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Databases and Streams

We have drawn some comparisons between message brokers and databases. Even though they have traditionally been considered separate categories of tools, we saw that log-based message brokers have been successful in taking ideas from databases and applying them to messaging. We can also go in reverse: take ideas from messaging and streams, and apply them to databases.

We said previously that an event is a record of something that happened at some point in time. The thing that happened may be a user action (e.g., typing a search query), or a sensor reading, but it may also be a write to a database. The fact that something was written to a database is an event that can be captured, stored, and pro?cessed. This observation suggests that the connection between databases and streams runs deeper than just the physical storage of logs on disk—it is quite fundamental.

In fact, a replication log (see “Implementation of Replication Logs” on page 158) is a stream of database write events, produced by the leader as it processes transactions. The followers apply that stream of writes to their own copy of the database and thus end up with an accurate copy of the same data. The events in the replication log describe the data changes that occurred.

We also came across the state machine replication principle in “Total Order Broadcast” on page 348, which states: if every event represents a write to the database, and every replica processes the same events in the same order, then the replicas will all end up in the same final state. (Processing an event is assumed to be a deterministic operation.) It’s just another case of event streams!

In this section we will first look at a problem that arises in heterogeneous data systems, and then explore how we can solve it by bringing ideas from event streams to databases.

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