There are several ways to install and use Debezium connectors, so we’ve documented a few of the most common ways to do this. The latest development version of Debezium is 0.10.0.Beta4.
If you’ve already installed Zookeeper, Kafka, and Kafka Connect, then using one of Debezium’s connectors is easy.
Simply download one or more connector plugin archives (see below), extract their files into your Kafka Connect environment, and add the parent directory of the extracted plugin(s) to Kafka Connect’s plugin path.
If not the case yet, specify the plugin path in your worker configuration (e.g. connect-distributed.properties) using the
As an example, let’s assume you have downloaded the Debezium MySQL connector archive and extracted its contents to /kafka/connect/debezium-connector-mysql.
Then you’d specify the following in the worker config:
Restart your Kafka Connect process to pick up the new JARs.
The connector plugins are available from Maven:
If immutable containers are your thing, then check out Debezium’s Docker images for Zookeeper, Kafka, and Kafka Connect with the MySQL and MongoDB connectors already pre-installed and ready to go. Our tutorial even walks you through using these images, and this is a great way to learn what Debezium is all about. You can even run Debezium on OpenShift.
By default, the directory /kafka/connect is used as plugin directory by the Debezium Docker image for Kafka Connect.
So any additional connectors you may wish to use should be added to that directory.
Alternatively, you can add further directories to the plugin path by specifying the
KAFKA_CONNECT_PLUGINS_DIR environment variable when starting the container
When using the Docker image for Kafka Connect provided by Confluent, you can specify the
CONNECT_PLUGIN_PATH environment variable to achieve the same.
Not that Java 8 or later is required to run the Debezium connectors.
Debezium executes nightly builds and deployments into the Sonatype snapshot repository. If you want to try latest and fresh or verify a bug fix you are interested in, then use plugins from oss.sonatype.org. The installation procedure is the same as for regular releases.
To use a connector to produce change events for a particular source server/cluster, simply create a configuration file for the MySQL Connector, Postgres Connector, MongoDB Connector, SQL Server Connector, Oracle Connector or Cassandra Connector and use the Kafka Connect REST API to add that connector configuration to your Kafka Connect cluster. When the connector starts, it will connect to the source and produce events for each inserted, updated, and deleted row or document.
See the Debezium Connectors documentation for more information.
Debezium uses (either via Kafka Connect or directly) multiple topics for storing data. The topics have to be either created by an administrator or by Kafka itself by enabling auto-creation for topics. There are certain limitations and recommendations which apply to topics:
Database history topic (for MySQL connector)
Infinite (or very long) retention (no compaction!)
Replication factor at least 3 for production
Optionally, log compaction enabled (if you wish to only keep the last change event for a given record); in this case the
delete.retention.mstopic-level settings in Apache Kafka should be configured, so that consumers have enough time to receive all events and delete markers; specifically, these values should be larger than the maximum downtime you anticipate for the sink connectors, e.g. when updating them
Replicated in production
You can relax the single partition rule but your application must handle out-of-order events for different rows in database (events for a single row are still totally ordered). If multiple partitions are used, Kafka will determine the partition by hashing the key by default. Other partition strategies require using SMTs to set the partition number for each record.
Although Debezium is intended to be used as turnkey services, all of JARs and other artifacts are available in Maven Central.
We do provide a small library so applications can embed any Kafka Connect connector and consume data change events read directly from the source system. This provides a light weight system (since Zookeeper, Kafka, and Kafka Connect services are not needed), but as a consequence it is not as fault tolerant or reliable since the application must manage and maintain all state normally kept inside Kafka’s distributed and replicated logs. It’s perfect for use in tests, and with careful consideration it may be useful in some applications.