Debezium Blog

Let’s talk about TOAST. Toast? No, TOAST!

So what’s that? TOAST (The Oversized-Attribute Storage Technique) is a mechanism in Postgres which stores large column values in multiple physical rows, circumventing the page size limit of 8 KB.

TOAST!

Typically, TOAST storage is transparent to the user, so you don’t really have to care about it. There’s an exception, though: if a table row has changed, any unchanged values that were stored using the TOAST mechanism are not included in the message that Debezium receives from the database, unless they are part of the table’s replica identity. Consequently, such unchanged TOAST column value will not be contained in Debezium data change events sent to Apache Kafka. In this post we’re going to discuss different strategies for dealing with this situation.

It is a common requirement for business applications to maintain some form of audit log, i.e. a persistent trail of all the changes to the application’s data. If you squint a bit, a Kafka topic with Debezium data change events is quite similar to that: sourced from database transaction logs, it describes all the changes to the records of an application. What’s missing though is some metadata: why, when and by whom was the data changed? In this post we’re going to explore how that metadata can be provided and exposed via change data capture (CDC), and how stream processing can be used to enrich the actual data change events with such metadata.

This is a guest post by Apache Pulsar PMC Member and Committer Jia Zhai.

Debezium is an open source project for change data capture (CDC). It is built on Apache Kafka Connect and supports multiple databases, such as MySQL, MongoDB, PostgreSQL, Oracle, and SQL Server. Apache Pulsar includes a set of built-in connectors based on Pulsar IO framework, which is counter part to Apache Kafka Connect.

As of version 2.3.0, Pulsar IO comes with support for the Debezium source connectors out of the box, so you can leverage Debezium to stream changes from your databases into Apache Pulsar. This tutorial walks you through setting up the Debezium connector for MySQL with Pulsar IO.

Last week’s announcement of Quarkus sparked a great amount of interest in the Java community: crafted from the best of breed Java libraries and standards, it allows to build Kubernetes-native applications based on GraalVM & OpenJDK HotSpot. In this blog post we are going to demonstrate how a Quarkus-based microservice can consume Debezium’s data change events via Apache Kafka. For that purpose, we’ll see what it takes to convert the shipment microservice from our recent post about the outbox pattern into Quarkus-based service.

As part of their business logic, microservices often do not only have to update their own local data store, but they also need to notify other services about data changes that happened. The outbox pattern describes an approach for letting services execute these two tasks in a safe and consistent manner; it provides source services with instant "read your own writes" semantics, while offering reliable, eventually consistent data exchange across service boundaries.

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