{"id":26350,"date":"2026-09-18T13:50:50","date_gmt":"2026-09-18T20:50:50","guid":{"rendered":"https:\/\/www.pingcap.com\/?post_type=article&p=26350"},"modified":"2026-09-18T13:52:39","modified_gmt":"2026-09-18T20:52:39","slug":"mastering-real-time-stream-processing-with-tidb","status":"publish","type":"article","link":"https:\/\/www.pingcap.com\/ko\/article\/mastering-real-time-stream-processing-with-tidb\/","title":{"rendered":"Mastering Real-Time Stream Processing with TiDB"},"content":{"rendered":"

A fraud-detection model or a dynamic pricing engine is only as good as its freshest data point; if a pipeline batches updates every few minutes, decisions get made on stale information. Real-time stream processing closes that gap by acting on data the moment it changes, and \ud2f0DB<\/a> feeds that pipeline directly through TiCDC, its built-in change data capture component. This piece covers how that connection works and what to watch when you’re running it at scale.<\/p>\n\n\n\n

<\/span>How Does TiDB Handle Real-Time Stream Processing?<\/strong><\/span><\/h2>\n\n\n\n

TiDB streams data changes to downstream systems through TiCDC<\/a>, a change data capture component that reads row-level updates from TiKV and pushes them to Kafka, from which Apache Flink or another stream processor picks them up. This removes the need for a separate polling job or ETL script to detect what changed since the last read.<\/p>\n\n\n\n

<\/span>How TiDB’s Changefeed Architecture Solves Streaming Consistency<\/strong><\/span><\/h2>\n\n\n\n

A polling-based pipeline risks missing updates between poll intervals, or double-processing rows if a poll overlaps with a write. TiCDC avoids both by capturing changes directly from TiKV’s replication log as they commit, in order, rather than re-scanning tables on a timer. A changefeed configuration tells TiCDC which tables to watch and which Kafka topic to publish to; Flink then consumes that topic to run the actual stream processing logic, whether that’s aggregation, enrichment, or triggering an action.<\/p>\n\n\n\n

MNC Bank<\/a> uses this pipeline to keep downstream fraud and risk systems working from current transaction data instead of a batch that’s already minutes old by the time it lands.<\/p>\n\n\n\n

Setting up a changefeed is a matter of pointing it at the tables to watch and the Kafka topic to publish to; TiCDC handles the ordering and delivery guarantees from there. Each changefeed tracks its own replication checkpoint, so if a downstream consumer falls behind or restarts, it resumes from that checkpoint instead of missing changes or reprocessing the entire table.<\/p>\n\n\n\n

<\/span>Performance Optimization for Streaming Workloads<\/strong><\/span><\/h2>\n\n\n\n

Improving throughput and reducing latency starts with efficient query execution and caching. TiDB’s execution plan cache cuts down compile time for repeated queries, which matters when a changefeed is triggering the same query shapes repeatedly. The TiDB Dashboard<\/a> gives visibility into system performance, and its Continuous Profiling view shows CPU and memory consumption so you can tune resource allocation against real load rather than guesswork.<\/p>\n\n\n\n

Horizontal scalability lets TiDB adjust node count as changefeed volume grows, and TiFlash<\/a> handles concurrent analytical queries on the same data without competing with the transactional writes the changefeed is capturing. For high-throughput tables, splitting a single changefeed into multiple changefeeds scoped to different table groups spreads replication load across more TiCDC nodes instead of funneling every change through one process.<\/p>\n\n\n\n

<\/span>\uc790\uc8fc \ubb3b\ub294 \uc9c8\ubb38<\/strong><\/span><\/h2>\n\n\n\n

What is TiCDC and how does it work with TiDB?<\/strong><\/p>\n\n\n\n