Reactive Microservices in Java Using the Lagom Framework

Microservice architecture is amongst the latest in best practices in software architecture. Essentially, microservices are small, independent applications that provide REST APIs for communication. These applications are akin to the Unix philosophy of small programs: each program does one thing and does it well. Each application should work on its own and be independently deployable, thus making each microservice independently maintainable. One simple way to check if a service is a microservice is if it is easily described in a single, simple sentence.

So why would you want to use microservices? There are a few advantages to this style of architecture:

  • Individual services are easier to maintain and deploy than a monolithic application. This makes the path towards continuous delivery much more attainable.
  • Each service can be written in the most appropriate programming language for its domain along with its related libraries and frameworks.
  • Services are far more modular and replaceable over time without requiring tremendous refactoring efforts.
  • Services own their own data, thus not only allowing data stores to be changed more easily, but this also makes it easier to horizontally scale large amounts of data.

Along with this concept of microservices, there is also the trend of making applications reactive. Reactive applications are responsive, resilient, elastic, and message driven, the combination of which allows them to scale more easily and deal with the deluge of data generated by users and devices. An application that follows reactive principles tends to provide for a better user experience, especially as the application gains a critical number of concurrent users.

Combining these two concepts, we come to the latest in backend software engineering patterns to make reactive microservices. The Lagom framework is a framework that provides an opinionated starting point for implementing some of your reactive microservices. Lagom is built on top of various open source Lightbend products like Akka and Play, managed by a Guice container, along with great integration with Cassandra and Kafka. Lagom requires Java 8 and uses many of its language and library features.

Background

Before delving into specifics on Lagom, there are some things to note about how microservices should generally be architected regardless of framework. Lagom does indeed follow these particular ideas, but it is not the only framework out there that does.

As mentioned earlier, microservices should have a specific function to serve. For example, a blog microservice might be one that allows for creating, updating, and reading blog posts. A related comments microservice could manage the comments attached to blog posts (or anything else). A users microservice could manage user details which could be relevant to both the blog and comments microservices. Note, however, that microservices should not create hard dependencies between each other.

Instead, microservices should be able to fall back to local data or default data when a related microservice is unable to provide its functionality. For example, if the users microservice is down, then a page displaying a blog post may fall back to only displaying usernames rather than the additional information the users service may provide such as an avatar, email address, real name, etc. If the comments service is down, a blog post can still be displayed without comments.

Another important architectural concern is that microservices should own their data. In that regard, each service should have its own database that only it owns. Other microservices should use the public API of the owner service in order to access that data. Services are completely allowed to cache or store data from other microservices for various reasons. While this does lead to duplication of data and goes against all normalization practices common to RDBMSs, the duplication of data in microservices is done in order to provide faster services and fallbacks when microservices may be down or otherwise unavailable.

Note that not all microservices need to be stateful. For example, while refactoring a large monolithic application into a microservice architecture, the first steps tend to be creating small, stateless microservice APIs such as for sending email, pushing notifications, etc. In general, though, an application should first be refactored to separate the front end user interface from the back end API before said API could ever be split into proper reactive microservices.

When it comes to implementing reactive microservices, following event sourcing and messaging patterns can help meet a lot of the technical challenges in following the reactive manifesto. In that regard, it is typical to use far more asynchronous communication and APIs. For example, instead of using typical SQL RDBMSs, NoSQL databases tend to be used far more often. Asynchronous communication between services can use messaging systems like Kafka or ActiveMQ. These types of systems also tend to be far more scalable and resilient, thus helping maintain reactivity. However, these types of systems do come at the cost of eventual consistency, the concept that your data will not maintain transactional consistency, but it will eventually be up to date as the events from your system propogate. As we’ll see later, Lagom does not hide this fact from the developer and instead embraces the CQRS paradigm.

Lagom

Lagom is a Java framework (with future support for Scala) that provides an opinionated set of libraries for implementing reactive microservices. Note that although Lagom is a rather interesting framework to use for implementing these services, it should not be the only tool in your box. Lagom works best when combined with microservices written in other languages and frameworks.

Microservices written in Lagom are exposed over HTTP as either REST APIs for synchronous request/reply services, or as WebSocket APIs for asynchronous or streaming services. Data is encoded in standard JSON. Using these web standards makes integration with other microservices much easier.

One important concept to follow when using Lagom is immutability. The standard JDK provides a lot of mutable classes and concepts, so third party libraries are useful here. For example, the recommended library for immutable collections is PCollections, although Guava also provides implementations of immutable collections as well. Immutable data structures can be written by hand (or more likely generated by an IDE), but using an annotation processor to generate that code such as Immutables or Lombok make this much easier. The use of immutable classes is pervasive throughout Lagom to the point where mutable classes can potentially cause fun to debug concurrency bugs.

Usage

Lagom sounds pretty cool, but what does it look like? You can follow along with sample code. What we’ll cover in this overview is a very simple blog microservice. First, we’ll create a service API and then the implementation. Note that these concerns should be separated so that other microservices can use Lagom as a client as well. In order to run the code from these examples, you’ll need to follow the installation guide.

Note that in this project, we will use Lombok instead of Immutables due to incomplete IntelliJ support. Immutables works without any need for workarounds in Eclipse, but there are workarounds required for IntelliJ that make using it inconvenient currently.

Blog API

In our API, we have two main things to provide that can be used by both clients and servers: the service interface and the data model. Our data model will be a simple blog post, and our service will provide APIs to create, update, and read these posts.

@Immutable
@JsonDeserialize
@Value
@Builder
@Wither
@AllArgsConstructor(onConstructor = @__(@JsonCreator))
public final class PostContent {

    @NonNull
    String title;
    @NonNull
    String body;
    @NonNull
    String author;

}

Wow, there are literally more lines of annotations than code here! Let’s go over them all.

  • @Immutable is from the javax.annotation.concurrent package which is a sort of marker annotation that indicates to developers that the annotated class is completely immutable and threadsafe. To that regard, all publicly accessible data in that class should also be immutable.
  • @JsonDeserialize is a Jackson annotation that indicates that the annotated class should be used for deserialized JSON data. This can be used to configure some details of that deserialization, but in our case, the default settings are fine.
  • The next four annotations all come from Lombok. Note that these annotations are similar to the functionality provided by the Immutables @Value.Immutable annotation used in the official Lagom documentation.
    • @Value makes all fields private final, generates getter methods for them, generates standard equals(), hashCode(), and toString() methods, and generates a constructor taking all fields as arguments. Essentially, this annotation makes creating an immutable value class far less verbose.
    • @Builder generates a builder class and method for constructing an object through the builder pattern. In this example, this generates a method called builder() which returns a new PostContentBuilder, and that class provides methods such as title(), body(), and author() to set these values, and a build() method to construct a PostContentobject. For classes with only one or two fields, including a builder is not very useful, but for classes with many fields, Java’s lack of named parameters in constructors makes the builder pattern the only way to readably construct an immutable object.
    • @Wither is an experimental feature of Lombok right now, but it generates withFoo()methods for each field. These methods construct a copy of the current object but with the specified field modified. This feature allows for more efficient “modification” of immutable objects rather than having to manually copy the objects by hand.
    • @AllArgsConstructor does what it sounds like: it generates a constructor that contains all the fields of the class as parameters. In this case, this constructor replaces the one that would’ve been generated by the @Value annotation. The reason we use this constructor is in order to decorate that constructor with the @JsonCreator annotation. This annotation is another Jackson annotation that indicates how to construct new instances of this class. By default, Jackson likes to use JavaBean style classes, but JavaBeans normally follow a mutable pattern, so we specify here our desired behavior.
  • Finally, the @NonNull annotation on the fields is another Lombok feature that generates null checks in generated constructors and setters for that field. This annotation can also be used in a parameter to a constructor or method and Lombok will generate the null check as well. This null check is implemented as throwing a NullPointerException if the value is null which is the pattern established by the JDK. One neat aspect of using this annotation is that it can also be configured in your IDE or static code checks as a non-null annotation that can aid in development.

That’s a lot of functionality! Thankfully, we can rely on these annotation processors to provide a lot of otherwise tedious functionality that we’ll come to rely on later when manipulating our data.

Our other class we need to specify is our service contract.

public interface BlogService extends Service {

    ServiceCall<NotUsed, Optional<PostContent>> getPost(String id);

    ServiceCall<PostContent, String> addPost();

    ServiceCall<PostContent, Done> updatePost(String id);

    @Override
    default Descriptor descriptor() {
        return named("blog").withCalls(
                restCall(Method.GET, "/api/blog/:id", this::getPost),
                restCall(Method.POST, "/api/blog/", this::addPost),
                restCall(Method.PUT, "/api/blog/:id", this::updatePost)
        ).withAutoAcl(true);
    }
}

First we note that all service APIs are simply interfaces that inherit from the Service interface. Next, we note that service calls are all specified by abstract methods that return a ServiceCall<Request, Response> object. There is also a descriptor() method that returns metadata about the REST API that these methods implement.

What is this ServiceCall class? It’s a functional interface that takes a Request object and returns a CompleteableFuture<Response> object. Note that Request and Response are generic parameters. There are two standard classes used above: NotUsed and Done, both of which are from the Akka API. The NotUsed class is similar to Void in the sense that it’s to be ignored. The Doneclass is essentially an “OK” response.

Thus, our API will implement three REST APIs: get a post, add a post, and update a post. Simple, right?

Blog Implementation

Now we delve into the actual implementation of this API. In this module, we’ll not only be implementing the above BlogService interface, but we’ll also be providing persistent entity classes for storing and retrieving blog posts. Let’s start with the entity itself.

@SuppressWarnings("unchecked")
public class BlogEntity
    extends PersistentEntity<BlogCommand, BlogEvent, BlogState> {

    @Override
    public Behavior initialBehavior(final Optional<BlogState> snapshotState) {
        final BehaviorBuilder b =
                newBehaviorBuilder(snapshotState.orElse(BlogState.EMPTY));
        addBehaviorForGetPost(b);
        addBehaviorForAddPost(b);
        addBehaviorForUpdatePost(b);
        return b.build();
    }

    private void addBehaviorForGetPost(final BehaviorBuilder b) {
        b.setReadOnlyCommandHandler(BlogCommand.GetPost.class,
                (cmd, ctx) -> ctx.reply(state().getContent()));
    }

    private void addBehaviorForAddPost(final BehaviorBuilder b) {
        b.setCommandHandler(BlogCommand.AddPost.class,
                (cmd, ctx) -> ctx.thenPersist(
                        new BlogEvent.PostAdded(entityId(), cmd.getContent()),
                        evt -> ctx.reply(entityId())
                )
        );
        b.setEventHandler(BlogEvent.PostAdded.class,
                evt -> new BlogState(Optional.of(evt.getContent()))
        );
    }

    private void addBehaviorForUpdatePost(final BehaviorBuilder b) {
        b.setCommandHandler(BlogCommand.UpdatePost.class,
                (cmd, ctx) -> ctx.thenPersist(
                        new BlogEvent.PostUpdated(entityId(), cmd.getContent()),
                        evt -> ctx.reply(Done.getInstance())
                )
        );
        b.setEventHandler(BlogEvent.PostUpdated.class,
                evt -> new BlogState(Optional.of(evt.getContent()))
        );
    }
}

Look at all the lambdas! Let’s break this down. A persistent entity in Lagom is the root class for manipulating data. The default implementation of this is via Cassandra, but there is also support for a few JDBC data stores such as Postgres, MySQL, and Oracle. In this API, we use what is known as event sourcing or CQRS as the pattern for reading and writing data.

A persistent entity relates to three classes: commands, events, and state. The command classes are used to provide a command to the entity to do something. This can be something like reading or writing the data of that entity. In our entity class, we specify how to interpret these commands via command handlers. These commands generally create events from the data provided in the command which can be persisted in the event log. An event log is a sort of write-only database structure where data updates are simply saved as new events, and the current state of a particular piece of data can be constructed by combining all the events that affected that state. Events are handled by event handlers which tend to handle the state updates. Finally, state objects are used to store the current accumulated state of an entity.

An entity begins its life with an optional snapshot state which is saved from time to time over the life of an entity. From this possible snapshot state, the entity describes its behavior which is essentially a set of command and event handlers for that entity. Using this snapshot, the initial behavior of the entity is specified, and this behavior can be changed at any time in response to an event. This allows for implementation of finite state machines as well which is pretty neat.

Delving into more detail in the types of behavior we’re giving this entity, the first one is a read-only command handler used for fetching posts. This command is simple to implement by replying with the current state of the entity.

Next, we implement a command handler for adding posts. When this command is received, we chain a persist call to save a PostAdded event to our event log. That is also chained with returning the entity ID from the event. We also add an event handler to update the current state when receiving aPostAdded event.

Finally, we implement a command handler for updating posts. This works almost exactly like creating posts, but instead we respond with Done rather than the entity ID. Our event handler also works very similarly to the post added event handler.

We could change the behavior of the entity after creating or updating posts (for instance, adding restrictions on editting certain attributes of the post).

So far, we know that commands are used to manipulate data, and events are used to react to that data by updating its state and optionally modifying its behavior. Let’s take a look at our commands.

public interface BlogCommand extends Jsonable {

    enum GetPost implements BlogCommand,
            PersistentEntity.ReplyType<Optional<PostContent>> {
        INSTANCE
    }

    @Immutable
    @JsonDeserialize
    @Value
    @AllArgsConstructor(onConstructor = @__(@JsonCreator))
    final class AddPost implements BlogCommand, CompressedJsonable,
            PersistentEntity.ReplyType<String> {
        @NonNull
        PostContent content;
    }

    @Immutable
    @JsonDeserialize
    @Value
    @AllArgsConstructor(onConstructor = @__(@JsonCreator))
    final class UpdatePost implements BlogCommand, CompressedJsonable,
            PersistentEntity.ReplyType<Done> {
        @NonNull
        PostContent content;
    }
}

First we’ll note the Jsonable interface. This is a marker interface to Lagom that indicates that the class should be serializable via JSON. There is also the CompressedJsonable interface which further specifies that if the JSON data exceeds a certain size threshold that it can be compressed.

Next, note that the three commands here all implement the BlogCommand interface which makes them available as command types in our BlogEntity class. Our commands are similar to the PostContent class in our annotation usage as they are also value objects.

The other important interface that all these commands implement is the ReplyType<T> interface which specifies the reply type returned by executing this command. In the first command, GetPostreturns an Optional<PostContent>, showing that it is possible to attempt to get a nonexistent post. This class is implemented as an enum because it has no state, and enums provide a very convenient way to implement a proper singleton object in Java. The second command, AddPost, returns a String which in our implementation will be the ID of the newly created entity so that it can be retrieved afterwards. The third command, UpdatePost, replies with a Done to indicate that the operation succeeded. All our commands revolve around the PostContent model, but this could be anything really.

Next, let’s take a look at our events.

public interface BlogEvent extends Jsonable, AggregateEvent<BlogEvent> {

    @Override
    default AggregateEventTagger<BlogEvent> aggregateTag() {
        return AggregateEventTag.of(BlogEvent.class);
    }

    @Immutable
    @JsonDeserialize
    @Value
    @AllArgsConstructor(onConstructor = @__(@JsonCreator))
    final class PostAdded implements BlogEvent, CompressedJsonable {
        @NonNull
        String id;
        @NonNull
        PostContent content;
    }

    @Immutable
    @JsonDeserialize
    @Value
    @AllArgsConstructor(onConstructor = @__(@JsonCreator))
    final class PostUpdated implements BlogEvent, CompressedJsonable {
        @NonNull
        String id;
        @NonNull
        PostContent content;
    }
}

Our first interface of note is AggregateEvent<E> which is used for sharding entities across database instances. In our case, we use the name of the class as our sharding key which would not be very efficient in a high throughput production scenario, but it is pretty simple. A more realistic aggregate event tag would probably include the author information as blog posts from a specific author may appear together.

All our event classes implement BlogEvent so that they can be used in our BlogEntity class as events. Other than the aggregate event tagging method, events are pretty straightforward data objects. The importance of events is found in the functions that response to the events.

Now we look at our state class.

@Immutable
@JsonDeserialize
@Value
@AllArgsConstructor(onConstructor = @__(@JsonCreator))
public class BlogState implements CompressedJsonable {

    public static final BlogState EMPTY = new BlogState(Optional.empty());

    Optional<PostContent> content;
}

Not much to this class. We use an Optional<PostContent> because the initial state of an entity contains no post data, and abusing null here to mean “empty” is a bit of a code smell to me.

Let’s put it all together now and implement the BlogService interface.

public class BlogServiceImpl implements BlogService {

    private final PersistentEntityRegistry registry;

    @Inject
    public BlogServiceImpl(final PersistentEntityRegistry registry) {
        this.registry = registry;
        registry.register(BlogEntity.class);
    }

    @Override
    public ServiceCall<NotUsed, Optional<PostContent>> getPost(final String id) {
        return request -> registry.refFor(BlogEntity.class, id)
                .ask(BlogCommand.GetPost.INSTANCE);
    }

    @Override
    public ServiceCall<PostContent, String> addPost() {
        return content -> registry.refFor(BlogEntity.class, UUID.randomUUID().toString())
                .ask(new BlogCommand.AddPost(content));
    }

    @Override
    public ServiceCall<PostContent, Done> updatePost(final String id) {
        return content -> registry.refFor(BlogEntity.class, id)
                .ask(new BlogCommand.UpdatePost(content));
    }
}

Our main point of contact with our entity class will be through the PersistentEntityRegistrywhich is injected via Guice. We need to register our entity class before being able to use the entities. The service calls are all relatively simple now that we’ve abstracted out our data access through the persistent entity API. Essentially, we can do simple lookups of entities by ID, then we “ask” it a command.

The only remaining class in this module is the class used for configuring Guice.

public class BlogModule extends AbstractModule implements ServiceGuiceSupport {
    @Override
    protected void configure() {
        bindServices(serviceBinding(BlogService.class, BlogServiceImpl.class));
    }
}

This adds our BlogServiceImpl class as an implementation of BlogService which allows it to be injected into other classes as well as makes itself manageable by Guice.

There are also various configuration files included in the project that are available on GitHub in order to get a complete project.

Writing Tests

Entities are complex sets of classes, and Lagom provides a test class used for simulating in-memory entities that can be used in unit tests. Let’s write some tests for various scenarios involving our BlogEntity example.

public class BlogEntityTest {

    private static ActorSystem system;

    @BeforeClass
    public static void beforeClass() {
        system = ActorSystem.create("BlogEntityTest");
    }

    @AfterClass
    public static void afterClass() {
        JavaTestKit.shutdownActorSystem(system);
        system = null;
    }

    @Rule
    public TestName testName = new TestName();

    private PersistentEntityTestDriver<BlogCommand, BlogEvent, BlogState> driver;

    @Before
    public void setUp() throws Exception {
        // given a default BlogEntity
        driver = new PersistentEntityTestDriver<>(
                system, new BlogEntity(), testName.getMethodName());
    }

    @Test
    public void initialStateShouldBeEmpty() throws Exception {
        // when we send a GetPost command
        final Outcome<BlogEvent, BlogState> getPostOutcome =
                driver.run(GetPost.INSTANCE);

        // then no events should have been created
        assertThat(getPostOutcome.events()).isEmpty();

        // and the state should still be empty
        assertThat(getPostOutcome.state().getContent()).isNotPresent();

        // and we should get back an empty Optional to indicate that
        // no post was found
        final Optional<PostContent> actual = getFirstReply(getPostOutcome);
        assertThat(actual).isNotPresent();
    }

    @Test
    public void addPost() throws Exception {
        // given entity ID of test name
        final String expectedEntityId = testName.getMethodName();

        // when we send an AddPost command
        final Outcome<BlogEvent, BlogState> addPostOutcome =
                driver.run(new AddPost(newPostContent()));

        // then a PostAdded event should be persisted
        final List<BlogEvent> events = addPostOutcome.events();
        assertThat(events).containsExactly(
                new PostAdded(expectedEntityId, newPostContent()));

        // and the state should contain that post content
        assertThat(addPostOutcome.state().getContent()).hasValue(
                newPostContent());

        // and the reply should give us the entity ID
        final String entityId = getFirstReply(addPostOutcome);
        assertThat(entityId).isEqualTo(expectedEntityId);

        // when we send a subsequent GetPost command
        final Outcome<BlogEvent, BlogState> getPostOutcome =
                driver.run(GetPost.INSTANCE);

        // then the reply should be our post content we added earlier
        final Optional<PostContent> content = getFirstReply(getPostOutcome);
        assertThat(content).hasValue(newPostContent());
    }

    @Test
    public void updatePost() throws Exception {
        // given entity ID of test name
        final String expectedEntityId = testName.getMethodName();

        // when we send an UpdatePost command
        final Outcome<BlogEvent, BlogState> updatePostOutcome =
                driver.run(new UpdatePost(newPostContent()));

        // then a PostUpdated event should be persisted
        final List<BlogEvent> events = updatePostOutcome.events();
        assertThat(events).containsExactly(
                new PostUpdated(expectedEntityId, newPostContent()));

        // and the state should contain the post content
        assertThat(updatePostOutcome.state().getContent()).hasValue(
                newPostContent());

        // and the reply should be Done
        final Done reply = getFirstReply(updatePostOutcome);
        assertThat(reply).isEqualTo(Done.getInstance());
    }

    @SuppressWarnings("unchecked")
    private static <T> T getFirstReply(final Outcome<?, ?> outcome) {
        return (T) outcome.getReplies().get(0);
    }

    private static PostContent newPostContent() {
        return new PostContent("z", "y", "x");
    }
}

The ActorSystem is essentially the entry point class to Akka which is how a lot of Lagom is built. The next key class here is the PersistentEntityTestDriver which allows you to simulate sending commands to an entity and inspecting the results of sending that command. The only other possibly strange thing here is the assertThat() method used here; this is from the fantastic AssertJ testing library.

As for writing service tests, Lagom’s test kit provides a way to run an embedded server that contains Cassandra, Kafka, and any other dependent applications of your service. These tests can interact directly with the service API and perform tests on the results. For a more complete example, see BlogServiceTest.

Finally, integration tests between multiple microservices can be implemented by setting up both an ActorSystem and a LagomClientFactory to run all your microservices together. Streaming microservices requires also setting up a Materializer instance from the Akka streams API. Included in the GitHub project are the sample integration tests from the Lagom Maven archetype sample project.

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Matt Sicker

Matt Sicker is a Senior Consultant in the Custom Development – Open Source Practice at SPR.