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Approaches to data auditing and introducing Slick-Eventsourcing

I’ve been working on various ways to audit data for quite some time now, probably because when working in IT (Information Technology), it’s just so painful to see all that historical data go to waste. You never know when it might be useful, but often it’s too late! Moreover, a good technical approach to auditing actually has real business value, which among a lot of the fancy libraries/frameworks that we use today is a nice change.

The first approach to auditing is to automatically capture changes done to the "current" data in the database, and store a log, deltas or older versions in history tables. In my case, this approach materialised in the form of Hibernate Envers, where if you annotate JPA entities with @Audited, a mirror table is created and automatically populated with historical data.

The second approach is to make the audit log the primary source of truth in the system. This is known as event sourcing: based on external (e.g. end-user) input, events are created and persisted. Each event describes what kind of change happened in the system. Basing on that, a read model is created (for serving user queries, and also validating input data), plus any kind of business logic can be run (e.g. communicating with external systems). There’s a lot of advantages to such an approach, which are described in a number of articles on the web, let me just mention the fact that it’s possible to rebuild the state of the system at any point in time; you get a very detailed audit log of who did exactly what and when; plus a lot of flexibility in reacting to changes in the system, on a more technical level.

This latter approach now also materialised, in the form of a micro-framework (I think the readme is longer than the actual code) slick-eventsourcing 0.1. As the name suggests, it builds on top of Slick, and implements an approach to event sourcing where storing events and resulting modifications to the read model are done in a single transaction, which makes it easier to maintain data consistency. I described this approach in more detail in an earlier blog post.

That’s of course only one of many ways to implement event sourcing, and heavily depends on the use-case (as always). For example, you could use a dedicated event storage, such as EventStore, or a concurrency framework such as Akka persistence or Eventuate. However, I think that for many applications, especially the "enterprise" ones, there’s no real need to use a clustered NoSQL system, and a traditional SQL database actually brings a lot of value. We are using slick-eventsourcing in a couple of our projects and so far it works quite well.

Using slick-eventsourcing you still get the familiar SQL tables with the "current" data (that’s the read model), which you can query as in a "traditional" CRUD application. The main difference is that any changes are driven through events, not done directly to the read model. There’s an extensive README which describes the main components, as well as a gitter channel if you’d need help.

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