# Spring Data MongoDB - Reference Documentation

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Mark Pollack·Thomas Risberg·Oliver Gierke·Costin Leau·Jon Brisbin·Thomas Darimont·Christoph Strobl·Mark Paluch·Jay Bryant
Version 2.1.9.RELEASE,2019-06-14

Copies of this document may be made for your own use and for distribution to others, provided that you do not charge any fee for such copies and further provided that each copy contains this Copyright Notice, whether distributed in print or electronically.

# Contents

Preface
1.Learning-Spring
2.学习NoSQL和文档数据库
3.Requirements
4.其他帮助资源
5.Following_Development
6.New_Noteworthy
7.Dependencies
8.使用Spring Data Repositories
- 8.6.Spring数据存储库的自定义实现
9.Introduction
10.MongoDB-support
11.MongoDB_Sessions
12.MongoDB_Transactions
13.Reactive_MongoDB_support
14.MongoDB_Repositories
16.Auditing
17.Mapping
18.Cross_Store_Support
19.JMX_support

# Preface

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

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

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

# Learning-Spring

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

IoC container
type conversion system
expression language
JMX integration
DAO exception hierarchy.

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

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

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

# 学习NoSQL和文档数据库

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

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

The manual introduces MongoDB and contains links to getting started guides, reference documentation, and tutorials.

The online shell provides a convenient way to interact with a MongoDB instance in combination with the online tutorial.

MongoDB Java Language Center.

Several books you can purchase.

Karl Seguin’s online book: The Little MongoDB Book.

# Requirements

The Spring Data MongoDB 2.x binaries require JDK level 8.0 and above and Spring Framework 5.1.8.RELEASE and above.

In terms of document stores, you need at least version 2.6 of MongoDB.

# 其他帮助资源

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

# Community Forum

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

# Professional Support

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

# Following_Development

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

# New_Noteworthy

# 6.1. What’s New in Spring Data MongoDB 2.1

Cursor-based aggregation execution.

Distinct queries for imperative and reactive Template APIs.

Support for Map/Reduce through the reactive Template API.

validator support for collections.

$jsonSchema support for queries and collection creation.

Change Stream support for imperative and reactive drivers.

Tailable cursors for imperative driver.

MongoDB 3.6 Session support for the imperative and reactive Template APIs.

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

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

findAndReplace support through imperative and reactive Template APIs.

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

# 6.2. What’s New in Spring Data MongoDB 2.0

Upgrade to Java 8.

Usage of the Document API, instead of DBObject.

Reactive MongoDB support.

Tailable Cursor queries.

Support for aggregation result streaming by using Java 8 Stream.

Fluent Collection API for CRUD and aggregation operations.

Kotlin extensions for Template and Collection APIs.

Integration of collations for collection and index creation and query operations.

Query-by-Example support without type matching.

Support for isolation Update operations.

Tooling support for null-safety by using Spring’s @NonNullApi and @Nullable annotations.

Deprecated cross-store support and removed Log4j appender.

# 6.3. What’s New in Spring Data MongoDB 1.10

Compatible with MongoDB Server 3.4 and the MongoDB Java Driver 3.4.

New annotations for @CountQuery, @DeleteQuery, and @ExistsQuery.

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

Support for partial filter expression when creating indexes.

Publishing lifecycle events when loading or converting DBRef instances.

Added any-match mode for Query By Example.

Support for $caseSensitive and $diacriticSensitive text search.

Support for GeoJSON Polygon with hole.

Performance improvements by bulk-fetching DBRef instances.

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

# 6.4. What’s New in Spring Data MongoDB 1.9

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

Support for Projections in repository query methods.

Support for Query by Example.

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

Support for the bulk operations introduced in MongoDB 2.6.

Upgrade to Querydsl 4.

Assert compatibility with MongoDB 3.0 and MongoDB Java Driver 3.2 (see: MongoDB 3.0 Support).

# 6.5. What’s New in Spring Data MongoDB 1.8

Criteria offers support for creating $geoIntersects.

Support for SpEL expressions in @Query.

MongoMappingEvents expose the collection name for which they are issued.

Improved support for <mongo:mongo-client credentials="…" />.

Improved index creation failure error message.

# 6.6. What’s New in Spring Data MongoDB 1.7

Assert compatibility with MongoDB 3.0 and MongoDB Java Driver 3-beta3 (see: MongoDB 3.0 Support).

Support JSR-310 and ThreeTen back-port date/time types.

Allow Stream as a query method return type (see: Query Methods).

GeoJSON support in both domain types and queries (see: GeoJSON Support).

QueryDslPredicateExcecutor now supports findAll(OrderSpecifier<?>… orders).

Support calling JavaScript functions with Script Operations.

Improve support for CONTAINS keyword on collection-like properties.

Support for $bit, $mul, and $position operators to Update.

# Dependencies

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

Example 1. Using the Spring Data release train BOM

<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>org.springframework.data</groupId>
      <artifactId>spring-data-releasetrain</artifactId>
      <version>Lovelace-SR9</version>
      <scope>import</scope>
      <type>pom</type>
    </dependency>
  </dependencies>
</dependencyManagement>

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The current release train version is Lovelace-SR9. The train names ascend alphabetically and the currently available trains are listed here. The version name follows the following pattern: ${name}-${release}, where release can be one of the following:

BUILD-SNAPSHOT: Current snapshots

M1, M2, and so on: Milestones

RC1, RC2, and so on: Release candidates

RELEASE: GA release

SR1, SR2, and so on: Service releases

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

Example 2. Declaring a dependency to a Spring Data module

<dependencies>
  <dependency>
    <groupId>org.springframework.data</groupId>
    <artifactId>spring-data-jpa</artifactId>
  </dependency>
<dependencies>

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# 7.1. Dependency Management with Spring Boot

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

# 7.2. Spring Framework

The current version of Spring Data modules require Spring Framework in version 5.1.8.RELEASE or better. The modules might also work with an older bugfix version of that minor version. However, using the most recent version within that generation is highly recommended.

# 使用Spring_Data_Repositories

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

Spring Data repository documentation and your module

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

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

Example 3. CrudRepository interface

public interface CrudRepository<T, ID extends Serializable>
  extends Repository<T, ID> {

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

  Optional<T> findById(ID primaryKey); 

  Iterable<T> findAll();               

  long count();                        

  void delete(T entity);               

  boolean existsById(ID primaryKey);   

  // … more functionality omitted.
}

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Saves the given entity. Returns the entity identified by the given ID. Returns all entities. Returns the number of entities. Deletes the given entity. Indicates whether an entity with the given ID exists. We also provide persistence technology-specific abstractions, such as JpaRepository or MongoRepository. Those interfaces extend CrudRepository and expose the capabilities of the underlying persistence technology in addition to the rather generic persistence technology-agnostic interfaces such as CrudRepository. On top of the CrudRepository, there is a PagingAndSortingRepository abstraction that adds additional methods to ease paginated access to entities:

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

Iterable findAll(Sort sort);

Page findAll(Pageable pageable); } To access the second page of User by a page size of 20, you could do something like the following:

PagingAndSortingRepository<User, Long> repository = // … get access to a bean Page users = repository.findAll(PageRequest.of(1, 20)); In addition to query methods, query derivation for both count and delete queries is available. The following list shows the interface definition for a derived count query:

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

long countByLastname(String lastname); } The following list shows the interface definition for a derived delete query:

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

long deleteByLastname(String lastname);

List removeByLastname(String lastname); } 8.2. Query methods Standard CRUD functionality repositories usually have queries on the underlying datastore. With Spring Data, declaring those queries becomes a four-step process:

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

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

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

To use Java configuration, create a class similar to the following:

import org.springframework.data.jpa.repository.config.EnableJpaRepositories;

@EnableJpaRepositories class Config {} To use XML configuration, define a bean similar to the following:

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

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

</beans>

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

+Also, note that the JavaConfig variant does not configure a package explicitly, because the package of the annotated class is used by default. To customize the package to scan, use one of the basePackage… attributes of the data-store-specific repository’s @Enable${store}Repositories-annotation.

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

class SomeClient {

  private final PersonRepository repository;

  SomeClient(PersonRepository repository) {
    this.repository = repository;
  }

  void doSomething() {
    List<Person> persons = repository.findByLastname("Matthews");
  }
}

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The sections that follow explain each step in detail:

Defining Repository Interfaces

Defining Query Methods

Creating Repository Instances

Custom Implementations for Spring Data Repositories

# 8.3. Defining Repository Interfaces

First, define a domain class-specific repository interface. The interface must extend Repository and be typed to the domain class and an ID type. If you want to expose CRUD methods for that domain type, extend CrudRepository instead of Repository.

# 8.3.1. Fine-tuning Repository Definition

Typically, your repository interface extends Repository, CrudRepository, or PagingAndSortingRepository. Alternatively, if you do not want to extend Spring Data interfaces, you can also annotate your repository interface with @RepositoryDefinition. Extending CrudRepository exposes a complete set of methods to manipulate your entities. If you prefer to be selective about the methods being exposed, copy the methods you want to expose from CrudRepository into your domain repository.

Doing so lets you define your own abstractions on top of the provided Spring Data Repositories functionality. The following example shows how to selectively expose CRUD methods (findById and save, in this case):

Example 7. Selectively exposing CRUD methods

@NoRepositoryBean
interface MyBaseRepository<T, ID extends Serializable> extends Repository<T, ID> {

  Optional<T> findById(ID id);

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

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

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In the prior example, you defined a common base interface for all your domain repositories and exposed findById(…) as well as save(…).These methods are routed into the base repository implementation of the store of your choice provided by Spring Data (for example, if you use JPA, the implementation is SimpleJpaRepository), because they match the method signatures in CrudRepository. So the UserRepository can now save users, find individual users by ID, and trigger a query to find Users by email address.

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

# 8.3.2. Null Handling of Repository Methods

As of Spring Data 2.0, repository CRUD methods that return an individual aggregate instance use Java 8’s Optional to indicate the potential absence of a value. Besides that, Spring Data supports returning the following wrapper types on query methods:

com.google.common.base.Optional
scala.Option
io.vavr.control.Option
javaslang.control.Option (deprecated as Javaslang is deprecated)

Alternatively, query methods can choose not to use a wrapper type at all. The absence of a query result is then indicated by returning null. Repository methods returning collections, collection alternatives, wrappers, and streams are guaranteed never to return null but rather the corresponding empty representation. See “Repository query return types” for details.

# Nullability Annotations

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

@NonNullApi:
Used on the package level to declare that the default behavior for parameters and return values is to not accept or produce null values.

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

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

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

Example 8. Declaring Non-nullability in package-info.java @org.springframework.lang.NonNullApi package com.acme; Once non-null defaulting is in place, repository query method invocations get validated at runtime for nullability constraints. If a query execution result violates the defined constraint, an exception is thrown. This happens when the method would return null but is declared as non-nullable (the default with the annotation defined on the package the repository resides in). If you want to opt-in to nullable results again, selectively use @Nullable on individual methods. Using the result wrapper types mentioned at the start of this section continues to work as expected: An empty result is translated into the value that represents absence.

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

Example 9. Using different nullability constraints package com.acme;

import org.springframework.lang.Nullable;

interface UserRepository extends Repository<User, Long> {

User getByEmailAddress(EmailAddress emailAddress);

@Nullable User findByEmailAddress(@Nullable EmailAddress emailAdress);

Optional findOptionalByEmailAddress(EmailAddress emailAddress); } The repository resides in a package (or sub-package) for which we have defined non-null behavior. Throws an EmptyResultDataAccessException when the query executed does not produce a result. Throws an IllegalArgumentException when the emailAddress handed to the method is null. Returns null when the query executed does not produce a result. Also accepts null as the value for emailAddress. Returns Optional.empty() when the query executed does not produce a result. Throws an IllegalArgumentException when the emailAddress handed to the method is null. Nullability in Kotlin-based Repositories Kotlin has the definition of nullability constraints baked into the language. Kotlin code compiles to bytecode, which does not express nullability constraints through method signatures but rather through compiled-in metadata. Make sure to include the kotlin-reflect JAR in your project to enable introspection of Kotlin’s nullability constraints. Spring Data repositories use the language mechanism to define those constraints to apply the same runtime checks, as follows:

Example 10. Using nullability constraints on Kotlin repositories interface UserRepository : Repository<User, String> {

fun findByUsername(username: String): User

fun findByFirstname(firstname: String?): User? } The method defines both the parameter and the result as non-nullable (the Kotlin default). The Kotlin compiler rejects method invocations that pass null to the method. If the query execution yields an empty result, an EmptyResultDataAccessException is thrown. This method accepts null for the firstname parameter and returns null if the query execution does not produce a result. 8.3.3. Using Repositories with Multiple Spring Data Modules Using a unique Spring Data module in your application makes things simple, because all repository interfaces in the defined scope are bound to the Spring Data module. Sometimes, applications require using more than one Spring Data module. In such cases, a repository definition must distinguish between persistence technologies. When it detects multiple repository factories on the class path, Spring Data enters strict repository configuration mode. Strict configuration uses details on the repository or the domain class to decide about Spring Data module binding for a repository definition:

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

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

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

Example 11. Repository definitions using module-specific interfaces interface MyRepository extends JpaRepository<User, Long> { }

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

interface UserRepository extends MyBaseRepository<User, Long> { … } MyRepository and UserRepository extend JpaRepository in their type hierarchy. They are valid candidates for the Spring Data JPA module.

The following example shows a repository that uses generic interfaces:

Example 12. Repository definitions using generic interfaces interface AmbiguousRepository extends Repository<User, Long> { … }

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

interface AmbiguousUserRepository extends MyBaseRepository<User, Long> { … } AmbiguousRepository and AmbiguousUserRepository extend only Repository and CrudRepository in their type hierarchy. While this is perfectly fine when using a unique Spring Data module, multiple modules cannot distinguish to which particular Spring Data these repositories should be bound.

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

Example 13. Repository definitions using domain classes with annotations interface PersonRepository extends Repository<Person, Long> { … }

@Entity class Person { … }

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

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

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

Example 14. Repository definitions using domain classes with mixed annotations interface JpaPersonRepository extends Repository<Person, Long> { … }

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

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

Repository type details and distinguishing domain class annotations are used for strict repository configuration to identify repository candidates for a particular Spring Data module. Using multiple persistence technology-specific annotations on the same domain type is possible and enables reuse of domain types across multiple persistence technologies. However, Spring Data can then no longer determine a unique module with which to bind the repository.

The last way to distinguish repositories is by scoping repository base packages. Base packages define the starting points for scanning for repository interface definitions, which implies having repository definitions located in the appropriate packages. By default, annotation-driven configuration uses the package of the configuration class. The base package in XML-based configuration is mandatory.

The following example shows annotation-driven configuration of base packages:

Example 15. Annotation-driven configuration of base packages @EnableJpaRepositories(basePackages = "com.acme.repositories.jpa") @EnableMongoRepositories(basePackages = "com.acme.repositories.mongo") interface Configuration { } 8.4. Defining Query Methods The repository proxy has two ways to derive a store-specific query from the method name:

By deriving the query from the method name directly.

By using a manually defined query.

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

8.4.1. Query Lookup Strategies The following strategies are available for the repository infrastructure to resolve the query. With XML configuration, you can configure the strategy at the namespace through the query-lookup-strategy attribute. For Java configuration, you can use the queryLookupStrategy attribute of the Enable${store}Repositories annotation. Some strategies may not be supported for particular datastores.

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

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

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

8.4.2. Query Creation The query builder mechanism built into Spring Data repository infrastructure is useful for building constraining queries over entities of the repository. The mechanism strips the prefixes find…By, read…By, query…By, count…By, and get…By from the method and starts parsing the rest of it. The introducing clause can contain further expressions, such as a Distinct to set a distinct flag on the query to be created. However, the first By acts as delimiter to indicate the start of the actual criteria. At a very basic level, you can define conditions on entity properties and concatenate them with And and Or. The following example shows how to create a number of queries:

Example 16. Query creation from method names interface PersonRepository extends Repository<User, Long> {

List findByEmailAddressAndLastname(EmailAddress emailAddress, String lastname);

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

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

// Enabling static ORDER BY for a query List findByLastnameOrderByFirstnameAsc(String lastname); List findByLastnameOrderByFirstnameDesc(String lastname); } The actual result of parsing the method depends on the persistence store for which you create the query. However, there are some general things to notice:

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

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

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

8.4.3. Property Expressions Property expressions can refer only to a direct property of the managed entity, as shown in the preceding example. At query creation time, you already make sure that the parsed property is a property of the managed domain class. However, you can also define constraints by traversing nested properties. Consider the following method signature:

List findByAddressZipCode(ZipCode zipCode); Assume a Person has an Address with a ZipCode. In that case, the method creates the property traversal x.address.zipCode. The resolution algorithm starts by interpreting the entire part (AddressZipCode) as the property and checks the domain class for a property with that name (uncapitalized). If the algorithm succeeds, it uses that property. If not, the algorithm splits up the source at the camel case parts from the right side into a head and a tail and tries to find the corresponding property — in our example, AddressZip and Code. If the algorithm finds a property with that head, it takes the tail and continues building the tree down from there, splitting the tail up in the way just described. If the first split does not match, the algorithm moves the split point to the left (Address, ZipCode) and continues.

Although this should work for most cases, it is possible for the algorithm to select the wrong property. Suppose the Person class has an addressZip property as well. The algorithm would match in the first split round already, choose the wrong property, and fail (as the type of addressZip probably has no code property).

To resolve this ambiguity you can use _ inside your method name to manually define traversal points. So our method name would be as follows:

List findByAddress_ZipCode(ZipCode zipCode); Because we treat the underscore character as a reserved character, we strongly advise following standard Java naming conventions (that is, not using underscores in property names but using camel case instead).

8.4.4. Special parameter handling To handle parameters in your query, define method parameters as already seen in the preceding examples. Besides that, the infrastructure recognizes certain specific types like Pageable and Sort, to apply pagination and sorting to your queries dynamically. The following example demonstrates these features:

Example 17. Using Pageable, Slice, and Sort in query methods Page findByLastname(String lastname, Pageable pageable);

Slice findByLastname(String lastname, Pageable pageable);

List findByLastname(String lastname, Sort sort);

List findByLastname(String lastname, Pageable pageable); The first method lets you pass an org.springframework.data.domain.Pageable instance to the query method to dynamically add paging to your statically defined query. A Page knows about the total number of elements and pages available. It does so by the infrastructure triggering a count query to calculate the overall number. As this might be expensive (depending on the store used), you can instead return a Slice. A Slice only knows about whether a next Slice is available, which might be sufficient when walking through a larger result set.

Sorting options are handled through the Pageable instance, too. If you only need sorting, add an org.springframework.data.domain.Sort parameter to your method. As you can see, returning a List is also possible. In this case, the additional metadata required to build the actual Page instance is not created (which, in turn, means that the additional count query that would have been necessary is not issued). Rather, it restricts the query to look up only the given range of entities.

To find out how many pages you get for an entire query, you have to trigger an additional count query. By default, this query is derived from the query you actually trigger. 8.4.5. Limiting Query Results The results of query methods can be limited by using the first or top keywords, which can be used interchangeably. An optional numeric value can be appended to top or first to specify the maximum result size to be returned. If the number is left out, a result size of 1 is assumed. The following example shows how to limit the query size:

Example 18. Limiting the result size of a query with Top and First User findFirstByOrderByLastnameAsc();

User findTopByOrderByAgeDesc();

Page queryFirst10ByLastname(String lastname, Pageable pageable);

Slice findTop3ByLastname(String lastname, Pageable pageable);

List findFirst10ByLastname(String lastname, Sort sort);

List findTop10ByLastname(String lastname, Pageable pageable); The limiting expressions also support the Distinct keyword. Also, for the queries limiting the result set to one instance, wrapping the result into with the Optional keyword is supported.

If pagination or slicing is applied to a limiting query pagination (and the calculation of the number of pages available), it is applied within the limited result.

Limiting the results in combination with dynamic sorting by using a Sort parameter lets you express query methods for the 'K' smallest as well as for the 'K' biggest elements. 8.4.6. Streaming query results The results of query methods can be processed incrementally by using a Java 8 Stream as return type. Instead of wrapping the query results in a Stream data store-specific methods are used to perform the streaming, as shown in the following example:

Example 19. Stream the result of a query with Java 8 Stream @Query("select u from User u") Stream findAllByCustomQueryAndStream();

Stream readAllByFirstnameNotNull();

@Query("select u from User u") Stream streamAllPaged(Pageable pageable); A Stream potentially wraps underlying data store-specific resources and must, therefore, be closed after usage. You can either manually close the Stream by using the close() method or by using a Java 7 try-with-resources block, as shown in the following example: Example 20. Working with a Stream result in a try-with-resources block try (Stream stream = repository.findAllByCustomQueryAndStream()) { stream.forEach(…); } Not all Spring Data modules currently support Stream as a return type. 8.4.7. Async query results Repository queries can be run asynchronously by using Spring’s asynchronous method execution capability. This means the method returns immediately upon invocation while the actual query execution occurs in a task that has been submitted to a Spring TaskExecutor. Asynchronous query execution is different from reactive query execution and should not be mixed. Refer to store-specific documentation for more details on reactive support. The following example shows a number of asynchronous queries:

@Async Future findByFirstname(String firstname);

@Async CompletableFuture findOneByFirstname(String firstname);

@Async ListenableFuture findOneByLastname(String lastname);
Use java.util.concurrent.Future as the return type. Use a Java 8 java.util.concurrent.CompletableFuture as the return type. Use a org.springframework.util.concurrent.ListenableFuture as the return type. 8.5. Creating Repository Instances In this section, you create instances and bean definitions for the defined repository interfaces. One way to do so is by using the Spring namespace that is shipped with each Spring Data module that supports the repository mechanism, although we generally recommend using Java configuration.

8.5.1. XML configuration Each Spring Data module includes a repositories element that lets you define a base package that Spring scans for you, as shown in the following example:

Example 21. Enabling Spring Data repositories via XML <?xml version="1.0" encoding="UTF-8"?> <beans:beans xmlns:beans="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.springframework.org/schema/data/jpa" xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd http://www.springframework.org/schema/data/jpa http://www.springframework.org/schema/data/jpa/spring-jpa.xsd">

</beans:beans> In the preceding example, Spring is instructed to scan com.acme.repositories and all its sub-packages for interfaces extending Repository or one of its sub-interfaces. For each interface found, the infrastructure registers the persistence technology-specific FactoryBean to create the appropriate proxies that handle invocations of the query methods. Each bean is registered under a bean name that is derived from the interface name, so an interface of UserRepository would be registered under userRepository. The base-package attribute allows wildcards so that you can define a pattern of scanned packages.

Using filters By default, the infrastructure picks up every interface extending the persistence technology-specific Repository sub-interface located under the configured base package and creates a bean instance for it. However, you might want more fine-grained control over which interfaces have bean instances created for them. To do so, use and elements inside the element. The semantics are exactly equivalent to the elements in Spring’s context namespace. For details, see the Spring reference documentation for these elements.

For example, to exclude certain interfaces from instantiation as repository beans, you could use the following configuration:

Example 22. Using exclude-filter element <context:exclude-filter type="regex" expression=".*SomeRepository" /> The preceding example excludes all interfaces ending in SomeRepository from being instantiated.

8.5.2. JavaConfig The repository infrastructure can also be triggered by using a store-specific @Enable${store}Repositories annotation on a JavaConfig class. For an introduction into Java-based configuration of the Spring container, see JavaConfig in the Spring reference documentation.

A sample configuration to enable Spring Data repositories resembles the following:

Example 23. Sample annotation based repository configuration @Configuration @EnableJpaRepositories("com.acme.repositories") class ApplicationConfiguration {

@Bean EntityManagerFactory entityManagerFactory() { // … } } The preceding example uses the JPA-specific annotation, which you would change according to the store module you actually use. The same applies to the definition of the EntityManagerFactory bean. See the sections covering the store-specific configuration. 8.5.3. Standalone usage You can also use the repository infrastructure outside of a Spring container — for example, in CDI environments. You still need some Spring libraries in your classpath, but, generally, you can set up repositories programmatically as well. The Spring Data modules that provide repository support ship a persistence technology-specific RepositoryFactory that you can use as follows:

Example 24. Standalone usage of repository factory RepositoryFactorySupport factory = … // Instantiate factory here UserRepository repository = factory.getRepository(UserRepository.class);

# Spring数据存储库的自定义实现

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8.6. Custom Implementations for Spring Data Repositories
本节介绍存储库自定义以及片段如何构成复合存储库。 This section covers repository customization and how fragments form a composite repository.

当查询方法需要不同的行为或无法通过查询派生实现时，则需要提供自定义实现。 Spring Data存储库允许您提供自定义存储库代码，并将其与通用CRUD抽象和查询方法功能集成。
When a query method requires a different behavior or cannot be implemented by query derivation, then it is necessary to provide a custom implementation. Spring Data repositories let you provide custom repository code and integrate it with generic CRUD abstraction and query method functionality.

# 自定义单个存储库

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8.6.1. Customizing Individual Repositories
要使用自定义功能丰富存储库，必须首先定义片段接口和自定义功能的实现，如以下示例所示：
To enrich a repository with custom functionality, you must first define a fragment interface and an implementation for the custom functionality, as shown in the following example:

自定义存储库功能的接口
Example 25. Interface for custom repository functionality

interface CustomizedUserRepository {
  void someCustomMethod(User user);
}

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Then you can let your repository interface additionally extend from the fragment interface, as shown in the following example:

Example 26. Implementation of custom repository functionality

class CustomizedUserRepositoryImpl implements CustomizedUserRepository {

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

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The most important part of the class name that corresponds to the fragment interface is the Impl postfix. The implementation itself does not depend on Spring Data and can be a regular Spring bean. Consequently, you can use standard dependency injection behavior to inject references to other beans (such as a JdbcTemplate), take part in aspects, and so on.

You can let your repository interface extend the fragment interface, as shown in the following example:

Example 27. Changes to your repository interface

interface UserRepository extends CrudRepository<User, Long>, CustomizedUserRepository {

  // Declare query methods here
}

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Extending the fragment interface with your repository interface combines the CRUD and custom functionality and makes it available to clients.

Spring Data repositories are implemented by using fragments that form a repository composition. Fragments are the base repository, functional aspects (such as QueryDsl), and custom interfaces along with their implementation. Each time you add an interface to your repository interface, you enhance the composition by adding a fragment. The base repository and repository aspect implementations are provided by each Spring Data module.

The following example shows custom interfaces and their implementations:

Example 28. Fragments with their implementations

interface HumanRepository {
  void someHumanMethod(User user);
}

class HumanRepositoryImpl implements HumanRepository {

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

interface ContactRepository {

  void someContactMethod(User user);

  User anotherContactMethod(User user);
}

class ContactRepositoryImpl implements ContactRepository {

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

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

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

Example 29. Changes to your repository interface

interface UserRepository extends CrudRepository<User, Long>, HumanRepository, ContactRepository {

  // Declare query methods here
}

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Repositories may be composed of multiple custom implementations that are imported in the order of their declaration. Custom implementations have a higher priority than the base implementation and repository aspects. This ordering lets you override base repository and aspect methods and resolves ambiguity if two fragments contribute the same method signature. Repository fragments are not limited to use in a single repository interface. Multiple repositories may use a fragment interface, letting you reuse customizations across different repositories.

The following example shows a repository fragment and its implementation:

Example 30. Fragments overriding save(…)

interface CustomizedSave<T> {
  <S extends T> S save(S entity);
}
class CustomizedSaveImpl<T> implements CustomizedSave<T> {

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

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以下示例显示了使用前面的存储库片段的存储库：
The following example shows a repository that uses the preceding repository fragment:

Example 31. Customized repository interfaces

interface UserRepository extends CrudRepository<User, Long>, CustomizedSave<User> {
}

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

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Configuration 如果使用命名空间配置，则存储库基础结构会尝试通过扫描其找到存储库的包下面的类来自动检测自定义实现片段。这些类需要遵循将命名空间元素的repository-impl-postfix属性附加到片段接口名称的命名约定。此后缀默认为Impl。以下示例显示了使用默认后缀的存储库以及为后缀设置自定义值的存储库：
If you use namespace configuration, the repository infrastructure tries to autodetect custom implementation fragments by scanning for classes below the package in which it found a repository. These classes need to follow the naming convention of appending the namespace element’s repository-impl-postfix attribute to the fragment interface name. This postfix defaults to Impl. The following example shows a repository that uses the default postfix and a repository that sets a custom value for the postfix:

Example 32. Configuration example

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

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

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

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

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

Example 33. Resolution of amibiguous implementations

package com.acme.impl.one;

class CustomizedUserRepositoryImpl implements CustomizedUserRepository {

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

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

  // Your custom implementation
}

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

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

Example 34. Manual wiring of custom implementations

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

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

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# 自定义Base-Repository

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8.6.2. Customize the Base Repository
当您要自定义基本存储库行为以便所有存储库都受到影响时，上一节中描述的方法需要自定义每个存储库接口。要改为更改所有存储库的行为，可以创建一个扩展特定于持久性技术的存储库基类的实现。然后，此类充当存储库代理的自定义基类，如以下示例所示：
The approach described in the preceding section requires customization of each repository interfaces when you want to customize the base repository behavior so that all repositories are affected. To instead change behavior for all repositories, you can create an implementation that extends the persistence technology-specific repository base class. This class then acts as a custom base class for the repository proxies, as shown in the following example:

Example 35. Custom repository base class

class MyRepositoryImpl<T, ID extends Serializable>
  extends SimpleJpaRepository<T, ID> {

  private final EntityManager entityManager;

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

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

  @Transactional
  public <S extends T> S save(S entity) {
    // implementation goes here
  }
}

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The class needs to have a constructor of the super class which the store-specific repository factory implementation uses. If the repository base class has multiple constructors, override the one taking an EntityInformation plus a store specific infrastructure object (such as an EntityManager or a template class). The final step is to make the Spring Data infrastructure aware of the customized repository base class. In Java configuration, you can do so by using the repositoryBaseClass attribute of the @Enable${store}Repositories annotation, as shown in the following example:

Example 36. Configuring a custom repository base class using JavaConfig

@Configuration
@EnableJpaRepositories(repositoryBaseClass = MyRepositoryImpl.class)
class ApplicationConfiguration { … }

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A corresponding attribute is available in the XML namespace, as shown in the following example:

Example 37. Configuring a custom repository base class using XML

<repositories base-package="com.acme.repository"
     base-class="….MyRepositoryImpl" />

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

Example 38. Exposing domain events from an aggregate root

class AnAggregateRoot {

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

    @AfterDomainEventPublication
    void callbackMethod() {
       // … potentially clean up domain events list
    }
}

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The method using @DomainEvents can return either a single event instance or a collection of events. It must not take any arguments. After all events have been published, we have a method annotated with @AfterDomainEventPublication. It can be used to potentially clean the list of events to be published (among other uses). The methods are called every time one of a Spring Data repository’s save(…) methods is called.

8.8. Spring Data Extensions
本节介绍了一组Spring Data扩展，它们可以在各种上下文中使用Spring Data。目前，大多数集成都针对Spring MVC。
This section documents a set of Spring Data extensions that enable Spring Data usage in a variety of contexts. Currently, most of the integration is targeted towards Spring MVC.

# Querydsl-Extension

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Querydsl是一个框架，可以通过其流畅的API构建静态类型的SQL类查询
Querydsl is a framework that enables the construction of statically typed SQL-like queries through its fluent API.

几个Spring Data模块通过QuerydslPredicateExecutor提供与Querydsl的集成，如以下示例所示：
Several Spring Data modules offer integration with Querydsl through QuerydslPredicateExecutor, as shown in the following example:

Example 39. QuerydslPredicateExecutor interface

public interface QuerydslPredicateExecutor<T> {

  Optional<T> findById(Predicate predicate);  

  Iterable<T> findAll(Predicate predicate);

  long count(Predicate predicate);

  boolean exists(Predicate predicate);

  // … more functionality omitted.
}

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Finds and returns a single entity matching the Predicate.
Finds and returns all entities matching the Predicate.
Returns the number of entities matching the Predicate.
Returns whether an entity that matches the Predicate exists.
To make use of Querydsl support, extend QuerydslPredicateExecutor on your repository interface, as shown in the following example

Example 40. Querydsl integration on repositories

interface UserRepository extends CrudRepository<User, Long>, QuerydslPredicateExecutor<User> {
}

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

userRepository.findAll(predicate);

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# Web-support

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This section contains the documentation for the Spring Data web support as it is implemented in the current (and later) versions of Spring Data Commons. As the newly introduced support changes many things, we kept the documentation of the former behavior in [web.legacy]. Spring Data modules that support the repository programming model ship with a variety of web support. The web related components require Spring MVC JARs to be on the classpath. Some of them even provide integration with Spring HATEOAS. In general, the integration support is enabled by using the @EnableSpringDataWebSupport annotation in your JavaConfig configuration class, as shown in the following example:

Example 41. Enabling Spring Data web support

@Configuration
@EnableWebMvc
@EnableSpringDataWebSupport
class WebConfiguration {}

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The @EnableSpringDataWebSupport annotation registers a few components we will discuss in a bit. It will also detect Spring HATEOAS on the classpath and register integration components for it as well if present.

Alternatively, if you use XML configuration, register either SpringDataWebConfiguration or HateoasAwareSpringDataWebConfiguration as Spring beans, as shown in the following example (for SpringDataWebConfiguration):

Example 42. Enabling Spring Data web support in XML

<bean class="org.springframework.data.web.config.SpringDataWebConfiguration" />

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

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# Basic Web Support

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

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

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

# DomainClassConverter

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

Example 43. A Spring MVC controller using domain types in method signatures

@Controller
@RequestMapping("/users")
class UserController {

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

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

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

Currently, the repository has to implement CrudRepository to be eligible to be discovered for conversion. HandlerMethodArgumentResolvers for Pageable and Sort The configuration snippet shown in the previous section also registers a PageableHandlerMethodArgumentResolver as well as an instance of SortHandlerMethodArgumentResolver. The registration enables Pageable and Sort as valid controller method arguments, as shown in the following example:

Example 44. Using Pageable as controller method argument

@Controller
@RequestMapping("/users")
class UserController {

  private final UserRepository repository;

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

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

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

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

Table 1. Request parameters evaluated for Pageable instances page

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

size

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

sort

Properties that should be sorted by in the format property,property(,ASC|DESC). Default sort direction is ascending. Use multiple sort parameters if you want to switch directions — for example, ?sort=firstname&sort=lastname,asc.

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

@Bean SortHandlerMethodArgumentResolverCustomizer sortCustomizer() {
    return s -> s.setPropertyDelimiter("<-->");
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If setting the properties of an existing MethodArgumentResolver is not sufficient for your purpose, extend either SpringDataWebConfiguration or the HATEOAS-enabled equivalent, override the pageableResolver() or sortResolver() methods, and import your customized configuration file instead of using the @Enable annotation.

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

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

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you have to populate thing1_page and thing2_page and so on.

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

# Hypermedia_Support_for_Pageables

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Spring HATEOAS附带了一个表示模型类（PagedResources），它允许使用必要的页面元数据丰富页面实例的内容以及允许客户端轻松浏览页面的链接。将Page转换为PagedResources是通过Spring HATEOAS ResourceAssembler接口的实现完成的，该接口称为PagedResourcesAssembler。以下示例显示如何将PagedResourcesAssembler用作控制器方法参数：
Spring HATEOAS ships with a representation model class (PagedResources) that allows enriching the content of a Page instance with the necessary Page metadata as well as links to let the clients easily navigate the pages. The conversion of a Page to a PagedResources is done by an implementation of the Spring HATEOAS ResourceAssembler interface, called the PagedResourcesAssembler. The following example shows how to use a PagedResourcesAssembler as a controller method argument:

Example 45. Using a PagedResourcesAssembler as controller method argument

@Controller
class PersonController {

  @Autowired PersonRepository repository;

  @RequestMapping(value = "/persons", method = RequestMethod.GET)
  HttpEntity<PagedResources<Person>> persons(Pageable pageable,
    PagedResourcesAssembler assembler) {

    Page<Person> persons = repository.findAll(pageable);
    return new ResponseEntity<>(assembler.toResources(persons), HttpStatus.OK);
  }
}

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

Page的内容成为PagedResources实例的内容。
The content of the Page becomes the content of the PagedResources instance.

PagedResources对象获取一个附加的PageMetadata实例，并使用来自Page和底层PageRequest的信息填充它。
The PagedResources object gets a PageMetadata instance attached, and it is populated with information from the Page and the underlying PageRequest.

根据页面的状态，PagedResources可能会显示并附加下一个链接。链接指向方法映射到的URI。添加到方法的分页参数与PageableHandlerMethodArgumentResolver的设置相匹配，以确保稍后可以解析链接
The PagedResources may get prev and next links attached, depending on the page’s state. The links point to the URI to which the method maps. The pagination parameters added to the method match the setup of the PageableHandlerMethodArgumentResolver to make sure the links can be resolved later.

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

{ "links" : [ { "rel" : "next",
                "href" : "http://localhost:8080/persons?page=1&size=20 }
  ],
  "content" : [
     … // 20 Person instances rendered here
  ],
  "pageMetadata" : {
    "size" : 20,
    "totalElements" : 30,
    "totalPages" : 2,
    "number" : 0
  }
}

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您会看到汇编程序生成了正确的URI，并且还选择了默认配置以将参数解析为即将发出的请求的Pageable。这意味着，如果更改该配置，链接将自动遵循更改。默认情况下，汇编程序指向它所调用的控制器方法，但是可以通过交换自定义链接来自定义链接以构建分页链接，这会重载PagedResourcesAssembler.toResource（...）方法。
You see that the assembler produced the correct URI and also picked up the default configuration to resolve the parameters into a Pageable for an upcoming request. This means that, if you change that configuration, the links automatically adhere to the change. By default, the assembler points to the controller method it was invoked in, but that can be customized by handing in a custom Link to be used as base to build the pagination links, which overloads the PagedResourcesAssembler.toResource(…) method.

# Web Databinding Support

Spring数据投影（在Projections中描述）可用于通过使用JSONPath表达式来绑定传入的请求有效负载（需要Jayway JsonPath或XPath表达式（需要XmlBeam），如以下示例所示：
Spring Data projections (described in Projections) can be used to bind incoming request payloads by either using JSONPath expressions (requires Jayway JsonPath or XPath expressions (requires XmlBeam), as shown in the following example:

Example 46. HTTP payload binding using JSONPath or XPath expressions

@ProjectedPayload
public interface UserPayload {

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

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

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前面示例中显示的类型可以用作Spring MVC处理程序方法参数，也可以在RestTemplate方法之一上使用ParameterizedTypeReference。前面的方法声明将尝试在给定文档中的任何位置查找firstname。 lastname XML查找在传入文档的顶级执行。其中JSON变体首先尝试顶级姓氏，但如果前者未返回值，则还尝试嵌套在用户子文档中的lastname。这样，可以轻松地减轻源文档结构的变化，而无需客户端调用公开的方法（通常是基于类的有效负载绑定的缺点）。
The type shown in the preceding example can be used as a Spring MVC handler method argument or by using ParameterizedTypeReference on one of RestTemplate's methods. The preceding method declarations would try to find firstname anywhere in the given document. The lastname XML lookup is performed on the top-level of the incoming document. The JSON variant of that tries a top-level lastname first but also tries lastname nested in a user sub-document if the former does not return a value. That way, changes in the structure of the source document can be mitigated easily without having clients calling the exposed methods (usually a drawback of class-based payload binding).

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

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

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

# Querydsl Web Support

For those stores having QueryDSL integration, it is possible to derive queries from the attributes contained in a Request query string.

Consider the following query string:

?firstname=Dave&lastname=Matthews
Given the User object from previous examples, a query string can be resolved to the following value by using the QuerydslPredicateArgumentResolver.

QUser.user.firstname.eq("Dave").and(QUser.user.lastname.eq("Matthews"))

The feature is automatically enabled, along with @EnableSpringDataWebSupport, when Querydsl is found on the classpath.
Adding a @QuerydslPredicate to the method signature provides a ready-to-use Predicate, which can be run by using the QuerydslPredicateExecutor.

Type information is typically resolved from the method’s return type. Since that information does not necessarily match the domain type, it might be a good idea to use the root attribute of QuerydslPredicate. The following exampe shows how to use @QuerydslPredicate in a method signature:

@Controller
class UserController {

  @Autowired UserRepository repository;

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

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

    return "index";
  }
}

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将查询字符串参数解析为匹配Predicate for User。默认绑定如下：

简单属性上的对象为eq。

集合上的对象，如包含的属性。

关于简单属性的集合，如。
Resolve query string arguments to matching Predicate for User. The default binding is as follows:
Object on simple properties as eq.
Object on collection like properties as contains.
Collection on simple properties as in.

Those bindings can be customized through the bindings attribute of @QuerydslPredicate or by making use of Java 8 default methods and adding the QuerydslBinderCustomizer method to the repository interface.

interface UserRepository extends CrudRepository<User, String>,
                                 QuerydslPredicateExecutor<User>,                
                                 QuerydslBinderCustomizer<QUser> {               

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

    bindings.bind(user.username).first((path, value) -> path.contains(value))    
    bindings.bind(String.class)
      .first((StringPath path, String value) -> path.containsIgnoreCase(value)); 
    bindings.excluding(user.password);                                           
  }
}

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QuerydslPredicateExecutor provides access to specific finder methods for Predicate. QuerydslBinderCustomizer defined on the repository interface is automatically picked up and shortcuts @QuerydslPredicate(bindings=…). Define the binding for the username property to be a simple contains binding. Define the default binding for String properties to be a case-insensitive contains match. Exclude the password property from Predicate resolution.

# Repository-Populators

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If you work with the Spring JDBC module, you are probably familiar with the support to populate a DataSource with SQL scripts. A similar abstraction is available on the repositories level, although it does not use SQL as the data definition language because it must be store-independent. Thus, the populators support XML (through Spring’s OXM abstraction) and JSON (through Jackson) to define data with which to populate the repositories.

Assume you have a file data.json with the following content:

Example 47. Data defined in JSON

[ { "_class" : "com.acme.Person",
 "firstname" : "Dave",
  "lastname" : "Matthews" },
  { "_class" : "com.acme.Person",
 "firstname" : "Carter",
  "lastname" : "Beauford" } ]

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You can populate your repositories by using the populator elements of the repository namespace provided in Spring Data Commons. To populate the preceding data to your PersonRepository, declare a populator similar to the following:

Example 48. Declaring a Jackson repository populator

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

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

</beans>

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The preceding declaration causes the data.json file to be read and deserialized by a Jackson ObjectMapper.

The type to which the JSON object is unmarshalled is determined by inspecting the _class attribute of the JSON document. The infrastructure eventually selects the appropriate repository to handle the object that was deserialized.

To instead use XML to define the data the repositories should be populated with, you can use the unmarshaller-populator element. You configure it to use one of the XML marshaller options available in Spring OXM. See the Spring reference documentation for details. The following example shows how to unmarshal a repository populator with JAXB:

Example 49. Declaring an unmarshalling repository populator (using JAXB)

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

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

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

</beans>

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# Reference Documentation

# Introduction

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# 9.1. Document Structure

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

“MongoDB support” introduces the MongoDB module feature set.

“MongoDB Repositories” introduces the repository support for MongoDB.

# MongoDB-support

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The MongoDB support contains a wide range of features:

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

MongoTemplate助手类，可在执行常见的Mongo操作时提高工作效率。包括文档和POJO之间的集成对象映射。
MongoTemplate helper class that increases productivity when performing common Mongo operations. Includes integrated object mapping between documents and POJOs.

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

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

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

Persistence and mapping lifecycle events.

Java-based Query, Criteria, and Update DSLs.

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

QueryDSL integration to support type-safe queries.

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

GeoSpatial integration.(地理空间整合)

对于大多数任务，您应该使用MongoTemplate或Repository支持，它们都利用丰富的映射功能。 MongoTemplate是寻找访问功能的地方，例如递增计数器或ad-hoc CRUD操作。 MongoTemplate还提供了回调方法，因此您可以轻松地获取低级API工件（例如com.mongodb.client.MongoDatabase）以直接与MongoDB进行通信。在各种API工件上命名约定的目标是复制基本MongoDB Java驱动程序中的那些，以便您可以轻松地将现有知识映射到Spring API。
For most tasks, you should use MongoTemplate or the Repository support, which both leverage the rich mapping functionality. MongoTemplate is the place to look for accessing functionality such as incrementing counters or ad-hoc CRUD operations. MongoTemplate also provides callback methods so that it is easy for you to get the low-level API artifacts, such as com.mongodb.client.MongoDatabase, to communicate directly with MongoDB. The goal with naming conventions on various API artifacts is to copy those in the base MongoDB Java driver so you can easily map your existing knowledge onto the Spring APIs.

# Getting-Started

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

首先，您需要设置一个正在运行的MongoDB服务器。有关如何启动MongoDB实例的说明，请参阅MongoDB快速入门指南。安装后，启动MongoDB通常需要运行以下命令：$ {MONGO_HOME} /bin /mongod
First, you need to set up a running MongoDB server. Refer to the MongoDB Quick Start guide for an explanation on how to startup a MongoDB instance. Once installed, starting MongoDB is typically a matter of running the following command: ${MONGO_HOME}/bin/mongod

To create a Spring project in STS:

Go to File → New → Spring Template Project → Simple Spring Utility Project, and press Yes when prompted. Then enter a project and a package name, such as org.spring.mongodb.example. .Add the following to the pom.xml files dependencies element:

<dependencies>

  <!-- other dependency elements omitted -->

  <dependency>
    <groupId>org.springframework.data</groupId>
    <artifactId>spring-data-mongodb</artifactId>
    <version>2.1.9.RELEASE</version>
  </dependency>

</dependencies>

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

<spring.framework.version>5.1.8.RELEASE</spring.framework.version>

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

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

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The repository is also browseable here.

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

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

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Then you can create a Person class to persist:

package org.spring.mongodb.example;

public class Person {

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

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

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

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

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You also need a main application to run:

package org.spring.mongodb.example;

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

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

import com.mongodb.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");
  }
}

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

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

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

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

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

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

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

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

# Examples-Repository

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

# Connecting-to-MongoDB-with-Spring

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

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

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

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

Example 50. Registering a com.mongodb.MongoClient object using Java-based bean metadata

@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");
   }
}

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This approach lets you 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 the use of @Repository is described in Spring’s DAO support features.

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

Example 51. Registering a com.mongodb.MongoClient object by using Spring’s MongoClientFactoryBean and enabling Spring’s exception translation support

@Configuration
public class AppConfig {

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

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

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

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

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

Example 52. XML schema to configure MongoDB

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

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

</beans>

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

Example 53. XML schema to configure a com.mongodb.MongoClient object with MongoClientOptions

<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>

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The following example shows a configuration using replica sets:

Example 54. XML schema to configure a com.mongodb.MongoClient object with Replica Sets

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

# The-MongoDbFactory-Interface

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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.client.MongoDatabase object and access all the functionality of a specific MongoDB database instance. Spring provides the org.springframework.data.mongodb.core.MongoDbFactory interface, shown in the following listing, to bootstrap connectivity to the database:

public interface MongoDbFactory {

  MongoDatabase getDb() throws DataAccessException;

  MongoDatabase getDb(String dbName) throws DataAccessException;
}

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The following sections show how you can use the container with either Java-based or 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 use them in standard Java code, as follows:

public class MongoApp {

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

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

    MongoOperations mongoOps = new MongoTemplate(new 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");
  }
}

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

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

# 10.3.4. Registering a MongoDbFactory Instance by 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. The following listing shows a simple example:

@Configuration
public class MongoConfiguration {

  public @Bean MongoDbFactory mongoDbFactory() {
    return new SimpleMongoDbFactory(new MongoClient(), "database");
  }
}

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

@Configuration
public class ApplicationContextEventTestsAppConfig extends 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())));
  }
}

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In order to use authentication with XML-based configuration, use the credentials attribute on the <mongo-client> element.

Username and password credentials used in XML-based configuration must be URL-encoded when these contain reserved characters, such as:, %, @, or ,.The following example shows encoded credentials: m0ng0@dmin:mo_res:bw6},Qsdxx@admin@database → m0ng0%40dmin:mo_res%3Abw6%7D%2CQsdxx%40admin@database See section 2.2 of RFC 3986 for further details.
As of MongoDB java driver 3.7.0 there is an alternative entry point to MongoClient via the mongodb-driver-sync artifact. com.mongodb.client.MongoClient is not compatible with com.mongodb.MongoClient and does not longer support the legacy DBObject codec. Therefore, it cannot be used with Querydsl and requires a different configuration. You can use AbstractMongoClientConfiguration to leverage the new MongoClients builder API.

@Configuration
public class MongoClientConfiguration extends AbstractMongoClientConfiguration {

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

	@Override
	public MongoClient mongoClient() {
		return MongoClients.create("mongodb://localhost:27017/?replicaSet=rs0&w=majority");
	}
}

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# 10.3.5. Registering a MongoDbFactory Instance by Using XML-based Metadata

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

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

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

<context:property-placeholder location="classpath:/com/myapp/mongodb/config/mongo.properties"/>

<mongo:mongo-client host="${mongo.host}" port="${mongo.port}">
  <mongo:client-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>

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# Introduction-to-MongoTemplate

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MongoTemplate类位于org.springframework.data.mongodb.core包中，是Spring的MongoDB支持的中心类，并提供了与数据库交互的丰富功能集。该模板提供了便捷的操作来创建，更新，删除和查询MongoDB文档，并提供域对象和MongoDB文档之间的映射。
The MongoTemplate class, located in the org.springframework.data.mongodb.core package, is the central class of Spring’s MongoDB support and provides a rich feature set for interacting with the database. The template offers convenience operations to create, update, delete, and query MongoDB documents and provides a mapping between your domain objects and MongoDB documents.

配置完成后，MongoTemplate是线程安全的，可以跨多个实例重用。 MongoDB文档和域类之间的映射是通过委托MongoConverter接口的实现来完成的。 Spring提供MappingMongoConverter，但您也可以编写自己的转换器。有关更多详细信息，请参阅“使用自定义转换器覆盖默认映射”。
Once configured, MongoTemplate is thread-safe and can be reused across multiple instances.The mapping between MongoDB documents and domain classes is done by delegating to an implementation of the MongoConverter interface. Spring provides MappingMongoConverter, but you can also write your own converter. See “Overriding Default Mapping with Custom Converters” for more detailed information.

MongoTemplate类实现了MongoOperations接口。在尽可能多的情况下，MongoOperations上的方法以MongoDB驱动程序Collection对象上可用的方法命名，以使API熟悉用于驱动程序API的现有MongoDB开发人员。例如，您可以找到find，findAndModify，findAndReplace，findOne，insert，remove，save，update和updateMulti等方法。设计目标是尽可能简单地在使用基本MongoDB驱动程序和MongoOperations之间进行转换。两个API之间的主要区别在于MongoOperations可以传递域对象而不是Document。此外，MongoOperations具有用于查询，标准和更新操作的流畅API，而不是填充文档以指定这些操作的参数。
The MongoTemplate class implements the interface MongoOperations. In as much as possible, the methods on MongoOperations are named after methods available on the MongoDB driver Collection object, to make the API familiar to existing MongoDB developers who are used to the driver API. For example, you can find methods such as find, findAndModify, findAndReplace, findOne, insert, remove, save, update, and updateMulti. The design goal was to make it as easy as possible to transition between the use of the base MongoDB driver and MongoOperations. A major difference between the two APIs is that MongoOperations can be passed domain objects instead of Document. Also, MongoOperations has fluent APIs for Query, Criteria, and Update operations instead of populating a Document to specify the parameters for those operations.

在MongoTemplate实例上引用操作的首选方法是通过它的接口MongoOperations。
MongoTemplate使用的默认转换器实现是MappingMongoConverter。虽然MappingMongoConverter可以使用其他元数据来指定对象到文档的映射，但它也可以通过使用一些约定来映射ID和集合名称，从而转换不包含其他元数据的对象。这些约定以及映射注释的使用在“映射”一章中进行了解释。
The preferred way to reference the operations on MongoTemplate instance is through its interface, MongoOperations.
The default converter implementation used by MongoTemplate is MappingMongoConverter. While the MappingMongoConverter can use additional metadata to specify the mapping of objects to documents, it can also convert objects that contain no additional metadata by using some conventions for the mapping of IDs and collection names. These conventions, as well as the use of mapping annotations, are explained in the “Mapping” chapter.

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

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

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

# 实例化MongoTemplate

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

Example 55. Registering a com.mongodb.MongoClient object and enabling Spring’s exception translation support

@Configuration
public class AppConfig {

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

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

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There are several overloaded constructors of MongoTemplate:

MongoTemplate(MongoClient mongo, String databaseName):
Takes the MongoClient object and the default database name to operate against.
MongoTemplate(MongoDbFactory mongoDbFactory):
Takes a MongoDbFactory object that encapsulated the 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 by using Spring’s XML <beans/> schema, as the following example shows:

<mongo:mongo-client host="localhost" port="27017"/>

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

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

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

# 10.4.2. WriteResultChecking Policy

在开发中，如果从任何MongoDB操作返回的com.mongodb.WriteResult包含错误，则记录或抛出异常非常方便。在开发过程中忘记这样做是很常见的，然后最终得到一个看起来像是成功运行的应用程序，事实上，数据库没有根据你的期望进行修改。您可以将MongoTemplate的WriteResultChecking属性设置为以下值之一：EXCEPTION或NONE，分别抛出异常或不执行任何操作。默认情况下使用WriteResultChecking值为NONE。
When in development, it is handy to either log or throw an exception if the com.mongodb.WriteResult returned from any MongoDB operation contains an error. It is quite common to forget to do this during development and then end up with an application that looks like it runs successfully when, in fact, the database was not modified according to your expectations. You can set the WriteResultChecking property of MongoTemplate to one of the following values: EXCEPTION or NONE, to either throw an Exception or do nothing, respectively. The default is to use a WriteResultChecking value of NONE.

# 10.4.3. WriteConcern

如果尚未通过更高级别的驱动程序指定（例如com.mongodb.MongoClient），则可以设置MongoTemplate用于写入操作的com.mongodb.WriteConcern属性。如果未设置WriteConcern属性，则默认为MongoDB驱动程序的DB或Collection设置中设置的属性。
If it has not yet been specified through the driver at a higher level (such as com.mongodb.MongoClient), you can set the com.mongodb.WriteConcern property that the MongoTemplate uses for write operations. If the WriteConcern property is not set, it defaults to the one set in the MongoDB driver’s DB or Collection setting.

# 10.4.4. WriteConcernResolver

对于要在每个操作基础上设置不同WriteConcern值的更高级情况（用于删除，更新，插入和保存操作），可以在MongoTemplate上配置名为WriteConcernResolver的策略接口。由于MongoTemplate用于持久保存POJO，因此WriteConcernResolver允许您创建可将特定POJO类映射到WriteConcern值的策略。以下清单显示了WriteConcernResolver接口：
For more advanced cases where you want to set different WriteConcern values on a per-operation basis (for remove, update, insert, and save operations), a strategy interface called WriteConcernResolver can be configured on MongoTemplate. Since MongoTemplate is used to persist POJOs, the WriteConcernResolver lets you create a policy that can map a specific POJO class to a WriteConcern value. The following listing shows the WriteConcernResolver interface:

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

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

private class MyAppWriteConcernResolver implements WriteConcernResolver {

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

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# 保存、更新和删除文档

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10.5. Saving, Updating, and Removing Documents MongoTemplate允许您保存，更新和删除域对象，并将这些对象映射到MongoDB中存储的文档。
MongoTemplate lets you save, update, and delete your domain objects and map those objects to documents stored in MongoDB.

Consider the following class:

public class Person {

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

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

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

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

}

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

MongoOperations is the interface that MongoTemplate implements.

package org.spring.example;

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

import java.util.List;

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

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

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

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

The preceding example is meant to show the use of save, update, and remove operations on MongoTemplate and not to show complex mapping functionality. The query syntax used in the preceding example is explained in more detail in the section “Querying Documents”.

# 如何在映射层中处理_id字段

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

MongoDB要求您为所有文档都有一个_id字段。如果您不提供，则驱动程序会为ObjectId分配生成的值。使用MappingMongoConverter时，某些规则控制Java类中的属性如何映射到此_id字段：
MongoDB requires that you have an _id field for all documents. If you do not provide one, the driver assigns an ObjectId with a generated value. When you use the MappingMongoConverter, certain rules govern how properties from the Java class are mapped to this _id field:

用@Id（org.springframework.data.annotation.Id）注释的属性或字段映射到_id字段。
A property or field annotated with @Id (org.springframework.data.annotation.Id) maps to the _id field.

没有注释但名为id的属性或字段映射到_id字段
A property or field without an annotation but named id maps to the _id field.

下面概述了在使用MappingMongoConverter（MongoTemplate的默认值）时，在映射到_id文档字段的属性上进行的类型转换（如果有）。
The following outlines what type conversion, if any, is done on the property mapped to the _id document field when using the MappingMongoConverter (the default for MongoTemplate).

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

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

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

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

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

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

Example 56. Type mapping

public class Sample {
  Contact value;
}

public abstract class Contact { … }

public class Person extends Contact { … }

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

mongoTemplate.save(sample);

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

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

# Customizing Type Mapping

如果要避免将整个Java类名称作为类型信息编写，而是希望使用键，则可以在实体类上使用@TypeAlias批注。如果您需要更多地自定义映射，请查看TypeInformationMapper接口。可以在DefaultMongoTypeMapper上配置该接口的实例，然后可以在MappingMongoConverter上配置该实例。以下示例显示如何为实体定义类型别名：
If you want to avoid writing the entire Java class name as type information but would rather like to use a key, you can use the @TypeAlias annotation on the entity class. If you need to customize the mapping even more, have a look at the TypeInformationMapper interface. An instance of that interface can be configured at the DefaultMongoTypeMapper, which can, in turn, be configured on MappingMongoConverter. The following example shows how to define a type alias for an entity:

Example 57. Defining a type alias for an Entity

@TypeAlias("pers")
class Person {

}

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

# Configuring Custom Type Mapping

以下示例显示如何在MappingMongoConverter中配置自定义MongoTypeMapper：
The following example shows how to configure a custom MongoTypeMapper in MappingMongoConverter:

Example 58. Configuring a custom MongoTypeMapper with Spring Java Config

class CustomMongoTypeMapper extends DefaultMongoTypeMapper {
  //implement custom type mapping here
}
@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();
  }
}

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

The following example shows how to use XML to configure a custom MongoTypeMapper:

Example 59. Configuring a custom MongoTypeMapper with XML

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

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

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# 保存和插入文档的方法

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10.5.3. Methods for Saving and Inserting Documents

MongoTemplate有几种方便的方法来保存和插入对象。要对转换过程进行更细粒度的控制，可以使用MappingMongoConverter注册Spring转换器 -例如Converter <Person，Document>和Converter <Document，Person>。
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>.

插入和保存操作之间的区别在于，如果对象尚不存在，则保存操作将执行插入操作。
使用保存操作的简单方法是保存POJO。在这种情况下，集合名称由类的名称（不完全限定）确定。您也可以使用特定的集合名称调用save操作。您可以使用映射元数据覆盖用于存储对象的集合。
The difference between insert and save operations is that a save operation performs an insert if the object is not already present.
The simple case of using the save operation is to save a POJO. In this case, the collection name is determined by name (not fully qualified) of the class. You may also call the save operation with a specific collection name. You can use mapping metadata to override the collection in which to store the object.

插入或保存时，如果未设置Id属性，则假设其值将由数据库自动生成。因此，要成功自动生成ObjectId，类中Id属性或字段的类型必须是String，ObjectId或BigInteger。
When inserting or saving, if the Id property is not set, the assumption is that its value will be auto-generated by the database. Consequently, for auto-generation of an ObjectId to succeed, the type of the Id property or field in your class must be a String, an ObjectId, or a BigInteger.

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

Example 60. Inserting and retrieving documents using the MongoTemplate

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

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

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

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The following insert and save operations are available:

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

A similar set of insert operations is also available:

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

######　Into Which Collection Are My Documents Saved?

有两种方法可以管理用于文档的集合名称。使用的默认集合名称是更改为以小写字母开头的类名。因此，com.test.Person类存储在person集合中。您可以通过使用@Document注释提供不同的集合名称来自定义此项。您还可以通过提供自己的集合名称作为所选MongoTemplate方法调用的最后一个参数来覆盖集合名称。
There are two ways to manage the collection name that is used for the documents. The default collection name that is used is the class name changed to start with a lower-case letter. So a com.test.Person class is stored in the person collection. You can customize this by providing a different collection name with the @Document annotation. You can also override the collection name by providing your own collection name as the last parameter for the selected MongoTemplate method calls.

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

insert:
Inserts an object. If there is an existing document with the same id, an error is generated.
insertAll:
Takes a Collection of objects as the first parameter. This method inspects each object and inserts it into the appropriate collection, based on the rules specified earlier.
save:
Saves the object, overwriting any object that might have the same id.

# Inserting Several Objects in a Batch

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

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

# 更新集合中的文档

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

Example 61. Updating documents by using the MongoTemplate

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

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

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In addition to the Query discussed earlier, we provide the update definition by using an Update object. The Update class has methods that match the update modifiers available for MongoDB.

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

Methods for Executing Updates for Documents

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

# 更新类中的方法

Methods in the Update Class

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

The Update class contains the following methods:

method	comment
Update addToSet (String key, Object value)	Update using the $addToSet update modifier
Update currentDate (String key)	Update using the $currentDate update modifier
Update currentTimestamp (String key)	Update using the $currentDate update modifier with $type timestamp
Update inc (String key, Number inc)	Update using the $inc update modifier
Update max (String key, Object max)	Update using the $max update modifier
Update min (String key, Object min)	Update using the $min update modifier
Update multiply (String key, Number multiplier)	Update using the $mul update modifier
Update pop (String key, Update.Position pos)	Update using the $pop update modifier
Update pull (String key, Object value)	Update using the $pull update modifier
Update pullAll (String key, Object[] values)	Update using the $pullAll update modifier
Update push (String key, Object value)	Update using the $push update modifier
Update pushAll (String key, Object[] values)	Update using the $pushAll update modifier
Update rename (String oldName, String newName)	Update using the $rename update modifier
Update set (String key, Object value)	Update using the $set update modifier
Update setOnInsert (String key, Object value)	Update using the $setOnInsert update modifier
Update unset (String key)	Update using the $unset update modifier

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

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

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

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

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

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# Upserting_Documents_in_a_Collection

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与执行updateFirst操作相关，您还可以执行“upsert”操作，如果找不到与查询匹配的文档，则会执行插入操作。插入的文档是查询文档和更新文档的组合。以下示例显示了如何使用upsert方法：
Related to performing an updateFirst operation, you can also perform an “upsert” operation, which will perform an insert if no document is found that matches the query. The document that is inserted is a combination of the query document and the update document. The following example shows how to use the upsert method:

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

10.5.6. Finding and Upserting Documents in a Collection

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

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

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

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

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

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

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));

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

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));

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# 查找和替换文档

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

Example 62. Find and Replace Documents

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

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Use the fluent update API with the domain type given for mapping the query and deriving the collection name or just use MongoOperations#findAndReplace.
The actual match query mapped against the given domain type. Provide sort, fields and collation settings via the query. Additional optional hook to provide options other than the defaults, like upsert.
An optional projection type used for mapping the operation result. If none given the initial domain type is used.
Trigger the actual execution. Use findAndReplaceValue to obtain the nullable result instead of an Optional.
Please note that the replacement must not hold an id itself as the id of the existing Document will be carried over to the replacement by the store itself. Also keep in mind that findAndReplace will only replace the first document matching the query criteria depending on a potentially given sort order.

# 删除文档的方法

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10.5.8. Methods for Removing Documents You can use one of five overloaded methods to remove an object from the database:

template.remove(tywin, "GOT");                                              

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

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

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

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

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

# 乐观锁定

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

@Document
class Person {

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

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

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

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

template.save(tmp); // throws OptimisticLockingFailureException

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最初插入文档。 version设置为0。
加载刚刚插入的文档。版本仍为0。
使用version = 0更新文档。将lastname和bump版本设置为1。
尝试更新仍然具有version = 0的先前加载的文档。操作因OptimisticLockingFailureException而失败，因为当前版本为1。
乐观锁定需要将WriteConcern设置为ACKNOWLEDGED。否则可以静默吞下OptimisticLockingFailureException。
Intially insert document. version is set to 0.
Load the just inserted document. version is still 0.
Update the document with version = 0. Set the lastname and bump version to 1.
Try to update the previously loaded document that still has version = 0. The operation fails with an OptimisticLockingFailureException, as the current version is 1.
Optimistic Locking requires to set the WriteConcern to ACKNOWLEDGED. Otherwise OptimisticLockingFailureException can be silently swallowed.

# Querying_Documents

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您可以使用Query和Criteria类来表达您的查询。它们具有镜像本机MongoDB运算符名称的方法名称，例如lt，lte，is和其他名称。 Query和Criteria类遵循流畅的API样式，因此您可以将多个方法标准和查询链接在一起，同时具有易于理解的代码。为了提高可读性，静态导入允许您避免使用“new”关键字来创建Query和Criteria实例。您还可以使用BasicQuery从普通JSON字符串创建Query实例，如以下示例所示：
You can use the Query and Criteria classes to express your queries. They have method names that mirror the native MongoDB operator names, such as lt, lte, is, and others. The Query and Criteria classes follow a fluent API style so that you can chain together multiple method criteria and queries while having easy-to-understand code. To improve readability, static imports let you avoid using the 'new' keyword for creating Query and Criteria instances. You can also use BasicQuery to create Query instances from plain JSON Strings, as shown in the following example:

Example 63. Creating a Query instance from a plain JSON String

BasicQuery query = new BasicQuery("{ age : { $lt : 50 }, accounts.balance : { $gt : 1000.00 }}");
List<Person> result = mongoTemplate.find(query, Person.class);

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# Querying_Documents_in_a_Collection

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

Example 64. Querying for documents using the MongoTemplate

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

…

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

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

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

# Methods for the Criteria Class

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

method	comment
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.

method	comment
Criteria lt (Object o)	Creates a criterion using the $lt operator
Criteria lte (Object o)	Creates a criterion using the $lte operator
Criteria mod (Number value, Number remainder)	Creates a criterion using the $mod operator
Criteria ne (Object o)	Creates a criterion using the $ne operator
Criteria nin (Object… o)	Creates a criterion using the $nin operator
Criteria norOperator (Criteria… criteria)	Creates an nor query using the $nor operator for all of the provided criteria
Criteria not ()	Creates a criterion using the $not meta operator which affects the clause directly following
Criteria orOperator (Criteria… criteria)	Creates an or query using the $or operator for all of the provided criteria
Criteria regex (String re)	Creates a criterion using a $regex
Criteria size (int s)	Creates a criterion using the $size operator
Criteria type (int t)	Creates a criterion using the $type operator

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

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

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

method	comment
Criteria within (Circle circle)	Creates a geospatial criterion using $geoWithin $center operators.
Criteria within (Box box)	Creates a geospatial criterion using a $geoWithin $box operation.
Criteria withinSphere (Circle circle)	Creates a geospatial criterion using $geoWithin $center operators.
Criteria near (Point point)	Creates a geospatial criterion using a $near operation
Criteria nearSphere (Point point)	Creates a geospatial criterion using $nearSphere$center operations. This is only available for MongoDB 1.7 and higher.
Criteria minDistance (double minDistance)	Creates a geospatial criterion using the $minDistance operation, for use with $near.
Criteria maxDistance (double maxDistance)	Creates a geospatial criterion using the $maxDistance operation, for use with $near.

# Methods for the Query class

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

method	comment
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

# 查询文档的方法

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

method	comment
findAll	Query for a list of objects of type T from the collection.
findOne	Map the results of an ad-hoc query on the collection to a single instance of an object of the specified type.
findById	Return an object of the given ID and target class.
find	Map the results of an ad-hoc query on the collection to a List of the specified type.
findAndRemove	Map the results of an ad-hoc query on the collection to a single instance of an object of the specified type. The first document that matches the query is returned and removed from the collection in the database.

# 10.6.3. Query Distinct Values

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

Example 65. Retrieving distinct values

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

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Query the Person collection.
Select distinct values of the lastname field. The field name is mapped according to the domain types property declaration, taking potential @Field annotations into account.
Retrieve all distinct values as a List of Object (due to no explicit result type being specified). Retrieving distinct values into a Collection of Object is the most flexible way, as it tries to determine the property value of the domain type and convert results to the desired type or mapping Document structures.

有时，当所需字段的所有值都固定为某种类型时，直接获取正确类型的Collection更方便，如下例所示：
Sometimes, when all values of the desired field are fixed to a certain type, it is more convenient to directly obtain a correctly typed Collection, as shown in the following example:

Example 66. Retrieving strongly typed distinct values

template.query(Person.class)  
  .distinct("lastname")       
  .as(String.class)           
  .all();

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Query the collection of Person.
Select distinct values of the lastname field. The fieldname is mapped according to the domain types property declaration, taking potential @Field annotations into account.
Retrieved values are converted into the desired target type — in this case, String. It is also possible to map the values to a more complex type if the stored field contains a document.
Retrieve all distinct values as a List of String. If the type cannot be converted into the desired target type, this method throws a DataAccessException.

# GeoSpatial_Queries

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MongoDB通过使用$ near，$ within，geoWithin和$ nearSphere等运算符支持GeoSpatial查询。 Criteria类提供了特定于地理空间查询的方法。还有一些形状类（Box，Circle和Point）与地理空间相关的Criteria方法结合使用。
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.

在MongoDB事务中使用GeoSpatial查询时需要注意，请参阅事务内部的特殊行为。
Using GeoSpatial queries requires attention when used within MongoDB transactions, see Special behavior inside transactions.
要了解如何执行GeoSpatial查询，请考虑以下Venue类（取自集成测试并依赖于丰富的MappingMongoConverter）：
To understand how to perform GeoSpatial queries, consider the following Venue class (taken from the integration tests and relying on the rich MappingMongoConverter):

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

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

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

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

  public String getName() {
    return name;
  }

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

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

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

Circle circle = new Circle(-73.99171, 40.738868, 0.01);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").within(circle)), Venue.class);
To find venues within a Circle using spherical coordinates, you can use the following query:

Circle circle = new Circle(-73.99171, 40.738868, 0.003712240453784);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").withinSphere(circle)), Venue.class);
To find venues within a Box, you can use the following query:

//lower-left then upper-right
Box box = new Box(new Point(-73.99756, 40.73083), new Point(-73.988135, 40.741404));
List<Venue> venues =
    template.find(new Query(Criteria.where("location").within(box)), Venue.class);
To find venues near a Point, you can use the following queries:

Point point = new Point(-73.99171, 40.738868);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").near(point).maxDistance(0.01)), Venue.class);
Point point = new Point(-73.99171, 40.738868);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").near(point).minDistance(0.01).maxDistance(100)), Venue.class);
To find venues near a Point using spherical coordinates, you can use the following query:

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

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# Geo-near Queries

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

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

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

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我们使用NearQuery构建器API来设置查询，以将给定Point周围的所有Restaurant实例返回到10英里。这里使用的Metrics枚举实际上实现了一个接口，以便其他指标也可以插入一个距离。度量标准由乘数支持，以将给定度量的距离值转换为本机距离。此处显示的示例将10视为英里。使用其中一个内置指标（英里和公里）会自动触发在查询上设置的球形标记。如果要避免这种情况，请将普通双精度值传递给maxDistance（...）。有关更多信息，请参阅NearQuery和Distance的JavaDoc。
We use the NearQuery builder API to set up a query to return all Restaurant instances surrounding the given Point out to 10 miles. The Metrics enum used here actually implements an interface so that other metrics could be plugged into a distance as well. A Metric is backed by a multiplier to transform the distance value of the given metric into native distances. The sample shown here would consider the 10 to be miles. Using one of the built-in metrics (miles and kilometers) automatically triggers the spherical flag to be set on the query. If you want to avoid that, pass plain double values into maxDistance(…). For more information, see the JavaDoc of NearQuery and Distance.

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

# 10.6.5. GeoJSON Support

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

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

public class Store {

	String id;

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

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# GeoJSON Types in Repository Query Methods

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

public interface StoreRepository extends CrudRepository<Store, String> {

	List<Store> findByLocationWithin(Polygon polygon);  

}

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

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

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Repository method definition using the commons type allows calling it with both the GeoJSON and the legacy format. Use GeoJSON type to make use of $geometry operator. Note that GeoJSON polygons need to define a closed ring. Use the legacy format $polygon operator. 10.6.6. Full-text Queries Since version 2.6 of MongoDB, you can run full-text queries by using the $text operator. Methods and operations specific to full-text queries are available in TextQuery and TextCriteria. When doing full text search, see the MongoDB reference for its behavior and limitations.

# Full-text Search

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

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

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

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

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您可以通过在术语前加上 -或使用notMatching来排除搜索词，如下例所示（请注意，这两行具有相同的效果，因此是多余的）：
You can exclude search terms by prefixing the term with - or by using notMatching, as shown in the following example (note that the two lines have the same effect and are thus redundant):

// search for 'coffee' and not 'cake'
TextQuery.searching(new TextCriteria().matching("coffee").matching("-cake"));
TextQuery.searching(new TextCriteria().matching("coffee").notMatching("cake"));
TextCriteria.matching takes the provided term as is. Therefore, you can define phrases by putting them between double quotation marks (for example, \"coffee cake\") or using by TextCriteria.phrase. The following example shows both ways of defining a phrase:

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

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

# 10.6.7. Collations

从3.4版开始，MongoDB支持用于收集和索引创建以及各种查询操作的排序规则。归类根据ICU归类定义字符串比较规则。排序规则文档包含在排序规则中封装的各种属性，如下面的清单所示：
Since version 3.4, MongoDB supports collations for collection and index creation and various query operations. Collations define string comparison rules based on the ICU collations. A collation document consists of various properties that are encapsulated in Collation, as the following listing shows:

Collation collation = Collation.of("fr")         

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

  .numericOrderingEnabled()                      

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

  .forwardDiacriticSort()                        

  .normalizationEnabled();

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排序规则需要创建区域设置。这可以是区域设置的字符串表示形式，区域设置（考虑语言，国家/地区和变体）或CollationLocale。区域设置是创建所必需的。
Collation requires a locale for creation. This can be either a string representation of the locale, a Locale (considering language, country, and variant) or a CollationLocale. The locale is mandatory for creation.
Collation strength defines comparison levels that denote differences between characters. You can configure various options (case-sensitivity, case-ordering, and others), depending on the selected strength.
Specify whether to compare numeric strings as numbers or as strings.
Specify whether the collation should consider whitespace and punctuation as base characters for purposes of comparison.
Specify whether strings with diacritics sort from back of the string, such as with some French dictionary ordering.
Specify whether to check whether text requires normalization and whether to perform normalization.
Collations can be used to create collections and indexes. If you create a collection that specifies a collation, the collation is applied to index creation and queries unless you specify a different collation. A collation is valid for a whole operation and cannot be specified on a per-field basis, as the following example shows:

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));

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MongoDB uses simple binary comparison if no collation is specified (Collation.simple()). Using collations with collection operations is a matter of specifying a Collation instance in your query or operation options, as the following two examples show:

Example 67. Using collation with find

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

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

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

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Example 68. Using collation with aggregate

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

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

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

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

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

# 10.6.8. JSON Schema

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

Example 69. Sample JSON schema

{
  "type": "object",                                                        

  "required": [ "firstname", "lastname" ],                                 

  "properties": {                                                          

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

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JSON schema documents always describe a whole document from its root. A schema is a schema object itself that can contain embedded schema objects that describe properties and subdocuments. required is a property that describes which properties are required in a document. It can be specified optionally, along with other schema constraints. See MongoDB’s documentation on available keywords. properties is related to a schema object that describes an object type. It contains property-specific schema constraints. firstname specifies constraints for the firsname field inside the document. Here, it is a string-based properties element declaring possible field values. address is a subdocument defining a schema for values in its postCode field. You can provide a schema either by specifying a schema document (that is, by using the Document API to parse or build a document object) or by building it with Spring Data’s JSON schema utilities in org.springframework.data.mongodb.core.schema. MongoJsonSchema is the entry point for all JSON schema-related operations. The following example shows how use MongoJsonSchema.builder() to create a JSON schema:

Example 70. Creating a JSON schema

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

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

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

    .build();

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Obtain a schema builder to configure the schema with a fluent API. Configure required properties. Configure the String-typed firstname field, allowing only luke and han values. Properties can be typed or untyped. Use a static import of JsonSchemaProperty to make the syntax slightly more compact and to get entry points such as string(…). Build the schema object. Use the schema to create either a collection or query documents. There are already some predefined and strongly typed schema objects (JsonSchemaObject and JsonSchemaProperty) available through static methods on the gateway interfaces. However, you may need to build custom property validation rules, which can be created through the builder API, as the following example shows:

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

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

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

Example 71. Create collection with $jsonSchema

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

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

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

Example 72. Query for Documents matching a $jsonSchema

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

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

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The following table shows the supported JSON schema types:

# Table 2. Supported JSON schema types

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

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

For more information, see $jsonSchema.

# 10.6.9. Fluent Template API

MongoOperations接口是与MongoDB进行更多低级别交互时的核心组件之一。它提供了广泛的方法，涵盖从集合创建，索引创建和CRUD操作到更高级功能（如Map-Reduce和聚合）的需求。您可以为每种方法找到多个重载。其中大多数涵盖了API的可选部分或可空部分。
The MongoOperations interface is one of the central components when it comes to more low-level interaction with MongoDB. It offers a wide range of methods covering needs from collection creation, index creation, and CRUD operations to more advanced functionality, such as Map-Reduce and aggregations. You can find multiple overloads for each method. Most of them cover optional or nullable parts of the API.

FluentMongoOperations为MongoOperations的常用方法提供了更窄的界面，并提供了更易读，更流畅的API。入口点（insert（...），find（...），update（...）和其他）遵循基于要运行的操作的自然命名模式。从入口点开始，API被设计为仅提供依赖于上下文的方法，这些方法导致调用实际MongoOperations对应的终止方法 -在以下示例的情况下为all方法：
FluentMongoOperations provides a more narrow interface for the common methods of MongoOperations and provides a more readable, fluent API. The entry points (insert(…), find(…), update(…), and others) follow a natural naming schema based on the operation to be run. Moving on from the entry point, the API is designed to offer only context-dependent methods that lead to a terminating method that invokes the actual MongoOperations counterpart — the all method in the case of the following example:

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

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Skip this step if SWCharacter defines the collection with @Document or if you use the class name as the collection name, which is fine. Sometimes, a collection in MongoDB holds entities of different types, such as a Jedi within a collection of SWCharacters. To use different types for Query and return value mapping, you can use as(Class<?> targetType) to map results differently, as the following example shows:

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

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The query fields are mapped against the SWCharacter type.
Resulting documents are mapped into Jedi.
You can directly apply Projections to result documents by providing the target type via as(Class<?>). Using projections allows MongoTemplate to optimize result mapping by limiting the actual response to fields required by the projection target type. This applies as long as the Query itself does not contain any field restriction and the target type is a closed interface or DTO projection. You can switch between retrieving a single entity and retrieving multiple entities as a List or a Stream through the terminating methods: first(), one(), all(), or stream().

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

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

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# 其他查询选项

back

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

Query query = query(where("firstname").is("luke"))
    .comment("find luke")         
    .batchSize(100)                                 
    .slaveOk();

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The comment propagated to the MongoDB profile log. The number of documents to return in each response batch. Allows querying a replica slave. On the repository level the @Meta annotation provides means to add query options in a declarative way.

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

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# Query_by_Example

back

# 10.7.1. Introduction

This chapter provides an introduction to Query by Example and explains how to use it.

按Example查询（QBE）是一种用户友好的查询技术，具有简单的界面。它允许动态创建查询，并且不需要您编写包含字段名称的查询。实际上，Query by Example不要求您使用特定于商店的查询语言来编写查询。
Query by Example (QBE) is a user-friendly querying technique with a simple interface. It allows dynamic query creation and does not require you to write queries that contain field names. In fact, Query by Example does not require you to write queries by using store-specific query languages at all.

# Usage

back

The Query by Example API consists of three parts:

Probe:
The actual example of a domain object with populated fields.
ExampleMatcher:
The ExampleMatcher carries details on how to match particular fields. It can be reused across multiple Examples.
Example:
An Example consists of the probe and the ExampleMatcher. It is used to create the query.

按示例查询非常适合几种用例：
Query by Example is well suited for several use cases:

使用一组静态或动态约束查询数据存储。
Querying your data store with a set of static or dynamic constraints.

频繁重构域对象，而不必担心破坏现有查询。
Frequent refactoring of the domain objects without worrying about breaking existing queries.

独立于底层数据存储API工作。
Working independently from the underlying data store API.

按示例查询也有几个限制：
Query by Example also has several limitations:

不支持嵌套或分组的属性约束，例如firstname =？0或（firstname =？1和lastname =？2）。
No support for nested or grouped property constraints, such as firstname = ?0 or (firstname = ?1 and lastname = ?2).

仅支持字符串的开始/包含/结束/正则表达式匹配以及其他属性类型的精确匹配。
Only supports starts/contains/ends/regex matching for strings and exact matching for other property types.

Before getting started with Query by Example, you need to have a domain object. To get started, create an interface for your repository, as shown in the following example:

Example 73. Sample Person object

public class Person {

  @Id
  private String id;
  private String firstname;
  private String lastname;
  private Address address;

  // … getters and setters omitted
}

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The preceding example shows a simple domain object. You can use it to create an Example. By default, fields having null values are ignored, and strings are matched by using the store specific defaults. Examples can be built by either using the of factory method or by using ExampleMatcher. Example is immutable. The following listing shows a simple Example:

Example 74. Simple Example

Person person = new Person();                         
person.setFirstname("Dave");                          

Example<Person> example = Example.of(person);

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Create a new instance of the domain object.
Set the properties to query.
Create the Example.
Examples are ideally be executed with repositories. To do so, let your repository interface extend QueryByExampleExecutor<T>. The following listing shows an excerpt from the QueryByExampleExecutor interface:

Example 75. The QueryByExampleExecutor

public interface QueryByExampleExecutor<T> {

  <S extends T> S findOne(Example<S> example);

  <S extends T> Iterable<S> findAll(Example<S> example);

  // … more functionality omitted.
}

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# Example_Matchers

back

Examples are not limited to default settings. You can specify your own defaults for string matching, null handling, and property-specific settings by using the ExampleMatcher, as shown in the following example:

Example 76. Example matcher with customized matching

Person person = new Person();                          
person.setFirstname("Dave");                           

ExampleMatcher matcher = ExampleMatcher.matching()     
  .withIgnorePaths("lastname")                         
  .withIncludeNullValues()                             
  .withStringMatcherEnding();                          
Example<Person> example = Example.of(person, matcher);

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Create a new instance of the domain object. Set properties. Create an ExampleMatcher to expect all values to match. It is usable at this stage even without further configuration. Construct a new ExampleMatcher to ignore the lastname property path. Construct a new ExampleMatcher to ignore the lastname property path and to include null values. Construct a new ExampleMatcher to ignore the lastname property path, to include null values, and to perform suffix string matching. Create a new Example based on the domain object and the configured ExampleMatcher. By default, the ExampleMatcher expects all values set on the probe to match. If you want to get results matching any of the predicates defined implicitly, use ExampleMatcher.matchingAny().

您可以指定单个属性的行为（例如“firstname”和“lastname”，或者对于嵌套属性，“address.city”）。您可以使用匹配选项和区分大小写来调整它，如以下示例所示：
You can specify behavior for individual properties (such as "firstname" and "lastname" or, for nested properties, "address.city"). You can tune it with matching options and case sensitivity, as shown in the following example:

Example 77. Configuring matcher options

ExampleMatcher matcher = ExampleMatcher.matching()
  .withMatcher("firstname", endsWith())
  .withMatcher("lastname", startsWith().ignoreCase());
}

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Another way to configure matcher options is to use lambdas (introduced in Java 8). This approach creates a callback that asks the implementor to modify the matcher. You need not return the matcher, because configuration options are held within the matcher instance. The following example shows a matcher that uses lambdas:

Example 78. Configuring matcher options with lambdas

ExampleMatcher matcher = ExampleMatcher.matching()
  .withMatcher("firstname", match -> match.endsWith())
  .withMatcher("firstname", match -> match.startsWith());
}

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Example创建的查询使用配置的合并视图。可以在ExampleMatcher级别设置默认匹配设置，而可以将单个设置应用于特定属性路径。除非明确定义，否则ExampleMatcher上设置的设置将由属性路径设置继承。属性修补程序上的设置优先于默认设置。下表描述了各种ExampleMatcher设置的范围：
Queries created by Example use a merged view of the configuration. Default matching settings can be set at the ExampleMatcher level, while individual settings can be applied to particular property paths. Settings that are set on ExampleMatcher are inherited by property path settings unless they are defined explicitly. Settings on a property patch have higher precedence than default settings. The following table describes the scope of the various ExampleMatcher settings:

Table 3. Scope of ExampleMatcher settings

Setting	Scope
Null-handling	ExampleMatcher
String matching	ExampleMatcher and property path
Ignoring properties	Property path
Case sensitivity	ExampleMatcher and property path
Value transformation	Property path

# 10.7.4. Running an Example

The following example shows how to query by example when using a repository (of Person objects, in this case):

Example 79. Query by Example using a repository

public interface PersonRepository extends QueryByExampleExecutor<Person> {

}

public class PersonService {

  @Autowired PersonRepository personRepository;

  public List<Person> findPeople(Person probe) {
    return personRepository.findAll(Example.of(probe));
  }
}

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包含无类型ExampleSpec的示例使用Repository类型及其集合名称。类型化的ExampleSpec实例使用其类型作为结果类型和Repository实例中的集合名称。
An Example containing an untyped ExampleSpec uses the Repository type and its collection name. Typed ExampleSpec instances use their type as the result type and the collection name from the Repository instance.

在ExampleSpec中包含空值时，Spring Data Mongo使用嵌入式文档匹配而不是点符号属性匹配。这样做会强制对嵌入文档中的所有属性值和属性顺序进行精确的文档匹配。 Spring Data MongoDB支持以下匹配选项：

When including null values in the ExampleSpec, Spring Data Mongo uses embedded document matching instead of dot notation property matching. Doing so forces exact document matching for all property values and the property order in the embedded document.Spring Data MongoDB provides support for the following matching options:

Table 4. StringMatcher options

Matching	Logical result
DEFAULT (case-sensitive)	{"firstname" : firstname}
DEFAULT (case-insensitive)	{"firstname" : { $regex: firstname, $options: 'i'}}
EXACT (case-sensitive)	{"firstname" : { $regex: /^firstname$/}}
EXACT (case-insensitive)	{"firstname" : { $regex: /^firstname$/, $options: 'i'}}
STARTING (case-sensitive)	{"firstname" : { $regex: /^firstname/}}
STARTING (case-insensitive)	{"firstname" : { $regex: /^firstname/, $options: 'i'}}
ENDING (case-sensitive)	{"firstname" : { $regex: /firstname$/}}
ENDING (case-insensitive)	{"firstname" : { $regex: /firstname$/, $options: 'i'}}
CONTAINING (case-sensitive)	{"firstname" : { $regex: /.firstname./}}
CONTAINING (case-insensitive)	{"firstname" : { $regex: /.firstname./, $options: 'i'}}
REGEX (case-sensitive)	{"firstname" : { $regex: /firstname/}}
REGEX (case-insensitive)	{"firstname" : { $regex: /firstname/, $options: 'i'}}

# 10.7.5. Untyped Example

默认情况下，严格键入示例。这意味着映射查询具有包含的类型匹配，将其限制为探测可分配类型。例如，当坚持使用默认类型键（_class）时，查询具有诸如（_class：{$ in：[com.acme.Person]}）之类的限制。
By default Example is strictly typed. This means that the mapped query has an included type match, restricting it to probe assignable types. For example, when sticking with the default type key (_class), the query has restrictions such as (_class : { $in : [ com.acme.Person] }).

By using the UntypedExampleMatcher, it is possible to bypass the default behavior and skip the type restriction. So, as long as field names match, nearly any domain type can be used as the probe for creating the reference, as the following example shows:

Example 80. Untyped Example Query

class JustAnArbitraryClassWithMatchingFieldName {
  @Field("lastname") String value;
}

JustAnArbitraryClassWithMatchingFieldNames probe = new JustAnArbitraryClassWithMatchingFieldNames();
probe.value = "stark";

Example example = Example.of(probe, UntypedExampleMatcher.matching());

Query query = new Query(new Criteria().alike(example));
List<Person> result = template.find(query, Person.class);

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# Map_Reduce_Operations

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您可以使用Map-Reduce查询MongoDB，这对批处理，数据聚合以及查询语言无法满足您的需求非常有用。
You can query MongoDB by using Map-Reduce, which is useful for batch processing, for data aggregation, and for when the query language does not fulfill your needs.

Spring通过在MongoOperations上提供方法来简化Map-Reduce操作的创建和执行，从而提供与MongoDB Map-Reduce的集成。它可以将Map-Reduce操作的结果转换为POJO，并与Spring的Resource抽象集成。这使您可以将JavaScript文件放在文件系统，类路径，HTTP服务器或任何其他Spring Resource实现上，然后通过简单的URI样式语法引用JavaScript资源 -例如，classpath：reduce.js;。在文件中外化JavaScript代码通常比在代码中将它们嵌入Java字符串更可取。请注意，如果您愿意，仍可以将JavaScript代码作为Java字符串传递。
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 and integrates with Spring’s Resource abstraction. This lets you place your JavaScript files on the file system, classpath, HTTP server, or any other Spring Resource implementation and then reference the JavaScript resources through an easy URI style syntax — for example, classpath:reduce.js;. Externalizing JavaScript code in files is often preferable to embedding them as Java strings in your code. Note that you can still pass JavaScript code as Java strings if you prefer.

# Example_Usage

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为了理解如何执行Map-Reduce操作，我们使用了MongoDB - The Definitive Guide [1]一书中的一个例子。在这个例子中，我们创建了三个分别具有值[a，b]，[b，c]和[c，d]的文档。每个文档中的值与键“x”相关联，如以下示例所示（假设这些文档位于名为jmr1的集合中）：
To understand how to perform Map-Reduce operations, we use an example from the book, MongoDB - The Definitive Guide [1]. In this example, we create three documents that have the values [a,b], [b,c], and [c,d], respectively. The values in each document are associated with the key, 'x', as the following example shows (assume these documents are in a collection named jmr1):

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

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以下map函数计算每个文档的数组中每个字母的出现次数：
The following map function counts the occurrence of each letter in the array for each document:

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

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以下reduce函数总结了所有文档中每个字母的出现次数：
The follwing reduce function sums up the occurrence of each letter across all the documents:

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

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运行上述函数会生成以下集合：
Running the preceding functions result in the following collection:

{ "_id" : "a", "value" : 1 }
{ "_id" : "b", "value" : 2 }
{ "_id" : "c", "value" : 2 }
{ "_id" : "d", "value" : 1 }

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假设map和reduce函数位于map.js和reduce.js中并捆绑在jar中，因此它们在类路径中可用，您可以按如下方式运行Map-Reduce操作：
Assuming that the map and reduce functions are located in map.js and reduce.js and bundled in your jar so they are available on the classpath, you can run a Map-Reduce operation as follows:

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

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The preceding exmaple produces the following output:

ValueObject [id=a, value=1.0]
ValueObject [id=b, value=2.0]
ValueObject [id=c, value=2.0]
ValueObject [id=d, value=1.0]

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The MapReduceResults class implements Iterable and provides access to the raw output and timing and count statistics. The following listing shows the ValueObject class:

public class ValueObject {

  private String id;
  private float value;

  public String getId() {
    return id;
  }

  public float getValue() {
    return value;
  }

  public void setValue(float value) {
    this.value = value;
  }

  @Override
  public String toString() {
    return "ValueObject [id=" + id + ", value=" + value + "]";
  }
}

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默认情况下，使用INLINE的输出类型，因此您无需指定输出集合。要指定其他Map-Reduce选项，请使用带有其他MapReduceOptions参数的重载方法。 MapReduceOptions类具有流畅的API，因此可以使用紧凑的语法添加其他选项。以下示例将输出集合设置为jmr1_out（请注意，仅设置输出集合采用默认输出类型REPLACE）：
By default, the output type of INLINE is used so that you need not specify an output collection. To specify additional Map-Reduce options, use an overloaded method that takes an additional MapReduceOptions argument. The class MapReduceOptions has a fluent API, so adding additional options can be done in a compact syntax. The following example sets the output collection to jmr1_out (note that setting only the output collection assumes a default output type of REPLACE):

MapReduceResults<ValueObject> results = mongoOperations.mapReduce("jmr1", "classpath:map.js", "classpath:reduce.js",
                                                                     new MapReduceOptions().outputCollection("jmr1_out"), ValueObject.class);

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There is also a static import (import static org.springframework.data.mongodb.core.mapreduce.MapReduceOptions.options;) that can be used to make the syntax slightly more compact, as the following example shows:

MapReduceResults<ValueObject> results = mongoOperations.mapReduce("jmr1", "classpath:map.js", "classpath:reduce.js",
                                                                     options().outputCollection("jmr1_out"), ValueObject.class);

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You can also specify a query to reduce the set of data that is fed into the Map-Reduce operation. The following example removes the document that contains [a,b] from consideration for Map-Reduce operations:

Query query = new Query(where("x").ne(new String[] { "a", "b" }));
MapReduceResults<ValueObject> results = mongoOperations.mapReduce(query, "jmr1", "classpath:map.js", "classpath:reduce.js",
                                                                     options().outputCollection("jmr1_out"), ValueObject.class);

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Note that you can specify additional limit and sort values on the query, but you cannot skip values.

# Script_Operations

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MongoDB允许通过直接发送脚本或调用存储的脚本在服务器上执行JavaScript函数。 ScriptOperations可以通过MongoTemplate访问，并为JavaScript使用提供基本抽象。以下示例显示了如何使用ScriptOperations类：
MongoDB allows executing JavaScript functions on the server by either directly sending the script or calling a stored one. ScriptOperations can be accessed through MongoTemplate and provides basic abstraction for JavaScript usage. The following example shows how to us the ScriptOperations class:

ScriptOperations scriptOps = template.scriptOps();

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

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

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直接执行脚本而不在服务器端存储该功能。
使用'echo'作为名称存储脚本。给定名称标识脚本并允许稍后调用它。
使用提供的参数执行名为'echo'的脚本。
Execute the script directly without storing the function on server side.
Store the script using 'echo' as its name. The given name identifies the script and allows calling it later.
Execute the script with name 'echo' using the provided parameters.

# Group_Operations

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作为使用Map-Reduce执行数据聚合的替代方法，您可以使用类似于按查询样式使用SQL组的组操作，因此与使用Map-Reduce相比，它可能更容易接近。使用组操作确实有一些限制，例如它在共享环境中不受支持，并且它在单个BSON对象中返回完整的结果集，因此结果应该很小，小于10,000个键。
As an alternative to using Map-Reduce to perform data aggregation, you can use the group operation which feels similar to using SQL’s group by query style, so it may feel more approachable vs. using Map-Reduce. Using the group operations does have some limitations, for example it is not supported in a shared environment and it returns the full result set in a single BSON object, so the result should be small, less than 10,000 keys.

Spring通过在MongoOperations上提供方法来简化组操作的创建和执行，从而提供与MongoDB组操作的集成。它可以将组操作的结果转换为POJO，还可以与Spring的资源抽象抽象集成。这将允许您将JavaScript文件放在文件系统，类路径，http服务器或任何其他Spring Resource实现上，然后通过简单的URI样式语法引用JavaScript资源，例如： “类路径：reduce.js ;.将JavaScript代码外部化为文件，如果通常更喜欢将它们作为Java字符串嵌入代码中。请注意，如果您愿意，仍可以将JavaScript代码作为Java字符串传递。
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 Resource abstraction abstraction. This will let you place your JavaScript files on the file system, classpath, http server or any other Spring Resource implementation and then reference the JavaScript resources via an easy URI style syntax, e.g. 'classpath:reduce.js;. Externalizing JavaScript code in files if often preferable to embedding them as Java strings in your code. Note that you can still pass JavaScript code as Java strings if you prefer.

# 10.10.1. Example Usage

为了理解组操作如何工作，使用了以下示例，这有点人为。有关更现实的示例，请参阅“MongoDB -权威指南”一书。使用以下行创建名为group_test_collection的集合。
In order to understand how group operations work the following example is used, which is somewhat artificial. For a more realistic example consult the book 'MongoDB - The definitive guide'. A collection named group_test_collection created with the following rows.

{ "_id" : ObjectId("4ec1d25d41421e2015da64f1"), "x" : 1 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f2"), "x" : 1 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f3"), "x" : 2 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f4"), "x" : 3 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f5"), "x" : 3 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f6"), "x" : 3 }

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我们希望按每行中的唯一字段分组，x字段并聚合x的每个特定值出现的次数。为此，我们需要创建一个包含count变量的初始文档，以及一个reduce函数，它会在每次遇到它时递增它。执行组操作的Java代码如下所示
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

GroupByResults<XObject> results = mongoTemplate.group("group_test_collection",
                                                      GroupBy.key("x").initialDocument("{ count: 0 }").reduceFunction("function(doc, prev) { prev.count += 1 }"),
                                                      XObject.class);

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第一个参数是用于运行组操作的集合的名称，第二个参数是一个流畅的API，它通过GroupBy类指定组操作的属性。在这个例子中，我们只使用intialDocument和reduceFunction方法。您还可以指定键功能，以及作为Fluent API的一部分的终结器。如果您有多个要分组的键，则可以传入逗号分隔的键列表。
The first argument is the name of the collection to run the group operation over, the second is a fluent API that specifies properties of the group operation via a GroupBy class. In this example we are using just the intialDocument and reduceFunction methods. You can also specify a key-function, as well as a finalizer as part of the fluent API. If you have multiple keys to group by, you can pass in a comma separated list of keys.

The raw results of the group operation is a JSON document that looks like this

{
  "retval" : [ { "x" : 1.0 , "count" : 2.0} ,
               { "x" : 2.0 , "count" : 1.0} ,
               { "x" : 3.0 , "count" : 3.0} ] ,
  "count" : 6.0 ,
  "keys" : 3 ,
  "ok" : 1.0
}

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“retval”字段下的文档被映射到group方法中的第三个参数，在本例中为XObject，如下所示。
The document under the "retval" field is mapped onto the third argument in the group method, in this case XObject which is shown below.

public class XObject {

  private float x;

  private float count;


  public float getX() {
    return x;
  }

  public void setX(float x) {
    this.x = x;
  }

  public float getCount() {
    return count;
  }

  public void setCount(float count) {
    this.count = count;
  }

  @Override
  public String toString() {
    return "XObject [x=" + x + " count = " + count + "]";
  }
}

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您还可以通过在GroupByResults类上调用方法getRawResults来获取原始结果作为Document。
You can also obtain the raw result as a Document by calling the method getRawResults on the GroupByResults class.

MongoOperations上的group方法还有一个额外的方法重载，它允许您指定Criteria对象以选择行的子集。使用Criteria对象，使用静态导入的一些语法糖，以及通过Spring Resource字符串引用键函数和减少函数javascript文件的示例如下所示。
There is an additional method overload of the group method on MongoOperations which lets you specify a Criteria object for selecting a subset of the rows. An example which uses a Criteria object, with some syntax sugar using static imports, as well as referencing a key-function and reduce function javascript files via a Spring Resource string is shown below.

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

GroupByResults<XObject> results = mongoTemplate.group(where("x").gt(0),
                                        "group_test_collection",
                                        keyFunction("classpath:keyFunction.js").initialDocument("{ count: 0 }").reduceFunction("classpath:groupReduce.js"), XObject.class);

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# Aggregation_Framework_Support

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Spring Data MongoDB支持在2.2版中引入MongoDB的聚合框架。
Spring Data MongoDB provides support for the Aggregation Framework introduced to MongoDB in version 2.2.

For further information, see the full reference documentation of the aggregation framework and other data aggregation tools for MongoDB.

# 10.11.1. Basic Concepts

Spring Data MongoDB中的聚合框架支持基于以下关键抽象：Aggregation，AggregationOperation和AggregationResults。
The Aggregation Framework support in Spring Data MongoDB is based on the following key abstractions: Aggregation, AggregationOperation, and AggregationResults.

######　Aggregation

Aggregation表示MongoDB聚合操作，并保存聚合管道指令的描述。通过调用Aggregation类的相应newAggregation（...）静态工厂方法来创建聚合，该方法获取AggregateOperation和可选输入类的列表。
An Aggregation represents a MongoDB aggregate operation and holds the description of the aggregation pipeline instructions. Aggregations are created by invoking the appropriate newAggregation(…) static factory method of the Aggregation class, which takes a list of AggregateOperation and an optional input class.

The actual aggregate operation is executed by the aggregate method of the MongoTemplate, which takes the desired output class as a parameter.

# AggregationOperation

AggregationOperation表示MongoDB聚合管道操作，并描述了应在此聚合步骤中执行的处理。虽然您可以手动创建AggregationOperation，但我们建议使用Aggregate类提供的静态工厂方法来构造AggregateOperation。
An AggregationOperation represents a MongoDB aggregation pipeline operation and describes the processing that should be performed in this aggregation step. Although you could manually create an AggregationOperation, we recommend using the static factory methods provided by the Aggregate class to construct an AggregateOperation.

# AggregationResults

AggregationResults是聚合操作结果的容器。它提供对原始聚合结果的访问，以Document的形式访问映射对象和有关聚合的其他信息。
AggregationResults is the container for the result of an aggregate operation. It provides access to the raw aggregation result, in the form of a Document to the mapped objects and other information about the aggregation.

The following listing shows the canonical example for using the Spring Data MongoDB support for the MongoDB Aggregation Framework:

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

Aggregation agg = newAggregation(
    pipelineOP1(),
    pipelineOP2(),
    pipelineOPn()
);

AggregationResults<OutputType> results = mongoTemplate.aggregate(agg, "INPUT_COLLECTION_NAME", OutputType.class);
List<OutputType> mappedResult = results.getMappedResults();

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请注意，如果您提供输入类作为newAggregation方法的第一个参数，MongoTemplate将从此类派生输入集合的名称。否则，如果未指定输入类，则必须显式提供输入集合的名称。如果同时提供输入类和输入集合，则后者优先。
Note that, if you provide an input class as the first parameter to the newAggregation method, the MongoTemplate derives the name of the input collection from this class. Otherwise, if you do not not specify an input class, you must provide the name of the input collection explicitly. If both an input class and an input collection are provided, the latter takes precedence.

# 10.11.2. Supported Aggregation Operations

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

Pipeline Aggregation Operators
Group Aggregation Operators
Boolean Aggregation Operators
Comparison Aggregation Operators
Arithmetic Aggregation Operators
String Aggregation Operators
Date Aggregation Operators
Array Aggregation Operators
Conditional Aggregation Operators
Lookup Aggregation Operators
Convert Aggregation Operators
Object Aggregation Operators

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

Table 5. Aggregation Operations currently supported by Spring Data MongoDB


Pipeline Aggregation Operators	bucket, bucketAuto, count, facet, geoNear, graphLookup, group, limit, lookup, match, project, replaceRoot, skip, sort, unwind
Set Aggregation Operators	setEquals, setIntersection, setUnion, setDifference, setIsSubset, anyElementTrue, allElementsTrue
Group Aggregation Operators	addToSet, first, last, max, min, avg, push, sum, (*count), stdDevPop, stdDevSamp
Arithmetic Aggregation Operators	abs, add (via plus), ceil, divide, exp, floor, ln, log, log10, mod, multiply, pow, sqrt, subtract (via minus), trunc
String Aggregation Operators	concat, substr, toLower, toUpper, stcasecmp, indexOfBytes, indexOfCP, split, strLenBytes, strLenCP, substrCP, trim, ltrim, rtim
Comparison Aggregation Operators	eq (*via: is), gt, gte, lt, lte, ne
Array Aggregation Operators	arrayElementAt, arrayToObject, concatArrays, filter, in, indexOfArray, isArray, range, reverseArray, reduce, size, slice, zip
Literal Operators	literal
Date Aggregation Operators	dayOfYear, dayOfMonth, dayOfWeek, year, month, week, hour, minute, second, millisecond, dateToString, dateFromString, dateFromParts, dateToParts, isoDayOfWeek, isoWeek, isoWeekYear
Variable Operators	map
Conditional Aggregation Operators	cond, ifNull, switch
Type Aggregation Operators	type
Convert Aggregation Operators	convert, toBool, toDate, toDecimal, toDouble, toInt, toLong, toObjectId, toString
Object Aggregation Operators	objectToArray, mergeObjects

The operation is mapped or added by Spring Data MongoDB.

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

# 10.11.3. Projection Expressions

投影表达式用于定义作为特定聚合步骤的结果的字段。可以通过Aggregation类的项目方法定义投影表达式，方法是传递String对象列表或聚合框架Fields对象。可以使用and（String）方法通过流畅的API使用其他字段扩展投影，并使用as（String）方法进行别名。请注意，您还可以使用聚合框架的Fields.field静态工厂方法定义具有别名的字段，然后可以使用该方法构建新的Fields实例。在后续聚合阶段中对投影字段的引用仅对包含字段的字段名称或其别名（包括新定义的字段及其别名）有效。未包含在投影中的字段无法在以后的聚合阶段中引用。以下列表显示了投影表达式的示例：
Projection expressions are used to define the fields that are the outcome of a particular aggregation step. Projection expressions can be defined through the project method of the Aggregation class, either by passing a list of String objects or an aggregation framework Fields object. The projection can be extended with additional fields through a fluent API by using the and(String) method and aliased by using the as(String) method. Note that you can also define fields with aliases by using the Fields.field static factory method of the aggregation framework, which you can then use to construct a new Fields instance. References to projected fields in later aggregation stages are valid only for the field names of included fields or their aliases (including newly defined fields and their aliases). Fields not included in the projection cannot be referenced in later aggregation stages. The following listings show examples of projection expression:

Example 81. Projection expression examples

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

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

// generates {$project: {a: 1, b: 1, thing2: $thing1}}
project("a","b").and("thing1").as("thing2")
Example 82. Multi-Stage Aggregation using Projection and Sorting
// generates {$project: {name: 1, netPrice: 1}}, {$sort: {name: 1}}
project("name", "netPrice"), sort(ASC, "name")

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

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

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

# 10.11.4. Faceted Classification

从版本3.4开始，MongoDB通过使用聚合框架支持分面分类。分面分类使用组合的语义类别（一般或特定主题）来创建完整的分类条目。流经聚合管道的文档被分类为桶。多面分类允许在同一组输入文档上进行各种聚合，而无需多次检索输入文档。
As of Version 3.4, MongoDB supports faceted classification by using the Aggregation Framework. A faceted classification uses semantic categories (either general or subject-specific) that are combined to create the full classification entry. Documents flowing through the aggregation pipeline are classified into buckets. A multi-faceted classification enables various aggregations on the same set of input documents, without needing to retrieve the input documents multiple times.

# Buckets

存储桶操作根据指定的表达式和存储区边界将传入文档分组（称为存储桶）。存储桶操作需要分组字段或分组表达式。您可以使用Aggregate类的bucket（）和bucketAuto（）方法来定义它们。 BucketOperation和BucketAutoOperation可以根据输入文档的聚合表达式公开累积。您可以使用with ...（）方法和andOutput（String）方法通过流畅的API扩展存储桶操作和其他参数。您可以使用as（String）方法对操作进行别名。每个存储桶在输出中表示为文档。
Bucket operations categorize incoming documents into groups, called buckets, based on a specified expression and bucket boundaries. Bucket operations require a grouping field or a grouping expression. You can define them by using the bucket() and bucketAuto() methods of the Aggregate class. BucketOperation and BucketAutoOperation can expose accumulations based on aggregation expressions for input documents. You can extend the bucket operation with additional parameters through a fluent API by using the with…() methods and the andOutput(String) method. You can alias the operation by using the as(String) method. Each bucket is represented as a document in the output.

BucketOperation takes a defined set of boundaries to group incoming documents into these categories. Boundaries are required to be sorted. The following listing shows some examples of bucket operations:

Example 83. Bucket operation examples

// generates {$bucket: {groupBy: $price, boundaries: [0, 100, 400]}}
bucket("price").withBoundaries(0, 100, 400);

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

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

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

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BucketAutoOperation determines boundaries in an attempt to evenly distribute documents into a specified number of buckets. BucketAutoOperation optionally takes a granularity value that specifies the preferred number series to use to ensure that the calculated boundary edges end on preferred round numbers or on powers of 10. The following listing shows examples of bucket operations:

Example 84. Bucket operation examples

// generates {$bucketAuto: {groupBy: $price, buckets: 5}}
bucketAuto("price", 5)

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

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

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To create output fields in buckets, bucket operations can use AggregationExpression through andOutput() and SpEL expressions through andOutputExpression().

Note that further details regarding bucket expressions can be found in the $bucket section and $bucketAuto section of the MongoDB Aggregation Framework reference documentation.

# Multi-faceted Aggregation

Multiple aggregation pipelines can be used to create multi-faceted aggregations that characterize data across multiple dimensions (or facets) within a single aggregation stage. Multi-faceted aggregations provide multiple filters and categorizations to guide data browsing and analysis. A common implementation of faceting is how many online retailers provide ways to narrow down search results by applying filters on product price, manufacturer, size, and other factors.

You can define a FacetOperation by using the facet() method of the Aggregation class. You can customize it with multiple aggregation pipelines by using the and() method. Each sub-pipeline has its own field in the output document where its results are stored as an array of documents.

Sub-pipelines can project and filter input documents prior to grouping. Common use cases include extraction of date parts or calculations before categorization. The following listing shows facet operation examples:

Example 85. Facet operation examples

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

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

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

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

# Sort By Count

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

Example 86. Sort by count example

// generates { $sortByCount: "$country" }
sortByCount("country");
A sort by count operation is equivalent to the following BSON (Binary JSON):

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

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# Spring Expression Support in Projection Expressions

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

Complex Calculations with SpEL expressions Consider the following SpEL expression:

1 + (q + 1) / (q - 1)
The preceding expression is translated into the following projection expression part:

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

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

Table 6. Supported SpEL transformations

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

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

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

# Aggregation Framework Examples

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

Aggregation Framework Example 1 In this introductory example, we want to aggregate a list of tags to get the occurrence count of a particular tag from a MongoDB collection (called tags) sorted by the occurrence count in descending order. This example demonstrates the usage of grouping, sorting, projections (selection), and unwinding (result splitting).

class TagCount {
 String tag;
 int n;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

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

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

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The preceding listing uses the following algorithm:

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

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

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

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

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

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

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

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

Aggregation Framework Example 2 This example is based on the Largest and Smallest Cities by State example from the MongoDB Aggregation Framework documentation. We added additional sorting to produce stable results with different MongoDB versions. Here we want to return the smallest and largest cities by population for each state by using the aggregation framework. This example demonstrates grouping, sorting, and projections (selection).

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

class City {
   String name;
   int population;
}

class ZipInfoStats {
   String id;
   String state;
   City biggestCity;
   City smallestCity;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

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

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

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

The preceding listings use the following algorithm:

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

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

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

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

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

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

Aggregation Framework Example 3 This example is based on the States with Populations Over 10 Million example from the MongoDB Aggregation Framework documentation. We added additional sorting to produce stable results with different MongoDB versions. Here we want to return all states with a population greater than 10 million, using the aggregation framework. This example demonstrates grouping, sorting, and matching (filtering).

class StateStats {
   @Id String id;
   String state;
   @Field("totalPop") int totalPopulation;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

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

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

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The preceding listings use the following algorithm:

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

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

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

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

Aggregation Framework Example 4 This example demonstrates the use of simple arithmetic operations in the projection operation.

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

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

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

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

Aggregation Framework Example 5 This example demonstrates the use of simple arithmetic operations derived from SpEL Expressions in the projection operation.

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

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

);

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

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Aggregation Framework Example 6 This example demonstrates the use of complex arithmetic operations derived from SpEL Expressions in the projection operation.

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

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

double shippingCosts = 1.2;

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

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

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

Aggregation Framework Example 7 This example uses conditional projection. It is derived from the $cond reference documentation.

public class InventoryItem {

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

public class InventoryItemProjection {

  @Id int id;
  String item;
  String description;
  int qty;
  int discount
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

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

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

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

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

Example 87. Conditional aggregation projection

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

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If the value of the field author.middle does not contain a value, then use $$REMOVE to exclude the field. Otherwise, add the field value of author.middle.

# 使用自定义转换器覆盖默认映射

back

10.12. Overriding Default Mapping with Custom Converters

要对映射过程进行更细粒度的控制，可以使用MongoConverter实现注册Spring转换器，例如MappingMongoConverter。
To have more fine-grained control over the mapping process, you can register Spring converters with the MongoConverter implementations, such as the MappingMongoConverter.

在尝试映射对象本身之前，MappingMongoConverter会检查是否有任何Spring转换器可以处理特定的类。要“劫持”MappingMongoConverter的常规映射策略，可能是为了提高性能或其他自定义映射需求，首先需要创建Spring Converter接口的实现，然后将其注册到MappingConverter。
The MappingMongoConverter checks to see if any Spring converters can handle a specific class before attempting to map the object itself. To 'hijack' the normal mapping strategies of the MappingMongoConverter, perhaps for increased performance or other custom mapping needs, you first need to create an implementation of the Spring Converter interface and then register it with the MappingConverter.

For more information on the Spring type conversion service, see the reference docs here.

# 10.12.1. Saving by Using a Registered Spring Converter

The following example shows an implementation of the Converter that converts from a Person object to a org.bson.Document:

import org.springframework.core.convert.converter.Converter;

import org.bson.Document;

public class PersonWriteConverter implements Converter<Person, Document> {

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

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# 10.12.2. Reading by Using a Spring Converter

The following example shows an implementation of a Converter that converts from a Document to a Person object:

public class PersonReadConverter implements Converter<Document, Person> {

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

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# 10.12.3. Registering Spring Converters with the MongoConverter

The Mongo Spring namespace provides a convenient way to register Spring Converter instances with the MappingMongoConverter. The following configuration snippet shows how to manually register converter beans as well as configure the wrapping MappingMongoConverter into a MongoTemplate:

<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>

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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, as the following example shows:

<mongo:mapping-converter>
  <mongo:custom-converters base-package="com.acme.**.converters" />
</mongo:mapping-converter>

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# 10.12.4. Converter Disambiguation

通常，我们检查Converter实现以获取它们转换的源类型和目标类型。根据其中一个是MongoDB本身可以处理的类型，我们将转换器实例注册为读取或写入转换器。以下示例显示了写入器转换器和读取转换器（请注意，差异在于Converter上的限定符的顺序）：
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 register the converter instance as a reading or a writing converter. The following examples show a writer converter and a read converter (note the difference is in the order of the qualifiers on Converter):

// Write converter as only the target type is one 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> { … }

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如果您编写一个源和目标类型都是本机Mongo类型的转换器，我们无法确定是否应将其视为读取或写入转换器。将转换器实例注册为两者可能会导致意外结果。例如，Converter <String，Long>是不明确的，尽管在编写时尝试将所有String实例转换为Long实例可能没有意义。为了让您强制基础架构仅为一种方式注册转换器，我们提供了@ReadingConverter和@WritingConverter注释，以便在转换器实现中使用。
If you write a Converter whose source and target type are native Mongo types, we cannot determine whether we should consider it as a reading or a writing converter. Registering the converter instance as both might lead to unwanted results. For example, a Converter<String, Long> is ambiguous, although it probably does not make sense to try to convert all String instances into Long instances when writing. To let you force the infrastructure to register a converter for only one way, we provide @ReadingConverter and @WritingConverter annotations to be used in the converter implementation.

# 索引和收集管理

back

10.13. Index and Collection Management

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

The following listing shows the IndexOperations interface:

public interface IndexOperations {

  void ensureIndex(IndexDefinition indexDefinition);

  void dropIndex(String name);

  void dropAllIndexes();

  void resetIndexCache();

  List<IndexInfo> getIndexInfo();
}

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# 10.13.1. Methods for Creating an Index

You can create an index on a collection to improve query performance by using the MongoTemplate class, as the following example shows:

mongoTemplate.indexOps(Person.class).ensureIndex(new Index().on("name",Order.ASCENDING));

ensureIndex makes sure that an index for the provided IndexDefinition exists for the collection.

You can create standard, geospatial, and text indexes by using the IndexDefinition, GeoSpatialIndex and TextIndexDefinition classes. For example, given the Venue class defined in a previous section, you could declare a geospatial query, as the following example shows:

mongoTemplate.indexOps(Venue.class).ensureIndex(new GeospatialIndex("location"));

Index and GeospatialIndex support configuration of collations.

# 10.13.2. Accessing Index Information

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

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

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

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

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# 10.13.3. Methods for Working with a Collection

The following example shows how to create a collection:

Example 88. Working with collections by using MongoTemplate

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

mongoTemplate.dropCollection("MyNewCollection");

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getCollectionNames: Returns a set of collection names.
collectionExists: Checks to see if a collection with a given name exists.
createCollection: Creates an uncapped collection.
dropCollection: Drops the collection.
getCollection: Gets a collection by name, creating it if it does not exist.

Collection creation allows customization with CollectionOptions and supports collations.

# Executing_Commands

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

# 10.14.1. Methods for executing commands

Document executeCommand (Document command):
Run a MongoDB command.
Document executeCommand (Document command, ReadPreference readPreference):
Run a MongoDB command with the given nullable MongoDB ReadPreference.
Document executeCommand (String jsonCommand):
Execute a MongoDB command expressed as a JSON string.

# Lifecycle_Events

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The MongoDB mapping framework includes several org.springframework.context.ApplicationEvent events that your application can respond to by registering special beans in the ApplicationContext. Being based on Spring’s ApplicationContext event infrastructure enables other products, such as Spring Integration, to easily receive these events, as they are a well known eventing mechanism in Spring-based applications.

To intercept an object before it goes through the conversion process (which turns your domain object into a org.bson.Document), you can register a subclass of AbstractMongoEventListener that overrides the onBeforeConvert method. When the event is dispatched, your listener is called and passed the domain object before it goes into the converter. The following example shows how to do so:

public class BeforeConvertListener extends AbstractMongoEventListener<Person> {
  @Override
  public void onBeforeConvert(BeforeConvertEvent<Person> event) {
    ... does some auditing manipulation, set timestamps, whatever ...
  }
}

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To intercept an object before it goes into the database, you can register a subclass of org.springframework.data.mongodb.core.mapping.event.AbstractMongoEventListener that overrides the onBeforeSave method. When the event is dispatched, your listener is called and passed the domain object and the converted com.mongodb.Document. The following example shows how to do so:

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

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Declaring these beans in your Spring ApplicationContext causes them to be invoked whenever the event is dispatched.

The following callback methods are present in AbstractMappingEventListener:

onBeforeConvert:
Called in MongoTemplate insert, insertList, and save operations before the object is converted to a Document by a MongoConverter.
onBeforeSave:
Called in MongoTemplate insert, insertList, and save operations before inserting or saving the Document in the database.
onAfterSave:
Called in MongoTemplate insert, insertList, and save operations after inserting or saving the Document in the database.
onAfterLoad:
Called in MongoTemplate find, findAndRemove, findOne, and getCollection methods after the Document has been retrieved from the database.
onAfterConvert:
Called in MongoTemplate find, findAndRemove, findOne, and getCollection methods after the Document has been retrieved from the database was converted to a POJO.

生命周期事件仅针对根级别类型发出。用作文档根目录中的属性的复杂类型不受事件发布的约束，除非它们是使用@DBRef注释的文档引用。
Lifecycle events are only emitted for root level types. Complex types used as properties within a document root are not subject to event publication unless they are document references annotated with @DBRef.

# Exception_Translation

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Spring框架为各种数据库和映射技术提供异常转换。传统上这适用于JDBC和JPA。 MongoDB的Spring支持通过提供org.springframework.dao.support.PersistenceExceptionTranslator接口的实现将此功能扩展到MongoDB数据库。
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.

映射到Spring的一致数据访问异常层次结构背后的动机是，您可以编写可移植和描述性的异常处理代码，而无需对MongoDB错误代码进行编码。所有Spring的数据访问异常都从根DataAccessException类继承，因此您可以确保在单个try-catch块中捕获所有与数据库相关的异常。请注意，并非MongoDB驱动程序抛出的所有异常都从MongoException类继承。保留内部异常和消息，以便不丢失任何信息。
The motivation behind mapping to Spring’s consistent data access exception hierarchy is that you are then able to write portable and descriptive exception handling code without resorting to coding against MongoDB error codes. All of Spring’s data access exceptions are inherited from the root DataAccessException class so that you can be sure to catch all database related exception within a single try-catch block. Note that not all exceptions thrown by the MongoDB driver inherit from the MongoException class. The inner exception and message are preserved so that no information is lost.

MongoExceptionTranslator执行的一些映射是com.mongodb.Network到DataAccessResourceFailureException，而MongoException错误代码1003,12001,12010,12011和12012到InvalidDataAccessApiUsageException。查看实现以获取有关映射的更多详细信息。
Some of the mappings performed by the MongoExceptionTranslator are com.mongodb.Network to DataAccessResourceFailureException and MongoException error codes 1003, 12001, 12010, 12011, and 12012 to InvalidDataAccessApiUsageException. Look into the implementation for more details on the mapping.

# Execution_Callbacks

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所有Spring模板类的一个常见设计特性是所有功能都被路由到模板的一个执行回调方法中。这样做有助于确保一致地执行可能需要的异常和任何资源管理。虽然JDBC和JMS比MongoDB更需要此功能，但它仍然提供了一个用于异常转换和日志记录的单点。因此，使用这些执行回调是访问MongoDB驱动程序的MongoDatabase和MongoCollection对象以执行未在MongoTemplate上作为方法公开的不常见操作的首选方法。
One common design feature of all Spring template classes is that all functionality is routed into one of the template’s execute callback methods. Doing so helps to ensure that exceptions and any resource management that may be required are performed consistently. While JDBC and JMS need this feature much more than MongoDB does, it still offers a single spot for exception translation and logging to occur. Consequently, using these execute callbacks is the preferred way to access the MongoDB driver’s MongoDatabase and MongoCollection objects to perform uncommon operations that were not exposed as methods on MongoTemplate.

The following list describes the execute callback methods.

<T> T execute (Class<?> entityClass, CollectionCallback<T> action):
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):
Executes a DbCallback translating any exceptions as necessary. Spring Data MongoDB provides support for the Aggregation Framework introduced to MongoDB in version 2.2.
<T> T execute (String collectionName, DbCallback<T> action):
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.

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

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

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# GridFS_Support

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MongoDB supports storing binary files inside its filesystem, GridFS. Spring Data MongoDB provides a GridFsOperations interface as well as the corresponding implementation, GridFsTemplate, to let you interact with the filesystem. You can set up a GridFsTemplate instance by handing it a MongoDbFactory as well as a MongoConverter, as the following example shows:

Example 89. JavaConfig setup for a GridFsTemplate

class GridFsConfiguration extends AbstractMongoConfiguration {

  // … further configuration omitted

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

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The corresponding XML configuration follows:

Example 90. XML configuration for a GridFsTemplate

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

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The template can now be injected and used to perform storage and retrieval operations, as the following example shows:

Example 91. Using GridFsTemplate to store files

class GridFsClient {

  @Autowired
  GridFsOperations operations;

  @Test
  public void storeFileToGridFs() {

    FileMetadata metadata = new FileMetadata();
    // populate metadata
    Resource file = … // lookup File or Resource

    operations.store(file.getInputStream(), "filename.txt", metadata);
  }
}

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The store(…) operations take an InputStream, a filename, and (optionally) metadata information about the file to store. The metadata can be an arbitrary object, which will be marshaled by the MongoConverter configured with the GridFsTemplate. Alternatively, you can also provide a Document.

You can read files from the filesystem through either the find(…) or the getResources(…) methods. Let’s have a look at the find(…) methods first. You can either find a single file or multiple files that match a Query. You can use the GridFsCriteria helper class to define queries. It provides static factory methods to encapsulate default metadata fields (such as whereFilename() and whereContentType()) or a custom one through whereMetaData(). The following example shows how to use GridFsTemplate to query for files:

Example 92. Using GridFsTemplate to query for files

class GridFsClient {

  @Autowired
  GridFsOperations operations;

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

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Currently, MongoDB does not support defining sort criteria when retrieving files from GridFS. For this reason, any sort criteria defined on the Query instance handed into the find(…) method are disregarded. The other option to read files from the GridFs is to use the methods introduced by the ResourcePatternResolver interface. They allow handing an Ant path into the method and can thus retrieve files matching the given pattern. The following example shows how to use GridFsTemplate to read files:

Example 93. Using GridFsTemplate to read files

class GridFsClient {

  @Autowired
  GridFsOperations operations;

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

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

# 具有Tailable游标的无限流

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10.19. Infinite Streams with Tailable Cursors

By default, MongoDB automatically closes a cursor when the client exhausts all results supplied by the cursor. Closing a cursor on exhaustion turns a stream into a finite stream. For capped collections, you can use a Tailable Cursor that remains open after the client consumed all initially returned data.

Capped collections can be created with MongoOperations.createCollection. To do so, provide the required CollectionOptions.empty().capped()…. Tailable cursors can be consumed with both, the imperative and the reactive MongoDB API. It is highly recommended to use the reactive variant, as it is less resource-intensive. However, if you cannot use the reactive API, you can still use a messaging concept that is already prevalent in the Spring ecosystem.

# 10.19.1. Tailable Cursors with MessageListener

Listening to a capped collection using a Sync Driver creates a long running, blocking task that needs to be delegated to a separate component. In this case, we need to first create a MessageListenerContainer, which will be the main entry point for running the specific SubscriptionRequest. Spring Data MongoDB already ships with a default implementation that operates on MongoTemplate and is capable of creating and executing Task instances for a TailableCursorRequest.

The following example shows how to use tailable cursors with MessageListener instances:

Example 94. Tailable Cursors with MessageListener instances

MessageListenerContainer container = new DefaultMessageListenerContainer(template);
container.start();                                                                  

MessageListener<Document, User> listener = System.out::println;                     

TailableCursorRequest request = TailableCursorRequest.builder()
  .collection("orders")                                                             
  .filter(query(where("value").lt(100)))                                            
  .publishTo(listener)                                                              
  .build();

container.register(request, User.class);                                            

// ...

container.stop();

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Starting the container intializes the resources and starts Task instances for already registered SubscriptionRequest instances. Requests added after startup are ran immediately. Define the listener called when a Message is received. The Message#getBody() is converted to the requested domain type. Use Document to receive raw results without conversion. Set the collection to listen to. Provide an optional filter for documents to receive. Set the message listener to publish incoming Messages to. Register the request. The returned Subscription can be used to check the current Task state and cancel its execution to free resources. Do not forget to stop the container once you are sure you no longer need it. Doing so stops all running Task instances within the container.

# 10.19.2. Reactive Tailable Cursors

Using tailable cursors with a reactive data types allows construction of infinite streams. A tailable cursor remains open until it is closed externally. It emits data as new documents arrive in a capped collection.

Tailable cursors may become dead, or invalid, if either the query returns no match or the cursor returns the document at the “end” of the collection and the application then deletes that document. The following example shows how to create and use an infinite stream query:

Example 95. Infinite Stream queries with ReactiveMongoOperations

Flux<Person> stream = template.tail(query(where("name").is("Joe")), Person.class);

Disposable subscription = stream.doOnNext(person -> System.out.println(person)).subscribe();

// …

// Later: Dispose the subscription to close the stream
subscription.dispose();

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Spring Data MongoDB Reactive repositories support infinite streams by annotating a query method with @Tailable. This works for methods that return Flux and other reactive types capable of emitting multiple elements, as the following example shows:

Example 96. Infinite Stream queries with ReactiveMongoRepository

public interface PersonRepository extends ReactiveMongoRepository<Person, String> {

  @Tailable
  Flux<Person> findByFirstname(String firstname);

}

Flux<Person> stream = repository.findByFirstname("Joe");

Disposable subscription = stream.doOnNext(System.out::println).subscribe();

// …

// Later: Dispose the subscription to close the stream
subscription.dispose();

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# 10.20. Change_Streams

As of MongoDB 3.6, Change Streams let applications get notified about changes without having to tail the oplog.

Change Stream support is only possible for replica sets or for a sharded cluster. Change Streams can be consumed with both, the imperative and the reactive MongoDB Java driver. It is highly recommended to use the reactive variant, as it is less resource-intensive. However, if you cannot use the reactive API, you can still obtain change events by using the messaging concept that is already prevalent in the Spring ecosystem.

It is possible to watch both on a collection as well as database level, whereas the database level variant publishes changes from all collections within the database. When subscribing to a database change stream, make sure to use a suitable type for the event type as conversion might not apply correctly across different entity types. In doubt, use Document.

# 10.20.1. Change Streams with MessageListener

Listening to a Change Stream by using a Sync Driver creates a long running, blocking task that needs to be delegated to a separate component. In this case, we need to first create a MessageListenerContainer, which will be the main entry point for running the specific SubscriptionRequest tasks. Spring Data MongoDB already ships with a default implementation that operates on MongoTemplate and is capable of creating and executing Task instances for a ChangeStreamRequest.

The following example shows how to use Change Streams with MessageListener instances:

Example 97. Change Streams with MessageListener instances

MessageListenerContainer container = new DefaultMessageListenerContainer(template);
container.start();                                                                                        

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

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

// ...

container.stop();

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Starting the container intializes the resources and starts Task instances for already registered SubscriptionRequest instances. Requests added after startup are ran immediately. Define the listener called when a Message is received. The Message#getBody() is converted to the requested domain type. Use Document to receive raw results without conversion. Set the collection to listen to and provide additional options through ChangeStreamOptions. Register the request. The returned Subscription can be used to check the current Task state and cancel its execution to free resources. Do not forget to stop the container once you are sure you no longer need it. Doing so stops all running Task instances within the container.

# 10.20.2. Reactive Change Streams

Subscribing to Change Streams with the reactive API is a more natural approach to work with streams. Still, the essential building blocks, such as ChangeStreamOptions, remain the same. The following example shows how to use Change Streams emitting ChangeStreamEvents:

Example 98. Change Streams emitting ChangeStreamEvent

ChangeStreamOptions options = ChangeStreamOptions.builder()
    .filter(newAggregation(User.class, match(where("age").gte(38)))                              
    .build();

Flux<ChangeStreamEvent<User>> flux = reactiveTemplate.changeStream("user", options, User.class);

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Use an aggregation pipeline to filter events. Obtain a Flux of change stream events. The ChangeStreamEvent#getBody() is converted to the requested domain type. Use Document to receive raw results without conversion.

# 10.20.3. Resuming Change Streams

Change Streams can be resumed and resume emitting events where you left. To resume the stream, you need to supply either a resume token or the last known server time (in UTC). Use ChangeStreamOptions to set the value accordingly.

The following example shows how to set the resume offset using server time:

Example 99. Resume a Change Stream

ChangeStreamOptions = ChangeStreamOptions.builder()
    .resumeAt(Instant.now().minusSeconds(1))        
    .build()

Flux<ChangeStreamEvent<Person>> resumed = template.changeStream("person", options, User.class)

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You may obtain the server time of an ChangeStreamEvent through the getTimestamp method or use the resumeToken exposed through getResumeToken.

# MongoDB_Sessions

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从版本3.6开始，MongoDB支持会话的概念。会话的使用启用了MongoDB的因果一致性模型，该模型保证以尊重其因果关系的顺序运行操作。它们分为ServerSession实例和ClientSession实例。在本节中，当我们谈到会话时，我们引用ClientSession。
As of version 3.6, MongoDB supports the concept of sessions. The use of sessions enables MongoDB’s Causal Consistency model, which guarantees running operations in an order that respects their causal relationships. Those are split into ServerSession instances and ClientSession instances. In this section, when we speak of a session, we refer to ClientSession.

Operations within a client session are not isolated from operations outside the session. Both MongoOperations and ReactiveMongoOperations provide gateway methods for tying a ClientSession to the operations. MongoCollection and MongoDatabase use session proxy objects that implement MongoDB’s collection and database interfaces, so you need not add a session on each call. This means that a potential call to MongoCollection#find() is delegated to MongoCollection#find(ClientSession).

Methods such as (Reactive)MongoOperations#getCollection return native MongoDB Java Driver gateway objects (such as MongoCollection) that themselves offer dedicated methods for ClientSession. These methods are NOT session-proxied. You should provide the ClientSession where needed when interacting directly with a MongoCollection or MongoDatabase and not through one of the #execute callbacks on MongoOperations.

# 11.1. Synchronous ClientSession support.

The following example shows the usage of a session:

Example 100. ClientSession with MongoOperations

ClientSessionOptions sessionOptions = ClientSessionOptions.builder()
    .causallyConsistent(true)
    .build();

ClientSession session = client.startSession(sessionOptions); 

template.withSession(() -> session)
    .execute(action -> {

        Query query = query(where("name").is("Durzo Blint"));
        Person durzo = action.findOne(query, Person.class);  

        Person azoth = new Person("Kylar Stern");
        azoth.setMaster(durzo);

        action.insert(azoth);                                

        return azoth;
    });

session.close()

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Obtain a new session from the server. Use MongoOperation methods as before. The ClientSession gets applied automatically. Make sure to close the ClientSession. Close the session. When dealing with DBRef instances, especially lazily loaded ones, it is essential to not close the ClientSession before all data is loaded. Otherwise, lazy fetch fails.

# 11.2. Reactive ClientSession support

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

Example 101. ClientSession with ReactiveMongoOperations

ClientSessionOptions sessionOptions = ClientSessionOptions.builder()
    .causallyConsistent(true)
    .build();

Publisher<ClientSession> session = client.startSession(sessionOptions); 

template.withSession(session)
    .execute(action -> {

        Query query = query(where("name").is("Durzo Blint"));
        return action.findOne(query, Person.class)
            .flatMap(durzo -> {

                Person azoth = new Person("Kylar Stern");
                azoth.setMaster(durzo);

                return action.insert(azoth);                            
            });
    }, ClientSession::close)                                            
    .subscribe();

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Obtain a Publisher for new session retrieval. Use ReactiveMongoOperation methods as before. The ClientSession is obtained and applied automatically. Make sure to close the ClientSession. Nothing happens until you subscribe. See the Project Reactor Reference Guide for details. By using a Publisher that provides the actual session, you can defer session acquisition to the point of actual subscription. Still, you need to close the session when done, so as to not pollute the server with stale sessions. Use the doFinally hook on execute to call ClientSession#close() when you no longer need the session. If you prefer having more control over the session itself, you can obtain the ClientSession through the driver and provide it through a Supplier.

Reactive use of ClientSession is limited to Template API usage. There’s currently no session integration with reactive repositories.

# MongoDB_Transactions

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As of version 4, MongoDB supports Transactions. Transactions are built on top of Sessions and, consequently, require an active ClientSession.

Unless you specify a MongoTransactionManager within your application context, transaction support is DISABLED. You can use setSessionSynchronization(ALWAYS) to participate in ongoing non-native MongoDB transactions. To get full programmatic control over transactions, you may want to use the session callback on MongoOperations.

The following example shows programmatic transaction control within a SessionCallback:

Example 102. Programmatic transactions

ClientSession session = client.startSession(options);                   

template.withSession(session)
    .execute(action -> {

        session.startTransaction();                                     

        try {

            Step step = // ...;
            action.insert(step);

            process(step);

            action.update(Step.class).apply(Update.set("state", // ...

            session.commitTransaction();                                

        } catch (RuntimeException e) {
            session.abortTransaction();                                 
        }
    }, ClientSession::close)

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Obtain a new ClientSession.
Start the transaction.
If everything works out as expected, commit the changes.
Something broke, so roll back everything.
Do not forget to close the session when done.
The preceding example lets you have full control over transactional behavior while using the session scoped MongoOperations instance within the callback to ensure the session is passed on to every server call. To avoid some of the overhead that comes with this approach, you can use a TransactionTemplate to take away some of the noise of manual transaction flow.

# 12.1. Transactions with TransactionTemplate

Spring Data MongoDB transactions support a TransactionTemplate. The following example shows how to create and use a TransactionTemplate:

Example 103. Transactions with TransactionTemplate

template.setSessionSynchronization(ALWAYS);                                     

// ...

TransactionTemplate txTemplate = new TransactionTemplate(anyTxManager);         

txTemplate.execute(new TransactionCallbackWithoutResult() {

    @Override
    protected void doInTransactionWithoutResult(TransactionStatus status) {     

        Step step = // ...;
        template.insert(step);

        process(step);

        template.update(Step.class).apply(Update.set("state", // ...
    };
});

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Enable transaction synchronization during Template API configuration. Create the TransactionTemplate using the provided PlatformTransactionManager. Within the callback the ClientSession and transaction are already registered. Changing state of MongoTemplate during runtime (as you might think would be possible in item 1 of the preceding listing) can cause threading and visibility issues.

# 12.2. Transactions with MongoTransactionManager

MongoTransactionManager是众所周知的Spring事务支持的入口。它允许应用程序使用Spring的托管事务功能。 MongoTransactionManager将ClientSession绑定到线程。 MongoTemplate检测会话并对与事务相关联的这些资源进行操作。 MongoTemplate还可以参与其他正在进行的交易。以下示例显示如何使用MongoTransactionManager创建和使用事务：
MongoTransactionManager is the gateway to the well known Spring transaction support. It lets applications use the managed transaction features of Spring. The MongoTransactionManager binds a ClientSession to the thread. MongoTemplate detects the session and operates on these resources which are associated with the transaction accordingly. MongoTemplate can also participate in other, ongoing transactions. The following example shows how to create and use transactions with a MongoTransactionManager:

Example 104. Transactions with MongoTransactionManager

@Configuration
static class Config extends AbstractMongoConfiguration {

    @Bean
    MongoTransactionManager transactionManager(MongoDbFactory dbFactory) {  
        return new MongoTransactionManager(dbFactory);
    }

    // ...
}

@Component
public class StateService {

    @Transactional
    void someBusinessFunction(Step step) {                                  

        template.insert(step);

        process(step);

        template.update(Step.class).apply(Update.set("state", // ...
    };
});

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Register MongoTransactionManager in the application context. Mark methods as transactional. @Transactional(readOnly = true) advises MongoTransactionManager to also start a transaction that adds the ClientSession to outgoing requests.

# 12.3. Reactive Transactions

Same as with the reactive ClientSession support, the ReactiveMongoTemplate offers dedicated methods for operating within a transaction without having to worry about the commit/abort actions depending on the operations outcome.

Reactive use of ClientSession and transactions is limited to Template API usage. There’s currently no session or transaction integration with reactive repositories. Using the plain MongoDB reactive driver API a delete within a transactional flow may look like this.

Example 105. Native driver support

Mono<DeleteResult> result = Mono
    .from(client.startSession())                                                             

    .flatMap(session -> {
        session.startTransaction();                                                          

        return Mono.from(collection.deleteMany(session, ...))                                

            .onErrorResume(e -> Mono.from(session.abortTransaction()).then(Mono.error(e)))   

            .flatMap(val -> Mono.from(session.commitTransaction()).then(Mono.just(val)))     

            .doFinally(signal -> session.close());                                           
      });

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First we obviously need to initiate the session.
Once we have the ClientSession at hand, start the transaction.
Operate within the transaction by passing on the ClientSession to the operation.
If the operations completes exceptionally, we need to abort the transaction and preserve the error.
Or of course, commit the changes in case of success. Still preserving the operations result.
Lastly, we need to make sure to close the session.
The culprit of the above operation is in keeping the main flows DeleteResult instead of the transaction outcome published via either commitTransaction() or abortTransaction(), which leads to a rather complicated setup.

MongoOperations.inTransaction() allows you to utilize the callback from for the reactive session support to actually preserve the flows outcome but also perform commit and abort actions accordingly. This allows you to express the above flow simply as the following:

Example 106. ReactiveMongoTemplate Transactions

Mono<DeleteResult> result = template.inTransaction()                                      

    .execute(action -> action.remove(query(where("id").is("step-1")), Step.class));

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Initiate the transaction.
Operate within the ClientSession. Each execute(…) unit of work callback initiates a new transaction in the scope of the same ClientSession. In case you need access to the ClientSession within the flow, you can use ReactiveMongoContext.getSession() to obtain in from the Reactor Context.
Everything happening inside the transactional callback is executed within a managed transaction. Errors within the reactive flow of execute(…) that are not propagated to outside of the callback do not affect the operations within the transaction.

template.inTransaction()                                                            

    .execute(action -> action.find(query(where("state").is("active")), Step.class)
        .flatMap(step -> action.update(Step.class)
            .matching(query(where("id").is(step.id)))
            .apply(update("state", "paused"))
            .all()))                                                                

    .flatMap(updated -> {
        // Exception could happen here                                              
    });

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Initiate the managed transaction.
Operate within the ClientSession. The transaction is committed after this is done or rolled back if an error occurs here.
An error outside the transaction flow has no affect on the previous transactional execution.
12.4. Special behavior inside transactions
Inside transactions, MongoDB server has a slightly different behavior.

# Connection Settings

The MongoDB drivers offer a dedicated replica set name configuration option turing the driver into auto detection mode. This option helps identifying replica set master nodes and command routing during a transaction.

Make sure to add replicaSet to the MongoDB URI. Please refer to connection string options for further details. Collection Operations

MongoDB does not support collection operations, such as collection creation, within a transaction. This also affects the on the fly collection creation that happens on first usage. Therefore make sure to have all required structures in place.

# Transient Errors

MongoDB can add special labels to errors raised during transactional execution. Those may indicate transient failures that might vanish by merely retrying the operation. We highly recommend Spring Retry for those purposes. Nevertheless one may override MongoTransactionManager#doCommit(MongoTransactionObject) to implement a Retry Commit Operation behavior as outlined in the MongoDB reference manual.

# Count

MongoDB count operates upon collection statistics which may not reflect the actual situation within a transaction. The server responds with error 50851 when issuing a count command inside of a multi-document transaction. Once MongoTemplate detects an active transaction, all exposed count() methods are converted and delegated to the aggregation framework using $match and $count operators, preserving Query settings, such as collation.

Restrictions apply when using geo commands inside of the aggregation count helper. The following operators cannot be used and must be replaced with a different operator:

$where → $expr
$near → $geoWithin with $center
$nearSphere → $geoWithin with $centerSphere

Queries using Criteria.near(…) and Criteria.nearSphere(…) must be rewritten to Criteria.within(…) respective Criteria.withinSphere(…). Same applies for the near query keyword in repository query methods that must be changed to within. See also MongoDB JIRA ticket DRIVERS-518 for further reference.

The following snippet shows count usage inside the session-bound closure:

session.startTransaction();

template.withSession(session)
    .execute(action -> {
        action.count(query(where("state").is("active")), Step.class)
        ...

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The snippet above materializes in the following command:

db.collection.aggregate(
   [
      { $match: { state: "active" } },
      { $count: "totalEntityCount" }
   ]
)

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instead of:

db.collection.find( { state: "active" } ).count()

# Reactive_MongoDB_support

back

The reactive MongoDB support contains the following basic set of features:

Spring configuration support that uses Java-based @Configuration classes, a MongoClient instance, and replica sets.

ReactiveMongoTemplate, which is a helper class that increases productivity by using MongoOperations in a reactive manner. It includes integrated object mapping between Document instances and POJOs.

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

Feature-rich Object Mapping integrated with Spring’s ConversionService.

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

Persistence and mapping lifecycle events.

Java based Query, Criteria, and Update DSLs.

Automatic implementation of reactive repository interfaces including support for custom query methods.

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

# 13.1. Getting Started

Spring MongoDB support requires MongoDB 2.6 or higher and Java SE 8 or higher.

First, you need to set up a running MongoDB server. Refer to the MongoDB Quick Start guide for an explanation on how to startup a MongoDB instance. Once installed, starting MongoDB is typically a matter of 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 and press Yes when prompted. Then enter a project and a package name, such as org.spring.mongodb.example.

Then add the following to the pom.xml dependencies section.

<dependencies>

  <!-- other dependency elements omitted -->

  <dependency>
    <groupId>org.springframework.data</groupId>
    <artifactId>spring-data-mongodb</artifactId>
    <version>2.1.9.RELEASE</version>
  </dependency>

  <dependency>
    <groupId>org.mongodb</groupId>
    <artifactId>mongodb-driver-reactivestreams</artifactId>
    <version>1.9.2</version>
  </dependency>

  <dependency>
    <groupId>io.projectreactor</groupId>
    <artifactId>reactor-core</artifactId>
    <version>Californium-SR6</version>
  </dependency>

</dependencies>

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MongoDB uses two different drivers for blocking and reactive (non-blocking) data access. While blocking operations are provided by default, you can opt-in for reactive usage. To get started with a working example, create a simple Person class to persist, as follows:

@Document
public class Person {

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

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

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

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

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Then create an application to run, as follows:

public class ReactiveMongoApp {

  private static final Logger log = LoggerFactory.getLogger(ReactiveMongoApp.class);

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

    CountDownLatch latch = new CountDownLatch(1);

    ReactiveMongoTemplate mongoOps = new ReactiveMongoTemplate(MongoClients.create(), "database");

    mongoOps.insert(new Person("Joe", 34))
          .flatMap(p -> mongoOps.findOne(new Query(where("name").is("Joe")), Person.class))
          .doOnNext(person -> log.info(person.toString()))
          .flatMap(person -> mongoOps.dropCollection("person"))
          .doOnComplete(latch::countDown)
          .subscribe();

    latch.await();
  }
}

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Running the preceding class produces the following output:

2016-09-20 14:56:57,373 DEBUG .index.MongoPersistentEntityIndexCreator: 124 - Analyzing class class example.ReactiveMongoApp$Person for index information.
2016-09-20 14:56:57,452 DEBUG .data.mongodb.core.ReactiveMongoTemplate: 975 - Inserting Document containing fields: [_class, name, age] in collection: person
2016-09-20 14:56:57,541 DEBUG .data.mongodb.core.ReactiveMongoTemplate:1503 - findOne using query: { "name" : "Joe"} fields: null for class: class example.ReactiveMongoApp$Person in collection: person
2016-09-20 14:56:57,545 DEBUG .data.mongodb.core.ReactiveMongoTemplate:1979 - findOne using query: { "name" : "Joe"} in db.collection: database.person
2016-09-20 14:56:57,567  INFO                 example.ReactiveMongoApp:  43 - Person [id=57e1321977ac501c68d73104, name=Joe, age=34]
2016-09-20 14:56:57,573 DEBUG .data.mongodb.core.ReactiveMongoTemplate: 528 - Dropped collection [person]

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

You can instantiate the central helper class of Spring Mongo (ReactiveMongoTemplate) by using the standard com.mongodb.reactivestreams.client.MongoClient object and the name of the database to use.

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

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

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

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

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

# 13.2. Connecting to MongoDB with Spring and the Reactive Streams Driver

One of the first tasks when using MongoDB and Spring is to create a com.mongodb.reactivestreams.client.MongoClient object by using the IoC container.

#####13.2.1. Registering a MongoClient Instance Using Java-based Metadata

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

Example 107. Registering a com.mongodb.MongoClient object using Java based bean metadata

@Configuration
public class AppConfig {

  /*
   * Use the Reactive Streams Mongo Client API to create a com.mongodb.reactivestreams.client.MongoClient instance.
   */
   public @Bean MongoClient reactiveMongoClient()  {
       return MongoClients.create("mongodb://localhost");
   }
}

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This approach lets you use the standard com.mongodb.reactivestreams.client.MongoClient API (which you may already know).

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

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

Example 108. Registering a com.mongodb.MongoClient object using Spring’s MongoClientFactoryBean and enabling Spring’s exception translation support

@Configuration
public class AppConfig {

    /*
     * Factory bean that creates the com.mongodb.reactivestreams.client.MongoClient instance
     */
     public @Bean ReactiveMongoClientFactoryBean mongoClient() {

          ReactiveMongoClientFactoryBean clientFactory = new ReactiveMongoClientFactoryBean();
          clientFactory.setHost("localhost");

          return clientFactory;
     }
}

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To access the com.mongodb.reactivestreams.client.MongoClient object created by the ReactiveMongoClientFactoryBean in other @Configuration or your own classes, get the MongoClient from the context.

# 13.2.2. The ReactiveMongoDatabaseFactory Interface

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

public interface ReactiveMongoDatabaseFactory {

  /**
   * Creates a default {@link MongoDatabase} instance.
   *
   * @return
   * @throws DataAccessException
   */
  MongoDatabase getMongoDatabase() throws DataAccessException;

  /**
   * Creates a {@link MongoDatabase} instance to access the database with the given name.
   *
   * @param dbName must not be {@literal null} or empty.
   * @return
   * @throws DataAccessException
   */
  MongoDatabase getMongoDatabase(String dbName) throws DataAccessException;

  /**
   * Exposes a shared {@link MongoExceptionTranslator}.
   *
   * @return will never be {@literal null}.
   */
  PersistenceExceptionTranslator getExceptionTranslator();
}

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The org.springframework.data.mongodb.core.SimpleReactiveMongoDatabaseFactory class implements the ReactiveMongoDatabaseFactory interface and is created with a standard com.mongodb.reactivestreams.client.MongoClient instance and the database name.

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

public class MongoApp {

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

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

    ReactiveMongoOperations mongoOps = new ReactiveMongoOperations(new SimpleReactiveMongoDatabaseFactory(MongoClient.create(), "database"));

    mongoOps.insert(new Person("Joe", 34))
        .flatMap(p -> mongoOps.findOne(new Query(where("name").is("Joe")), Person.class))
        .doOnNext(person -> log.info(person.toString()))
        .flatMap(person -> mongoOps.dropCollection("person"))
        .subscribe();
  }
}

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The use of SimpleMongoDbFactory is the only difference between the listing shown in the getting started section.

# 13.2.3. Registering a ReactiveMongoDatabaseFactory Instance by Using Java-based Metadata

To register a ReactiveMongoDatabaseFactory instance with the container, you can write code much like what was highlighted in the previous code listing, as the following example shows:

@Configuration
public class MongoConfiguration {

  public @Bean ReactiveMongoDatabaseFactory reactiveMongoDatabaseFactory() {
    return new SimpleReactiveMongoDatabaseFactory(MongoClients.create(), "database");
  }
}

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To define the username and password, create a MongoDB connection string and pass it into the factory method, as the next listing shows. The following listing also shows how to use ReactiveMongoDatabaseFactory to register an instance of ReactiveMongoTemplate with the container:

@Configuration
public class MongoConfiguration {

  public @Bean ReactiveMongoDatabaseFactory reactiveMongoDatabaseFactory() {
    return new SimpleReactiveMongoDatabaseFactory(MongoClients.create("mongodb://joe:secret@localhost"), "database");
  }

  public @Bean ReactiveMongoTemplate reactiveMongoTemplate() {
    return new ReactiveMongoTemplate(reactiveMongoDatabaseFactory());
  }
}

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# 13.3. Introduction to ReactiveMongoTemplate

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

Once configured, ReactiveMongoTemplate is thread-safe and can be reused across multiple instances. The mapping between MongoDB documents and domain classes is done by delegating to an implementation of the MongoConverter interface. Spring provides a default implementation with MongoMappingConverter, but you can also write your own converter. See the section on MongoConverter instances for more detailed information.

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

The preferred way to reference the operations on ReactiveMongoTemplate instance is through its ReactiveMongoOperations interface. The default converter implementation used by ReactiveMongoTemplate is MappingMongoConverter. While the MappingMongoConverter can use additional metadata to specify the mapping of objects to documents, it can also convert objects that contain no additional metadata by using some conventions for the mapping of IDs and collection names. These conventions as well as the use of mapping annotations are explained in the Mapping chapter.

Another central feature of ReactiveMongoTemplate is exception translation of exceptions thrown in the MongoDB Java driver into Spring’s portable Data Access Exception hierarchy. See the section on exception translation for more information.

There are many convenience methods on ReactiveMongoTemplate to help you easily perform common tasks. However, if you need to access the MongoDB driver API directly to access functionality not explicitly exposed by the MongoTemplate, you can use one of several execute callback methods to access underlying driver APIs. The execute callbacks give you a reference to either a com.mongodb.reactivestreams.client.MongoCollection or a com.mongodb.reactivestreams.client.MongoDatabase object. See Execution Callbacks for more information.

# 13.3.1. Instantiating ReactiveMongoTemplate

You can use Java to create and register an instance of ReactiveMongoTemplate, as follows:

Example 109. Registering a com.mongodb.reactivestreams.client.MongoClient object and enabling Spring’s exception translation support

@Configuration
public class AppConfig {

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

  public @Bean ReactiveMongoTemplate reactiveMongoTemplate() {
      return new ReactiveMongoTemplate(reactiveMongoClient(), "mydatabase");
  }
}

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There are several overloaded constructors of ReactiveMongoTemplate, including:

ReactiveMongoTemplate(MongoClient mongo, String databaseName):
Takes the com.mongodb.MongoClient object and the default database name to operate against.
ReactiveMongoTemplate(ReactiveMongoDatabaseFactory mongoDatabaseFactory):
Takes a ReactiveMongoDatabaseFactory object that encapsulated the com.mongodb.reactivestreams.client.MongoClient object and database name.
ReactiveMongoTemplate(ReactiveMongoDatabaseFactory mongoDatabaseFactory, MongoConverter mongoConverter):
Adds a MongoConverter to use for mapping.

When creating a ReactiveMongoTemplate, you might also want to set the following properties:

WriteResultCheckingPolicy
WriteConcern
ReadPreference

The preferred way to reference the operations on ReactiveMongoTemplate instance is through its ReactiveMongoOperations interface.

# 13.3.2. WriteResultChecking Policy

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

# 13.3.3. WriteConcern

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

# 13.3.4. WriteConcernResolver

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

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

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The argument, MongoAction, determines the WriteConcern value to be used and whether to use the value of the template itself as a default. MongoAction contains the collection name being written to, the java.lang.Class of the POJO, the converted DBObject, the operation as a value from the MongoActionOperation enumeration (one of REMOVE, UPDATE, INSERT, INSERT_LIST, and SAVE), and a few other pieces of contextual information. The following example shows how to create a WriteConcernResolver:

private class MyAppWriteConcernResolver implements WriteConcernResolver {

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

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# 13.4. Saving, Updating, and Removing Documents

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

Consider the following Person class:

public class Person {

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

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

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

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

}

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The following listing shows how you can save, update, and delete the Person object:

public class ReactiveMongoApp {

  private static final Logger log = LoggerFactory.getLogger(ReactiveMongoApp.class);

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

    CountDownLatch latch = new CountDownLatch(1);

    ReactiveMongoTemplate mongoOps = new ReactiveMongoTemplate(MongoClients.create(), "database");

    mongoOps.insert(new Person("Joe", 34)).doOnNext(person -> log.info("Insert: " + person))
      .flatMap(person -> mongoOps.findById(person.getId(), Person.class))
      .doOnNext(person -> log.info("Found: " + person))
      .zipWith(person -> mongoOps.updateFirst(query(where("name").is("Joe")), update("age", 35), Person.class))
      .flatMap(tuple -> mongoOps.remove(tuple.getT1())).flatMap(deleteResult -> mongoOps.findAll(Person.class))
      .count().doOnSuccess(count -> {
        log.info("Number of people: " + count);
        latch.countDown();
      })

      .subscribe();

    latch.await();
  }
}

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The preceding example includes implicit conversion between a String and ObjectId (by using the MongoConverter) as stored in the database and recognizing a convention of the property Id name.

The preceding example is meant to show the use of save, update, and remove operations on ReactiveMongoTemplate and not to show complex mapping or chaining functionality. “Querying Documents” explains the query syntax used in the preceding example in more detail. Additional documentation can be found in the blocking MongoTemplate section.

# 13.5. Execution Callbacks

One common design feature of all Spring template classes is that all functionality is routed into one of the templates 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 the execute callback is the preferred way to access the MongoDB driver’s MongoDatabase and MongoCollection objects to perform uncommon operations that were not exposed as methods on ReactiveMongoTemplate.

Here is a list of execute callback methods.

<T> Flux<T> execute (Class<?> entityClass, ReactiveCollectionCallback<T> action):
Executes the given ReactiveCollectionCallback for the entity collection of the specified class.
<T> Flux<T> execute (String collectionName, ReactiveCollectionCallback<T> action):
Executes the given ReactiveCollectionCallback on the collection of the given name.
<T> Flux<T> execute (ReactiveDatabaseCallback<T> action):
Executes a ReactiveDatabaseCallback translating any exceptions as necessary.

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

Flux<Boolean> hasIndex = operations.execute("geolocation",
    collection -> Flux.from(collection.listIndexes(Document.class))
      .filter(document -> document.get("name").equals("fancy-index-name"))
      .flatMap(document -> Mono.just(true))
      .defaultIfEmpty(false));

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# MongoDB_Repositories

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# Introduction14

This chapter points out the specialties for repository support for MongoDB. This chapter builds on the core repository support explained in Working with Spring Data Repositories. You should have a sound understanding of the basic concepts explained there.

# Usage14

To access domain entities stored in a MongoDB, you can use our sophisticated repository support that eases implementation quite significantly. To do so, create an interface for your repository, as the following example shows:

Example 110. Sample Person entity

public class Person {

  @Id
  private String id;
  private String firstname;
  private String lastname;
  private Address address;

  // … getters and setters omitted
}

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Note that the domain type shown in the preceding example has a property named id of type ObjectId. The default serialization mechanism used in MongoTemplate (which backs the repository support) regards properties named id as the document ID. Currently, we support String, ObjectId, and BigInteger as ID types. Now that we have a domain object, we can define an interface that uses it, as follows:

Example 111. Basic repository interface to persist Person entities

public interface PersonRepository extends PagingAndSortingRepository<Person, String> {

  // additional custom query methods go here
}

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Right now this interface serves only to provide type information, but we can add additional methods to it later. To do so, in your Spring configuration, add the following content:

Example 112. General MongoDB repository Spring configuration

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:mongo="http://www.springframework.org/schema/data/mongo"
  xsi:schemaLocation="http://www.springframework.org/schema/beans
    http://www.springframework.org/schema/beans/spring-beans-3.0.xsd
    http://www.springframework.org/schema/data/mongo
    http://www.springframework.org/schema/data/mongo/spring-mongo-1.0.xsd">

  <mongo:mongo-client id="mongoClient" />

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

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

</beans>

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This namespace element causes the base packages to be scanned for interfaces that extend MongoRepository and create Spring beans for each one found. By default, the repositories get a MongoTemplate Spring bean wired that is called mongoTemplate, so you only need to configure mongo-template-ref explicitly if you deviate from this convention.

If you would rather go with Java-based configuration, use the @EnableMongoRepositories annotation. That annotation carries the same attributes as the namespace element. If no base package is configured, the infrastructure scans the package of the annotated configuration class. The following example shows how to use Java configuration for a repository:

Example 113. Java configuration for repositories

@Configuration
@EnableMongoRepositories
class ApplicationConfig extends AbstractMongoConfiguration {

  @Override
  protected String getDatabaseName() {
    return "e-store";
  }

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

  @Override
  protected String getMappingBasePackage() {
    return "com.oreilly.springdata.mongodb"
  }
}

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Because our domain repository extends PagingAndSortingRepository, it provides you with CRUD operations as well as methods for paginated and sorted access to the entities. Working with the repository instance is just a matter of dependency injecting it into a client. Consequently, accessing the second page of Person objects at a page size of 10 would resemble the following code:

Example 114. Paging access to Person entities

@RunWith(SpringJUnit4ClassRunner.class)
@ContextConfiguration
public class PersonRepositoryTests {

    @Autowired PersonRepository repository;

    @Test
    public void readsFirstPageCorrectly() {

      Page<Person> persons = repository.findAll(PageRequest.of(0, 10));
      assertThat(persons.isFirstPage(), is(true));
    }
}

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The preceding example creates an application context with Spring’s unit test support, which performs annotation-based dependency injection into test cases. Inside the test method, we use the repository to query the datastore. We hand the repository a PageRequest instance that requests the first page of Person objects at a page size of 10.

# Query_Methods

back

Most of the data access operations you usually trigger on a repository result in a query being executed against the MongoDB databases. Defining such a query is a matter of declaring a method on the repository interface, as the following example shows:

Example 115. PersonRepository with query methods

public interface PersonRepository extends PagingAndSortingRepository<Person, String> {

    List<Person> findByLastname(String lastname);                      

    Page<Person> findByFirstname(String firstname, Pageable pageable); 

    Person findByShippingAddresses(Address address);                   

    Person findFirstByLastname(String lastname)                        

    Stream<Person> findAllBy();                                        
}

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The findByLastname method shows a query for all people with the given last name. The query is derived by parsing the method name for constraints that can be concatenated with And and Or. Thus, the method name results in a query expression of {"lastname" : lastname}. Applies pagination to a query. You can equip your method signature with a Pageable parameter and let the method return a Page instance and Spring Data automatically pages the query accordingly. Shows that you can query based on properties that are not primitive types. Throws IncorrectResultSizeDataAccessException if more than one match is found. Uses the First keyword to restrict the query to only the first result. Unlike <3>, this method does not throw an exception if more than one match is found. Uses a Java 8 Stream that reads and converts individual elements while iterating the stream. We do not support referring to parameters that are mapped as DBRef in the domain class. The following table shows the keywords that are supported for query methods:

Table 7. Supported keywords for query methods

Keyword	Sample	Logical result
After	findByBirthdateAfter(Date date)	{"birthdate" : {"$gt" : date}}
GreaterThan	findByAgeGreaterThan(int age)	{"age" : {"$gt" : age}}
GreaterThanEqual	findByAgeGreaterThanEqual(int age)	{"age" : {"$gte" : age}}
Before	findByBirthdateBefore(Date date)	{"birthdate" : {"$lt" : date}}
LessThan	findByAgeLessThan(int age)	{"age" : {"$lt" : age}}
LessThanEqual	findByAgeLessThanEqual(int age)	{"age" : {"$lte" : age}}
Between	findByAgeBetween(int from, int to)	{"age" : {"$gt" : from, "$lt" : to}}
In	findByAgeIn(Collection ages)	{"age" : {"$in" : [ages…]}}
NotIn	findByAgeNotIn(Collection ages)	{"age" : {"$nin" : [ages…]}}
IsNotNull, NotNull	findByFirstnameNotNull()	{"firstname" : {"$ne" : null}}
IsNull, Null	findByFirstnameNull()	{"firstname" : null}
Like, StartingWith, EndingWith	findByFirstnameLike(String name)	{"firstname" : name} (name as regex)
NotLike, IsNotLike	findByFirstnameNotLike(String name)	{"firstname" : { "$not" : name }} (name as regex)
Containing on String	findByFirstnameContaining(String name)	{"firstname" : name} (name as regex)
NotContaining on String	findByFirstnameNotContaining(String name)	{"firstname" : { "$not" : name}} (name as regex)
Containing on Collection	findByAddressesContaining(Address address)	{"addresses" : { "$in" : address}}
NotContaining on Collection	findByAddressesNotContaining(Address address)	{"addresses" : { "$not" : { "$in" : address}}}
Regex	findByFirstnameRegex(String firstname)	{"firstname" : {"$regex" : firstname }}
(No keyword)	findByFirstname(String name)	{"firstname" : name}
Not	findByFirstnameNot(String name)	{"firstname" : {"$ne" : name}}
Near	findByLocationNear(Point point)	{"location" : {"$near" : [x,y]}}
Near	findByLocationNear(Point point, Distance max)	{"location" : {"$near" : [x,y], "$maxDistance" : max}}
Near	findByLocationNear(Point point, Distance min, Distance max)	{"location" : {"$near" : [x,y], "$minDistance" : min, "$maxDistance" : max}}
Within	findByLocationWithin(Circle circle)	{"location" : {"$geoWithin" : {"$center" : [ [x, y], distance]}}}
Within	findByLocationWithin(Box box)	{"location" : {"$geoWithin" : {"$box" : [ [x1, y1], x2, y2]}}}
IsTrue, True	findByActiveIsTrue()	{"active" : true}
IsFalse, False	findByActiveIsFalse()	{"active" : false}
Exists	findByLocationExists(boolean exists)	{"location" : {"$exists" : exists }}

If the property criterion compares a document, the order of the fields and exact equality in the document matters.

# Repository_Delete_Queries

back

The keywords in the preceding table can be used in conjunction with delete…By or remove…By to create queries that delete matching documents.

Example 116. Delete…By Query

public interface PersonRepository extends MongoRepository<Person, String> {

  List <Person> deleteByLastname(String lastname);

  Long deletePersonByLastname(String lastname);
}

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Using a return type of List retrieves and returns all matching documents before actually deleting them. A numeric return type directly removes the matching documents, returning the total number of documents removed.

# 14.3.2. Geo-spatial Repository Queries

back

As you saw in the preceding table of keywords, a few keywords trigger geo-spatial operations within a MongoDB query. The Near keyword allows some further modification, as the next few examples show.

The following example shows how to define a near query that finds all persons with a given distance of a given point:

Example 117. Advanced Near queries

public interface PersonRepository extends MongoRepository<Person, String>

  // { 'location' : { '$near' : [point.x, point.y], '$maxDistance' : distance}}
  List<Person> findByLocationNear(Point location, Distance distance);
}

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Adding a Distance parameter to the query method allows restricting results to those within the given distance. If the Distance was set up containing a Metric, we transparently use $nearSphere instead of $code, as the following example shows:

Example 118. Using Distance with Metrics

Point point = new Point(43.7, 48.8);
Distance distance = new Distance(200, Metrics.KILOMETERS);
… = repository.findByLocationNear(point, distance);
// {'location' : {'$nearSphere' : [43.7, 48.8], '$maxDistance' : 0.03135711885774796}}

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Using a Distance with a Metric causes a $nearSphere (instead of a plain $near) clause to be added. Beyond that, the actual distance gets calculated according to the Metrics used.

(Note that Metric does not refer to metric units of measure. It could be miles rather than kilometers. Rather, metric refers to the concept of a system of measurement, regardless of which system you use.)

Using @GeoSpatialIndexed(type = GeoSpatialIndexType.GEO_2DSPHERE) on the target property forces usage of the $nearSphere operator. Geo-near Queries Spring Data MongoDb supports geo-near queries, as the following example shows:

public interface PersonRepository extends MongoRepository<Person, String>

  // {'geoNear' : 'location', 'near' : [x, y] }
  GeoResults<Person> findByLocationNear(Point location);

  // No metric: {'geoNear' : 'person', 'near' : [x, y], maxDistance : distance }
  // Metric: {'geoNear' : 'person', 'near' : [x, y], 'maxDistance' : distance,
  //          'distanceMultiplier' : metric.multiplier, 'spherical' : true }
  GeoResults<Person> findByLocationNear(Point location, Distance distance);

  // Metric: {'geoNear' : 'person', 'near' : [x, y], 'minDistance' : min,
  //          'maxDistance' : max, 'distanceMultiplier' : metric.multiplier,
  //          'spherical' : true }
  GeoResults<Person> findByLocationNear(Point location, Distance min, Distance max);

  // {'geoNear' : 'location', 'near' : [x, y] }
  GeoResults<Person> findByLocationNear(Point location);
}

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# 14.3.3. MongoDB JSON-based Query Methods and Field Restriction

back

By adding the org.springframework.data.mongodb.repository.Query annotation to your repository query methods, you can specify a MongoDB JSON query string to use instead of having the query be derived from the method name, as the following example shows:

public interface PersonRepository extends MongoRepository<Person, String>

  @Query("{ 'firstname' : ?0 }")
  List<Person> findByThePersonsFirstname(String firstname);

}

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The ?0 placeholder lets you substitute the value from the method arguments into the JSON query string.

String parameter values are escaped during the binding process, which means that it is not possible to add MongoDB specific operators through the argument. You can also use the filter property to restrict the set of properties that is mapped into the Java object, as the following example shows:

public interface PersonRepository extends MongoRepository<Person, String>

  @Query(value="{ 'firstname' : ?0 }", fields="{ 'firstname' : 1, 'lastname' : 1}")
  List<Person> findByThePersonsFirstname(String firstname);

}

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The query in the preceding example returns only the firstname, lastname and Id properties of the Person objects. The age property, a java.lang.Integer, is not set and its value is therefore null.

# 14.3.4. Sorting Query Method results

MongoDB repositories allow various approaches to define sorting order. Let’s take a look at the following example:

Example 119. Sorting Query Results

public interface PersonRepository extends MongoRepository<Person, String> {

  List<Person> findByFirstnameSortByAgeDesc(String firstname); 

  List<Person> findByFirstname(String firstname, Sort sort);   

  @Query(sort = "{ age : -1 }")
  List<Person> findByFirstname(String firstname);              

  @Query(sort = "{ age : -1 }")
  List<Person> findByLastname(String lastname, Sort sort);     
}

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Static sorting derived from method name. SortByAgeDesc results in { age : -1 } for the sort parameter.
Dynamic sorting using a method argument. Sort.by(DESC, "age") creates { age : -1 } for the sort parameter.
Static sorting via Query annotation. Sort parameter applied as stated in the sort attribute.
Default sorting via Query annotation combined with dynamic one via a method argument. Sort.unsorted() results in { age : -1 }. Using Sort.by(ASC, "age") overrides the defaults and creates { age : 1 }. Sort.by (ASC, "firstname") alters the default and results in { age : -1, firstname : 1 }.

# 14.3.5. JSON-based Queries with SpEL Expressions

查询字符串和字段定义可与SpEL表达式一起使用，以在运行时创建动态查询。 SpEL表达式可以提供谓词值，并可用于扩展带有子文档的谓词。
Query strings and field definitions can be used together with SpEL expressions to create dynamic queries at runtime. SpEL expressions can provide predicate values and can be used to extend predicates with subdocuments.

表达式通过包含所有参数的数组公开方法参数。以下查询使用[0]声明lastname的谓词值（相当于？0参数绑定）：
Expressions expose method arguments through an array that contains all the arguments. The following query uses [0] to declare the predicate value for lastname (which is equivalent to the ?0 parameter binding):

public interface PersonRepository extends MongoRepository<Person, String>

  @Query("{'lastname': ?#{[0]} }")
  List<Person> findByQueryWithExpression(String param0);
}

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表达式可用于调用函数，评估条件和构造值。与JSON结合使用的SpEL表达式揭示了副作用，因为SpEL内部的类似Map的声明读起来像JSON，如下例所示
Expressions can be used to invoke functions, evaluate conditionals, and construct values. SpEL expressions used in conjunction with JSON reveal a side-effect, because Map-like declarations inside of SpEL read like JSON, as the following example shows:

public interface PersonRepository extends MongoRepository<Person, String>

  @Query("{'id': ?#{ [0] ? {$exists :true} : [1] }}")
  List<Person> findByQueryWithExpressionAndNestedObject(boolean param0, String param1);
}

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查询字符串中的SpEL可以是增强查询的强大方法。但是，他们也可以接受各种不受欢迎的论点。您应确保在将字符串传递给查询之前清理字符串，以避免对查询进行不必要的更改。
SpEL in query strings can be a powerful way to enhance queries. However, they can also accept a broad range of unwanted arguments. You should make sure to sanitize strings before passing them to the query to avoid unwanted changes to your query.

Expression support is extensible through the Query SPI: org.springframework.data.repository.query.spi.EvaluationContextExtension. The Query SPI can contribute properties and functions and can customize the root object. Extensions are retrieved from the application context at the time of SpEL evaluation when the query is built. The following example shows how to use EvaluationContextExtension:

public class SampleEvaluationContextExtension extends EvaluationContextExtensionSupport {

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

  @Override
  public Map<String, Object> getProperties() {
    return Collections.singletonMap("principal", SecurityContextHolder.getCurrent().getPrincipal());
  }
}

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Bootstrapping MongoRepositoryFactory yourself is not application context-aware and requires further configuration to pick up Query SPI extensions.

# 14.3.6. Type-safe Query Methods

MongoDB repository support integrates with the Querydsl project, which provides a way to perform type-safe queries. To quote from the project description, "Instead of writing queries as inline strings or externalizing them into XML files they are constructed via a fluent API." It provides the following features:

Code completion in the IDE (all properties, methods, and operations can be expanded in your favorite Java IDE).

Almost no syntactically invalid queries allowed (type-safe on all levels).

Domain types and properties can be referenced safely — no strings involved!

Adapts better to refactoring changes in domain types.

Incremental query definition is easier.

See the QueryDSL documentation for how to bootstrap your environment for APT-based code generation using Maven or Ant.

QueryDSL lets you write queries such as the following:

QPerson person = new QPerson("person");
List<Person> result = repository.findAll(person.address.zipCode.eq("C0123"));

Page<Person> page = repository.findAll(person.lastname.contains("a"),
                                       PageRequest.of(0, 2, Direction.ASC, "lastname"));

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QPerson is a class that is generated by the Java annotation post-processing tool. It is a Predicate that lets you write type-safe queries. Notice that there are no strings in the query other than the C0123 value.

You can use the generated Predicate class by using the QuerydslPredicateExecutor interface, which the following listing shows:

public interface QuerydslPredicateExecutor<T> {

  T findOne(Predicate predicate);

  List<T> findAll(Predicate predicate);

  List<T> findAll(Predicate predicate, OrderSpecifier<?>... orders);

  Page<T> findAll(Predicate predicate, Pageable pageable);

  Long count(Predicate predicate);
}

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To use this in your repository implementation, add it to the list of repository interfaces from which your interface inherits, as the following example shows:

public interface PersonRepository extends MongoRepository<Person, String>, QuerydslPredicateExecutor<Person> {

   // additional query methods go here
}

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# 14.3.7. Full-text Search Queries

MongoDB’s full-text search feature is store-specific and, therefore, can be found on MongoRepository rather than on the more general CrudRepository. We need a document with a full-text index (see “Text Indexes” to learn how to create a full-text index).

Additional methods on MongoRepository take TextCriteria as an input parameter. In addition to those explicit methods, it is also possible to add a TextCriteria-derived repository method. The criteria are added as an additional AND criteria. Once the entity contains a @TextScore-annotated property, the document’s full-text score can be retrieved. Furthermore, the @TextScore annotated also makes it possible to sort by the document’s score, as the following example shows:

@Document
class FullTextDocument {

  @Id String id;
  @TextIndexed String title;
  @TextIndexed String content;
  @TextScore Float score;
}

interface FullTextRepository extends Repository<FullTextDocument, String> {

  // Execute a full-text search and define sorting dynamically
  List<FullTextDocument> findAllBy(TextCriteria criteria, Sort sort);

  // Paginate over a full-text search result
  Page<FullTextDocument> findAllBy(TextCriteria criteria, Pageable pageable);

  // Combine a derived query with a full-text search
  List<FullTextDocument> findByTitleOrderByScoreDesc(String title, TextCriteria criteria);
}


Sort sort = Sort.by("score");
TextCriteria criteria = TextCriteria.forDefaultLanguage().matchingAny("spring", "data");
List<FullTextDocument> result = repository.findAllBy(criteria, sort);

criteria = TextCriteria.forDefaultLanguage().matching("film");
Page<FullTextDocument> page = repository.findAllBy(criteria, PageRequest.of(1, 1, sort));
List<FullTextDocument> result = repository.findByTitleOrderByScoreDesc("mongodb", criteria);

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# 14.3.8. Projections

Spring Data query methods usually return one or multiple instances of the aggregate root managed by the repository. However, it might sometimes be desirable to create projections based on certain attributes of those types. Spring Data allows modeling dedicated return types, to more selectively retrieve partial views of the managed aggregates.

Imagine a repository and aggregate root type such as the following example:

Example 120. A sample aggregate and repository

class Person {

  @Id UUID id;
  String firstname, lastname;
  Address address;

  static class Address {
    String zipCode, city, street;
  }
}

interface PersonRepository extends Repository<Person, UUID> {

  Collection<Person> findByLastname(String lastname);
}

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Now imagine that we want to retrieve the person’s name attributes only. What means does Spring Data offer to achieve this? The rest of this chapter answers that question.

# Interface-based Projections

The easiest way to limit the result of the queries to only the name attributes is by declaring an interface that exposes accessor methods for the properties to be read, as shown in the following example:

Example 121. A projection interface to retrieve a subset of attributes

interface NamesOnly {

  String getFirstname();
  String getLastname();
}

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The important bit here is that the properties defined here exactly match properties in the aggregate root. Doing so lets a query method be added as follows:

Example 122. A repository using an interface based projection with a query method

interface PersonRepository extends Repository<Person, UUID> {

  Collection<NamesOnly> findByLastname(String lastname);
}

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The query execution engine creates proxy instances of that interface at runtime for each element returned and forwards calls to the exposed methods to the target object.

Projections can be used recursively. If you want to include some of the Address information as well, create a projection interface for that and return that interface from the declaration of getAddress(), as shown in the following example:

Example 123. A projection interface to retrieve a subset of attributes

interface PersonSummary {

  String getFirstname();
  String getLastname();
  AddressSummary getAddress();

  interface AddressSummary {
    String getCity();
  }
}

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On method invocation, the address property of the target instance is obtained and wrapped into a projecting proxy in turn.

# Closed Projections

A projection interface whose accessor methods all match properties of the target aggregate is considered to be a closed projection. The following example (which we used earlier in this chapter, too) is a closed projection:

Example 124. A closed projection

interface NamesOnly {

  String getFirstname();
  String getLastname();
}

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If you use a closed projection, Spring Data can optimize the query execution, because we know about all the attributes that are needed to back the projection proxy. For more details on that, see the module-specific part of the reference documentation.

# Open Projections

Accessor methods in projection interfaces can also be used to compute new values by using the @Value annotation, as shown in the following example:

Example 125. An Open Projection

interface NamesOnly {

  @Value("#{target.firstname + ' ' + target.lastname}")
  String getFullName();
  …
}

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The aggregate root backing the projection is available in the target variable. A projection interface using @Value is an open projection. Spring Data cannot apply query execution optimizations in this case, because the SpEL expression could use any attribute of the aggregate root.

The expressions used in @Value should not be too complex — you want to avoid programming in String variables. For very simple expressions, one option might be to resort to default methods (introduced in Java 8), as shown in the following example:

Example 126. A projection interface using a default method for custom logic

interface NamesOnly {

  String getFirstname();
  String getLastname();

  default String getFullName() {
    return getFirstname.concat(" ").concat(getLastname());
  }
}

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This approach requires you to be able to implement logic purely based on the other accessor methods exposed on the projection interface. A second, more flexible, option is to implement the custom logic in a Spring bean and then invoke that from the SpEL expression, as shown in the following example:

Example 127. Sample Person object

@Component
class MyBean {

  String getFullName(Person person) {
    …
  }
}

interface NamesOnly {

  @Value("#{@myBean.getFullName(target)}")
  String getFullName();
  …
}

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Notice how the SpEL expression refers to myBean and invokes the getFullName(…) method and forwards the projection target as a method parameter. Methods backed by SpEL expression evaluation can also use method parameters, which can then be referred to from the expression. The method parameters are available through an Object array named args. The following example shows how to get a method parameter from the args array:

Example 128. Sample Person object

interface NamesOnly {

  @Value("#{args[0] + ' ' + target.firstname + '!'}")
  String getSalutation(String prefix);
}

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Again, for more complex expressions, you should use a Spring bean and let the expression invoke a method, as described earlier.

# Class-based Projections (DTOs)

Another way of defining projections is by using value type DTOs (Data Transfer Objects) that hold properties for the fields that are supposed to be retrieved. These DTO types can be used in exactly the same way projection interfaces are used, except that no proxying happens and no nested projections can be applied.

If the store optimizes the query execution by limiting the fields to be loaded, the fields to be loaded are determined from the parameter names of the constructor that is exposed.

The following example shows a projecting DTO:

Example 129. A projecting DTO

class NamesOnly {

  private final String firstname, lastname;

  NamesOnly(String firstname, String lastname) {

    this.firstname = firstname;
    this.lastname = lastname;
  }

  String getFirstname() {
    return this.firstname;
  }

  String getLastname() {
    return this.lastname;
  }

  // equals(…) and hashCode() implementations
}

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Avoid boilerplate code for projection DTOs You can dramatically simplify the code for a DTO by using Project Lombok, which provides an @Value annotation (not to be confused with Spring’s @Value annotation shown in the earlier interface examples). If you use Project Lombok’s @Value annotation, the sample DTO shown earlier would become the following:

@Value
class NamesOnly {
String firstname, lastname;
}

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Fields are private final by default, and the class exposes a constructor that takes all fields and automatically gets equals(…) and hashCode() methods implemented.

# Dynamic Projections

So far, we have used the projection type as the return type or element type of a collection. However, you might want to select the type to be used at invocation time (which makes it dynamic). To apply dynamic projections, use a query method such as the one shown in the following example:

Example 130. A repository using a dynamic projection parameter

interface PersonRepository extends Repository<Person, UUID> {

  <T> Collection<T> findByLastname(String lastname, Class<T> type);
}
This way, the method can be used to obtain the aggregates as is or with a projection applied, as shown in the following example:

Example 131. Using a repository with dynamic projections
void someMethod(PersonRepository people) {

  Collection<Person> aggregates =
    people.findByLastname("Matthews", Person.class);

  Collection<NamesOnly> aggregates =
    people.findByLastname("Matthews", NamesOnly.class);
}

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# 14.4. CDI Integration

Instances of the repository interfaces are usually created by a container, and Spring is the most natural choice when working with Spring Data. As of version 1.3.0, Spring Data MongoDB ships with a custom CDI extension that lets you use the repository abstraction in CDI environments. The extension is part of the JAR. To activate it, drop the Spring Data MongoDB JAR into your classpath. You can now set up the infrastructure by implementing a CDI Producer for the MongoTemplate, as the following example shows:

class MongoTemplateProducer {

    @Produces
    @ApplicationScoped
    public MongoOperations createMongoTemplate() {

        MongoDbFactory factory = new SimpleMongoDbFactory(new MongoClient(), "database");
        return new MongoTemplate(factory);
    }
}

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The Spring Data MongoDB CDI extension picks up the MongoTemplate available as a CDI bean and creates a proxy for a Spring Data repository whenever a bean of a repository type is requested by the container. Thus, obtaining an instance of a Spring Data repository is a matter of declaring an @Inject-ed property, as the following example shows:

class RepositoryClient {

  @Inject
  PersonRepository repository;

  public void businessMethod() {
    List<Person> people = repository.findAll();
  }
}

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# 15. Reactive MongoDB repositories

This chapter describes the specialties for reactive repository support for MongoDB. This chapter builds on the core repository support explained in Working with Spring Data Repositories. You should have a sound understanding of the basic concepts explained there.

# 15.1. Reactive Composition Libraries

The reactive space offers various reactive composition libraries. The most common libraries are RxJava and Project Reactor.

Spring Data MongoDB is built on top of the MongoDB Reactive Streams driver, to provide maximal interoperability by relying on the Reactive Streams initiative. Static APIs, such as ReactiveMongoOperations, are provided by using Project Reactor’s Flux and Mono types. Project Reactor offers various adapters to convert reactive wrapper types (Flux to Observable and vice versa), but conversion can easily clutter your code.

Spring Data’s Repository abstraction is a dynamic API, mostly defined by you and your requirements as you declare query methods. Reactive MongoDB repositories can be implemented by using either RxJava or Project Reactor wrapper types by extending from one of the following library-specific repository interfaces:

ReactiveCrudRepository
ReactiveSortingRepository
RxJava2CrudRepository
RxJava2SortingRepository

Spring Data converts reactive wrapper types behind the scenes so that you can stick to your favorite composition library.

# 15.2. Usage

To access domain entities stored in a MongoDB database, you can use our sophisticated repository support that eases implementing those quite significantly. To do so, create an interface similar for your repository. Before you can do that, though, you need an entity, such as the entity defined in the following example:

Example 132. Sample Person entity

public class Person {

  @Id
  private String id;
  private String firstname;
  private String lastname;
  private Address address;

  // … getters and setters omitted
}

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Note that the entity defined in the preceding example has a property named id of type ObjectId. The default serialization mechanism used in MongoTemplate (which backs the repository support) regards properties named id as the document ID. Currently, we support String, ObjectId, and BigInteger as id-types. The following example shows how to create an interface that defines queries against the Person object from the preceding example:

Example 133. Basic repository interface to persist Person entities

public interface ReactivePersonRepository extends ReactiveSortingRepository<Person, String> {

  Flux<Person> findByFirstname(String firstname);                                   

  Flux<Person> findByFirstname(Publisher<String> firstname);                        

  Flux<Person> findByFirstnameOrderByLastname(String firstname, Pageable pageable); 

  Mono<Person> findByFirstnameAndLastname(String firstname, String lastname);       

  Mono<Person> findFirstByLastname(String lastname);                                
}

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The method shows a query for all people with the given lastname. The query is derived by parsing the method name for constraints that can be concatenated with And and Or. Thus, the method name results in a query expression of {"lastname" : lastname}. The method shows a query for all people with the given firstname once the firstname is emitted by the given Publisher. Use Pageable to pass offset and sorting parameters to the database. Find a single entity for the given criteria. It completes with IncorrectResultSizeDataAccessException on non-unique results. Unless <4>, the first entity is always emitted even if the query yields more result documents. For Java configuration, use the @EnableReactiveMongoRepositories annotation. The annotation carries the same attributes as the namespace element. If no base package is configured, the infrastructure scans the package of the annotated configuration class.

MongoDB uses two different drivers for imperative (synchronous/blocking) and reactive (non-blocking) data access. You must create a connection by using the Reactive Streams driver to provide the required infrastructure for Spring Data’s Reactive MongoDB support. Consequently, you must provide a separate configuration for MongoDB’s Reactive Streams driver. Note that your application operates on two different connections if you use reactive and blocking Spring Data MongoDB templates and repositories. The following listing shows how to use Java configuration for a repository:

Example 134. Java configuration for repositories

@Configuration
@EnableReactiveMongoRepositories
class ApplicationConfig extends AbstractReactiveMongoConfiguration {

  @Override
  protected String getDatabaseName() {
    return "e-store";
  }

  @Override
  public MongoClient reactiveMongoClient() {
    return MongoClients.create();
  }

  @Override
  protected String getMappingBasePackage() {
    return "com.oreilly.springdata.mongodb"
  }
}

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Because our domain repository extends ReactiveSortingRepository, it provides you with CRUD operations as well as methods for sorted access to the entities. Working with the repository instance is a matter of dependency injecting it into a client, as the following example shows:

Example 135. Sorted access to Person entities

public class PersonRepositoryTests {

    @Autowired ReactivePersonRepository repository;

    @Test
    public void sortsElementsCorrectly() {
      Flux<Person> persons = repository.findAll(Sort.by(new Order(ASC, "lastname")));
    }
}

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# 15.3. Features

Spring Data’s Reactive MongoDB support comes with a reduced feature set compared to the blocking MongoDB Repositories.

It supports the following features:

Query Methods using String queries and Query Derivation
Geo-spatial Repository Queries
Repository Delete Queries
MongoDB JSON-based Query Methods and Field Restriction
Full-text Search Queries
Projections

Reactive Repositories do not support type-safe query methods that use Querydsl.

# 15.3.1. Geo-spatial Repository Queries

As you saw earlier in “Geo-spatial Repository Queries”, a few keywords trigger geo-spatial operations within a MongoDB query. The Near keyword allows some further modification, as the next few examples show.

The following example shows how to define a near query that finds all persons with a given distance of a given point:

Example 136. Advanced Near queries

public interface PersonRepository extends ReactiveMongoRepository<Person, String>

  // { 'location' : { '$near' : [point.x, point.y], '$maxDistance' : distance}}
  Flux<Person> findByLocationNear(Point location, Distance distance);
}

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Example 137. Using Distance with Metrics

Point point = new Point(43.7, 48.8);
Distance distance = new Distance(200, Metrics.KILOMETERS);
… = repository.findByLocationNear(point, distance);
// {'location' : {'$nearSphere' : [43.7, 48.8], '$maxDistance' : 0.03135711885774796}}

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Reactive Geo-spatial repository queries support the domain type and GeoResult results within a reactive wrapper type. GeoPage and GeoResults are not supported as they contradict the deferred result approach with pre-calculating the average distance. Howevery, you can still pass in a Pageable argument to page results yourself. Using a Distance with a Metric causes a $nearSphere (instead of a plain $near) clause to be added. Beyond that, the actual distance gets calculated according to the Metrics used.

Using @GeoSpatialIndexed(type = GeoSpatialIndexType.GEO_2DSPHERE) on the target property forces usage of $nearSphere operator. Geo-near Queries Spring Data MongoDB supports geo-near queries, as the following example shows:

public interface PersonRepository extends ReactiveMongoRepository<Person, String>

  // {'geoNear' : 'location', 'near' : [x, y] }
  Flux<GeoResult<Person>> findByLocationNear(Point location);

  // No metric: {'geoNear' : 'person', 'near' : [x, y], maxDistance : distance }
  // Metric: {'geoNear' : 'person', 'near' : [x, y], 'maxDistance' : distance,
  //          'distanceMultiplier' : metric.multiplier, 'spherical' : true }
  Flux<GeoResult<Person>> findByLocationNear(Point location, Distance distance);

  // Metric: {'geoNear' : 'person', 'near' : [x, y], 'minDistance' : min,
  //          'maxDistance' : max, 'distanceMultiplier' : metric.multiplier,
  //          'spherical' : true }
  Flux<GeoResult<Person>> findByLocationNear(Point location, Distance min, Distance max);

  // {'geoNear' : 'location', 'near' : [x, y] }
  Flux<GeoResult<Person>> findByLocationNear(Point location);
}

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# Auditing

back

# 16.1. Basics

Spring Data provides sophisticated support to transparently keep track of who created or changed an entity and when the change happened. To benefit from that functionality, you have to equip your entity classes with auditing metadata that can be defined either using annotations or by implementing an interface.

# 16.1.1. Annotation-based Auditing Metadata

We provide @CreatedBy and @LastModifiedBy to capture the user who created or modified the entity as well as @CreatedDate and @LastModifiedDate to capture when the change happened.

Example 138. An audited entity

class Customer {

  @CreatedBy
  private User user;

  @CreatedDate
  private DateTime createdDate;

  // … further properties omitted
}

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As you can see, the annotations can be applied selectively, depending on which information you want to capture. The annotations capturing when changes were made can be used on properties of type Joda-Time, DateTime, legacy Java Date and Calendar, JDK8 date and time types, and long or Long.

# 16.1.2. Interface-based Auditing Metadata

In case you do not want to use annotations to define auditing metadata, you can let your domain class implement the Auditable interface. It exposes setter methods for all of the auditing properties.

There is also a convenience base class, AbstractAuditable, which you can extend to avoid the need to manually implement the interface methods. Doing so increases the coupling of your domain classes to Spring Data, which might be something you want to avoid. Usually, the annotation-based way of defining auditing metadata is preferred as it is less invasive and more flexible.

# 16.1.3. AuditorAware

In case you use either @CreatedBy or @LastModifiedBy, the auditing infrastructure somehow needs to become aware of the current principal. To do so, we provide an AuditorAware<T> SPI interface that you have to implement to tell the infrastructure who the current user or system interacting with the application is. The generic type T defines what type the properties annotated with @CreatedBy or @LastModifiedBy have to be.

The following example shows an implementation of the interface that uses Spring Security’s Authentication object:

Example 139. Implementation of AuditorAware based on Spring Security

class SpringSecurityAuditorAware implements AuditorAware<User> {

  public Optional<User> getCurrentAuditor() {

    return Optional.ofNullable(SecurityContextHolder.getContext())
			  .map(SecurityContext::getAuthentication)
			  .filter(Authentication::isAuthenticated)
			  .map(Authentication::getPrincipal)
			  .map(User.class::cast);
  }
}

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The implementation accesses the Authentication object provided by Spring Security and looks up the custom UserDetails instance that you have created in your UserDetailsService implementation. We assume here that you are exposing the domain user through the UserDetails implementation but that, based on the Authentication found, you could also look it up from anywhere.

16.2. General Auditing Configuration for MongoDB To activate auditing functionality, add the Spring Data Mongo auditing namespace element to your configuration, as the following example shows:

Example 140. Activating auditing by using XML configuration

<mongo:auditing mapping-context-ref="customMappingContext" auditor-aware-ref="yourAuditorAwareImpl"/>

Since Spring Data MongoDB 1.4, auditing can be enabled by annotating a configuration class with the @EnableMongoAuditing annotation, as the followign example shows:

Example 141. Activating auditing using JavaConfig

@Configuration
@EnableMongoAuditing
class Config {

  @Bean
  public AuditorAware<AuditableUser> myAuditorProvider() {
      return new AuditorAwareImpl();
  }
}

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If you expose a bean of type AuditorAware to the ApplicationContext, the auditing infrastructure picks it up automatically and uses it to determine the current user to be set on domain types. If you have multiple implementations registered in the ApplicationContext, you can select the one to be used by explicitly setting the auditorAwareRef attribute of @EnableMongoAuditing.

# Mapping

back

MappingMongoConverter提供了丰富的映射支持。 MappingMongoConverter具有丰富的元数据模型，可提供将域对象映射到MongoDB文档的完整功能集。通过在域对象上使用注释来填充映射元数据模型。但是，基础结构不限于使用注释作为元数据信息的唯一来源。 MappingMongoConverter还允许您通过遵循一组约定将对象映射到文档而不提供任何其他元数据。
Rich mapping support is provided by the MappingMongoConverter. MappingMongoConverter has a rich metadata model that provides a full feature set to map domain objects to MongoDB documents. The mapping metadata model is populated by using annotations on your domain objects. However, the infrastructure is not limited to using annotations as the only source of metadata information. The MappingMongoConverter also lets you map objects to documents without providing any additional metadata, by following a set of conventions.

This section describes the features of the MappingMongoConverter, including fundamentals, how to use conventions for mapping objects to documents and how to override those conventions with annotation-based mapping metadata.

# 17.1. Object Mapping Fundamentals

This section covers the fundamentals of Spring Data object mapping, object creation, field and property access, mutability and immutability. Note, that this section only applies to Spring Data modules that do not use the object mapping of the underlying data store (like JPA). Also be sure to consult the store-specific sections for store-specific object mapping, like indexes, customizing column or field names or the like.

Core responsibility of the Spring Data object mapping is to create instances of domain objects and map the store-native data structures onto those. This means we need two fundamental steps:

Instance creation by using one of the constructors exposed.

Instance population to materialize all exposed properties.

# 17.1.1. Object creation

Spring Data automatically tries to detect a persistent entity’s constructor to be used to materialize objects of that type. The resolution algorithm works as follows:

If there’s a no-argument constructor, it will be used. Other constructors will be ignored.

If there’s a single constructor taking arguments, it will be used.

If there are multiple constructors taking arguments, the one to be used by Spring Data will have to be annotated with @PersistenceConstructor.

The value resolution assumes constructor argument names to match the property names of the entity, i.e. the resolution will be performed as if the property was to be populated, including all customizations in mapping (different datastore column or field name etc.). This also requires either parameter names information available in the class file or an @ConstructorProperties annotation being present on the constructor.

The value resolution can be customized by using Spring Framework’s @Value value annotation using a store-specific SpEL expression. Please consult the section on store specific mappings for further details.

Object creation internals To avoid the overhead of reflection, Spring Data object creation uses a factory class generated at runtime by default, which will call the domain classes constructor directly. I.e. for this example type:

class Person {
  Person(String firstname, String lastname) { … }
}

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we will create a factory class semantically equivalent to this one at runtime:

class PersonObjectInstantiator implements ObjectInstantiator {

  Object newInstance(Object... args) {
    return new Person((String) args[0], (String) args[1]);
  }
}

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This gives us a roundabout 10% performance boost over reflection. For the domain class to be eligible for such optimization, it needs to adhere to a set of constraints:

it must not be a private class

it must not be a non-static inner class

it must not be a CGLib proxy class

the constructor to be used by Spring Data must not be private

If any of these criteria match, Spring Data will fall back to entity instantiation via reflection.

# 17.1.2. Property population

Once an instance of the entity has been created, Spring Data populates all remaining persistent properties of that class. Unless already populated by the entity’s constructor (i.e. consumed through its constructor argument list), the identifier property will be populated first to allow the resolution of cyclic object references. After that, all non-transient properties that have not already been populated by the constructor are set on the entity instance. For that we use the following algorithm:

If the property is immutable but exposes a wither method (see below), we use the wither to create a new entity instance with the new property value.

If property access (i.e. access through getters and setters) is defined, we’re invoking the setter method.

By default, we set the field value directly.

Property population internals Similarly to our optimizations in object construction we also use Spring Data runtime generated accessor classes to interact with the entity instance.

class Person {

  private final Long id;
  private String firstname;
  private @AccessType(Type.PROPERTY) String lastname;

  Person() {
    this.id = null;
  }

  Person(Long id, String firstname, String lastname) {
    // Field assignments
  }

  Person withId(Long id) {
    return new Person(id, this.firstname, this.lastame);
  }

  void setLastname(String lastname) {
    this.lastname = lastname;
  }
}

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Example 142. A generated Property Accessor

class PersonPropertyAccessor implements PersistentPropertyAccessor {

  private static final MethodHandle firstname;              

  private Person person;                                    

  public void setProperty(PersistentProperty property, Object value) {

    String name = property.getName();

    if ("firstname".equals(name)) {
      firstname.invoke(person, (String) value);             
    } else if ("id".equals(name)) {
      this.person = person.withId((Long) value);            
    } else if ("lastname".equals(name)) {
      this.person.setLastname((String) value);              
    }
  }
}

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PropertyAccessor’s hold a mutable instance of the underlying object. This is, to enable mutations of otherwise immutable properties. By default, Spring Data uses field-access to read and write property values. As per visibility rules of private fields, MethodHandles are used to interact with fields. The class exposes a withId(…) method that’s used to set the identifier, e.g. when an instance is inserted into the datastore and an identifier has been generated. Calling withId(…) creates a new Person object. All subsequent mutations will take place in the new instance leaving the previous untouched. Using property-access allows direct method invocations without using MethodHandles. This gives us a roundabout 25% performance boost over reflection. For the domain class to be eligible for such optimization, it needs to adhere to a set of constraints:

Types must not reside in the default or under the java package.

Types and their constructors must be public

Types that are inner classes must be static.

The used Java Runtime must allow for declaring classes in the originating ClassLoader. Java 9 and newer impose certain limitations.

By default, Spring Data attempts to use generated property accessors and falls back to reflection-based ones if a limitation is detected.

Let’s have a look at the following entity:

Example 143. A sample entity

class Person {

  private final @Id Long id;                                                
  private final String firstname, lastname;                                 
  private final LocalDate birthday;
  private final int age; 

  private String comment;                                                   
  private @AccessType(Type.PROPERTY) String remarks;                        

  static Person of(String firstname, String lastname, LocalDate birthday) { 

    return new Person(null, firstname, lastname, birthday,
      Period.between(birthday, LocalDate.now()).getYears());
  }

  Person(Long id, String firstname, String lastname, LocalDate birthday, int age) { 

    this.id = id;
    this.firstname = firstname;
    this.lastname = lastname;
    this.birthday = birthday;
    this.age = age;
  }

  Person withId(Long id) {                                                  
    return new Person(id, this.firstname, this.lastname, this.birthday);
  }

  void setRemarks(String remarks) {                                         
    this.remarks = remarks;
  }
}

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identifier属性是final，但在构造函数中设置为null。该类公开了一个withId（...）方法，该方法用于设置标识符，例如将实例插入数据存储区并生成标识符时。原始Person实例在创建新实例时保持不变。相同的模式通常应用于存储管理的其他属性，但可能必须更改以进行持久性操作。
firstname和lastname属性是可能通过getter公开的普通不可变属性。
age属性是一个不可变的，但是来自生日财产。如图所示，当Spring Data使用唯一声明的构造函数时，数据库值将胜过默认值。即使意图是计算应该是首选，重要的是这个构造函数也将age作为参数（可能忽略它），否则属性填充步骤将尝试设置age字段并因为它是不可变的而失败枯萎的存在。
注释属性是可变的，通过直接设置其字段来填充。
通过直接设置注释字段或通过调用setter方法，remarks属性是可变的和填充的
该类公开了一个工厂方法和一个用于创建对象的构造函数。这里的核心思想是使用工厂方法而不是其他构造函数来避免通过@PersistenceConstructor进行构造函数消歧。相反，在工厂方法中处理属性的默认值。
The identifier property is final but set to null in the constructor. The class exposes a withId(…) method that’s used to set the identifier, e.g. when an instance is inserted into the datastore and an identifier has been generated. The original Person instance stays unchanged as a new one is created. The same pattern is usually applied for other properties that are store managed but might have to be changed for persistence operations. The firstname and lastname properties are ordinary immutable properties potentially exposed through getters. The age property is an immutable but derived one from the birthday property. With the design shown, the database value will trump the defaulting as Spring Data uses the only declared constructor. Even if the intent is that the calculation should be preferred, it’s important that this constructor also takes age as parameter (to potentially ignore it) as otherwise the property population step will attempt to set the age field and fail due to it being immutable and no wither being present. The comment property is mutable is populated by setting its field directly. The remarks properties are mutable and populated by setting the comment field directly or by invoking the setter method for The class exposes a factory method and a constructor for object creation. The core idea here is to use factory methods instead of additional constructors to avoid the need for constructor disambiguation through @PersistenceConstructor. Instead, defaulting of properties is handled within the factory method.

# 17.1.3. General recommendations

Try to stick to immutable objects — Immutable objects are straightforward to create as materializing an object is then a matter of calling its constructor only. Also, this avoids your domain objects to be littered with setter methods that allow client code to manipulate the objects state. If you need those, prefer to make them package protected so that they can only be invoked by a limited amount of co-located types. Constructor-only materialization is up to 30% faster than properties population.

Provide an all-args constructor — Even if you cannot or don’t want to model your entities as immutable values, there’s still value in providing a constructor that takes all properties of the entity as arguments, including the mutable ones, as this allows the object mapping to skip the property population for optimal performance.

Use factory methods instead of overloaded constructors to avoid @PersistenceConstructor — With an all-argument constructor needed for optimal performance, we usually want to expose more application use case specific constructors that omit things like auto-generated identifiers etc. It’s an established pattern to rather use static factory methods to expose these variants of the all-args constructor.

Make sure you adhere to the constraints that allow the generated instantiator and property accessor classes to be used —

For identifiers to be generated, still use a final field in combination with a wither method —

Use Lombok to avoid boilerplate code — As persistence operations usually require a constructor taking all arguments, their declaration becomes a tedious repetition of boilerplate parameter to field assignments that can best be avoided by using Lombok’s @AllArgsConstructor.

# 17.1.4. Kotlin support

Spring Data adapts specifics of Kotlin to allow object creation and mutation.

Kotlin object creation Kotlin classes are supported to be instantiated , all classes are immutable by default and require explicit property declarations to define mutable properties. Consider the following data class Person:

data class Person(val id: String, val name: String) The class above compiles to a typical class with an explicit constructor. We can customize this class by adding another constructor and annotate it with @PersistenceConstructor to indicate a constructor preference:

data class Person(var id: String, val name: String) {

@PersistenceConstructor
constructor(id: String) : this(id, "unknown")

} Kotlin supports parameter optionality by allowing default values to be used if a parameter is not provided. When Spring Data detects a constructor with parameter defaulting, then it leaves these parameters absent if the data store does not provide a value (or simply returns null) so Kotlin can apply parameter defaulting. Consider the following class that applies parameter defaulting for name

data class Person(var id: String, val name: String = "unknown") Every time the name parameter is either not part of the result or its value is null, then the name defaults to unknown.

Property population of Kotlin data classes In Kotlin, all classes are immutable by default and require explicit property declarations to define mutable properties. Consider the following data class Person:

data class Person(val id: String, val name: String) This class is effectively immutable. It allows to create new instances as Kotlin generates a copy(…) method that creates new object instances copying all property values from the existing object and applying property values provided as arguments to the method.

# 17.2. Convention-based Mapping

MappingMongoConverter has a few conventions for mapping objects to documents when no additional mapping metadata is provided. The conventions are:

The short Java class name is mapped to the collection name in the following manner. The class com.bigbank.SavingsAccount maps to the savingsAccount collection name.

All nested objects are stored as nested objects in the document and not as DBRefs.

The converter uses any Spring Converters registered with it to override the default mapping of object properties to document fields and values.

The fields of an object are used to convert to and from fields in the document. Public JavaBean properties are not used.

If you have a single non-zero-argument constructor whose constructor argument names match top-level field names of document, that constructor is used. Otherwise, the zero-argument constructor is used. If there is more than one non-zero-argument constructor, an exception will be thrown.

# 17.2.1. How the _id field is handled in the mapping layer.

MongoDB requires that you have an _id field for all documents. If you don’t provide one the driver will assign a ObjectId with a generated value. The "_id" field can be of any type the, other than arrays, so long as it is unique. The driver naturally supports all primitive types and Dates. When using the MappingMongoConverter there are certain rules that govern how properties from the Java class is mapped to this _id field.

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

A field annotated with @Id (org.springframework.data.annotation.Id) will be mapped to the _id field.

A field without an annotation but named id will be mapped to the _id field.

The default field name for identifiers is _id and can be customized via the @Field annotation.

Table 8. Examples for the translation of _id field definitions

Field definition	Resulting Id-Fieldname in MongoDB
String id	_id
@Field String id	_id
@Field("x") String id	x
@Id String x	_id
@Field("x") @Id String x	_id

The following outlines what type conversion, if any, will be done on the property mapped to the _id document field.

If a field named id is declared as a String or BigInteger in the Java class it will be converted to and stored as an ObjectId if possible. ObjectId as a field type is also valid. If you specify a value for id in your application, the conversion to an ObjectId is detected to the MongoDBdriver. If the specified id value cannot be converted to an ObjectId, then the value will be stored as is in the document’s _id field.

If a field named id id field is not declared as a String, BigInteger, or ObjectID in the Java class then you should assign it a value in your application so it can be stored 'as-is' in the document’s _id field.

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

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

# 17.3. Data Mapping and Type Conversion

This section explains how types are mapped to and from a MongoDB representation. Spring Data MongoDB supports all types that can be represented as BSON, MongoDB’s internal document format. In addition to these types, Spring Data MongoDB provides a set of built-in converters to map additional types. You can provide your own converters to adjust type conversion. See Overriding Mapping with Explicit Converters for further details.

The following provides samples of each available type conversion:

Table 9. Type

Type	Type conversion	Sample
String	native	{"firstname" : "Dave"}
double, Double, float, Float	native	{"weight" : 42.5}
int, Integer, short, Short	native	32-bit integer
long, Long	native 64-bit integer	{"height" : 42}
Date, Timestamp	native	{"date" : ISODate("2019-11-12T23:00:00.809Z")}
byte[]	native	{"bin" : { "$binary" : "AQIDBA==", "$type" : "00" }}
java.util.UUID (Legacy UUID)	native	{"uuid" : { "$binary" : "MEaf1CFQ6lSphaa3b9AtlA==", "$type" : "03" }}
Date	native	{"date" : ISODate("2019-11-12T23:00:00.809Z")}
ObjectId	native	{"_id" : ObjectId("5707a2690364aba3136ab870")}
Array, List, BasicDBList	native	{"cookies" : [ … ]}
boolean, Boolean	native	{"active" : true}
null	native	{"value" : null}
Document	native	{"value" : { … }}
Decimal128	native	{"value" : NumberDecimal(…)}
AtomicInteger calling get() before the actual conversion	converter 32-bit integer	{"value" : "741" }
AtomicLong calling get() before the actual conversion	converter 64-bit integer	{"value" : "741" }
converter	String	{"value" : "741" }
BigDecimal	converter String	{"value" : "741.99" }
URL	converter	{"website" : "http://projects.spring.io/spring-data-mongodb/" }
Locale	converter	{"locale : "en_US" }

char, Character

converter

{"char" : "a" }

NamedMongoScript

converter Code

{"_id" : "script name", value: (some javascript code)}

java.util.Currency

converter

{"currencyCode" : "EUR"}

LocalDate (Joda, Java 8, JSR310-BackPort)

converter

{"date" : ISODate("2019-11-12T00:00:00.000Z")}

LocalDateTime, LocalTime, Instant (Joda, Java 8, JSR310-BackPort)

converter

{"date" : ISODate("2019-11-12T23:00:00.809Z")}

DateTime (Joda)

converter

{"date" : ISODate("2019-11-12T23:00:00.809Z")}

ZoneId (Java 8, JSR310-BackPort)

converter

{"zoneId" : "ECT - Europe/Paris"}

Box

converter

{"box" : { "first" : { "x" : 1.0 , "y" : 2.0} , "second" : { "x" : 3.0 , "y" : 4.0}}

Polygon

converter

{"polygon" : { "points" : [ { "x" : 1.0 , "y" : 2.0} , { "x" : 3.0 , "y" : 4.0} , { "x" : 4.0 , "y" : 5.0}]}}

Circle

converter

{"circle" : { "center" : { "x" : 1.0 , "y" : 2.0} , "radius" : 3.0 , "metric" : "NEUTRAL"}}

Point

converter

{"point" : { "x" : 1.0 , "y" : 2.0}}

GeoJsonPoint

converter

{"point" : { "type" : "Point" , "coordinates" : [3.0 , 4.0] }}

GeoJsonMultiPoint

converter

{"geoJsonLineString" : {"type":"MultiPoint", "coordinates": [ [ 0 , 0 ], [ 0 , 1 ], [ 1 , 1 ] ] }}

Sphere

converter

{"sphere" : { "center" : { "x" : 1.0 , "y" : 2.0} , "radius" : 3.0 , "metric" : "NEUTRAL"}}

GeoJsonPolygon

converter

{"polygon" : { "type" : "Polygon", "coordinates" : [[ [ 0 , 0 ], [ 3 , 6 ], [ 6 , 1 ], [ 0 , 0 ] ]] }}

GeoJsonMultiPolygon

converter

{"geoJsonMultiPolygon" : { "type" : "MultiPolygon", "coordinates" : [ [ [ [ -73.958 , 40.8003 ] , [ -73.9498 , 40.7968 ] ] ], [ [ [ -73.973 , 40.7648 ] , [ -73.9588 , 40.8003 ] ] ] ] }}

GeoJsonLineString

converter

{ "geoJsonLineString" : { "type" : "LineString", "coordinates" : [ [ 40 , 5 ], [ 41 , 6 ] ] }}

GeoJsonMultiLineString

converter

{"geoJsonLineString" : { "type" : "MultiLineString", coordinates: [ [ [ -73.97162 , 40.78205 ], [ -73.96374 , 40.77715 ] ], [ [ -73.97880 , 40.77247 ], [ -73.97036 , 40.76811 ] ] ] }}

# 17.4. Mapping Configuration

Unless explicitly configured, an instance of MappingMongoConverter is created by default when you create a MongoTemplate. You can create your own instance of the MappingMongoConverter. Doing so lets you dictate where in the classpath your domain classes can be found, so that Spring Data MongoDB can extract metadata and construct indexes. Also, by creating your own instance, you can register Spring converters to map specific classes to and from the database.

You can configure the MappingMongoConverter as well as com.mongodb.MongoClient and MongoTemplate by using either Java-based or XML-based metadata. The following example uses Spring’s Java-based configuration:

Example 144. @Configuration class to configure MongoDB mapping support

@Configuration
public class GeoSpatialAppConfig extends AbstractMongoConfiguration {

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

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

  @Override
  public String getMappingBasePackage() {
    return "com.bigbank.domain";
  }

  // the following are optional


  @Bean
  @Override
  public CustomConversions customConversions() throws Exception {
    List<Converter<?, ?>> converterList = new ArrayList<Converter<?, ?>>();
    converterList.add(new org.springframework.data.mongodb.test.PersonReadConverter());
    converterList.add(new org.springframework.data.mongodb.test.PersonWriteConverter());
    return new CustomConversions(converterList);
  }

  @Bean
  public LoggingEventListener<MongoMappingEvent> mappingEventsListener() {
    return new LoggingEventListener<MongoMappingEvent>();
  }
}

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AbstractMongoConfiguration requires you to implement methods that define a com.mongodb.MongoClient as well as provide a database name. AbstractMongoConfiguration also has a method named getMappingBasePackage(…) that you can override to tell the converter where to scan for classes annotated with the @Document annotation.

You can add additional converters to the converter by overriding the customConversions method. Also shown in the preceding example is a LoggingEventListener, which logs MongoMappingEvent instances that are posted onto Spring’s ApplicationContextEvent infrastructure.

AbstractMongoConfiguration creates a MongoTemplate instance and registers it with the container under the name mongoTemplate. Spring’s MongoDB namespace lets you enable mapping functionality in XML, as the following example shows:

Example 145. XML schema to configure MongoDB mapping support

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

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

  <!-- by default look for a Mongo object named 'mongo' - default name used for the converter is 'mappingConverter' -->
  <mongo:mapping-converter base-package="com.bigbank.domain">
    <mongo:custom-converters>
      <mongo:converter ref="readConverter"/>
      <mongo:converter>
        <bean class="org.springframework.data.mongodb.test.PersonWriteConverter"/>
      </mongo:converter>
    </mongo:custom-converters>
  </mongo:mapping-converter>

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

  <!-- set the mapping converter to be used by the MongoTemplate -->
  <bean id="mongoTemplate" class="org.springframework.data.mongodb.core.MongoTemplate">
    <constructor-arg name="mongoDbFactory" ref="mongoDbFactory"/>
    <constructor-arg name="mongoConverter" ref="mappingConverter"/>
  </bean>

  <bean class="org.springframework.data.mongodb.core.mapping.event.LoggingEventListener"/>

</beans>

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The base-package property tells it where to scan for classes annotated with the @org.springframework.data.mongodb.core.mapping.Document annotation.

# 17.5. Metadata-based Mapping

To take full advantage of the object mapping functionality inside the Spring Data MongoDB support, you should annotate your mapped objects with the @Document annotation. Although it is not necessary for the mapping framework to have this annotation (your POJOs are mapped correctly, even without any annotations), it lets the classpath scanner find and pre-process your domain objects to extract the necessary metadata. If you do not use this annotation, your application takes a slight performance hit the first time you store a domain object, because the mapping framework needs to build up its internal metadata model so that it knows about the properties of your domain object and how to persist them. The following example shows a domain object:

Example 146. Example domain object

package com.mycompany.domain;

@Document
public class Person {

  @Id
  private ObjectId id;

  @Indexed
  private Integer ssn;

  private String firstName;

  @Indexed
  private String lastName;
}

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The @Id annotation tells the mapper which property you want to use for the MongoDB _id property, and the @Indexed annotation tells the mapping framework to call createIndex(…) on that property of your document, making searches faster. Automatic index creation is only done for types annotated with @Document.

# 17.5.1. Mapping Annotation Overview

The MappingMongoConverter can use metadata to drive the mapping of objects to documents. The following annotations are available:

@Id:
Applied at the field level to mark the field used for identity purpose.
@Document:
Applied at the class level to indicate this class is a candidate for mapping to the database. You can specify the name of the collection where the database will be stored.
@DBRef:
Applied at the field to indicate it is to be stored using a com.mongodb.DBRef.
@Indexed:
Applied at the field level to describe how to index the field.
@CompoundIndex:
Applied at the type level to declare Compound Indexes
@GeoSpatialIndexed:
Applied at the field level to describe how to geoindex the field.
@TextIndexed:
Applied at the field level to mark the field to be included in the text index.
@Language:
Applied at the field level to set the language override property for text index.
@Transient:
By default all private fields are mapped to the document, this annotation excludes the field where it is applied from being stored in the database
@PersistenceConstructor:
Marks a given constructor - even a package protected one - to use when instantiating the object from the database. Constructor arguments are mapped by name to the key values in the retrieved Document.
@Value:
This annotation is part of the Spring Framework . Within the mapping framework it can be applied to constructor arguments. This lets you use a Spring Expression Language statement to transform a key’s value retrieved in the database before it is used to construct a domain object. In order to reference a property of a given document one has to use expressions like: @Value("#root.myProperty") where root refers to the root of the given document.
@Field:
Applied at the field level and described the name of the field as it will be represented in the MongoDB BSON document thus allowing the name to be different than the fieldname of the class.
@Version:
Applied at field level is used for optimistic locking and checked for modification on save operations. The initial value is zero which is bumped automatically on every update.

The mapping metadata infrastructure is defined in a separate spring-data-commons project that is technology agnostic. Specific subclasses are using in the MongoDB support to support annotation based metadata. Other strategies are also possible to put in place if there is demand.

Here is an example of a more complex mapping.

@Document
@CompoundIndexes({
    @CompoundIndex(name = "age_idx", def = "{'lastName': 1, 'age': -1}")
})
public class Person<T extends Address> {

  @Id
  private String id;

  @Indexed(unique = true)
  private Integer ssn;

  @Field("fName")
  private String firstName;

  @Indexed
  private String lastName;

  private Integer age;

  @Transient
  private Integer accountTotal;

  @DBRef
  private List<Account> accounts;

  private T address;


  public Person(Integer ssn) {
    this.ssn = ssn;
  }

  @PersistenceConstructor
  public Person(Integer ssn, String firstName, String lastName, Integer age, T address) {
    this.ssn = ssn;
    this.firstName = firstName;
    this.lastName = lastName;
    this.age = age;
    this.address = address;
  }

  public String getId() {
    return id;
  }

  // no setter for Id.  (getter is only exposed for some unit testing)

  public Integer getSsn() {
    return ssn;
  }

// other getters/setters omitted

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# 17.5.2. Customized Object Construction

The mapping subsystem allows the customization of the object construction by annotating a constructor with the @PersistenceConstructor annotation. The values to be used for the constructor parameters are resolved in the following way:

If a parameter is annotated with the @Value annotation, the given expression is evaluated and the result is used as the parameter value.

If the Java type has a property whose name matches the given field of the input document, then it’s property information is used to select the appropriate constructor parameter to pass the input field value to. This works only if the parameter name information is present in the java .class files which can be achieved by compiling the source with debug information or using the new -parameters command-line switch for javac in Java 8.

Otherwise a MappingException will be thrown indicating that the given constructor parameter could not be bound.

class OrderItem {

  private @Id String id;
  private int quantity;
  private double unitPrice;

  OrderItem(String id, @Value("#root.qty ?: 0") int quantity, double unitPrice) {
    this.id = id;
    this.quantity = quantity;
    this.unitPrice = unitPrice;
  }

  // getters/setters ommitted
}

Document input = new Document("id", "4711");
input.put("unitPrice", 2.5);
input.put("qty",5);
OrderItem item = converter.read(OrderItem.class, input);

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The SpEL expression in the @Value annotation of the quantity parameter falls back to the value 0 if the given property path cannot be resolved. Additional examples for using the @PersistenceConstructor annotation can be found in the MappingMongoConverterUnitTests test suite.

# 17.5.3. Compound Indexes

Compound indexes are also supported. They are defined at the class level, rather than on individual properties.

Compound indexes are very important to improve the performance of queries that involve criteria on multiple fields Here’s an example that creates a compound index of lastName in ascending order and age in descending order:

Example 147. Example Compound Index Usage package com.mycompany.domain;

@Document
@CompoundIndexes({
    @CompoundIndex(name = "age_idx", def = "{'lastName': 1, 'age': -1}")
})
public class Person {

  @Id
  private ObjectId id;
  private Integer age;
  private String firstName;
  private String lastName;

}

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# 17.5.4. Text Indexes

The text index feature is disabled by default for mongodb v.2.4. Creating a text index allows accumulating several fields into a searchable full-text index. It is only possible to have one text index per collection, so all fields marked with @TextIndexed are combined into this index. Properties can be weighted to influence the document score for ranking results. The default language for the text index is English. To change the default language, set the language attribute to whichever language you want (for example,@Document(language="spanish")). Using a property called language or @Language lets you define a language override on a per document base. The following example shows how to created a text index and set the language to Spanish:

Example 148. Example Text Index Usage

@Document(language = "spanish")
class SomeEntity {

    @TextIndexed String foo;

    @Language String lang;

    Nested nested;
}

class Nested {

    @TextIndexed(weight=5) String bar;
    String roo;
}

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# 17.5.5. Using DBRefs

The mapping framework does not have to store child objects embedded within the document. You can also store them separately and use a DBRef to refer to that document. When the object is loaded from MongoDB, those references are eagerly resolved so that you get back a mapped object that looks the same as if it had been stored embedded within your master document.

The following example uses a DBRef to refer to a specific document that exists independently of the object in which it is referenced (both classes are shown in-line for brevity’s sake):

@Document
public class Account {

  @Id
  private ObjectId id;
  private Float total;
}

@Document
public class Person {

  @Id
  private ObjectId id;
  @Indexed
  private Integer ssn;
  @DBRef
  private List<Account> accounts;
}

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您不需要使用@OneToMany或类似的机制，因为对象列表告诉映射框架您想要一对多的关系。当对象存储在MongoDB中时，有一个DBRef列表而不是Account对象本身。在加载DBRef集合时，建议将集合类型中包含的引用限制为特定的MongoDB集合。这允许批量加载所有引用，而指向不同MongoDB集合的引用需要逐个解析。
You need not use @OneToMany or similar mechanisms because the List of objects tells the mapping framework that you want a one-to-many relationship. When the object is stored in MongoDB, there is a list of DBRefs rather than the Account objects themselves. When it comes to loading collections of DBRefs it is advisable to restrict references held in collection types to a specific MongoDB collection. This allows bulk loading of all references, whereas references pointing to different MongoDB collections need to be resolved one by one.

映射框架不处理级联保存。如果更改Person对象引用的Account对象，则必须单独保存Account对象。调用Person对象上的save不会自动将Account对象保存在accounts属性中。 DBRefs也可以懒惰地解决。在这种情况下，实际的对象或引用集合在首次访问属性时得到解析。使用@DBRef的lazy属性指定它。还定义为延迟加载DBRef并用作构造函数参数的必需属性也使用延迟加载代理进行修饰，确保尽可能减轻数据库和网络的压力。
The mapping framework does not handle cascading saves. If you change an Account object that is referenced by a Person object, you must save the Account object separately. Calling save on the Person object does not automatically save the Account objects in the accounts property. DBRefs can also be resolved lazily. In this case the actual Object or Collection of references is resolved on first access of the property. Use the lazy attribute of @DBRef to specify this. Required properties that are also defined as lazy loading DBRef and used as constructor arguments are also decorated with the lazy loading proxy making sure to put as little pressure on the database and network as possible.

Lazily loaded DBRefs can be hard to debug. Make sure tooling does not accidentally trigger proxy resolution by eg. calling toString() or some inline debug rendering invoking property getters. Please consider to enable trace logging for org.springframework.data.mongodb.core.convert.DefaultDbRefResolver to gain insight on DBRef resolution.

# 17.5.6. Mapping Framework Events

Events are fired throughout the lifecycle of the mapping process. This is described in the Lifecycle Events section.

Declaring these beans in your Spring ApplicationContext causes them to be invoked whenever the event is dispatched.

# 17.5.7. Overriding Mapping with Explicit Converters

When storing and querying your objects, it is convenient to have a MongoConverter instance handle the mapping of all Java types to Document instances. However, sometimes you may want the MongoConverter instances do most of the work but let you selectively handle the conversion for a particular type — perhaps to optimize performance.

To selectively handle the conversion yourself, register one or more one or more org.springframework.core.convert.converter.Converter instances with the MongoConverter.

Spring 3.0 introduced a core.convert package that provides a general type conversion system. This is described in detail in the Spring reference documentation section entitled “Spring Type Conversion”. You can use the customConversions method in AbstractMongoConfiguration to configure converters. The examples at the beginning of this chapter show how to perform the configuration using Java and XML.

The following example of a Spring Converter implementation converts from a Document to a Person POJO:

@ReadingConverter
 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;
  }
}
The following example converts from a Person to a Document:

@WritingConverter
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;
  }
}

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# Cross_Store_Support

back

This feature has been deprecated and will be removed without replacement. Sometimes you need to store data in multiple data stores, and these data stores need to be of different types. One might be relational while the other is a document store. For this use case, we created a separate module in the MongoDB support that handles what we call “cross-store support”. The current implementation is based on JPA as the driver for the relational database and we let select fields in the Entities be stored in a Mongo database. In addition to letting you store your data in two stores, we also coordinate persistence operations for the non-transactional MongoDB store with the transaction life-cycle for the relational database.

18.1. Cross Store Configuration Assuming that you have a working JPA application and would like to add some cross-store persistence for MongoDB, what do you have to add to your configuration?

First, you need to add a dependency on the cross-store module. If you use Maven, you can add the following dependency to your pom:

Example 149. Example Maven pom.xml with spring-data-mongodb-cross-store dependency

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>

  ...

    <!-- Spring Data -->
    <dependency>
      <groupId>org.springframework.data</groupId>
      <artifactId>spring-data-mongodb-cross-store</artifactId>
      <version>${spring.data.mongo.version}</version>
    </dependency>

  ...

</project>

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Once you have added the dependency, you need to enable AspectJ for the project. The cross-store support is implemented with AspectJ aspects so, if you enable compile-time AspectJ support, the cross-store features become available to your project. In Maven, you would add an additional plugin to the section of the pom, as follows:

Example 150. Example Maven pom.xml with AspectJ plugin enabled

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>

  ...

  <build>
    <plugins>

      …

      <plugin>
        <groupId>org.codehaus.mojo</groupId>
        <artifactId>aspectj-maven-plugin</artifactId>
        <version>1.0</version>
        <dependencies>
          <!-- NB: You must use Maven 2.0.9 or above or these are ignored (see MNG-2972) -->
          <dependency>
            <groupId>org.aspectj</groupId>
            <artifactId>aspectjrt</artifactId>
            <version>${aspectj.version}</version>
          </dependency>
          <dependency>
            <groupId>org.aspectj</groupId>
            <artifactId>aspectjtools</artifactId>
            <version>${aspectj.version}</version>
          </dependency>
        </dependencies>
        <executions>
          <execution>
            <goals>
              <goal>compile</goal>
              <goal>test-compile</goal>
            </goals>
          </execution>
        </executions>
        <configuration>
          <outxml>true</outxml>
          <aspectLibraries>
            <aspectLibrary>
              <groupId>org.springframework</groupId>
              <artifactId>spring-aspects</artifactId>
            </aspectLibrary>
            <aspectLibrary>
              <groupId>org.springframework.data</groupId>
              <artifactId>spring-data-mongodb-cross-store</artifactId>
            </aspectLibrary>
          </aspectLibraries>
          <source>1.6</source>
          <target>1.6</target>
        </configuration>
      </plugin>

      ...

    </plugins>
  </build>

...

</project>

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Finally, you need to configure your project to use MongoDB and also configure which aspects are used. You should add the following XML snippet to your application context:

Example 151. Example application context with MongoDB and cross-store aspect support

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:jdbc="http://www.springframework.org/schema/jdbc"
  xmlns:jpa="http://www.springframework.org/schema/data/jpa"
  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/jdbc
    http://www.springframework.org/schema/jdbc/spring-jdbc-3.0.xsd
    http://www.springframework.org/schema/beans
    http://www.springframework.org/schema/beans/spring-beans-3.0.xsd
    http://www.springframework.org/schema/data/jpa
    http://www.springframework.org/schema/data/jpa/spring-jpa-1.0.xsd">

  ...

  <!--  Mongo config -->
  <mongo:mongo-client host="localhost" port="27017"/>

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

  <bean class="org.springframework.data.mongodb.core.MongoExceptionTranslator"/>

  <!--  Mongo cross-store aspect config -->
  <bean class="org.springframework.data.persistence.document.mongo.MongoDocumentBacking"
        factory-method="aspectOf">
    <property name="changeSetPersister" ref="mongoChangeSetPersister"/>
  </bean>
  <bean id="mongoChangeSetPersister"
      class="org.springframework.data.persistence.document.mongo.MongoChangeSetPersister">
    <property name="mongoTemplate" ref="mongoTemplate"/>
    <property name="entityManagerFactory" ref="entityManagerFactory"/>
  </bean>

  ...

</beans>

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# 18.2. Writing the Cross Store Application

We assume that you have a working JPA application, so we cover only the additional steps needed to persist part of your entity in your Mongo database. To do so, you need to identify the field you want to persist. It should be a domain class and follow the general rules for the Mongo mapping support covered in previous chapters. The field you want to persist in MongoDB should be annotated with the @RelatedDocument annotation. That is really all you need to do. The cross-store aspects take care of the rest, including:

Marking the field with @Transient so that it will not be persisted by JPA

Keeping track of any changes made to the field value and writing them to the database on successful transaction completion

Loading the document from MongoDB the first time the value is used in your application.

The following example shows an entity that has a field annotated with @RelatedDocument:

Example 152. Example of Entity with @RelatedDocument

@Entity
public class Customer {

  @Id
  @GeneratedValue(strategy = GenerationType.IDENTITY)
  private Long id;

  private String firstName;

  private String lastName;

  @RelatedDocument
  private SurveyInfo surveyInfo;

  // getters and setters omitted
}

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The following example shows a domain class that is to be stored as a Document:

Example 153. Example of a domain class to be stored as a Document

public class SurveyInfo {

  private Map<String, String> questionsAndAnswers;

  public SurveyInfo() {
    this.questionsAndAnswers = new HashMap<String, String>();
  }

  public SurveyInfo(Map<String, String> questionsAndAnswers) {
    this.questionsAndAnswers = questionsAndAnswers;
  }

  public Map<String, String> getQuestionsAndAnswers() {
    return questionsAndAnswers;
  }

  public void setQuestionsAndAnswers(Map<String, String> questionsAndAnswers) {
    this.questionsAndAnswers = questionsAndAnswers;
  }

  public SurveyInfo addQuestionAndAnswer(String question, String answer) {
    this.questionsAndAnswers.put(question, answer);
    return this;
  }
}

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In the preceding example, once the SurveyInfo has been set on the Customer object, the MongoTemplate that was configured previously is used to save the SurveyInfo (along with some metadata about the JPA Entity) in a MongoDB collection named after the fully qualified name of the JPA Entity class. The following code shows how to configure a JPA entity for cross-store persistence with MongoDB:

Example 154. Example of code using the JPA Entity configured for cross-store persistence

Customer customer = new Customer();
customer.setFirstName("Sven");
customer.setLastName("Olafsen");
SurveyInfo surveyInfo = new SurveyInfo()
  .addQuestionAndAnswer("age", "22")
  .addQuestionAndAnswer("married", "Yes")
  .addQuestionAndAnswer("citizenship", "Norwegian");
customer.setSurveyInfo(surveyInfo);
customerRepository.save(customer);

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Running the preceding above results in the following JSON document being stored in MongoDB:

Example 155. Example of JSON document stored in MongoDB

{ "_id" : ObjectId( "4d9e8b6e3c55287f87d4b79e" ),
  "_entity_id" : 1,
  "_entity_class" : "org.springframework.data.mongodb.examples.custsvc.domain.Customer",
  "_entity_field_name" : "surveyInfo",
  "questionsAndAnswers" : { "married" : "Yes",
    "age" : "22",
    "citizenship" : "Norwegian" },
  "_entity_field_class" : "org.springframework.data.mongodb.examples.custsvc.domain.SurveyInfo" }

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# JMX_support

back

The JMX support for MongoDB exposes the results of executing the 'serverStatus' command on the admin database for a single MongoDB server instance. It also exposes an administrative MBean, MongoAdmin, that lets you perform administrative operations, such as dropping or creating a database. The JMX features build upon the JMX feature set available in the Spring Framework. See here for more details.

19.1. MongoDB JMX Configuration Spring’s Mongo namespace lets you enable JMX functionality, as the following example shows:

Example 156. XML schema to configure MongoDB

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

    <!-- by default look for a Mongo object named 'mongo' -->
    <mongo:jmx/>

    <context:mbean-export/>

    <!-- To translate any MongoExceptions thrown in @Repository annotated classes -->
    <context:annotation-config/>

    <bean id="registry" class="org.springframework.remoting.rmi.RmiRegistryFactoryBean" p:port="1099" />

    <!-- Expose JMX over RMI -->
    <bean id="serverConnector" class="org.springframework.jmx.support.ConnectorServerFactoryBean"
        depends-on="registry"
        p:objectName="connector:name=rmi"
        p:serviceUrl="service:jmx:rmi://localhost/jndi/rmi://localhost:1099/myconnector" />

</beans>

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The preceding code exposes several MBeans:

AssertMetrics

BackgroundFlushingMetrics

BtreeIndexCounters

ConnectionMetrics

GlobalLockMetrics

MemoryMetrics

OperationCounters

ServerInfo

MongoAdmin

The following screenshot from JConsole shows the resulting configuration:

jconsole

# 20. MongoDB 3.0 Support

Spring Data MongoDB requires MongoDB Java driver generations 3 when connecting to a MongoDB 2.6/3.0 server running MMap.v1 or a MongoDB server 3.0 using MMap.v1 or the WiredTiger storage engine.

See the driver- and database-specific documentation for major differences between those engines. Operations that are no longer valid when using a 3.x MongoDB Java driver have been deprecated within Spring Data and will be removed in a subsequent release. 20.1. Using Spring Data MongoDB with MongoDB 3.0 The rest of this section describes how to use Spring Data MongoDB with MongoDB 3.0.

# 20.1.1. Configuration Options

Some of the configuration options have been changed or removed for the mongo-java-driver. The following options are ignored when using the generation 3 driver:

autoConnectRetry

maxAutoConnectRetryTime

slaveOk

Generally, you should use the <mongo:mongo-client … /> and <mongo:client-options … /> elements instead of <mongo:mongo … /> when doing XML based configuration, since those elements provide you with attributes that are only valid for the third generation Java driver. The follwoing example shows how to configure a Mongo client connection:

<?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:mongo-client host="127.0.0.1" port="27017">
    <mongo:client-options write-concern="NORMAL" />
  </mongo:mongo-client>

</beans>

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# 20.1.2. WriteConcern and WriteConcernChecking

WriteConcern.NONE, which had been used as the default by Spring Data MongoDB, was removed in 3.0. Therefore, in a MongoDB 3 environment, the WriteConcern defaults to WriteConcern.UNACKNOWLEGED. If WriteResultChecking.EXCEPTION is enabled, the WriteConcern is altered to WriteConcern.ACKNOWLEDGED for write operations. Otherwise, errors during execution would not be thrown correctly, since they are not raised by the driver.

# 20.1.3. Authentication

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

@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())));
  }
}

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In order to use authentication with XML configuration, you can use the credentials attribute on , as the following example shows:

<?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:mongo-client credentials="user:password@database" />

</beans>

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# 20.1.4. Server-side Validation

MongoDB supports Schema Validation as of version 3.2 with query operators and as of version 3.6 JSON-schema based validation.

This chapter will point out the specialties for validation in MongoDB and how to apply JSON schema validation.

# JSON Schema Validation

MongoDB 3.6 allows validation and querying of documents with JSON schema draft 4 (including core specification and validation specification) with some differences. $jsonSchema can be used in a document validator (when creating a collection), which enforces that inserted or updated documents are valid against the schema. It can also be used to query for documents with the find command or $match aggregation stage.

Spring Data MongoDB supports MongoDB’s specific JSON schema implementation to define and use schemas. See JSON Schema for further details.

Query Expression Validation In addition to the JSON Schema Validation, MongoDB supports (as of version 3.2) validating documents against a given structure described by a query. The structure can be built from Criteria objects in the same way as they are used for defining queries. The following example shows how to create and use such a validator:

Criteria queryExpression = Criteria.where("lastname").ne(null).type(2)
    .and("age").ne(null).type(16).gt(0).lte(150);
Validator validator = Validator.criteria(queryExpression);

template.createCollection(Person.class, CollectionOptions.empty().validator(validator));

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The field names used within the query expression are mapped to the domain types property names, taking potential @Field annotations into account.

# 20.1.5. Miscellaneous Details

This section covers briefly lists additional things to keep in mind when using the 3.0 driver:

IndexOperations.resetIndexCache() is no longer supported.

Any MapReduceOptions.extraOption is silently ignored.

WriteResult no longer holds error information but, instead, throws an Exception.

MongoOperations.executeInSession(…) no longer calls requestStart and requestDone.

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

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

Appendix Appendix A: Namespace reference The <repositories /> Element The <repositories /> element triggers the setup of the Spring Data repository infrastructure. The most important attribute is base-package, which defines the package to scan for Spring Data repository interfaces. See “XML configuration”. The following table describes the attributes of the <repositories /> element:

Table 10. Attributes Name Description base-package

Defines the package to be scanned for repository interfaces that extend *Repository (the actual interface is determined by the specific Spring Data module) in auto-detection mode. All packages below the configured package are scanned, too. Wildcards are allowed.

repository-impl-postfix

Defines the postfix to autodetect custom repository implementations. Classes whose names end with the configured postfix are considered as candidates. Defaults to Impl.

query-lookup-strategy

Determines the strategy to be used to create finder queries. See “Query Lookup Strategies” for details. Defaults to create-if-not-found.

named-queries-location

Defines the location to search for a Properties file containing externally defined queries.

consider-nested-repositories

Whether nested repository interface definitions should be considered. Defaults to false.

Appendix B: Populators namespace reference The element The element allows to populate the a data store via the Spring Data repository infrastructure.[2]

Table 11. Attributes Name Description locations

Where to find the files to read the objects from the repository shall be populated with.

Appendix C: Repository query keywords Supported query keywords The following table lists the keywords generally supported by the Spring Data repository query derivation mechanism. However, consult the store-specific documentation for the exact list of supported keywords, because some keywords listed here might not be supported in a particular store.

Table 12. Query keywords Logical keyword Keyword expressions AND

And

AFTER

After, IsAfter

BEFORE

Before, IsBefore

CONTAINING

Containing, IsContaining, Contains

BETWEEN

Between, IsBetween

ENDING_WITH

EndingWith, IsEndingWith, EndsWith

EXISTS

Exists

FALSE

False, IsFalse

GREATER_THAN

GreaterThan, IsGreaterThan

GREATER_THAN_EQUALS

GreaterThanEqual, IsGreaterThanEqual

In, IsIn

Is, Equals, (or no keyword)

IS_EMPTY

IsEmpty, Empty

IS_NOT_EMPTY

IsNotEmpty, NotEmpty

IS_NOT_NULL

NotNull, IsNotNull

IS_NULL

Null, IsNull

LESS_THAN

LessThan, IsLessThan

LESS_THAN_EQUAL

LessThanEqual, IsLessThanEqual

Like, IsLike

NEAR

Near, IsNear

NOT

Not, IsNot

NOT_IN

NotIn, IsNotIn

NOT_LIKE

NotLike, IsNotLike

REGEX

Regex, MatchesRegex, Matches

STARTING_WITH

StartingWith, IsStartingWith, StartsWith

TRUE

True, IsTrue

WITHIN

Within, IsWithin

Appendix D: Repository query return types Supported Query Return Types The following table lists the return types generally supported by Spring Data repositories. However, consult the store-specific documentation for the exact list of supported return types, because some types listed here might not be supported in a particular store.

Geospatial types (such as GeoResult, GeoResults, and GeoPage) are available only for data stores that support geospatial queries. Table 13. Query return types Return type Description void

Denotes no return value.

Primitives

Java primitives.

Wrapper types

Java wrapper types.

An unique entity. Expects the query method to return one result at most. If no result is found, null is returned. More than one result triggers an IncorrectResultSizeDataAccessException.

Iterator

An Iterator.

Collection

A Collection.

List

A List.

Optional

A Java 8 or Guava Optional. Expects the query method to return one result at most. If no result is found, Optional.empty() or Optional.absent() is returned. More than one result triggers an IncorrectResultSizeDataAccessException.

Option

Either a Scala or Javaslang Option type. Semantically the same behavior as Java 8’s Optional, described earlier.

Stream

A Java 8 Stream.

Future

A Future. Expects a method to be annotated with @Async and requires Spring’s asynchronous method execution capability to be enabled.

CompletableFuture

A Java 8 CompletableFuture. Expects a method to be annotated with @Async and requires Spring’s asynchronous method execution capability to be enabled.

ListenableFuture

A org.springframework.util.concurrent.ListenableFuture. Expects a method to be annotated with @Async and requires Spring’s asynchronous method execution capability to be enabled.

Slice

A sized chunk of data with an indication of whether there is more data available. Requires a Pageable method parameter.

Page

A Slice with additional information, such as the total number of results. Requires a Pageable method parameter.

GeoResult

A result entry with additional information, such as the distance to a reference location.

GeoResults

A list of GeoResult with additional information, such as the average distance to a reference location.

GeoPage

A Page with GeoResult, such as the average distance to a reference location.

Mono

A Project Reactor Mono emitting zero or one element using reactive repositories. Expects the query method to return one result at most. If no result is found, Mono.empty() is returned. More than one result triggers an IncorrectResultSizeDataAccessException.

Flux

A Project Reactor Flux emitting zero, one, or many elements using reactive repositories. Queries returning Flux can emit also an infinite number of elements.

Single

A RxJava Single emitting a single element using reactive repositories. Expects the query method to return one result at most. If no result is found, Mono.empty() is returned. More than one result triggers an IncorrectResultSizeDataAccessException.

Maybe

A RxJava Maybe emitting zero or one element using reactive repositories. Expects the query method to return one result at most. If no result is found, Mono.empty() is returned. More than one result triggers an IncorrectResultSizeDataAccessException.

Flowable

A RxJava Flowable emitting zero, one, or many elements using reactive repositories. Queries returning Flowable can emit also an infinite number of elements.