Change data capture (CDC) EARLY ACCESS

Change data capture (CDC) in YugabyteDB provides technology to ensure that any changes in data due to operations such as inserts, updates, and deletions are identified, captured, and automatically applied to another data repository instance, or made available for consumption by applications and other tools. CDC provides the following guarantees.


Stateless CDC Service

Every YB-TServer has a CDC service that is stateless. The main APIs provided by the CDC service are the following:

  • createCDCSDKStream API for creating the stream on the database.
  • getChangesCDCSDK API that can be used by the client to get the latest set of changes.

CDC streams

Creating a new CDC stream returns a stream UUID. This is facilitated via the yb-admin tool.


To consume the events generated by CDC, Debezium is used as the connector. Debezium is an open-source distributed platform that needs to be pointed at the database using the stream ID. For information on how to set up Debezium for YugabyteDB CDC, see Debezium integration.

Pushing changes to external systems

Using the Debezium connector for YugabyteDB, changes are pushed from YugabyteDB to a Kafka topic, which can then be used by any end-user application for the processing and analysis of the records.

Per-tablet ordered delivery

All data changes for one row or multiple rows in the same tablet are received in the order in which they occur. Due to the distributed nature of the problem, however, there is no guarantee for the order across tablets.

Consider the following scenario:

  • Two rows are being updated concurrently.
  • These two rows belong to different tablets.
  • The first row row #1 was updated at time t1, and the second row row #2 was updated at time t2.

In this case, it is possible for CDC to push the later update corresponding to row #2 change to Kafka before pushing the earlier update, corresponding to row #1.

At-least-once delivery

Updates for rows are pushed at least once. With the at-least-once delivery, you never lose a message, however the message might be delivered to a CDC consumer more than once. This can happen in case of a tablet leader change, where the old leader already pushed changes to Kafka, but the latest pushed op id was not updated in the CDC metadata.

For example, a CDC client has received changes for a row at times t1 and t3. It is possible for the client to receive those updates again.

No gaps in change stream

When you have received a change for a row for timestamp t, you do not receive a previously unseen change for that row from an earlier timestamp. This guarantees that receiving any change implies that all earlier changes have been received for a row.


See Change data capture in Explore for more details and limitations.