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 TiDB 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.
How Does TiDB Handle Real-Time Stream Processing?
TiDB streams data changes to downstream systems through TiCDC, 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.
How TiDB’s Changefeed Architecture Solves Streaming Consistency
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.
MNC Bank 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.
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.
Performance Optimization for Streaming Workloads
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 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.
Horizontal scalability lets TiDB adjust node count as changefeed volume grows, and TiFlash 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.
FAQ
What is TiCDC and how does it work with TiDB?
- TiCDC is TiDB’s built-in change data capture component
- It reads row-level changes directly from TiKV’s replication log
- Changes publish to a Kafka topic through a configured changefeed
- Downstream tools like Flink consume that topic for processing
Can TiDB stream data to Kafka in real time?
- Yes, TiCDC pushes committed changes to Kafka as they happen
- No separate polling job or ETL script is required
- Changes stream in commit order, not on a fixed timer
- Kafka acts as the buffer between TiDB and downstream consumers
Does streaming data into TiDB affect transactional performance?
- TiCDC reads from the replication log, not the primary write path
- TiFlash handles concurrent analytical queries separately from TiKV’s transactional load
- The execution plan cache reduces repeated query compile time
- Horizontal scaling adds capacity if changefeed volume grows
What’s the difference between this page and TiDB’s other real-time processing content?
- This page focuses specifically on TiCDC changefeed mechanics
- Other PingCAP articles cover IoT, high-frequency trading, and event-driven framings of similar underlying architecture
- Start here if you need the Kafka/Flink integration details specifically
- See the TiDB architecture guide for the broader system design
Streaming Data Without a Separate Pipeline to Maintain
TiCDC removes the polling and ETL layer a real-time pipeline usually needs, streaming committed changes straight from TiKV into Kafka and Flink. Start a free TiDB Cloud Starter cluster to configure a changefeed against your own workload.