Implementing change data capture

We can call the log consumers derived data systems, as discussed in the introduction to Part III: the data stored in the search index and the data warehouse is just another view onto the data in the system of record. Change data capture is a mechanism for ensuring that all changes made to the system of record are also reflected in the derived data systems so that the derived systems have an accurate copy of the data.

Essentially, change data capture makes one database the leader (the one from which the changes are captured), and turns the others into followers. A log-based message broker is well suited for transporting the change events from the source database, since it preserves the ordering of messages (avoiding the reordering issue of Figure 11-2).

Database triggers can be used to implement change data capture (see “Trigger-based replication” on page 161) by registering triggers that observe all changes to data tables and add corresponding entries to a changelog table. However, they tend to be fragile and have significant performance overheads. Parsing the replication log can be a more robust approach, although it also comes with challenges, such as handling schema changes.

LinkedIn’s Databus [25], Facebook’s Wormhole [26], and Yahoo!’s Sherpa [27] use this idea at large scale. Bottled Water implements CDC for PostgreSQL using an API that decodes the write-ahead log [28], Maxwell and Debezium do something similar for MySQL by parsing the binlog [29, 30, 31], Mongoriver reads the MongoDB oplog [32, 33], and GoldenGate provides similar facilities for Oracle [34, 35].

Like message brokers, change data capture is usually asynchronous: the system of record database does not wait for the change to be applied to consumers before committing it. This design has the operational advantage that adding a slow consumer does not affect the system of record too much, but it has the downside that all the issues of replication lag apply (see “Problems with Replication Lag” on page 161).

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