Principle:Apache Kafka Connect Distributed Invocation
| Knowledge Sources | |
|---|---|
| Domains | Kafka_Connect, Distributed_Systems |
| Last Updated | 2026-02-09 12:00 GMT |
Overview
Principle for launching a Kafka Connect distributed worker process that joins a cluster of workers to provide scalable, fault-tolerant connector execution.
Description
Connect Distributed Invocation is the principle of starting a Kafka Connect worker in distributed mode, where multiple workers coordinate via Kafka internal topics to share connector and task assignments. The distributed architecture ensures that if a worker fails, its tasks are automatically rebalanced to surviving workers. This is the recommended production deployment model for Kafka Connect, as opposed to standalone mode which runs all tasks in a single process.
The invocation principle encompasses configuring the JVM environment (heap, logging), selecting the correct Java entry point class (ConnectDistributed), and providing a worker properties file that specifies the group.id, bootstrap servers, and internal topic configurations for offset, config, and status storage.
Usage
Use this principle when deploying Kafka Connect for production workloads where connector scalability, fault tolerance, and rolling upgrades are required. Distributed mode should be preferred over standalone mode whenever running more than one connector or when high availability is a concern.
Theoretical Basis
The distributed mode of Kafka Connect implements a group membership protocol similar to Kafka consumer groups. Workers join a logical group identified by group.id and perform rebalancing when members join or leave. Three compacted Kafka topics (offset storage, config storage, status storage) serve as the distributed state store:
Pseudo-code Logic:
# Abstract algorithm description (NOT real implementation)
worker = ConnectDistributed(worker_properties)
worker.join_group(group_id)
worker.await_rebalance() # Receive task assignments from the leader
worker.start_assigned_connectors_and_tasks()
# On failure of another worker -> rebalance -> reassign tasks
The architecture provides exactly-once delivery semantics (when configured) and incremental cooperative rebalancing to minimize task disruption during scaling events.