Spring Data Neo4j or in short SDN is a next-generation Spring Data module, created and maintained by Neo4j, Inc. in close collaboration with VMware’s Spring Data Team.

SDN relies completely on the Neo4j Java Driver, without introducing another "driver" or "transport" layer between the mapping framework and the driver. The Neo4j Java Driver - sometimes dubbed Bolt or the Bolt driver - is used as a protocol much like JDBC is with relational databases.

Noteworthy features that differentiate the new SDN from Spring Data Neo4j + OGM are

SDN has several features not present in SDN+OGM, notably

Neo4j-OGM is an Object Graph Mapping library, which is mainly used by previous versions of Spring Data Neo4j as its backend for the heavy lifting of mapping nodes and relationships into domain object. The current SDN does not need and does not support Neo4j-OGM. SDN uses Spring Data’s mapping context exclusively for scanning classes and building the meta model.

While this pins SDN to the Spring ecosystem, it has several advantages, among them the smaller footprint regarding CPU and memory usage and especially, all the features of Spring’s mapping context.

No.

Embedded Neo4j has multiple facets to it:

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

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 Enable${store}Repositories 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 B” 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:

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.

You can also trigger the repository infrastructure by using a store-specific @Enable${store}Repositories annotation on a Java configuration class. 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:

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 Java configuration, you can do so by using the repositoryBaseClass attribute of the @Enable${store}Repositories annotation, as shown in the following example:

A corresponding attribute is available in the XML namespace, 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.

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

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:

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 6 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 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, than 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.

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 an 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 later 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.

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.

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 24:

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 26:

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 27. You need the following dependencies:

And a complete test:

There are some scenarios in which the above annotation does not fit your usecase. 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 capturing when changes were made can be used on properties of type Joda-Time, DateTime, legacy Java Date and Calendar, JDK8 date and time types, and long or Long.

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 snipped 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.

There is also a convenience base class, AbstractAuditable, which you can extend to avoid the need to manually implement the interface methods. Doing so increases the coupling of your domain classes to Spring Data, which might be something you want to avoid. Usually, the annotation-based way of defining auditing metadata is preferred as it is less invasive and more flexible.

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.

Configure the database name to use in your Spring Boot configuration like this (the same property applies of course for YML or environment based configuration, with Spring Boot’s conventions applied):

With that configuration in place, all queries generated by all instances of SDN repositories (both reactive and imperative) and by the ReactiveNeo4jTemplate respectively Neo4jTemplate will be executed against the database yourDatabase.

Provide a bean with the type Neo4jDatabaseNameProvider or ReactiveDatabaseSelectionProvider depending on the type of your Spring application.

That bean could use for example Spring’s security context to retrieve a tenant. Here is a working example for an imperative application secured with Spring Security:

Spring Boot comes with both imperative and reactive Neo4j health indicators. Both variants are able to detect multiple beans of org.neo4j.driver.Driver inside the application context and provide a contribution to the overall health for each instance. The Neo4j driver however does connect to a server and not to a specific database inside that server. Spring Boot is able to configure the driver without Spring Data Neo4j and as the information which database is to be used is tied to Spring Data Neo4j, this information is not available to the built-in health indicator.

This is most likely not a problem in many deployment scenarios. However, if configured database user does not have at least access rights to the default database, the health checks will fail.

This can be mitigated by custom Neo4j health contributors that are aware of the database selection.

The fragment DomainResults declares one additional method findMoviesAlongShortestPath:

This method is annotated with @Transactional(readOnly = true) to indicate that readers can answer it. It cannot be derived by SDN but would need a custom query. This custom query is provided by the one implementation of that interface. The implementation has the same name with the suffix Impl:

The template has overloads for String-based queries as well, so you could write down the query as String as well. The important takeaway here is:

Often times a custom query indicates custom results. Should all of those results be mapepd as @Node? Of course not! Many times those objects represents read commands and are not meant to be used as write commands. It is also not unlikely that SDN 6 cannot or want not map everything that is possible with Cypher. It does however offer several hooks to run your own mapping: On the Neo4jClient. The benefit of using the SDN 6 Neo4jClient over the driver:

Declaring the fragment is exactly the same as before:

Without an implementation for that fragment, startup would fail, so here it is:

The whole query runs in the context of a Spring transaction, in this case, a read-only one.

We provide two abstract configuration classes to support you in bringing in the necessary beans: AbstractNeo4jConfig for imperative database access and AbstractReactiveNeo4jConfig for the reactive version. They are meant to be used with @EnableNeo4jRepositories and @EnableReactiveNeo4jRepositories respectively. See Listing 48 and Listing 49 for an example usage. Both classes require you to override driver() in which you are supposed to create the driver.

To get the imperative version of the Neo4j client, the template and support for imperative repositories, use something similar as shown here:

The following listing provides the reactive Neo4j client and template, enables reactive transaction management and discovers Neo4j related repositories:

For your convenience we provide a CDI extension with Neo4jCdiExtension. When run in a compatible CDI 2.0 container, it will be automatically be registered and loaded through Java’s service loader SPI.

The only thing you have to bring into your application is an annotated type that produces the Neo4j Java Driver:

If you are running in a SE Container - like the one Weld provides for example, you can enable the extension like that:

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 a 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).

Spring Expression Language (SpEL) can be used in custom queries inside :#{}. 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 than 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 34 from custom queries? With the orderBy extension you can pass in a Pageable with a dynamic sort to a custom query:

The literal extension can be used to make things like labels or relationship-types "dynamic" in custom queries. Neither labels nor relationship types can be parameterized in Cypher, so they must be given literal.

Here, the literal value has been used to match dynamically on a Label. If you pass in SomeLabel as a parameter to the method, MATCH (n:SomeLabel) RETURN n will be generated. Ticks have been added to correctly escape values. SDN won’t do this for you as this is probably not what you want in all cases.

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 6 for both Gradle and Maven.

You’re then ready to replace annotations:

The following sections are alternatives and roughly sorted by increased effort.

All builds require a local copy of the project:

Before you proceed, verify your locally installed JDK version. The output should be similar:

There is no quality gate in place at the moment to ensure that the code/test ratio stays as is, but please consider adding tests to your contributions.

We have some rather mild checkstyle rules in place, enforcing more or less default Java formatting rules. Your build will break on formatting errors or something like unused imports.