debezium VS pgaudit

They manage data in the application layer and your original data stays where it is. This way data consistency is no longer an issue as it was with streaming databases. You can use Change Data Capture (CDC) services like Debezium by directly connecting to your primary database, doing computational work, and saving the result back or sending real-time data to output streams.

Both of these articles mention a key player, Debezium. In fact, Debezium has had a place in the modern infrastructure. Let's use a diagram to understand why.

This might be overkill, but you could use an extra tool like https://debezium.io to capture logs about all creates, updates, and deletes in your table

If you're considering a relational database for an event-driven architecture, check out Debezium. It lets you stream changes to relational databases, and subscribe to change events.

Debezium

https://debezium.io/ comes to mind as an open source product, but there are a gazillion of these tools out there.

Contrary to what it sounds, it’s quite straightforward. The database changes are captured via Debezium and published as events on Apache Kafka. ClickHouse consumes those changes in partial order by Kafka Engine. Real-time and eventually consistent.

I think pgAudit it still the best and it's not a major issue. You can try my PR that fixes this issue https://github.com/pgaudit/pgaudit/pull/219 it should work and it should handle the other types of SELECT's that need update permissions but are not actually updating anything https://pglocks.org/?pglock=RowShareLock

You can use pgaudit, it's an extension that let's you audit DDL/DML statements. It's a great auditing mechanism. I use it on all our prod postgres instances, but have only "DML" enabled, because of the potential performance overhead

https://github.com/pgaudit/pgaudit

pgAudit, with all its capabilities, simplifies the process of auditing by generating the audit trail log. Though there are a few caveats, like logging of renamed objects under the same name, it is still a robust tool that provides the required functionality. However, the audit information written in logs may not be just ideal for the auditing process - the auditing process is even better when those logs can be converted to a database schema, and audit data can be loaded to the database so you can easily query the information. This is where the PostgreSQL Audit Log Analyzer (pgAudit Analyze) is helpful. For more information, refer to the github pages of pgAudit and pgAudit Analyze.