Version: 1.7

Kafka

Prerequisites

To fully understand how Nussknacker works with Kafka topics, it's best to read the following first:

If you want to use Flink engine, this is also recommended:

Flink keyed state

Sources and sinks

Kafka topics are native streaming data input to Nussknacker and the native output where results of Nussknacker scenarios processing are placed. In Nussknacker terminology input topics are called sources, output topics are called sinks. This section provides important details of Nussknacker's integration with Kafka and schema registry.

Schema registry integration

Nussknacker integrates with a schema registry. It is the source of topics available to choose from in sources and sinks. It also allows Nussknacker to provide syntax suggestions and validation. Nussknacker assumes that for the topic topic-name a schema topic-name-value and optionally topic-name-key (for the Kafka topic key) will be defined in the schema registry.

Schemas are stored and managed by Confluent Schema Registry; it is bundled with Nussknacker in all deployment versions. Schemas can be registered in Schema Registry by means of REST API based CLI or using AKHQ, an open source GUI for Apache Kafka and Confluent Schema Registry. AKHQ is bundled with Nussknacker in all deployment versions.

Nussknacker supports both JSON and AVRO schemas, and JSON and AVRO topic payloads. Detailed information on how AVRO data should be serialized/deserialized can be found in Confluent Wire Documentation.

Schema and payload types

By default, Nussknacker supports two combinations of schema type and payload type:

AVRO schema + AVRO payload
JSON schema + JSON payload

If you prefer using JSON payload with AVRO schema, you can use avroAsJsonSerialization configuration setting to change that behaviour (see Configuration for details).

Schema ID

Nussknacker supports schema evolution.

For sources and sinks, you can choose which schema version should be used for syntax suggestions and validation.

At runtime Nussknacker determines the schema version of a message value and key in the following way:

it checks value.schemaId and key.schemaId headers;
if there are no such headers, it looks for a magic byte and schema version in the message, in a format used by Confluent;
if the magic byte is not found, it assumes the schema version chosen by the user in the scenario.

Configuration

Common part

The Kafka configuration is part of the Model configuration. All the settings below should be placed relative to scenarioTypes.ScenarioTypeName.modelConfig key. You can find the high level structure of the configuration file here

Both streaming Engines (Lite and Flink) share some common Kafka settings this section describes them, see respective sections below for details on configuring Kafka for particular Engine (e.g. the keys where the common settings should be placed at).

Kafka connection configuration

Important thing to remember is that Kafka server addresses/schema registry addresses have to be resolvable from:

Nussknacker Designer host (to enable schema discovery and scenario testing)
Lite/Flink engine - to be able to run job

Name	Importance	Type	Default value	Description
kafkaProperties."bootstrap.servers"	High	string		Comma separated list of bootstrap servers
kafkaProperties."schema.registry.url"	High	string		Comma separated list of schema registry
kafkaProperties	Medium	map		Additional configuration of producers or consumers
useStringForKey	Medium	boolean	true	Should we assume that Kafka message keys are in plain string format (not in Avro)
kafkaEspProperties.forceLatestRead	Medium	boolean	false	If scenario is restarted, should offsets of source consumers be reset to latest (can be useful in test enrivonments)
topicsExistenceValidationConfig.enabled	Low	boolean	false	Should we validate existence of topics if no auto.create.topics.enable is false on Kafka cluster - note, that it may require permissions to access Kafka cluster settings
topicsExistenceValidationConfig.validatorConfig.autoCreateFlagFetchCacheTtl	Low	duration	5 minutes	TTL for checking Kafka cluster settings
topicsExistenceValidationConfig.validatorConfig.topicsFetchCacheTtl	Low	duration	30 seconds	TTL for caching list of existing topics
topicsExistenceValidationConfig.validatorConfig.adminClientTimeout	Low	duration	500 milliseconds	Timeout for communicating with Kafka cluster
schemaRegistryCacheConfig.availableSchemasExpirationTime	Low	duration	10 seconds	How often available schemas cache will be invalidated. This determines the maximum time you'll have to wait after adding new schema or new schema version until it will be available in Designer
schemaRegistryCacheConfig.parsedSchemaAccessExpirationTime	Low	duration	2 hours	How long parsed schema will be cached after first access to it
schemaRegistryCacheConfig.maximumSize	Low	number	10000	Maximum entries size for each caches: available schemas cache and parsed schema cache
lowLevelComponentsEnabled	Medium	boolean	false	Add low level (deprecated) kafka components: 'kafka-json', 'kafka-avro', 'kafka-registry-typed-json'
avroAsJsonSerialization	Low	boolean	false	Send and receive json messages serialized using Avro schema

Exception handling

Errors can be sent to specified Kafka topic in following json format (see below for format configuration options):

{
  "processName" : "Premium Customer Scenario",
  "nodeId" : "filter premium customers",
  "message" : "Unknown exception",
  "exceptionInput" : "SpelExpressionEvaluationException:Expression [1/0 != 10] evaluation failed, message: / by zero",
  "inputEvent" : "{ \" field1\": \"vaulue1\" }",
  "stackTrace" : "pl.touk.nussknacker.engine.api.exception.NonTransientException: mess\n\tat pl.touk.nussknacker.engine.kafka.exception.KafkaExceptionConsumerSerializationSpec.<init>(KafkaExceptionConsumerSerializationSpec.scala:24)\n\tat java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat java.base/jdk.internal.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.base/java.lang.reflect.Constructor.newInstance(Constructor.java:490)\n\tat java.base/java.lang.Class.newInstance(Class.java:584)\n\tat org.scalatest.tools.Runner$.genSuiteConfig(Runner.scala:1431)\n\tat org.scalatest.tools.Runner$.$anonfun$doRunRunRunDaDoRunRun$8(Runner.scala:1239)\n\tat scala.collection.immutable.List.map(List.scala:286)\n\tat org.scalatest.tools.Runner$.doRunRunRunDaDoRunRun(Runner.scala:1238)\n\tat org.scalatest.tools.Runner$.$anonfun$runOptionallyWithPassFailReporter$24(Runner.scala:1033)\n\tat org.scalatest.tools.Runner$.$anonfun$runOptionallyWithPassFailReporter$24$adapted(Runner.scala:1011)\n\tat org.scalatest.tools.Runner$.withClassLoaderAndDispatchReporter(Runner.scala:1509)\n\tat org.scalatest.tools.Runner$.runOptionallyWithPassFailReporter(Runner.scala:1011)\n\tat org.scalatest.tools.Runner$.run(Runner.scala:850)\n\tat org.scalatest.tools.Runner.run(Runner.scala)\n\tat org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.runScalaTest2or3(ScalaTestRunner.java:38)\n\tat org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.main(ScalaTestRunner.java:25)",
  "timestamp" : 1623758738000,
  "host" : "teriberka.pl",
  "additionalData" : {
    "scenarioCategory" : "Marketing"
  }
}

Following properties can be configured (please look at correct engine page : Lite or Flink, to see where they should be set):

Name	Default value	Description
topic	-	Topic where errors will be sent. If the topic does not exist, it will be created (with default settings - for production deployments make sure the config is ok) during deploy. If (e.g. due to ACL settings) the topic cannot be created, the scenarios will fail, in that case, the topic has to be configured manually.
stackTraceLengthLimit	50	Limit of stacktrace length that will be sent (0 to omit stacktrace at all)
includeHost	true	Should name of host where error occurred (e.g. TaskManager in case of Flink) be included. Can be misleading if there are many network interfaces or hostname is improperly configured)
includeInputEvent	false	Should input event be serialized (can be large or contain sensitive data so use with care)
useSharedProducer	false	For better performance shared Kafka producer can be used (by default it's created and closed for each error), shared Producer is kind of experimental feature and should be used with care
additionalParams	{}	Map of fixed parameters that can be added to Kafka message

Configuration for Flink engine

With Flink engine, the Kafka sources and sinks are configured as any other component. In particular, you can configure multiple Kafka component providers - e.g. when you want to connect to multiple clusters. Below we give two example configurations, one for default setup with one Kafka cluster and standard component names:

components.kafka {
  config: {
    kafkaProperties {
      "bootstrap.servers": "kafakaBroker1.sample.pl:9092,kafkaBroker2.sample.pl:9092"
      "schema.registry.url": "http://schemaRegistry.pl:8081"
    }
  }
}

And now - more complex, with two clusters. In the latter case, we configure prefix which will be added to component names, resulting in clusterA-kafka-avro etc.

components.kafkaA {
  providerType: "kafka"
  componentPrefix: "clusterA-"
  config: {
    kafkaProperties {
      "bootstrap.servers": "clusterA-broker1.sample.pl:9092,clusterA-broker2.sample.pl:9092"
      "schema.registry.url": "http://clusterA-schemaRegistry.pl:8081"
    }
  }
}
components.kafkaB {
  providerType: "kafka"
  componentPrefix: "clusterB-"
  config: {
    kafkaProperties {
      "bootstrap.servers": "clusterB-broker1.sample.pl:9092,clusterB-broker2.sample.pl:9092"
      "schema.registry.url": "http://clusterB-schemaRegistry.pl:8081"
    }
  }
}

Important thing to remember is that Kafka server addresses/schema registry addresses have to be resolvable from:

Nussknacker Designer host (to enable schema discovery and scenario testing)
Flink cluster (both jobmanagers and taskmanagers) hosts - to be able to run job

See common config for the details of Kafka configuration, the table below presents additional options available only in Flink engine:

Name	Importance	Type	Default value	Description
kafkaEspProperties.defaultMaxOutOfOrdernessMillis	Medium	duration	60s	Configuration of bounded of orderness watermark generator used by Kafka sources
consumerGroupNamingStrategy	Low	processId/processId-nodeId	processId-nodeId	How consumer groups for sources should be named
avroKryoGenericRecordSchemaIdSerialization	Low	boolean	true	Should AVRO messages from topics registered in schema registry be serialized in optimized way, by serializing only schema id, not the whole schema

Configuration for Lite engine

The Lite engine in Streaming processing mode uses Kafka as it's core part (e.g. delivery guarantees are based on Kafka transactions), so it's configured separately from other components. Therefore, it's only possible to use one Kafka cluster for one model configuration. This configuration is used for all Kafka based sources and sinks (you don't need to configure them separately). See common config for the details.

modelConfig {
  kafka {
    kafkaProperties {
      "bootstrap.servers": "broker1:9092,broker2:9092"
      "schema.registry.url": "http://schemaregistry:8081"
    }
  }
}  

Kafka

Prerequisites​

Sources and sinks​

Schema registry integration​

Schema and payload types​

Schema ID​

Configuration​

Common part​

Kafka connection configuration​

Exception handling​

Configuration for Flink engine​

Configuration for Lite engine​