[[mongo.core]]
= MongoDB support

The MongoDB support contains a wide range of features which are summarized below.

* Spring configuration support using Java based @Configuration classes or an XML namespace for a Mongo driver instance and replica sets
* MongoTemplate helper class that increases productivity 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 but 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/retrieved using MongoDB (deprecated - will be removed without replacement)
* GeoSpatial integration

For most tasks you will find yourself using `MongoTemplate` or the Repository support that 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 a hold of the low level API artifacts such as `com.mongo.DB` 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.

[[mongodb-getting-started]]
== Getting Started

Spring MongoDB support requires MongoDB 2.6 or higher and Java SE 8 or higher. An easy way to bootstrap setting up a working environment is to create a Spring based project in http://spring.io/tools/sts[STS].

First you need to set up a running Mongodb server. Refer to the http://docs.mongodb.org/manual/core/introduction/[Mongodb Quick Start guide] for an explanation on how to startup a MongoDB instance. Once installed starting MongoDB is typically a matter of executing the following command: `MONGO_HOME/bin/mongod`

To create a Spring project in STS go to File -> New -> Spring Template Project -> Simple Spring Utility Project -> press Yes when prompted. Then enter a project and a package name such as org.spring.mongodb.example.

Then add the following to pom.xml dependencies section.

[source,xml]
----
<dependencies>

  <!-- other dependency elements omitted -->

  <dependency>
    <groupId>org.springframework.data</groupId>
    <artifactId>spring-data-mongodb</artifactId>
    <version>{version}</version>
  </dependency>

</dependencies>
----

Also change the version of Spring in the pom.xml to be

[source,xml]
----
<spring.framework.version>{springVersion}</spring.framework.version>
----

You will also need to add the location of the Spring Milestone repository for maven to your `pom.xml` which is at the same level of your `<dependencies/>` element

[source,xml]
----
<repositories>
  <repository>
    <id>spring-milestone</id>
    <name>Spring Maven MILESTONE Repository</name>
    <url>http://repo.spring.io/libs-milestone</url>
  </repository>
</repositories>
----

The repository is also http://repo.spring.io/milestone/org/springframework/data/[browseable here].

You may also want to set the logging level to `DEBUG` to see some additional information, edit the `log4j.properties` file to have

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

Create a simple Person class to persist:

[source,java]
----
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 + "]";
  }
}
----

And a main application to run

[source,java]
----
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.MongoClient;

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

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

    mongoOps.dropCollection("person");
  }
}
----

This will produce the following output

[source]
----
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 take notice of

* You can instantiate the central helper class of Spring Mongo, <<mongo-template,`MongoTemplate`>>, using the standard `com.mongodb.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 <<mongo.mapping,here>>.).
* Conventions are used for handling the id field, converting it to be a `ObjectId` when stored in the database.
* Mapping conventions can use field access. Notice the Person class has only getters.
* If the constructor argument names match the field names of the stored document, they will be used to instantiate the object

[[mongo.examples-repo]]
== Examples Repository

There is an https://github.com/spring-projects/spring-data-examples[github repository with several examples] that you can download and play around with to get a feel for how the library works.

[[mongodb-connectors]]
== Connecting to MongoDB with Spring

One of the first tasks when using MongoDB and Spring is to create a `com.mongodb.MongoClient` object using the IoC container. There are two main ways to do this, either using Java based bean metadata or XML based bean metadata. These 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 http://docs.spring.io/spring/docs/3.2.x/spring-framework-reference/html/new-in-3.0.html#new-java-configuration[here ] as well as the detailed documentation http://docs.spring.io/spring/docs/{springVersion}/spring-framework-reference/core.html#beans-java-instantiating-container[ here].

[[mongo.mongo-java-config]]
=== Registering a Mongo instance using Java based metadata

An example of using Java based bean metadata to register an instance of a `com.mongodb.MongoClient` is shown below

.Registering a com.mongodb.MongoClient object using Java based bean metadata
====
[source,java]
----
@Configuration
public class AppConfig {

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

This approach allows you to use the standard `com.mongodb.MongoClient` instance with the container using Spring's `MongoClientFactoryBean`. As compared to instantiating a `com.mongodb.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 use of `@Repository` is described in http://docs.spring.io/spring/docs/{springVersion}/spring-framework-reference/data-access.html[Spring's DAO support features].

An example of a Java based bean metadata that supports exception translation on `@Repository` annotated classes is shown below:

.Registering a com.mongodb.MongoClient object using Spring's MongoClientFactoryBean and enabling Spring's exception translation support
====
[source,java]
----
@Configuration
public class AppConfig {

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

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

[[mongo.mongo-xml-config]]
=== Registering a Mongo instance using XML based metadata

While you can use Spring's traditional `<beans/>` XML namespace to register an instance of `com.mongodb.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 allows you to create a Mongo instance server location, replica-sets, and options.

To use the Mongo namespace elements you will need to reference the Mongo schema:

.XML schema to configure MongoDB
====
[source,xml]
----
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
          xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
          xmlns:context="http://www.springframework.org/schema/context"
          xmlns:mongo="http://www.springframework.org/schema/data/mongo"
          xsi:schemaLocation=
          "http://www.springframework.org/schema/context
          http://www.springframework.org/schema/context/spring-context-3.0.xsd
          *http://www.springframework.org/schema/data/mongo http://www.springframework.org/schema/data/mongo/spring-mongo-1.0.xsd*
          http://www.springframework.org/schema/beans
          http://www.springframework.org/schema/beans/spring-beans-3.0.xsd">

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

</beans>
----
====

A more advanced configuration with `MongoClientOptions` is shown below (note these are not recommended values)

.XML schema to configure a com.mongodb.MongoClient object with MongoClientOptions
====
[source,xml]
----
<beans>

  <mongo:mongo-client host="localhost" port="27017">
    <mongo:client-options connections-per-host="8"
                   threads-allowed-to-block-for-connection-multiplier="4"
                   connect-timeout="1000"
                   max-wait-time="1500}"
                   auto-connect-retry="true"
                   socket-keep-alive="true"
                   socket-timeout="1500"
                   slave-ok="true"
                   write-number="1"
                   write-timeout="0"
                   write-fsync="true"/>
  </mongo:mongo-client>

</beans>
----
====

A configuration using replica sets is shown below.

.XML schema to configure com.mongodb.MongoClient object with Replica Sets
====
[source,xml]
----
<mongo:mongo-client id="replicaSetMongo" replica-set="127.0.0.1:27017,localhost:27018"/>
----
====

[[mongo.mongo-db-factory]]
=== The MongoDbFactory interface

While `com.mongodb.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.DB object and access all the functionality of a specific MongoDB database instance. Spring provides the `org.springframework.data.mongodb.core.MongoDbFactory` interface shown below to bootstrap connectivity to the database.

[source,java]
----
public interface MongoDbFactory {

  MongoDatabase getDb() throws DataAccessException;

  MongoDatabase getDb(String dbName) throws DataAccessException;
}
----

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

Instead of using the IoC container to create an instance of MongoTemplate, you can just use them in standard Java code as shown below.

[source,java]
----
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 SimpleMongoDbFactory(new MongoClient(), "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 SimpleMongoDbFactory and is the only difference between the listing shown in the <<mongodb-getting-started,getting started section>>.

[[mongo.mongo-db-factory-java]]
=== Registering a MongoDbFactory instance using Java based metadata

To register a MongoDbFactory instance with the container, you write code much like what was highlighted in the previous code listing. A simple example is shown below

[source,java]
----
@Configuration
public class MongoConfiguration {

  public @Bean MongoDbFactory mongoDbFactory() {
    return new SimpleMongoDbFactory(new MongoClient(), "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. Please use the `MongoClient` specific options for setting credentials via `MongoCredential` to provide authentication data.

[source,java]
----
@Configuration
public class ApplicationContextEventTestsAppConfig extends AbstractMongoConfiguration {

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

  @Override
  @Bean
  public MongoClient mongoClient() {
    return new MongoClient(singletonList(new ServerAddress("127.0.0.1", 27017)),
      singletonList(MongoCredential.createCredential("name", "db", "pwd".toCharArray())));
  }
}
----

In order to use authentication with XML configuration use the `credentials` attribue on `<mongo-client>`.

NOTE: Username/password credentials used in XML configuration must be URL encoded when these contain reserved characters such as `:`, `%`, `@`, `,`.
Example: `m0ng0@dmin:mo_res:bw6},Qsdxx@admin@database` -> `m0ng0%40dmin:mo_res%3Abw6%7D%2CQsdxx%40admin@database`
See https://tools.ietf.org/html/rfc3986#section-2.2[section 2.2 of RFC 3986] for further details.


[[mongo.mongo-db-factory-xml]]
=== Registering a MongoDbFactory instance using XML based metadata

The mongo namespace provides a convenient way to create a `SimpleMongoDbFactory` as compared to using the `<beans/>` namespace. Simple usage is shown below

[source,xml]
----
<mongo:db-factory dbname="database">
----

If you need to configure additional options on the `com.mongodb.MongoClient` instance that is used to create a `SimpleMongoDbFactory` you can refer to an existing bean using the `mongo-ref` attribute as shown below. To show another common usage pattern, this listing shows the use of a property placeholder to parametrise the configuration and creating `MongoTemplate`.

[source,xml]
----
<context:property-placeholder location="classpath:/com/myapp/mongodb/config/mongo.properties"/>

<mongo:mongo-client host="${mongo.host}" port="${mongo.port}">
  <mongo:client-options
     connections-per-host="${mongo.connectionsPerHost}"
     threads-allowed-to-block-for-connection-multiplier="${mongo.threadsAllowedToBlockForConnectionMultiplier}"
     connect-timeout="${mongo.connectTimeout}"
     max-wait-time="${mongo.maxWaitTime}"
     auto-connect-retry="${mongo.autoConnectRetry}"
     socket-keep-alive="${mongo.socketKeepAlive}"
     socket-timeout="${mongo.socketTimeout}"
     slave-ok="${mongo.slaveOk}"
     write-number="1"
     write-timeout="0"
     write-fsync="true"/>
</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>
----

[[mongo-template]]
== Introduction to MongoTemplate

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

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 interface `MongoConverter`. Spring provides the `MappingMongoConverter`, but you can also write your own converter. Please refer to the section on MongoConverters 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 will find methods such as "find", "findAndModify", "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 in between the two APIs is that MongoOperations can be passed domain objects instead of `Document` and there are fluent APIs for `Query`, `Criteria`, and `Update` operations instead of populating a `Document` to specify the parameters for those operations.

NOTE: The preferred way to reference the operations on `MongoTemplate` instance is via its interface `MongoOperations`.

The default converter implementation used by `MongoTemplate` is MappingMongoConverter. While the `MappingMongoConverter` can make use of additional metadata to specify the mapping of objects to documents it is also capable of converting 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 is explained in the <<mongo.mapping,Mapping chapter>>.

Another central feature of MongoTemplate is exception translation of exceptions thrown in the MongoDB Java driver into Spring's portable Data Access Exception hierarchy. Refer to the section on <<mongo.exception,exception translation>> for more information.

While there are many convenience methods on `MongoTemplate` to help you easily perform common tasks if you should need to access the MongoDB driver API directly to access functionality not explicitly exposed by the MongoTemplate you can use one of several Execute callback methods to access underlying driver APIs. The execute callbacks will give you a reference to either a `com.mongodb.Collection` or a `com.mongodb.DB` object. Please see the section mongo.executioncallback[Execution Callbacks] for more information.

Now let's look at an example of how to work with the `MongoTemplate` in the context of the Spring container.

[[mongo-template.instantiating]]
=== Instantiating MongoTemplate

You can use Java to create and register an instance of `MongoTemplate` as shown below.

.Registering a com.mongodb.MongoClient object and enabling Spring's exception translation support
====
[source,java]
----
@Configuration
public class AppConfig {

  public @Bean MongoClient mongoClient() {
      return new MongoClient("localhost");
  }

  public @Bean MongoTemplate mongoTemplate() {
      return new MongoTemplate(mongoClient(), "mydatabase");
  }
}
----
====

There are several overloaded constructors of MongoTemplate. These are

* `MongoTemplate(MongoClient mongo, String databaseName)` - takes the `com.mongodb.MongoClient` object and the default database name to operate against.
* `MongoTemplate(MongoDbFactory mongoDbFactory)` - takes a MongoDbFactory object that encapsulated the `com.mongodb.MongoClient` object, database name, and username and password.
* `MongoTemplate(MongoDbFactory mongoDbFactory, MongoConverter mongoConverter)` - adds a MongoConverter to use for mapping.

You can also configure a MongoTemplate using Spring's XML <beans/> schema.

[source,java]
----
<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`.

NOTE: The preferred way to reference the operations on `MongoTemplate` instance is via its interface `MongoOperations`.

[[mongo-template.writeresultchecking]]
=== WriteResultChecking Policy

When in development it is very 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 but in fact the database was not modified according to your expectations. Set MongoTemplate's  property to an enum with the following values, `EXCEPTION`, or `NONE` to either throw an Exception or do nothing. The default is to use a `WriteResultChecking` value of `NONE`.

[[mongo-template.writeconcern]]
=== WriteConcern

You can set the `com.mongodb.WriteConcern` property that the `MongoTemplate` will use for write operations if it has not yet been specified via the driver at a higher level such as `com.mongodb.MongoClient`. If MongoTemplate's `WriteConcern` property is not set it will default to the one set in the MongoDB driver's DB or Collection setting.

[[mongo-template.writeconcernresolver]]
=== 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 `WriteConcernResolver` interface is shown below.

[source,java]
----
public interface WriteConcernResolver {
  WriteConcern resolve(MongoAction action);
}
----

The passed in argument, MongoAction, is what you use to determine the `WriteConcern` value to be used or to use the value of the Template itself as a default. `MongoAction` contains the collection name being written to, the `java.lang.Class` of the POJO, the converted `Document`, as well as the operation as an enumeration (`MongoActionOperation`: REMOVE, UPDATE, INSERT, INSERT_LIST, SAVE) and a few other pieces of contextual information. For example,

[source]
----
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();
  }
}
----

[[mongo-template.save-update-remove]]
== Saving, Updating, and Removing Documents

`MongoTemplate` provides a simple way for you to save, update, and delete your domain objects and map those objects to documents stored in MongoDB.

Given a simple class such as Person

[source,java]
----
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 can save, update and delete the object as shown below.

NOTE: `MongoOperations` is the interface that `MongoTemplate` implements.

[source,java]
----
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.SimpleMongoDbFactory;

import com.mongodb.MongoClient;

public class MongoApp {

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

  public static void main(String[] args) {

    MongoOperations mongoOps = new MongoTemplate(new SimpleMongoDbFactory(new MongoClient(), "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);
  }
}
----

This would produce the following log output (including debug messages from `MongoTemplate` itself)

[source]
----
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]
----

There was implicit conversion using the `MongoConverter` between a `String` and `ObjectId` as stored in the database and recognizing a convention of the property "Id" name.

NOTE: This 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 example is explained in more detail in the section <<mongo.query,Querying Documents>>.

[[mongo-template.id-handling]]
=== How the `_id` field is handled in the mapping layer

MongoDB requires that you have an `_id` field for all documents. If you don't provide one the driver will assign a `ObjectId` with a generated value. When using the `MappingMongoConverter` there are certain rules that govern how properties from the Java class is mapped to this `_id` field.

The following outlines what property will be mapped to the `_id` document field:

* A property or field annotated with `@Id` (`org.springframework.data.annotation.Id`) will be mapped to the `_id` field.
* A property or field without an annotation but named `id` will be mapped to the `_id` field.

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

* An id property or field declared as a String in the Java class will be converted to and stored as an `ObjectId` if possible 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 will be stored as a string in the database.
* An id property or field declared as `BigInteger` in the Java class will be converted to and stored as an `ObjectId` using a Spring `Converter<BigInteger, ObjectId>`.

If no field or property specified above is present in the Java class then an implicit `_id` file will be generated by the driver but not mapped to a property or field of the Java class.

When querying and updating `MongoTemplate` will use the converter to handle conversions of the `Query` and `Update` objects that correspond to the above rules for saving documents so field names and types used in your queries will be able to match what is in your domain classes.

[[mongo-template.type-mapping]]
=== Type mapping

As MongoDB collections can contain documents that represent instances of a variety of types. A great example here is if you store a hierarchy of classes or simply 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 it's main implementation. Its default behavior is storing 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's a complex type and a subtype of the property type declared.


.Type mapping
====
[source,java]
----
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"
}
----
====

As you can see we store the type information as last field for the actual root class as well as for the nested type as it is complex and a subtype of `Contact`. So if you're now using `mongoTemplate.findAll(Object.class, "sample")` we are able to find out that the document stored shall be a `Sample` instance. We are also able to find out that the value property shall be a `Person` actually.

==== Customizing type mapping

In case you want to avoid writing the entire Java class name as type information but rather like to use some key you can use the `@TypeAlias` annotation at the entity class being persisted. 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 be configured in turn on `MappingMongoConverter`.

.Defining a TypeAlias for an Entity
====
[source,java]
----
@TypeAlias("pers")
class Person {

}
----
====

Note that the resulting document will contain `"pers"` as the value in the `_class` Field.

==== Configuring custom type mapping

The following example demonstrates how to configure a custom `MongoTypeMapper` in `MappingMongoConverter`.

.Configuring a custom MongoTypeMapper via Spring Java Config
====
[source,java]
----
class CustomMongoTypeMapper extends DefaultMongoTypeMapper {
  //implement custom type mapping here
}
----
====

[source,java]
----
@Configuration
class SampleMongoConfiguration extends AbstractMongoConfiguration {

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

  @Override
  public MongoClient mongoClient() {
    return new MongoClient();
  }

  @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 we are extending the `AbstractMongoConfiguration` class and override the bean definition of the `MappingMongoConverter` where we configure our custom `MongoTypeMapper`.

.Configuring a custom MongoTypeMapper via XML
====
[source,xml]
----
<mongo:mapping-converter type-mapper-ref="customMongoTypeMapper"/>

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

[[mongo-template.save-insert]]
=== 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 will perform 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 will be determined by name (not fully qualified) of the class. You may also call the save operation with a specific collection name. The collection to store the object can be overridden using mapping metadata.

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

Here is a basic example of using the save operation and retrieving its contents.

.Inserting and retrieving documents using the MongoTemplate
====
[source,java]
----
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 insert/save operations available to you are listed below.

* `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 listed below

* `void` *insert* `(Object objectToSave)` Insert the object to the default collection.
* `void` *insert* `(Object objectToSave, String collectionName)` Insert the object to the specified collection.

[[mongo-template.save-insert.collection]]
==== Which collection will my documents be saved into?

There are two ways to manage the collection name that is used for operating on 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 would be stored in the "person" collection. You can customize this by providing a different collection name using 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.

[[mongo-template.save-insert.individual]]
==== Inserting or saving individual objects

The MongoDB driver supports inserting a collection of documents in one operation. The methods in the MongoOperations interface that support this functionality are listed below

* *insert* inserts an object. If there is an existing document with the same id then an error is generated.
* *insertAll* takes a `Collection` of objects as the first parameter. This method inspects each object and inserts it to the appropriate collection based on the rules specified above.
* *save* saves the object overwriting any object that might exist with the same id.

[[mongo-template.save-insert.batch]]
==== Inserting several objects in a batch

The MongoDB driver supports inserting a collection of documents in one operation. The methods in the MongoOperations interface that support this functionality are listed below

* *insert* methods that take a `Collection` as the first argument. This inserts a list of objects in a single batch write to the database.

[[mongodb-template-update]]
=== Updating documents in a collection

For updates we can elect to update the first document found using `MongoOperation` 's method `updateFirst` or we can update all documents that were found to match the query using the method `updateMulti`. Here is an example of an update of all SAVINGS accounts where we are adding a one-time $50.00 bonus to the balance using the `$inc` operator.

.Updating documents using the MongoTemplate
====
[source,java]
----
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 above we provide the update definition using an `Update` object. The `Update` class has methods that match the update modifiers available for MongoDB.

As you can see most methods return the `Update` object to provide a fluent style for the API.

[[mongodb-template-update.methods]]
==== Methods for executing updates for documents

* *updateFirst* Updates the first document that matches the query document criteria with the provided updated document.
* *updateMulti* Updates all objects that match the query document criteria with the provided updated document.

[[mongodb-template-update.update]]
==== Methods for the Update class

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

Here is a listing of methods on the Update class

* `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 like `$push` and `$addToSet` allow nesting of additional operators.

[source]
----
// { $push : { "category" : { "$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"));
----

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

[[mongo-template.upserts]]
=== Upserting documents in a collection

Related to performing an `updateFirst` operations, 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. Here is an example

[source]
----
template.upsert(query(where("ssn").is(1111).and("firstName").is("Joe").and("Fraizer").is("Update")), update("address", addr), Person.class);
----

[[mongo-template.find-and-upsert]]
=== Finding and Upserting documents in a collection

The `findAndModify(…)` method on DBCollection can update a document and return either the old or newly updated document in a single operation. `MongoTemplate` provides a findAndModify method that takes `Query` and `Update` classes and converts from `Document` to your POJOs. Here are the methods

[source,java]
----
<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);
----

As an example usage, we will insert of few `Person` objects into the container and perform a simple findAndUpdate operation

[source,java]
----
mongoTemplate.insert(new Person("Tom", 21));
mongoTemplate.insert(new Person("Dick", 22));
mongoTemplate.insert(new Person("Harry", 23));

Query query = new Query(Criteria.where("firstName").is("Harry"));
Update update = new Update().inc("age", 1);
Person p = mongoTemplate.findAndModify(query, update, Person.class); // return's old person object

assertThat(p.getFirstName(), is("Harry"));
assertThat(p.getAge(), is(23));
p = mongoTemplate.findOne(query, Person.class);
assertThat(p.getAge(), is(24));

// Now return the newly updated document when updating
p = template.findAndModify(query, update, new FindAndModifyOptions().returnNew(true), Person.class);
assertThat(p.getAge(), is(25));
----

The `FindAndModifyOptions` lets you set the options of returnNew, upsert, and remove. An example extending off the previous code snippet is shown below

[source,java]
----
Query query2 = new Query(Criteria.where("firstName").is("Mary"));
p = mongoTemplate.findAndModify(query2, update, new FindAndModifyOptions().returnNew(true).upsert(true), Person.class);
assertThat(p.getFirstName(), is("Mary"));
assertThat(p.getAge(), is(1));
----

[[mongo-template.delete]]
=== Methods for removing documents

You can use several overloaded methods to remove an object from the database.

====
[source,java]
----
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 via its `_id` from the associated collection.
<2> Remove all documents matching the criteria of the query from the `GOT` collection.
<3> Remove the first 3 documents in the `GOT` collection. Unlike <2>, the documents to remove are identified via their `_id` executing the given query applying `sort`, `limit` and `skip` options first and then remove 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 3 documents in the `GOT` collection. Unlike <3>, documents do not get deleted in a batch but one by one.
====

[[mongo-template.optimistic-locking]]
=== Optimistic locking

The `@Version` annotation provides a JPA similar semantic in the context of MongoDB and makes sure updates are only applied to documents with matching version. Therefore the actual value of the version property is added to the update query in a way that the update won't have any effect if another operation altered the document in between. In that case an `OptimisticLockingFailureException` is thrown.

====
[source,java]
----
@Document
class Person {

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

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

Person tmp = teplate.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 document with `version = 0`. Set the `lastname` and bump `version` to `1`.
<4> Try to update previously loaded document sill having `version = 0` fails with `OptimisticLockingFailureException` as the current `version` is `1`.
====

IMPORTANT: Using MongoDB driver version 3 requires to set the `WriteConcern` to `ACKNOWLEDGED`. Otherwise `OptimisticLockingFailureException` can be silently swallowed.

[[mongo.query]]
== Querying Documents

You can express your queries using the `Query` and `Criteria` classes which 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 easily chain together multiple method criteria and queries while having easy to understand the code. Static imports in Java are used to help remove the need to see the 'new' keyword for creating `Query` and `Criteria` instances so as to improve readability. If you like to create `Query` instances from a plain JSON String use `BasicQuery`.

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

GeoSpatial queries are also supported and are described more in the section <<mongo.geospatial,GeoSpatial Queries>>.

Map-Reduce operations are also supported and are described more in the section <<mongo.mapreduce,Map-Reduce>>.

[[mongodb-template-query]]
=== Querying documents in a collection

We saw how to retrieve a single document using the findOne and findById methods on MongoTemplate in previous sections which 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.

.Querying for documents using the MongoTemplate
====
[source,java]
----
import static org.springframework.data.mongodb.core.query.Criteria.where;
import static org.springframework.data.mongodb.core.query.Query.query;

…

List<Person> result = mongoTemplate.find(query(where("age").lt(50)
  .and("accounts.balance").gt(1000.00d)), Person.class);
----
====

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

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

As you can see most methods return the `Criteria` object to provide a fluent style for the API.

[[mongodb-template-query.criteria]]
==== Methods for the Criteria class

* `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 <<mongo.jsonSchema,JSON schema criteria>>. `$jsonSchema` can only be applied on the top level of a query and not property specific. Use the `properties` attribute of the schema to match against nested fields.
* `Criteria` *bits()* is the gateway to https://docs.mongodb.com/manual/reference/operator/query-bitwise/[MongoDB bitwise query operators] like `$bitsAllClear`.


There are also methods on the Criteria class for geospatial queries. Here is a listing but look at the section on <<mongo.geospatial,GeoSpatial Queries>> 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.

The `Query` class has some additional methods used to provide options for the query.

[[mongodb-template-query.query]]
==== Methods for the Query class

* `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

[[mongo-template.querying]]
=== Methods for querying for documents

The query methods need to specify the target type T that will be returned and they are also overloaded with an explicit collection name for queries that should operate on a collection other than the one indicated by the return type.

* *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 also removed from the collection in the database.

[[mongo-template.query.distinct]]
=== Query distinct values

MongoDB provides an operation to obtain distinct values for a single field 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 retriaval the actual result type does matter for the sake of conversion and typing.

.Retrieving distinct values
====
[source,java]
----
template.query(Person.class)  <1>
  .distinct("lastname")       <2>
  .all();                     <3>
----
<1> Query the collection of `Person`.
<2> Select _distinct_  values of the `lastname` field. The fieldname will be mapped according to the domain types property declaration, taking potential `@Field` annotations into account.
<3> Retrieve all distinct values as `List` of `Object` due to no explicit result type specification.
====

Retrieving distinct values into a `Collection` of `Object` is the most flexible way as it will try to determine the property value of the domain type converting 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`

.Retrieving strongly typed distinct values
====
[source,java]
----
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 will be mapped according to the domain types property declaration, taking potential `@Field` annotations into account.
<3> Retrieved values will be converted into the desired target type. In this case `String`. It would also be 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`. Throws a `DataAccessException` if the type cannot be converted into the desired target type.
====

[[mongo.geospatial]]
=== 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.

To understand how to perform GeoSpatial queries we will use the following Venue class taken from the integration tests which relies on using the rich `MappingMongoConverter`.

[source,java]
----
@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`, the following query can be used.

[source,java]
----
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 the following query can be used

[source,java]
----
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` the following query can be used

[source,java]
----
//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`, the following queries can be used

[source,java]
----
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);
----

[source,java]
----
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 the following query can be used

[source,java]
----
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);
----

[[mongo.geo-near]]
==== Geo near queries

MongoDB supports querying the database for geo locations and calculation the distance from a given origin at the very same time. With geo-near queries it's possible to express queries like: "find all restaurants in the surrounding 10 miles". To do so `MongoOperations` provides `geoNear(…)` methods taking a `NearQuery` as argument as well as the already familiar entity type and collection

[source,java]
----
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);
----

As you can see we use the `NearQuery` builder API to set up a query to return all `Restaurant` instances surrounding the given `Point` by 10 miles maximum. 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 pre-built in metrics (miles and kilometers) will automatically trigger the spherical flag to be set on the query. If you want to avoid that, simply hand in 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. The wrapping `GeoResults` allows accessing the average distance of all results. A single `GeoResult` object simply carries the entity found plus its distance from the origin.

[[mongo.geo-json]]
=== GeoJSON Support

MongoDB supports http://geojson.org/[GeoJSON] and simple (legacy) coordinate pairs for geospatial data. Those formats can both be used for storing as well as querying data.

NOTE: Please refer to the http://docs.mongodb.org/manual/core/2dsphere/#geospatial-indexes-store-geojson/[MongoDB manual on GeoJSON support] to learn about requirements and restrictions.

==== GeoJSON types in domain classes

Usage of http://geojson.org/[GeoJSON] types in domain classes is straight forward. The `org.springframework.data.mongodb.core.geo` package contains types like `GeoJsonPoint`, `GeoJsonPolygon` and others. Those are extensions to the existing `org.springframework.data.geo` types.

====
[source,java]
----
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.

====
[source,java]
----
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 GeoJSON and legacy format.
<2> Use GeoJSON type the make use of `$geometry` operator.
<3> Plase note that GeoJSON polygons need the define a closed ring.
<4> Use legacy format `$polygon` operator.
====

[[mongo.textsearch]]
=== Full Text Queries

Since MongoDB 2.6 full text queries can be executed using the `$text` operator. Methods and operations specific for full text queries are available in `TextQuery` and `TextCriteria`. When doing full text search please refer to the http://docs.mongodb.org/manual/reference/operator/query/text/#behavior[MongoDB reference] for its behavior and limitations.

==== Full Text Search

Before we are actually able to use full text search we have to ensure to set up the search index correctly. Please refer to section <<mapping-usage-indexes.text-index,Text Index>> for creating index structures.

[source,javascript]
----
db.foo.createIndex(
{
  title : "text",
  content : "text"
},
{
  weights : {
              title : 3
            }
}
)
----

A query searching for `coffee cake`, sorted by relevance according to the `weights` can be defined and executed as:

[source,java]
----
Query query = TextQuery.searching(new TextCriteria().matchingAny("coffee", "cake")).sortByScore();
List<Document> page = template.find(query, Document.class);
----

Exclusion of search terms can directly be done by prefixing the term with `-` or using `notMatching`

[source,java]
----
// search for 'coffee' and not 'cake'
TextQuery.searching(new TextCriteria().matching("coffee").matching("-cake"));
TextQuery.searching(new TextCriteria().matching("coffee").notMatching("cake"));
----

As `TextCriteria.matching` takes the provided term as is. Therefore phrases can be defined by putting them between double quotes (eg. `\"coffee cake\")` or using `TextCriteria.phrase.`

[source,java]
----
// search for phrase 'coffee cake'
TextQuery.searching(new TextCriteria().matching("\"coffee cake\""));
TextQuery.searching(new TextCriteria().phrase("coffee cake"));
----

The flags for `$caseSensitive` and `$diacriticSensitive` can be set via the according methods on `TextCriteria`. Please note that these two optional flags have been introduced in MongoDB 3.2 and will not be included in the query unless explicitly set.

[[mongo.collation]]
=== Collations

MongoDB supports since 3.4 collations for collection and index creation and various query operations. Collations define string comparison rules based on the http://userguide.icu-project.org/collation/concepts[ICU collations]. A collation document consists of various properties that are encapsulated in `Collation`:

====
[source,java]
----
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 denoting differences between characters. You can configure various options (case-sensitivity, case-ordering) 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 if text requires normalization and to perform normalization.
====

Collations can be used to create collections and indexes. If you create a collection specifying 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.

[source,java]
----
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.

.Using collation with `find`
====
[source,java]
----
Collation collation = Collation.of("de");

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

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

.Using collation with `aggregate`
====
[source,java]
----
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 and the index collation matches.

[[mongo.jsonSchema]]
=== JSON Schema

As of version 3.6 MongoDB supports collections that validate ``Document``s against a provided https://docs.mongodb.com/manual/core/schema-validation/#json-schema[JSON Schema].
The schema itself and both validation action and level can be defined when creating the collection.

.Sample JSON schema
====
[source,json]
----
{
  "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 describing properties and subdocuments.
<2> `required` is a property describing which properties are required in a document. It can be specified optionally along of other
schema constraints. See MongoDB's documentation on https://docs.mongodb.com/manual/reference/operator/query/jsonSchema/#available-keywords[available keywords].
<3> `properties` is related to a schema object describing an `object` type. It contains property-specific schema constraints.
<4> `firstname` specifies constrains for the `firsname` field inside the document. Here it's a string-based properties 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 (i.e. using the `Document` API by parsing or building 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.

.Creating a JSON schema
====
[source,java]
----
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 entrypoints like `string(…)`.
<4> Build the schema object. Use the schema to either create a collection or <<mongodb-template-query.criteria,query documents>>.
====

There are already some predefined and strongly typed schema objects (`JsonSchemaObject` and `JsonSchemaProperty`) available
via static methods on the gateway interfaces.
However there may arise the need to build custom property validation rules which can be created via builder API.

[source,java]
----
// "birthdate" : { "bsonType": "date" }
JsonSchemaProperty.named("birthdate").ofType(Type.dateType());

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

`CollectionOptions` provides the entry point to schema support for collections.

.Create collection with `$jsonSchema`
====
[source,java]
----
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.

.Query for Documents matching a `$jsonSchema`
====
[source,java]
----
MongoJsonSchema schema = MongoJsonSchema.builder().required("firstname", "lastname").build();

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

[cols="3,1,6", options="header"]
.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[]`
|

| `boolean`
| `boolean`, `Boolean`
|

| `null`
| `null`
|

| `objectId`
| `ObjectId`
|

| `date`
| `java.util.Date`
|

| `timestamp`
| `BsonTimestamp`
|

| `regex`
| `java.util.regex.Pattern`
|

|===

NOTE: `untyped` is a generic type that is inherited by all typed schema types providing all `untyped` schema properties to typed schema types.

For more information, see https://docs.mongodb.com/manual/reference/operator/query/jsonSchema/#op._S_jsonSchema[$jsonSchema].

[[mongo.query.fluent-template-api]]
=== 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 / index creation and CRUD operations to more advanced functionality like map-reduce and aggregations.
One can find multiple overloads for each and every method. Most of them just cover optional / nullable parts of the API.

`FluentMongoOperations` provide a more narrow interface for common methods of `MongoOperations` providing a more readable, fluent API.
The entry points `insert(…)`, `find(…)`, `update(…)`, etc. follow a natural naming schema based on the operation to execute. Moving on from the entry point the API is designed to only offer context dependent methods guiding towards a terminating method that invokes the actual `MongoOperations` counterpart.

====
[source,java]
----
List<SWCharacter> all = ops.find(SWCharacter.class)
  .inCollection("star-wars")                        <1>
  .all();
----
<1> Skip this step if `SWCharacter` defines the collection via `@Document` or if using the class name as the collection name is just fine.
====

Sometimes a collection in MongoDB holds entities of different types. Like a `Jedi` within a collection of `SWCharacters`.
To use different types for `Query` and return value mapping one can use `as(Class<?> targetType)` map results differently.

====
[source,java]
----
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: It is possible to directly apply <<projections>> to resulting documents by providing just the `interface` type via `as(Class<?>)`.

Switching between retrieving a single entity, multiple ones as `List` or `Stream` like is done via the terminating methods `first()`, `one()`, `all()` or `stream()`.

When writing a geo-spatial query via `near(NearQuery)` the number of terminating methods is altered to just the ones valid for executing a `geoNear` command in MongoDB fetching entities as `GeoResult` within `GeoResults`.

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

include::../{spring-data-commons-docs}/query-by-example.adoc[leveloffset=+1]
include::query-by-example.adoc[leveloffset=+1]

[[mongo.mapreduce]]
== Map-Reduce Operations

You can query MongoDB using Map-Reduce which is useful for batch processing, data aggregation, and for when the query language doesn't fulfill your needs.

Spring provides integration with MongoDB's map reduce by providing methods on MongoOperations to simplify the creation and execution of Map-Reduce operations. It can convert the results of a Map-Reduce operation to a POJO also integrates with Spring's http://docs.spring.io/spring/docs/{springVersion}/spring-framework-reference/core.html#resources[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 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.

[[mongo.mapreduce.example]]
=== Example Usage

To understand how to perform Map-Reduce operations an example from the book 'MongoDB - The definitive guide' is used. In this example we will create three documents that have the values [a,b], [b,c], and [c,d] respectfully. The values in each document are associated with the key 'x' as shown below. For this example assume these documents are in the collection named "jmr1".

[source]
----
{ "_id" : ObjectId("4e5ff893c0277826074ec533"), "x" : [ "a", "b" ] }
{ "_id" : ObjectId("4e5ff893c0277826074ec534"), "x" : [ "b", "c" ] }
{ "_id" : ObjectId("4e5ff893c0277826074ec535"), "x" : [ "c", "d" ] }
----

A map function that will count the occurrence of each letter in the array for each document is shown below

[source,java]
----
function () {
    for (var i = 0; i < this.x.length; i++) {
        emit(this.x[i], 1);
    }
}
----

The reduce function that will sum up the occurrence of each letter across all the documents is shown below

[source,java]
----
function (key, values) {
    var sum = 0;
    for (var i = 0; i < values.length; i++)
        sum += values[i];
    return sum;
}
----

Executing this will result in a collection as shown below.

[source]
----
{ "_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 execute a map-reduce operation and obtain the results as shown below

[source,java]
----
MapReduceResults<ValueObject> results = mongoOperations.mapReduce("jmr1", "classpath:map.js", "classpath:reduce.js", ValueObject.class);
for (ValueObject valueObject : results) {
  System.out.println(valueObject);
}
----

The output of the above code is

[source]
----
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, as well as timing and count statistics. The `ValueObject` class is simply

[source,java]
----
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 you don't have to 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 very compact syntax. Here an example that sets the output collection to "jmr1_out". Note that setting only the output collection assumes a default output type of REPLACE.

[source,java]
----
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

[source,java]
----
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 will be used to feed into the map-reduce operation. This will remove the document that contains [a,b] from consideration for map-reduce operations.

[source,java]
----
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 as well on the query but not skip values.

[[mongo.server-side-scripts]]
== Script Operations

MongoDB allows executing JavaScript functions on the server by either directly sending the script or calling a stored one. `ScriptOperations` can be accessed via `MongoTemplate` and provides basic abstraction for `JavaScript` usage.

=== Example Usage

====
[source,java]
----
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> Execute 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> Execute the script with name 'echo' using the provided parameters.
====

[[mongo.group]]
== Group Operations

As an alternative to using Map-Reduce to perform data aggregation, you can use the http://www.mongodb.org/display/DOCS/Aggregation#Aggregation-Group[`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 execution of group operations. It can convert the results of the group operation to a POJO and also integrates with Spring's http://docs.spring.io/spring/docs/{springVersion}/spring-framework-reference/core.html#resources[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.

[[mongo.group.example]]
=== 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.

[source]
----
{ "_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 execute the group operation is shown below

[source,java]
----
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

[source]
----
{
  "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.

[source,java]
----
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.

[source]
----
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);
----

[[mongo.aggregation]]
== Aggregation Framework Support

Spring Data MongoDB provides support for the Aggregation Framework introduced to MongoDB in version 2.2.

The MongoDB Documentation describes the http://docs.mongodb.org/manual/core/aggregation/[Aggregation Framework] as follows:

For further information see the full http://docs.mongodb.org/manual/aggregation/[reference documentation] of the aggregation framework and other data aggregation tools for MongoDB.

[[mongo.aggregation.basic-concepts]]
=== 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 the list of `AggregateOperation` as a parameter next to the optional input class.
+
The actual aggregate operation is executed by the `aggregate` method of the `MongoTemplate` which also takes the desired output class as parameter.
+
* `AggregationOperation`
+
An `AggregationOperation` represents a MongoDB aggregation pipeline operation and describes the processing that should be performed in this aggregation step. Although one could manually create an `AggregationOperation` the recommended way to construct an `AggregateOperation` is to use the static factory methods provided by the `Aggregate` class.
+
* `AggregationResults`
+
`AggregationResults` is the container for the result of an aggregate operation. It provides access to the raw aggregation result in the form of an `Document`, to the mapped objects and information which performed the aggregation.
+
The canonical example for using the Spring Data MongoDB support for the MongoDB Aggregation Framework looks as follows:

[source,java]
----
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` will derive the name of the input collection from this class. Otherwise if you don't not specify an input class you must provide the name of the input collection explicitly. If an input-class and an input-collection is provided the latter takes precedence.

[[mongo.aggregation.supported-aggregation-operations]]
=== 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

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

.Aggregation Operations currently supported by Spring Data MongoDB
[cols="2*"]
|===
| 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, sqrt, subtract (*via minus), trunc

| String Aggregation Operators
| concat, substr, toLower, toUpper, stcasecmp, indexOfBytes, indexOfCP, split, strLenBytes, strLenCP, substrCP,

| Comparison Aggregation Operators
| eq (*via: is), gt, gte, lt, lte, ne

| Array Aggregation Operators
| arrayElementAt, 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

|===

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

*) The operation is mapped or added by Spring Data MongoDB.

[[mongo.aggregation.projection]]
=== Projection Expressions

Projection expressions are used to define the fields that are the outcome of a particular aggregation step. Projection expressions can be defined via the `project` method of the `Aggregation` class either by passing a list of ``String``'s or an aggregation framework `Fields` object. The projection can be extended with additional fields through a fluent API via the `and(String)` method and aliased via the `as(String)` method.
Note that one can also define fields with aliases via the static factory method `Fields.field` of the aggregation framework that can then be used to construct a new `Fields` instance. References to projected fields in later aggregation stages are only valid by using the field name of included fields or their alias of aliased or newly defined fields. Fields not included in the projection cannot be referenced in later aggregation stages.

.Projection expression examples
====
[source,java]
----
// will generate {$project: {name: 1, netPrice: 1}}
project("name", "netPrice")

// will generate {$project: {bar: $foo}}
project().and("foo").as("bar")

// will generate {$project: {a: 1, b: 1, bar: $foo}}
project("a","b").and("foo").as("bar")
----
====

.Multi-Stage Aggregation using Projection and Sorting
====
[source,java]
----
// will generate {$project: {name: 1, netPrice: 1}}, {$sort: {name: 1}}
project("name", "netPrice"), sort(ASC, "name")

// will generate {$project: {bar: $foo}}, {$sort: {bar: 1}}
project().and("foo").as("bar"), sort(ASC, "bar")

// this will not work
project().and("foo").as("bar"), sort(ASC, "foo")
----
====

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 http://docs.mongodb.org/manual/reference/operator/aggregation/project/#pipe._S_project[corresponding section] of the MongoDB Aggregation Framework reference documentation.

[[mongo.aggregation.facet]]
=== Faceted classification

MongoDB supports as of Version 3.4 faceted classification 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 classificated 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 grouping expression. They can be defined via the `bucket()`/`bucketAuto()` methods of the `Aggregate` class. `BucketOperation` and `BucketAutoOperation` can expose accumulations based on aggregation expressions for input documents. The bucket operation can be extended with additional parameters through a fluent API via the `with…()` methods, the `andOutput(String)` method and aliased via 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.

.Bucket operation examples
====
[source,java]
----
// will generate {$bucket: {groupBy: $price, boundaries: [0, 100, 400]}}
bucket("price").withBoundaries(0, 100, 400);

// will generate {$bucket: {groupBy: $price, default: "Other" boundaries: [0, 100]}}
bucket("price").withBoundaries(0, 100).withDefault("Other");

// will generate {$bucket: {groupBy: $price, boundaries: [0, 100], output: { count: { $sum: 1}}}}
bucket("price").withBoundaries(0, 100).andOutputCount().as("count");

// will generate {$bucket: {groupBy: $price, boundaries: [0, 100], 5, output: { titles: { $push: "$title"}}}
bucket("price").withBoundaries(0, 100).andOutput("title").push().as("titles");
----
====

`BucketAutoOperation` determines boundaries itself in an attempt to evenly distribute documents into a specified number of buckets. `BucketAutoOperation` optionally takes a granularity specifies the https://en.wikipedia.org/wiki/Preferred_number[preferred number] series to use to ensure that the calculated boundary edges end on preferred round numbers or their powers of 10.

.Bucket operation examples
====
[source,java]
----
// will generate {$bucketAuto: {groupBy: $price, buckets: 5}}
bucketAuto("price", 5)

// will generate {$bucketAuto: {groupBy: $price, buckets: 5, granularity: "E24"}}
bucketAuto("price", 5).withGranularity(Granularities.E24).withDefault("Other");

// will generate {$bucketAuto: {groupBy: $price, buckets: 5, output: { titles: { $push: "$title"}}}
bucketAuto("price", 5).andOutput("title").push().as("titles");
----
====

Bucket operations can use `AggregationExpression` via `andOutput()` and <<mongo.aggregation.projection.expressions, SpEL expressions>> via `andOutputExpression()` to create output fields in buckets.

Note that further details regarding bucket expressions can be found in the http://docs.mongodb.org/manual/reference/operator/aggregation/bucket/[`$bucket` section] and
http://docs.mongodb.org/manual/reference/operator/aggregation/bucketAuto/[`$bucketAuto` section] of the MongoDB Aggregation Framework reference documentation.

==== Multi-faceted aggregation

Multiple aggregation pipelines can be used to create multi-faceted aggregations which 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, etc.

A `FacetOperation` can be defined via the `facet()` method of the `Aggregation` class. It can be customized with multiple aggregation pipelines via 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 grouping. Common cases are extraction of date parts or calculations before categorization.

.Facet operation examples
====
[source,java]
----
// will generate {$facet: {categorizedByPrice: [ { $match: { price: {$exists : true}}}, { $bucketAuto: {groupBy: $price, buckets: 5}}]}}
facet(match(Criteria.where("price").exists(true)), bucketAuto("price", 5)).as("categorizedByPrice"))

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

// will generate {$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 http://docs.mongodb.org/manual/reference/operator/aggregation/facet/[`$facet` section] of the MongoDB Aggregation Framework reference documentation.

[[mongo.aggregation.sort-by-count]]
==== SortByCount

Sort by count operations group incoming documents based on the value of a specified expression, then compute the count of documents in each distinct group and sort the results by count. It's a handy shortcut to apply sorting for when using <<mongo.aggregation.facet>>. Sort by count operations require a grouping field or grouping expression.

.Sort by count example
====
[source,java]
----
// will generate { $sortByCount: "$country" }
sortByCount("country");
----
====

A sort by count operation is equivalent to the following BSON:

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

[[mongo.aggregation.projection.expressions]]
==== Spring Expression Support in Projection Expressions

We support the use of SpEL expression in projection expressions via the `andExpression` method of the `ProjectionOperation` and `BucketOperation` classes. This allows you to define the desired expression as a SpEL expression which is translated into a corresponding MongoDB projection expression part on query execution. This makes it much easier to express complex calculations.

===== Complex calculations with SpEL expressions

The following SpEL expression:

[source,java]
----
1 + (q + 1) / (q - 1)
----

will be translated into the following projection expression part:

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

Have a look at an example in more context in <<mongo.aggregation.examples.example5>> and <<mongo.aggregation.examples.example6>>. You can find more usage examples for supported SpEL expression constructs in `SpelExpressionTransformerUnitTests`.

.Supported SpEL transformations
[cols="2"]
|===
| 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] }
|===

Next to the transformations shown in Supported SpEL transformations it is possible to use standard SpEL operations like `new` to eg. create arrays and reference expressions via their name followed by the arguments to use in brackets.

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

[[mongo.aggregation.examples]]
==== Aggregation Framework Examples

The following examples demonstrate the usage patterns for the MongoDB Aggregation Framework with Spring Data MongoDB.

[[mongo.aggregation.examples.example1]]
.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).

[source,java]
----
class TagCount {
 String tag;
 int n;
}
----

[source,java]
----
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();
----

* In order to do this we first create a new aggregation via the `newAggregation` static factory method to which we pass a list of aggregation operations. These aggregate operations define the aggregation pipeline of our `Aggregation`.
* As a second step we select the `"tags"` field (which is an array of strings) from the input collection with the `project` operation.
* In a third step we use the `unwind` operation to generate a new document for each tag within the `"tags"` array.
* In the forth step we use the `group` operation to define a group for each `"tags"`-value for which we aggregate the occurrence count via the `count` aggregation operator and collect the result in a new field called `"n"`.
* As a fifth step we select the field `"n"` and create an alias for the id-field generated from the previous group operation (hence the call to `previousOperation()`) with the name `"tag"`.
* As the sixth step we sort the resulting list of tags by their occurrence count in descending order via the `sort` operation.
* Finally we call the `aggregate` Method on the MongoTemplate in order 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 first parameter to the `newAggreation` Method.

[[mongo.aggregation.examples.example2]]
.Aggregation Framework Example 2

This example is based on the http://docs.mongodb.org/manual/tutorial/aggregation-examples/#largest-and-smallest-cities-by-state[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, using the aggregation framework. This example demonstrates the usage of grouping, sorting and projections (selection).

[source,java]
----
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;
}
----

[source,java]
----
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);
----

* The class `ZipInfo` maps the structure of the given input-collection. The class `ZipInfoStats` defines the structure in the desired output format.
* As a first step we use the `group` operation to define a group from the input-collection. The grouping criteria is the combination of the fields `"state"` and `"city"` which forms the id structure of the group. We aggregate the value of the `"population"` property from the grouped elements with by using the `sum` operator saving the result in the field `"pop"`.
* In a second step we use the `sort` operation to sort the intermediate-result by the fields `"pop"`, `"state"` and `"city"` 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 took care of.
* In the third step we use a `group` operation again to group the intermediate result by `"state"`. Note that `"state"` again implicitly references an group-id field. We select the name and the population count of the biggest and smallest city with calls to the `last(…)` and `first(...)` operator respectively via the `project` operation.
* As the forth step we select the `"state"` field from the previous `group` operation. Note that `"state"` again implicitly references an group-id field. As we do not want an implicitly generated id to appear, we exclude the id from the previous operation via `and(previousOperation()).exclude()`. As we want to populate the nested `City` structures in our output-class accordingly we have to emit appropriate sub-documents with the nested method.
* Finally as the fifth step we sort the resulting list of `StateStats` by their state name in ascending order via the `sort` operation.

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

[[mongo.aggregation.examples.example3]]
.Aggregation Framework Example 3

This example is based on the http://docs.mongodb.org/manual/tutorial/aggregation-examples/#states-with-populations-over-10-million[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 the usage of grouping, sorting and matching (filtering).

[source,java]
----
class StateStats {
   @Id String id;
   String state;
   @Field("totalPop") int totalPopulation;
}
----

[source,java]
----
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();
----

* As a first step we 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"`.
* In the second step we sort the intermediate result by the id-reference of the previous group operation in addition to the `"totalPop"` field in ascending order.
* Finally in the third step we 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.

[[mongo.aggregation.examples.example4]]
.Aggregation Framework Example 4

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

[source,java]
----
class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}
----

[source,java]
----
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.

[[mongo.aggregation.examples.example5]]
.Aggregation Framework Example 5

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

[source,java]
----
class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}
----

[source,java]
----
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();
----

[[mongo.aggregation.examples.example6]]
.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 via indexer expressions according to their position. In this example we reference the parameter which is the first parameter of the parameters array via `[0]`. External parameter expressions are replaced with their respective values when the SpEL expression is transformed into a MongoDB aggregation framework expression.

[source,java]
----
class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}
----

[source,java]
----
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.

[[mongo.aggregation.examples.example7]]
.Aggregation Framework Example 7

This example uses conditional projection. It's derived from the https://docs.mongodb.com/manual/reference/operator/aggregation/cond/[$cond reference documentation].

[source,java]
----
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
}
----

[source,java]
----
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 using a conditional operation for all inventory items that have a `qty` greater or equal to `250`. A second conditional projection is performed for the `description` field. We apply the description `Unspecified` to all items that either do not have a `description` field of items that have a `null` description.

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

.Conditional aggregation projection
====
[source,java]
----
TypedAggregation<Book> agg = Aggregation.newAggregation(Book.class,
  project("title")
    .and(ConditionalOperators.when(ComparisonOperators.valueOf("author.middle")     <1>
        .equalToValue(""))                                                          <2>
        .then("$$REMOVE")                                                           <3>
        .otherwiseValueOf("author.middle")                                          <4>
    )
	.as("author.middle"));
----
<1> If the value of the field `author.middle`
<2> does not contain a value
<3> then use https://docs.mongodb.com/manual/reference/aggregation-variables/#variable.REMOVE[``$$REMOVE``] to exclude the field
<4> else add the field value of `author.middle`.
====

[[mongo.custom-converters]]
== Overriding default mapping with custom converters

In order to have more fine-grained control over the mapping process you can register Spring converters with the `MongoConverter` implementations such as the `MappingMongoConverter`.

The `MappingMongoConverter` checks to see if there are any Spring converters that can handle a specific class before attempting to map the object itself. To 'hijack' the normal mapping strategies of the `MappingMongoConverter`, perhaps for increased performance or other custom mapping needs, you first need to create an implementation of the Spring `Converter` interface and then register it with the MappingConverter.

NOTE: For more information on the Spring type conversion service see the reference docs http://docs.spring.io/spring/docs/{springVersion}/spring-framework-reference/core.html#validation[here].

[[mongo.custom-converters.writer]]
=== Saving using a registered Spring Converter

An example implementation of the `Converter` that converts from a Person object to a `org.bson.Document` is shown below

[source,java]
----
import org.springframework.core.convert.converter.Converter;

import org.bson.Document;

public class PersonWriteConverter implements Converter<Person, Document> {

  public Document convert(Person source) {
    Document document = new Document();
    document.put("_id", source.getId());
    document.put("name", source.getFirstName());
    document.put("age", source.getAge());
    return document;
  }
}
----

[[mongo.custom-converters.reader]]
=== Reading using a Spring Converter

An example implementation of a Converter that converts from a Document to a Person object is shown below.

[source,java]
----
public class PersonReadConverter implements Converter<Document, Person> {

  public Person convert(Document source) {
    Person p = new Person((ObjectId) source.get("_id"), (String) source.get("name"));
    p.setAge((Integer) source.get("age"));
    return p;
  }
}
----

[[mongo.custom-converters.xml]]
=== Registering Spring Converters with the MongoConverter

The Mongo Spring namespace provides a convenience way to register Spring `Converter` s with the `MappingMongoConverter`. The configuration snippet below shows how to manually register converter beans as well as configuring the wrapping `MappingMongoConverter` into a `MongoTemplate`.

[source,xml]
----
<mongo:db-factory dbname="database"/>

<mongo:mapping-converter>
  <mongo:custom-converters>
    <mongo:converter ref="readConverter"/>
    <mongo:converter>
      <bean class="org.springframework.data.mongodb.test.PersonWriteConverter"/>
    </mongo:converter>
  </mongo:custom-converters>
</mongo:mapping-converter>

<bean id="readConverter" class="org.springframework.data.mongodb.test.PersonReadConverter"/>

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

You can also use the base-package attribute of the custom-converters element to enable classpath scanning for all `Converter` and `GenericConverter` implementations below the given package.

[source,xml]
----
<mongo:mapping-converter>
  <mongo:custom-converters base-package="com.acme.**.converters" />
</mongo:mapping-converter>
----

[[mongo.converter-disambiguation]]
=== Converter disambiguation

Generally we inspect the `Converter` implementations for the source and target types they convert from and to. Depending on whether one of those is a type MongoDB can handle natively we will register the converter instance as reading or writing one. Have a look at the following samples:

[source,java]
----
// Write converter as only the target type is one Mongo can handle natively
class MyConverter implements Converter<Person, String> { … }

// Read converter as only the source type is one Mongo can handle natively
class MyConverter implements Converter<String, Person> { … }
----

In case you write a `Converter` whose source and target type are native Mongo types there's no way for us to determine whether we should consider it as reading or writing converter. Registering the converter instance as both might lead to unwanted results then. E.g. a `Converter<String, Long>` is ambiguous although it probably does not make sense to try to convert all `String` instances into `Long` instances when writing. To be generally able to force the infrastructure to register a converter for one way only we provide `@ReadingConverter` as well as `@WritingConverter` to be used in the converter implementation.

[[mongo-template.index-and-collections]]
== Index and Collection management

`MongoTemplate` provides a few methods for managing indexes and collections. These are collected into a helper interface called `IndexOperations`. You access these operations by calling the method `indexOps` and pass in either the collection name or the `java.lang.Class` of your entity (the collection name will be derived from the .class either by name or via annotation metadata).

The `IndexOperations` interface is shown below

[source,java]
----
public interface IndexOperations {

  void ensureIndex(IndexDefinition indexDefinition);

  void dropIndex(String name);

  void dropAllIndexes();

  void resetIndexCache();

  List<IndexInfo> getIndexInfo();
}
----

[[mongo-template.index-and-collections.index]]
=== Methods for creating an Index

We can create an index on a collection to improve query performance.

==== Creating an index using the MongoTemplate

[source,java]
----
mongoTemplate.indexOps(Person.class).ensureIndex(new Index().on("name",Order.ASCENDING));
----

* *ensureIndex* Ensure that an index for the provided IndexDefinition exists for the collection.

You can create standard, geospatial and text indexes using the classes `IndexDefinition`, `GeoSpatialIndex` and `TextIndexDefinition`. For example, given the Venue class defined in a previous section, you would declare a geospatial query as shown below.

[source,java]
----
mongoTemplate.indexOps(Venue.class).ensureIndex(new GeospatialIndex("location"));
----

NOTE: `Index` and `GeospatialIndex` support configuration of <<mongo.collation,collations>>.

[[mongo-template.index-and-collections.access]]
=== Accessing index information

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

[source,java]
----
template.indexOps(Person.class).ensureIndex(new Index().on("age", Order.DESCENDING).unique(Duplicates.DROP));

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

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

[[mongo-template.index-and-collections.collection]]
=== Methods for working with a Collection

It's time to look at some code examples showing how to use the `MongoTemplate`. First we look at creating our first collection.

.Working with collections using the MongoTemplate
====
[source,java]
----
DBCollection collection = null;
if (!mongoTemplate.getCollectionNames().contains("MyNewCollection")) {
    collection = mongoTemplate.createCollection("MyNewCollection");
}

mongoTemplate.dropCollection("MyNewCollection");
----
====

* *getCollectionNames* Returns a set of collection names.
* *collectionExists* Check to see if a collection with a given name exists.
* *createCollection* Create an uncapped collection
* *dropCollection* Drop the collection
* *getCollection* Get a collection by name, creating it if it doesn't exist.

NOTE: Collection creation allows customization via `CollectionOptions` and supports <<mongo.collation,collations>>.

[[mongo-template.commands]]
== Executing Commands

You can also get at the MongoDB driver's `MongoDatabase.runCommand( )` method using the `executeCommand(…)` methods on `MongoTemplate`. These will also perform exception translation into Spring's `DataAccessException` hierarchy.

[[mongo-template.commands.execution]]
=== Methods for executing commands

* `Document` *executeCommand* `(Document command)` Execute a MongoDB command.
* `Document` *executeCommand* `(Document command, ReadPreference readPreference)` Execute a MongoDB command using the given nullable MongoDB `ReadPreference`.
* `Document` *executeCommand* `(String jsonCommand)` Execute the a MongoDB command expressed as a JSON string.

[[mongodb.mapping-usage.events]]
== Lifecycle Events

Built into the MongoDB mapping framework are several `org.springframework.context.ApplicationEvent` events that your application can respond to by registering special beans in the `ApplicationContext`. By being based off Spring's ApplicationContext event infrastructure this 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'd register a subclass of `AbstractMongoEventListener` that overrides the `onBeforeConvert` method. When the event is dispatched, your listener will be called and passed the domain object before it goes into the converter.

====
[source,java]
----
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'd register a subclass of `org.springframework.data.mongodb.core.mapping.event.AbstractMongoEventListener` that overrides the `onBeforeSave` method. When the event is dispatched, your listener will be called and passed the domain object and the converted `com.mongodb.Document`.

====
[source,java]
----
public class BeforeSaveListener extends AbstractMongoEventListener<Person> {
  @Override
  public void onBeforeSave(BeforeSaveEvent<Person> event) {
    … change values, delete them, whatever …
  }
}
----
====

Simply declaring these beans in your Spring ApplicationContext will cause them to be invoked whenever the event is dispatched.

The list of callback methods that are present in AbstractMappingEventListener are

* `onBeforeConvert` - called in MongoTemplate insert, insertList and save operations before the object is converted to a Document using a MongoConveter.
* `onBeforeSave` - called in MongoTemplate insert, insertList and save operations *before* inserting/saving the Document in the database.
* `onAfterSave` - called in MongoTemplate insert, insertList and save operations *after* inserting/saving the Document in the database.
* `onAfterLoad` - called in MongoTemplate find, findAndRemove, findOne and getCollection methods after the Document is retrieved from the database.
* `onAfterConvert` - called in MongoTemplate find, findAndRemove, findOne and getCollection methods after the Document 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 of event publication unless they are document references annotated with `@DBRef`.

[[mongo.exception]]
== 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 http://docs.spring.io/spring/docs/{springVersion}/spring-framework-reference/data-access.html#dao-exceptions[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 you can be sure that you will be able 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 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, 12012 to `InvalidDataAccessApiUsageException`. Look into the implementation for more details on the mapping.

[[mongo.executioncallback]]
== Execution callbacks

One common design feature of all Spring template classes is that all functionality is routed into one of the templates execute callback methods. This helps ensure that exceptions and any resource management that maybe required are performed consistency. While this was of much greater need in the case of JDBC and JMS than with MongoDB, it still offers a single spot for exception translation and logging to occur. As such, using these execute callback is the preferred way to access the MongoDB driver's `DB` and `DBCollection` objects to perform uncommon operations that were not exposed as methods on `MongoTemplate`.

Here is a list of execute callback methods.

* `<T> T` *execute* `(Class<?> entityClass, CollectionCallback<T> action)` Executes the given CollectionCallback for the entity collection of the specified class.

* `<T> T` *execute* `(String collectionName, CollectionCallback<T> action)` Executes the given CollectionCallback on the collection of the given name.

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

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

* `<T> T` *executeInSession* `(DbCallback<T> action)` Executes 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.

Here is an example that uses the `CollectionCallback` to return information about an index

[source,java]
----
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]]
== GridFS support

MongoDB supports storing binary files inside it's filesystem GridFS. Spring Data MongoDB provides a `GridFsOperations` interface as well as the according implementation `GridFsTemplate` to easily interact with the filesystem. You can setup a `GridFsTemplate` instance by handing it a `MongoDbFactory` as well as a `MongoConverter`:

.JavaConfig setup for a GridFsTemplate
====
[source,java]
----
class GridFsConfiguration extends AbstractMongoConfiguration {

  // … further configuration omitted

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

An according XML configuration looks like this:

.XML configuration for a GridFsTemplate
====
[source,xml]
----
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:mongo="http://www.springframework.org/schema/data/mongo"
  xsi:schemaLocation="http://www.springframework.org/schema/data/mongo
                      http://www.springframework.org/schema/data/mongo/spring-mongo.xsd
                      http://www.springframework.org/schema/beans
                      http://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.

.Using GridFsTemplate to store files
====
[source,java]
----
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` as well.

Reading files from the filesystem can either be achieved through the `find(…)` or `getResources(…)` methods. Let's have a look at the `find(…)` methods first. You can either find a single file matching a `Query` or multiple ones. To easily define file queries we provide the `GridFsCriteria` helper class. It provides static factory methods to encapsulate default metadata fields (e.g. `whereFilename()`, `whereContentType()`) or the custom one through `whereMetaData()`.

.Using GridFsTemplate to query for files
====
[source,java]
----
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. Thus any sort criteria defined on the `Query` instance handed into the `find(…)` method will be disregarded.

The other option to read files from the GridFs is using the methods introduced by the `ResourcePatternResolver` interface. They allow handing an Ant path into the method ar thus retrieve files matching the given pattern.

.Using GridFsTemplate to read files
====
[source,java]
----
class GridFsClient {

  @Autowired
  GridFsOperations operations;

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

`GridFsOperations` extending `ResourcePatternResolver` allows the `GridFsTemplate` e.g. to be plugged into an `ApplicationContext` to read Spring Config files from a MongoDB.

[[change-streams]]
== Change Streams

As of MongoDB 3.6, https://docs.mongodb.com/manual/changeStreams/[Change Streams] allow application to get notified about changes without having to tail the oplog.

NOTE: Change Stream support is only possible for replica sets or a sharded cluster.

Change Streams can be subscribed to with both the imperative and the reactive MongoDB Java driver. It is highly recommended to use the reactive variant as it is less resource intensive. However if you do not feel comfortable using the reactive API for whatever reason, you can sill obtain the change events via a Messaging concept already common in the Spring ecosystem.

=== Change Streams using MessageListener

Listening to a https://docs.mongodb.com/manual/tutorial/change-streams-example/[Change Stream using a Sync Driver] is a long running, blocking task that needs to be delegated to a separate component.
In this case we need to create a `MessageListenerContainer` first which will be the main entry point for running the specific ``SubscriptionRequest``s.
Spring Data MongoDB already ships with a default implementation that operates upon `MongoTemplate` and is capable of creating and executing ``Task``s for a `ChangeStreamRequest`.

.Change Streams with `MessageListeners`
====
[source,java]
----
MessageListenerContainer container = new DefaultMessageListenerContainer(template);
container.start();    <1>

MessageListener<ChangeStreamDocument<Document>, User> listener = System.out::println; <2>
ChangeStreamRequestOptions options = new ChangeStreamRequestOptions("user", ChangeStreamOptions.empty()); <3>

Subscription subscription = container.register(new ChangeStreamRequest<>(listener, options), User.class); <4>

// ...

container.stop(); <5>
----
<1> Starting the container intializes the resources and starts the ``Task``s for already registered ``SubscriptionRequest``s. Requests added after the statup are run immediately.
<2> Define the listener called when a `Message` is received. The `Message#getBody()` is converted to the requested domain type. Use `Document` to receive raw results without conversion.
<3> Set the collection to listen to and provide additional options via `ChangeStreamOptions`.
<4> Register the request. The returned `Subscription` can be used to check the current `Task` state and cancel the execution to free resources.
<5> Do not forget to stop the container once you're sure you won't need it any more. This will stop all running ``Task``s within the container.
====

=== Change Streams - Reactive

Subscribing to Change Stream via the reactive API is clearly more straight forward. Still the building blocks like `ChangeStreamOptions` remain the same.

.Change Streams with `MessageListeners`
====
[source,java]
----
Aggregation filter = newAggregation(User.class, match(where("age").gte(38)); <1>
Flux<ChangeStreamEvent<User>> flux = reactiveTemplate.changeStream(filter), User.class, ChangeStreamOptions.empty()); <2>
----
<1> Use an aggregation pipeline to filter events.
<2> Obtain a `Flux` of change stream events. The `ChangeStreamEvent#getBody()` is converted to requested domain type. Use `Document` to receive raw results without conversion.
====
