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Typically, your repository interface will extend 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, simply copy the ones you want to expose from CrudRepository into your domain repository.

In this first step you defined a common base interface for all your domain repositories and exposed findById(…) as well as save(…).These methods will be routed into the base repository implementation of the store of your choice provided by Spring Data ,e.g. in the case if JPA SimpleJpaRepository, because they are matching the method signatures in CrudRepository. So the UserRepository will now be able to save users, and find single ones by id, as well as triggering a query to find Users by their email address.

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 to return other wrapper types on query methods:

Alternatively query methods can choose not to use a wrapper type at all. The absence of a query result will then be 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.

Using a unique Spring Data module in your application makes things simple hence, 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 case, it’s required for a repository definition to distinguish between persistence technologies. Spring Data enters strict repository configuration mode because it detects multiple repository factories on the class path. Strict configuration requires details on the repository or the domain class to decide about Spring Data module binding for a repository definition:

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

The last way to distinguish repositories is scoping repository base packages. Base packages define the starting points for scanning for repository interface definitions which implies to have 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 strategies are available for the repository infrastructure to resolve the query. You can configure the strategy at the namespace through the query-lookup-strategy attribute in case of XML configuration or via the queryLookupStrategy attribute of the Enable${store}Repositories annotation in case of Java config. Some strategies may not be supported for particular datastores.

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 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.

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. Assume a Person has an Address with a ZipCode. In that case a method name of

creates the property traversal x.address.zipCode. The resolution algorithm starts with 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 continue building the tree down from there, splitting the tail up in the way just described. If the first split does not match, the algorithm move 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 and essentially choose the wrong property and finally 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 end up like so:

As we treat underscore as a reserved character we strongly advise to follow standard Java naming conventions (i.e. not using underscores in property names but camel case instead).

To handle parameters in your query you simply define method parameters as already seen in the examples above. Besides that the infrastructure will recognize certain specific types like Pageable and Sort to apply pagination and sorting to your queries dynamically.

The first method allows you to 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, Slice can be used as return instead. A Slice only knows about whether there’s a next Slice available which might be just sufficient when walking through a larger result set.

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

The results of query methods can be limited via the keywords first or top, which can be used interchangeably. An optional numeric value can be appended to top/first to specify the maximum result size to be returned. If the number is left out, a result size of 1 is assumed.

The limiting expressions also support the Distinct keyword. Also, for the queries limiting the result set to one instance, wrapping the result into an Optional is supported.

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

The results of query methods can be processed incrementally by using a Java 8 Stream<T> as return type. Instead of simply wrapping the query results in a Stream data store specific methods are used to perform the streaming.

Repository queries can be executed asynchronously using Spring’s asynchronous method execution capability. This means the method will return immediately upon invocation and the actual query execution will occur in a task that has been submitted to a Spring TaskExecutor.

Each Spring Data module includes a repositories element that allows you to simply define a base package that Spring scans for you.

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.

The repository infrastructure can also be triggered using a store-specific @Enable${store}Repositories annotation on a JavaConfig class. For an introduction into Java-based configuration of the Spring container, see the reference documentation.^[1]

A sample configuration to enable Spring Data repositories looks something like this.

You can also use the repository infrastructure outside of a Spring container, e.g. 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.

To enrich a repository with custom functionality, you first define a fragment interface and an implementation for the custom functionality. Then let your repository interface additionally extend from the fragment interface.

The implementation itself does not depend on Spring Data and can be a regular Spring bean. So you can use standard dependency injection behavior to inject references to other beans like a JdbcTemplate, take part in aspects, and so on.

Let your repository interface extend the fragment one. Doing so 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.

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 allows you to override base repository and aspect methods and resolves ambiguity if two fragments contribute the same method signature. Repository fragments are not limited to be used in a single repository interface. Multiple repositories may use a fragment interface to reuse customizations across different repositories.

The preceding approach requires customization of all repository interfaces when you want to customize the base repository behavior, so all repositories are affected. To change behavior for all repositories, you need to create an implementation that extends the persistence technology-specific repository base class. This class will then act as a custom base class for the repository proxies.

The final step is to make the Spring Data infrastructure aware of the customized repository base class. In JavaConfig this is achieved by using the repositoryBaseClass attribute of the @Enable…Repositories annotation:

A corresponding attribute is available in the XML namespace.

Querydsl is a framework which enables the construction of statically typed SQL-like queries via its fluent API.

Several Spring Data modules offer integration with Querydsl via QueryDslPredicateExecutor.

To make use of Querydsl support simply extend QueryDslPredicateExecutor on your repository interface.

The above enables to write typesafe queries using Querydsl Predicate s.

Spring Data modules ships with a variety of web support if the module supports the repository programming model. The web related stuff requires Spring MVC JARs on the classpath, some of them even provide integration with Spring HATEOAS ^[2]. In general, the integration support is enabled by using the @EnableSpringDataWebSupport annotation in your JavaConfig configuration class.

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 are using XML configuration, register either SpringDataWebSupport or HateoasAwareSpringDataWebSupport as Spring beans:

If you work with the Spring JDBC module, you probably are familiar with the support to populate a DataSource using 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:

You can easily 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 , do the following:

This declaration causes the data.json file to be read and deserialized via a Jackson ObjectMapper.

The type to which the JSON object will be unmarshalled to will be determined by inspecting the \_class attribute of the JSON document. The infrastructure will eventually select the appropriate repository to handle the object just deserialized.

To rather use XML to define the data the repositories shall be populated with, you can use the unmarshaller-populator element. You configure it to use one of the XML marshaller options Spring OXM provides you with. See the Spring reference documentation for details.

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

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

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

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

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

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

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

A configuration using replica sets is shown below.

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

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

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

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

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

MongoDB Server generation 3 changed the authentication model when connecting to the DB. Therefore some of the configuration options available for authentication are no longer valid. Please use the MongoClient specific options for setting credentials via MongoCredential to provide authentication data.

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

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

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

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

There are several overloaded constructors of MongoTemplate. These are

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

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

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

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

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

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

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

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

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

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

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

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

To achieve that the MappingMongoConverter uses a MongoTypeMapper abstraction with DefaultMongoTypeMapper as it’s main implementation. Its default behavior is storing the fully qualified classname under _class inside the document. Type hints are written for top-level documents as well as for every value if it’s a complex type and a subtype of the property type declared.

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

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

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

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

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

The insert/save operations available to you are listed below.

A similar set of insert operations is listed below

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

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

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

Related to performing an updateFirst operations, you can also perform an upsert operation which will perform an insert if no document is found that matches the query. The document that is inserted is a combination of the query document and the update document. Here is an example

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

To find locations within a Circle, the following query can be used.

To find venues within a Circle using spherical coordinates the following query can be used

To find venues within a Box the following query can be used

To find venues near a Point, the following queries can be used

To find venues near a Point using spherical coordinates the following query can be used

MongoDB supports GeoJSON and simple (legacy) coordinate pairs for geospatial data. Those formats can both be used for storing as well as querying data.

Since MongoDB 2.6 full text queries can be executed using the $text operator. Methods and operations specific for full text queries are available in TextQuery and TextCriteria. When doing full text search please refer to the MongoDB reference for its behavior and limitations.

MongoDB supports since 3.4 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:

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

Using collations with collection operations is a matter of specifying a Collation instance in your query or operation options.

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.

You can provide a schema either by specifying a schema document (i.e. using the Document API by parsing or building a document object) or by building it with Spring Data’s JSON schema utilities in org.springframework.data.mongodb.core.schema. MongoJsonSchema is the entry point for all JSON schema-related operations.

CollectionOptions provides the entry point to schema support for collections.

You can use a schema to query any collection for documents that match a given structure defined by a JSON schema.

For more information, see $jsonSchema.

The MongoOperations interface is one of the central components when it comes to more low level interaction with MongoDB. It offers a wide range of methods covering needs from collection / index creation and CRUD operations to more advanced functionality like map-reduce and aggregations. One can find multiple overloads for each and every method. Most of them just cover optional / nullable parts of the API.

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

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

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

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

This chapter will give you an introduction to Query by Example and explain how to use Examples.

Query by Example (QBE) is a user-friendly querying technique with a simple interface. It allows dynamic query creation and does not require to write queries containing field names. In fact, Query by Example does not require to write queries using store-specific query languages at all.

The Query by Example API consists of three parts:

Query by Example is suited for several use-cases but also comes with limitations:

When to use

Limitations

Before getting started with Query by Example, you need to have a domain object. To get started, simply create an interface for your repository:

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

Examples are ideally be executed with repositories. To do so, let your repository interface extend QueryByExampleExecutor<T>. Here’s an excerpt from the QueryByExampleExecutor interface:

Examples are not limited to default settings. You can specify own defaults for string matching, null handling and property-specific settings using the ExampleMatcher.

By default the ExampleMatcher will expect all values set on the probe to match. If you want to get results matching any of the predicates defined implicitly, use ExampleMatcher.matchingAny().

You can specify behavior for individual properties (e.g. "firstname" and "lastname", "address.city" for nested properties). You can tune it with matching options and case sensitivity.

Another style to configure matcher options is by using Java 8 lambdas. This approach is a callback that asks the implementor to modify the matcher. It’s not required to return the matcher because configuration options are held within the matcher instance.

Queries created by Example use a merged view of the configuration. Default matching settings can be set at 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.

An Example containing an untyped ExampleSpec uses the Repository type and its collection name. Typed ExampleSpec use their type as result type and the collection name from the Repository.

Spring Data MongoDB provides support for the following matching options:

By default Example is strictly typed. This means the mapped query will have a type match included restricting it to probe assignable types. Eg. when sticking with the default type key _class the query has restrictions like _class : { $in : [ com.acme.Person] }.

By using the UntypedExampleMatcher it is possible bypasses 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.

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

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

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

Executing this will result in a collection as shown below.

Assuming that the map and reduce functions are located in map.js and reduce.js and bundled in your jar so they are available on the classpath, you can execute a map-reduce operation and obtain the results as shown below

The output of the above code is

The MapReduceResults class implements Iterable and provides access to the raw output, as well as timing and count statistics. The ValueObject class is simply

By default the output type of INLINE is used so you don’t have to specify an output collection. To specify additional map-reduce options use an overloaded method that takes an additional MapReduceOptions argument. The class MapReduceOptions has a fluent API so adding additional options can be done in a very compact syntax. Here an example that sets the output collection to "jmr1_out". Note that setting only the output collection assumes a default output type of REPLACE.

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

You can also specify a query to reduce the set of data that will be used to feed into the map-reduce operation. This will remove the document that contains [a,b] from consideration for map-reduce operations.

Note that you can specify additional limit and sort values as well on the query but not skip values.

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.

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

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

The document under the "retval" field is mapped onto the third argument in the group method, in this case XObject which is shown below.

You can also obtain the raw result as a Document by calling the method getRawResults on the GroupByResults class.

There is an additional method overload of the group method on MongoOperations which lets you specify a Criteria object for selecting a subset of the rows. An example which uses a Criteria object, with some syntax sugar using static imports, as well as referencing a key-function and reduce function javascript files via a Spring Resource string is shown below.

The Aggregation Framework support in Spring Data MongoDB is based on the following key abstractions Aggregation, AggregationOperation and AggregationResults.

Note that if you provide an input class as the first parameter to the newAggregation method the MongoTemplate will derive the name of the input collection from this class. Otherwise if you don’t not specify an input class you must provide the name of the input collection explicitly. If an input-class and an input-collection is provided the latter takes precedence.

The MongoDB Aggregation Framework provides the following types of Aggregation Operations:

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

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

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

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

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.

MongoDB supports as of Version 3.4 faceted classification using the Aggregation Framework. A faceted classification uses semantic categories, either general or subject-specific, that are combined to create the full classification entry. Documents flowing through the aggregation pipeline are classificated into buckets. A multi-faceted classification enables various aggregations on the same set of input documents, without needing to retrieve the input documents multiple times.

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

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

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

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.

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

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

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

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

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

Listening to a Change Stream using a Sync Driver is a long running, blocking task that needs to be delegated to a separate component. In this case we need to create a MessageListenerContainer first which will be the main entry point for running the specific SubscriptionRequests. Spring Data MongoDB already ships with a default implementation that operates upon MongoTemplate and is capable of creating and executing Tasks for a ChangeStreamRequest.

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

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

This approach allows you to use the standard com.mongodb.reactivestreams.client.MongoClient API that you may already be used to using.

An alternative is to register an instance of com.mongodb.reactivestreams.client.MongoClient instance with the container 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.

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

To access the com.mongodb.reactivestreams.client.MongoClient object created by the ReactiveMongoClientFactoryBean in other @Configuration or your own classes, just obtain the MongoClient from the context.

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 shown below to bootstrap connectivity to the database.

The class org.springframework.data.mongodb.core.SimpleReactiveMongoDatabaseFactory provides 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 just use them in standard Java code as shown below.

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

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

To define the username and password create MongoDB connection string and pass it into the factory method as shown below. This listing also shows using ReactiveMongoDatabaseFactory register an instance of ReactiveMongoTemplate with the container.

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

There are several overloaded constructors of ReactiveMongoTemplate. These are

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

When in development it is very handy to either log or throw an Exception if the com.mongodb.WriteResult returned from any MongoDB operation contains an error. It is quite common to forget to do this during development and then end up with an application that looks like it runs successfully but in fact the database was not modified according to your expectations. Set MongoTemplate’s property to an enum with the following values, 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.

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

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 WriteConcernResolver interface is shown below.

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

The above keywords can be used in conjunction with delete…By or remove…By to create queries deleting matching documents.

Using return type List will retrieve and return all matching documents before actually deleting them. A numeric return type directly removes the matching documents returning the total number of documents removed.

As you’ve just seen there are a few keywords triggering geo-spatial operations within a MongoDB query. The Near keyword allows some further modification. Let’s have a look at some examples:

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 will transparently use $nearSphere instead of $code.

As you can see using a Distance equipped with a Metric causes $nearSphere clause to be added instead of a plain $near. Beyond that the actual distance gets calculated according to the Metrics used.

By adding the annotation org.springframework.data.mongodb.repository.Query repository finder methods you can specify a MongoDB JSON query string to use instead of having the query derived from the method name. For example

The placeholder ?0 lets you substitute the value from the method arguments into the JSON query string.

You can also use the filter property to restrict the set of properties that will be mapped into the Java object. For example,

This will return only the firstname, lastname and Id properties of the Person objects. The age property, a java.lang.Integer, will not be set and its value will therefore be null.

Query strings and field definitions can be used together with SpEL expressions to create dynamic queries at runtime. SpEL expressions can provide predicate values and can be used to extend predicates with subdocuments.

Expressions expose method arguments through an array that contains all arguments. The the following query uses [0] to declare the predicate value for lastname that is equivalent to the ?0 parameter binding.

Expressions can be used to invoke functions, evaluate conditionals and construct values. SpEL expressions reveal in conjunction with JSON a side-effect as Map-like declarations inside of SpEL read like JSON.

SpEL in query strings can be a powerful way to enhance queries and can accept a broad range of unwanted arguments. You should make sure to sanitize strings before passing these 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 than can contribute properties, functions and customize the root object. Extensions are retrieved from the application context at the time of SpEL evaluation when the query is build.

MongoDB repository support integrates with the QueryDSL project which provides a means to perform type-safe queries in Java. 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

Please refer to the QueryDSL documentation which describes how to bootstrap your environment for APT based code generation using Maven or Ant.

Using QueryDSL you will be able to write queries as shown below

QPerson is a class that is generated (via the Java annotation post processing tool) which is a Predicate that allows you to write type safe queries. Notice that there are no strings in the query other than the value "C0123".

You can use the generated Predicate class via the interface QueryDslPredicateExecutor which is shown below

To use this in your repository implementation, simply inherit from it in addition to other repository interfaces. This is shown below

We think you will find this an extremely powerful tool for writing MongoDB queries.

MongoDBs full text search feature is very store specific and therefore can rather be found on MongoRepository than on the more general CrudRepository. What we need is a document with a full-text index defined for (Please see section Text Indexes for creating).

Additional methods on MongoRepository take TextCriteria as input parameter. In addition to those explicit methods, it is also possible to add a TextCriteria derived repository method. The criteria will be added as an additional AND criteria. Once the entity contains a @TextScore annotated property the documents full-text score will be retrieved. Furthermore the @TextScore annotated property will also make it possible to sort by the documents score.

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 rather project on certain attributes of those types. Spring Data allows to model dedicated return types to more selectively retrieve partial views onto the managed aggregates.

Imagine a sample repository and aggregate root type like this:

Now imagine we’d want to retrieve the person’s name attributes only. What means does Spring Data offer to achieve this?

Instances of the repository interfaces are usually created by a container, which 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 allows using the repository abstraction in CDI environments. The extension is part of the JAR so all you need to do to activate it is dropping the Spring Data MongoDB JAR into your classpath. You can now set up the infrastructure by implementing a CDI Producer for the MongoTemplate:

The Spring Data MongoDB CDI extension will pick up the MongoTemplate available as CDI bean and create 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 you’ve just seen there are a few keywords triggering geo-spatial operations within a MongoDB query. The Near keyword allows some further modification. Let’s have look at some examples:

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 will transparently use $nearSphere instead of $code.

As you can see using a Distance equipped with a Metric causes $nearSphere clause to be added instead of a plain $near. Beyond that the actual distance gets calculated according to the Metrics used.

We provide @CreatedBy, @LastModifiedBy to capture the user who created or modified the entity as well as @CreatedDate and @LastModifiedDate to capture the point in time this happened.

As you can see, the annotations can be applied selectively, depending on which information you’d like to capture. For the annotations capturing the points in time can be used on properties of type JodaTimes DateTime, legacy Java Date and Calendar, JDK8 date/time types as well as long/Long.

In case you don’t 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’s also a convenience base class AbstractAuditable which you can extend to avoid the need to manually implement the interface methods. Be aware that this 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.

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 of what type the properties annotated with @CreatedBy or @LastModifiedBy have to be.

Here’s an example implementation of the interface using Spring Security’s Authentication object:

The implementation is accessing the Authentication object provided by Spring Security and looks up the custom UserDetails instance from it that you have created in your UserDetailsService implementation. We’re assuming here that you are exposing the domain user through that UserDetails implementation but you could also look it up from anywhere based on the Authentication found.

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:

The following outlines what type conversion, if any, will be done on the property mapped to the _id document field.

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.