Spring Data MongoDB - Reference Documentation

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 .

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.

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

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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 ).

Reactive auditing enabled through @EnableReactiveMongoAuditing . @EnableMongoAuditing no longer registers ReactiveAuditingEntityCallback .

Reactive SpEL support in @Query and @Aggregation query methods.

Aggregation hints via AggregationOptions.builder().hint(bson).build() .

Extension Function KProperty.asPath() to render property references into a property path representation.

Server-side JavaScript aggregation expressions $function and $accumulator via ScriptOperators .

Compatibility with MongoDB 4.2 deprecating eval , group and geoNear Template API methods.

Extended SpEL aggregation support for MongoDB 3.4 and MongoDB 4.0 operators (see Spring Expression Support in Projection Expressions ).

Querydsl support for reactive repositories via ReactiveQuerydslPredicateExecutor .

Reactive GridFS support .

Aggregation framework support via repository query methods.

Declarative reactive transactions using @Transactional .

Template API delete by entity considers the version property in delete queries.

Repository deletes now throw OptimisticLockingFailureException when a versioned entity cannot be deleted.

Support Range<T> in repository between queries.

Changed behavior of Reactive/MongoOperations#count now limiting the range to count matches within by passing on offset & limit to the server.

Support of array filters in Update operations.

JSON Schema generation from domain types.

SpEL support in for expressions in @Indexed .

Support for Hashed Indexes .

Annotation-based Collation support through @Document and @Query .

Type-safe Queries for Kotlin .

Kotlin extension methods accepting KClass are deprecated now in favor of reified methods.

Kotlin [kotlin.coroutines] support.

MongoDB 4.0 Transaction support and a MongoDB-specific transaction manager implementation.

Default sort specifications for repository query methods using @Query(sort=…) .

findAndReplace support through imperative and reactive Template APIs.

Deprecation of dropDups in @Indexed and @CompoundIndex as MongoDB server 3.0 and newer do not support dropDups anymore.

Extended support for MongoDB 3.2 and MongoDB 3.4 aggregation operators (see Supported Aggregation Operations ).

Support for partial filter expression when creating indexes.

Publishing lifecycle events when loading or converting DBRef instances.

Added any-match mode for Query By Example.

Support for $caseSensitive and $diacriticSensitive text search.

Support for GeoJSON Polygon with hole.

Performance improvements by bulk-fetching DBRef instances.

Multi-faceted aggregations using $facet , $bucket , and $bucketAuto with Aggregation .

The following annotations have been enabled to build your own composed annotations: @Document , @Id , @Field , @Indexed , @CompoundIndex , @GeoSpatialIndexed , @TextIndexed , @Query , and @Meta .

Support for [projections] in repository query methods.

Support for [query-by-example] .

Out-of-the-box support for java.util.Currency in object mapping.

Support for the bulk operations introduced in MongoDB 2.6.

Upgrade to Querydsl 4.

Assert compatibility with MongoDB 3.0 and MongoDB Java Driver 3.2.

Spring Data MongoDB 3.x requires the MongoDB Java Driver 4.x.
The 4.0 MongoDB Java Driver does no longer support certain features that have already been deprecated in one of the last minor versions. Some of the changes affect the initial setup configuration as well as compile/runtime features. We summarized the most typical changes one might encounter.

Things to keep in mind when using the 4.0 driver:

Index name generation has become a driver-internal operation. Spring Data MongoDB still uses the 2.x schema to generate names.

Some Exception messages differ between the generation 2 and 3 servers as well as between the MMap.v1 and WiredTiger storage engines.

Depending on the application one of the mongodb-driver-sync , mongodb-driver-reactivestreams artifacts is is required next to the mandatory mongodb-driver-core . It is possible to combine the sync and reactive drivers in one application if needed.

7.2. Java Configuration

Table 1. Java API changes

MongoClientFactoryBean

Creates com.mongodb.client.MongoClient instead of com.mongodb.MongoClient
Uses MongoClientSettings instead of MongoClientOptions .

MongoDataIntegrityViolationException

Uses WriteConcernResult instead of WriteResult .

BulkOperationException

Uses MongoBulkWriteException and com.mongodb.bulk.BulkWriteError instead of BulkWriteException and com.mongodb.BulkWriteError

ReactiveMongoClientFactoryBean

Uses com.mongodb.MongoClientSettings instead of com.mongodb.async.client.MongoClientSettings

ReactiveMongoClientSettingsFactoryBean

Now produces com.mongodb.MongoClientSettings instead of com.mongodb.async.client.MongoClientSettings

AbstractMongoClientConfiguration , AbstractReactiveMongoConfiguration

Configuration methods use parameter injection instead of calling local methods to avoid the need for cglib proxies

MongoClientOptionsFactoryBean

MongoClientSettingsFactoryBean

Creating a com.mongodb.MongoClientSettings .

AbstractMongoConfiguration

AbstractMongoClientConfiguration
(Available since 2.1)

Using com.mongodb.client.MongoClient .

MongoDbFactory#getLegacyDb()

SimpleMongoDbFactory

SimpleMongoClientDbFactory
(Available since 2.1)

MapReduceOptions#getOutputType()

MapReduceOptions#getMapReduceAction()

Returns MapReduceAction instead of MapReduceCommand.OutputType .

Meta|Query maxScan & snapshot

<mongo:mongo-client />

Used to create a com.mongodb.MongoClient

Now exposes a com.mongodb.client.MongoClient

<mongo:mongo-client replica-set="…" />

Was a comma delimited list of replica set members (host/port)

Now defines the replica set name.
Use <mongo:client-settings cluster-hosts="…" /> instead

<mongo:db-factory writeConcern="…" />

NONE, NORMAL, SAFE, FSYNC_SAFE, REPLICAS_SAFE, MAJORITY

W1, W2, W3, UNAKNOWLEDGED, AKNOWLEDGED, JOURNALED, MAJORITY

<mongo:db-factory mongo-ref="…" />

<mongo:db-factory mongo-client-ref="…" />

Referencing a com.mongodb.client.MongoClient .

<mongo:mongo-client credentials="…" />

<mongo:mongo-client credential="…" />

Single authentication data instead of list.

<mongo:client-options />

<mongo:client-settings />

See com.mongodb.MongoClientSettings for details.

<mongo:db-factory mongo-client-ref="…" />

Replacement for <mongo:db-factory mongo-ref="…" />

<mongo:db-factory connection-string="…" />

Replacement for uri and client-uri .

<mongo:mongo-client connection-string="…" />

Replacement for uri and client-uri .

<mongo:client-settings />

Namespace element for com.mongodb.MongoClientSettings .

7.4.1. Auto Index Creation

Annotation based index creation is now turned OFF by default and needs to be enabled eg. when relying on @GeoSpatialIndexed . Please refer to Index Creation on how to create indexes programmatically.

Example 1. Enable Auto Index Creation

XML Namespace

<mongo:mapping-converter auto-index-creation="true" />    (1)

Java Config

@Configuration public class Config extends AbstractMongoClientConfiguration { @Override protected boolean autoIndexCreation() { (2) return true; // ... Programmatic MongoDatabaseFactory dbFactory = new SimpleMongoClientDatabaseFactory(...); DefaultDbRefResolver dbRefResolver = new DefaultDbRefResolver(dbFactory); MongoMappingContext mappingContext = new MongoMappingContext(); mappingContext.setAutoIndexCreation(true); (3) // ... mappingContext.afterPropertiesSet(); MongoTemplate template = new MongoTemplate(dbFactory, new MappingMongoConverter(dbRefResolver, mappingContext)); Override autoIndexCreation via AbstractMongoClientConfiguration or AbstractReactiveMongoClientConfiguration. Set the flag on MongoMappingContext. 7.4.2. UUID Types The MongoDB UUID representation can now be configured with different formats. This has to be done via MongoClientSettings as shown in the snippet below. Example 2. UUid Codec Configuration @Configuration public class Config extends AbstractMongoClientConfiguration { @Override public void configureClientSettings(MongoClientSettings.Builder builder) { builder.uuidRepresentation(UuidRepresentation.STANDARD); // ... 7.4.3. Deferred MongoDatabase lookup in ReactiveMongoDatabaseFactory ReactiveMongoDatabaseFactory now returns Mono<MongoDatabase> instead of MongoDatabase to allow access to the Reactor Subscriber context to enable context-specific routing functionality. This change affects ReactiveMongoTemplate.getMongoDatabase() and ReactiveMongoTemplate.getCollection() so both methods must follow deferred retrieval. Unresolved directive in index.adoc - include::../../../../../spring-data-commons/src/main/asciidoc/dependencies.adoc[leveloffset=+1] Unresolved directive in index.adoc - include::../../../../../spring-data-commons/src/main/asciidoc/repositories.adoc[leveloffset=+1] 8.1. 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. 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. 9.1. 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: 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. Add the following to the pom.xml files dependencies element: <dependencies>  <dependency> <groupId>org.springframework.data</groupId> <artifactId>spring-data-mongodb</artifactId> <version>3.3.0-SNAPSHOT</version> </dependency> </dependencies> 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/libs-milestone</url> </repository> </repositories> @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 9.3.1. 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 3. 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 4. 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; 9.3.2. 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 5. 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">  <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 6. 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> <mongo:mongo-client id="replicaSetMongo" replica-set="rs0"> <mongo:client-settings cluster-hosts="127.0.0.1:27017,localhost:27018" /> </mongo:mongo-client> 9.3.3. 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. public class MongoConfiguration { public @Bean 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: @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))); In order to use authentication with XML-based configuration, use the credential attribute on the <mongo-client> element. 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: [email protected]:mo_res:bw6},[email protected]@database → m0ng0%40dmin:mo_res%3Abw6%7D%2CQsdxx%[email protected] 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 by using the mongo-ref attribute 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: <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> 9.4. 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. 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 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. 9.4.1. Instantiating MongoTemplate You can use Java to create and register an instance of MongoTemplate, as the following example shows: Example 8. Registering a com.mongodb.client.MongoClient object and enabling Spring’s exception translation support @Configuration public class AppConfig { public @Bean MongoClient mongoClient() { return MongoClients.create("mongodb://localhost:27017"); public @Bean MongoTemplate mongoTemplate() { return new MongoTemplate(mongoClient(), "mydatabase"); 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. <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> Other optional properties that you might like to set when creating a MongoTemplate are the default WriteResultCheckingPolicy, WriteConcern, and ReadPreference properties. 9.4.2. 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. 9.4.3. 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. 9.4.4. 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(); 9.5. 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: 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 query syntax used in the preceding example is explained in more detail in the section “Querying Documents”. 9.5.1. 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: A property or field annotated with @Id (org.springframework.data.annotation.Id) maps to the _id field. A property or field without an annotation but named id maps to the _id field. 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. 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 9. @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) 9.5.2. 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 10. Type mapping public class Sample { Contact value; public abstract class Contact { … } public 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 11. Defining a type alias for an Entity @TypeAlias("pers") class Person { 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 public 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: Example 12. Configuring a custom MongoTypeMapper with Spring Java Config class CustomMongoTypeMapper extends DefaultMongoTypeMapper { //implement custom type mapping here @Override public MappingMongoConverter mappingMongoConverter() throws Exception { MappingMongoConverter mmc = super.mappingMongoConverter(); mmc.setTypeMapper(customTypeMapper()); return mmc; @Bean public MongoTypeMapper customTypeMapper() { return new CustomMongoTypeMapper(); Note that the preceding example extends the AbstractMongoClientConfiguration class and overrides the bean definition of the MappingMongoConverter where we configured our custom MongoTypeMapper. The following example shows how to use XML to configure a custom MongoTypeMapper: Example 13. Configuring a custom MongoTypeMapper with XML <mongo:mapping-converter type-mapper-ref="customMongoTypeMapper"/> <bean name="customMongoTypeMapper" class="com.bubu.mongo.CustomMongoTypeMapper"/> 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 14. 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); 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. 9.5.4. 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 $inc operator: Example 15. Updating documents by using the MongoTemplate import static org.springframework.data.mongodb.core.query.Criteria.where; import static org.springframework.data.mongodb.core.query.Query; import static org.springframework.data.mongodb.core.query.Update; WriteResult wr = mongoTemplate.updateMulti(new Query(where("accounts.accountType").is(Account.Type.SAVINGS)), new Update().inc("accounts.$.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. 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 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 // { $push : { "category" : { "$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"); 9.5.5. “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(); <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(); 9.5.7. 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 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) { $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) The 1st $set stage calculates a new field average based on the average of the tests field. The 2nd $set stage calculates a new field grade based on the average field calculated by the first aggregation stage. The pipeline is run on the students collection and uses Student for the aggregation field mapping. Apply the update to all matching documents in the collection. 9.5.8. 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 17. 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) Use the fluent update API with the domain type given for mapping the query and deriving the collection name or just use MongoOperations#findAndReplace. The actual match query mapped against the given domain type. Provide sort, fields and collation settings via the query. Additional optional hook to provide options other than the defaults, like upsert. An optional projection type used for mapping the operation result. If none given the initial domain type is used. 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. 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) 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. 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. Remove the first three documents in the GOT collection. Unlike <3>, documents do not get deleted in a batch but one by one. 9.5.10. 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: 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) 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. 9.6. 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 18. 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.). 9.6.1. 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 19. 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 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. 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 $near. Query limit (int limit) used to limit the size of the returned results to the provided limit (used for paging) Query skip (int skip) used to skip the provided number of documents in the results (used for paging) Query with (Sort sort) used to provide sort definition for the results MongoDB supports projecting fields returned by a query. A projection can include and exclude fields (the _id field is always included unless explicitly excluded) based on their name. Example 20. Selecting result fields public class Person { @Id String id; String firstname; @Field("last_name") String lastname; Address address; query.fields().include("lastname"); (1) query.fields().exclude("id").include("lastname") (2) query.fields().include("address") (3) query.fields().include("address.city") (4) Starting with MongoDB 4.4 you can use aggregation expressions for field projections as shown below: Example 21. Computing result fields using expressions query.fields() .project(MongoExpression.create("'$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"); Use a native expression. The used field name must refer to field names within the database document. Assign the field name to which the expression result is projected. The resulting field name is not mapped against the domain model. Use an AggregationExpression. Other than native MongoExpression, field names are mapped to the ones used in the domain model. Use SpEL along with an AggregationExpression to invoke expression functions. Field names are mapped to the ones used in the domain model. 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. 9.6.3. 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 22. Retrieving distinct values template.query(Person.class) (1) .distinct("lastname") (2) .all(); (3) 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. 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 23. Retrieving strongly typed distinct values template.query(Person.class) (1) .distinct("lastname") (2) .as(String.class) (3) .all(); (4) Select distinct values of the lastname field. The fieldname is mapped according to the domain types property declaration, taking potential @Field annotations into account. 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. 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. 9.6.4. 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. @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").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(); 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. 9.6.5. 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 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))); Repository method definition using the commons type allows calling it with both the GeoJSON and the legacy format. Use GeoJSON type to make use of $geometry operator. Note that GeoJSON polygons need to define a closed ring. Use the legacy format $polygon operator. Though syntactically different the server is fine accepting both no matter what format the target Document within the collection is using. To avoid a serious headache make sure to set the Metric to the desired unit of measure which ensures the distance to be calculated correctly. In other words: "_id" : ObjectId("5c10f3735d38908db52796a5"), "name" : "Penn Station", "location" : { "type" : "Point", "coordinates" : [ -73.99408, 40.75057 ] } "_id" : ObjectId("5c10f3735d38908db52796a6"), "name" : "10gen Office", "location" : { "type" : "Point", "coordinates" : [ -73.99171, 40.738868 ] } "_id" : ObjectId("5c10f3735d38908db52796a9"), "name" : "City Bakery ", "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] } "_id" : ObjectId("5c10f3735d38908db52796aa"), "name" : "Splash Bar", "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] } "_id" : ObjectId("5c10f3735d38908db52796ab"), "name" : "Momofuku Milk Bar", "location" : { "type" : "Point", "coordinates" : [ -73.985839, 40.731698 ] } Fetching all Documents within a 400 Meter radius from [-73.99171, 40.738868] would look like this using GeoJSON: Example 24. GeoNear with GeoJSON "$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) Now, when using legacy coordinate pairs one operates upon Radians as discussed before. So we use Metrics#KILOMETERS when constructing the `$geoNear command. The Metric makes sure the distance multiplier is set correctly. Example 25. GeoNear with Legacy Coordinate Pairs "$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) GeoJSON Jackson Modules By using the [core.web], Spring Data registers additional Jackson Moduless to the ObjectMapper for de-/serializing common Spring Data domain types. Please refer to the [core.web.basic.jackson-mappers] 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 9.6.6. Full-text Queries Since version 2.6 of MongoDB, you can run full-text queries by using the $text operator. Methods and operations specific to full-text queries are available in TextQuery and TextCriteria. When doing full text search, see the MongoDB reference for its behavior and limitations. Full-text Search Before you can actually use full-text search, you must set up the search index correctly. See Text Index for more detail on how to create index structures. The following example shows how to set up a full-text search: db.foo.createIndex( title : "text", content : "text" weights : { title : 3 A query searching for coffee cake can be defined and run as follows: Example 26. Full Text Query Query query = TextQuery .queryText(new TextCriteria().matchingAny("coffee", "cake")); List<Document> page = template.find(query, Document.class); Query query = TextQuery .queryText(new TextCriteria().matchingAny("coffee", "cake")) .sortByScore() (1) .includeScore(); (2) List<Document> page = template.find(query, Document.class); Use the score property for sorting results by relevance which triggers .sort({'score': {'$meta': 'textScore'}}). Use TextQuery.includeScore() to include the calculated relevance in the resulting Document. // 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. 9.6.7. 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 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. 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. Specify whether to compare numeric strings as numbers or as strings. Specify whether the collation should consider whitespace and punctuation as base characters for purposes of comparison. Specify whether strings with diacritics sort from back of the string, such as with some French dictionary ordering. 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 german = Collation.of("de"); template.createCollection(Person.class, CollectionOptions.just(collation)); template.indexOps(Person.class).ensureIndex(new Index("name", Direction.ASC).collation(german));

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 28. 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);

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);

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

Example 30. 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)
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"))
Dynamic collation depending on 2nd method argument.
Apply the Collation method parameter to the query.
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.
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 31. 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 }
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.
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.
properties is related to a schema object that describes an object type. It contains property-specific schema constraints.
firstname specifies constraints for the firsname field inside the document. Here, it is a string-based properties element declaring
possible field values.
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 32. 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)

Configure required properties either directly as shown here or with more details as in 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(…).
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 33. 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 34. 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));

    '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)
_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.
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 35. 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 36. 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 37. 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;
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
To combine derived encryption settings with AutoEncryptionSettings in a Spring Boot application use the 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);
object
Object
required, additionalProperties, properties, minProperties, maxProperties, patternProperties
array
any array except byte[]
uniqueItems, additionalItems, items, minItems, maxItems
string
String
minLength, maxLentgth, pattern
int, Integer
multipleOf, minimum, exclusiveMinimum, maximum, exclusiveMaximum
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)
(none)
objectId
ObjectId
(none)
java.util.Date
(none)
timestamp
BsonTimestamp
(none)
regex
java.util.regex.Pattern
(none)
9.6.8. 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:
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();

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.
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();
9.6.9. 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:
// 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)
isEqualTo() is an infix extension function with receiver type KProperty<T> that returns Criteria.
For bitwise operators, pass a lambda argument where you call one of the methods of Criteria.BitwiseCriteriaOperators.
To construct nested properties, use the / character (overloaded operator div).
9.6.10. 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.
Unresolved directive in reference/mongodb.adoc - include::../../../../../../spring-data-commons/src/main/asciidoc/query-by-example.adoc[leveloffset=+1]
:leveloffset: +1
9.7. Running an Example

The following example shows how to query by example when using a repository (of Person objects, in this case):
Example 38. 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));
9.8. 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 39. 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.
9.9. 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.
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)

9.10. 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.
9.10.1. 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.
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)

Store the script using 'echo' as its name. The given name identifies the script and allows calling it later.
Run the script with name 'echo' using the provided parameters.
9.12. 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.
9.12.1. 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);
9.13. 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.
9.13.1. 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.
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.
9.13.2. Supported Aggregation Operations

The MongoDB Aggregation Framework provides the following types of aggregation operations:
At the time of this writing, we provide support for the following Aggregation Operations in Spring Data MongoDB:
Table 10. Aggregation Operations currently supported by Spring Data MongoDB
Pipeline Aggregation Operators
bucket, bucketAuto, count, facet, geoNear, graphLookup, group, limit, lookup, match, project, rand, replaceRoot, skip, sort, unwind
Set Aggregation Operators
setEquals, setIntersection, setUnion, setDifference, setIsSubset, anyElementTrue, allElementsTrue
Group/Accumulator Aggregation Operators
addToSet, covariancePop, covarianceSamp, expMovingAvg, first, last, max, min, avg, push, sum, count (*), stdDevPop, stdDevSamp
Arithmetic Aggregation Operators
abs, 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, 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, in, indexOfArray, isArray, range, reverseArray, reduce, size, slice, zip
Literal Operators
literal
Date Aggregation Operators
dayOfYear, dayOfMonth, dayOfWeek, year, month, week, hour, minute, second, millisecond, dateAdd, dateDiff, dateToString, dateFromString, dateFromParts, dateToParts, isoDayOfWeek, isoWeek, isoWeekYear
Variable Operators
Conditional Aggregation Operators
cond, ifNull, switch
Type Aggregation Operators
Convert Aggregation Operators
convert, degreesToRadians, toBool, toDate, toDecimal, toDouble, toInt, toLong, toObjectId, toString
Object Aggregation Operators
objectToArray, mergeObjects
Script Aggregation Operators
function, accumulator
9.13.3. 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 40. 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: $thing1}}
project("a","b").and("thing1").as("thing2")
// generates {$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")
9.13.4. 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 42. 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 43. 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
$bucketAuto section of the MongoDB Aggregation Framework reference documentation.
Multi-faceted Aggregation
Multiple aggregation pipelines can be used to create multi-faceted aggregations that characterize data across multiple dimensions (or facets) within a single aggregation stage. Multi-faceted aggregations provide multiple filters and categorizations to guide data browsing and analysis. A common implementation of faceting is how many online retailers provide ways to narrow down search results by applying filters on product price, manufacturer, size, and other factors.
You can define a FacetOperation by using the facet() method of the Aggregation class. You can customize it with multiple aggregation pipelines by using the and() method. Each sub-pipeline has its own field in the output document where its results are stored as an array of documents.
Sub-pipelines can project and filter input documents prior to grouping. Common use cases include extraction of date parts or calculations before categorization. The following listing shows facet operation examples:
Example 44. Facet operation examples
// generates {$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"))
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 45. Sort by count example
// generates { $sortByCount: "$country" }
sortByCount("country");
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 11. Supported SpEL transformations
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:
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.
Use the project operation to select the tags field (which is an array of strings) from the input collection.
Use the unwind operation to generate a new document for each tag within the tags array.
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).
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.
Use the sort operation to sort the resulting list of tags by their occurrence count in descending order.
Call the aggregate method on MongoTemplate to let MongoDB perform the actual aggregation operation, with the created Aggregation as an argument.
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:
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.
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).
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.
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.
Sort the resulting list of StateStats by their state name in ascending order in the sort operation.
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:
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".
Sort the intermediate result by the id-reference of the previous group operation in addition to the "totalPop" field in ascending order.
Filter the intermediate result by using a match operation which accepts a Criteria query as an argument.
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 46. 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"));

9.14. 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();
9.14.1. 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"));
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]]
9.14.3. Methods for Working with a Collection

The following example shows how to create a collection:
Example 47. Working with collections by using MongoTemplate
MongoCollection<Document> collection = null;
if (!mongoTemplate.getCollectionNames().contains("MyNewCollection")) {
    collection = mongoTemplate.createCollection("MyNewCollection");
mongoTemplate.dropCollection("MyNewCollection");
9.15. 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.
9.15.1. 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.
9.16. 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.
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.
Unresolved directive in reference/mongodb.adoc - include::../../../../../../spring-data-commons/src/main/asciidoc/entity-callbacks.adoc[leveloffset=+1]
:leveloffset: +2
Spring Data MongoDB uses the EntityCallback API for its auditing support and reacts on the following callbacks.
Table 12. Supported Entity Callbacks
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
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
10.1. 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.
10.2. 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.
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;
10.3. 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:
Example 48. JavaConfig setup for a GridFsTemplate




    

class GridFsConfiguration extends AbstractMongoClientConfiguration {
  // … further configuration omitted
  @Bean
  public GridFsTemplate gridFsTemplate() {
    return new GridFsTemplate(mongoDbFactory(), mappingMongoConverter());
<?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 50. 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 51. Using GridFsTemplate to query for files
class GridFsClient {
  @Autowired
  GridFsOperations operations;
  @Test
  public void findFilesInGridFs() {
    GridFSFindIterable result = operations.find(query(whereFilename().is("filename.txt")))
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 52. Using GridFsTemplate to read files
class GridFsClient {
  @Autowired
  GridFsOperations operations;
  @Test
  public void readFilesFromGridFs() {
    GridFsResources[] txtFiles = operations.getResources("*.txt");
10.4.  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.
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.
10.4.1. 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 53. 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)

Starting the container intializes the resources and starts Task instances for already registered SubscriptionRequest instances. Requests added after startup are ran immediately.
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.
Set the collection to listen to.
Provide an optional filter for documents to receive.
Set the message listener to publish incoming Messages to.
Register the request. The returned Subscription can be used to check the current Task state and cancel it to free resources.
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.
10.4.2. 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 54. 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 55. 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();
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.
10.5.1. 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 56. 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)

Starting the container initializes the resources and starts Task instances for already registered SubscriptionRequest instances. Requests added after startup are ran immediately.
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.
Set the collection to listen to and provide additional options through ChangeStreamOptions.
Register the request. The returned Subscription can be used to check the current Task state and cancel it to free resources.
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.
10.5.2. 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 57. Change Streams emitting ChangeStreamEvent
Flux<ChangeStreamEvent<User>> flux = reactiveTemplate.changeStream(User.class) (1)
    .watchCollection("people")
    .filter(where("age").gte(38))                                              (2)
    .listen();                                                                 (3)

The event target type the underlying document should be converted to. Leave this out to receive raw results without conversion.
Use an aggregation pipeline or just a query Criteria to filter events.
Obtain a Flux of change stream events. The ChangeStreamEvent#getBody() is converted to the requested domain type from (2).
10.5.3. 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 58. Resume a Change Stream
Flux<ChangeStreamEvent<User>> resumed = template.changeStream(User.class)
    .watchCollection("people")
    .resumeAt(Instant.now().minusSeconds(1)) (1)
    .listen();

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.
10.6. 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 59. 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.
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.
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 60. 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)

11.2. Reactive ClientSession support

The reactive counterpart uses the same building blocks as the imperative one, as the following example shows:
Example 61. 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)

Use ReactiveMongoOperation methods as before. The ClientSession is obtained and applied automatically.
Make sure to close the ClientSession.
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.
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 62. 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)

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 63. 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", // ...
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 64. 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", // ...
@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.
Using the plain MongoDB reactive driver API a delete within a transactional flow may look like this.
Example 65. 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)
If the operations completes exceptionally, we need to stop the transaction and preserve the error.
Or of course, commit the changes in case of success. Still preserving the operations result.
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 66. 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();

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 67. Transactions with ReactiveMongoTransactionManager
@Configuration
static class Config extends AbstractMongoClientConfiguration {
    @Bean
    ReactiveMongoTransactionManager transactionManager(ReactiveDatabaseFactory 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", …));
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.
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:
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:
template.withSession(session)
    .execute(action -> {
        action.count(query(where("state").is("active")), Step.class)
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: ${MONGO_HOME}/bin/mongod
To create a Spring project in STS, go to File → New → Spring Template Project → Simple Spring Utility Project and press Yes when prompted. Then enter a project and a package name, such as org.spring.mongodb.example.
Then add the following to the pom.xml dependencies section.
<dependencies>
  <!-- other dependency elements omitted -->
  <dependency>
    <groupId>org.springframework.data</groupId>
    <artifactId>spring-data-mongodb</artifactId>
    <version>3.3.0-SNAPSHOT</version>
  </dependency>
  <dependency>
    <groupId>org.mongodb</groupId>
    <artifactId>mongodb-driver-reactivestreams</artifactId>
    <version>4.3.2</version>
  </dependency>
  <dependency>
    <groupId>io.projectreactor</groupId>
    <artifactId>reactor-core</artifactId>
    <version>2020.0.11</version>
  </dependency>
</dependencies>
  @Override
  public String toString() {
    return "Person [id=" + id + ", name=" + name + ", age=" + age + "]";
Then create an application to run, as follows:
public class ReactiveMongoApp {
  private static final Logger log = LoggerFactory.getLogger(ReactiveMongoApp.class);
  public static void main(String[] args) throws Exception {
    CountDownLatch latch = new CountDownLatch(1);
    ReactiveMongoTemplate mongoOps = new ReactiveMongoTemplate(MongoClients.create(), "database");
    mongoOps.insert(new Person("Joe", 34))
          .flatMap(p -> mongoOps.findOne(new Query(where("name").is("Joe")), Person.class))
          .doOnNext(person -> log.info(person.toString()))
          .flatMap(person -> mongoOps.dropCollection("person"))
          .doOnComplete(latch::countDown)
          .subscribe();
    latch.await();
Running the preceding class produces the following output:
2016-09-20 14:56:57,373 DEBUG .index.MongoPersistentEntityIndexCreator: 124 - Analyzing class class example.ReactiveMongoApp$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
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 68. 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 69. 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;
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:[email protected]"), "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.
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 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 70. 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");
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.
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.
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.
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));
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 71. 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 72. 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.
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 73. Using ReactiveGridFsTemplate to query for files
class ReactiveGridFsClient {
  @Autowired
  ReactiveGridFsTemplate operations;
  @Test
  public Flux<GridFSFile> findFilesInGridFs() {
    return operations.find(query(whereFilename().is("filename.txt")))
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 74. Using ReactiveGridFsTemplate to read files
class ReactiveGridFsClient {
  @Autowired
  ReactiveGridFsOperations operations;
  @Test
  public void readFilesFromGridFs() {
     Flux<ReactiveGridFsResource> txtFiles = operations.getResources("*.txt");
14.2. 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 75. Sample Person entity
public class Person {
  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 76. 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 use Java configuration for a repository:
Example 77. Java configuration for repositories
@Configuration
@EnableMongoRepositories
class ApplicationConfig extends AbstractMongoClientConfiguration {
  @Override
  protected String getDatabaseName() {
    return "e-store";
  @Override
  protected String getMappingBasePackage() {
    return "com.oreilly.springdata.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/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 79. Paging access to Person entities
@RunWith(SpringRunner.class)
@ContextConfiguration
public class PersonRepositoryTests {
    @Autowired PersonRepository repository;
    @Test
    public 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.
14.3. 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 80. 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)
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}.
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.
Shows that you can query based on properties that are not primitive types. Throws IncorrectResultSizeDataAccessException if more than one match is found.
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.
Uses a Java 8 Stream that reads and converts individual elements while iterating the stream.
findByAgeBetween(int from, int to)

findByAgeBetween(Range<Integer> range)
{"age" : {"$gt" : from, "$lt" : to}}

lower / upper bounds ($gt / $gte & $lt / $lte) according to Range
findByAgeIn(Collection ages)
{"age" : {"$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}
findByFirstnameNot(String name)
{"firstname" : {"$ne" : name}}
findByLocationNear(Point point)
{"location" : {"$near" : [x,y]}}
findByLocationNear(Point point, Distance max)
{"location" : {"$near" : [x,y], "$maxDistance" : max}}
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 }}
14.3.1. 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 81. 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)
Using a return type of List retrieves and returns all matching documents before actually deleting them.
A numeric return type directly removes the matching documents, returning the total number of documents removed.
A single domain type result retrieves and removes the first matching document.
Same as in 3 but wrapped in an Optional type.
14.3.2. 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 82. 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 $code, as the following example shows:
Example 83. Using Distance with Metrics
Point point = new Point(43.7, 48.8);
Distance distance = new Distance(200, Metrics.KILOMETERS);
… = repository.findByLocationNear(point, distance);
// {'location' : {'$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.)
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);
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.
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.
14.3.4. Sorting Query Method results

MongoDB repositories allow various approaches to define sorting order. Let’s take a look at the following example:
Example 84. 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)
Static sorting derived from method name. SortByAgeDesc results in { age : -1 } for the sort parameter.
Dynamic sorting using a method argument. Sort.by(DESC, "age") creates { age : -1 } for the sort parameter.
Static sorting via Query annotation. Sort parameter applied as stated in the sort attribute.
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 }.
14.3.5. 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.
You should make sure to sanitize strings before passing them to the query to avoid 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());
14.3.6. 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
14.3.7. 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);
Unresolved directive in reference/mongo-repositories.adoc - include::../../../../../../spring-data-commons/src/main/asciidoc/repository-projections.adoc[leveloffset=+2]
14.3.8. 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 85. Aggregating Repository Method




    

public interface PersonRepository extends CrudReppsitory<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 to group first names by lastname in the Person collection returning these as PersonAggregate.
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.
Replaces ?0 with the given value for property for a dynamic aggregation pipeline.
$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.
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.
Map the result of an aggregation returning a single Document to an instance of a desired SumValue target type.
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>.
Obtain the raw AggregationResults mapped to the generic target wrapper type SumValue or org.bson.Document.
Like in <6>, a single value can be directly obtained from multiple result Documents.
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.
public interface PersonRepository extends CrudReppsitory<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 { }
public interface PersonRepository extends CrudReppsitory<Person, String> {
  @AllowDiskUse
  @Aggregation("{ $group: { _id : $lastname, names : { $addToSet : $firstname } } }")
  List<PersonAggregate> groupByLastnameAndFirstnames();
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.
14.4. 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();
This chapter describes the specialties for reactive repository support for MongoDB. This chapter builds on the core repository support explained in [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:
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 86. Sample Person entity
public class Person {
  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 87. 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)
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}.
The method shows a query for all people with the given firstname once the firstname is emitted by the given Publisher.
Use Pageable to pass offset and sorting parameters to the database.
Find a single entity for the given criteria. It completes with IncorrectResultSizeDataAccessException on non-unique results.
Unless <4>, the first entity is always emitted even if the query yields more result documents.
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.
@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 89. Sorted access to Person entities
public class PersonRepositoryTests {
    @Autowired ReactivePersonRepository repository;
    @Test
    public void sortsElementsCorrectly() {
      Flux<Person> persons = repository.findAll(Sort.by(new Order(ASC, "lastname")));
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 90. 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 91. Advanced Near queries
public 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 $code, as the following example shows:
Example 92. Using Distance with Metrics
Point point = new Point(43.7, 48.8);
Distance distance = new Distance(200, Metrics.KILOMETERS);
… = repository.findByLocationNear(point, distance);
// {'location' : {'$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.)
public 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);
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 93. The Gateway to Reactive Querydsl - The ReactiveQuerydslPredicateExecutor




    

public 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 94. Reactive Querydsl Respository Declaration
public interface PersonRepository extends ReactiveMongoRepository<Person, String>, ReactiveQuerydslPredicateExecutor<Person> {
   // additional query methods go here
Unresolved directive in index.adoc - include::../../../../../spring-data-commons/src/main/asciidoc/auditing.adoc[leveloffset=+1]
:leveloffset: +1
Since Spring Data MongoDB 1.4, auditing can be enabled by annotating a configuration class with the @EnableMongoAuditing annotation, as the followign example shows:
Example 95. Activating auditing using JavaConfig
@Configuration
@EnableMongoAuditing
class Config {
  @Bean
  public AuditorAware<AuditableUser> myAuditorProvider() {
      return new AuditorAwareImpl();
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 activate auditing functionality via XML, add the Spring Data Mongo auditing namespace element to your configuration, as the following example shows:
Example 96. Activating auditing by using XML configuration
<mongo:auditing mapping-context-ref="customMappingContext" auditor-aware-ref="yourAuditorAwareImpl"/>
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 97. Activating reactive auditing using JavaConfig
@Configuration
@EnableReactiveMongoAuditing
class Config {
  @Bean
  public ReactiveAuditorAware<AuditableUser> myAuditorProvider() {
      return new AuditorAwareImpl();
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.
Unresolved directive in reference/mapping.adoc - include::../../../../../../spring-data-commons/src/main/asciidoc/object-mapping.adoc[leveloffset=+1]
17.1. 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.
17.1.1. 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.
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 a field is annotated with @MongoId(FieldType.…) in the Java class it will be attempted to convert the value to the declared FieldType.
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.
17.2. 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 [mapping-explicit-converters] for further details.
The following provides samples of each available type conversion:
Table 15. Type
java.util.UUID (Legacy UUID)
native
{"uuid" : { "$binary" : "MEaf1CFQ6lSphaa3b9AtlA==", "$type" : "03" }}
native
{"date" : ISODate("2019-11-12T23:00:00.809Z")}
ObjectId
native
{"_id" : ObjectId("5707a2690364aba3136ab870")}
Array, List, BasicDBList
native
{"cookies" : [ … ]}
boolean, Boolean
native
{"active" : true}
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" }
converter
{"website" : "https://projects.spring.io/spring-data-mongodb/" }
Locale
converter
{"locale : "en_US" }
char, Character
converter
{"char" : "a" }
NamedMongoScript
converter

{"_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"}
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 ] ]
17.3. 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 uses Spring’s Java-based configuration:
Example 98. @Configuration class to configure MongoDB mapping support




    

@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>();
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.
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.
Spring’s MongoDB namespace lets you enable mapping functionality in XML, as the following example shows:
Example 99. XML schema to configure MongoDB mapping support
<?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>
17.4. 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 100. Example domain object
package com.mycompany.domain;
@Document
public class Person {
  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.
17.4.1. 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.
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.
Example 101. 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);
  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);
@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> {
  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;
  // ...
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.
  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);
Here’s an example that creates a compound index of lastName in ascending order and age in descending order:
Example 103. Example Compound Index Usage
package com.mycompany.domain;
@Document
@CompoundIndex(name = "age_idx", def = "{'lastName': 1, 'age': -1}")
public class Person {
  private ObjectId id;
  private Integer age;
  private String firstName;
  private String lastName;
@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;
  // ...
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 104. 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 105. 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 106. 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 "";
17.4.6. 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 107. Programmatic WildcardIndex setup
mongoOperations
    .indexOps(User.class)
    .ensureIndex(new WildcardIndex("userMetadata"));
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 108. Wildcard index on domain type
@Document
@WildcardIndexed
public class Product {
@Document
@WildcardIndexed(wildcardProjection = "{ 'userMetadata.age' : 0 }")
public class User {
    private @Id String id;
    private UserMetadata userMetadata;
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 110. Wildcard index on property
@Document
public class User {
    private @Id String id;
    @WildcardIndexed
    private UserMetadata userMetadata;
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 111. 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;
17.4.8. 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):
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.
17.4.9. 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.
template.update(Person.class)
  .matching(where("id").is(…))
  .apply(new Update().push("accounts").value(account)) (3)
  .first();

The mapping framework does not handle cascading saves, so make sure to persist the referenced entity individually.
Add the reference to the existing entity.
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
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.
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.
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.
  @Field("publisher_ac")
  @DocumentReference(lookup = "{ 'acronym' : ?#{#target} }") (1)
  Publisher publisher;
@Document
class Publisher {
  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",
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.
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.
  @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"
Set up the link from Book (reference) to Publisher (owner) by storing the Publisher.id within the Book document.
Mark the property holding the references to be readonly.
This prevents storing references to individual Books with the Publisher document.
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 112. Simple Document Reference using id field
class Entity {
  @DocumentReference
  ReferencedObject ref;
class Entity {
  @DocumentReference(lookup = "{ '_id' : '?#{#target}' }") (1)
  ReferencedObject ref;
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)
class Entity {
  @DocumentReference(lookup = "{ 'firstname' : '?#{fn}', 'lastname' : '?#{ln}' }") (1) (2)
  ReferencedObject ref;
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)
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:
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.
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.
17.4.10. 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.
17.5. 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.
17.5.1. 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 117. Sample Code of unwrapping objects




    

class User {
    String userId;
    @Unwrapped(onEmpty = USE_NULL) (1)
    UserName name;
class UserName {
    String firstname;
    String lastname;
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.
17.5.2. 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 118. Sample Code of unwrapped object with name prefix
class User {
    String userId;
    @Unwrapped.Nullable(prefix = "u_") (1)
    UserName name;
    @Unwrapped.Nullable(prefix = "a_") (2)
    UserName name;
class UserName {
    String firstname;
    String lastname;
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 119. Sample Code unwrapping objects with @Field annotation
public class User {
    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;
17.5.3. 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 120. Query on unwrapped object
UserName userName = new UserName("Carol", "Danvers")
Query findByUserName = query(where("name").is(userName));
User user = template.findOne(findByUserName, User.class);
It is also possible to address any field of the unwrapped object directly using its property name as shown in the snippet below.
Example 121. Query on field of unwrapped object
Query findByUserFirstName = query(where("name.firstname").is("Shuri"));
List<User> users = template.findAll(findByUserFirstName, User.class);
Sort by unwrapped field.

Fields of unwrapped objects can be used for sorting via their property path as shown in the sample below.
Example 122. 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);
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 123. 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);

Query findByUserLastName = query(where("name.lastname").is("Smoak"));
findByUserLastName.fields().include("name.firstname");                   (1)
List<User> user = template.findAll(findByUserName, User.class);

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 125. Repository queries on unwrapped objects.
interface UserRepository extends CrudRepository<User, String> {
	List<User> findByName(UserName username);         (1)
	List<User> findByNameFirstname(String firstname); (2)
17.5.4. 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 126. 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()
Update update = new Update().set("name", new Name("Janet", "van Dyne"));
template.update(User.class).matching(where("id").is("Wasp"))
   .apply(update).first()
17.5.5. 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.
17.5.6. 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.
    // Invalid -> InvalidDataAccessApiUsageException
    @Indexed                          (2)
    @Unwrapped(onEmpty = USE_Empty)
    Address address;
public class UserName {
    private String firstname;
    @Indexed
    private String lastname;           (1)
17.6. 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 128. 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)
String id values that represent a valid ObjectId are converted automatically. See How the _id Field is Handled in the Mapping Layer
for details.
The desired target type is explicitly defined as Decimal128 which translates to NumberDecimal. Otherwise the
BigDecimal value would have been truned into a String.
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.
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;
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;
	@Override
	protected void configureConverters(MongoConverterConfigurationAdapter adapter) {
		adapter.registerConverter(new com.example.PersonReadConverter());
		adapter.registerConverter(new com.example.PersonWriteConverter());
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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.
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.
    .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);

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.
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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
[kotlin.coroutines] extensions for ReactiveFluentMongoOperations.
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.
19.1. MongoDB JMX Configuration

Spring’s Mongo namespace lets you enable JMX functionality, as the following example shows:
Example 129. 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>
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