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Deriving current state from the event log

An event log by itself is not very useful, because users generally expect to see the current state of a system, not the history of modifications. For example, on a shopping website, users expect to be able to see the current contents of their cart, not an append-only list of all the changes they have ever made to their cart.

Thus, applications that use event sourcing need to take the log of events (representing the data written to the system) and transform it into application state that is suitable for showing to a user (the way in which data is read from the system [47]). This transformation can use arbitrary logic, but it should be deterministic so that you can run it again and derive the same application state from the event log.

Like with change data capture, replaying the event log allows you to reconstruct the current state of the system. However, log compaction needs to be handled differently:

  • • A CDC event for the update of a record typically contains the entire new version of the record, so the current value for a primary key is entirely determined by the most recent event for that primary key, and log compaction can discard previous events for the same key.
  • • On the other hand, with event sourcing, events are modeled at a higher level: an event typically expresses the intent of a user action, not the mechanics of the state update that occurred as a result of the action. In this case, later events typically do not override prior events, and so you need the full history of events to reconstruct the final state. Log compaction is not possible in the same way.

Applications that use event sourcing typically have some mechanism for storing snapshots of the current state that is derived from the log of events, so they don’t need to repeatedly reprocess the full log. However, this is only a performance optimization to speed up reads and recovery from crashes; the intention is that the system is able to store all raw events forever and reprocess the full event log whenever required. We discuss this assumption in “Limitations of immutability” on page 463.

 
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