Canal is a popular change data capture (CDC) tool for MySQL, but its default architecture struggles with petabyte-scale data. This analysis examines key architectural extensions needed for massive scale: horizontal partitioning of binlog streams, tiered storage with hot/warm/cold separation, and distributed checkpointing for fault tolerance. It also discusses optimizing memory and disk usage to prevent bottlenecks. For data engineers and backend architects, these insights are critical for building reliable, high-throughput CDC pipelines that can handle enterprise-scale data volumes without degradation.
This article explores how to extend Canal's architecture to handle TB/PB-level data volumes, covering partitioning, storage optimization, and fault tolerance. It provides actionable insights for engineers managing large-scale change data capture pipelines.