Failure Modes and Edge Cases

Trigger Path Blocking: The most dangerous failure mode is that problems in trigger execution directly impact your core transactional workload. Triggers run synchronously within transactions. If a trigger becomes slow or blocks, every write to that table slows or blocks. Consider a production incident: a poorly chosen index on the change table causes inserts to suddenly take 50 milliseconds instead of 2 milliseconds. Write latency p99 spikes from 30 milliseconds to 200 milliseconds. User facing requests start timing out. Upstream services experience cascading failures. The operational database is no longer just a data store; it is also running CDC infrastructure that can bring down the entire write path. Schema Drift and Missing Triggers: Operational teams deploy schema changes. A developer adds a new table without corresponding triggers. Changes to that table never get captured. In regulated environments, missing audit records can cause compliance violations. Detection requires active monitoring. Periodic reconciliation comparing row counts or checksums between source tables and downstream systems catches drift. One pattern is a nightly job that checks COUNT(*) on source versus COUNT(DISTINCT primary_key) in change tables for each monitored table. Schema evolution creates similar problems. Adding a column to the source without updating the trigger and change table schema means downstream consumers never see that field. Dropping a column that triggers reference causes write failures. Robust continuous integration and continuous deployment (CI/CD) processes with automated schema migration testing are essential. Bulk Operation Overload: A one time data migration updates 100 million rows. This generates 100 million change records. At 10000 events per second processing rate, downstream systems face 2.7 hours of catch up. The change table can experience lock contention on hot pages as millions of inserts compete. Many teams temporarily disable CDC or adjust triggers for bulk operations, accepting temporary inconsistency. This requires careful coordination and verification that downstream systems can tolerate staleness during the maintenance window. Backpressure and Storage: If downstream systems or the CDC reader fail, change tables accumulate unbounded rows. At 5000 writes per second, even a 30 minute outage produces 9 million rows. Without storage limits or alerts, you can exhaust disk space, causing write failures on the primary database. Production systems set aggressive retention policies (7 to 14 days) and monitor change table size and reader lag continuously. Alerting on lag over 5 minutes or change table size over 10 gigabytes gives operations time to intervene before critical failures. Multi Table Transaction Boundaries: Preserving transactional consistency across tables requires careful handling. If a business transaction updates orders and inventory tables, both triggers write rows with the same transaction_id. But if the CDC reader processes these tables independently, downstream systems might see partial state (order created but inventory not decremented). Solutions include transaction aware batching where the CDC reader groups changes by transaction_id and commit_timestamp, or idempotent downstream consumers that handle out of order delivery gracefully using sequence numbers.