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Implementing Kafka Event Publishing for Task Completion Events in Camunda 8.5 Workflows

Integrating Apache Kafka with Camunda Platform 8.5 allows for robust event-driven architectures, enabling workflow events to be broadcasted to other systems seamlessly. This article provides a guide on how to track only task completion events with minimal per-process changes, using a common and efficient approach.

Overview

  • Objective: Publish task completion events to Kafka with minimal modifications to existing BPMN processes.
  • Approach: Utilize the Zeebe Kafka Exporter to automatically export task completion events without altering BPMN models.
  • Benefits:
    • Minimal custom code and configuration.
    • No need to modify individual BPMN processes.
    • Efficient tracking of task completion events.

Understanding Integration Options

Before implementing, it's essential to understand the available integration options between Camunda 8.5 and Kafka:

  1. Camunda Kafka Connectors:

    • Kafka Producer and Consumer Connectors available for use within BPMN models.
    • Limitation: Requires adding service tasks to each process where events need to be published.

  2. Zeebe Kafka Exporter:

    • A community-maintained exporter that streams Zeebe records to Kafka topics.
    • Advantage: Exports events globally without modifications to BPMN models.

  3. Kafka Connect Zeebe:

    • Facilitates integration between Kafka and Zeebe.
    • Use Case: Suited for complex integrations requiring bidirectional communication.

For tracking only task completion events with minimal changes, the Zeebe Kafka Exporter is the recommended approach.

Setting Up the Zeebe Kafka Exporter

Prerequisites

  • Camunda Platform 8.5 installed and configured.
  • Access to an Apache Kafka cluster.
  • Zeebe Kafka Exporter JAR file.

Installation Steps

  1. Download the Zeebe Kafka Exporter:
  • Visit the GitHub repository to download the latest version (e.g., zeebe-kafka-exporter-3.0.0-uber.jar).
  1. Place the Exporter JAR:
  • Copy the JAR file to the Zeebe broker's exporters directory.
  • If running in a containerized environment (e.g., Kubernetes), use an init container to place the JAR in the correct directory.
  1. Configure the Exporter:

  • Add the exporter configuration to the application.yaml file of the Zeebe broker:

     zeebe:
   broker:
     exporters:
       kafka:
         className: io.zeebe.exporters.kafka.KafkaExporter
         jarPath: exporters/zeebe-kafka-exporter-3.0.0-uber.jar
         args:
           producer:
             servers: "kafka-broker-1:9092,kafka-broker-2:9092"
           maxInFlightRecords: 1000
           format:
             type: JSON
           topic:
             name: "zeebe-task-events"
           filters:
             - eventType: "ELEMENT_COMPLETED"
               elementType: "SERVICE_TASK"

  • Explanation of Configuration:

    • className: Specifies the exporter class.
    • jarPath: Path to the exporter JAR file.
    • producer.servers: Kafka brokers' addresses.
    • topic.name: Kafka topic where events will be published.
    • filters: Filters to export only task completion events.
  1. Apply Configuration and Restart:
  • Restart the Zeebe broker to apply the new exporter configuration.
  • Verify that the exporter is properly loaded by checking the broker logs.

Filtering for Task Completion Events

To ensure only task completion events are published:

  • Configure Filters:

    • In the exporter configuration, set filters to include only ELEMENT_COMPLETED events for TASK elements.
    • Update the filters section as follows: 
    filters:
  - eventType: "ELEMENT_COMPLETED"
    elementType: "TASK"

  • Result:

    • Only task completion events will be exported to the specified Kafka topic.
    • No changes are required in individual BPMN processes.

Consuming Task Completion Events from Kafka

Once the events are published to Kafka:

  1. Set Up a Kafka Consumer:
  • Configure a consumer application to listen to the zeebe-task-events topic.
  • Use Kafka client libraries appropriate for your programming language.
  1. Process Event Data:
  • Each message contains JSON-formatted event data.
  • Extract relevant information like processInstanceId, taskId, taskName, and timestamp.
  1. Integrate with Downstream Systems:
  • Use the event data to trigger actions in other systems.
  • Example use cases:
    • Update a dashboard or monitoring tool.
    • Trigger notifications or alerts.
    • Synchronize with external databases.

Advanced Configuration and Best Practices

Handling High-Throughput Scenarios

  • Adjust maxInFlightRecords:

    • Increase maxInFlightRecords in the exporter configuration for higher throughput.
    • Example: 
    maxInFlightRecords: 5000

  • Optimize Kafka Producer Settings:

    • Tune Kafka producer configurations like batch.size and linger.ms for better performance.

Error Handling and Resilience

  • Fault Tolerance:

    • The exporter retries on transient errors.
    • Configure appropriate retry policies if needed.

  • Monitoring:

    • Monitor exporter metrics to detect issues.
    • Use Kafka and Zeebe monitoring tools for end-to-end visibility.

Security Considerations

  • Secure Connections:

    • Enable SSL/TLS encryption for Kafka connections.
    • Configure authentication mechanisms like SASL if required.

  • Access Control:

    • Ensure appropriate permissions are set for Kafka topics.
    • Restrict access to sensitive event data.

Deployment Considerations

Kubernetes Deployments

  • Init Containers for Exporter JAR:

    • Use an init container to download or copy the exporter JAR into the read-only filesystem: 
    extraInitContainers:
  - name: init-exporters-kafka
    image: busybox:1.35
    command: ["/bin/sh", "-c"]
    args:
      - "wget https://path/to/zeebe-kafka-exporter-3.0.0-uber.jar -O /exporters/zeebe-kafka-exporter-3.0.0-uber.jar"
    volumeMounts:
      - name: exporters
        mountPath: /exporters/

  • Volume Mounts:

    • Use a readWriteMany volume for the exporters directory if necessary.

Resource Allocation

  • Scaling Zeebe Brokers:

    • Ensure brokers have sufficient resources (CPU, memory) to handle the additional load.
    • Consider horizontal scaling if required.

  • Kafka Cluster Capacity:

    • Ensure the Kafka cluster can handle the incoming event throughput.
    • Monitor Kafka performance and add brokers as needed.

Advantages of Using Zeebe Kafka Exporter

  • Minimal BPMN Changes:

    • No need to add service tasks or modify existing workflows.
    • Maintains the integrity of the BPMN models.

  • Automated Event Exporting:

    • Exports events globally across all processes.
    • Simplifies the event publishing mechanism.

  • Flexible Filtering:

    • Customize filters to include other event types if needed.
    • Supports fine-grained control over exported events.

Conclusion

By leveraging the Zeebe Kafka Exporter, you can efficiently track and publish task completion events in Camunda 8.5 workflows with minimal per-process changes. This approach simplifies the integration with Apache Kafka, reducing the need for custom code and modifications to existing BPMN processes.

The exporter automatically handles the event streaming, allowing you to focus on consuming and reacting to these events in downstream systems. By following the steps and best practices outlined in this article, you can build a robust, event-driven architecture that enhances the capabilities of your workflow management using Camunda and Kafka.

References

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