Constructing a reliable data ingestion layer often tempts engineers to write custom producers and consumers for standard data systems like PostgreSQL, Elasticsearch, or S3. This approach introduces significant technical debt. Custom integration code requires ongoing maintenance to handle network partitions, schema changes, and backpressure mechanisms. Apache Kafka Connect addresses this by providing a scalable, declarative framework for moving data between Kafka and other systems. Instead of writing code, you define configuration JSON that dictates how data flows, while the Connect framework handles distribution, fault tolerance, and offset management.
In a production environment, Kafka Connect should run in distributed mode. Unlike standalone mode, which runs connectors in a single process and lacks high availability, distributed mode spreads work across a cluster of worker nodes. These nodes form a group coordinated via the Kafka group membership protocol, similar to consumer groups.
The runtime hierarchy consists of three components:
When you submit a connector configuration to the REST API, the workers elect a leader to determine task assignment. If a worker fails, the cluster detects the heartbeat loss and triggers a rebalance, redistributing the failed worker's tasks to the remaining active nodes.
Architecture of a distributed Kafka Connect cluster showing the relationship between workers, tasks, and the internal state topics used for coordination.
Source connectors import data. While simple connectors might poll a database using SELECT * FROM table WHERE updated_at > ?, this query-based approach often misses hard deletes and places significant load on the database execution engine.
For high-fidelity pipelines, Change Data Capture (CDC) is the preferred pattern. CDC connectors, such as those provided by Debezium, act as clients to the database's transaction log (e.g., PostgreSQL WAL or MySQL binlog). They capture every row-level change, inserts, updates, and deletes, as an event.
State management in source connectors relies on the source offset. The connector periodically saves its position (e.g., Log Sequence Number) to the connect-offsets topic. Upon restart, the connector reads this topic to resume processing exactly where it left off.
To optimize throughput for source connectors, you must align the number of tasks with the parallelism capabilities of the source system. For a database, parallelism is often limited to the number of shards or partitions.
Sink connectors export data from Kafka topics to external systems. The most critical engineering challenge here is ensuring data consistency. Kafka Connect guarantees at-least-once delivery. In failure scenarios, such as a worker crashing after writing to a database but before committing the offset to Kafka, messages will be redelivered and reprocessed.
To achieve exactly-once semantics (or effectively exactly-once), the sink system must support idempotency. An idempotent sink operation produces the same result regardless of how many times it is applied.
For example, when writing to a key-value store or a relational database, you should utilize "upsert" semantics (Update or Insert) rather than simple append operations. If the target system is an object store like S3, exactly-once is harder to achieve directly due to eventual consistency, but can be managed by partitioning files deterministically based on Kafka offsets.
Batching is another optimization vector. Sink tasks accumulate records from Kafka until they reach a size threshold or a time limit. The relationship between batch size () and throughput () in high-latency sinks (like S3 or Snowflake) generally follows a logarithmic curve of diminishing returns.
Where is the round-trip latency and is the time the target system takes to ingest the batch.
The impact of
consumer.max.poll.recordsand connector-specific batch sizes on ingestion throughput. Small batches incur high network overhead, while extremely large batches hit processing limits.
Kafka Connect allows for lightweight data manipulation through Single Message Transforms. SMTs operate on messages as they pass through the Connect framework, either before they are written to Kafka (Source) or before they are sent to the sink (Sink).
Common use cases for SMTs include:
However, SMTs are designed for simple, stateless transformations. Do not use SMTs for complex aggregations, joining streams, or heavy computation. Those tasks belong in the Flink layer of your architecture. SMTs run synchronously within the Connect worker's IO thread; heavy processing here will block the pipeline and increase consumer lag.
In a strong Kappa architecture, Kafka Connect and Flink perform complementary roles. Kafka Connect handles the "Last Mile" integration, getting raw data into Kafka (EL) and getting processed data out (L). Flink handles the Transformation (T) and complex event processing.
You might ask why one would use Kafka Connect Sinks instead of Flink's native sinks (e.g., JdbcSink or StreamingFileSink).
Use Kafka Connect when:
Use Flink Sinks when:
Deploying Kafka Connect requires careful configuration of internal topics. These topics store the cluster's state, and if they are deleted or corrupted, the cluster loses its configuration and offsets.
config.storage.topic: Stores connector configurations. Must have cleanup.policy=compact and replication.factor of at least 3.offset.storage.topic: Stores source offsets. Must be compacted and highly replicated.status.storage.topic: Stores the current state of connectors and tasks.When defining a connector, always define a unique name. If you delete a connector and create a new one with the same name, it may inherit the old offsets from the offset.storage.topic. To reset a source connector completely, you must often manually delete the specific key associated with that connector name from the offset topic.
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