Kafka Connect Sources: Ingesting Data into Apache Kafka

Introduction

Apache Kafka is a powerful distributed streaming platform that allows you to build real-time data pipelines and streaming applications. It provides a unified, high-throughput, low-latency platform for handling real-time data feeds. One of the key components of Kafka is Kafka Connect, a framework for ingesting and exporting data to and from Kafka.

Kafka Connect allows you to connect Kafka with external systems in a scalable and fault-tolerant way. It provides a simple and efficient method for capturing data changes from various sources and streaming them into Kafka topics. In this blog post, we will specifically focus on Kafka Connect Sources and how they can be used to ingest data into Apache Kafka.

What are Kafka Connect Sources?

Kafka Connect Sources are connectors that pull data from various external systems and load it into Kafka topics. They act as data producers, capturing data changes and events from different sources and streaming them into Kafka for further processing and analysis.

With Kafka Connect Sources, you can easily integrate Kafka with a wide range of data sources such as databases, message queues, file systems, and more. These connectors are designed to be highly scalable and fault-tolerant, ensuring reliable and efficient ingestion of data into Apache Kafka.

Common Kafka Connect Sources

Kafka Connect offers a variety of connectors out of the box, allowing you to connect Kafka with popular data sources. Here are some common Kafka Connect Sources:

JDBC Source Connector

The JDBC Source Connector allows you to capture database changes and stream them into Kafka topics. It can monitor specified database tables or execute user-defined SQL queries to select the data to be ingested into Kafka. This connector supports a wide range of databases, making it an ideal choice for integrating with relational databases.

Example configuration for the JDBC Source Connector:

{
  "name": "jdbc-source",
  "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector",
  "tasks.max": "1",
  "connection.url": "jdbc:mysql://localhost:3306/mydb",
  "connection.user": "username",
  "connection.password": "password",
  "table.whitelist": "users",
  "mode": "timestamp",
  "timestamp.column.name": "modified_at"
}

Debezium Connector

The Debezium Connector is a powerful connector that captures data changes from supported databases and streams them into Kafka. It uses the CDC (Change Data Capture) feature of the database to track and capture changes in real-time. Debezium supports multiple databases, including MySQL, PostgreSQL, Oracle, and SQL Server.

Example configuration for the Debezium Connector:

{
  "name": "debezium-source",
  "connector.class": "io.debezium.connector.mysql.MySqlConnector",
  "tasks.max": "1",
  "database.hostname": "localhost",
  "database.port": "3306",
  "database.user": "username",
  "database.password": "password",
  "database.server.id": "1",
  "database.server.name": "mydb",
  "table.whitelist": "users"
}

Kafka Connect Spooldir Connector

The Kafka Connect Spooldir Connector is a file-based connector that monitors a directory for new files and streams their contents into Kafka topics. It can be used to ingest data from various file formats, such as CSV, JSON, Avro, and more. This connector is especially useful for scenarios where data is generated in files and needs to be continuously ingested into Kafka.

Example configuration for the Kafka Connect Spooldir Connector:

{
  "name": "spooldir-source",
  "connector.class": "org.apache.kafka.connect.file.FileStreamSourceConnector",
  "tasks.max": "1",
  "file.reader.class": "org.apache.kafka.connect.storage.StringConverter",
  "topic": "mytopic",
  "input.path": "/path/to/directory"
}

These are just a few examples of the Kafka Connect Sources available. Kafka Connect provides a rich ecosystem of connectors, and you can also develop your own custom connectors to meet your specific requirements.

Working with Kafka Connect Sources

Using Kafka Connect Sources is straightforward. First, you need to configure the connector by providing the necessary configuration properties for the specific source connector you are using. These properties define the connection details, input data format, and other specific settings for the source system.

Once the source connector is configured, Kafka Connect will handle the data ingestion process automatically. It will continuously monitor the configured source system, capture data changes, and stream them into Kafka topics. The streaming of data is done in an efficient and fault-tolerant manner, ensuring data integrity and reliable ingestion.

To start the Kafka Connect Source connector, use the following command:

$ bin/connect-standalone.sh config/connect-standalone.properties my-source-config.properties

Where config/connect-standalone.properties is the configuration file for the Kafka Connect worker, and my-source-config.properties is the configuration file for your specific source connector.

Conclusion

Ingesting data into Apache Kafka is a vital step in building real-time streaming applications. Kafka Connect Sources simplify this process by providing a scalable and fault-tolerant framework for capturing data changes from various sources and streaming them into Kafka topics. By leveraging Kafka Connect Sources, you can easily integrate Kafka with popular data sources and take advantage of Kafka's powerful streaming capabilities.

In this blog post, we explored what Kafka Connect Sources are and discussed some common examples. We also briefly covered how to work with Kafka Connect Sources and start the ingestion process. Armed with this knowledge, you are now well-equipped to start ingesting data into Apache Kafka using Kafka Connect Sources.

Happy data streaming!