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Version: 1.3

Model configuration

This part of configuration defines how to configure Components and some of the runtime behaviour (e.g. error handling) for a given scenario type (Streaming-Lite or Streaming-Flink). It is processed not only at the Designer but also passed to the execution engine (e.g. Flink), that’s why it’s parsed and processed a bit differently:

  • Defaults can be defined in defaultModelConfig.conf. Standard deployment (e.g. with docker sample) has it here.
  • defaultModelConfig.conf is currently resolved both on designer (to extract information about types of data or during scenario testing) and on execution engine (e.g. on Flink or in Streaming-Lite runtime). That’s why all environment variables used there have to be defined also on all Flink/Streaming-Lite runtime hosts (!). This is a technical limitation and may change in the future.
  • Some Components can use a special mechanism which resolves and adds additional configuration during deployment, which is then passed to the execution engine. Such configuration is read and resolved only at the designer. Example: OpenAPI enrichers need to read its definition from external sites - so e.g. Flink cluster does not have to have access to the site with the definition.

Look at configuration areas to understand where Model configuration should be placed in Nussknacker configuration.

ClassPath configuration

Nussknacker looks for components and various extensions in jar on Model classpath. Make sure you have all necessary entries properly configured:

  • Jar with model - unless you used custom model, this should be model/defaultModel.jar
  • All jars with additional components, e.g. "components/flink/flinkBase.jar", "components/flink/flinkKafka.jar"
  • flinkExecutor.jar for Flink Engine. This contains executor of scenarios in Flink cluster. By default, following configuration is used:
classPath: ["model/defaultModel.jar", "model/flinkExecutor.jar", "components/flink"]

Please note that in classpath elements you can use:

Components configuration

Nussknacker comes with a set of provided components. Some of them (e.g. filter, variable, aggregations in Flink, for-each, union) are predefined and accessible by default. Others need additional configuration - the most important ones are enrichers, where you have to set e.g. JDBC URL or external service address.

Check documentation of available components that you can configure:

  • OpenAPI Supports accessing external APIs directly from scenario
  • SQL Supports access to SQL database engines

Configuration of component providers

Below you can see typical component configuration, each section describes configuration of one component provider.

  components {
sqlHsql {
providerType: sql
jdbcUrl: jdbc:hsql...//
sqlOracle {
providerType: sql
jdbcUrl: jdbc:oracle...//
prinzH20 {
providerType: prinzH20
prinzMlFlow {
#this is not needed, as configuration is named just as provider
#providerType: prinzMLFlow
#we can disable particular component provider, if it's not needed in our installation
#note: you cannot disable certain basic components like filter, variable, switch and split
aggregation {
disabled: true

Configuration of UI attributes of components

In model configuration you can also define some UI attributes of components. This can be useful for tweaking of appearance of generated components (like from OpenAPI), in most cases you should not need to defined these settings. The settings you can configure include:

  • icons - icon property
  • documentation - docsUrl property
  • should component be disabled - disabled property
  • in which toolbox panel the component should appear (componentGroup property)
  • params configuration (allows to override default component settings):
    • editor - BoolParameterEditor, StringParameterEditor, DateParameterEditor etc.
    • validators - MandatoryParameterValidator, NotBlankParameterValidator, RegexpParameterValidator
    • defaultValue
    • label

Example (see dev application config for more examples):

  componentsUiConfig {
customerService {
params {
serviceIdParameter {
defaultValue: "customerId-10"
editor: "StringParameterEditor"
validators: [
type: "RegExpParameterValidator"
pattern: "customerId-[0-9]*"
message: "has to match customer id format"
description: "really has to match..."
label: "Customer id (from CRM!)
docsUrl: ""
icon: "icon_file.svg"

You can add additional links that will be shown in Components tab. They can be used e.g. to point to custom dashboards with component performance or point to some external system (link to documentation is configured by default). The config format is as follows:

componentLinks: [
id: "sourceSystem"
title: "Source system"
icon: "/assets/components/CustomNode.svg"
url: "$componentName"
supportedComponentTypes: ["openAPIEnricher1"]

Fields title, icon, url can contain templates: $componentId nad $componentName which are replaced by component data. Param supportedComponentTypes means component's types which can support links.

Component group mapping

You can override default grouping of basic components in toolbox panels with componentsGroupMapping setting. Component names are keys, while values are toolbox panels name (e.g. sources, enrichers etc.) |

Common Kafka configuration

Kafka connection configuration

Both engines share common Kafka configuration, see Streaming-Lite or Streaming-Flink docs for details on configuring sources/sinks.

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)
  • Streaming-Lite runtime - to be able to run job
NameImportanceTypeDefault valueDescription
kafkaAddressHighstringComma separated list of bootstrap servers
kafkaProperties."schema.registry.url"HighstringComma separated list of schema registry
kafkaPropertiesMediummapAdditional configuration of producers or consumers
useStringForKeyMediumbooleantrueShould we assume that Kafka message keys are in plain string format (not in Avro)
kafkaEspProperties.forceLatestReadMediumbooleanfalseIf scenario is restarted, should offsets of source consumers be reset to latest (can be useful in test enrivonments)
topicsExistenceValidationConfig.enabledLowbooleanfalseShould 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.autoCreateFlagFetchCacheTtlLowduration5 minutesTTL for checking Kafka cluster settings
topicsExistenceValidationConfig.validatorConfig.topicsFetchCacheTtlLowduration30 secondsTTL for caching list of existing topics
topicsExistenceValidationConfig.validatorConfig.adminClientTimeoutLowduration500 millisecondsTimeout for communicating with Kafka cluster

Kafka 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(\n\tat java.base/jdk.internal.reflect.DelegatingConstructorAccessorImpl.newInstance(\n\tat java.base/java.lang.reflect.Constructor.newInstance(\n\tat java.base/java.lang.Class.newInstance(\n\tat$.genSuiteConfig(Runner.scala:1431)\n\tat$.$anonfun$doRunRunRunDaDoRunRun$8(Runner.scala:1239)\n\tat\n\tat$.doRunRunRunDaDoRunRun(Runner.scala:1238)\n\tat$.$anonfun$runOptionallyWithPassFailReporter$24(Runner.scala:1033)\n\tat$.$anonfun$runOptionallyWithPassFailReporter$24$adapted(Runner.scala:1011)\n\tat$.withClassLoaderAndDispatchReporter(Runner.scala:1509)\n\tat$.runOptionallyWithPassFailReporter(Runner.scala:1011)\n\tat$.run(Runner.scala:850)\n\tat\n\tat org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.runScalaTest2or3(\n\tat org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.main(",
"timestamp" : 1623758738000,
"host" : "",
"additionalData" : {
"scenarioCategory" : "Marketing"

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

NameDefault valueDescription
topic-Topic where errors will be sent. It should be configured separately (or topic auto.create setting should be enabled on Kafka cluster)
stackTraceLengthLimit50Limit of stacktrace length that will be sent (0 to omit stacktrace at all)
includeHosttrueShould 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)
includeInputEventfalseShould input event be serialized (can be large or contain sensitive data so use with care)
useSharedProducerfalseFor 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

Scenario's additional properties

It's possible to add additional properties for scenario. They can be used for allowing more detailed scenario information (e.g. pass information about marketing campaign target etc.), they can also be used in various Nussknacker extensions:

Example (see dev application config for more examples):

additionalPropertiesConfig {
campaignType: {
editor: { type: "StringParameterEditor" }
validators: [ { type: "MandatoryParameterValidator" } ]
label: "Campaign type"
defaultValue: "Generic campaign"

Configuration is similar to component configuration