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reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-03T19:00:22.335Z","updated_at":"2026-03-04T20:08:38.228Z","avatar_url":"https://github.com/embeddedkafka.png","language":"Scala","funding_links":[],"categories":["测试","Testing \u0026 Development","Development"],"sub_categories":["Testing Frameworks","Testing"],"readme":"# embedded-kafka\n\n[![Maven Central](https://maven-badges.sml.io/sonatype-central/io.github.embeddedkafka/embedded-kafka_2.13/badge.svg)](https://central.sonatype.com/artifact/io.github.embeddedkafka/embedded-kafka_2.13/overview)\n[![Test](https://github.com/embeddedkafka/embedded-kafka/actions/workflows/test.yml/badge.svg)](https://github.com/embeddedkafka/embedded-kafka/actions/workflows/test.yml)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/e6fea84592574f7dabffd44117ceb9e2)](https://www.codacy.com/gh/embeddedkafka/embedded-kafka)\n[![Codacy Coverage Badge](https://api.codacy.com/project/badge/Coverage/e6fea84592574f7dabffd44117ceb9e2)](https://www.codacy.com/gh/embeddedkafka/embedded-kafka)\n[![Mergify Status](https://img.shields.io/endpoint.svg?url=https://api.mergify.com/v1/badges/embeddedkafka/embedded-kafka\u0026style=flat)](https://mergify.io)\n[![Scala Steward badge](https://img.shields.io/badge/Scala_Steward-helping-blue.svg?style=flat\u0026logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA4AAAAQCAMAAAARSr4IAAAAVFBMVEUAAACHjojlOy5NWlrKzcYRKjGFjIbp293YycuLa3pYY2LSqql4f3pCUFTgSjNodYRmcXUsPD/NTTbjRS+2jomhgnzNc223cGvZS0HaSD0XLjbaSjElhIr+AAAAAXRSTlMAQObYZgAAAHlJREFUCNdNyosOwyAIhWHAQS1Vt7a77/3fcxxdmv0xwmckutAR1nkm4ggbyEcg/wWmlGLDAA3oL50xi6fk5ffZ3E2E3QfZDCcCN2YtbEWZt+Drc6u6rlqv7Uk0LdKqqr5rk2UCRXOk0vmQKGfc94nOJyQjouF9H/wCc9gECEYfONoAAAAASUVORK5CYII=)](https://scala-steward.org)\n\nA library that provides an in-memory Kafka instance to run your tests against.\n\nInspired by [kafka-unit](https://github.com/chbatey/kafka-unit).\n\n1. [Version compatibility matrix](#version-compatibility-matrix)\n2. [Upgrade notes](#upgrade-notes)\n3. [Usage](#usage)\n   1. [embedded-kafka](#embedded-kafka-1)\n   2. [embedded-kafka-streams](#embedded-kafka-streams)\n   3. [embedded-kafka-connect](#embedded-kafka-connect)\n\n---\n\n## Version compatibility matrix\n\nThe library is available on Maven Central.\n\nVersions match the version of Kafka they're built against.\n\n| embedded-kafka version | Kafka version | Scala versions  | Java version |\n|------------------------|---------------|-----------------|--------------|\n| 4.x                    | 4.x           | 2.13, 3.3       | 17+          |\n| 3.4.0.1 - 3.9.0        | 3.4.0 - 3.9.0 | 2,12, 2.13, 3.3 | 8+           |\n\n_Note that [prior to v2.8.0](https://github.com/apache/kafka/pull/10174) 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](https://github.com/apache/kafka/blob/trunk/gradle/dependencies.gradle#L30)._\n\n---\n\n## Upgrade notes\n\n### 4.0.0\n\nMajor changes:\n- **Java 17+:** as [Kafka Server 4.x requires Java 17+](https://kafka.apache.org/40/documentation/compatibility.html), so does embedded-kafka even though Kafka Clients/Streams are still available for Java 11+.\n- **Scala 2.13+**: Kafka is not compiled against Scala 2.12 anymore, so neither is embedded-kafka.\n- embedded-kafka 4.0.0 starts a Kafka server in combined mode (broker and controller) and no more Zookeeper.\n\nAs a user, you'll have to change your code to use `controllerPort` instead of `zookeeperPort` in places you were doing so:\n```diff\n- EmbeddedKafkaConfig(kafkaPort = 12345, zooKeeperPort = 54321)\n+ EmbeddedKafkaConfig(kafkaPort = 12345, controllerPort = 54321)\n```\n\n### 2.8.0\n\n**Package name change:** from v2.8.0 onwards package name has been updated to reflect the library group id (i.e. `io.github.embeddedkafka`).\nAliases to the old package name have been added, along with a one-time [Scalafix rule](https://github.com/embeddedkafka/embedded-kafka-scalafix) to ensure the smoothest migration.\n\n---\n\n## Usage\n\n### embedded-kafka\n\n#### How to use\n\n* In your `build.sbt` file add the following dependency (replace `x.x.x` with the appropriate version): `\"io.github.embeddedkafka\" %% \"embedded-kafka\" % \"x.x.x\" % Test`\n* Have your class extend the `EmbeddedKafka` trait.\n* Enclose the code that needs a running instance of Kafka within the `withRunningKafka` closure.\n\nAn example, using ScalaTest:\n\n```scala\nclass MySpec extends AnyWordSpecLike with Matchers with EmbeddedKafka {\n\n  \"runs with embedded kafka\" should {\n\n    \"work\" in {\n\n      withRunningKafka {\n        // ... code goes here\n      }\n    }\n  }\n}\n```\n\n* In-memory Kafka broker and controller (combined mode) will be instantiated respectively on port 6000 and 6001 and automatically shutdown at the end of the test.\n\n#### Use without the `withRunningKafka` method\n\nA `EmbeddedKafka` companion object is provided for usage without extending the `EmbeddedKafka` trait. 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 on every test.\n\n```scala\nclass MySpec extends AnyWordSpecLike with Matchers {\n\n  \"runs with embedded kafka\" should {\n\n    \"work\" in {\n      EmbeddedKafka.start()\n\n      // ... code goes here\n\n      EmbeddedKafka.stop()\n    }\n  }\n}\n```\n\nPlease 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.\n\n#### Configuration\n\nIt's possible to change the ports on which Kafka broker and controller are started by providing an implicit `EmbeddedKafkaConfig`\n\n```scala\nclass MySpec extends AnyWordSpecLike with Matchers with EmbeddedKafka {\n\n  \"runs with embedded kafka on a specific port\" should {\n\n    \"work\" in {\n      implicit val config = EmbeddedKafkaConfig(kafkaPort = 12345)\n\n      withRunningKafka {\n        // now a kafka broker is listening on port 12345\n      }\n    }\n  }\n}\n```\n\nIf you want to run Kafka broker and controller on arbitrary available ports, you can\nuse the `withRunningKafkaOnFoundPort` method. This is useful to make tests more\nreliable, especially when running tests in parallel or on machines where other\ntests or services may be running with port numbers you can't control.\n\n```scala\nclass MySpec extends AnyWordSpecLike with Matchers with EmbeddedKafka {\n\n  \"runs with embedded kafka on arbitrary available ports\" should {\n\n    \"work\" in {\n      val userDefinedConfig = EmbeddedKafkaConfig(kafkaPort = 0, controllerPort = 0)\n\n      withRunningKafkaOnFoundPort(userDefinedConfig) { implicit actualConfig =\u003e\n        // now a kafka broker is listening on actualConfig.kafkaPort\n        publishStringMessageToKafka(\"topic\", \"message\")\n        consumeFirstStringMessageFrom(\"topic\") shouldBe \"message\"\n      }\n    }\n  }\n}\n```\n\nThe same implicit `EmbeddedKafkaConfig` is used to define custom consumer or producer properties\n\n```scala\nclass MySpec extends AnyWordSpecLike with Matchers with EmbeddedKafka {\n\n  \"runs with custom producer and consumer properties\" should {\n    \"work\" in {\n      val customBrokerConfig = Map(\"replica.fetch.max.bytes\" -\u003e \"2000000\",\n        \"message.max.bytes\" -\u003e \"2000000\")\n\n      val customProducerConfig = Map(\"max.request.size\" -\u003e \"2000000\")\n      val customConsumerConfig = Map(\"max.partition.fetch.bytes\" -\u003e \"2000000\")\n\n      implicit val customKafkaConfig = EmbeddedKafkaConfig(\n        customBrokerProperties = customBrokerConfig,\n        customProducerProperties = customProducerConfig,\n        customConsumerProperties = customConsumerConfig)\n\n      withRunningKafka {\n        // now a kafka broker is listening on port 12345\n      }\n    }\n  }\n}\n```\n\nThis works for `withRunningKafka`, `withRunningKafkaOnFoundPort`, and `EmbeddedKafka.start()`\n\nAlso, it is now possible to provide custom properties to the broker while starting Kafka. `EmbeddedKafkaConfig` has a\n`customBrokerProperties` field which can be used to provide extra properties contained in a `Map[String, String]`.\nThose properties will be added to the broker configuration, be careful some properties are set by the library itself and\nin case of conflict the `customBrokerProperties` values will take precedence. Please look at the source code to see what these properties\nare.\n\n#### Utility methods\n\nThe `EmbeddedKafka` trait provides also some utility methods to interact with the embedded kafka, in order to set preconditions or verifications in your specs:\n\n```scala\ndef publishToKafka(topic: String, message: String): Unit\n\ndef consumeFirstMessageFrom(topic: String): String\n\ndef createCustomTopic(topic: String, topicConfig: Map[String,String], partitions: Int, replicationFactor: Int): Unit\n```\n\n#### Custom producers\n\nGiven implicits `Deserializer`s 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.\n\nFor more information about how to use the utility methods, you can either look at the Scaladocs or at the tests of this project.\n\n#### Custom consumers\n\nGiven implicits `Serializer`s 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.\n\n#### Loan methods example\n\nA simple test using loan methods can be as simple as this:\n\n```scala\n  implicit val serializer: Serializer[String]     = new StringSerializer()\n  implicit val deserializer: Deserializer[String] = new StringDeserializer()\n  val key                                         = \"key\"\n  val value                                       = \"value\"\n  val topic                                       = \"loan_method_example\"\n\n  EmbeddedKafka.withProducer[String, String, Unit](producer =\u003e\n    producer.send(new ProducerRecord[String, String](topic, key, value)))\n\n  EmbeddedKafka.withConsumer[String, String, Assertion](consumer =\u003e {\n    consumer.subscribe(Collections.singletonList(topic))\n\n    eventually {\n      val records = consumer.poll(java.time.Duration.ofMillis(1.seconds.toMillis)).asScala\n      records should have size 1\n      records.head.key shouldBe key\n      records.head.value shouldBe value\n    }\n  })\n```\n\n### embedded-kafka-streams\n\nA library that builds on top of `embedded-kafka` to offer easy testing of [Kafka Streams](https://kafka.apache.org/documentation/streams).\n\nIt takes care of instantiating and starting your streams as well as closing them after running your test-case code.\n\n#### How to use\n\n* In your `build.sbt` file add the following dependency (replace `x.x.x` with the appropriate version): `\"io.github.embeddedkafka\" %% \"embedded-kafka-streams\" % \"x.x.x\" % Test`\n* Have a look at the [example test](kafka-streams/src/test/scala/io/github/embeddedkafka/streams/ExampleKafkaStreamsSpec.scala)\n* For most of the cases have your class extend the `EmbeddedKafkaStreams` trait. This offers both streams management and easy loaning of producers and consumers for asserting resulting messages in output/sink topics.\n* Use `EmbeddedKafkaStreams.runStreams` and `EmbeddedKafka.withConsumer` and `EmbeddedKafka.withProducer`. This allows you to create your own consumers of custom types as seen in the [example test](kafka-streams/src/test/scala/io/github/embeddedkafka/streams/ExampleKafkaStreamsSpec.scala).\n\n### embedded-kafka-connect\n\nA library that builds on top of `embedded-kafka` to offer easy testing of [Kafka Connect](https://kafka.apache.org/documentation/#connect).\n\nIt takes care of instantiating and starting a Kafka Connect server as well as closing it after running your test-case code.\n\n#### How to use\n\n* In your `build.sbt` file add the following dependency (replace `x.x.x` with the appropriate version): `\"io.github.embeddedkafka\" %% \"embedded-kafka-connect\" % \"x.x.x\" % Test`\n* Have a look at the [example test](kafka-connect/src/test/scala/io/github/embeddedkafka/connect/ExampleKafkaConnectSpec.scala)\n* For most of the cases have your class extend the `EmbeddedKafkaConnect` trait.\n* Use `EmbeddedKafkaConnect.startConnect`. This allows you to start a Kafka Connect server to interact with as seen in the [example test](kafka-connect/src/test/scala/io/github/embeddedkafka/connect/ExampleKafkaConnectSpec.scala).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fembeddedkafka%2Fembedded-kafka","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fembeddedkafka%2Fembedded-kafka","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fembeddedkafka%2Fembedded-kafka/lists"}