You can issue a curl request against the Spring Initializer to create a basic Maven project:

This will create a new folder Neo4jSpringBootExample. As this starter is not yet on the initializer, you will have to add the following dependency manually to your pom.xml:

You would also add the dependency manually in case of an existing project.

The idea is the same, just generate a Gradle project:

The dependency for Gradle looks like this and must be added to build.gradle:

You would also add the dependency manually in case of an existing project.

SDN fully supports unmodifiable entities, for both Java and data classes in Kotlin. Therefore, we will focus on immutable entities here, Listing 7 shows a such an entity.

As a general remark: immutable entities using internally generated ids are a bit contradictory, as SDN needs a way to set the field with the value generated by the database.

If you don’t find a good business key or don’t want to use a generator for IDs, here’s the same entity using the internally generated id together with a regular constructor and a so called wither-Method, that is used by SDN:

You can of course use SDN with Kotlin and model your domain with Kotlin’s data classes. Project Lombok is an alternative if you want or need to stay purely within Java.

You basically have two options here: you can work in a store-agnostic fashion with SDN and make your domain specific extend one of

Choose imperative and reactive accordingly.

The other option is to settle on a store specific implementation and gain all the methods we support out of the box. The advantage of this approach is also its biggest disadvantage: once out, all those methods will be part of your API. Most of the time it’s harder to take something away, than to add stuff afterwards. Furthermore, using store specifics leaks your store into your domain. From a performance point of view, there is no penalty.

A reactive repository fitting to any of the movie entities above looks like this:

The @Node annotation is used to mark a class as a managed domain class, subject to the classpath scanning by the mapping context.

To map an Object to nodes in the graph and vice versa, we need a label to identify the class to map to and from.

@Node has an attribute labels that allows you to configure one or more labels to be used when reading and writing instances of the annotated class. The value attribute is an alias for labels. If you don’t specify a label, then the simple class name will be used as the primary label. In case you want to provide multiple labels, you could either:

The primary label should always be the most concrete label that reflects your domain class.

For each instance of an annotated class that is written through a repository or through the Neo4j template, one node in the graph with at least the primary label will be written. Vice versa, all nodes with the primary label will be mapped to the instances of the annotated class.

While @Node creates a mapping between a class and nodes having a specific label, we also need to make the connection between individual instances of that class (objects) and instances of the node.

This is where @Id comes into play. @Id marks an attribute of the class to be the unique identifier of the object. That unique identifier is in an optimal world a unique business key or in other words, a natural key. @Id can be used on all attributes with a supported simple type.

Natural keys are however pretty hard to find. Peoples names for example are seldom unique, change over time or worse, not everyone has a first and last name.

We therefore support two different kind of surrogate keys.

On an attribute of type long or Long, @Id can be used with @GeneratedValue. This maps the Neo4j internal id, which is not a property on a node or relationship and usually not visible, to the attribute and allows SDN to retrieve individual instances of the class.

@GeneratedValue provides the attribute generatorClass. generatorClass can be used to specify a class implementing IdGenerator. An IdGenerator is a functional interface and its generateId takes the primary label and the instance to generate an Id for. We support UUIDStringGenerator as one implementation out of the box.

You can also specify a Spring Bean from the application context on @GeneratedValue via generatorRef. That bean also needs to implement IdGenerator, but can make use of everything in the context, including the Neo4j client or template to interact with the database.

Spring Data Neo4j supports optimistic locking by using the @Version annotation on a Long typed field. This attribute will get incremented automatically during updates and must not be manually modified.

If, e.g., two transactions in different threads want to modify the same object with version x, the first operation will get successfully persisted to the database. At this moment, the version field will get incremented, so it is x+1. The second operation will fail with a OptimisticLockingFailureException because it wants to modify the object with the version x that does not exist anymore in the database. In such cases the operation needs to get retried, beginning with a fresh fetch of the object with the current version from the database.

The @Version attribute is also mandatory if business ids are used. Spring Data Neo4j will check this field to determine if the entity is new or has already been persisted before.

All attributes of a @Node-annotated class will be persisted as properties of Neo4j nodes and relationships. Without further configuration, the name of the attribute in the Java or Kotlin class will be used as Neo4j property.

If you are working with an existing Neo4j schema or just like to adapt the mapping to your needs, you will need to use @Property. The name is used to specify the name of the property inside the database.

The @Relationship annotation can be used on all attributes that are not a simple type. It is applicable on attributes of other types annotated with @Node or collections and maps thereof.

The type or the value attribute allow configuration of the relationship’s type, direction allows specifying the direction. The default direction in SDN is Relationship.Direction#OUTGOING.

We support dynamic relationships. Dynamic relationships are represented as a Map<String, AnnotatedDomainClass> or Map<Enum, AnnotatedDomainClass>. In such a case, the type of the relationship to the other domain class is given by the maps key and must not be configured through the @Relationship.

Putting all those together, we can create a simple domain. We use movies and people with different roles:

People are mapped in two roles here, actors and directors. The domain class is the same:

The easiest way to give your domain classes a unique identifier is the combination of @Id and @GeneratedValue on a field of type Long (preferable the object, not the scalar long, as literal null is the better indicator whether an instance is new or not):

You don’t need to provide a setter for the field, SDN will use reflection to assign the field, but use a setter if there is one. If you want to create an immutable entity with an internally generated id, you have to provide a wither.

You either have to provide a setter for the id attribute or something like a wither, if you want to have

The @GeneratedValue annotation can take a class implementing org.springframework.data.neo4j.core.schema.IdGenerator as parameter. SDN provides InternalIdGenerator (the default) and UUIDStringGenerator out of the box. The latter generates new UUIDs for each entity and returns them as java.lang.String. An application entity using that would look like this:

We have to discuss two separate things regarding advantages and disadvantages. The assignment itself and the UUID-Strategy. A universally unique identifier is meant to be unique for practical purposes. To quote Wikipedia: “Thus, anyone can create a UUID and use it to identify something with near certainty that the identifier does not duplicate one that has already been, or will be, created to identify something else.” Our strategy uses Java internal UUID mechanism, employing a cryptographically strong pseudo random number generator. In most cases that should work fine, but your mileage might vary.

That leaves the assignment itself:

You have several options to roll out your own ID generator. One is a POJO implementing a generator:

Another option is to provide an additional Spring Bean like this:

The generator above would be configured as a bean reference like this:

We have been using a business key in the complete example’s MovieEntity and PersonEntity. The name of the person is assigned at construction time, both by your application and while being loaded through Spring Data.

This is only possible, if you find a stable, unique business key, but makes great immutable domain objects.

Please keep in mind that a business key is always set on the domain entity before Spring Data Neo4j processes it. This means that it cannot determine if the entity was new or not (it always assumes that the entity is new), unless also a @Version field is provided.

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:

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.

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:

Let’s have a look at the following entity:

Spring Data adapts specifics of Kotlin to allow object creation and mutation.

There are a few variants how you can get started with your repository interface.

The typical approach is to extend CrudRepository, which gives you methods for CRUD functionality. CRUD stands for Create, Read, Update, Delete. With version 3.0 we also introduced ListCrudRepository which is very similar to the CrudRepository but for those methods that return multiple entities it returns a List instead of an Iterable which you might find easier to use.

If you are using a reactive store you might choose ReactiveCrudRepository, or RxJava3CrudRepository depending on which reactive framework you are using.

If you are using Kotlin you might pick CoroutineCrudRepository which utilizes Kotlin’s coroutines.

Additional you can extend PagingAndSortingRepository, ReactiveSortingRepository, RxJava3SortingRepository, or CoroutineSortingRepository if you need methods that allow to specify a Sort abstraction or in the first case a Pageable abstraction. Note that the various sorting repositories no longer extended their respective CRUD repository as they did in Spring Data Versions pre 3.0. Therefore, you need to extend both interfaces if you want functionality of both.

If you do not want to extend Spring Data interfaces, you can also annotate your repository interface with @RepositoryDefinition. Extending one of the CRUD repository interfaces 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 the CRUD repository into your domain repository. When doing so, you may change the return type of methods. Spring Data will honor the return type if possible. For example, for methods returning multiple entities you may choose Iterable<T>, List<T>, Collection<T> or a VAVR list.

If many repositories in your application should have the same set of methods you can define your own base interface to inherit from. Such an interface must be annotated with @NoRepositoryBean. This prevents Spring Data to try to create an instance of it directly and failing because it can’t determine the entity for that repository, since it still contains a generic type variable.

The following example shows how to selectively expose CRUD methods (findById and save, in this case):

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.

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:

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

The following example shows a repository that uses generic interfaces:

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

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

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:

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 EnableJpaRepositories annotation. Some strategies may not be supported for particular datastores.

The query builder mechanism built into the Spring Data repository infrastructure is useful for building constraining queries over entities of the repository.

The following example shows how to create a number of queries:

Parsing query method names is divided into subject and predicate. The first part (find…By, exists…By) defines the subject of the query, the second part forms the predicate. The introducing clause (subject) can contain further expressions. Any text between find (or other introducing keywords) and By is considered to be descriptive unless using one of the result-limiting keywords such as a Distinct to set a distinct flag on the query to be created or Top/First to limit query results.

The appendix contains the full list of query method subject keywords and query method predicate keywords including sorting and letter-casing modifiers. However, the first By acts as a delimiter to indicate the start of the actual criteria predicate. 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. Consider the following method signature:

Assume a Person has an Address with a ZipCode. In that case, the method creates the x.address.zipCode property traversal. 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:

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).

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:

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 knows only 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 need only 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.

You can limit the results of query methods by using the first or top keywords, which you can use interchangeably. You can append an optional numeric value 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:

The limiting expressions also support the Distinct keyword for datastores that support distinct queries. Also, for the queries that limit 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 available pages), it is applied within the limited result.

Query methods that return multiple results can use standard Java Iterable, List, and Set. Beyond that, we support returning Spring Data’s Streamable, a custom extension of Iterable, as well as collection types provided by Vavr. Refer to the appendix explaining all possible query method return types.

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:

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 “Appendix C” for details.

You can process the results of query methods incrementally by using a Java 8 Stream<T> as the 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:

You can run repository queries asynchronously by using Spring’s asynchronous method running capability. This means the method returns immediately upon invocation while the actual query occurs in a task that has been submitted to a Spring TaskExecutor. Asynchronous queries differ from reactive queries and should not be mixed. See the store-specific documentation for more details on reactive support. The following example shows a number of asynchronous queries:

Use the store-specific @EnableJpaRepositories annotation on a Java configuration class to define a configuration for repository activation. For an introduction to 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:

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:

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. Bean names for nested repository interfaces are prefixed with their enclosing type name. The base package attribute allows wildcards so that you can define a pattern of scanned packages.

By default, the infrastructure picks up every interface that extends 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 filter elements inside the repository declaration. The semantics are exactly equivalent to the elements in Spring’s component filters. 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:

The preceding example excludes all interfaces ending in SomeRepository from being instantiated and includes those ending with SomeOtherRepository.

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 with a persistence technology-specific RepositoryFactory that you can use, as follows:

To enrich a repository with custom functionality, you must first define a fragment interface and an implementation for the custom functionality, as follows:

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.

Then you can let your repository interface extend the fragment interface, as follows:

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 implementations. 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:

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

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:

The following example shows a repository that uses the preceding repository fragment:

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:

The final step is to make the Spring Data infrastructure aware of the customized repository base class. In configuration, you can do so by using the repositoryBaseClass, as shown in the following example:

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

Several Spring Data modules offer integration with Querydsl through QuerydslPredicateExecutor, as the following example shows:

To use the Querydsl support, extend QuerydslPredicateExecutor on your repository interface, as the following example shows:

The preceding example lets you write type-safe queries by using Querydsl Predicate instances, as the following example shows:

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 the following example shows:

The @EnableSpringDataWebSupport annotation registers a few components. We discuss those later in this section. It also detects Spring HATEOAS on the classpath and registers integration components (if present) for it as well.

If you work with the Spring JDBC module, you are probably familiar with the support for populating 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 called data.json with the following content:

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:

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 unmarshall a repository populator with JAXB:

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

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.

The Query by Example API consists of four parts:

Query by Example is well suited for several use cases:

Query by Example also has several limitations:

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:

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:

You can run the example queries by using repositories. To do so, let your repository interface extend QueryByExampleExecutor<T>. The following listing shows an excerpt from the QueryByExampleExecutor interface:

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:

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().

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:

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:

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:

QueryByExampleExecutor offers one more method, which we did not mention so far: <S extends T, R> R findBy(Example<S> example, Function<FluentQuery.FetchableFluentQuery<S>, R> queryFunction). As with other methods, it executes a query derived from an Example. However, with the second argument, you can control aspects of that execution that you cannot dynamically control otherwise. You do so by invoking the various methods of the FetchableFluentQuery in the second argument. sortBy lets you specify an ordering for your result. as lets you specify the type to which you want the result to be transformed. project limits the queried attributes. first, firstValue, one, oneValue, all, page, stream, count, and exists define what kind of result you get and how the query behaves when more than the expected number of results are available.

Spring Data Neo4j offers a couple of extensions or "mixins" that can be added to repositories. What is a mixin? According to Wikipedia mixins are a language concept that allows a programmer to inject some code into a class. Mixin programming is a style of software development, in which units of functionality are created in a class and then mixed in with other classes.

Java does not support that concept on the language level, but we do emulate it via a couple of interfaces and a runtime that adds appropriate implementations and interceptors for.

Mixins added by default are QueryByExampleExecutor and ReactiveQueryByExampleExecutor respectively. Those interfaces are explained in detail in Section 10.9.

Additional mixins provided are:

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:

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.

Accessor methods in projection interfaces can also be used to compute new values by using the @Value annotation, as shown in the following example:

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:

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:

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:

Again, for more complex expressions, you should use a Spring bean and let the expression invoke a method, as described earlier.

Getters in projection interfaces can make use of nullable wrappers for improved null-safety. Currently supported wrapper types are:

If the underlying projection value is not null, then values are returned using the present-representation of the wrapper type. In case the backing value is null, then the getter method returns the empty representation of the used wrapper type.

You need the following dependencies to run Listing 40:

The dependencies for the enterprise version of Neo4j 3.5 are available under the com.neo4j.test:neo4j-harness-enterprise and an appropriate repository configuration.

You need the following dependencies to run Listing 42:

The dependencies for the enterprise version of Neo4j 4.x are available under the com.neo4j.test:neo4j-harness-enterprise and an appropriate repository configuration.

The principal of configuring the connection is of course still the same with Testcontainers as shown in Listing 43. You need the following dependencies:

And a complete test:

There are some scenarios in which the above annotation does not fit your use case. One of those might be that you want to have 100% control over how the driver is initialized. With a test container running, you could do this with a nested, static configuration class like this:

If you want to use the properties but cannot use a @DynamicPropertySource, you would use an initializer:

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.

As you can see, the annotations can be applied selectively, depending on which information you want to capture. The annotations, indicating to capture when changes are made, can be used on properties of type JDK8 date and time types, long, Long, and legacy Java Date and Calendar.

Auditing metadata does not necessarily need to live in the root level entity but can be added to an embedded one (depending on the actual store in use), as shown in the snippet below.

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.

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:

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.

When using reactive infrastructure you might want to make use of contextual information to provide @CreatedBy or @LastModifiedBy information. We provide an ReactiveAuditorAware<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 reactive Spring Security’s Authentication object:

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.

This uses the available database selection to run the same query that Boot runs to check whether a connection is healthy or not. Use the following configuration to apply it:

The reactive variant is basically the same, using reactive types and the corresponding reactive infrastructure classes:

And of course, the reactive variant of the configuration. It needs two different registry cleaners, as Spring Boot will wrap existing reactive indicators to be used with the non-reactive actuator endpoint, too.

Sometimes you might want to do bulk loadings from a repository or delete whole subgraphs or interact in very specific ways with the Neo4j Java-Driver. This is possible as well. The following example shows how:

This interaction does of course not run in a Spring transaction, as the driver does not know about Spring.

Putting it all together, this test succeeds:

As a final word: All three interfaces and implementations are picked up by Spring Data Neo4j automatically. There is no need for further configuration. Also, the same overall repository could have been created with only one additional fragment (the interface defining all three methods) and one implementation. The implementation would than have had all three abstractions injected (template, client and driver).

All of this applies of course to reactive repositories as well. They would work with the ReactiveNeo4jTemplate and ReactiveNeo4jClient and the reactive session provided by the driver.

If you have recurring methods for all repositories, you could swap out the default repository implementation.

We support a broad range of conversions out of the box. Find the list of supported cypher types in the official drivers manual: Working with Cypher values.

Primitive types of wrapper types are equally supported.

Interactions with a Neo4j Client usually ends with a call to

The imperative version will interact at this moment with the database and get the requested results or summary, wrapped in an Optional<> or a Collection.

The reactive version will in contrast return a publisher of the requested type. Interaction with the database and retrieval of the results will not happen until the publisher is subscribed to. The publisher can only be subscribed once.

As with most things in SDN, both clients depend on a configured driver instance.

The driver can only open a reactive session against a 4.0 database and will fail with an exception on any lower version.

Our Spring Boot starter provide a ready to use bean of the Neo4j Client that fits the environment (imperative or reactive) and you usually don’t have to configure your own instance.

Beside the find/load operations the save operation is one of the most used when working with data. A save operation call in general issues multiple statements against the database to ensure that the resulting graph model matches the given Java model.

The load documentation will not only show you how the MATCH part of the query looks like but also how the data gets returned.

The simplest kind of load operation is a findById call. It will match all nodes with the label of the type you queried for and does a filter on the id value.

MATCH (n:Person) WHERE id(n) = 1364

If there is a custom id provided SDN will use the property you have defined as the id.

MATCH (n:Person) WHERE n.customId = 'anId'

The data to return is defined as a map projection.

RETURN n{.first_name, .personNumber, __internalNeo4jId__: id(n), __nodeLabels__: labels(n)}

As you can see there are two special fields in there: The __internalNeo4jId__ and the __nodeLabels__. Both are critical when it comes to mapping the data to Java objects. The value of the __internalNeo4jId__ is either id(n) or the provided custom id but in the mapping process one known field to refer to has to exist. The __nodeLabels__ ensures that all defined labels on this node can be found and mapped. This is needed for situations when inheritance is used and you query not for the concrete classes or have relationships defined that only define a super-type.

Talking about relationships: If you have defined relationships in your entity, they will get added to the returned map as pattern comprehensions. The above return part will then look like:

RETURN n{.first_name, …​, Person_Has_Hobby: [(n)-[:Has]→(n_hobbies:Hobby)|n_hobbies{__internalNeo4jId__: id(n_hobbies), .name, nodeLabels: labels(n_hobbies)}]}

The map projection and pattern comprehension used by SDN ensures that only the properties and relationships you have defined are getting queried.

In cases where you have self-referencing nodes or creating schemas that potentially lead to cycles in the data that gets returned, SDN falls back to a cascading / data-driven query creation. Starting with an initial query that looks for the specific node and considering the conditions, it steps through the resulting nodes and, if their relationships are also mapped, would create further queries on the fly. This query creation and execution loop will continue until no query finds new relationships or nodes. The way of the creation can be seen analogue to the save/update process.

Given are a graph that looks like this:

and a domain model as shown in the mapping (Constructors and accessors have been omitted for brevity):

As you see, the relationships are only outgoing. Generated finder methods (including findById) will always try to match a root node to be mapped. From there on onwards, all related objects will be mapped. In queries that should return only one object, that root object is returned. In queries that return many objects, all matching objects are returned. Out- and incoming relationships from those objects returned are of course populated.

Assume the following Cypher query:

It follows the recommendation from Getting one record per root node and it works great for the leaf node you want to match here. However: That is only the case in all scenarios that return 0 or 1 mapped objects. While that query will populate all relationships like before, it won’t return all 4 objects.

This can be changed by returning the whole path:

Here we do want to use the fact that the path p actually returns 3 rows with paths to all 4 nodes. All 4 nodes will be populated, linked together and returned.

You do this exactly the same way as in a standard Cypher query issued in the Neo4j Browser or the Cypher-Shell, with the $ syntax (from Neo4j 4.0 on upwards, the old {foo} syntax for Cypher parameters has been removed from the database).

Mapped entities (everything with a @Node) passed as parameter to a function that is annotated with a custom query will be turned into a nested map. The following example represents the structure as Neo4j parameters.

Given are a Movie, Vertex and Actor classes annotated as shown in the movie model:

Passing an instance of Movie to the repository method above, will generate the following Neo4j map parameter:

A node is represented by a map. The map will always contain id which is the mapped id property. Under labels all labels, static and dynamic, will be available. All properties - and type of relationships - appear in those maps as they would appear in the graph when the entity would have been written by SDN. Values will have the correct Cypher type and won’t need further conversion.

If an entity has a relationship with the same type to different types of others nodes, they will all appear in the same list. If you need such a mapping and also have the need to work with those custom parameters, you have to unroll it accordingly. One way to do this are correlated subqueries (Neo4j 4.1+ required).

Spring Expression Language (SpEL) can be used in custom queries inside :#{}. The colon here refers to a parameter and such an expression should be used where parameters make sense. However, when using our literal extension you can use SpEL expression in places where standard Cypher won’t allow parameters (such as for labels or relationship types). This is the standard Spring Data way of defining a block of text inside a query that undergoes SpEL evaluation.

The following example basically defines the same query as above, but uses a WHERE clause to avoid even more curly braces:

The SpEL blocked starts with :#{ and then refers to the given String parameters by name (#pt1). Don’t confuse this with the above Cypher syntax! The SpEL expression concatenates both parameters into one single value that is eventually passed on to the Neo4jClient. The SpEL block ends with }.

SpEL also solves two additional problems. We provide two extensions that allow to pass in a Sort object into custom queries. Remember Listing 62 from custom queries? With the orderBy extension you can pass in a Pageable with a dynamic sort to a custom query:

If you are using the native Neo4j Java driver org.neo4j.driver.types.Point type, you can make use of the following keywords and parameter types in derived finder methods.

Query inside an area:

Query near a certain point:

SDN+OGM has had quite a history over the years and we understand that migrating big application systems is neither fun nor something that provides immediate profit. The main issues we observed when migrating from older versions of Spring Data Neo4j to newer ones are roughly in order the following:

First, you must make sure that your application runs against Neo4j in server mode over the Bolt protocol, which means work in two of three cases:

Once you have made sure, that your SDN+OGM application works over Bolt as expected, you can start migrating to SDN.

And finally, add the new dependency, see Chapter 8 for both Gradle and Maven.

You’re then ready to replace annotations: