Distributed Mode Kafka Connect: What It Is and How It Works

Distributed mode in Kafka Connect allows multiple worker nodes to run connectors together, sharing the workload and providing fault tolerance. It manages connector tasks across the cluster automatically, making it easy to scale and maintain data integration pipelines.

How It Works

Imagine you have a team working together to move boxes from one place to another. Instead of one person doing all the work, the team splits the boxes among themselves to finish faster and cover for each other if someone is absent. Kafka Connect in distributed mode works similarly by running multiple worker nodes that share the job of moving data between Kafka and other systems.

Each worker node runs parts of connectors called tasks. These tasks are automatically balanced across the workers, so if one worker stops, others take over its tasks without losing data. This setup helps keep your data pipelines running smoothly and lets you add more workers to handle more data easily.

Example

This example shows a simple connect-distributed.properties configuration file to start Kafka Connect in distributed mode. It sets the Kafka cluster address and a group ID to coordinate workers.

properties

bootstrap.servers=localhost:9092
key.converter=org.apache.kafka.connect.json.JsonConverter
value.converter=org.apache.kafka.connect.json.JsonConverter
key.converter.schemas.enable=false
value.converter.schemas.enable=false
group.id=connect-cluster
config.storage.topic=connect-configs
offset.storage.topic=connect-offsets
status.storage.topic=connect-status
config.storage.replication.factor=1
offset.storage.replication.factor=1
status.storage.replication.factor=1

Output

Kafka Connect workers start and join the group 'connect-cluster', sharing connector tasks automatically.

When to Use

Use distributed mode when you need to run Kafka Connect at scale or want high availability. It is ideal for production environments where data integration must continue without interruption even if some workers fail.

For example, if you have multiple databases or systems to connect to Kafka, distributed mode lets you run many connectors and tasks across several machines. This setup helps handle large data volumes and provides fault tolerance by redistributing tasks if a worker goes down.

Key Points

• Scalability: Add more workers to handle more connectors and tasks.
• Fault tolerance: Tasks move to healthy workers if one fails.
• Automatic coordination: Workers share the load without manual setup.
• Production-ready: Recommended for real-world, large-scale data pipelines.

Key Takeaways

Distributed mode runs Kafka Connect across multiple workers for scalability and fault tolerance.

Workers automatically share and balance connector tasks in a cluster.

Use distributed mode for production environments needing high availability.

Configuration involves setting Kafka brokers and group ID for worker coordination.

It ensures continuous data integration even if some workers fail.