A library that provides an in-memory Kafka instance to run your tests against.
Inspired by kafka-unit.
embedded-kafka is available on Maven Central, compiled for Scala 2.12, 2.13 and Scala 3 (since v3.4.0.1).
Versions match the version of Kafka they're built against.
Prior to v2.8.0 Kafka core was inlining the Scala library, so you couldn't use a different Scala patch version than what Kafka used to compile its jars!
From v2.8.0 onwards package name has been updated to reflect the library group id (i.e. io.github.embeddedkafka).
Aliases to the old package name have been added, along with a one-time Scalafix rule to ensure the smoothest migration.
- In your
build.sbtfile add the following dependency (replacex.x.xwith the appropriate version):"io.github.embeddedkafka" %% "embedded-kafka" % "x.x.x" % Test - Have your class extend the
EmbeddedKafkatrait. - Enclose the code that needs a running instance of Kafka within the
withRunningKafkaclosure.
An example, using ScalaTest:
class MySpec extends AnyWordSpecLike with Matchers with EmbeddedKafka {
"runs with embedded kafka" should {
"work" in {
withRunningKafka {
// ... code goes here
}
}
}
}- In-memory Zookeeper and Kafka will be instantiated respectively on port 6000 and 6001 and automatically shutdown at the end of the test.
A EmbeddedKafka companion object is provided for usage without extending the EmbeddedKafka trait. Zookeeper and Kafka can be started and stopped in a programmatic way. This is the recommended usage if you have more than one test in your file and you don't want to start and stop Kafka and Zookeeper on every test.
class MySpec extends AnyWordSpecLike with Matchers {
"runs with embedded kafka" should {
"work" in {
EmbeddedKafka.start()
// ... code goes here
EmbeddedKafka.stop()
}
}
}Please note that in order to avoid Kafka instances not shutting down properly, it's recommended to call EmbeddedKafka.stop() in a after block or in a similar teardown logic.
It's possible to change the ports on which Zookeeper and Kafka are started by providing an implicit EmbeddedKafkaConfig
class MySpec extends AnyWordSpecLike with Matchers with EmbeddedKafka {
"runs with embedded kafka on a specific port" should {
"work" in {
implicit val config = EmbeddedKafkaConfig(kafkaPort = 12345)
withRunningKafka {
// now a kafka broker is listening on port 12345
}
}
}
}If you want to run ZooKeeper and Kafka on arbitrary available ports, you can
use the withRunningKafkaOnFoundPort method. This is useful to make tests more
reliable, especially when running tests in parallel or on machines where other
tests or services may be running with port numbers you can't control.
class MySpec extends AnyWordSpecLike with Matchers with EmbeddedKafka {
"runs with embedded kafka on arbitrary available ports" should {
"work" in {
val userDefinedConfig = EmbeddedKafkaConfig(kafkaPort = 0, zooKeeperPort = 0)
withRunningKafkaOnFoundPort(userDefinedConfig) { implicit actualConfig =>
// now a kafka broker is listening on actualConfig.kafkaPort
publishStringMessageToKafka("topic", "message")
consumeFirstStringMessageFrom("topic") shouldBe "message"
}
}
}
}The same implicit EmbeddedKafkaConfig is used to define custom consumer or producer properties
class MySpec extends AnyWordSpecLike with Matchers with EmbeddedKafka {
"runs with custom producer and consumer properties" should {
"work" in {
val customBrokerConfig = Map("replica.fetch.max.bytes" -> "2000000",
"message.max.bytes" -> "2000000")
val customProducerConfig = Map("max.request.size" -> "2000000")
val customConsumerConfig = Map("max.partition.fetch.bytes" -> "2000000")
implicit val customKafkaConfig = EmbeddedKafkaConfig(
customBrokerProperties = customBrokerConfig,
customProducerProperties = customProducerConfig,
customConsumerProperties = customConsumerConfig)
withRunningKafka {
// now a kafka broker is listening on port 12345
}
}
}
}This works for withRunningKafka, withRunningKafkaOnFoundPort, and EmbeddedKafka.start()
Also, it is now possible to provide custom properties to the broker while starting Kafka. EmbeddedKafkaConfig has a
customBrokerProperties field which can be used to provide extra properties contained in a Map[String, String].
Those properties will be added to the broker configuration, be careful some properties are set by the library itself and
in case of conflict the customBrokerProperties values will take precedence. Please look at the source code to see what these properties
are.
The EmbeddedKafka trait provides also some utility methods to interact with the embedded kafka, in order to set preconditions or verifications in your specs:
def publishToKafka(topic: String, message: String): Unit
def consumeFirstMessageFrom(topic: String): String
def createCustomTopic(topic: String, topicConfig: Map[String,String], partitions: Int, replicationFactor: Int): UnitGiven implicits Deserializers for each type and an EmbeddedKafkaConfig it is possible to use withProducer[A, B, R] { your code here } where R is the code return type.
For more information about how to use the utility methods, you can either look at the Scaladocs or at the tests of this project.
Given implicits Serializers for each type and an EmbeddedKafkaConfig it is possible to use withConsumer[A, B, R] { your code here } where R is the code return type.
A simple test using loan methods can be as simple as this:
implicit val serializer: Serializer[String] = new StringSerializer()
implicit val deserializer: Deserializer[String] = new StringDeserializer()
val key = "key"
val value = "value"
val topic = "loan_method_example"
EmbeddedKafka.withProducer[String, String, Unit](producer =>
producer.send(new ProducerRecord[String, String](topic, key, value)))
EmbeddedKafka.withConsumer[String, String, Assertion](consumer => {
consumer.subscribe(Collections.singletonList(topic))
eventually {
val records = consumer.poll(java.time.Duration.ofMillis(1.seconds.toMillis)).asScala
records should have size 1
records.head.key shouldBe key
records.head.value shouldBe value
}
})A library that builds on top of embedded-kafka to offer easy testing of Kafka Streams.
It takes care of instantiating and starting your streams as well as closing them after running your test-case code.
- In your
build.sbtfile add the following dependency (replacex.x.xwith the appropriate version):"io.github.embeddedkafka" %% "embedded-kafka-streams" % "x.x.x" % Test - Have a look at the example test
- For most of the cases have your class extend the
EmbeddedKafkaStreamstrait. This offers both streams management and easy loaning of producers and consumers for asserting resulting messages in output/sink topics. - Use
EmbeddedKafkaStreams.runStreamsandEmbeddedKafka.withConsumerandEmbeddedKafka.withProducer. This allows you to create your own consumers of custom types as seen in the example test.
A library that builds on top of embedded-kafka to offer easy testing of Kafka Connect.
It takes care of instantiating and starting a Kafka Connect server as well as closing it after running your test-case code.
- In your
build.sbtfile add the following dependency (replacex.x.xwith the appropriate version):"io.github.embeddedkafka" %% "embedded-kafka-connect" % "x.x.x" % Test - Have a look at the example test
- For most of the cases have your class extend the
EmbeddedKafkaConnecttrait. - Use
EmbeddedKafkaConnect.startConnect. This allows you to start a Kafka Connect server to interact with as seen in the example test.
![dependabot[bot] avatar](https://avatars.githubusercontent.com/in/29110?v=4 "dependabot[bot]")
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent