One of the typical Debezium uses cases is to use change data capture to integrate a legacy system with other systems in the organization. There are multiple ways how to achieve this goal
Write data to Kafka using Debezium and follow with a combination of Kafka Streams pipelines and Kafka Connect connectors to deliver the changes to other systems
Use Debezium Embedded engine in a Java standalone application and write the integration code using plain Java; that’s often used to send change events to alternative messaging infrastructure such as Amazon Kinesis, Google Pub/Sub etc.
Use an existing integration framework or service bus to express the pipeline logic
This article is focusing on the third option - a dedicated integration framework.
Release early, release often! After the 1.1 Beta1 and 1.0.1 Final releases earlier this week, I’m today happy to share the news about the release of Debezium 1.1.0.Beta2!
The main addition in Beta2 is support for integration tests of your change data capture (CDC) set-up using Testcontainers. In addition, the Quarkus extension for implementing the outbox pattern as well as the SMT for extracting the
after state of change events have been re-worked and offer more configuration flexibility now.
It’s my pleasure to announce the release of Debezium 1.1.0.Beta1!
This release adds support for transaction marker events, an incubating connector for the IBM Db2 database as well as a wide range of bug fixes. As the 1.1 release still is under active development, we’ve backported an asorted set of bug fixes to the 1.0 branch and released Debezium 1.0.1.Final, too.
At the time of writing this, not all connector archives have been synched to Maven Central yet; this should be the case within the next few others.
This article is a dive into the realms of Event Sourcing, Command Query Responsibility Segregation (CQRS), Change Data Capture (CDC), and the Outbox Pattern. Much needed clarity on the value of these solutions will be presented. Additionally, two differing designs will be explained in detail with the pros/cons of each.
So why do all these solutions even matter? They matter because many teams are building microservices and distributing data across multiple data stores. One system of microservices might involve relational databases, object stores, in-memory caches, and even searchable indexes of data. Data can quickly become lost, out of sync, or even corrupted therefore resulting in disastrous consequences for mission critical systems.
Solutions that help avoid these serious problems are of paramount importance for many organizations. Unfortunately, many vital solutions are somewhat difficult to understand; Event Sourcing, CQRS, CDC, and Outbox are no exception. Please look at these solutions as an opportunity to learn and understand how they could apply to your specific use cases.
As you will find out at the end of this article, I will propose that three of these four solutions have high value, while the other should be discouraged except for the rarest of circumstances. The advice given in this article should be evaluated against your specific needs, because, in some cases, none of these four solutions would be a good fit.
Outbox as in that folder in my email client? No, not exactly but there are some similarities!
The term outbox describes a pattern that allows independent components or services to perform read your own write semantics while concurrently providing a reliable, eventually consistent view to those writes across component or service boundaries.
You can read more about the Outbox pattern and how it applies to microservices in our blog post, Reliable Microservices Data Exchange With the Outbox Patttern.
So what exactly is an Outbox Event Router?
In Debezium version 0.9.3.Final, we introduced a ready-to-use Single Message Transform (SMT) that builds on the Outbox pattern to propagate data change events using Debezium and Kafka. Please see the documentation for details on how to use this transformation.