As a follow up to the recent Building Audit Logs with Change Data Capture and Stream Processing blog post, we’d like to extend the example with admin features to make it possible to capture and fix any missing transactional data.
In the above mentioned blog post, there is a log enricher service used to combine data inserted or updated in the Vegetable database table with transaction context data such as
User name who performed the work
Use case that was behind the actual change e.g. "CREATE VEGETABLE"
This all works well as long as all the changes are done via the vegetable service. But is this always the case?
What about maintenance activities or migration scripts executed directly on the database level? There are still a lot of such activities going on, either on purpose or because that is our old habits we are trying to change…
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.
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.