Production Reality: Overhead and Scale Limits

The Write Path Impact: Every write operation now performs extra work. At 1000 writes per second, adding triggers that insert into change tables and update indexes increases primary database CPU usage by 10 to 30 percent. Write latency grows measurably. The numbers matter for capacity planning. Without triggers, your database might handle writes at 20 milliseconds p99. With triggers performing one additional insert and a couple of index updates, p99 latency rises to 40 milliseconds or more. For write heavy workloads at tens of thousands of operations per second, this overhead becomes problematic. When It Works Well: Trigger based CDC is practical for systems below roughly 2000 to 3000 writes per second per database node. Internal line of business applications, content management systems, and many Software as a Service (SaaS) products fall into this range. The added latency and CPU overhead are acceptable trade offs for the simplicity of not needing log access. Companies in regulated industries like banking and insurance have historically used this pattern. When you cannot get permissions to read transaction logs on legacy or mainframe systems, triggers provide an auditable change capture mechanism. Storage Growth: Change tables accumulate rows rapidly. At 5000 writes per second, you generate 432 million change records per day. Even with compact representations averaging 200 bytes per row, that is 86 gigabytes daily. Without proper retention policies and partition management, you hit storage limits or slow down queries scanning the change table. Production systems typically implement time based partitioning on the change table, dropping partitions older than 7 to 30 days after downstream systems have processed them. The CDC reader must maintain its offset correctly so it does not miss data during partition maintenance. Compared to Log Based CDC: Transaction log based CDC tools like Debezium read directly from database write ahead logs or binary logs. They capture every change with minimal overhead (typically under 5 percent CPU increase) and handle much higher throughput, often 50000 to 100000 events per second per node. However, log based CDC requires log access, which managed database services often restrict. It also couples tightly to database internals and version compatibility. Trigger based CDC trades performance for portability and control. You can filter, mask, or enrich changes at capture time using trigger logic, something log readers cannot easily do.