Change Data Capture

Change Data Capture (CDC) solves a common problem: keeping different systems in sync with your database. Think search indexes, caches, or data warehouses. Instead of your application updating multiple places, CDC watches database changes and shares them automatically.

Here's how it works: Your database already keeps a log of changes (like MySQL's binlog or Postgres' WAL). CDC tools read this log and turn database changes into events. These events then go to other systems that need to know about the changes.

Example: When you update a user record in MySQL:

UPDATE users SET status = 'active' WHERE id = 123;

CDC creates an event like this:

{
  "table": "users",
  "operation": "UPDATE",
  "before": { "id": 123, "status": "inactive" },
  "after": { "id": 123, "status": "active" }
}

This event can then update Elasticsearch, invalidate Redis caches, or update analytics tables. The receiving systems only care about the change, not where it came from.

CDC uses log compaction to save space. It only keeps the latest state for each record. If a user changes status ten times, we only need the latest one. This is different from event sourcing, where history matters.

Common CDC tools:

• Debezium for MySQL, Postgres

• Maxwell for MySQL

• Kafka Connect for various databases

Note: CDC isn't magic. You still need to handle failures, lag (it's async, which is good because it doesn't impact the database performance), and ordering. But it's better than trying to update multiple systems from your application code. That approach leads to race conditions and inconsistencies.

When to use CDC:

• Keeping search indexes fresh

• Updating caches automatically

• Feeding analytics systems

• Syncing microservices

When not to use CDC:

• When you need full history (use event sourcing)

• Simple, low-volume updates (direct API calls might be simpler)

• When immediate consistency is required (CDC has some delay, it's small though)

Remember: CDC reads from database logs (async). It doesn't impact your database performance like triggers or polling would. That's why it's the preferred pattern for keeping data in sync at scale.