Debezium Blog

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

  • Transaction id

  • 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…

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 post originally appeared on the WePay Engineering blog.

Historically, MySQL had been the de-facto database of choice for microservices at WePay. As WePay scales, the sheer volume of data written into some of our microservice databases demanded us to make a scaling decision between sharded MySQL (i.e. Vitess) and switching to a natively sharded NoSQL database. After a series of evaluations, we picked Cassandra, a NoSQL database, primarily because of its high availability, horizontal scalability, and ability to handle high write throughput.

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.

The second-level cache of Hibernate ORM / JPA is a proven and efficient way to increase application performance: caching read-only or rarely modified entities avoids roundtrips to the database, resulting in improved response times of the application.

Unlike the first-level cache, the second-level cache is associated with the session factory (or entity manager factory in JPA terms), so its contents are shared across transactions and concurrent sessions. Naturally, if a cached entity gets modified, the corresponding cache entry must be updated (or purged from the cache), too. As long as the data changes are done through Hibernate ORM, this is nothing to worry about: the ORM will update the cache automatically.

Things get tricky, though, when bypassing the application, e.g. when modifying records directly in the database. Hibernate ORM then has no way of knowing that the cached data has become stale, and it’s necessary to invalidate the affected items explicitly. A common way for doing so is to foresee some admin functionality that allows to clear an application’s caches. For this to work, it’s vital to not forget about calling that invalidation functionality, or the application will keep working with outdated cached data.

In the following we’re going to explore an alternative approach for cache invalidation, which works in a reliable and fully automated way: by employing Debezium and its change data capture (CDC) capabilities, you can track data changes in the database itself and react to any applied change. This allows to invalidate affected cache entries in near-realtime, without the risk of stale data due to missed changes. If an entry has been evicted from the cache, Hibernate ORM will load the latest version of the entity from the database the next time is requested.

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