it can also convert objects that contain no additional metadata by using some conventions for the mapping of IDs and collection names. These conventions as well as the use of mapping annotations are explained in the Mapping chapter.

Another central feature of ReactiveMongoTemplate is exception translation of exceptions thrown in the MongoDB Java driver into Spring’s portable Data Access Exception hierarchy. See the section on exception translation for more information.

There are many convenience methods on ReactiveMongoTemplate to help you easily perform common tasks. However, if you need to access the MongoDB driver API directly to access functionality not explicitly exposed by the MongoTemplate, you can use one of several execute callback methods to access underlying driver APIs. The execute callbacks give you a reference to either a com.mongodb.reactivestreams.client.MongoCollection or a com.mongodb.reactivestreams.client.MongoDatabase object. See Execution Callbacks for more information.

13.3.1. Instantiating ReactiveMongoTemplate

You can use Java to create and register an instance of ReactiveMongoTemplate, as follows:

Example 134. Registering a com.mongodb.reactivestreams.client.MongoClient object and enabling Spring’s exception translation support
@Configuration
public class AppConfig {

  public @Bean MongoClient reactiveMongoClient() {
      return MongoClients.create("mongodb://localhost");
  }

  public @Bean ReactiveMongoTemplate reactiveMongoTemplate() {
      return new ReactiveMongoTemplate(reactiveMongoClient(), "mydatabase");
  }
}

There are several overloaded constructors of ReactiveMongoTemplate, including:

  • ReactiveMongoTemplate(MongoClient mongo, String databaseName): Takes the com.mongodb.reactivestreams.client.MongoClient object and the default database name to operate against.

  • ReactiveMongoTemplate(ReactiveMongoDatabaseFactory mongoDatabaseFactory): Takes a ReactiveMongoDatabaseFactory object that encapsulated the com.mongodb.reactivestreams.client.MongoClient object and database name.

  • ReactiveMongoTemplate(ReactiveMongoDatabaseFactory mongoDatabaseFactory, MongoConverter mongoConverter): Adds a MongoConverter to use for mapping.

When creating a ReactiveMongoTemplate, you might also want to set the following properties:

  • WriteResultCheckingPolicy

  • WriteConcern

  • ReadPreference

The preferred way to reference the operations on ReactiveMongoTemplate instance is through its ReactiveMongoOperations interface.

13.3.2. WriteResultChecking Policy

When in development, it is handy to either log or throw an Exception if the com.mongodb.WriteResult returned from any MongoDB operation contains an error. It is quite common to forget to do this during development and then end up with an application that looks like it runs successfully when, in fact, the database was not modified according to your expectations. Set the MongoTemplate WriteResultChecking property to an enum with the following values, LOG, EXCEPTION, or NONE to either log the error, throw and exception or do nothing. The default is to use a WriteResultChecking value of NONE.

13.3.3. WriteConcern

If it has not yet been specified through the driver at a higher level (such as MongoDatabase), you can set the com.mongodb.WriteConcern property that the ReactiveMongoTemplate uses for write operations. If ReactiveMongoTemplate’s WriteConcern property is not set, it defaults to the one set in the MongoDB driver’s MongoDatabase or MongoCollection setting.

13.3.4. WriteConcernResolver

For more advanced cases where you want to set different WriteConcern values on a per-operation basis (for remove, update, insert, and save operations), a strategy interface called WriteConcernResolver can be configured on ReactiveMongoTemplate. Since ReactiveMongoTemplate is used to persist POJOs, the WriteConcernResolver lets you create a policy that can map a specific POJO class to a WriteConcern value. The following listing shows the WriteConcernResolver interface:

public interface WriteConcernResolver {
  WriteConcern resolve(MongoAction action);
}

The argument, MongoAction, determines the WriteConcern value to be used and whether to use the value of the template itself as a default. MongoAction contains the collection name being written to, the java.lang.Class of the POJO, the converted DBObject, the operation as a value from the MongoActionOperation enumeration (one of REMOVE, UPDATE, INSERT, INSERT_LIST, and SAVE), and a few other pieces of contextual information. The following example shows how to create a WriteConcernResolver:

private class MyAppWriteConcernResolver implements WriteConcernResolver {

  public WriteConcern resolve(MongoAction action) {
    if (action.getEntityClass().getSimpleName().contains("Audit")) {
      return WriteConcern.NONE;
    } else if (action.getEntityClass().getSimpleName().contains("Metadata")) {
      return WriteConcern.JOURNAL_SAFE;
    }
    return action.getDefaultWriteConcern();
  }
}

13.4. Saving, Updating, and Removing Documents

ReactiveMongoTemplate lets you save, update, and delete your domain objects and map those objects to documents stored in MongoDB.

Consider the following Person class:

public class Person {

  private String id;
  private String name;
  private int age;

  public Person(String name, int age) {
    this.name = name;
    this.age = age;
  }

  public String getId() {
    return id;
  }
  public String getName() {
    return name;
  }
  public int getAge() {
    return age;
  }

  @Override
  public String toString() {
    return "Person [id=" + id + ", name=" + name + ", age=" + age + "]";
  }

}

The following listing shows how you can save, update, and delete the Person object:

public class ReactiveMongoApp {

  private static final Logger log = LoggerFactory.getLogger(ReactiveMongoApp.class);

  public static void main(String[] args) throws Exception {

    CountDownLatch latch = new CountDownLatch(1);

    ReactiveMongoTemplate mongoOps = new ReactiveMongoTemplate(MongoClients.create(), "database");

    mongoOps.insert(new Person("Joe", 34)).doOnNext(person -> log.info("Insert: " + person))
      .flatMap(person -> mongoOps.findById(person.getId(), Person.class))
      .doOnNext(person -> log.info("Found: " + person))
      .zipWith(person -> mongoOps.updateFirst(query(where("name").is("Joe")), update("age", 35), Person.class))
      .flatMap(tuple -> mongoOps.remove(tuple.getT1())).flatMap(deleteResult -> mongoOps.findAll(Person.class))
      .count().doOnSuccess(count -> {
        log.info("Number of people: " + count);
        latch.countDown();
      })

      .subscribe();

    latch.await();
  }
}

The preceding example includes implicit conversion between a String and ObjectId (by using the MongoConverter) as stored in the database and recognizing a convention of the property Id name.

The preceding example is meant to show the use of save, update, and remove operations on ReactiveMongoTemplate and not to show complex mapping or chaining functionality.

Querying Documents” explains the query syntax used in the preceding example in more detail. Additional documentation can be found in the blocking MongoTemplate section.

13.5. Execution Callbacks

One common design feature of all Spring template classes is that all functionality is routed into one of the templates that run callback methods. This helps ensure that exceptions and any resource management that maybe required are performed consistency. While this was of much greater need in the case of JDBC and JMS than with MongoDB, it still offers a single spot for exception translation and logging to occur. As such, using the execute callback is the preferred way to access the MongoDB driver’s MongoDatabase and MongoCollection objects to perform uncommon operations that were not exposed as methods on ReactiveMongoTemplate.

Here is a list of execute callback methods.

  • <T> Flux<T> execute (Class<?> entityClass, ReactiveCollectionCallback<T> action): Runs the given ReactiveCollectionCallback for the entity collection of the specified class.

  • <T> Flux<T> execute (String collectionName, ReactiveCollectionCallback<T> action): Runs the given ReactiveCollectionCallback on the collection of the given name.

  • <T> Flux<T> execute (ReactiveDatabaseCallback<T> action): Runs a ReactiveDatabaseCallback translating any exceptions as necessary.

The following example uses the ReactiveCollectionCallback to return information about an index:

Flux<Boolean> hasIndex = operations.execute("geolocation",
    collection -> Flux.from(collection.listIndexes(Document.class))
      .filter(document -> document.get("name").equals("fancy-index-name"))
      .flatMap(document -> Mono.just(true))
      .defaultIfEmpty(false));

13.6. GridFS Support

MongoDB supports storing binary files inside its filesystem, GridFS. Spring Data MongoDB provides a ReactiveGridFsOperations interface as well as the corresponding implementation, ReactiveGridFsTemplate, to let you interact with the filesystem. You can set up a ReactiveGridFsTemplate instance by handing it a ReactiveMongoDatabaseFactory as well as a MongoConverter, as the following example shows:

Example 135. JavaConfig setup for a ReactiveGridFsTemplate
class GridFsConfiguration extends AbstractReactiveMongoConfiguration {

  // … further configuration omitted

  @Bean
  public ReactiveGridFsTemplate reactiveGridFsTemplate() {
    return new ReactiveGridFsTemplate(reactiveMongoDbFactory(), mappingMongoConverter());
  }
}

The template can now be injected and used to perform storage and retrieval operations, as the following example shows:

Example 136. Using ReactiveGridFsTemplate to store files
class ReactiveGridFsClient {

  @Autowired
  ReactiveGridFsTemplate operations;

  @Test
  public Mono<ObjectId> storeFileToGridFs() {

    FileMetadata metadata = new FileMetadata();
    // populate metadata
    Publisher<DataBuffer> file = … // lookup File or Resource

    return operations.store(file, "filename.txt", metadata);
  }
}

The store(…) operations take an Publisher<DataBuffer>, a filename, and (optionally) metadata information about the file to store. The metadata can be an arbitrary object, which will be marshaled by the MongoConverter configured with the ReactiveGridFsTemplate. Alternatively, you can also provide a Document.

MongoDB’s driver uses AsyncInputStream and AsyncOutputStream interfaces to exchange binary streams. Spring Data MongoDB adapts these interfaces to Publisher<DataBuffer>. Read more about DataBuffer in Spring’s reference documentation.

You can read files from the filesystem through either the find(…) or the getResources(…) methods. Let’s have a look at the find(…) methods first. You can either find a single file or multiple files that match a Query. You can use the GridFsCriteria helper class to define queries. It provides static factory methods to encapsulate default metadata fields (such as whereFilename() and whereContentType()) or a custom one through whereMetaData(). The following example shows how to use ReactiveGridFsTemplate to query for files:

Example 137. Using ReactiveGridFsTemplate to query for files
class ReactiveGridFsClient {

  @Autowired
  ReactiveGridFsTemplate operations;

  @Test
  public Flux<GridFSFile> findFilesInGridFs() {
    return operations.find(query(whereFilename().is("filename.txt")))
  }
}
Currently, MongoDB does not support defining sort criteria when retrieving files from GridFS. For this reason, any sort criteria defined on the Query instance handed into the find(…) method are disregarded.

The other option to read files from the GridFs is to use the methods modeled along the lines of ResourcePatternResolver. ReactiveGridFsOperations uses reactive types to defer running while ResourcePatternResolver uses a synchronous interface. These methods allow handing an Ant path into the method and can thus retrieve files matching the given pattern. The following example shows how to use ReactiveGridFsTemplate to read files:

Example 138. Using ReactiveGridFsTemplate to read files
class ReactiveGridFsClient {

  @Autowired
  ReactiveGridFsOperations operations;

  @Test
  public void readFilesFromGridFs() {
     Flux<ReactiveGridFsResource> txtFiles = operations.getResources("*.txt");
  }
}

14. MongoDB Repositories

This chapter points out the specialties for repository support for MongoDB. This chapter builds on the core repository support explained in Working with Spring Data Repositories. You should have a sound understanding of the basic concepts explained there.

14.1. Usage

To access domain entities stored in a MongoDB, you can use our sophisticated repository support that eases implementation quite significantly. To do so, create an interface for your repository, as the following example shows:

Example 139. Sample Person entity
public class Person {

  @Id
  private String id;
  private String firstname;
  private String lastname;
  private Address address;

  // … getters and setters omitted
}

Note that the domain type shown in the preceding example has a property named id of type String.The default serialization mechanism used in MongoTemplate (which backs the repository support) regards properties named id as the document ID. Currently, we support String, ObjectId, and BigInteger as ID types. Please see ID mapping for more information about on how the id field is handled in the mapping layer.

Now that we have a domain object, we can define an interface that uses it, as follows:

Example 140. Basic repository interface to persist Person entities
public interface PersonRepository extends PagingAndSortingRepository<Person, String> {

  // additional custom query methods go here
}

Right now this interface serves only to provide type information, but we can add additional methods to it later.

To start using the repository, use the @EnableMongoRepositories annotation. That annotation carries the same attributes as the namespace element. If no base package is configured, the infrastructure scans the package of the annotated configuration class. The following example shows how to configuration your application to use MongoDB repositories:

Java
@Configuration
@EnableMongoRepositories("com.acme.*.repositories")
class ApplicationConfig extends AbstractMongoClientConfiguration {

  @Override
  protected String getDatabaseName() {
    return "e-store";
  }

  @Override
  protected String getMappingBasePackage() {
    return "com.acme.*.repositories";
  }
}
XML
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:mongo="http://www.springframework.org/schema/data/mongo"
  xsi:schemaLocation="http://www.springframework.org/schema/beans
    https://www.springframework.org/schema/beans/spring-beans-3.0.xsd
    http://www.springframework.org/schema/data/mongo
    https://www.springframework.org/schema/data/mongo/spring-mongo-1.0.xsd">

  <mongo:mongo-client id="mongoClient" />

  <bean id="mongoTemplate" class="org.springframework.data.mongodb.core.MongoTemplate">
    <constructor-arg ref="mongoClient" />
    <constructor-arg value="databaseName" />
  </bean>

  <mongo:repositories base-package="com.acme.*.repositories" />

</beans>

This namespace element causes the base packages to be scanned for interfaces that extend MongoRepository and create Spring beans for each one found. By default, the repositories get a MongoTemplate Spring bean wired that is called mongoTemplate, so you only need to configure mongo-template-ref explicitly if you deviate from this convention.

Because our domain repository extends PagingAndSortingRepository, it provides you with CRUD operations as well as methods for paginated and sorted access to the entities. Working with the repository instance is just a matter of dependency injecting it into a client . Consequently, accessing the second page of Person objects at a page size of 10 would resemble the following code:

Example 141. Paging access to Person entities
@ExtendWith(SpringExtension.class)
@ContextConfiguration
class PersonRepositoryTests {

    @Autowired PersonRepository repository;

    @Test
    void readsFirstPageCorrectly() {

      Page<Person> persons = repository.findAll(PageRequest.of(0, 10));
      assertThat(persons.isFirstPage()).isTrue();
    }
}

The preceding example creates an application context with Spring’s unit test support, which performs annotation-based dependency injection into test cases. Inside the test method, we use the repository to query the datastore. We hand the repository a PageRequest instance that requests the first page of Person objects at a page size of 10.

14.2. Query Methods

Most of the data access operations you usually trigger on a repository result in a query being executed against the MongoDB databases. Defining such a query is a matter of declaring a method on the repository interface, as the following example shows:

Example 142. PersonRepository with query methods
public interface PersonRepository extends PagingAndSortingRepository<Person, String> {

    List<Person> findByLastname(String lastname);                      (1)

    Page<Person> findByFirstname(String firstname, Pageable pageable); (2)

    Person findByShippingAddresses(Address address);                   (3)

    Person findFirstByLastname(String lastname)                        (4)

    Stream<Person> findAllBy();                                        (5)
}
1 The findByLastname method shows a query for all people with the given last name. The query is derived by parsing the method name for constraints that can be concatenated with And and Or. Thus, the method name results in a query expression of {"lastname" : lastname}.
2 Applies pagination to a query. You can equip your method signature with a Pageable parameter and let the method return a Page instance and Spring Data automatically pages the query accordingly.
3 Shows that you can query based on properties that are not primitive types. Throws IncorrectResultSizeDataAccessException if more than one match is found.
4 Uses the First keyword to restrict the query to only the first result. Unlike <3>, this method does not throw an exception if more than one match is found.
5 Uses a Java 8 Stream that reads and converts individual elements while iterating the stream.
We do not support referring to parameters that are mapped as DBRef in the domain class.

The following table shows the keywords that are supported for query methods:

Table 13. Supported keywords for query methods
Keyword Sample Logical result

After

findByBirthdateAfter(Date date)

{"birthdate" : {"$gt" : date}}

GreaterThan

findByAgeGreaterThan(int age)

{"age" : {"$gt" : age}}

GreaterThanEqual

findByAgeGreaterThanEqual(int age)

{"age" : {"$gte" : age}}

Before

findByBirthdateBefore(Date date)

{"birthdate" : {"$lt" : date}}

LessThan

findByAgeLessThan(int age)

{"age" : {"$lt" : age}}

LessThanEqual

findByAgeLessThanEqual(int age)

{"age" : {"$lte" : age}}

Between

findByAgeBetween(int from, int to)
findByAgeBetween(Range<Integer> range)

{"age" : {"$gt" : from, "$lt" : to}}
lower / upper bounds ($gt / $gte & $lt / $lte) according to Range

In

findByAgeIn(Collection ages)

{"age" : {"$in" : [ages…​]}}

NotIn

findByAgeNotIn(Collection ages)

{"age" : {"$nin" : [ages…​]}}

IsNotNull, NotNull

findByFirstnameNotNull()

{"firstname" : {"$ne" : null}}

IsNull, Null

findByFirstnameNull()

{"firstname" : null}

Like, StartingWith, EndingWith

findByFirstnameLike(String name)

{"firstname" : name} (name as regex)

NotLike, IsNotLike

findByFirstnameNotLike(String name)

{"firstname" : { "$not" : name }} (name as regex)

Containing on String

findByFirstnameContaining(String name)

{"firstname" : name} (name as regex)

NotContaining on String

findByFirstnameNotContaining(String name)

{"firstname" : { "$not" : name}} (name as regex)

Containing on Collection

findByAddressesContaining(Address address)

{"addresses" : { "$in" : address}}

NotContaining on Collection

findByAddressesNotContaining(Address address)

{"addresses" : { "$not" : { "$in" : address}}}

Regex

findByFirstnameRegex(String firstname)

{"firstname" : {"$regex" : firstname }}

(No keyword)

findByFirstname(String name)

{"firstname" : name}

Not

findByFirstnameNot(String name)

{"firstname" : {"$ne" : name}}

Near

findByLocationNear(Point point)

{"location" : {"$near" : [x,y]}}

Near

findByLocationNear(Point point, Distance max)

{"location" : {"$near" : [x,y], "$maxDistance" : max}}

Near

findByLocationNear(Point point, Distance min, Distance max)

{"location" : {"$near" : [x,y], "$minDistance" : min, "$maxDistance" : max}}

Within

findByLocationWithin(Circle circle)

{"location" : {"$geoWithin" : {"$center" : [ [x, y], distance]}}}

Within

findByLocationWithin(Box box)

{"location" : {"$geoWithin" : {"$box" : [ [x1, y1], x2, y2]}}}

IsTrue, True

findByActiveIsTrue()

{"active" : true}

IsFalse, False

findByActiveIsFalse()

{"active" : false}

Exists

findByLocationExists(boolean exists)

{"location" : {"$exists" : exists }}

IgnoreCase

findByUsernameIgnoreCase(String username)

{"username" : {"$regex" : "^username$", "$options" : "i" }}

If the property criterion compares a document, the order of the fields and exact equality in the document matters.

14.2.1. Repository Index Hints

The @Hint annotation allows to override MongoDB’s default index selection and forces the database to use the specified index instead.

Example 143. Example of index hints
@Hint("lastname-idx")                                          (1)
List<Person> findByLastname(String lastname);

@Query(value = "{ 'firstname' : ?0 }", hint = "firstname-idx") (2)
List<Person> findByFirstname(String firstname);
1 Use the index with name lastname-idx.
2 The @Query annotation defines the hint alias which is equivalent to adding the @Hint annotation.

14.2.2. Repository Update Methods

You can also use the keywords in the preceding table to create queries that identify matching documents for running updates on them. The actual update action is defined by the @Update annotation on the method itself, as the following listing shows. Note that the naming schema for derived queries starts with find. Using update (as in updateAllByLastname(…​)) is allowed only in combination with @Query.

The update is applied to all matching documents and it is not possible to limit the scope by passing in a Page or by using any of the limiting keywords. The return type can be either void or a numeric type, such as long, to hold the number of modified documents.

Example 144. Update Methods
public interface PersonRepository extends CrudRepository<Person, String> {

  @Update("{ '$inc' : { 'visits' : 1 } }")
  long findAndIncrementVisitsByLastname(String lastname); (1)

  @Update("{ '$inc' : { 'visits' : ?1 } }")
  void findAndIncrementVisitsByLastname(String lastname, int increment); (2)

  @Update("{ '$inc' : { 'visits' : ?#{[1]} } }")
  long findAndIncrementVisitsUsingSpELByLastname(String lastname, int increment); (3)

  @Update(pipeline = {"{ '$set' : { 'visits' : { '$add' : [ '$visits', ?1 ] } } }"})
  void findAndIncrementVisitsViaPipelineByLastname(String lastname, int increment); (4)

  @Update("{ '$push' : { 'shippingAddresses' : ?1 } }")
  long findAndPushShippingAddressByEmail(String email, Address address); (5)

  @Query("{ 'lastname' : ?0 }")
  @Update("{ '$inc' : { 'visits' : ?1 } }")
  void updateAllByLastname(String lastname, int increment); (6)
}
1 The filter query for the update is derived from the method name. The update is “as is” and does not bind any parameters.
2 The actual increment value is defined by the increment method argument that is bound to the ?1 placeholder.
3 Use the Spring Expression Language (SpEL) for parameter binding.
4 Use the pipeline attribute to issue aggregation pipeline updates.
5 The update may contain complex objects.
6 Combine a string based query with an update.
Repository updates do not emit persistence nor mapping lifecycle events.

14.2.3. Repository Delete Queries

The keywords in the preceding table can be used in conjunction with delete…By or remove…By to create queries that delete matching documents.

Example 145. Delete…By Query
public interface PersonRepository extends MongoRepository<Person, String> {

  List <Person> deleteByLastname(String lastname);      (1)

  Long deletePersonByLastname(String lastname);         (2)

  @Nullable
  Person deleteSingleByLastname(String lastname);       (3)

  Optional<Person> deleteByBirthdate(Date birthdate);   (4)
}
1 Using a return type of List retrieves and returns all matching documents before actually deleting them.
2 A numeric return type directly removes the matching documents, returning the total number of documents removed.
3 A single domain type result retrieves and removes the first matching document.
4 Same as in 3 but wrapped in an Optional type.

14.2.4. Geo-spatial Repository Queries

As you saw in the preceding table of keywords, a few keywords trigger geo-spatial operations within a MongoDB query. The Near keyword allows some further modification, as the next few examples show.

The following example shows how to define a near query that finds all persons with a given distance of a given point:

Example 146. Advanced Near queries
public interface PersonRepository extends MongoRepository<Person, String> {

  // { 'location' : { '$near' : [point.x, point.y], '$maxDistance' : distance}}
  List<Person> findByLocationNear(Point location, Distance distance);
}

Adding a Distance parameter to the query method allows restricting results to those within the given distance. If the Distance was set up containing a Metric, we transparently use $nearSphere instead of $code, as the following example shows:

Example 147. Using Distance with Metrics
Point point = new Point(43.7, 48.8);
Distance distance = new Distance(200, Metrics.KILOMETERS);
… = repository.findByLocationNear(point, distance);
// {'location' : {'$nearSphere' : [43.7, 48.8], '$maxDistance' : 0.03135711885774796}}

Using a Distance with a Metric causes a $nearSphere (instead of a plain $near) clause to be added. Beyond that, the actual distance gets calculated according to the Metrics used.

(Note that Metric does not refer to metric units of measure. It could be miles rather than kilometers. Rather, metric refers to the concept of a system of measurement, regardless of which system you use.)

Using @GeoSpatialIndexed(type = GeoSpatialIndexType.GEO_2DSPHERE) on the target property forces usage of the $nearSphere operator.
Geo-near Queries

Spring Data MongoDb supports geo-near queries, as the following example shows:

public interface PersonRepository extends MongoRepository<Person, String> {

  // {'geoNear' : 'location', 'near' : [x, y] }
  GeoResults<Person> findByLocationNear(Point location);

  // No metric: {'geoNear' : 'person', 'near' : [x, y], maxDistance : distance }
  // Metric: {'geoNear' : 'person', 'near' : [x, y], 'maxDistance' : distance,
  //          'distanceMultiplier' : metric.multiplier, 'spherical' : true }
  GeoResults<Person> findByLocationNear(Point location, Distance distance);

  // Metric: {'geoNear' : 'person', 'near' : [x, y], 'minDistance' : min,
  //          'maxDistance' : max, 'distanceMultiplier' : metric.multiplier,
  //          'spherical' : true }
  GeoResults<Person> findByLocationNear(Point location, Distance min, Distance max);

  // {'geoNear' : 'location', 'near' : [x, y] }
  GeoResults<Person> findByLocationNear(Point location);
}

14.2.5. MongoDB JSON-based Query Methods and Field Restriction

By adding the org.springframework.data.mongodb.repository.Query annotation to your repository query methods, you can specify a MongoDB JSON query string to use instead of having the query be derived from the method name, as the following example shows:

public interface PersonRepository extends MongoRepository<Person, String> {

  @Query("{ 'firstname' : ?0 }")
  List<Person> findByThePersonsFirstname(String firstname);

}

The ?0 placeholder lets you substitute the value from the method arguments into the JSON query string.

String parameter values are escaped during the binding process, which means that it is not possible to add MongoDB specific operators through the argument.

You can also use the filter property to restrict the set of properties that is mapped into the Java object, as the following example shows:

public interface PersonRepository extends MongoRepository<Person, String> {

  @Query(value="{ 'firstname' : ?0 }", fields="{ 'firstname' : 1, 'lastname' : 1}")
  List<Person> findByThePersonsFirstname(String firstname);

}

The query in the preceding example returns only the firstname, lastname and Id properties of the Person objects. The age property, a java.lang.Integer, is not set and its value is therefore null.

14.2.6. Sorting Query Method results

MongoDB repositories allow various approaches to define sorting order. Let’s take a look at the following example:

Example 148. Sorting Query Results
public interface PersonRepository extends MongoRepository<Person, String> {

  List<Person> findByFirstnameSortByAgeDesc(String firstname); (1)

  List<Person> findByFirstname(String firstname, Sort sort);   (2)

  @Query(sort = "{ age : -1 }")
  List<Person> findByFirstname(String firstname);              (3)

  @Query(sort = "{ age : -1 }")
  List<Person> findByLastname(String lastname, Sort sort);     (4)
}
1 Static sorting derived from method name. SortByAgeDesc results in { age : -1 } for the sort parameter.
2 Dynamic sorting using a method argument. Sort.by(DESC, "age") creates { age : -1 } for the sort parameter.
3 Static sorting via Query annotation. Sort parameter applied as stated in the sort attribute.
4 Default sorting via Query annotation combined with dynamic one via a method argument. Sort.unsorted() results in { age : -1 }. Using Sort.by(ASC, "age") overrides the defaults and creates { age : 1 }. Sort.by (ASC, "firstname") alters the default and results in { age : -1, firstname : 1 }.

14.2.7. JSON-based Queries with SpEL Expressions

Query strings and field definitions can be used together with SpEL expressions to create dynamic queries at runtime. SpEL expressions can provide predicate values and can be used to extend predicates with subdocuments.

Expressions expose method arguments through an array that contains all the arguments. The following query uses [0] to declare the predicate value for lastname (which is equivalent to the ?0 parameter binding):

public interface PersonRepository extends MongoRepository<Person, String> {

  @Query("{'lastname': ?#{[0]} }")
  List<Person> findByQueryWithExpression(String param0);
}

Expressions can be used to invoke functions, evaluate conditionals, and construct values. SpEL expressions used in conjunction with JSON reveal a side-effect, because Map-like declarations inside of SpEL read like JSON, as the following example shows:

public interface PersonRepository extends MongoRepository<Person, String> {

  @Query("{'id': ?#{ [0] ? {$exists :true} : [1] }}")
  List<Person> findByQueryWithExpressionAndNestedObject(boolean param0, String param1);
}
SpEL in query strings can be a powerful way to enhance queries. However, they can also accept a broad range of unwanted arguments. Make sure to sanitize strings before passing them to the query to avoid creation of vulnerabilities or unwanted changes to your query.

Expression support is extensible through the Query SPI: org.springframework.data.repository.query.spi.EvaluationContextExtension. The Query SPI can contribute properties and functions and can customize the root object. Extensions are retrieved from the application context at the time of SpEL evaluation when the query is built. The following example shows how to use EvaluationContextExtension:

public class SampleEvaluationContextExtension extends EvaluationContextExtensionSupport {

  @Override
  public String getExtensionId() {
    return "security";
  }

  @Override
  public Map<String, Object> getProperties() {
    return Collections.singletonMap("principal", SecurityContextHolder.getCurrent().getPrincipal());
  }
}
Bootstrapping MongoRepositoryFactory yourself is not application context-aware and requires further configuration to pick up Query SPI extensions.
Reactive query methods can make use of org.springframework.data.spel.spi.ReactiveEvaluationContextExtension.

14.2.8. Type-safe Query Methods

MongoDB repository support integrates with the Querydsl project, which provides a way to perform type-safe queries. To quote from the project description, "Instead of writing queries as inline strings or externalizing them into XML files they are constructed via a fluent API." It provides the following features:

  • Code completion in the IDE (all properties, methods, and operations can be expanded in your favorite Java IDE).

  • Almost no syntactically invalid queries allowed (type-safe on all levels).

  • Domain types and properties can be referenced safely — no strings involved!

  • Adapts better to refactoring changes in domain types.

  • Incremental query definition is easier.

See the QueryDSL documentation for how to bootstrap your environment for APT-based code generation using Maven or Ant.

QueryDSL lets you write queries such as the following:

QPerson person = new QPerson("person");
List<Person> result = repository.findAll(person.address.zipCode.eq("C0123"));

Page<Person> page = repository.findAll(person.lastname.contains("a"),
                                       PageRequest.of(0, 2, Direction.ASC, "lastname"));

QPerson is a class that is generated by the Java annotation post-processing tool. It is a Predicate that lets you write type-safe queries. Notice that there are no strings in the query other than the C0123 value.

You can use the generated Predicate class by using the QuerydslPredicateExecutor interface, which the following listing shows:

public interface QuerydslPredicateExecutor<T> {

  T findOne(Predicate predicate);

  List<T> findAll(Predicate predicate);

  List<T> findAll(Predicate predicate, OrderSpecifier<?>... orders);

  Page<T> findAll(Predicate predicate, Pageable pageable);

  Long count(Predicate predicate);
}

To use this in your repository implementation, add it to the list of repository interfaces from which your interface inherits, as the following example shows:

public interface PersonRepository extends MongoRepository<Person, String>, QuerydslPredicateExecutor<Person> {

   // additional query methods go here
}

14.2.9. Full-text Search Queries

MongoDB’s full-text search feature is store-specific and, therefore, can be found on MongoRepository rather than on the more general CrudRepository. We need a document with a full-text index (see “Text Indexes” to learn how to create a full-text index).

Additional methods on MongoRepository take TextCriteria as an input parameter. In addition to those explicit methods, it is also possible to add a TextCriteria-derived repository method. The criteria are added as an additional AND criteria. Once the entity contains a @TextScore-annotated property, the document’s full-text score can be retrieved. Furthermore, the @TextScore annotated also makes it possible to sort by the document’s score, as the following example shows:

@Document
class FullTextDocument {

  @Id String id;
  @TextIndexed String title;
  @TextIndexed String content;
  @TextScore Float score;
}

interface FullTextRepository extends Repository<FullTextDocument, String> {

  // Execute a full-text search and define sorting dynamically
  List<FullTextDocument> findAllBy(TextCriteria criteria, Sort sort);

  // Paginate over a full-text search result
  Page<FullTextDocument> findAllBy(TextCriteria criteria, Pageable pageable);

  // Combine a derived query with a full-text search
  List<FullTextDocument> findByTitleOrderByScoreDesc(String title, TextCriteria criteria);
}


Sort sort = Sort.by("score");
TextCriteria criteria = TextCriteria.forDefaultLanguage().matchingAny("spring", "data");
List<FullTextDocument> result = repository.findAllBy(criteria, sort);

criteria = TextCriteria.forDefaultLanguage().matching("film");
Page<FullTextDocument> page = repository.findAllBy(criteria, PageRequest.of(1, 1, sort));
List<FullTextDocument> result = repository.findByTitleOrderByScoreDesc("mongodb", criteria);

14.2.10. Projections

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:

Example 149. A sample aggregate and repository
class Person {

  @Id UUID id;
  String firstname, lastname;
  Address address;

  static class Address {
    String zipCode, city, street;
  }
}

interface PersonRepository extends Repository<Person, UUID> {

  Collection<Person> findByLastname(String lastname);
}

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.

Interface-based Projections

The easiest way to limit the result of the queries to only the name attributes is by declaring an interface that exposes accessor methods for the properties to be read, as shown in the following example:

Example 150. A projection interface to retrieve a subset of attributes
interface NamesOnly {

  String getFirstname();
  String getLastname();
}

The important bit here is that the properties defined here exactly match properties in the aggregate root. Doing so lets a query method be added as follows:

Example 151. A repository using an interface based projection with a query method
interface PersonRepository extends Repository<Person, UUID> {

  Collection<NamesOnly> findByLastname(String lastname);
}

The query execution engine creates proxy instances of that interface at runtime for each element returned and forwards calls to the exposed methods to the target object.

Declaring a method in your Repository that overrides a base method (e.g. declared in CrudRepository, a store-specific repository interface, or the Simple…Repository) results in a call to the base method regardless of the declared return type. Make sure to use a compatible return type as base methods cannot be used for projections. Some store modules support @Query annotations to turn an overridden base method into a query method that then can be used to return projections.

Projections can be used recursively. If you want to include some of the Address information as well, create a projection interface for that and return that interface from the declaration of getAddress(), as shown in the following example:

Example 152. A projection interface to retrieve a subset of attributes
interface PersonSummary {

  String getFirstname();
  String getLastname();
  AddressSummary getAddress();

  interface AddressSummary {
    String getCity();
  }
}

On method invocation, the address property of the target instance is obtained and wrapped into a projecting proxy in turn.

Closed Projections

A projection interface whose accessor methods all match properties of the target aggregate is considered to be a closed projection. The following example (which we used earlier in this chapter, too) is a closed projection:

Example 153. A closed projection
interface NamesOnly {

  String getFirstname();
  String getLastname();
}

If you use a closed projection, Spring Data can optimize the query execution, because we know about all the attributes that are needed to back the projection proxy. For more details on that, see the module-specific part of the reference documentation.

Open Projections

Accessor methods in projection interfaces can also be used to compute new values by using the @Value annotation, as shown in the following example:

Example 154. An Open Projection
interface NamesOnly {

  @Value("#{target.firstname + ' ' + target.lastname}")
  String getFullName();
  …
}

The aggregate root backing the projection is available in the target variable. A projection interface using @Value is an open projection. Spring Data cannot apply query execution optimizations in this case, because the SpEL expression could use any attribute of the aggregate root.

The expressions used in @Value should not be too complex — you want to avoid programming in String variables. For very simple expressions, one option might be to resort to default methods (introduced in Java 8), as shown in the following example:

Example 155. A projection interface using a default method for custom logic
interface NamesOnly {

  String getFirstname();
  String getLastname();

  default String getFullName() {
    return getFirstname().concat(" ").concat(getLastname());
  }
}

This approach requires you to be able to implement logic purely based on the other accessor methods exposed on the projection interface. A second, more flexible, option is to implement the custom logic in a Spring bean and then invoke that from the SpEL expression, as shown in the following example:

Example 156. Sample Person object
@Component
class MyBean {

  String getFullName(Person person) {
    …
  }
}

interface NamesOnly {

  @Value("#{@myBean.getFullName(target)}")
  String getFullName();
  …
}

Notice how the SpEL expression refers to myBean and invokes the getFullName(…) method and forwards the projection target as a method parameter. Methods backed by SpEL expression evaluation can also use method parameters, which can then be referred to from the expression. The method parameters are available through an Object array named args. The following example shows how to get a method parameter from the args array:

Example 157. Sample Person object
interface NamesOnly {

  @Value("#{args[0] + ' ' + target.firstname + '!'}")
  String getSalutation(String prefix);
}

Again, for more complex expressions, you should use a Spring bean and let the expression invoke a method, as described earlier.

Nullable Wrappers

Getters in projection interfaces can make use of nullable wrappers for improved null-safety. Currently supported wrapper types are:

  • java.util.Optional

  • com.google.common.base.Optional

  • scala.Option

  • io.vavr.control.Option

Example 158. A projection interface using nullable wrappers
interface NamesOnly {

  Optional<String> getFirstname();
}

If the underlying projection value is not null, then values are returned using the present-representation of the wrapper type. In case the backing value is null, then the getter method returns the empty representation of the used wrapper type.

Class-based Projections (DTOs)

Another way of defining projections is by using value type DTOs (Data Transfer Objects) that hold properties for the fields that are supposed to be retrieved. These DTO types can be used in exactly the same way projection interfaces are used, except that no proxying happens and no nested projections can be applied.

If the store optimizes the query execution by limiting the fields to be loaded, the fields to be loaded are determined from the parameter names of the constructor that is exposed.

The following example shows a projecting DTO:

Example 159. A projecting DTO
record NamesOnly(String firstname, String lastname) {
}

Java Records are ideal to define DTO types since they adhere to value semantics: All fields are private final and equals(…)/hashCode()/toString() methods are created automatically. Alternatively, you can use any class that defines the properties you want to project.

Dynamic Projections

So far, we have used the projection type as the return type or element type of a collection. However, you might want to select the type to be used at invocation time (which makes it dynamic). To apply dynamic projections, use a query method such as the one shown in the following example:

Example 160. A repository using a dynamic projection parameter
interface PersonRepository extends Repository<Person, UUID> {

  <T> Collection<T> findByLastname(String lastname, Class<T> type);
}

This way, the method can be used to obtain the aggregates as is or with a projection applied, as shown in the following example:

Example 161. Using a repository with dynamic projections
void someMethod(PersonRepository people) {

  Collection<Person> aggregates =
    people.findByLastname("Matthews", Person.class);

  Collection<NamesOnly> aggregates =
    people.findByLastname("Matthews", NamesOnly.class);
}
Query parameters of type Class are inspected whether they qualify as dynamic projection parameter. If the actual return type of the query equals the generic parameter type of the Class parameter, then the matching Class parameter is not available for usage within the query or SpEL expressions. If you want to use a Class parameter as query argument then make sure to use a different generic parameter, for example Class<?>.

14.2.11. Aggregation Repository Methods

The repository layer offers means to interact with the aggregation framework via annotated repository query methods. Similar to the JSON based queries, you can define a pipeline using the org.springframework.data.mongodb.repository.Aggregation annotation. The definition may contain simple placeholders like ?0 as well as SpEL expressions ?#{ … }.

Example 162. Aggregating Repository Method
public interface PersonRepository extends CrudRepository<Person, String> {

  @Aggregation("{ $group: { _id : $lastname, names : { $addToSet : $firstname } } }")
  List<PersonAggregate> groupByLastnameAndFirstnames();                            (1)

  @Aggregation("{ $group: { _id : $lastname, names : { $addToSet : $firstname } } }")
  List<PersonAggregate> groupByLastnameAndFirstnames(Sort sort);                   (2)

  @Aggregation("{ $group: { _id : $lastname, names : { $addToSet : ?0 } } }")
  List<PersonAggregate> groupByLastnameAnd(String property);                       (3)

  @Aggregation("{ $group: { _id : $lastname, names : { $addToSet : ?0 } } }")
  Slice<PersonAggregate> groupByLastnameAnd(String property, Pageable page);       (4)

  @Aggregation("{ $group: { _id : $lastname, names : { $addToSet : $firstname } } }")
  Stream<PersonAggregate> groupByLastnameAndFirstnamesAsStream();                  (5)

  @Aggregation("{ $group : { _id : null, total : { $sum : $age } } }")
  SumValue sumAgeUsingValueWrapper();                                              (6)

  @Aggregation("{ $group : { _id : null, total : { $sum : $age } } }")
  Long sumAge();                                                                   (7)

  @Aggregation("{ $group : { _id : null, total : { $sum : $age } } }")
  AggregationResults<SumValue> sumAgeRaw();                                        (8)

  @Aggregation("{ '$project': { '_id' : '$lastname' } }")
  List<String> findAllLastnames();                                                 (9)

  @Aggregation(pipeline = {
		  "{ $group : { _id : '$author', books: { $push: '$title' } } }",
		  "{ $out : 'authors' }"
  })
  void groupAndOutSkippingOutput();                                                (10)
}
public class PersonAggregate {

  private @Id String lastname;                                                     (2)
  private List<String> names;

  public PersonAggregate(String lastname, List<String> names) {
     // ...
  }

  // Getter / Setter omitted
}

public class SumValue {

  private final Long total;                                                        (6) (8)

  public SumValue(Long total) {
    // ...
  }

  // Getter omitted
}
1 Aggregation pipeline to group first names by lastname in the Person collection returning these as PersonAggregate.
2 If Sort argument is present, $sort is appended after the declared pipeline stages so that it only affects the order of the final results after having passed all other aggregation stages. Therefore, the Sort properties are mapped against the methods return type PersonAggregate which turns Sort.by("lastname") into { $sort : { '_id', 1 } } because PersonAggregate.lastname is annotated with @Id.
3 Replaces ?0 with the given value for property for a dynamic aggregation pipeline.
4 $skip, $limit and $sort can be passed on via a Pageable argument. Same as in <2>, the operators are appended to the pipeline definition. Methods accepting Pageable can return Slice for easier pagination.
5 Aggregation methods can return Stream to consume results directly from an underlying cursor. Make sure to close the stream after consuming it to release the server-side cursor by either calling close() or through try-with-resources.
6 Map the result of an aggregation returning a single Document to an instance of a desired SumValue target type.
7 Aggregations resulting in single document holding just an accumulation result like eg. $sum can be extracted directly from the result Document. To gain more control, you might consider AggregationResult as method return type as shown in <7>.
8 Obtain the raw AggregationResults mapped to the generic target wrapper type SumValue or org.bson.Document.
9 Like in <6>, a single value can be directly obtained from multiple result Documents.
10 Skips the output of the $out stage when return type is void.

In some scenarios, aggregations might require additional options, such as a maximum run time, additional log comments, or the permission to temporarily write data to disk. Use the @Meta annotation to set those options via maxExecutionTimeMs, comment or allowDiskUse.

interface PersonRepository extends CrudRepository<Person, String> {

  @Meta(allowDiskUse = true)
  @Aggregation("{ $group: { _id : $lastname, names : { $addToSet : $firstname } } }")
  List<PersonAggregate> groupByLastnameAndFirstnames();
}

Or use @Meta to create your own annotation as shown in the sample below.

@Retention(RetentionPolicy.RUNTIME)
@Target({ ElementType.METHOD })
@Meta(allowDiskUse = true)
@interface AllowDiskUse { }

interface PersonRepository extends CrudRepository<Person, String> {

  @AllowDiskUse
  @Aggregation("{ $group: { _id : $lastname, names : { $addToSet : $firstname } } }")
  List<PersonAggregate> groupByLastnameAndFirstnames();
}
You can use @Aggregation also with Reactive Repositories.

Simple-type single-result inspects the returned Document and checks for the following:

  1. Only one entry in the document, return it.

  2. Two entries, one is the _id value. Return the other.

  3. Return for the first value assignable to the return type.

  4. Throw an exception if none of the above is applicable.

The Page return type is not supported for repository methods using @Aggregation. However, you can use a Pageable argument to add $skip, $limit and $sort to the pipeline and let the method return Slice.

14.3. CDI Integration

Instances of the repository interfaces are usually created by a container, and Spring is the most natural choice when working with Spring Data. As of version 1.3.0, Spring Data MongoDB ships with a custom CDI extension that lets you use the repository abstraction in CDI environments. The extension is part of the JAR. To activate it, drop the Spring Data MongoDB JAR into your classpath. You can now set up the infrastructure by implementing a CDI Producer for the MongoTemplate, as the following example shows:

class MongoTemplateProducer {

    @Produces
    @ApplicationScoped
    public MongoOperations createMongoTemplate() {

        MongoDatabaseFactory factory = new SimpleMongoClientDatabaseFactory(MongoClients.create(), "database");
        return new MongoTemplate(factory);
    }
}

The Spring Data MongoDB CDI extension picks up the MongoTemplate available 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 @Inject-ed property, as the following example shows:

class RepositoryClient {

  @Inject
  PersonRepository repository;

  public void businessMethod() {
    List<Person> people = repository.findAll();
  }
}

15. Reactive MongoDB repositories

This chapter describes the specialties for reactive repository support for MongoDB. This chapter builds on the core repository support explained in Working with Spring Data Repositories. You should have a sound understanding of the basic concepts explained there.

15.1. Reactive Composition Libraries

The reactive space offers various reactive composition libraries. The most common libraries are RxJava and Project Reactor.

Spring Data MongoDB is built on top of the MongoDB Reactive Streams driver, to provide maximal interoperability by relying on the Reactive Streams initiative. Static APIs, such as ReactiveMongoOperations, are provided by using Project Reactor’s Flux and Mono types. Project Reactor offers various adapters to convert reactive wrapper types (Flux to Observable and vice versa), but conversion can easily clutter your code.

Spring Data’s Repository abstraction is a dynamic API, mostly defined by you and your requirements as you declare query methods. Reactive MongoDB repositories can be implemented by using either RxJava or Project Reactor wrapper types by extending from one of the following library-specific repository interfaces:

  • ReactiveCrudRepository

  • ReactiveSortingRepository

  • RxJava2CrudRepository

  • RxJava2SortingRepository

  • RxJava3CrudRepository

  • RxJava3SortingRepository

Spring Data converts reactive wrapper types behind the scenes so that you can stick to your favorite composition library.

15.2. Usage

To access domain entities stored in a MongoDB database, you can use our sophisticated repository support that eases implementing those quite significantly. To do so, create an interface similar for your repository. Before you can do that, though, you need an entity, such as the entity defined in the following example:

Example 163. Sample Person entity
public class Person {

  @Id
  private String id;
  private String firstname;
  private String lastname;
  private Address address;

  // … getters and setters omitted
}

Note that the entity defined in the preceding example has a property named id of type String. The default serialization mechanism used in MongoTemplate (which backs the repository support) regards properties named id as the document ID. Currently, we support String, ObjectId, and BigInteger as id-types. Please see ID mapping for more information about on how the id field is handled in the mapping layer.

The following example shows how to create an interface that defines queries against the Person object from the preceding example:

Example 164. Basic repository interface to persist Person entities
public interface ReactivePersonRepository extends ReactiveSortingRepository<Person, String> {

  Flux<Person> findByFirstname(String firstname);                                   (1)

  Flux<Person> findByFirstname(Publisher<String> firstname);                        (2)

  Flux<Person> findByFirstnameOrderByLastname(String firstname, Pageable pageable); (3)

  Mono<Person> findByFirstnameAndLastname(String firstname, String lastname);       (4)

  Mono<Person> findFirstByLastname(String lastname);                                (5)
}
1 The method shows a query for all people with the given lastname. The query is derived by parsing the method name for constraints that can be concatenated with And and Or. Thus, the method name results in a query expression of {"lastname" : lastname}.
2 The method shows a query for all people with the given firstname once the firstname is emitted by the given Publisher.
3 Use Pageable to pass offset and sorting parameters to the database.
4 Find a single entity for the given criteria. It completes with IncorrectResultSizeDataAccessException on non-unique results.
5 Unless <4>, the first entity is always emitted even if the query yields more result documents.

For Java configuration, use the @EnableReactiveMongoRepositories annotation. The annotation carries the same attributes as the namespace element. If no base package is configured, the infrastructure scans the package of the annotated configuration class.

MongoDB uses two different drivers for imperative (synchronous/blocking) and reactive (non-blocking) data access. You must create a connection by using the Reactive Streams driver to provide the required infrastructure for Spring Data’s Reactive MongoDB support. Consequently, you must provide a separate configuration for MongoDB’s Reactive Streams driver. Note that your application operates on two different connections if you use reactive and blocking Spring Data MongoDB templates and repositories.

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

Example 165. Java configuration for repositories
@Configuration
@EnableReactiveMongoRepositories
class ApplicationConfig extends AbstractReactiveMongoConfiguration {

  @Override
  protected String getDatabaseName() {
    return "e-store";
  }

  @Override
  public MongoClient reactiveMongoClient() {
    return MongoClients.create();
  }

  @Override
  protected String getMappingBasePackage() {
    return "com.oreilly.springdata.mongodb";
  }
}

Because our domain repository extends ReactiveSortingRepository, it provides you with CRUD operations 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:

Example 166. Sorted access to Person entities
@ExtendWith(SpringExtension.class)
@ContextConfiguration
class PersonRepositoryTests {

    @Autowired ReactivePersonRepository repository;

    @Test
    public void sortsElementsCorrectly() {
      Flux<Person> persons = repository.findAll(Sort.by(new Order(ASC, "lastname")));
    }
}
The Page return type (as in Mono<Page>) is not supported by reactive repositories.

It is possible to use Pageable in derived finder methods, to pass on sort, limit and offset parameters to the query to reduce load and network traffic. The returned Flux will only emit data within the declared range.

Example 167. Limit and Offset with reactive repositories
Pageable page = PageRequest.of(1, 10, Sort.by("lastname"));
Flux<Person> persons = repository.findByFirstnameOrderByLastname("luke", page);

15.3. Features

Spring Data’s Reactive MongoDB support comes with a reduced feature set compared to the blocking MongoDB Repositories.

It supports the following features:

15.3.1. Geo-spatial Repository Queries

As you saw earlier in “Geo-spatial Repository Queries”, a few keywords trigger geo-spatial operations within a MongoDB query. The Near keyword allows some further modification, as the next few examples show.

The following example shows how to define a near query that finds all persons with a given distance of a given point:

Example 168. Advanced Near queries
interface PersonRepository extends ReactiveMongoRepository<Person, String> {

  // { 'location' : { '$near' : [point.x, point.y], '$maxDistance' : distance}}
  Flux<Person> findByLocationNear(Point location, Distance distance);
}

Adding a Distance parameter to the query method allows restricting results to those within the given distance. If the Distance was set up containing a Metric, we transparently use $nearSphere instead of $code, as the following example shows:

Example 169. Using Distance with Metrics
Point point = new Point(43.7, 48.8);
Distance distance = new Distance(200, Metrics.KILOMETERS);
… = repository.findByLocationNear(point, distance);
// {'location' : {'$nearSphere' : [43.7, 48.8], '$maxDistance' : 0.03135711885774796}}
Reactive Geo-spatial repository queries support the domain type and GeoResult<T> results within a reactive wrapper type. GeoPage and GeoResults are not supported as they contradict the deferred result approach with pre-calculating the average distance. Howevery, you can still pass in a Pageable argument to page results yourself.

Using a Distance with a Metric causes a $nearSphere (instead of a plain $near) clause to be added. Beyond that, the actual distance gets calculated according to the Metrics used.

(Note that Metric does not refer to metric units of measure. It could be miles rather than kilometers. Rather, metric refers to the concept of a system of measurement, regardless of which system you use.)

Using @GeoSpatialIndexed(type = GeoSpatialIndexType.GEO_2DSPHERE) on the target property forces usage of $nearSphere operator.
Geo-near Queries

Spring Data MongoDB supports geo-near queries, as the following example shows:

interface PersonRepository extends ReactiveMongoRepository<Person, String>  {

  // {'geoNear' : 'location', 'near' : [x, y] }
  Flux<GeoResult<Person>> findByLocationNear(Point location);

  // No metric: {'geoNear' : 'person', 'near' : [x, y], maxDistance : distance }
  // Metric: {'geoNear' : 'person', 'near' : [x, y], 'maxDistance' : distance,
  //          'distanceMultiplier' : metric.multiplier, 'spherical' : true }
  Flux<GeoResult<Person>> findByLocationNear(Point location, Distance distance);

  // Metric: {'geoNear' : 'person', 'near' : [x, y], 'minDistance' : min,
  //          'maxDistance' : max, 'distanceMultiplier' : metric.multiplier,
  //          'spherical' : true }
  Flux<GeoResult<Person>> findByLocationNear(Point location, Distance min, Distance max);

  // {'geoNear' : 'location', 'near' : [x, y] }
  Flux<GeoResult<Person>> findByLocationNear(Point location);
}

15.3.2. Type-safe Query Methods

Reactive MongoDB repository support integrates with the Querydsl project, which provides a way to perform type-safe queries.

Instead of writing queries as inline strings or externalizing them into XML files they are constructed via a fluent API.
— Querydsl Team

It provides the following features:

  • Code completion in the IDE (all properties, methods, and operations can be expanded in your favorite Java IDE).

  • Almost no syntactically invalid queries allowed (type-safe on all levels).

  • Domain types and properties can be referenced safely — no strings involved!

  • Adapts better to refactoring changes in domain types.

  • Incremental query definition is easier.

See the Querydsl documentation for how to bootstrap your environment for APT-based code generation using Maven or Ant.

The Querydsl repository support lets you write and run queries, such as the following:

QPerson person = QPerson.person;

Flux<Person> result = repository.findAll(person.address.zipCode.eq("C0123"));

QPerson is a class that is generated by the Java annotation post-processing tool. It is a Predicate that lets you write type-safe queries. Note that there are no strings in the query other than the C0123 value.

You can use the generated Predicate class by using the ReactiveQuerydslPredicateExecutor interface, which the following listing shows:

Example 170. The Gateway to Reactive Querydsl - The ReactiveQuerydslPredicateExecutor
interface ReactiveQuerydslPredicateExecutor<T> {

	Mono<T> findOne(Predicate predicate);

	Flux<T> findAll(Predicate predicate);

	Flux<T> findAll(Predicate predicate, Sort sort);

	Flux<T> findAll(Predicate predicate, OrderSpecifier<?>... orders);

	Flux<T> findAll(OrderSpecifier<?>... orders);

	Mono<Long> count(Predicate predicate);

	Mono<Boolean> exists(Predicate predicate);
}

To use this in your repository implementation, add it to the list of repository interfaces from which your interface inherits, as the following example shows:

Example 171. Reactive Querydsl Respository Declaration
interface PersonRepository extends ReactiveMongoRepository<Person, String>, ReactiveQuerydslPredicateExecutor<Person> {

   // additional query methods go here
}
Please note that joins (DBRef’s) are not supported with Reactive MongoDB support.

16. Auditing

16.1. Basics

Spring Data provides sophisticated support to transparently keep track of who created or changed an entity and when the change happened.To benefit from that functionality, you have to equip your entity classes with auditing metadata that can be defined either using annotations or by implementing an interface. Additionally, auditing has to be enabled either through Annotation configuration or XML configuration to register the required infrastructure components. Please refer to the store-specific section for configuration samples.

Applications that only track creation and modification dates are not required do make their entities implement AuditorAware.

16.1.1. Annotation-based Auditing Metadata

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.

Example 172. An audited entity
class Customer {

  @CreatedBy
  private User user;

  @CreatedDate
  private Instant createdDate;

  // … further properties omitted
}

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.

Example 173. Audit metadata in embedded entity
class Customer {

  private AuditMetadata auditingMetadata;

  // … further properties omitted
}

class AuditMetadata {

  @CreatedBy
  private User user;

  @CreatedDate
  private Instant createdDate;

}

16.1.2. Interface-based Auditing Metadata

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.

16.1.3. AuditorAware

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:

Example 174. Implementation of AuditorAware based on Spring Security
class SpringSecurityAuditorAware implements AuditorAware<User> {

  @Override
  public Optional<User> getCurrentAuditor() {

    return Optional.ofNullable(SecurityContextHolder.getContext())
            .map(SecurityContext::getAuthentication)
            .filter(Authentication::isAuthenticated)
            .map(Authentication::getPrincipal)
            .map(User.class::cast);
  }
}

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.

16.1.4. ReactiveAuditorAware

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:

Example 175. Implementation of ReactiveAuditorAware based on Spring Security
class SpringSecurityAuditorAware implements ReactiveAuditorAware<User> {

  @Override
  public Mono<User> getCurrentAuditor() {

    return ReactiveSecurityContextHolder.getContext()
                .map(SecurityContext::getAuthentication)
                .filter(Authentication::isAuthenticated)
                .map(Authentication::getPrincipal)
                .map(User.class::cast);
  }
}

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.

16.2. General Auditing Configuration for MongoDB

Since Spring Data MongoDB 1.4, auditing can be enabled by annotating a configuration class with the @EnableMongoAuditing annotation, as the following example shows:

Java
@Configuration
@EnableMongoAuditing
class Config {

  @Bean
  public AuditorAware<AuditableUser> myAuditorProvider() {
      return new AuditorAwareImpl();
  }
}
XML
<mongo:auditing mapping-context-ref="customMappingContext" auditor-aware-ref="yourAuditorAwareImpl"/>

If you expose a bean of type AuditorAware to the ApplicationContext, the auditing infrastructure picks it up automatically and uses it to determine the current user to be set on domain types. If you have multiple implementations registered in the ApplicationContext, you can select the one to be used by explicitly setting the auditorAwareRef attribute of @EnableMongoAuditing.

To enable auditing, leveraging a reactive programming model, use the @EnableReactiveMongoAuditing annotation.
If you expose a bean of type ReactiveAuditorAware to the ApplicationContext, the auditing infrastructure picks it up automatically and uses it to determine the current user to be set on domain types. If you have multiple implementations registered in the ApplicationContext, you can select the one to be used by explicitly setting the auditorAwareRef attribute of @EnableReactiveMongoAuditing.

Example 176. Activating reactive auditing using JavaConfig
@Configuration
@EnableReactiveMongoAuditing
class Config {

  @Bean
  public ReactiveAuditorAware<AuditableUser> myAuditorProvider() {
      return new AuditorAwareImpl();
  }
}

17. Mapping

Rich mapping support is provided by the MappingMongoConverter. MappingMongoConverter has a rich metadata model that provides a full feature set to map domain objects to MongoDB documents. The mapping metadata model is populated by using annotations on your domain objects. However, the infrastructure is not limited to using annotations as the only source of metadata information. The MappingMongoConverter also lets you map objects to documents without providing any additional metadata, by following a set of conventions.

This section describes the features of the MappingMongoConverter, including fundamentals, how to use conventions for mapping objects to documents and how to override those conventions with annotation-based mapping metadata.

17.1. Object Mapping Fundamentals

This section covers the fundamentals of Spring Data object mapping, object creation, field and property access, mutability and immutability. Note, that this section only applies to Spring Data modules that do not use the object mapping of the underlying data store (like JPA). Also be sure to consult the store-specific sections for store-specific object mapping, like indexes, customizing column or field names or the like.

Core responsibility of the Spring Data object mapping is to create instances of domain objects and map the store-native data structures onto those. This means we need two fundamental steps:

  1. Instance creation by using one of the constructors exposed.

  2. Instance population to materialize all exposed properties.

17.1.1. Object creation

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:

  1. If there is a single static factory method annotated with @PersistenceCreator then it is used.

  2. If there is a single constructor, it is used.

  3. If there are multiple constructors and exactly one is annotated with @PersistenceCreator, it is used.

  4. If the type is a Java Record the canonical constructor is used.

  5. If there’s a no-argument constructor, it is used. Other constructors will be ignored.

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.

Object creation internals

To avoid the overhead of reflection, Spring Data object creation uses a factory class generated at runtime by default, which will call the domain classes constructor directly. I.e. for this example type:

class Person {
  Person(String firstname, String lastname) { … }
}

we will create a factory class semantically equivalent to this one at runtime:

class PersonObjectInstantiator implements ObjectInstantiator {

  Object newInstance(Object... args) {
    return new Person((String) args[0], (String) args[1]);
  }
}

This gives us a roundabout 10% performance boost over reflection. For the domain class to be eligible for such optimization, it needs to adhere to a set of constraints:

  • it must not be a private class

  • it must not be a non-static inner class

  • it must not be a CGLib proxy class

  • the constructor to be used by Spring Data must not be private

If any of these criteria match, Spring Data will fall back to entity instantiation via reflection.

17.1.2. Property population

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:

  1. If the property is immutable but exposes a with… method (see below), we use the with… method to create a new entity instance with the new property value.

  2. If property access (i.e. access through getters and setters) is defined, we’re invoking the setter method.

  3. If the property is mutable we set the field directly.

  4. If the property is immutable we’re using the constructor to be used by persistence operations (see Object creation) to create a copy of the instance.

  5. By default, we set the field value directly.

Property population internals

Similarly to our optimizations in object construction we also use Spring Data runtime generated accessor classes to interact with the entity instance.

class Person {

  private final Long id;
  private String firstname;
  private @AccessType(Type.PROPERTY) String lastname;

  Person() {
    this.id = null;
  }

  Person(Long id, String firstname, String lastname) {
    // Field assignments
  }

  Person withId(Long id) {
    return new Person(id, this.firstname, this.lastame);
  }

  void setLastname(String lastname) {
    this.lastname = lastname;
  }
}
Example 177. A generated Property Accessor
class PersonPropertyAccessor implements PersistentPropertyAccessor {

  private static final MethodHandle firstname;              (2)

  private Person person;                                    (1)

  public void setProperty(PersistentProperty property, Object value) {

    String name = property.getName();

    if ("firstname".equals(name)) {
      firstname.invoke(person, (String) value);             (2)
    } else if ("id".equals(name)) {
      this.person = person.withId((Long) value);            (3)
    } else if ("lastname".equals(name)) {
      this.person.setLastname((String) value);              (4)
    }
  }
}
1 PropertyAccessor’s hold a mutable instance of the underlying object. This is, to enable mutations of otherwise immutable properties.
2 By default, Spring Data uses field-access to read and write property values. As per visibility rules of private fields, MethodHandles are used to interact with fields.
3 The class exposes a withId(…) method that’s used to set the identifier, e.g. when an instance is inserted into the datastore and an identifier has been generated. Calling withId(…) creates a new Person object. All subsequent mutations will take place in the new instance leaving the previous untouched.
4 Using property-access allows direct method invocations without using MethodHandles.

This gives us a roundabout 25% performance boost over reflection. For the domain class to be eligible for such optimization, it needs to adhere to a set of constraints:

  • Types must not reside in the default or under the java package.

  • Types and their constructors must be public

  • Types that are inner classes must be static.

  • The used Java Runtime must allow for declaring classes in the originating ClassLoader. Java 9 and newer impose certain limitations.

By default, Spring Data attempts to use generated property accessors and falls back to reflection-based ones if a limitation is detected.

Let’s have a look at the following entity:

Example 178. A sample entity
class Person {

  private final @Id Long id;                                                (1)
  private final String firstname, lastname;                                 (2)
  private final LocalDate birthday;
  private final int age;                                                    (3)

  private String comment;                                                   (4)
  private @AccessType(Type.PROPERTY) String remarks;                        (5)

  static Person of(String firstname, String lastname, LocalDate birthday) { (6)

    return new Person(null, firstname, lastname, birthday,
      Period.between(birthday, LocalDate.now()).getYears());
  }

  Person(Long id, String firstname, String lastname, LocalDate birthday, int age) { (6)

    this.id = id;
    this.firstname = firstname;
    this.lastname = lastname;
    this.birthday = birthday;
    this.age = age;
  }

  Person withId(Long id) {                                                  (1)
    return new Person(id, this.firstname, this.lastname, this.birthday, this.age);
  }

  void setRemarks(String remarks) {                                         (5)
    this.remarks = remarks;
  }
}
1 The identifier property is final but set to null in the constructor. The class exposes a withId(…) method that’s used to set the identifier, e.g. when an instance is inserted into the datastore and an identifier has been generated. The original Person instance stays unchanged as a new one is created. The same pattern is usually applied for other properties that are store managed but might have to be changed for persistence operations. The wither method is optional as the persistence constructor (see 6) is effectively a copy constructor and setting the property will be translated into creating a fresh instance with the new identifier value applied.
2 The firstname and lastname properties are ordinary immutable properties potentially exposed through getters.
3 The age property is an immutable but derived one from the birthday property. With the design shown, the database value will trump the defaulting as Spring Data uses the only declared constructor. Even if the intent is that the calculation should be preferred, it’s important that this constructor also takes age as parameter (to potentially ignore it) as otherwise the property population step will attempt to set the age field and fail due to it being immutable and no with… method being present.
4 The comment property is mutable and is populated by setting its field directly.
5 The remarks property is mutable and is populated by invoking the setter method.
6 The class exposes a factory method and a constructor for object creation. The core idea here is to use factory methods instead of additional constructors to avoid the need for constructor disambiguation through @PersistenceCreator. Instead, defaulting of properties is handled within the factory method. If you want Spring Data to use the factory method for object instantiation, annotate it with @PersistenceCreator.

17.1.3. General recommendations

  • Try to stick to immutable objects — Immutable objects are straightforward to create as materializing an object is then a matter of calling its constructor only. Also, this avoids your domain objects to be littered with setter methods that allow client code to manipulate the objects state. If you need those, prefer to make them package protected so that they can only be invoked by a limited amount of co-located types. Constructor-only materialization is up to 30% faster than properties population.

  • Provide an all-args constructor — Even if you cannot or don’t want to model your entities as immutable values, there’s still value in providing a constructor that takes all properties of the entity as arguments, including the mutable ones, as this allows the object mapping to skip the property population for optimal performance.

  • Use factory methods instead of overloaded constructors to avoid @PersistenceCreator — With an all-argument constructor needed for optimal performance, we usually want to expose more application use case specific constructors that omit things like auto-generated identifiers etc. It’s an established pattern to rather use static factory methods to expose these variants of the all-args constructor.

  • Make sure you adhere to the constraints that allow the generated instantiator and property accessor classes to be used — 

  • For identifiers to be generated, still use a final field in combination with an all-arguments persistence constructor (preferred) or a with… method — 

  • Use Lombok to avoid boilerplate code — As persistence operations usually require a constructor taking all arguments, their declaration becomes a tedious repetition of boilerplate parameter to field assignments that can best be avoided by using Lombok’s @AllArgsConstructor.

Overriding Properties

Java’s allows a flexible design of domain classes where a subclass could define a property that is already declared with the same name in its superclass. Consider the following example:

public class SuperType {

   private CharSequence field;

   public SuperType(CharSequence field) {
      this.field = field;
   }

   public CharSequence getField() {
      return this.field;
   }

   public void setField(CharSequence field) {
      this.field = field;
   }
}

public class SubType extends SuperType {

   private String field;

   public SubType(String field) {
      super(field);
      this.field = field;
   }

   @Override
   public String getField() {
      return this.field;
   }

   public void setField(String field) {
      this.field = field;

      // optional
      super.setField(field);
   }
}

Both classes define a field using assignable types. SubType however shadows SuperType.field. Depending on the class design, using the constructor could be the only default approach to set SuperType.field. Alternatively, calling super.setField(…) in the setter could set the field in SuperType. All these mechanisms create conflicts to some degree because the properties share the same name yet might represent two distinct values. Spring Data skips super-type properties if types are not assignable. That is, the type of the overridden property must be assignable to its super-type property type to be registered as override, otherwise the super-type property is considered transient. We generally recommend using distinct property names.

Spring Data modules generally support overridden properties holding different values. From a programming model perspective there are a few things to consider:

  1. Which property should be persisted (default to all declared properties)? You can exclude properties by annotating these with @Transient.

  2. How to represent properties in your data store? Using the same field/column name for different values typically leads to corrupt data so you should annotate least one of the properties using an explicit field/column name.

  3. Using @AccessType(PROPERTY) cannot be used as the super-property cannot be generally set without making any further assumptions of the setter implementation.

17.1.4. Kotlin support

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

Kotlin object creation

Kotlin classes are supported to be instantiated, all classes are immutable by default and require explicit property declarations to define mutable properties.

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:

  1. If there is a constructor that is annotated with @PersistenceCreator, it is used.

  2. If the type is a Kotlin data cass the primary constructor is used.

  3. If there is a single static factory method annotated with @PersistenceCreator then it is used.

  4. If there is a single constructor, it is used.

  5. If there are multiple constructors and exactly one is annotated with @PersistenceCreator, it is used.

  6. If the type is a Java Record the canonical constructor is used.

  7. If there’s a no-argument constructor, it is used. Other constructors will be ignored.

Consider the following data class Person:

data class Person(val id: String, val name: String)

The class above compiles to a typical class with an explicit constructor.We can customize this class by adding another constructor and annotate it with @PersistenceCreator to indicate a constructor preference:

data class Person(var id: String, val name: String) {

    @PersistenceCreator
    constructor(id: String) : this(id, "unknown")
}

Kotlin supports parameter optionality by allowing default values to be used if a parameter is not provided. When Spring Data detects a constructor with parameter defaulting, then it leaves these parameters absent if the data store does not provide a value (or simply returns null) so Kotlin can apply parameter defaulting.Consider the following class that applies parameter defaulting for name

data class Person(var id: String, val name: String = "unknown")

Every time the name parameter is either not part of the result or its value is null, then the name defaults to unknown.

Property population of Kotlin data classes

In Kotlin, all classes are immutable by default and require explicit property declarations to define mutable properties. Consider the following data class Person:

data class Person(val id: String, val name: String)

This class is effectively immutable. It allows creating new instances as Kotlin generates a copy(…) method that creates new object instances copying all property values from the existing object and applying property values provided as arguments to the method.

Kotlin Overriding Properties

Kotlin allows declaring property overrides to alter properties in subclasses.

open class SuperType(open var field: Int)

class SubType(override var field: Int = 1) :
	SuperType(field) {
}

Such an arrangement renders two properties with the name field. Kotlin generates property accessors (getters and setters) for each property in each class. Effectively, the code looks like as follows:

public class SuperType {

   private int field;

   public SuperType(int field) {
      this.field = field;
   }

   public int getField() {
      return this.field;
   }

   public void setField(int field) {
      this.field = field;
   }
}

public final class SubType extends SuperType {

   private int field;

   public SubType(int field) {
      super(field);
      this.field = field;
   }

   public int getField() {
      return this.field;
   }

   public void setField(int field) {
      this.field = field;
   }
}

Getters and setters on SubType set only SubType.field and not SuperType.field. In such an arrangement, using the constructor is the only default approach to set SuperType.field. Adding a method to SubType to set SuperType.field via this.SuperType.field = … is possible but falls outside of supported conventions. Property overrides create conflicts to some degree because the properties share the same name yet might represent two distinct values. We generally recommend using distinct property names.

Spring Data modules generally support overridden properties holding different values. From a programming model perspective there are a few things to consider:

  1. Which property should be persisted (default to all declared properties)? You can exclude properties by annotating these with @Transient.

  2. How to represent properties in your data store? Using the same field/column name for different values typically leads to corrupt data so you should annotate least one of the properties using an explicit field/column name.

  3. Using @AccessType(PROPERTY) cannot be used as the super-property cannot be set.

17.2. Convention-based Mapping

MappingMongoConverter has a few conventions for mapping objects to documents when no additional mapping metadata is provided. The conventions are:

  • The short Java class name is mapped to the collection name in the following manner. The class com.bigbank.SavingsAccount maps to the savingsAccount collection name.

  • All nested objects are stored as nested objects in the document and not as DBRefs.

  • The converter uses any Spring Converters registered with it to override the default mapping of object properties to document fields and values.

  • The fields of an object are used to convert to and from fields in the document. Public JavaBean properties are not used.

  • If you have a single non-zero-argument constructor whose constructor argument names match top-level field names of document, that constructor is used. Otherwise, the zero-argument constructor is used. If there is more than one non-zero-argument constructor, an exception will be thrown.

17.2.1. How the _id field is handled in the mapping layer.

MongoDB requires that you have an _id field for all documents. If you don’t provide one the driver will assign a ObjectId with a generated value. The "_id" field can be of any type the, other than arrays, so long as it is unique. The driver naturally supports all primitive types and Dates. When using the MappingMongoConverter there are certain rules that govern how properties from the Java class is mapped to this _id field.

The following outlines what field will be mapped to the _id document field:

  • A field annotated with @Id (org.springframework.data.annotation.Id) will be mapped to the _id field.

  • A field without an annotation but named id will be mapped to the _id field.

  • The default field name for identifiers is _id and can be customized via the @Field annotation.

Table 14. Examples for the translation of _id field definitions
Field definition Resulting Id-Fieldname in MongoDB

String id

_id

@Field String id

_id

@Field("x") String id

x

@Id String x

_id

@Field("x") @Id String x

_id

The following outlines what type conversion, if any, will be done on the property mapped to the _id document field.

  • If a field named id is declared as a String or BigInteger in the Java class it will be converted to and stored as an ObjectId if possible. ObjectId as a field type is also valid. If you specify a value for id in your application, the conversion to an ObjectId is detected to the MongoDB driver. If the specified id value cannot be converted to an ObjectId, then the value will be stored as is in the document’s _id field. This also applies if the field is annotated with @Id.

  • If a field is annotated with @MongoId in the Java class it will be converted to and stored as using its actual type. No further conversion happens unless @MongoId declares a desired field type. If no value is provided for the id field, a new ObjectId will be created and converted to the properties type.

  • If a field is annotated with @MongoId(FieldType.…) in the Java class it will be attempted to convert the value to the declared FieldType. If no value is provided for the id field, a new ObjectId will be created and converted to the declared type.

  • If a field named id id field is not declared as a String, BigInteger, or ObjectID in the Java class then you should assign it a value in your application so it can be stored 'as-is' in the document’s _id field.

  • If no field named id is present in the Java class then an implicit _id file will be generated by the driver but not mapped to a property or field of the Java class.

When querying and updating MongoTemplate will use the converter to handle conversions of the Query and Update objects that correspond to the above rules for saving documents so field names and types used in your queries will be able to match what is in your domain classes.

17.3. Data Mapping and Type Conversion

This section explains how types are mapped to and from a MongoDB representation. Spring Data MongoDB supports all types that can be represented as BSON, MongoDB’s internal document format. In addition to these types, Spring Data MongoDB provides a set of built-in converters to map additional types. You can provide your own converters to adjust type conversion. See Custom Conversions - Overriding Default Mapping for further details.

The following provides samples of each available type conversion:

Table 15. Type
Type Type conversion Sample

String

native

{"firstname" : "Dave"}

double, Double, float, Float

native

{"weight" : 42.5}

int, Integer, short, Short

native
32-bit integer

{"height" : 42}

long, Long

native
64-bit integer

{"height" : 42}

Date, Timestamp

native

{"date" : ISODate("2019-11-12T23:00:00.809Z")}

byte[]

native

{"bin" : { "$binary" : "AQIDBA==", "$type" : "00" }}

java.util.UUID (Legacy UUID)

native

{"uuid" : { "$binary" : "MEaf1CFQ6lSphaa3b9AtlA==", "$type" : "03" }}

Date

native

{"date" : ISODate("2019-11-12T23:00:00.809Z")}

ObjectId

native

{"_id" : ObjectId("5707a2690364aba3136ab870")}

Array, List, BasicDBList

native

{"cookies" : [ … ]}

boolean, Boolean

native

{"active" : true}

null

native

{"value" : null}

Document

native

{"value" : { … }}

Decimal128

native

{"value" : NumberDecimal(…)}

AtomicInteger
calling get() before the actual conversion

converter
32-bit integer

{"value" : "741" }

AtomicLong
calling get() before the actual conversion

converter
64-bit integer

{"value" : "741" }

BigInteger

converter
String

{"value" : "741" }

BigDecimal

converter
String

{"value" : "741.99" }

URL

converter

{"website" : "https://spring.io/projects/spring-data-mongodb/" }

Locale

converter

{"locale : "en_US" }

char, Character

converter

{"char" : "a" }

NamedMongoScript

converter
Code

{"_id" : "script name", value: (some javascript code)}

java.util.Currency

converter

{"currencyCode" : "EUR"}

Instant
(Java 8)

native

{"date" : ISODate("2019-11-12T23:00:00.809Z")}

Instant
(Joda, JSR310-BackPort)

converter

{"date" : ISODate("2019-11-12T23:00:00.809Z")}

LocalDate
(Joda, Java 8, JSR310-BackPort)

converter / native (Java8)[2]

{"date" : ISODate("2019-11-12T00:00:00.000Z")}

LocalDateTime, LocalTime
(Joda, Java 8, JSR310-BackPort)

converter / native (Java8)[3]

{"date" : ISODate("2019-11-12T23:00:00.809Z")}

DateTime (Joda)

converter

{"date" : ISODate("2019-11-12T23:00:00.809Z")}

ZoneId (Java 8, JSR310-BackPort)

converter

{"zoneId" : "ECT - Europe/Paris"}

Box

converter

{"box" : { "first" : { "x" : 1.0 , "y" : 2.0} , "second" : { "x" : 3.0 , "y" : 4.0}}

Polygon

converter

{"polygon" : { "points" : [ { "x" : 1.0 , "y" : 2.0} , { "x" : 3.0 , "y" : 4.0} , { "x" : 4.0 , "y" : 5.0}]}}

Circle

converter

{"circle" : { "center" : { "x" : 1.0 , "y" : 2.0} , "radius" : 3.0 , "metric" : "NEUTRAL"}}

Point

converter

{"point" : { "x" : 1.0 , "y" : 2.0}}

GeoJsonPoint

converter

{"point" : { "type" : "Point" , "coordinates" : [3.0 , 4.0] }}

GeoJsonMultiPoint

converter

{"geoJsonLineString" : {"type":"MultiPoint", "coordinates": [ [ 0 , 0 ], [ 0 , 1 ], [ 1 , 1 ] ] }}

Sphere

converter

{"sphere" : { "center" : { "x" : 1.0 , "y" : 2.0} , "radius" : 3.0 , "metric" : "NEUTRAL"}}

GeoJsonPolygon

converter

{"polygon" : { "type" : "Polygon", "coordinates" : [[ [ 0 , 0 ], [ 3 , 6 ], [ 6 , 1 ], [ 0 , 0 ] ]] }}

GeoJsonMultiPolygon

converter

{"geoJsonMultiPolygon" : { "type" : "MultiPolygon", "coordinates" : [ [ [ [ -73.958 , 40.8003 ] , [ -73.9498 , 40.7968 ] ] ], [ [ [ -73.973 , 40.7648 ] , [ -73.9588 , 40.8003 ] ] ] ] }}

GeoJsonLineString

converter

{ "geoJsonLineString" : { "type" : "LineString", "coordinates" : [ [ 40 , 5 ], [ 41 , 6 ] ] }}

GeoJsonMultiLineString

converter

{"geoJsonLineString" : { "type" : "MultiLineString", coordinates: [ [ [ -73.97162 , 40.78205 ], [ -73.96374 , 40.77715 ] ], [ [ -73.97880 , 40.77247 ], [ -73.97036 , 40.76811 ] ] ] }}

17.4. Mapping Configuration

Unless explicitly configured, an instance of MappingMongoConverter is created by default when you create a MongoTemplate. You can create your own instance of the MappingMongoConverter. Doing so lets you dictate where in the classpath your domain classes can be found, so that Spring Data MongoDB can extract metadata and construct indexes. Also, by creating your own instance, you can register Spring converters to map specific classes to and from the database.

You can configure the MappingMongoConverter as well as com.mongodb.client.MongoClient and MongoTemplate by using either Java-based or XML-based metadata. The following example shows the configuration:

Java
@Configuration
public class MongoConfig extends AbstractMongoClientConfiguration {

  @Override
  public String getDatabaseName() {
    return "database";
  }

  // the following are optional

  @Override
  public String getMappingBasePackage() { (1)
    return "com.bigbank.domain";
  }

  @Override
  void configureConverters(MongoConverterConfigurationAdapter adapter) { (2)

  	adapter.registerConverter(new org.springframework.data.mongodb.test.PersonReadConverter());
  	adapter.registerConverter(new org.springframework.data.mongodb.test.PersonWriteConverter());
  }

  @Bean
  public LoggingEventListener<MongoMappingEvent> mappingEventsListener() {
    return new LoggingEventListener<MongoMappingEvent>();
  }
}
XML
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:mongo="http://www.springframework.org/schema/data/mongo"
  xsi:schemaLocation="
    http://www.springframework.org/schema/data/mongo https://www.springframework.org/schema/data/mongo/spring-mongo.xsd
    http://www.springframework.org/schema/beans https://www.springframework.org/schema/beans/spring-beans-3.0.xsd">

  <!-- Default bean name is 'mongo' -->
  <mongo:mongo-client host="localhost" port="27017"/>

  <mongo:db-factory dbname="database" mongo-ref="mongoClient"/>

  <!-- by default look for a Mongo object named 'mongo' - default name used for the converter is 'mappingConverter' -->
  <mongo:mapping-converter base-package="com.bigbank.domain">
    <mongo:custom-converters>
      <mongo:converter ref="readConverter"/>
      <mongo:converter>
        <bean class="org.springframework.data.mongodb.test.PersonWriteConverter"/>
      </mongo:converter>
    </mongo:custom-converters>
  </mongo:mapping-converter>

  <bean id="readConverter" class="org.springframework.data.mongodb.test.PersonReadConverter"/>

  <!-- set the mapping converter to be used by the MongoTemplate -->
  <bean id="mongoTemplate" class="org.springframework.data.mongodb.core.MongoTemplate">
    <constructor-arg name="mongoDbFactory" ref="mongoDbFactory"/>
    <constructor-arg name="mongoConverter" ref="mappingConverter"/>
  </bean>

  <bean class="org.springframework.data.mongodb.core.mapping.event.LoggingEventListener"/>

</beans>
1 The mapping base package defines the root path used to scan for entities used to pre initialize the MappingContext. By default the configuration classes package is used.
2 Configure additional custom converters for specific domain types that replace the default mapping procedure for those types with your custom implementation.

AbstractMongoClientConfiguration requires you to implement methods that define a com.mongodb.client.MongoClient as well as provide a database name. AbstractMongoClientConfiguration also has a method named getMappingBasePackage(…) that you can override to tell the converter where to scan for classes annotated with the @Document annotation.

You can add additional converters to the converter by overriding the customConversionsConfiguration method. MongoDB’s native JSR-310 support can be enabled through MongoConverterConfigurationAdapter.useNativeDriverJavaTimeCodecs(). Also shown in the preceding example is a LoggingEventListener, which logs MongoMappingEvent instances that are posted onto Spring’s ApplicationContextEvent infrastructure.

AbstractMongoClientConfiguration creates a MongoTemplate instance and registers it with the container under the name mongoTemplate.

The base-package property tells it where to scan for classes annotated with the @org.springframework.data.mongodb.core.mapping.Document annotation.

If you want to rely on Spring Boot to bootstrap Data MongoDB, but still want to override certain aspects of the configuration, you may want to expose beans of that type. For custom conversions you may eg. choose to register a bean of type MongoCustomConversions that will be picked up the by the Boot infrastructure. To learn more about this please make sure to read the Spring Boot Reference Documentation.

17.5. Metadata-based Mapping

To take full advantage of the object mapping functionality inside the Spring Data MongoDB support, you should annotate your mapped objects with the @Document annotation. Although it is not necessary for the mapping framework to have this annotation (your POJOs are mapped correctly, even without any annotations), it lets the classpath scanner find and pre-process your domain objects to extract the necessary metadata. If you do not use this annotation, your application takes a slight performance hit the first time you store a domain object, because the mapping framework needs to build up its internal metadata model so that it knows about the properties of your domain object and how to persist them. The following example shows a domain object:

Example 179. Example domain object
package com.mycompany.domain;

@Document
public class Person {

  @Id
  private ObjectId id;

  @Indexed
  private Integer ssn;

  private String firstName;

  @Indexed
  private String lastName;
}
The @Id annotation tells the mapper which property you want to use for the MongoDB _id property, and the @Indexed annotation tells the mapping framework to call createIndex(…) on that property of your document, making searches faster. Automatic index creation is only done for types annotated with @Document.
Auto index creation is disabled by default and needs to be enabled through the configuration (see Index Creation).

17.5.1. Index Creation

Spring Data MongoDB can automatically create indexes for entity types annotated with @Document. Index creation must be explicitly enabled since version 3.0 to prevent undesired effects with collection lifecyle and performance impact. Indexes are automatically created for the initial entity set on application startup and when accessing an entity type for the first time while the application runs.

We generally recommend explicit index creation for application-based control of indexes as Spring Data cannot automatically create indexes for collections that were recreated while the application was running.

IndexResolver provides an abstraction for programmatic index definition creation if you want to make use of @Indexed annotations such as @GeoSpatialIndexed, @TextIndexed, @CompoundIndex and @WildcardIndexed. You can use index definitions with IndexOperations to create indexes. A good point in time for index creation is on application startup, specifically after the application context was refreshed, triggered by observing ContextRefreshedEvent. This event guarantees that the context is fully initialized. Note that at this time other components, especially bean factories might have access to the MongoDB database.

Map-like properties are skipped by the IndexResolver unless annotated with @WildcardIndexed because the map key must be part of the index definition. Since the purpose of maps is the usage of dynamic keys and values, the keys cannot be resolved from static mapping metadata.

Example 180. Programmatic Index Creation for a single Domain Type
class MyListener {

  @EventListener(ContextRefreshedEvent.class)
  public void initIndicesAfterStartup() {

    MappingContext<? extends MongoPersistentEntity<?>, MongoPersistentProperty> mappingContext = mongoTemplate
                .getConverter().getMappingContext();

    IndexResolver resolver = new MongoPersistentEntityIndexResolver(mappingContext);

    IndexOperations indexOps = mongoTemplate.indexOps(DomainType.class);
    resolver.resolveIndexFor(DomainType.class).forEach(indexOps::ensureIndex);
  }
}
Example 181. Programmatic Index Creation for all Initial Entities
class MyListener{

  @EventListener(ContextRefreshedEvent.class)
  public void initIndicesAfterStartup() {

    MappingContext<? extends MongoPersistentEntity<?>, MongoPersistentProperty> mappingContext = mongoTemplate
        .getConverter().getMappingContext();

    // consider only entities that are annotated with @Document
    mappingContext.getPersistentEntities()
                            .stream()
                            .filter(it -> it.isAnnotationPresent(Document.class))
                            .forEach(it -> {

    IndexOperations indexOps = mongoTemplate.indexOps(it.getType());
    resolver.resolveIndexFor(it.getType()).forEach(indexOps::ensureIndex);
    });
  }
}

Alternatively, if you want to ensure index and collection presence before any component is able to access your database from your application, declare a @Bean method for MongoTemplate and include the code from above before returning the MongoTemplate object.

To turn automatic index creation ON please override autoIndexCreation() in your configuration.

@Configuration
public class Config extends AbstractMongoClientConfiguration {

  @Override
  public boolean autoIndexCreation() {
    return true;
  }

// ...
}
Automatic index creation is turned OFF by default as of version 3.0.

17.5.2. Mapping Annotation Overview

The MappingMongoConverter can use metadata to drive the mapping of objects to documents. The following annotations are available:

  • @Id: Applied at the field level to mark the field used for identity purpose.

  • @MongoId: Applied at the field level to mark the field used for identity purpose. Accepts an optional FieldType to customize id conversion.

  • @Document: Applied at the class level to indicate this class is a candidate for mapping to the database. You can specify the name of the collection where the data will be stored.

  • @DBRef: Applied at the field to indicate it is to be stored using a com.mongodb.DBRef.

  • @DocumentReference: Applied at the field to indicate it is to be stored as a pointer to another document. This can be a single value (the id by default), or a Document provided via a converter.

  • @Indexed: Applied at the field level to describe how to index the field.

  • @CompoundIndex (repeatable): Applied at the type level to declare Compound Indexes.

  • @GeoSpatialIndexed: Applied at the field level to describe how to geoindex the field.

  • @TextIndexed: Applied at the field level to mark the field to be included in the text index.

  • @HashIndexed: Applied at the field level for usage within a hashed index to partition data across a sharded cluster.

  • @Language: Applied at the field level to set the language override property for text index.

  • @Transient: By default, all fields are mapped to the document. This annotation excludes the field where it is applied from being stored in the database. Transient properties cannot be used within a persistence constructor as the converter cannot materialize a value for the constructor argument.

  • @PersistenceConstructor: Marks a given constructor - even a package protected one - to use when instantiating the object from the database. Constructor arguments are mapped by name to the key values in the retrieved Document.

  • @Value: This annotation is part of the Spring Framework . Within the mapping framework it can be applied to constructor arguments. This lets you use a Spring Expression Language statement to transform a key’s value retrieved in the database before it is used to construct a domain object. In order to reference a property of a given document one has to use expressions like: @Value("#root.myProperty") where root refers to the root of the given document.

  • @Field: Applied at the field level it allows to describe the name and type of the field as it will be represented in the MongoDB BSON document thus allowing the name and type to be different than the fieldname of the class as well as the property type.

  • @Version: Applied at field level is used for optimistic locking and checked for modification on save operations. The initial value is zero (one for primitive types) which is bumped automatically on every update.

The mapping metadata infrastructure is defined in a separate spring-data-commons project that is technology agnostic. Specific subclasses are using in the MongoDB support to support annotation based metadata. Other strategies are also possible to put in place if there is demand.

Here is an example of a more complex mapping.

@Document
@CompoundIndex(name = "age_idx", def = "{'lastName': 1, 'age': -1}")
public class Person<T extends Address> {

  @Id
  private String id;

  @Indexed(unique = true)
  private Integer ssn;

  @Field("fName")
  private String firstName;

  @Indexed
  private String lastName;

  private Integer age;

  @Transient
  private Integer accountTotal;

  @DBRef
  private List<Account> accounts;

  private T address;

  public Person(Integer ssn) {
    this.ssn = ssn;
  }

  @PersistenceConstructor
  public Person(Integer ssn, String firstName, String lastName, Integer age, T address) {
    this.ssn = ssn;
    this.firstName = firstName;
    this.lastName = lastName;
    this.age = age;
    this.address = address;
  }

  public String getId() {
    return id;
  }

  // no setter for Id.  (getter is only exposed for some unit testing)

  public Integer getSsn() {
    return ssn;
  }

// other getters/setters omitted
}

@Field(targetType=…​) can come in handy when the native MongoDB type inferred by the mapping infrastructure does not match the expected one. Like for BigDecimal, which is represented as String instead of Decimal128, just because earlier versions of MongoDB Server did not have support for it.

public class Balance {

  @Field(targetType = DECIMAL128)
  private BigDecimal value;

  // ...
}

You may even consider your own, custom annotation.

@Target(ElementType.FIELD)
@Retention(RetentionPolicy.RUNTIME)
@Field(targetType = FieldType.DECIMAL128)
public @interface Decimal128 { }

// ...

public class Balance {

  @Decimal128
  private BigDecimal value;

  // ...
}

17.5.3. Customized Object Construction

The mapping subsystem allows the customization of the object construction by annotating a constructor with the @PersistenceConstructor annotation. The values to be used for the constructor parameters are resolved in the following way:

  • If a parameter is annotated with the @Value annotation, the given expression is evaluated and the result is used as the parameter value.

  • If the Java type has a property whose name matches the given field of the input document, then it’s property information is used to select the appropriate constructor parameter to pass the input field value to. This works only if the parameter name information is present in the java .class files which can be achieved by compiling the source with debug information or using the new -parameters command-line switch for javac in Java 8.

  • Otherwise, a MappingException will be thrown indicating that the given constructor parameter could not be bound.

class OrderItem {

  private @Id String id;
  private int quantity;
  private double unitPrice;

  OrderItem(String id, @Value("#root.qty ?: 0") int quantity, double unitPrice) {
    this.id = id;
    this.quantity = quantity;
    this.unitPrice = unitPrice;
  }

  // getters/setters ommitted
}

Document input = new Document("id", "4711");
input.put("unitPrice", 2.5);
input.put("qty",5);
OrderItem item = converter.read(OrderItem.class, input);
The SpEL expression in the @Value annotation of the quantity parameter falls back to the value 0 if the given property path cannot be resolved.

Additional examples for using the @PersistenceConstructor annotation can be found in the MappingMongoConverterUnitTests test suite.

17.5.4. Compound Indexes

Compound indexes are also supported. They are defined at the class level, rather than on individual properties.

Compound indexes are very important to improve the performance of queries that involve criteria on multiple fields

Here’s an example that creates a compound index of lastName in ascending order and age in descending order:

Example 182. Example Compound Index Usage
package com.mycompany.domain;

@Document
@CompoundIndex(name = "age_idx", def = "{'lastName': 1, 'age': -1}")
public class Person {

  @Id
  private ObjectId id;
  private Integer age;
  private String firstName;
  private String lastName;

}

@CompoundIndex is repeatable using @CompoundIndexes as its container.

@Document
@CompoundIndex(name = "cmp-idx-one", def = "{'firstname': 1, 'lastname': -1}")
@CompoundIndex(name = "cmp-idx-two", def = "{'address.city': -1, 'address.street': 1}")
public class Person {

  String firstname;
  String lastname;

  Address address;

  // ...
}

17.5.5. Hashed Indexes

Hashed indexes allow hash based sharding within a sharded cluster. Using hashed field values to shard collections results in a more random distribution. For details, refer to the MongoDB Documentation.

Here’s an example that creates a hashed index for _id:

Example 183. Example Hashed Index Usage
@Document
public class DomainType {

  @HashIndexed @Id String id;

  // ...
}

Hashed indexes can be created next to other index definitions like shown below, in that case both indices are created:

Example 184. Example Hashed Index Usage togehter with simple index
@Document
public class DomainType {

  @Indexed
  @HashIndexed
  String value;

  // ...
}

In case the example above is too verbose, a compound annotation allows to reduce the number of annotations that need to be declared on a property:

Example 185. Example Composed Hashed Index Usage
@Document
public class DomainType {

  @IndexAndHash(name = "idx...")                            (1)
  String value;

  // ...
}

@Indexed
@HashIndexed
@Retention(RetentionPolicy.RUNTIME)
public @interface IndexAndHash {

  @AliasFor(annotation = Indexed.class, attribute = "name") (1)
  String name() default "";
}
1 Potentially register an alias for certain attributes of the meta annotation.

Although index creation via annotations comes in handy for many scenarios cosider taking over more control by setting up indices manually via IndexOperations.

mongoOperations.indexOpsFor(Jedi.class)
  .ensureIndex(HashedIndex.hashed("useTheForce"));

17.5.6. Wildcard Indexes

A WildcardIndex is an index that can be used to include all fields or specific ones based a given (wildcard) pattern. For details, refer to the MongoDB Documentation.

The index can be set up programmatically using WildcardIndex via IndexOperations.

Example 186. Programmatic WildcardIndex setup
mongoOperations
    .indexOps(User.class)
    .ensureIndex(new WildcardIndex("userMetadata"));
db.user.createIndex({ "userMetadata.$**" : 1 }, {})

The @WildcardIndex annotation allows a declarative index setup that can used either with a document type or property.

If placed on a type that is a root level domain entity (one annotated with @Document) , the index resolver will create a wildcard index for it.

Example 187. Wildcard index on domain type
@Document
@WildcardIndexed
public class Product {
	// …
}
db.product.createIndex({ "$**" : 1 },{})

The wildcardProjection can be used to specify keys to in-/exclude in the index.

Example 188. Wildcard index with wildcardProjection
@Document
@WildcardIndexed(wildcardProjection = "{ 'userMetadata.age' : 0 }")
public class User {
    private @Id String id;
    private UserMetadata userMetadata;
}
db.user.createIndex(
  { "$**" : 1 },
  { "wildcardProjection" :
    { "userMetadata.age" : 0 }
  }
)

Wildcard indexes can also be expressed by adding the annotation directly to the field. Please note that wildcardProjection is not allowed on nested paths such as properties. Projections on types annotated with @WildcardIndexed are omitted during index creation.

Example 189. Wildcard index on property
@Document
public class User {
    private @Id String id;

    @WildcardIndexed
    private UserMetadata userMetadata;
}
db.user.createIndex({ "userMetadata.$**" : 1 }, {})

17.5.7. Text Indexes

The text index feature is disabled by default for MongoDB v.2.4.

Creating a text index allows accumulating several fields into a searchable full-text index. It is only possible to have one text index per collection, so all fields marked with @TextIndexed are combined into this index. Properties can be weighted to influence the document score for ranking results. The default language for the text index is English.To change the default language, set the language attribute to whichever language you want (for example,@Document(language="spanish")). Using a property called language or @Language lets you define a language override on a per-document base. The following example shows how to created a text index and set the language to Spanish:

Example 190. Example Text Index Usage
@Document(language = "spanish")
class SomeEntity {

    @TextIndexed String foo;

    @Language String lang;

    Nested nested;
}

class Nested {

    @TextIndexed(weight=5) String bar;
    String roo;
}

17.5.8. Using DBRefs

The mapping framework does not have to store child objects embedded within the document. You can also store them separately and use a DBRef to refer to that document. When the object is loaded from MongoDB, those references are eagerly resolved so that you get back a mapped object that looks the same as if it had been stored embedded within your top-level document.

The following example uses a DBRef to refer to a specific document that exists independently of the object in which it is referenced (both classes are shown in-line for brevity’s sake):

@Document
public class Account {

  @Id
  private ObjectId id;
  private Float total;
}

@Document
public class Person {

  @Id
  private ObjectId id;
  @Indexed
  private Integer ssn;
  @DBRef
  private List<Account> accounts;
}

You need not use @OneToMany or similar mechanisms because the List of objects tells the mapping framework that you want a one-to-many relationship. When the object is stored in MongoDB, there is a list of DBRefs rather than the Account objects themselves. When it comes to loading collections of DBRefs it is advisable to restrict references held in collection types to a specific MongoDB collection. This allows bulk loading of all references, whereas references pointing to different MongoDB collections need to be resolved one by one.

The mapping framework does not handle cascading saves. If you change an Account object that is referenced by a Person object, you must save the Account object separately. Calling save on the Person object does not automatically save the Account objects in the accounts property.

DBRefs can also be resolved lazily. In this case the actual Object or Collection of references is resolved on first access of the property. Use the lazy attribute of @DBRef to specify this. Required properties that are also defined as lazy loading DBRef and used as constructor arguments are also decorated with the lazy loading proxy making sure to put as little pressure on the database and network as possible.

Lazily loaded DBRefs can be hard to debug. Make sure tooling does not accidentally trigger proxy resolution by e.g. calling toString() or some inline debug rendering invoking property getters. Please consider to enable trace logging for org.springframework.data.mongodb.core.convert.DefaultDbRefResolver to gain insight on DBRef resolution.
Lazy loading may require class proxies, that in turn, might need access to jdk internals, that are not open, starting with Java 16+, due to JEP 396: Strongly Encapsulate JDK Internals by Default. For those cases please consider falling back to an interface type (eg. switch from ArrayList to List) or provide the required --add-opens argument.

17.5.9. Using Document References

Using @DocumentReference offers a flexible way of referencing entities in MongoDB. While the goal is the same as when using DBRefs, the store representation is different. DBRef resolves to a document with a fixed structure as outlined in the MongoDB Reference documentation.
Document references, do not follow a specific format. They can be literally anything, a single value, an entire document, basically everything that can be stored in MongoDB. By default, the mapping layer will use the referenced entities id value for storage and retrieval, like in the sample below.

@Document
class Account {

  @Id
  String id;
  Float total;
}

@Document
class Person {

  @Id
  String id;

  @DocumentReference                                   (1)
  List<Account> accounts;
}
Account account = …

tempate.insert(account);                               (2)

template.update(Person.class)
  .matching(where("id").is(…))
  .apply(new Update().push("accounts").value(account)) (3)
  .first();
{
  "_id" : …,
  "accounts" : [ "6509b9e" … ]                        (4)
}
1 Mark the collection of Account values to be referenced.
2 The mapping framework does not handle cascading saves, so make sure to persist the referenced entity individually.
3 Add the reference to the existing entity.
4 Referenced Account entities are represented as an array of their _id values.

The sample above uses an _id-based fetch query ({ '_id' : ?#{#target} }) for data retrieval and resolves linked entities eagerly. It is possible to alter resolution defaults (listed below) using the attributes of @DocumentReference

Table 16. @DocumentReference defaults
Attribute Description Default

db

The target database name for collection lookup.

MongoDatabaseFactory.getMongoDatabase()

collection

The target collection name.

The annotated property’s domain type, respectively the value type in case of Collection like or Map properties, collection name.

lookup

The single document lookup query evaluating placeholders via SpEL expressions using #target as the marker for a given source value. Collection like or Map properties combine individual lookups via an $or operator.

An _id field based query ({ '_id' : ?#{#target} }) using the loaded source value.

sort

Used for sorting result documents on server side.

None by default. Result order of Collection like properties is restored based on the used lookup query on a best-effort basis.

lazy

If set to true value resolution is delayed upon first access of the property.

Resolves properties eagerly by default.

Lazy loading may require class proxies, that in turn, might need access to jdk internals, that are not open, starting with Java 16+, due to JEP 396: Strongly Encapsulate JDK Internals by Default. For those cases please consider falling back to an interface type (eg. switch from ArrayList to List) or provide the required --add-opens argument.

@DocumentReference(lookup) allows defining filter queries that can be different from the _id field and therefore offer a flexible way of defining references between entities as demonstrated in the sample below, where the Publisher of a book is referenced by its acronym instead of the internal id.

@Document
class Book {

  @Id
  ObjectId id;
  String title;
  List<String> author;

  @Field("publisher_ac")
  @DocumentReference(lookup = "{ 'acronym' : ?#{#target} }") (1)
  Publisher publisher;
}

@Document
class Publisher {

  @Id
  ObjectId id;
  String acronym;                                            (1)
  String name;

  @DocumentReference(lazy = true)                            (2)
  List<Book> books;

}
Book document
{
  "_id" : 9a48e32,
  "title" : "The Warded Man",
  "author" : ["Peter V. Brett"],
  "publisher_ac" : "DR"
}
Publisher document
{
  "_id" : 1a23e45,
  "acronym" : "DR",
  "name" : "Del Rey",
  …
}
1 Use the acronym field to query for entities in the Publisher collection.
2 Lazy load back references to the Book collection.

The above snippet shows the reading side of things when working with custom referenced objects. Writing requires a bit of additional setup as the mapping information do not express where #target stems from. The mapping layer requires registration of a Converter between the target document and DocumentPointer, like the one below:

@WritingConverter
class PublisherReferenceConverter implements Converter<Publisher, DocumentPointer<String>> {

	@Override
	public DocumentPointer<String> convert(Publisher source) {
		return () -> source.getAcronym();
	}
}

If no DocumentPointer converter is provided the target reference document can be computed based on the given lookup query. In this case the association target properties are evaluated as shown in the following sample.

@Document
class Book {

  @Id
  ObjectId id;
  String title;
  List<String> author;

  @DocumentReference(lookup = "{ 'acronym' : ?#{acc} }") (1) (2)
  Publisher publisher;
}

@Document
class Publisher {

  @Id
  ObjectId id;
  String acronym;                                        (1)
  String name;

  // ...
}
{
  "_id" : 9a48e32,
  "title" : "The Warded Man",
  "author" : ["Peter V. Brett"],
  "publisher" : {
    "acc" : "DOC"
  }
}
1 Use the acronym field to query for entities in the Publisher collection.
2 The field value placeholders of the lookup query (like acc) is used to form the reference document.

It is also possible to model relational style One-To-Many references using a combination of @ReadonlyProperty and @DocumentReference. This approach allows link types without storing the linking values within the owning document but rather on the referencing document as shown in the example below.

@Document
class Book {

  @Id
  ObjectId id;
  String title;
  List<String> author;

  ObjectId publisherId;                                        (1)
}

@Document
class Publisher {

  @Id
  ObjectId id;
  String acronym;
  String name;

  @ReadOnlyProperty                                            (2)
  @DocumentReference(lookup="{'publisherId':?#{#self._id} }")  (3)
  List<Book> books;
}
Book document
{
  "_id" : 9a48e32,
  "title" : "The Warded Man",
  "author" : ["Peter V. Brett"],
  "publisherId" : 8cfb002
}
Publisher document
{
  "_id" : 8cfb002,
  "acronym" : "DR",
  "name" : "Del Rey"
}
1 Set up the link from Book (reference) to Publisher (owner) by storing the Publisher.id within the Book document.
2 Mark the property holding the references to be readonly. This prevents storing references to individual Books with the Publisher document.
3 Use the #self variable to access values within the Publisher document and in this retrieve Books with matching publisherId.

With all the above in place it is possible to model all kind of associations between entities. Have a look at the non-exhaustive list of samples below to get feeling for what is possible.

Example 191. Simple Document Reference using id field
class Entity {
  @DocumentReference
  ReferencedObject ref;
}
// entity
{
  "_id" : "8cfb002",
  "ref" : "9a48e32" (1)
}

// referenced object
{
  "_id" : "9a48e32" (1)
}
1 MongoDB simple type can be directly used without further configuration.
Example 192. Simple Document Reference using id field with explicit lookup query
class Entity {
  @DocumentReference(lookup = "{ '_id' : '?#{#target}' }") (1)
  ReferencedObject ref;
}
// entity
{
  "_id" : "8cfb002",
  "ref" : "9a48e32"                                        (1)
}

// referenced object
{
  "_id" : "9a48e32"
}
1 target defines the reference value itself.
Example 193. Document Reference extracting the refKey field for the lookup query
class Entity {
  @DocumentReference(lookup = "{ '_id' : '?#{refKey}' }")  (1) (2)
  private ReferencedObject ref;
}
@WritingConverter
class ToDocumentPointerConverter implements Converter<ReferencedObject, DocumentPointer<Document>> {
	public DocumentPointer<Document> convert(ReferencedObject source) {
		return () -> new Document("refKey", source.id);    (1)
	}
}
// entity
{
  "_id" : "8cfb002",
  "ref" : {
    "refKey" : "9a48e32"                                   (1)
  }
}

// referenced object
{
  "_id" : "9a48e32"
}
1 The key used for obtaining the reference value must be the one used during write.
2 refKey is short for target.refKey.
Example 194. Document Reference with multiple values forming the lookup query
class Entity {
  @DocumentReference(lookup = "{ 'firstname' : '?#{fn}', 'lastname' : '?#{ln}' }") (1) (2)
  ReferencedObject ref;
}
// entity
{
  "_id" : "8cfb002",
  "ref" : {
    "fn" : "Josh",           (1)
    "ln" : "Long"            (1)
  }
}

// referenced object
{
  "_id" : "9a48e32",
  "firstname" : "Josh",      (2)
  "lastname" : "Long",       (2)
}
1 Read/wirte the keys fn & ln from/to the linkage document based on the lookup query.
2 Use non id fields for the lookup of the target documents.
Example 195. Document Reference reading from a target collection
class Entity {
  @DocumentReference(lookup = "{ '_id' : '?#{id}' }", collection = "?#{collection}") (2)
  private ReferencedObject ref;
}
@WritingConverter
class ToDocumentPointerConverter implements Converter<ReferencedObject, DocumentPointer<Document>> {
	public DocumentPointer<Document> convert(ReferencedObject source) {
		return () -> new Document("id", source.id)                                   (1)
                           .append("collection", … );                                (2)
	}
}
// entity
{
  "_id" : "8cfb002",
  "ref" : {
    "id" : "9a48e32",                                                                (1)
    "collection" : "…"                                                               (2)
  }
}
1 Read/wirte the keys _id from/to the reference document to use them in the lookup query.
2 The collection name can be read from the reference document using its key.

We know it is tempting to use all kinds of MongoDB query operators in the lookup query and this is fine. But there a few aspects to consider:

  • Make sure to have indexes in place that support your lookup.

  • Mind that resolution requires a server rountrip inducing latency, consider a lazy strategy.

  • A collection of document references is bulk loaded using the $or operator.
    The original element order is restored in memory on a best-effort basis. Restoring the order is only possible when using equality expressions and cannot be done when using MongoDB query operators. In this case results will be ordered as they are received from the store or via the provided @DocumentReference(sort) attribute.

A few more general remarks:

  • Do you use cyclic references? Ask your self if you need them.

  • Lazy document references are hard to debug. Make sure tooling does not accidentally trigger proxy resolution by e.g. calling toString().

  • There is no support for reading document references using reactive infrastructure.

17.5.10. Mapping Framework Events

Events are fired throughout the lifecycle of the mapping process. This is described in the Lifecycle Events section.

Declaring these beans in your Spring ApplicationContext causes them to be invoked whenever the event is dispatched.

17.6. Unwrapping Types

Unwrapped entities are used to design value objects in your Java domain model whose properties are flattened out into the parent’s MongoDB Document.

17.6.1. Unwrapped Types Mapping

Consider the following domain model where User.name is annotated with @Unwrapped. The @Unwrapped annotation signals that all properties of UserName should be flattened out into the user document that owns the name property.

Example 196. Sample Code of unwrapping objects
class User {

    @Id
    String userId;

    @Unwrapped(onEmpty = USE_NULL) (1)
    UserName name;
}

class UserName {

    String firstname;

    String lastname;

}
{
  "_id" : "1da2ba06-3ba7",
  "firstname" : "Emma",
  "lastname" : "Frost"
}
1 When loading the name property its value is set to null if both firstname and lastname are either null or not present. By using onEmpty=USE_EMPTY an empty UserName, with potential null value for its properties, will be created.

For less verbose embeddable type declarations use @Unwrapped.Nullable and @Unwrapped.Empty instead @Unwrapped(onEmpty = USE_NULL) and @Unwrapped(onEmpty = USE_EMPTY). Both annotations are meta-annotated with JSR-305 @javax.annotation.Nonnull to aid with nullability inspections.

It is possible to use complex types within an unwrapped object. However, those must not be, nor contain unwrapped fields themselves.

17.6.2. Unwrapped Types field names

A value object can be unwrapped multiple times by using the optional prefix attribute of the @Unwrapped annotation. By dosing so the chosen prefix is prepended to each property or @Field("…") name in the unwrapped object. Please note that values will overwrite each other if multiple properties render to the same field name.

Example 197. Sample Code of unwrapped object with name prefix
class User {

    @Id
    String userId;

    @Unwrapped.Nullable(prefix = "u_") (1)
    UserName name;

    @Unwrapped.Nullable(prefix = "a_") (2)
    UserName name;
}

class UserName {

    String firstname;

    String lastname;
}
{
  "_id" : "a6a805bd-f95f",
  "u_firstname" : "Jean",             (1)
  "u_lastname" : "Grey",
  "a_firstname" : "Something",        (2)
  "a_lastname" : "Else"
}
1 All properties of UserName are prefixed with u_.
2 All properties of UserName are prefixed with a_.

While combining the @Field annotation with @Unwrapped on the very same property does not make sense and therefore leads to an error. It is a totally valid approach to use @Field on any of the unwrapped types properties.

Example 198. Sample Code unwrapping objects with @Field annotation
public class User {

	@Id
    private String userId;

    @Unwrapped.Nullable(prefix = "u-") (1)
    UserName name;
}

public class UserName {

	@Field("first-name")              (2)
    private String firstname;

	@Field("last-name")
    private String lastname;
}
{
  "_id" : "2647f7b9-89da",
  "u-first-name" : "Barbara",         (2)
  "u-last-name" : "Gordon"
}
1 All properties of UserName are prefixed with u-.
2 Final field names are a result of concatenating @Unwrapped(prefix) and @Field(name).

17.6.3. Query on Unwrapped Objects

Defining queries on unwrapped properties is possible on type- as well as field-level as the provided Criteria is matched against the domain type. Prefixes and potential custom field names will be considered when rendering the actual query. Use the property name of the unwrapped object to match against all contained fields as shown in the sample below.

Example 199. Query on unwrapped object
UserName userName = new UserName("Carol", "Danvers")
Query findByUserName = query(where("name").is(userName));
User user = template.findOne(findByUserName, User.class);
db.collection.find({
  "firstname" : "Carol",
  "lastname" : "Danvers"
})

It is also possible to address any field of the unwrapped object directly using its property name as shown in the snippet below.

Example 200. Query on field of unwrapped object
Query findByUserFirstName = query(where("name.firstname").is("Shuri"));
List<User> users = template.findAll(findByUserFirstName, User.class);
db.collection.find({
  "firstname" : "Shuri"
})
Sort by unwrapped field.

Fields of unwrapped objects can be used for sorting via their property path as shown in the sample below.

Example 201. Sort on unwrapped field
Query findByUserLastName = query(where("name.lastname").is("Romanoff"));
List<User> user = template.findAll(findByUserName.withSort(Sort.by("name.firstname")), User.class);
db.collection.find({
  "lastname" : "Romanoff"
}).sort({ "firstname" : 1 })

Though possible, using the unwrapped object itself as sort criteria includes all of its fields in unpredictable order and may result in inaccurate ordering.

Field projection on unwrapped objects

Fields of unwrapped objects can be subject for projection either as a whole or via single fields as shown in the samples below.

Example 202. Project on unwrapped object.
Query findByUserLastName = query(where("name.firstname").is("Gamora"));
findByUserLastName.fields().include("name");                             (1)
List<User> user = template.findAll(findByUserName, User.class);
db.collection.find({
  "lastname" : "Gamora"
},
{
  "firstname" : 1,
  "lastname" : 1
})
1 A field projection on an unwrapped object includes all of its properties.
Example 203. Project on a field of an unwrapped object.
Query findByUserLastName = query(where("name.lastname").is("Smoak"));
findByUserLastName.fields().include("name.firstname");                   (1)
List<User> user = template.findAll(findByUserName, User.class);
db.collection.find({
  "lastname" : "Smoak"
},
{
  "firstname" : 1
})
1 A field projection on an unwrapped object includes all of its properties.
Query By Example on unwrapped object.

Unwrapped objects can be used within an Example probe just as any other type. Please review the Query By Example section, to learn more about this feature.

Repository Queries on unwrapped objects.

The Repository abstraction allows deriving queries on fields of unwrapped objects as well as the entire object.

Example 204. Repository queries on unwrapped objects.
interface UserRepository extends CrudRepository<User, String> {

	List<User> findByName(UserName username);         (1)

	List<User> findByNameFirstname(String firstname); (2)
}
1 Matches against all fields of the unwrapped object.
2 Matches against the firstname.

Index creation for unwrapped objects is suspended even if the repository create-query-indexes namespace attribute is set to true.

17.6.4. Update on Unwrapped Objects

Unwrapped objects can be updated as any other object that is part of the domain model. The mapping layer takes care of flattening structures into their surroundings. It is possible to update single attributes of the unwrapped object as well as the entire value as shown in the examples below.

Example 205. Update a single field of an unwrapped object.
Update update = new Update().set("name.firstname", "Janet");
template.update(User.class).matching(where("id").is("Wasp"))
   .apply(update).first()
db.collection.update({
  "_id" : "Wasp"
},
{
  "$set" { "firstname" : "Janet" }
},
{ ... }
)
Example 206. Update an unwrapped object.
Update update = new Update().set("name", new Name("Janet", "van Dyne"));
template.update(User.class).matching(where("id").is("Wasp"))
   .apply(update).first()
db.collection.update({
  "_id" : "Wasp"
},
{
  "$set" {
    "firstname" : "Janet",
    "lastname" : "van Dyne",
  }
},
{ ... }
)

17.6.5. Aggregations on Unwrapped Objects

The Aggregation Framework will attempt to map unwrapped values of typed aggregations. Please make sure to work with the property path including the wrapper object when referencing one of its values. Other than that no special action is required.

17.6.6. Index on Unwrapped Objects

It is possible to attach the @Indexed annotation to properties of an unwrapped type just as it is done with regular objects. It is not possible to use @Indexed along with the @Unwrapped annotation on the owning property.

public class User {

	@Id
    private String userId;

    @Unwrapped(onEmpty = USE_NULL)
    UserName name;                    (1)

    // Invalid -> InvalidDataAccessApiUsageException
    @Indexed                          (2)
    @Unwrapped(onEmpty = USE_Empty)
    Address address;
}

public class UserName {

    private String firstname;

    @Indexed
    private String lastname;           (1)
}
1 Index created for lastname in users collection.
2 Invalid @Indexed usage along with @Unwrapped

17.7. Custom Conversions - Overriding Default Mapping

The most trivial way of influencing the mapping result is by specifying the desired native MongoDB target type via the @Field annotation. This allows to work with non MongoDB types like BigDecimal in the domain model while persisting values in native org.bson.types.Decimal128 format.

Example 207. Explicit target type mapping
public class Payment {

  @Id String id; (1)

  @Field(targetType = FieldType.DECIMAL128) (2)
  BigDecimal value;

  Date date; (3)

}
{
  "_id"   : ObjectId("5ca4a34fa264a01503b36af8"), (1)
  "value" : NumberDecimal(2.099), (2)
  "date"   : ISODate("2019-04-03T12:11:01.870Z") (3)
}
1 String id values that represent a valid ObjectId are converted automatically. See How the _id Field is Handled in the Mapping Layer for details.
2 The desired target type is explicitly defined as Decimal128 which translates to NumberDecimal. Otherwise the BigDecimal value would have been truned into a String.
3 Date values are handled by the MongoDB driver itself an are stored as ISODate.

The snippet above is handy for providing simple type hints. To gain more fine-grained control over the mapping process, you can register Spring converters with the MongoConverter implementations, such as the MappingMongoConverter.

The MappingMongoConverter checks to see if any Spring converters can handle a specific class before attempting to map the object itself. To 'hijack' the normal mapping strategies of the MappingMongoConverter, perhaps for increased performance or other custom mapping needs, you first need to create an implementation of the Spring Converter interface and then register it with the MappingConverter.

For more information on the Spring type conversion service, see the reference docs here.

17.7.1. Saving by Using a Registered Spring Converter

The following example shows an implementation of the Converter that converts from a Person object to a org.bson.Document:

import org.springframework.core.convert.converter.Converter;

import org.bson.Document;

public class PersonWriteConverter implements Converter<Person, Document> {

  public Document convert(Person source) {
    Document document = new Document();
    document.put("_id", source.getId());
    document.put("name", source.getFirstName());
    document.put("age", source.getAge());
    return document;
  }
}

17.7.2. Reading by Using a Spring Converter

The following example shows an implementation of a Converter that converts from a Document to a Person object:

public class PersonReadConverter implements Converter<Document, Person> {

  public Person convert(Document source) {
    Person p = new Person((ObjectId) source.get("_id"), (String) source.get("name"));
    p.setAge((Integer) source.get("age"));
    return p;
  }
}

17.7.3. Registering Spring Converters with the MongoConverter

class MyMongoConfiguration extends AbstractMongoClientConfiguration {

	@Override
	public String getDatabaseName() {
		return "database";
	}

	@Override
	protected void configureConverters(MongoConverterConfigurationAdapter adapter) {
		adapter.registerConverter(new com.example.PersonReadConverter());
		adapter.registerConverter(new com.example.PersonWriteConverter());
	}
}

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

@ReadingConverter
public class EmailReadConverter implements Converter<String, Email> {

  public Email convert(String source) {
    return Email.valueOf(source);
  }
}

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:

  • JSR-310 Converters for conversion between java.time, java.util.Date and String types.

Default converters for local temporal types (e.g. LocalDateTime to java.util.Date) rely on system-default timezone settings to convert between those types. You can override the default converter, by registering your own converter.
Converter Disambiguation

Generally, we inspect the Converter implementations for the source and target types they convert from and to. Depending on whether one of those is a type the underlying data access API can handle natively, we register the converter instance as a reading or a writing converter. The following examples show a writing- and a read converter (note the difference is in the order of the qualifiers on Converter):

// Write converter as only the target type is one that can be handled natively
class MyConverter implements Converter<Person, String> { … }

// Read converter as only the source type is one that can be handled natively
class MyConverter implements Converter<String, Person> { … }

17.8. Property Converters - Mapping specific fields

While type-based conversion already offers ways to influence the conversion and representation of certain types within the target store, it has limitations when only certain values or properties of a particular type should be considered for conversion. Property-based converters allow configuring conversion rules on a per-property basis, either declaratively (via @ValueConverter) or programmatically (by registering a PropertyValueConverter for a specific property).

A PropertyValueConverter can transform a given value into its store representation (write) and back (read) as the following listing shows. The additional ValueConversionContext provides additional information, such as mapping metadata and direct read and write methods.

Example 208. A simple PropertyValueConverter
class ReversingValueConverter implements PropertyValueConverter<String, String, ValueConversionContext> {

  @Override
  public String read(String value, ValueConversionContext context) {
    return reverse(value);
  }

  @Override
  public String write(String value, ValueConversionContext context) {
    return reverse(value);
  }
}

You can obtain PropertyValueConverter instances from CustomConversions#getPropertyValueConverter(…) by delegating to PropertyValueConversions, typically by using a PropertyValueConverterFactory to provide the actual converter. Depending on your application’s needs, you can chain or decorate multiple instances of PropertyValueConverterFactory — for example, to apply caching. By default, Spring Data MongoDB uses a caching implementation that can serve types with a default constructor or enum values. A set of predefined factories is available through the factory methods in PropertyValueConverterFactory. You can use PropertyValueConverterFactory.beanFactoryAware(…) to obtain a PropertyValueConverter instance from an ApplicationContext.

You can change the default behavior through ConverterConfiguration.

17.8.1. Declarative Value Converter

The most straight forward usage of a PropertyValueConverter is by annotating properties with the @ValueConverter annotation that defines the converter type:

Example 209. Declarative PropertyValueConverter
class Person {

  @ValueConverter(ReversingValueConverter.class)
  String ssn;
}

17.8.2. Programmatic Value Converter Registration

Programmatic registration registers PropertyValueConverter instances for properties within an entity model by using a PropertyValueConverterRegistrar, as the following example shows. The difference between declarative registration and programmatic registration is that programmatic registration happens entirely outside of the entity model. Such an approach is useful if you cannot or do not want to annotate the entity model.

Example 210. Programmatic PropertyValueConverter registration
PropertyValueConverterRegistrar registrar = new PropertyValueConverterRegistrar();

registrar.registerConverter(Address.class, "street", new PropertyValueConverter() { … }); (1)

// type safe registration
registrar.registerConverter(Person.class, Person::getSsn())                               (2)
  .writing(value -> encrypt(value))
  .reading(value -> decrypt(value));
1 Register a converter for the field identified by its name.
2 Type safe variant that allows to register a converter and its conversion functions.
Dot notation (such as registerConverter(Person.class, "address.street", …)) for nagivating across properties into subdocuments is not supported when registering converters.

17.8.3. MongoDB property value conversions

The preceding sections outlined the purpose an overall structure of PropertyValueConverters. This section focuses on MongoDB specific aspects.

MongoValueConverter and MongoConversionContext

MongoValueConverter offers a pre-typed PropertyValueConverter interface that uses MongoConversionContext.

MongoCustomConversions configuration

By default, MongoCustomConversions can handle declarative value converters, depending on the configured PropertyValueConverterFactory. MongoConverterConfigurationAdapter helps to set up programmatic value conversions or define the PropertyValueConverterFactory to be used.

Example 211. Configuration Sample
MongoCustomConversions.create(configurationAdapter -> {

    SimplePropertyValueConversions valueConversions = new SimplePropertyValueConversions();
    valueConversions.setConverterFactory(…);
    valueConversions.setValueConverterRegistry(new PropertyValueConverterRegistrar()
        .registerConverter(…)
        .buildRegistry());

    configurationAdapter.setPropertyValueConversions(valueConversions);
});

18. Sharding

MongoDB supports large data sets via sharding, a method for distributing data across multiple database servers. Please refer to the MongoDB Documentation to learn how to set up a sharded cluster, its requirements and limitations.

Spring Data MongoDB uses the @Sharded annotation to identify entities stored in sharded collections as shown below.

@Document("users")
@Sharded(shardKey = { "country", "userId" }) (1)
public class User {

	@Id
	Long id;

	@Field("userid")
	String userId;

	String country;
}
1 The properties of the shard key get mapped to the actual field names.

18.1. Sharded Collections

Spring Data MongoDB does not auto set up sharding for collections nor indexes required for it. The snippet below shows how to do so using the MongoDB client API.

MongoDatabase adminDB = template.getMongoDbFactory()
    .getMongoDatabase("admin");                                     (1)

adminDB.runCommand(new Document("enableSharding", "db"));           (2)

Document shardCmd = new Document("shardCollection", "db.users")     (3)
	.append("key", new Document("country", 1).append("userid", 1)); (4)

adminDB.runCommand(shardCmd);
1 Sharding commands need to be run against the admin database.
2 Enable sharding for a specific database if necessary.
3 Shard a collection within the database having sharding enabled.
4 Specify the shard key. This example uses range based sharding.

18.2. Shard Key Handling

The shard key consists of a single or multiple properties that must exist in every document in the target collection. It is used to distribute documents across shards.

Adding the @Sharded annotation to an entity enables Spring Data MongoDB to apply best effort optimisations required for sharded scenarios. This means essentially adding required shard key information, if not already present, to replaceOne filter queries when upserting entities. This may require an additional server round trip to determine the actual value of the current shard key.

By setting @Sharded(immutableKey = true) Spring Data does not attempt to check if an entity shard key was changed.

Please see the MongoDB Documentation for further details. The following list contains which operations are eligible for shard key auto-inclusion:

  • (Reactive)CrudRepository.save(…)

  • (Reactive)CrudRepository.saveAll(…)

  • (Reactive)MongoTemplate.save(…)

19. Client Side Field Level Encryption (CSFLE)

Client Side Encryption is a feature that encrypts data in your application before it is sent to MongoDB. We recommend you get familiar with the concepts, ideally from the MongoDB Documentation to learn more about its capabilities and restrictions before you continue applying Encryption through Spring Data.

Make sure to set the drivers com.mongodb.AutoEncryptionSettings to use client-side encryption. MongoDB does not support encryption for all field types. Specific data types require deterministic encryption to preserve equality comparison functionality.

19.1. Automatic Encryption

MongoDB supports Client-Side Field Level Encryption out of the box using the MongoDB driver with its Automatic Encryption feature. Automatic Encryption requires a JSON Schema that allows to perform encrypted read and write operations without the need to provide an explicit en-/decryption step.

Please refer to the JSON Schema section for more information on defining a JSON Schema that holds encryption information.

To make use of a the MongoJsonSchema it needs to be combined with AutoEncryptionSettings which can be done eg. via a MongoClientSettingsBuilderCustomizer.

@Bean
MongoClientSettingsBuilderCustomizer customizer(MappingContext mappingContext) {
    return (builder) -> {

        // ... keyVaultCollection, kmsProvider, ...

        MongoJsonSchemaCreator schemaCreator = MongoJsonSchemaCreator.create(mappingContext);
        MongoJsonSchema patientSchema = schemaCreator
            .filter(MongoJsonSchemaCreator.encryptedOnly())
            .createSchemaFor(Patient.class);

        AutoEncryptionSettings autoEncryptionSettings = AutoEncryptionSettings.builder()
            .keyVaultNamespace(keyVaultCollection)
            .kmsProviders(kmsProviders)
            .extraOptions(extraOpts)
            .schemaMap(Collections.singletonMap("db.patient", patientSchema.schemaDocument().toBsonDocument()))
            .build();

        builder.autoEncryptionSettings(autoEncryptionSettings);
    };
}

19.2. Explicit Encryption

Explicit encryption uses the MongoDB driver’s encryption library (org.mongodb:mongodb-crypt) to perform encryption and decryption tasks. The @ExplicitEncrypted annotation is a combination of the @Encrypted annotation used for JSON Schema creation and a Property Converter. In other words, @ExplicitEncrypted uses existing building blocks to combine them for simplified explicit encryption support.

Fields annotated with @ExplicitEncrypted are always encrypted as whole. Consider the following example:

@ExplicitEncrypted(…)
String simpleValue;        (1)

@ExplicitEncrypted(…)
Address address;           (2)

@ExplicitEncrypted(…)
List<...> list;            (3)

@ExplicitEncrypted(…)
Map<..., ...> mapOfString; (4)
1 Encrypts the value of the simple type such as a String if not null.
2 Encrypts the entire Address object and all its nested fields as Document. To only encrypt parts of the Address, like Address#street the street field within Address needs to be annotated with @ExplicitEncrypted.
3 Collection-like fields are encrypted as single value and not per entry.
4 Map-like fields are encrypted as single value and not as a key/value entry.

Depending on the encryption algorithm, MongoDB supports certain operations on an encrypted field using its Queryable Encryption feature. To pick a certain algorithm use @ExplicitEncrypted(algorithm), see EncryptionAlgorithms for algorithm constants. Please read the Encryption Types manual for more information on algorithms and their usage.

To perform the actual encryption we require a Data Encryption Key (DEK). Please refer to the MongoDB Documentation for more information on how to set up key management and create a Data Encryption Key. The DEK can be referenced directly via its id or a defined alternative name. The @EncryptedField annotation only allows referencing a DEK via an alternative name. It is possible to provide an EncryptionKeyResolver, which will be discussed later, to any DEK.

Example 212. Reference the Data Encryption Key
@EncryptedField(algorithm=…, altKeyName = "secret-key") (1)
String ssn;
@EncryptedField(algorithm=…, altKeyName = "/name")      (2)
String ssn;
1 Use the DEK stored with the alternative name secret-key.
2 Uses a field reference that will read the actual field value and use that for key lookup. Always requires the full document to be present for save operations. Fields cannot be used in queries/aggregations.

By default, the @ExplicitEncrypted(value=…) attribute references a MongoEncryptionConverter. It is possible to change the default implementation and exchange it with any PropertyValueConverter implementation by providing the according type reference. To learn more about custom PropertyValueConverters and the required configuration, please refer to the Property Converters - Mapping specific fields section.

19.2.1. MongoEncryptionConverter Setup

The converter setup for MongoEncryptionConverter requires a few steps as several components are involved. The bean setup consists of the following:

  1. The ClientEncryption engine

  2. A MongoEncryptionConverter instance configured with ClientEncryption and a EncryptionKeyResolver.

  3. A PropertyValueConverterFactory that uses the registered MongoEncryptionConverter bean.

A side effect of using annotated key resolution is that the @ExplicitEncrypted annotation does not need to specify an alt key name. The EncryptionKeyResolver uses an EncryptionContext providing access to the property allowing for dynamic DEK resolution.

Example 213. Sample MongoEncryptionConverter Configuration
class Config extends AbstractMongoClientConfiguration {

    @Autowired ApplicationContext appContext;

    @Bean
    ClientEncryption clientEncryption() {                                                            (1)
        ClientEncryptionSettings encryptionSettings = ClientEncryptionSettings.builder();
        // …

        return ClientEncryptions.create(encryptionSettings);
    }

    @Bean
    MongoEncryptionConverter encryptingConverter(ClientEncryption clientEncryption) {

        Encryption<BsonValue, BsonBinary> encryption = MongoClientEncryption.just(clientEncryption);
        EncryptionKeyResolver keyResolver = EncryptionKeyResolver.annotated((ctx) -> …);             (2)

        return new MongoEncryptionConverter(encryption, keyResolver);                                (3)
    }

    @Override
    protected void configureConverters(MongoConverterConfigurationAdapter adapter) {

        adapter
            .registerPropertyValueConverterFactory(PropertyValueConverterFactory.beanFactoryAware(appContext)); (4)
    }
}
1 Set up a Encryption engine using com.mongodb.client.vault.ClientEncryption. The instance is stateful and must be closed after usage. Spring takes care of this because ClientEncryption is Closeable.
2 Set up an annotation-based EncryptionKeyResolver to determine the EncryptionKey from annotations.
3 Create the MongoEncryptionConverter.
4 Enable for a PropertyValueConverter lookup from the BeanFactory.

20. Kotlin Support

Kotlin is a statically typed language that targets the JVM (and other platforms) which allows writing concise and elegant code while providing excellent interoperability with existing libraries written in Java.

Spring Data provides first-class support for Kotlin and lets developers write Kotlin applications almost as if Spring Data was a Kotlin native framework.

The easiest way to build a Spring application with Kotlin is to leverage Spring Boot and its dedicated Kotlin support. This comprehensive tutorial will teach you how to build Spring Boot applications with Kotlin using start.spring.io.

20.1. Requirements

Spring Data supports Kotlin 1.3 and requires kotlin-stdlib (or one of its variants, such as kotlin-stdlib-jdk8) and kotlin-reflect to be present on the classpath. Those are provided by default if you bootstrap a Kotlin project via start.spring.io.

20.2. Null Safety

One of Kotlin’s key features is null safety, which cleanly deals with null values at compile time. This makes applications safer through nullability declarations and the expression of “value or no value” semantics without paying the cost of wrappers, such as Optional. (Kotlin allows using functional constructs with nullable values. See this comprehensive guide to Kotlin null safety.)

Although Java does not let you express null safety in its type system, Spring Data API is annotated with JSR-305 tooling friendly annotations declared in the org.springframework.lang package. By default, types from Java APIs used in Kotlin are recognized as platform types, for which null checks are relaxed. Kotlin support for JSR-305 annotations and Spring nullability annotations provide null safety for the whole Spring Data API to Kotlin developers, with the advantage of dealing with null related issues at compile time.

See Null Handling of Repository Methods how null safety applies to Spring Data Repositories.

You can configure JSR-305 checks by adding the -Xjsr305 compiler flag with the following options: -Xjsr305={strict|warn|ignore}.

For Kotlin versions 1.1+, the default behavior is the same as -Xjsr305=warn. The strict value is required take Spring Data API null-safety into account. Kotlin types inferred from Spring API but should be used with the knowledge that Spring API nullability declaration could evolve, even between minor releases and that more checks may be added in the future.

Generic type arguments, varargs, and array elements nullability are not supported yet, but should be in an upcoming release.

20.3. Object Mapping

See Kotlin support for details on how Kotlin objects are materialized.

20.4. Extensions

Kotlin extensions provide the ability to extend existing classes with additional functionality. Spring Data Kotlin APIs use these extensions to add new Kotlin-specific conveniences to existing Spring APIs.

Keep in mind that Kotlin extensions need to be imported to be used. Similar to static imports, an IDE should automatically suggest the import in most cases.

For example, Kotlin reified type parameters provide a workaround for JVM generics type erasure, and Spring Data provides some extensions to take advantage of this feature. This allows for a better Kotlin API.

To retrieve a list of SWCharacter objects in Java, you would normally write the following:

Flux<SWCharacter> characters  = template.find(SWCharacter.class).inCollection("star-wars").all()

With Kotlin and the Spring Data extensions, you can instead write the following:

val characters = template.find<SWCharacter>().inCollection("star-wars").all()
// or (both are equivalent)
val characters : Flux<SWCharacter> = template.find().inCollection("star-wars").all()

As in Java, characters in Kotlin is strongly typed, but Kotlin’s clever type inference allows for shorter syntax.

Spring Data MongoDB provides the following extensions:

  • Reified generics support for MongoOperations, ReactiveMongoOperations, FluentMongoOperations, ReactiveFluentMongoOperations, and Criteria.

  • Type-safe Queries for Kotlin

  • Coroutines extensions for ReactiveFluentMongoOperations.

20.5. Coroutines

Kotlin Coroutines are lightweight threads allowing to write non-blocking code imperatively. On language side, suspend functions provides an abstraction for asynchronous operations while on library side kotlinx.coroutines provides functions like async { } and types like Flow.

Spring Data modules provide support for Coroutines on the following scope:

  • Deferred and Flow return values support in Kotlin extensions

20.5.1. Dependencies

Coroutines support is enabled when kotlinx-coroutines-core, kotlinx-coroutines-reactive and kotlinx-coroutines-reactor dependencies are in the classpath:

Example 214. Dependencies to add in Maven pom.xml
<dependency>
	<groupId>org.jetbrains.kotlinx</groupId>
	<artifactId>kotlinx-coroutines-core</artifactId>
</dependency>

<dependency>
	<groupId>org.jetbrains.kotlinx</groupId>
	<artifactId>kotlinx-coroutines-reactive</artifactId>
</dependency>

<dependency>
	<groupId>org.jetbrains.kotlinx</groupId>
	<artifactId>kotlinx-coroutines-reactor</artifactId>
</dependency>
Supported versions 1.3.0 and above.

20.5.2. How Reactive translates to Coroutines?

For return values, the translation from Reactive to Coroutines APIs is the following:

  • fun handler(): Mono<Void> becomes suspend fun handler()

  • fun handler(): Mono<T> becomes suspend fun handler(): T or suspend fun handler(): T? depending on if the Mono can be empty or not (with the advantage of being more statically typed)

  • fun handler(): Flux<T> becomes fun handler(): Flow<T>

Flow is Flux equivalent in Coroutines world, suitable for hot or cold stream, finite or infinite streams, with the following main differences:

  • Flow is push-based while Flux is push-pull hybrid

  • Backpressure is implemented via suspending functions

  • Flow has only a single suspending collect method and operators are implemented as extensions

  • Operators are easy to implement thanks to Coroutines

  • Extensions allow adding custom operators to Flow

  • Collect operations are suspending functions

  • map operator supports asynchronous operation (no need for flatMap) since it takes a suspending function parameter

Read this blog post about Going Reactive with Spring, Coroutines and Kotlin Flow for more details, including how to run code concurrently with Coroutines.

20.5.3. Repositories

Here is an example of a Coroutines repository:

interface CoroutineRepository : CoroutineCrudRepository<User, String> {

    suspend fun findOne(id: String): User

    fun findByFirstname(firstname: String): Flow<User>

    suspend fun findAllByFirstname(id: String): List<User>
}

Coroutines repositories are built on reactive repositories to expose the non-blocking nature of data access through Kotlin’s Coroutines. Methods on a Coroutines repository can be backed either by a query method or a custom implementation. Invoking a custom implementation method propagates the Coroutines invocation to the actual implementation method if the custom method is suspend-able without requiring the implementation method to return a reactive type such as Mono or Flux.

Note that depending on the method declaration the coroutine context may or may not be available. To retain access to the context, either declare your method using suspend or return a type that enables context propagation such as Flow.

  • suspend fun findOne(id: String): User: Retrieve the data once and synchronously by suspending.

  • fun findByFirstname(firstname: String): Flow<User>: Retrieve a stream of data. The Flow is created eagerly while data is fetched upon Flow interaction (Flow.collect(…)).

  • fun getUser(): User: Retrieve data once blocking the thread and without context propagation. This should be avoided.

Coroutines repositories are only discovered when the repository extends the CoroutineCrudRepository interface.

21. JMX support

The JMX support for MongoDB exposes the results of running the 'serverStatus' command on the admin database for a single MongoDB server instance. It also exposes an administrative MBean, MongoAdmin, that lets you perform administrative operations, such as dropping or creating a database. The JMX features build upon the JMX feature set available in the Spring Framework. See here for more details.

21.1. MongoDB JMX Configuration

Spring’s Mongo namespace lets you enable JMX functionality, as the following example shows:

Example 215. XML schema to configure MongoDB
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:context="http://www.springframework.org/schema/context"
  xmlns:mongo="http://www.springframework.org/schema/data/mongo"
  xsi:schemaLocation="
    http://www.springframework.org/schema/context
    https://www.springframework.org/schema/context/spring-context-3.0.xsd
    http://www.springframework.org/schema/data/mongo
    https://www.springframework.org/schema/data/mongo/spring-mongo-1.0.xsd
    http://www.springframework.org/schema/beans https://www.springframework.org/schema/beans/spring-beans-3.0.xsd">

    <!-- Default bean name is 'mongo' -->
    <mongo:mongo-client host="localhost" port="27017"/>

    <!-- by default look for a Mongo object named 'mongo' -->
    <mongo:jmx/>

    <context:mbean-export/>

    <!-- To translate any MongoExceptions thrown in @Repository annotated classes -->
    <context:annotation-config/>

    <bean id="registry" class="org.springframework.remoting.rmi.RmiRegistryFactoryBean" p:port="1099" />

    <!-- Expose JMX over RMI -->
    <bean id="serverConnector" class="org.springframework.jmx.support.ConnectorServerFactoryBean"
        depends-on="registry"
        p:objectName="connector:name=rmi"
        p:serviceUrl="service:jmx:rmi://localhost/jndi/rmi://localhost:1099/myconnector" />

</beans>

The preceding code exposes several MBeans:

  • AssertMetrics

  • BackgroundFlushingMetrics

  • BtreeIndexCounters

  • ConnectionMetrics

  • GlobalLockMetrics

  • MemoryMetrics

  • OperationCounters

  • ServerInfo

  • MongoAdmin

The following screenshot from JConsole shows the resulting configuration:

jconsole

Appendix

Appendix A: Namespace reference

The <repositories /> Element

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

Table 17. Attributes
Name Description

base-package

Defines the package to be scanned for repository interfaces that extend *Repository (the actual interface is determined by the specific Spring Data module) in auto-detection mode. All packages below the configured package are scanned, too. Wildcards are allowed.

repository-impl-postfix

Defines the postfix to autodetect custom repository implementations. Classes whose names end with the configured postfix are considered as candidates. Defaults to Impl.

query-lookup-strategy

Determines the strategy to be used to create finder queries. See “Query Lookup Strategies” for details. Defaults to create-if-not-found.

named-queries-location

Defines the location to search for a Properties file containing externally defined queries.

consider-nested-repositories

Whether nested repository interface definitions should be considered. Defaults to false.

Appendix B: Populators namespace reference

The <populator /> element

The <populator /> element allows to populate a data store via the Spring Data repository infrastructure.[4]

Table 18. Attributes
Name Description

locations

Where to find the files to read the objects from the repository shall be populated with.

Appendix C: Repository query keywords

Supported query method subject keywords

The following table lists the subject keywords generally supported by the Spring Data repository query derivation mechanism to express the predicate. Consult the store-specific documentation for the exact list of supported keywords, because some keywords listed here might not be supported in a particular store.

Table 19. Query subject keywords
Keyword Description

find…By, read…By, get…By, query…By, search…By, stream…By

General query method returning typically the repository type, a Collection or Streamable subtype or a result wrapper such as Page, GeoResults or any other store-specific result wrapper. Can be used as findBy…, findMyDomainTypeBy… or in combination with additional keywords.

exists…By

Exists projection, returning typically a boolean result.

count…By

Count projection returning a numeric result.

delete…By, remove…By

Delete query method returning either no result (void) or the delete count.

…First<number>…, …Top<number>…

Limit the query results to the first <number> of results. This keyword can occur in any place of the subject between find (and the other keywords) and by.

…Distinct…

Use a distinct query to return only unique results. Consult the store-specific documentation whether that feature is supported. This keyword can occur in any place of the subject between find (and the other keywords) and by.

Supported query method predicate keywords and modifiers

The following table lists the predicate keywords generally supported by the Spring Data repository query derivation mechanism. However, consult the store-specific documentation for the exact list of supported keywords, because some keywords listed here might not be supported in a particular store.

Table 20. Query predicate keywords
Logical keyword Keyword expressions

AND

And

OR

Or

AFTER

After, IsAfter

BEFORE

Before, IsBefore

CONTAINING

Containing, IsContaining, Contains

BETWEEN

Between, IsBetween

ENDING_WITH

EndingWith, IsEndingWith, EndsWith

EXISTS

Exists

FALSE

False, IsFalse

GREATER_THAN

GreaterThan, IsGreaterThan

GREATER_THAN_EQUALS

GreaterThanEqual, IsGreaterThanEqual

IN

In, IsIn

IS

Is, Equals, (or no keyword)

IS_EMPTY

IsEmpty, Empty

IS_NOT_EMPTY

IsNotEmpty, NotEmpty

IS_NOT_NULL

NotNull, IsNotNull

IS_NULL

Null, IsNull

LESS_THAN

LessThan, IsLessThan

LESS_THAN_EQUAL

LessThanEqual, IsLessThanEqual

LIKE

Like, IsLike

NEAR

Near, IsNear

NOT

Not, IsNot

NOT_IN

NotIn, IsNotIn

NOT_LIKE

NotLike, IsNotLike

REGEX

Regex, MatchesRegex, Matches

STARTING_WITH

StartingWith, IsStartingWith, StartsWith

TRUE

True, IsTrue

WITHIN

Within, IsWithin

In addition to filter predicates, the following list of modifiers is supported:

Table 21. Query predicate modifier keywords
Keyword Description

IgnoreCase, IgnoringCase

Used with a predicate keyword for case-insensitive comparison.

AllIgnoreCase, AllIgnoringCase

Ignore case for all suitable properties. Used somewhere in the query method predicate.

OrderBy…

Specify a static sorting order followed by the property path and direction (e. g. OrderByFirstnameAscLastnameDesc).

Appendix D: Repository query return types

Supported Query Return Types

The following table lists the return types generally supported by Spring Data repositories. However, consult the store-specific documentation for the exact list of supported return types, because some types listed here might not be supported in a particular store.

Geospatial types (such as GeoResult, GeoResults, and GeoPage) are available only for data stores that support geospatial queries. Some store modules may define their own result wrapper types.
Table 22. Query return types
Return type Description

void

Denotes no return value.

Primitives

Java primitives.

Wrapper types

Java wrapper types.

T

A unique entity. Expects the query method to return one result at most. If no result is found, null is returned. More than one result triggers an IncorrectResultSizeDataAccessException.

Iterator<T>

An Iterator.

Collection<T>

A Collection.

List<T>

A List.

Optional<T>

A Java 8 or Guava Optional. Expects the query method to return one result at most. If no result is found, Optional.empty() or Optional.absent() is returned. More than one result triggers an IncorrectResultSizeDataAccessException.

Option<T>

Either a Scala or Vavr Option type. Semantically the same behavior as Java 8’s Optional, described earlier.

Stream<T>

A Java 8 Stream.

Streamable<T>

A convenience extension of Iterable that directy exposes methods to stream, map and filter results, concatenate them etc.

Types that implement Streamable and take a Streamable constructor or factory method argument

Types that expose a constructor or ….of(…)/….valueOf(…) factory method taking a Streamable as argument. See Returning Custom Streamable Wrapper Types for details.

Vavr Seq, List, Map, Set

Vavr collection types. See Support for Vavr Collections for details.

Future<T>

A Future. Expects a method to be annotated with @Async and requires Spring’s asynchronous method execution capability to be enabled.

CompletableFuture<T>

A Java 8 CompletableFuture. Expects a method to be annotated with @Async and requires Spring’s asynchronous method execution capability to be enabled.

Slice<T>

A sized chunk of data with an indication of whether there is more data available. Requires a Pageable method parameter.

Page<T>

A Slice with additional information, such as the total number of results. Requires a Pageable method parameter.

GeoResult<T>

A result entry with additional information, such as the distance to a reference location.

GeoResults<T>

A list of GeoResult<T> with additional information, such as the average distance to a reference location.

GeoPage<T>

A Page with GeoResult<T>, such as the average distance to a reference location.

Mono<T>

A Project Reactor Mono emitting zero or one element using reactive repositories. Expects the query method to return one result at most. If no result is found, Mono.empty() is returned. More than one result triggers an IncorrectResultSizeDataAccessException.

Flux<T>

A Project Reactor Flux emitting zero, one, or many elements using reactive repositories. Queries returning Flux can emit also an infinite number of elements.

Single<T>

A RxJava Single emitting a single element using reactive repositories. Expects the query method to return one result at most. If no result is found, Mono.empty() is returned. More than one result triggers an IncorrectResultSizeDataAccessException.

Maybe<T>

A RxJava Maybe emitting zero or one element using reactive repositories. Expects the query method to return one result at most. If no result is found, Mono.empty() is returned. More than one result triggers an IncorrectResultSizeDataAccessException.

Flowable<T>

A RxJava Flowable emitting zero, one, or many elements using reactive repositories. Queries returning Flowable can emit also an infinite number of elements.


1. Kristina Chodorow. MongoDB - The Definitive Guide. O’Reilly Media, 2013
2. Uses UTC zone offset. Configure via MongoConverterConfigurationAdapter
3. Uses UTC zone offset. Configure via MongoConverterConfigurationAdapter