mongodb.adoc

MongoDB support

The MongoDB support contains a wide range of features:

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

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

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

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

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

• Persistence and mapping lifecycle events.

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

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

• QueryDSL integration to support type-safe queries.

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

• GeoSpatial integration.

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

Getting Started

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

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

To create a Spring project in STS:

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

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

   <dependencies>
   
     <!-- other dependency elements omitted -->
   
     <dependency>
       <groupId>org.springframework.data</groupId>
       <artifactId>spring-data-mongodb</artifactId>
       <version>{version}</version>
     </dependency>
   
   </dependencies>

3. Change the version of Spring in the pom.xml to be

   <spring.framework.version>{springVersion}</spring.framework.version>

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

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

The repository is also browseable here.

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

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

Then you can create a Person class to persist:

package org.spring.mongodb.example;

public class Person {

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

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

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

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

You also need a main application to run:

package org.spring.mongodb.example;

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

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

import com.mongodb.client.MongoClients;

public class MongoApp {

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

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

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

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

    mongoOps.dropCollection("person");
  }
}

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

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

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

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

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

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

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

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

Examples Repository

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

Connecting to MongoDB with Spring

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

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

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 1. 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 2. Registering a com.mongodb.client.MongoClient object by using Spring’s MongoClientFactoryBean and enabling Spring’s exception translation support

@Configuration
public class AppConfig {

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

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

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 3. XML schema to configure MongoDB

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

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

</beans>

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

Example 4. XML schema to configure a com.mongodb.client.MongoClient object with MongoClientSettings

<beans>

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

</beans>

The following example shows a configuration using replica sets:

Example 5. XML schema to configure a com.mongodb.client.MongoClient object with Replica Sets

<mongo:mongo-client id="replicaSetMongo" replica-set="rs0">
    <mongo:client-settings cluster-hosts="127.0.0.1:27017,localhost:27018" />
</mongo:mongo-client>

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.

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

Registering a MongoDatabaseFactory Instance by Using Java-based Metadata

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

@Configuration
public class MongoConfiguration {

  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.

Note

Username and password credentials used in XML-based configuration must be URL-encoded when these contain reserved characters, such as :, %, @, or ,. The following example shows encoded credentials: m0ng0@dmin:mo_res:bw6},Qsdxx@admin@database → m0ng0%40dmin:mo_res%3Abw6%7D%2CQsdxx%40admin@database See section 2.2 of RFC 3986 for further details.

Registering a MongoDatabaseFactory Instance by Using XML-based Metadata

The mongo namespace provides a convenient way to create a SimpleMongoClientDbFactory, as compared to using the <beans/> namespace, as shown in the following example:

<mongo:db-factory dbname="database">

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>

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.

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

The mapping between MongoDB documents and domain classes is done by delegating to an implementation of the MongoConverter interface. Spring provides MappingMongoConverter, but you can also write your own converter. See “[mongo.custom-converters]” 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.

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

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

Instantiating MongoTemplate

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

Example 6. 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");
  }
}

There are several overloaded constructors of MongoTemplate:

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

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

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

You can also configure a MongoTemplate by using Spring’s XML <beans/> schema, as the following example shows:

<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.

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

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.

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.

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();
  }
}

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:

Note	MongoOperations is the interface that MongoTemplate implements.

package org.spring.example;

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

import java.util.List;

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

import com.mongodb.client.MongoClients;

public class MongoApp {

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

  public static void main(String[] args) {

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

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

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

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

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

    // Delete
    mongoOps.remove(p);

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


    mongoOps.dropCollection(Person.class);
  }
}

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

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

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

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

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

How the _id Field is Handled in the Mapping Layer

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

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

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

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

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

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

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

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

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

Example 7. @MongoId mapping

public class PlainStringId {
  @MongoId String id; (1)
}

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

public class StringToObjectId {
  @MongoId(FieldType.OBJECT_ID) String id; (3)
}

1. The id is treated as String without further conversion.

2. The id is treated as ObjectId.

3. The id is treated as ObjectId if the given String is a valid ObjectId hex, otherwise as String. Corresponds to @Id usage.

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 8. 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 9. Defining a type alias for an Entity

@TypeAlias("pers")
class Person {

}

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

Warning

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 10. Configuring a custom MongoTypeMapper with Spring Java Config

class CustomMongoTypeMapper extends DefaultMongoTypeMapper {
  //implement custom type mapping here
}

@Configuration
class SampleMongoConfiguration extends AbstractMongoClientConfiguration {

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

  @Bean
  @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 11. Configuring a custom MongoTypeMapper with XML

<mongo:mapping-converter type-mapper-ref="customMongoTypeMapper"/>

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

Methods for Saving and Inserting Documents

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

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

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

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

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

Example 12. Inserting and retrieving documents using the MongoTemplate

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

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

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

The following insert and save operations are available:

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

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

A similar set of insert operations is also available:

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

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

Into Which Collection Are My Documents Saved?

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

Inserting or Saving Individual Objects

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

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

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

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

Inserting Several Objects in a Batch

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

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

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 13. 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.

Warning

updateFirst does not support ordering. Please use findAndModify to apply Sort.

Methods in the Update Class

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

The Update class contains the following methods:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

“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();

Warning

upsert does not support ordering. Please use findAndModify to apply Sort.

Finding and Upserting Documents in a Collection

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Aggregation Pipeline Updates

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

The update can consist of the following stages:

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

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

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

Example 14. Update Aggregation

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

template.update(Student.class)                                              (3)
    .apply(update)
    .all();                                                                 (4)

db.students.update(                                                         (3)
   { },
   [
     { $set: { average : { $avg: "$tests" } } },                            (1)
     { $set: { grade: { $switch: {                                          (2)
                           branches: [
                               { case: { $gte: [ "$average", 90 ] }, then: "A" },
                               { case: { $gte: [ "$average", 80 ] }, then: "B" },
                               { case: { $gte: [ "$average", 70 ] }, then: "C" },
                               { case: { $gte: [ "$average", 60 ] }, then: "D" }
                           ],
                           default: "F"
     } } } }
   ],
   { multi: true }                                                          (4)
)

1. The 1st $set stage calculates a new field average based on the average of the tests field.

2. The 2nd $set stage calculates a new field grade based on the average field calculated by the first aggregation stage.

3. The pipeline is run on the students collection and uses Student for the aggregation field mapping.

4. Apply the update to all matching documents in the collection.

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 15. Find and Replace Documents

Optional<User> result = template.update(Person.class)      (1)
    .matching(query(where("firstame").is("Tom")))          (2)
    .replaceWith(new Person("Dick"))
    .withOptions(FindAndReplaceOptions.options().upsert()) (3)
    .as(User.class)                                        (4)
    .findAndReplace();                                     (5)

1. Use the fluent update API with the domain type given for mapping the query and deriving the collection name or just use MongoOperations#findAndReplace.

2. The actual match query mapped against the given domain type. Provide sort, fields and collation settings via the query.

3. Additional optional hook to provide options other than the defaults, like upsert.

4. An optional projection type used for mapping the operation result. If none given the initial domain type is used.

5. Trigger the actual processing. Use findAndReplaceValue to obtain the nullable result instead of an Optional.

Important

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.

Methods for Removing Documents

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

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

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

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

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

template.findAllAndRemove(new Query().limit(3), "GOT");                     (5)

1. Remove a single entity specified by its _id from the associated collection.

2. Remove all documents that match the criteria of the query from the GOT collection.

3. Remove the first three documents in the GOT collection. Unlike <2>, the documents to remove are identified by their _id, running the given query, applying sort, limit, and skip options first, and then removing all at once in a separate step.

4. Remove all documents matching the criteria of the query from the GOT collection. Unlike <3>, documents do not get deleted in a batch but one by one.

5. Remove the first three documents in the GOT collection. Unlike <3>, documents do not get deleted in a batch but one by one.

Optimistic Locking

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

@Document
class Person {

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

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

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

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

template.save(tmp); // throws OptimisticLockingFailureException                       (4)

1. Intially insert document. version is set to 0.

2. Load the just inserted document. version is still 0.

3. Update the document with version = 0. Set the lastname and bump version to 1.

4. Try to update the previously loaded document that still has version = 0. The operation fails with an OptimisticLockingFailureException, as the current version is 1.

Important

Optimistic Locking requires to set the WriteConcern to ACKNOWLEDGED. Otherwise OptimisticLockingFailureException can be silently swallowed.

Note	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).

Note

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.

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 16. 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.).

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 17. Querying for documents using the MongoTemplate

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

// ...

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

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

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

Methods for the Criteria Class

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Methods for the Query class

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

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

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

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

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

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

Methods for Querying for Documents

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

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

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

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

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

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

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 18. Retrieving distinct values

template.query(Person.class)  (1)
  .distinct("lastname")       (2)
  .all();                     (3)

1. Query the Person collection.

2. Select distinct values of the lastname field. The field name is mapped according to the domain types property declaration, taking potential @Field annotations into account.

3. Retrieve all distinct values as a List of Object (due to no explicit result type being specified).

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

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

Example 19. Retrieving strongly typed distinct values

template.query(Person.class)  (1)
  .distinct("lastname")       (2)
  .as(String.class)           (3)
  .all();                     (4)

1. Query the collection of Person.

2. Select distinct values of the lastname field. The fieldname is mapped according to the domain types property declaration, taking potential @Field annotations into account.

3. Retrieved values are converted into the desired target type — in this case, String. It is also possible to map the values to a more complex type if the stored field contains a document.

4. Retrieve all distinct values as a List of String. If the type cannot be converted into the desired target type, this method throws a DataAccessException.

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.

Note	Using GeoSpatial queries requires attention when used within MongoDB transactions, see [mongo.transactions.behavior].

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

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

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

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

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

  public String getName() {
    return name;
  }

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

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

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

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

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

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

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

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

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

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

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

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

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

Geo-near Queries

Warning

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(); Domain type used to identify the target collection and potential query mapping. Target type containing a dis field of type Number.

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

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

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

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

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

GeoJSON Support

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

GeoJSON Types in Domain Classes

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

public class Store {

	String id;

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

GeoJSON Types in Repository Query Methods

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

public interface StoreRepository extends CrudRepository<Store, String> {

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

}

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

/*
 * {
 *   "location" : {
 *     "$geoWithin" : {
 *        "$polygon" : [ [-73.992514,40.758934] , [-73.961138,40.760348] , [-73.991658,40.730006] ]
 *     }
 *   }
 * }
 */
repo.findByLocationWithin(                              (4)
  new Polygon(
    new Point(-73.992514, 40.758934),
    new Point(-73.961138, 40.760348),
    new Point(-73.991658, 40.730006)));

1. Repository method definition using the commons type allows calling it with both the GeoJSON and the legacy format.

2. Use GeoJSON type to make use of $geometry operator.

3. Note that GeoJSON polygons need to define a closed ring.

4. Use the legacy format $polygon operator.

Metrics and Distance calculation

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

NearQuery.near(new Point(-73.99171, 40.738868))

{
  "$geoNear": {
    //...
    "near": [-73.99171, 40.738868]
  }
}

NearQuery.near(new GeoJsonPoint(-73.99171, 40.738868))

{
  "$geoNear": {
    //...
    "near": { "type": "Point", "coordinates": [-73.99171, 40.738868] }
  }
}

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

Warning

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

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

In other words:

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

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

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

Example 20. GeoNear with GeoJSON

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

Returning the following 3 Documents:

{
    "_id" : ObjectId("5c10f3735d38908db52796a6"),
    "name" : "10gen Office",
    "location" : { "type" : "Point", "coordinates" : [ -73.99171, 40.738868 ] }
    "distance" : 0.0 (3)
}
{
    "_id" : ObjectId("5c10f3735d38908db52796a9"),
    "name" : "City Bakery ",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
    "distance" : 69.3582262492474 (3)
}
{
    "_id" : ObjectId("5c10f3735d38908db52796aa"),
    "name" : "Splash Bar",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
    "distance" : 69.3582262492474 (3)
}

1. Maximum distance from center point in Meters.

2. GeoJSON always operates upon a sphere.

3. Distance from center point in Meters.

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

Example 21. GeoNear with Legacy Coordinate Pairs

{
    "$geoNear": {
        "maxDistance": 0.0000627142377, (1)
        "distanceMultiplier": 6378.137, (2)
        "num": 10,
        "near": [-73.99171, 40.738868],
        "spherical":true, (3)
        "key": "location",
        "distanceField": "distance"
    }
}

Returning the 3 Documents just like the GeoJSON variant:

{
    "_id" : ObjectId("5c10f3735d38908db52796a6"),
    "name" : "10gen Office",
    "location" : { "type" : "Point", "coordinates" : [ -73.99171, 40.738868 ] }
    "distance" : 0.0 (4)
}
{
    "_id" : ObjectId("5c10f3735d38908db52796a9"),
    "name" : "City Bakery ",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
    "distance" : 0.0693586286032982 (4)
}
{
    "_id" : ObjectId("5c10f3735d38908db52796aa"),
    "name" : "Splash Bar",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
    "distance" : 0.0693586286032982 (4)
}

1. Maximum distance from center point in Radians.

2. The distance multiplier so we get Kilometers as resulting distance.

3. Make sure we operate on a 2d_sphere index.

4. Distance from center point in Kilometers - take it times 1000 to match Meters of the GeoJSON variant.

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 22. Full Text Query

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

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

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

Example 23. Full Text Query - Sort by Score

Query query = TextQuery
  .queryText(new TextCriteria().matchingAny("coffee", "cake"))
  .sortByScore() (1)
  .includeScore(); (2)

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

1. Use the score property for sorting results by relevance which triggers .sort({'score': {'$meta': 'textScore'}}).

2. Use TextQuery.includeScore() to include the calculated relevance in the resulting Document.

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

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

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

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

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

Collations

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

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

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

  .numericOrderingEnabled()                      (3)

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

  .forwardDiacriticSort()                        (5)

  .normalizationEnabled();                       (6)

1. Collation requires a locale for creation. This can be either a string representation of the locale, a Locale (considering language, country, and variant) or a CollationLocale. The locale is mandatory for creation.

2. Collation strength defines comparison levels that denote differences between characters. You can configure various options (case-sensitivity, case-ordering, and others), depending on the selected strength.

3. Specify whether to compare numeric strings as numbers or as strings.

4. Specify whether the collation should consider whitespace and punctuation as base characters for purposes of comparison.

5. Specify whether strings with diacritics sort from back of the string, such as with some French dictionary ordering.

6. Specify whether to check whether text requires normalization and whether to perform normalization.

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

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

Note	Annotated collations will not be used when a collection is auto created by MongoDB on first interaction. This would require additional store interaction delaying the entire process. Please use MongoOperations.createCollection for those cases.

Collation french = Collation.of("fr");
Collation german = Collation.of("de");

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

template.indexOps(Person.class).ensureIndex(new Index("name", Direction.ASC).collation(german));

Note	MongoDB uses simple binary comparison if no collation is specified (Collation.simple()).

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

Example 24. Using collation with find

Collation collation = Collation.of("de");

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

List<Person> results = template.find(query, Person.class);

Example 25. Using collation with aggregate

Collation collation = Collation.of("de");

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

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

AggregationResults<TagCount> results = template.aggregate(aggregation, "tags", TagCount.class);

Warning

Indexes are only used if the collation used for the operation matches the index collation.

./mongo-json-schema.adoc

[mongo.repositories] support Collations via the collation attribute of the @Query annotation.

Example 26. Collation support for Repositories

public interface PersonRepository extends MongoRepository<Person, String> {

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

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

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

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

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

  @Query(collation = "{ 'locale' : 'en_US' }")
  List<Person> findByFirstname(String firstname, @Nullable Collation collation); (6)
}

1. Static collation definition resulting in { 'locale' : 'en_US' }.

2. Static collation definition resulting in { 'locale' : 'en_US' }.

3. Dynamic collation depending on 2nd method argument. Allowed types include String (eg. 'en_US'), Locacle (eg. Locacle.US) and Document (eg. new Document("locale", "en_US"))

4. Dynamic collation depending on 2nd method argument.

5. Apply the Collation method parameter to the query.

6. The Collation method parameter overrides the default collation from @Query if not null.

Note	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.

Tip	The most specifc Collation outroules potentially defined others. Which means Method argument over query method annotation over doamin type annotation.

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 27. Sample JSON schema

{
  "type": "object",                                                        (1)

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

  "properties": {                                                          (3)

    "firstname": {                                                         (4)
      "type": "string",
      "enum": [ "luke", "han" ]
    },
    "address": {                                                           (5)
      "type": "object",
      "properties": {
        "postCode": { "type": "string", "minLength": 4, "maxLength": 5 }
      }
    }
  }
}

1. JSON schema documents always describe a whole document from its root. A schema is a schema object itself that can contain embedded schema objects that describe properties and subdocuments.

2. required is a property that describes which properties are required in a document. It can be specified optionally, along with other schema constraints. See MongoDB’s documentation on available keywords.

3. properties is related to a schema object that describes an object type. It contains property-specific schema constraints.

4. firstname specifies constraints for the firsname field inside the document. Here, it is a string-based properties element declaring possible field values.

5. address is a subdocument defining a schema for values in its postCode field.

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

Example 28. Creating a JSON schema

MongoJsonSchema.builder()                                                  (1)
    .required("firstname", "lastname")                                     (2)

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

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

    .build();                                                              (4)

1. Obtain a schema builder to configure the schema with a fluent API.

2. Configure required properties.

3. Configure the String-typed firstname field, allowing only luke and han values. Properties can be typed or untyped. Use a static import of JsonSchemaProperty to make the syntax slightly more compact and to get entry points such as string(…).

4. Build the schema object. Use the schema to create either a collection or query documents.

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

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

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

The Schema builder also provides support for Client-Side Field Level Encryption. Please refer to [mongo.jsonSchema.encrypted-fields] for more information,

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

Example 29. Create collection with $jsonSchema

MongoJsonSchema schema = MongoJsonSchema.builder().required("firstname", "lastname").build();

template.createCollection(Person.class, CollectionOptions.empty().schema(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 30. Query for Documents matching a $jsonSchema

MongoJsonSchema schema = MongoJsonSchema.builder().required("firstname", "lastname").build();

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

The following table shows the supported JSON schema types:

Table 1. Supported JSON schema types

Schema Type	Java Type	Schema Properties
untyped	-	description, generated description, enum, allOf, anyOf, oneOf, not
object	Object	required, additionalProperties, properties, minProperties, maxProperties, patternProperties
array	any array except byte[]	uniqueItems, additionalItems, items, minItems, maxItems
string	String	minLength, maxLentgth, pattern
int	int, Integer	multipleOf, minimum, exclusiveMinimum, maximum, exclusiveMaximum
long	long, Long	multipleOf, minimum, exclusiveMinimum, maximum, exclusiveMaximum
double	float, Float, double, Double	multipleOf, minimum, exclusiveMinimum, maximum, exclusiveMaximum
decimal	BigDecimal	multipleOf, minimum, exclusiveMinimum, maximum, exclusiveMaximum
number	Number	multipleOf, minimum, exclusiveMinimum, maximum, exclusiveMaximum
binData	byte[]	(none)
boolean	boolean, Boolean	(none)
null	null	(none)
objectId	ObjectId	(none)
date	java.util.Date	(none)
timestamp	BsonTimestamp	(none)
regex	java.util.regex.Pattern	(none)

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

For more information, see $jsonSchema.

Fluent Template API

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

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

List<SWCharacter> all = ops.find(SWCharacter.class)
  .inCollection("star-wars")                        (1)
  .all();

1. Skip this step if SWCharacter defines the collection with @Document or if you use the class name as the collection name, which is fine.

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

List<Jedi> all = ops.find(SWCharacter.class)    (1)
  .as(Jedi.class)                               (2)
  .matching(query(where("jedi").is(true)))
  .all();

1. The query fields are mapped against the SWCharacter type.

2. Resulting documents are mapped into Jedi.

Tip	You can directly apply [projections] to result documents by providing the target type via as(Class<?>).

Note	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();

Type-safe Queries for Kotlin

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

Consider the following example explaining Type-safe Queries:

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

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

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

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

// Binary operators
mongoOperations.find<BinaryMessage>(
  Query(BinaryMessage::payload bits { allClear(0b101) }) (2)
)

// Nested Properties (i.e. refer to "book.author")
mongoOperations.find<Book>(
  Query(Book::author / Author::name regex "^H")          (3)
)

1. isEqualTo() is an infix extension function with receiver type KProperty<T> that returns Criteria.

2. For bitwise operators, pass a lambda argument where you call one of the methods of Criteria.BitwiseCriteriaOperators.

3. To construct nested properties, use the / character (overloaded operator div).

Additional Query Options

MongoDB offers various ways of applying meta information, like a comment or a batch size, to a query.Using the Query API directly there are several methods for those options.

Query query = query(where("firstname").is("luke"))
    .comment("find luke")         (1)
    .batchSize(100)                                 (2)

1. The comment propagated to the MongoDB profile log.

2. The number of documents to return in each response batch.

On the repository level the @Meta annotation provides means to add query options in a declarative way.

@Meta(comment = "find luke", batchSize = 100, flags = { SLAVE_OK })
List<Person> findByFirstname(String firstname);

../{spring-data-commons-docs}/query-by-example.adoc query-by-example.adoc

Counting Documents

In pre-3.x versions of SpringData MongoDB the count operation used MongoDBs internal collection statistics. With the introduction of [mongo.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.

Note

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) Count source query using $near. Rewritten query now using $geoWithin with $center. Count source query using $near with $minDistance and $maxDistance. Rewritten query now a combination of $nor $geowithin critierias to work around unsupported $minDistance.

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.

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.

Script Operations

Warning

MongoDB 4.2 removed support for the eval command used by ScriptOperations. There is no replacement for the removed functionality.

MongoDB allows running JavaScript functions on the server by either directly sending the script or calling a stored one. ScriptOperations can be accessed through MongoTemplate and provides basic abstraction for JavaScript usage. The following example shows how to us the ScriptOperations class:

ScriptOperations scriptOps = template.scriptOps();

ExecutableMongoScript echoScript = new ExecutableMongoScript("function(x) { return x; }");
scriptOps.execute(echoScript, "directly execute script");     (1)

scriptOps.register(new NamedMongoScript("echo", echoScript)); (2)
scriptOps.call("echo", "execute script via name");            (3)

1. Run the script directly without storing the function on server side.

2. Store the script using 'echo' as its name. The given name identifies the script and allows calling it later.

3. Run the script with name 'echo' using the provided parameters.

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.

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);

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.

Basic Concepts

The Aggregation Framework support in Spring Data MongoDB is based on the following key abstractions: Aggregation, AggregationOperation, 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 and raising errors when referencing nonexistent properties.

• AggregationOperation

  An AggregationOperation represents a MongoDB aggregation pipeline operation and describes the processing that should be performed in this aggregation step. Although you could manually create an AggregationOperation, 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.

Supported Aggregation Operations

The MongoDB Aggregation Framework provides the following types of aggregation operations:

• Pipeline Aggregation Operators

• Group Aggregation Operators

• Boolean Aggregation Operators

• Comparison Aggregation Operators

• Arithmetic Aggregation Operators

• String Aggregation Operators

• Date Aggregation Operators

• Array Aggregation Operators

• Conditional Aggregation Operators

• Lookup Aggregation Operators

• Convert Aggregation Operators

• Object Aggregation Operators

• Script Aggregation Operators

At the time of this writing, we provide support for the following Aggregation Operations in Spring Data MongoDB:

Table 2. Aggregation Operations currently supported by Spring Data MongoDB

Pipeline Aggregation Operators	bucket, bucketAuto, count, facet, geoNear, graphLookup, group, limit, lookup, match, project, replaceRoot, skip, sort, unwind
Set Aggregation Operators	setEquals, setIntersection, setUnion, setDifference, setIsSubset, anyElementTrue, allElementsTrue
Group Aggregation Operators	addToSet, first, last, max, min, avg, push, sum, (*count), stdDevPop, stdDevSamp
Arithmetic Aggregation Operators	abs, add (*via plus), ceil, divide, exp, floor, ln, log, log10, mod, multiply, pow, round, sqrt, subtract (*via minus), trunc
String Aggregation Operators	concat, substr, toLower, toUpper, stcasecmp, indexOfBytes, indexOfCP, 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, dateToString, dateFromString, dateFromParts, dateToParts, isoDayOfWeek, isoWeek, isoWeekYear
Variable Operators	map
Conditional Aggregation Operators	cond, ifNull, switch
Type Aggregation Operators	type
Convert Aggregation Operators	convert, toBool, toDate, toDecimal, toDouble, toInt, toLong, toObjectId, toString
Object Aggregation Operators	objectToArray, mergeObjects
Script Aggregation Operators	function, accumulator

• The operation is mapped or added by Spring Data MongoDB.

Note that the aggregation operations not listed here are currently not supported by Spring Data MongoDB. Comparison aggregation operators are expressed as Criteria expressions.

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 31. Projection expression examples

// generates {$project: {name: 1, netPrice: 1}}
project("name", "netPrice")

// generates {$project: {thing1: $thing2}}
project().and("thing1").as("thing2")

// generates {$project: {a: 1, b: 1, thing2: $thing1}}
project("a","b").and("thing1").as("thing2")

Example 32. Multi-Stage Aggregation using Projection and Sorting

// generates {$project: {name: 1, netPrice: 1}}, {$sort: {name: 1}}
project("name", "netPrice"), sort(ASC, "name")

// generates {$project: {name: $firstname}}, {$sort: {name: 1}}
project().and("firstname").as("name"), sort(ASC, "name")

// does not work
project().and("firstname").as("name"), sort(ASC, "firstname")

More examples for project operations can be found in the AggregationTests class. Note that further details regarding the projection expressions can be found in the corresponding section of the MongoDB Aggregation Framework reference documentation.

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 33. 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 34. 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 35. Facet operation examples

// generates {$facet: {categorizedByPrice: [ { $match: { price: {$exists : true}}}, { $bucketAuto: {groupBy: $price, buckets: 5}}]}}
facet(match(Criteria.where("price").exists(true)), bucketAuto("price", 5)).as("categorizedByPrice"))

// generates {$facet: {categorizedByCountry: [ { $match: { country: {$exists : true}}}, { $sortByCount: "$country"}]}}
facet(match(Criteria.where("country").exists(true)), sortByCount("country")).as("categorizedByCountry"))

// generates {$facet: {categorizedByYear: [
//     { $project: { title: 1, publicationYear: { $year: "publicationDate"}}},
//     { $bucketAuto: {groupBy: $price, buckets: 5, output: { titles: {$push:"$title"}}}
// ]}}
facet(project("title").and("publicationDate").extractYear().as("publicationYear"),
      bucketAuto("publicationYear", 5).andOutput("title").push().as("titles"))
  .as("categorizedByYear"))

Note that further details regarding facet operation can be found in the $facet section of the MongoDB Aggregation Framework reference documentation.

Sort By Count

Sort by count operations group incoming documents based on the value of a specified expression, compute the count of documents in each distinct group, and sort the results by count. It offers a handy shortcut to apply sorting when using Faceted Classification. Sort by count operations require a grouping field or grouping expression. The following listing shows a sort by count example:

Example 36. Sort by count example

// generates { $sortByCount: "$country" }
sortByCount("country");

A sort by count operation is equivalent to the following BSON (Binary JSON):

{ $group: { _id: <expression>, count: { $sum: 1 } } },
{ $sort: { count: -1 } }

Spring Expression Support in Projection Expressions

We support the use of SpEL expressions in projection expressions through the andExpression method of the ProjectionOperation and BucketOperation classes. This feature lets you define the desired expression as a SpEL expression. On running a query, the SpEL expression is translated into a corresponding MongoDB projection expression part. This arrangement makes it much easier to express complex calculations.

Complex Calculations with SpEL expressions

Consider the following SpEL expression:

1 + (q + 1) / (q - 1)

The preceding expression is translated into the following projection expression part:

{ "$add" : [ 1, {
    "$divide" : [ {
        "$add":["$q", 1]}, {
        "$subtract":[ "$q", 1]}
    ]
}]}

You can see examples in more context in Aggregation Framework Example 5 and Aggregation Framework Example 6. You can find more usage examples for supported SpEL expression constructs in SpelExpressionTransformerUnitTests. The following table shows the SpEL transformations supported by Spring Data MongoDB:

Table 3. Supported SpEL transformations

SpEL Expression	Mongo Expression Part
a == b	{ $eq : [$a, $b] }
a != b	{ $ne : [$a , $b] }
a > b	{ $gt : [$a, $b] }
a >= b	{ $gte : [$a, $b] }
a < b	{ $lt : [$a, $b] }
a ⇐ b	{ $lte : [$a, $b] }
a + b	{ $add : [$a, $b] }
a - b	{ $subtract : [$a, $b] }
a * b	{ $multiply : [$a, $b] }
a / b	{ $divide : [$a, $b] }
a^b	{ $pow : [$a, $b] }
a % b	{ $mod : [$a, $b] }
a && b	{ $and : [$a, $b] }
a || b	{ $or : [$a, $b] }
!a	{ $not : [$a] }

In addition to the transformations shown in the preceding table, you can use standard SpEL operations such as new to (for example) create arrays and reference expressions through their names (followed by the arguments to use in brackets). The following example shows how to create an array in this fashion:

// { $setEquals : [$a, [5, 8, 13] ] }
.andExpression("setEquals(a, new int[]{5, 8, 13})");

Aggregation Framework Examples

The examples in this section demonstrate the usage patterns for the MongoDB Aggregation Framework with Spring Data MongoDB.

Aggregation Framework Example 1

In this introductory example, we want to aggregate a list of tags to get the occurrence count of a particular tag from a MongoDB collection (called tags) sorted by the occurrence count in descending order. This example demonstrates the usage of grouping, sorting, projections (selection), and unwinding (result splitting).

class TagCount {
 String tag;
 int n;
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

Aggregation agg = newAggregation(
    project("tags"),
    unwind("tags"),
    group("tags").count().as("n"),
    project("n").and("tag").previousOperation(),
    sort(DESC, "n")
);

AggregationResults<TagCount> results = mongoTemplate.aggregate(agg, "tags", TagCount.class);
List<TagCount> tagCount = results.getMappedResults();

The preceding listing uses the following algorithm:

1. Create a new aggregation by using the newAggregation static factory method, to which we pass a list of aggregation operations. These aggregate operations define the aggregation pipeline of our Aggregation.

2. Use the project operation to select the tags field (which is an array of strings) from the input collection.

3. Use the unwind operation to generate a new document for each tag within the tags array.

4. Use the group operation to define a group for each tags value for which we aggregate the occurrence count (by using the count aggregation operator and collecting the result in a new field called n).

5. Select the n field and create an alias for the ID field generated from the previous group operation (hence the call to previousOperation()) with a name of tag.

6. Use the sort operation to sort the resulting list of tags by their occurrence count in descending order.

7. Call the aggregate method on MongoTemplate to let MongoDB perform the actual aggregation operation, with the created Aggregation as an argument.

Note that the input collection is explicitly specified as the tags parameter to the aggregate Method. If the name of the input collection is not specified explicitly, it is derived from the input class passed as the first parameter to the newAggreation method.

Aggregation Framework Example 2

This example is based on the Largest and Smallest Cities by State example from the MongoDB Aggregation Framework documentation. We added additional sorting to produce stable results with different MongoDB versions. Here we want to return the smallest and largest cities by population for each state by using the aggregation framework. This example demonstrates grouping, sorting, and projections (selection).

class ZipInfo {
   String id;
   String city;
   String state;
   @Field("pop") int population;
   @Field("loc") double[] location;
}

class City {
   String name;
   int population;
}

class ZipInfoStats {
   String id;
   String state;
   City biggestCity;
   City smallestCity;
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<ZipInfo> aggregation = newAggregation(ZipInfo.class,
    group("state", "city")
       .sum("population").as("pop"),
    sort(ASC, "pop", "state", "city"),
    group("state")
       .last("city").as("biggestCity")
       .last("pop").as("biggestPop")
       .first("city").as("smallestCity")
       .first("pop").as("smallestPop"),
    project()
       .and("state").previousOperation()
       .and("biggestCity")
          .nested(bind("name", "biggestCity").and("population", "biggestPop"))
       .and("smallestCity")
          .nested(bind("name", "smallestCity").and("population", "smallestPop")),
    sort(ASC, "state")
);

AggregationResults<ZipInfoStats> result = mongoTemplate.aggregate(aggregation, ZipInfoStats.class);
ZipInfoStats firstZipInfoStats = result.getMappedResults().get(0);

Note that the ZipInfo class maps the structure of the given input-collection. The ZipInfoStats class defines the structure in the desired output format.

The preceding listings use the following algorithm:

1. Use the group operation to define a group from the input-collection. The grouping criteria is the combination of the state and city fields, which forms the ID structure of the group. We aggregate the value of the population property from the grouped elements by using the sum operator and save the result in the pop field.

2. Use the sort operation to sort the intermediate-result by the pop, state and city fields, in ascending order, such that the smallest city is at the top and the biggest city is at the bottom of the result. Note that the sorting on state and city is implicitly performed against the group ID fields (which Spring Data MongoDB handled).

3. Use a group operation again to group the intermediate result by state. Note that state again implicitly references a group ID field. We select the name and the population count of the biggest and smallest city with calls to the last(…) and first(…​) operators, respectively, in the project operation.

4. Select the state field from the previous group operation. Note that state again implicitly references a group ID field. Because we do not want an implicitly generated ID to appear, we exclude the ID from the previous operation by using and(previousOperation()).exclude(). Because we want to populate the nested City structures in our output class, we have to emit appropriate sub-documents by using the nested method.

5. Sort the resulting list of StateStats by their state name in ascending order in the sort operation.

Note that we derive the name of the input collection from the ZipInfo class passed as the first parameter to the newAggregation method.

Aggregation Framework Example 3

This example is based on the States with Populations Over 10 Million example from the MongoDB Aggregation Framework documentation. We added additional sorting to produce stable results with different MongoDB versions. Here we want to return all states with a population greater than 10 million, using the aggregation framework. This example demonstrates grouping, sorting, and matching (filtering).

class StateStats {
   @Id String id;
   String state;
   @Field("totalPop") int totalPopulation;
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<ZipInfo> agg = newAggregation(ZipInfo.class,
    group("state").sum("population").as("totalPop"),
    sort(ASC, previousOperation(), "totalPop"),
    match(where("totalPop").gte(10 * 1000 * 1000))
);

AggregationResults<StateStats> result = mongoTemplate.aggregate(agg, StateStats.class);
List<StateStats> stateStatsList = result.getMappedResults();

The preceding listings use the following algorithm:

1. Group the input collection by the state field and calculate the sum of the population field and store the result in the new field "totalPop".

2. Sort the intermediate result by the id-reference of the previous group operation in addition to the "totalPop" field in ascending order.

3. Filter the intermediate result by using a match operation which accepts a Criteria query as an argument.

Note that we derive the name of the input collection from the ZipInfo class passed as first parameter to the newAggregation method.

Aggregation Framework Example 4

This example demonstrates the use of simple arithmetic operations in the projection operation.

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<Product> agg = newAggregation(Product.class,
    project("name", "netPrice")
        .and("netPrice").plus(1).as("netPricePlus1")
        .and("netPrice").minus(1).as("netPriceMinus1")
        .and("netPrice").multiply(1.19).as("grossPrice")
        .and("netPrice").divide(2).as("netPriceDiv2")
        .and("spaceUnits").mod(2).as("spaceUnitsMod2")
);

AggregationResults<Document> result = mongoTemplate.aggregate(agg, Document.class);
List<Document> resultList = result.getMappedResults();

Note that we derive the name of the input collection from the Product class passed as first parameter to the newAggregation method.

Aggregation Framework Example 5

This example demonstrates the use of simple arithmetic operations derived from SpEL Expressions in the projection operation.

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<Product> agg = newAggregation(Product.class,
    project("name", "netPrice")
        .andExpression("netPrice + 1").as("netPricePlus1")
        .andExpression("netPrice - 1").as("netPriceMinus1")
        .andExpression("netPrice / 2").as("netPriceDiv2")
        .andExpression("netPrice * 1.19").as("grossPrice")
        .andExpression("spaceUnits % 2").as("spaceUnitsMod2")
        .andExpression("(netPrice * 0.8  + 1.2) * 1.19").as("grossPriceIncludingDiscountAndCharge")

);

AggregationResults<Document> result = mongoTemplate.aggregate(agg, Document.class);
List<Document> resultList = result.getMappedResults();

Aggregation Framework Example 6

This example demonstrates the use of complex arithmetic operations derived from SpEL Expressions in the projection operation.

Note: The additional parameters passed to the addExpression method can be referenced with indexer expressions according to their position. In this example, we reference the first parameter of the parameters array with [0]. When the SpEL expression is transformed into a MongoDB aggregation framework expression, external parameter expressions are replaced with their respective values.

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

double shippingCosts = 1.2;

TypedAggregation<Product> agg = newAggregation(Product.class,
    project("name", "netPrice")
        .andExpression("(netPrice * (1-discountRate)  + [0]) * (1+taxRate)", shippingCosts).as("salesPrice")
);

AggregationResults<Document> result = mongoTemplate.aggregate(agg, Document.class);
List<Document> resultList = result.getMappedResults();

Note that we can also refer to other fields of the document within the SpEL expression.

Aggregation Framework Example 7

This example uses conditional projection. It is derived from the $cond reference documentation.

public class InventoryItem {

  @Id int id;
  String item;
  String description;
  int qty;
}

public class InventoryItemProjection {

  @Id int id;
  String item;
  String description;
  int qty;
  int discount
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<InventoryItem> agg = newAggregation(InventoryItem.class,
  project("item").and("discount")
    .applyCondition(ConditionalOperator.newBuilder().when(Criteria.where("qty").gte(250))
      .then(30)
      .otherwise(20))
    .and(ifNull("description", "Unspecified")).as("description")
);

AggregationResults<InventoryItemProjection> result = mongoTemplate.aggregate(agg, "inventory", InventoryItemProjection.class);
List<InventoryItemProjection> stateStatsList = result.getMappedResults();

This one-step aggregation uses a projection operation with the inventory collection. We project the discount field by using a conditional operation for all inventory items that have a qty greater than or equal to 250. A second conditional projection is performed for the description field. We apply the Unspecified description to all items that either do not have a description field or items that have a null description.

As of MongoDB 3.6, it is possible to exclude fields from the projection by using a conditional expression.

Example 37. Conditional aggregation projection

TypedAggregation<Book> agg = Aggregation.newAggregation(Book.class,
  project("title")
    .and(ConditionalOperators.when(ComparisonOperators.valueOf("author.middle")     (1)
        .equalToValue(""))                                                          (2)
        .then("$$REMOVE")                                                           (3)
        .otherwiseValueOf("author.middle")                                          (4)
    )
	.as("author.middle"));

1. If the value of the field author.middle

2. does not contain a value,

3. then use $$REMOVE to exclude the field.

4. Otherwise, add the field value of author.middle.

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();
}

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"));

Note	Index and GeospatialIndex support configuration of collations.

Accessing Index Information

The IndexOperations interface has the getIndexInfo method that returns a list of IndexInfo objects. This list contains all the indexes defined on the collection. The following example defines an index on the Person class that has an age property:

template.indexOps(Person.class).ensureIndex(new Index().on("age", Order.DESCENDING).unique());

List<IndexInfo> indexInfoList = template.indexOps(Person.class).getIndexInfo();

// Contains
// [IndexInfo [fieldSpec={_id=ASCENDING}, name=_id_, unique=false, sparse=false],
//  IndexInfo [fieldSpec={age=DESCENDING}, name=age_-1, unique=true, sparse=false]]

Methods for Working with a Collection

The following example shows how to create a collection:

Example 38. Working with collections by using MongoTemplate

MongoCollection<Document> collection = null;
if (!mongoTemplate.getCollectionNames().contains("MyNewCollection")) {
    collection = mongoTemplate.createCollection("MyNewCollection");
}

mongoTemplate.dropCollection("MyNewCollection");

• getCollectionNames: Returns a set of collection names.

• collectionExists: Checks to see if a collection with a given name exists.

• createCollection: Creates an uncapped collection.

• dropCollection: Drops the collection.

• getCollection: Gets a collection by name, creating it if it does not exist.

Note	Collection creation allows customization with CollectionOptions and supports collations.

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.

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.

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.

Note	Lifecycle events are only emitted for root level types. Complex types used as properties within a document root are not subject to event publication unless they are document references annotated with @DBRef.

Warning

Lifecycle events depend on an ApplicationEventMulticaster, which in case of the SimpleApplicationEventMulticaster can be configured with a TaskExecutor, and therefore gives no guarantees when an Event is processed.

../{spring-data-commons-docs}/entity-callbacks.adoc ./mongo-entity-callbacks.adoc

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.

Execution Callbacks

One common design feature of all Spring template classes is that all functionality is routed into one of the template’s execute callback methods. Doing so helps to ensure that exceptions and any resource management that may be required are performed consistently. While JDBC and JMS need this feature much more than MongoDB does, it still offers a single spot for exception translation and logging to occur. Consequently, using these execute callbacks is the preferred way to access the MongoDB driver’s MongoDatabase and MongoCollection objects to perform uncommon operations that were not exposed as methods on MongoTemplate.

The following list describes the execute callback methods.

• <T> T execute (Class<?> entityClass, CollectionCallback<T> action): Runs the given CollectionCallback for the entity collection of the specified class.

• <T> T execute (String collectionName, CollectionCallback<T> action): Runs the given CollectionCallback on the collection of the given name.

• <T> T execute (DbCallback<T> action): Runs a DbCallback, translating any exceptions as necessary. Spring Data MongoDB provides support for the Aggregation Framework introduced to MongoDB in version 2.2.

• <T> T execute (String collectionName, DbCallback<T> action): Runs a DbCallback on the collection of the given name translating any exceptions as necessary.

• <T> T executeInSession (DbCallback<T> action): Runs the given DbCallback within the same connection to the database so as to ensure consistency in a write-heavy environment where you may read the data that you wrote.

The following example uses the CollectionCallback to return information about an index:

boolean hasIndex = template.execute("geolocation", new CollectionCallbackBoolean>() {
  public Boolean doInCollection(Venue.class, DBCollection collection) throws MongoException, DataAccessException {
    List<Document> indexes = collection.getIndexInfo();
    for (Document document : indexes) {
      if ("location_2d".equals(document.get("name"))) {
        return true;
      }
    }
    return false;
  }
});

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 39. JavaConfig setup for a GridFsTemplate

class GridFsConfiguration extends AbstractMongoClientConfiguration {

  // … further configuration omitted

  @Bean
  public GridFsTemplate gridFsTemplate() {
    return new GridFsTemplate(mongoDbFactory(), mappingMongoConverter());
  }
}

The corresponding XML configuration follows:

Example 40. XML configuration for a GridFsTemplate

<?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 41. 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 42. Using GridFsTemplate to query for files

class GridFsClient {

  @Autowired
  GridFsOperations operations;

  @Test
  public void findFilesInGridFs() {
    GridFSFindIterable result = operations.find(query(whereFilename().is("filename.txt")))
  }
}

Note	Currently, MongoDB does not support defining sort criteria when retrieving files from GridFS. For this reason, any sort criteria defined on the Query instance handed into the find(…) method are disregarded.

The other option to read files from the GridFs is to use the methods introduced by the ResourcePatternResolver interface. They allow handing an Ant path into the method and can thus retrieve files matching the given pattern. The following example shows how to use GridFsTemplate to read files:

Example 43. Using GridFsTemplate to read files

class GridFsClient {

  @Autowired
  GridFsOperations operations;

  @Test
  public void readFilesFromGridFs() {
    GridFsResources[] txtFiles = operations.getResources("*.txt");
  }
}

GridFsOperations extends ResourcePatternResolver and lets the GridFsTemplate (for example) to be plugged into an ApplicationContext to read Spring Config files from MongoDB database.

tailable-cursors.adoc change-streams.adoc

---

1. Kristina Chodorow. MongoDB - The Definitive Guide. O’Reilly Media, 2013