Skip to content

Click to use (opens in a new tab)

What is a Kafka Connector?

Introduction to Kafka Connect

Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems. It makes it simple to quickly define connectors that move large collections of data into and out of Kafka. Kafka Connect can ingest entire databases or collect metrics from all your application servers into Kafka topics, making the data available for stream processing with low latency. Additionally, it can export data from Kafka topics into secondary storage and databases, or even transform and load data into Kafka as part of a stream processing pipeline.

Key Characteristics

  • Scalability: Kafka Connect can scale horizontally by adding more workers to handle larger volumes of data.
  • Reliability: Ensures reliable delivery of data through fault-tolerant mechanisms.
  • Reusability: Connectors are reusable components that can be easily shared and deployed across different environments.
  • Extensibility: Supports custom connector development, allowing integration with virtually any system.

Components of Kafka Connect

1. Connectors

A connector is a high-level abstraction that represents the configuration and management of data streams between Kafka and an external system. Each connector instance manages one or more tasks that perform the actual data transfer.

Types of Connectors

  • Source Connectors: Pull data from external systems into Kafka.
  • Sink Connectors: Push data from Kafka to external systems.

Example Connectors

  • JDBC Source Connector: Ingests data from relational databases using JDBC.
  • S3 Sink Connector: Exports data from Kafka topics to Amazon S3.
  • Elasticsearch Sink Connector: Loads data into Elasticsearch for indexing and search.

2. Tasks

A task is the unit of execution within a connector. Each task handles a portion of the data movement. Kafka Connect automatically partitions the data movement work among multiple tasks to achieve parallelism.

3. Workers

A worker is a process that runs connectors and tasks. There are two types of worker configurations:

  • Standalone Worker: Runs in a single process, suitable for testing and small-scale deployments.
  • Distributed Workers: Multiple workers can be configured to run in a cluster, providing scalability and fault tolerance.

Distributed Mode Example

bootstrap.servers=localhost:9092
group.id=my-connect-cluster
key.converter=org.apache.kafka.connect.storage.StringConverter
value.converter=org.apache.kafka.connect.storage.StringConverter
offset.storage.topic=connect-offsets
config.storage.topic=connect-configs
status.storage.topic=connect-statuses

4. Converter

A converter is responsible for serializing and deserializing messages as they enter and leave Kafka. Common converters include JSON, Avro, Protobuf, and String.

Example Configuration

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=true

5. Transforms

Single Message Transforms (SMTs) allow you to modify individual records during the data transfer process. They can be used to filter, cast, or manipulate data before it's written to the destination system.

Example Transform

transforms=unwrap
transforms.unwrap.type=io.confluent.connect.transforms.ExtractKey$Key

Benefits of Using Kafka Connect

  • Ease of Use: Simplifies the setup and management of data pipelines without writing custom code.
  • Integration: Provides built-in connectors for common data sources and sinks, reducing integration effort.
  • Scalability: Easily scales to handle large volumes of data by adding more workers.
  • Reliability: Ensures reliable data transfer with fault-tolerant design and automatic recovery from failures.
  • Extensibility: Supports custom connector development, enabling integration with proprietary or less common systems.

Example Use Cases

Data Ingestion

  • Log Aggregation: Collect logs from multiple servers and centralize them in Kafka for real-time analysis.
  • Database Replication: Stream changes from a database (e.g., MySQL) to Kafka for near-real-time replication.

Data Export

  • Data Warehouse Loading: Export data from Kafka topics to a data warehouse (e.g., Redshift) for batch analytics.
  • Search Indexing: Load data into search engines like Elasticsearch for fast querying.

Real-Time Processing Pipelines

  • Event Streaming Applications: Integrate Kafka with stream processing frameworks like Apache Flink or Kafka Streams to build real-time applications.

Conclusion

Kafka Connect provides a robust framework for building scalable, reliable, and maintainable data pipelines between Kafka and other systems. By leveraging pre-built connectors and extending the platform with custom implementations, organizations can efficiently manage data flows and integrate diverse data sources and sinks. Understanding its architecture and capabilities enables developers and administrators to harness the power of Kafka Connect for their specific use cases.

Chat2DB - AI Text2SQL Tool for Easy Database Management

Click to use (opens in a new tab)

What can Chat2DB do?