Change Data Capture (CDC) has become foundational for real-time analytics, cross-region replication, event-driven systems, and streaming ingestion pipelines. Databases like MySQL and Postgres replication expose change streams through a single-writer log. CDC in these systems is trivial. Modern distributed SQL databases like TiDB require a fundamentally different design and handle bigger challenges because they need to order the writes of multiple writers and deal with millions of tables,

This talk is about TiCDC’s architecture and how it handles multiple writers, millions of tables, 1000s of writers with its event-driven pipeline. To preserve total order, TiCDC must merge, order, and stream updates coming concurrently from multiple regions, Raft groups, and storage nodes—while preserving correctness and low latency.

This talk will explore the challenges and the evolution of TiCDC design over several iterations, With lessons learnt the hard way.