수집을 위한 소스 커넥터

Welcome! In this lesson, we'll dive into Source Connectors, a powerful feature of Kafka Connect.

Source connectors are like bridges. They help you bring data from external systems, such as databases or files, into Kafka topics.

This allows your data to flow seamlessly into your real-time data pipelines.

Imagine you need to move data from a database into Kafka. You could write custom code, but that takes time and effort.

Source Connectors simplify this process by:

• Reducing boilerplate code: No need to write custom producers.
• Providing fault tolerance: They handle failures and resume data transfer.
• Scaling easily: Distribute work across multiple Kafka Connect workers.
• Offering pre-built solutions: Many common connectors are already available.

At a high level, a source connector operates by:

• Polling Data: It continuously checks the external system for new or updated data.
• Converting Data: It transforms the external data format into Kafka records.
• Producing to Kafka: These records are then sent to a specified Kafka topic.

The Kafka Connect framework manages the connector's lifecycle and distributes its tasks.

Kafka Connect has a rich ecosystem of connectors. Here are a few popular examples:

• FileStreamSourceConnector: Reads data from local files. Great for initial testing!
• JdbcSourceConnector: Connects to relational databases (like PostgreSQL, MySQL) to pull data.
• S3 Source Connector: Ingests data from Amazon S3 buckets.
• Cloud-specific connectors: For Google Cloud Storage, Azure Blob Storage, etc.

Each connector is designed for a specific data source.

When you set up a connector, you provide a configuration. This is typically a JSON or properties file.

Key properties include:

• name: A unique name for your connector instance.
• connector.class: The fully qualified class name of the connector to use (e.g., FileStreamSourceConnector).
• tasks.max: The maximum number of tasks the connector can use to parallelize data ingestion.

Other properties are specific to the connector's type.

Let's use the FileStreamSourceConnector for a practical example. It's simple and helps demonstrate the core concepts.

This connector reads new lines appended to a specified file and publishes each line as a message to a Kafka topic.

First, let's create a simple input file named test.txt with some initial content.

To tell the FileStreamSourceConnector what to do, we create a configuration file. Let's call it file-source-connector.json:

{
  "name": "local-file-source",
  "config": {
    "connector.class": "org.apache.kafka.connect.file.FileStreamSourceConnector",
    "tasks.max": "1",
    "file": "/path/to/your/test.txt",
    "topic": "file-input-topic"
  }
}

Remember to replace /path/to/your/test.txt with the actual path to your file.

Kafka Connect clusters expose a REST API to manage connectors. We can use curl to deploy our connector.

Assuming your Kafka Connect worker is running on localhost:8083, you'd send a POST request:

curl -X POST -H "Content-Type: application/json" \
      --data @file-source-connector.json \
      http://localhost:8083/connectors

This command tells Kafka Connect to create a new connector using the configuration in our JSON file.

After deploying, you'll want to ensure your connector started correctly. You can check its status using another REST API call:

curl http://localhost:8083/connectors/local-file-source/status

Look for the "state": "RUNNING" in the response. If it's FAILED, check the Kafka Connect worker logs for error details.

Once running, any new lines added to test.txt will appear in the file-input-topic in Kafka!

Congratulations! You've learned the essentials of Kafka Connect Source Connectors.

• Source Connectors ingest data from external systems into Kafka.
• They offer a code-free, fault-tolerant, and scalable way to integrate data.
• You configure them with properties like connector.class, name, and tasks.max.
• Deployment and monitoring are done via the Kafka Connect REST API.

Next, we'll explore the other side of the coin: Sink Connectors, for exporting data from Kafka!