Tips on how to Set Up Kafka Integration Take a look at – Grape Up

Do you take into account unit testing as not sufficient resolution for retaining the applying’s reliability and stability? Are you afraid that one way or the other or someplace there’s a potential bug hiding within the assumption that unit assessments ought to cowl all circumstances? And likewise is mocking Kafka not sufficient for undertaking necessities? If even one reply is  ‘sure’, then welcome to a pleasant and straightforward information on how one can arrange Integration Exams for Kafka utilizing TestContainers and Embedded Kafka for Spring!

What’s TestContainers?

TestContainers is an open-source Java library specialised in offering all wanted options for the combination and testing of exterior sources. It implies that we’re in a position to mimic an precise database, net server, and even an occasion bus surroundings and deal with that as a dependable place to check app performance. All these fancy options are hooked into docker photos, outlined as containers. Do we have to take a look at the database layer with precise MongoDB? No worries, we have now a take a look at container for that. We can’t additionally overlook about UI assessments – Selenium Container will do something that we really want.
In our case, we are going to deal with Kafka Testcontainer.

What’s Embedded Kafka?

Because the identify suggests, we’re going to cope with an in-memory Kafka occasion, prepared for use as a traditional dealer with full performance. It permits us to work with producers and shoppers, as common, making our integration assessments light-weight. 

Earlier than we begin

The idea for our take a look at is easy – I wish to take a look at Kafka client and producer utilizing two completely different approaches and verify how we will make the most of them in precise circumstances. 

Kafka Messages are serialized utilizing Avro schemas.

Embedded Kafka – Producer Take a look at

The idea is simple – let’s create a easy undertaking with the controller, which invokes a service technique to push a Kafka Avro serialized message.

Dependencies:

dependencies 
implementation "org.apache.avro:avro:1.10.1"
implementation("io.confluent:kafka-avro-serializer:6.1.0")
implementation 'org.springframework.boot:spring-boot-starter-validation'
implementation 'org.springframework.kafka:spring-kafka'
implementation('org.springframework.cloud:spring-cloud-stream:3.1.1')
implementation('org.springframework.cloud:spring-cloud-stream-binder-kafka:3.1.1')

implementation('org.springframework.boot:spring-boot-starter-web:2.4.3')
implementation 'org.projectlombok:lombok:1.18.16'

compileOnly 'org.projectlombok:lombok'
annotationProcessor 'org.projectlombok:lombok'
testImplementation('org.springframework.cloud:spring-cloud-stream-test-support:3.1.1')
testImplementation 'org.springframework.boot:spring-boot-starter-test'
testImplementation 'org.springframework.kafka:spring-kafka-test'

Additionally price mentioning unbelievable plugin for Avro. Right here plugins part:

plugins 
	id 'org.springframework.boot' model '2.6.8'
	id 'io.spring.dependency-management' model '1.0.11.RELEASE'
	id 'java'
	id "com.github.davidmc24.gradle.plugin.avro" model "1.3.0"

Avro Plugin helps schema auto-generating. This can be a must-have.

Hyperlink to plugin: https://github.com/davidmc24/gradle-avro-plugin

Now let’s outline the Avro schema:

  "namespace": "com.grapeup.myawesome.myawesomeproducer",
  "kind": "report",
  "identify": "RegisterRequest",
  "fields": [
    "name": "id", "type": "long",
    "name": "address", "type": "string", "avro.java.string": "String"
    

  ]

Our ProducerService will likely be centered solely on sending messages to Kafka utilizing a template, nothing thrilling about that half. Major performance might be completed simply utilizing this line:

ListenableFuture<SendResult<String, RegisterRequest>> future = this.kafkaTemplate.ship("register-request", kafkaMessage);

We will’t overlook about take a look at properties:

spring:
  principal:
    allow-bean-definition-overriding: true
  kafka:
    client:
      group-id: group_id
      auto-offset-reset: earliest
      key-deserializer: org.apache.kafka.widespread.serialization.StringDeserializer
      value-deserializer: com.grapeup.myawesome.myawesomeconsumer.widespread.CustomKafkaAvroDeserializer
    producer:
      auto.register.schemas: true
      key-serializer: org.apache.kafka.widespread.serialization.StringSerializer
      value-serializer: com.grapeup.myawesome.myawesomeconsumer.widespread.CustomKafkaAvroSerializer
    properties:
      particular.avro.reader: true

As we see within the talked about take a look at properties, we declare a customized deserializer/serializer for KafkaMessages. It’s extremely really helpful to make use of Kafka with Avro – don’t let JSONs keep object construction, let’s use civilized mapper and object definition like Avro.

Serializer:

public class CustomKafkaAvroSerializer extends KafkaAvroSerializer 
    public CustomKafkaAvroSerializer() 
        tremendous();
        tremendous.schemaRegistry = new MockSchemaRegistryClient();
    

    public CustomKafkaAvroSerializer(SchemaRegistryClient shopper) 
        tremendous(new MockSchemaRegistryClient());
    

    public CustomKafkaAvroSerializer(SchemaRegistryClient shopper, Map<String, ?> props) 
        tremendous(new MockSchemaRegistryClient(), props);
    

Deserializer:

public class CustomKafkaAvroSerializer extends KafkaAvroSerializer 
    public CustomKafkaAvroSerializer() 
        tremendous();
        tremendous.schemaRegistry = new MockSchemaRegistryClient();
    

    public CustomKafkaAvroSerializer(SchemaRegistryClient shopper) 
        tremendous(new MockSchemaRegistryClient());
    

    public CustomKafkaAvroSerializer(SchemaRegistryClient shopper, Map<String, ?> props) 
        tremendous(new MockSchemaRegistryClient(), props);
    

And we have now every thing to start out writing our take a look at.

@ExtendWith(SpringExtension.class)
@SpringBootTest
@AutoConfigureMockMvc
@TestInstance(TestInstance.Lifecycle.PER_CLASS)
@ActiveProfiles("take a look at")
@EmbeddedKafka(partitions = 1, matters = "register-request")
class ProducerControllerTest {

All we have to do is add @EmbeddedKafka annotation with listed matters and partitions. Software Context will boot Kafka Dealer with supplied configuration identical to that. Take into account that @TestInstance needs to be used with particular consideration. Lifecycle.PER_CLASS will keep away from creating the identical objects/context for every take a look at technique. Value checking if assessments are too time-consuming.

Client<String, RegisterRequest> consumerServiceTest;

@BeforeEach
void setUp() 
DefaultKafkaConsumerFactory<String, RegisterRequest> client = new DefaultKafkaConsumerFactory<>(kafkaProperties.buildConsumerProperties();

consumerServiceTest = client.createConsumer();
consumerServiceTest.subscribe(Collections.singletonList(TOPIC_NAME));

Right here we will declare the take a look at client, primarily based on the Avro schema return kind. All Kafka properties are already supplied within the .yml file. That client will likely be used as a verify if the producer truly pushed a message.

Right here is the precise take a look at technique:

@Take a look at
void whenValidInput_therReturns200() throws Exception 
        RegisterRequestDto request = RegisterRequestDto.builder()
                .id(12)
                .deal with("tempAddress")
                .construct();

        mockMvc.carry out(
                put up("/register-request")
                      .contentType("software/json")
                      .content material(objectMapper.writeValueAsBytes(request)))
                .andExpect(standing().isOk());

      ConsumerRecord<String, RegisterRequest> consumedRegisterRequest =  KafkaTestUtils.getSingleRecord(consumerServiceTest, TOPIC_NAME);

        RegisterRequest valueReceived = consumedRegisterRequest.worth();

        assertEquals(12, valueReceived.getId());
        assertEquals("tempAddress", valueReceived.getAddress());
    

To begin with, we use MockMvc to carry out an motion on our endpoint. That endpoint makes use of ProducerService to push messages to Kafka. KafkaConsumer is used to confirm if the producer labored as anticipated. And that’s it – we have now a totally working take a look at with embedded Kafka.

Take a look at Containers – Client Take a look at

TestContainers are nothing else like impartial docker photos prepared for being dockerized. The next take a look at situation will likely be enhanced by a MongoDB picture. Why not maintain our information within the database proper after something occurred in Kafka movement?

Dependencies aren’t a lot completely different than within the earlier instance. The next steps are wanted for take a look at containers:

testImplementation 'org.testcontainers:junit-jupiter'
	testImplementation 'org.testcontainers:kafka'
	testImplementation 'org.testcontainers:mongodb'

ext 
	set('testcontainersVersion', "1.17.1")


dependencyManagement 
	imports 
		mavenBom "org.testcontainers:testcontainers-bom:$testcontainersVersion"
	

Let’s focus now on the Client half. The take a look at case will likely be easy – one client service will likely be answerable for getting the Kafka message and storing the parsed payload within the MongoDB assortment. All that we have to find out about KafkaListeners, for now, is that annotation:

@KafkaListener(matters = "register-request")

By the performance of the annotation processor, KafkaListenerContainerFactory will likely be accountable to create a listener on our technique. From this second our technique will react to any upcoming Kafka message with the talked about matter.

Avro serializer and deserializer configs are the identical as within the earlier take a look at.

Concerning TestContainer, we must always begin with the next annotations:

@SpringBootTest
@ActiveProfiles("take a look at")
@Testcontainers
public class AbstractIntegrationTest {

Throughout startup, all configured TestContainers modules will likely be activated. It means that we’ll get entry to the complete working surroundings of the chosen supply. As instance:

@Autowired
personal KafkaListenerEndpointRegistry kafkaListenerEndpointRegistry;

@Container
public static KafkaContainer kafkaContainer = new KafkaContainer(DockerImageName.parse("confluentinc/cp-kafka:6.2.1"));

@Container
static MongoDBContainer mongoDBContainer = new MongoDBContainer("mongo:4.4.2").withExposedPorts(27017);

Because of booting the take a look at, we will count on two docker containers to start out with the supplied configuration.

What is admittedly necessary for the mongo container – it provides us full entry to the database utilizing only a easy connection uri. With such a function, we’re ready to have a look what’s the present state in our collections, even throughout debug mode and ready breakpoints.
Have a look additionally on the Ryuk container – it really works like overwatch and checks if our containers have began accurately.

And right here is the final a part of the configuration:

@DynamicPropertySource
static void dataSourceProperties(DynamicPropertyRegistry registry) 
   registry.add("spring.kafka.bootstrap-servers", kafkaContainer::getBootstrapServers);
   registry.add("spring.kafka.client.bootstrap-servers", kafkaContainer::getBootstrapServers);
   registry.add("spring.kafka.producer.bootstrap-servers", kafkaContainer::getBootstrapServers);
   registry.add("spring.information.mongodb.uri", mongoDBContainer::getReplicaSetUrl);


static 
   kafkaContainer.begin();
   mongoDBContainer.begin();

   mongoDBContainer.waitingFor(Wait.forListeningPort()
           .withStartupTimeout(Period.ofSeconds(180L)));


@BeforeTestClass
public void beforeTest() 

   kafkaListenerEndpointRegistry.getListenerContainers().forEach(
           messageListenerContainer -> 
               ContainerTestUtils
                       .waitForAssignment(messageListenerContainer, 1);

           
   );


@AfterAll
static void tearDown() 
   kafkaContainer.cease();
   mongoDBContainer.cease();

DynamicPropertySource provides us the choice to set all wanted surroundings variables in the course of the take a look at lifecycle. Strongly wanted for any config functions for TestContainers. Additionally, beforeTestClass kafkaListenerEndpointRegistry waits for every listener to get anticipated partitions throughout container startup.

And the final a part of the Kafka take a look at containers journey – the principle physique of the take a look at:

@Take a look at
public void containerStartsAndPublicPortIsAvailable() throws Exception 
   writeToTopic("register-request", RegisterRequest.newBuilder().setId(123).setAddress("dummyAddress").construct());

   //Await KafkaListener
   TimeUnit.SECONDS.sleep(5);
   Assertions.assertEquals(1, taxiRepository.findAll().dimension());



personal KafkaProducer<String, RegisterRequest> createProducer() 
   return new KafkaProducer<>(kafkaProperties.buildProducerProperties());


personal void writeToTopic(String topicName, RegisterRequest... registerRequests) 

   attempt (KafkaProducer<String, RegisterRequest> producer = createProducer()) 
       Arrays.stream(registerRequests)
               .forEach(registerRequest -> 
                           ProducerRecord<String, RegisterRequest> report = new ProducerRecord<>(topicName, registerRequest);
                           producer.ship(report);
                       
               );
   

The customized producer is answerable for writing our message to KafkaBroker. Additionally, it is strongly recommended to offer a while for shoppers to deal with messages correctly. As we see, the message was not simply consumed by the listener, but in addition saved within the MongoDB assortment.

Conclusions

As we will see, present options for integration assessments are fairly simple to implement and keep in tasks. There isn’t a level in retaining simply unit assessments and relying on all strains lined as an indication of code/logic high quality. Now the query is, ought to we use an Embedded resolution or TestContainers? I recommend to begin with specializing in the phrase “Embedded”. As an ideal integration take a look at, we wish to get an nearly perfect copy of the manufacturing surroundings with all properties/options included. In-memory options are good, however principally, not sufficient for big enterprise tasks. Positively, the benefit of Embedded providers is the straightforward method to implement such assessments and keep configuration, simply when something occurs in reminiscence.
TestContainers on the first sight would possibly seem like overkill, however they provide us crucial function, which is a separate surroundings. We don’t should even depend on current docker photos – if we wish we will use customized ones. This can be a big enchancment for potential take a look at eventualities.
What about Jenkins? There isn’t a purpose to be afraid additionally to make use of TestContainers in Jenkins. I firmly advocate checking TestContainers documentation on how simply we will arrange the configuration for Jenkins brokers.
To sum up – if there is no such thing as a blocker or any undesirable situation for utilizing TestContainers, then don’t hesitate. It’s at all times good to maintain all providers managed and secured with integration take a look at contracts.