Unlock Real-Time Data Sync with Flink CDC: YAML Integration, Transform & Route Explained

Abstract

This article is compiled from a talk by Alibaba Cloud senior engineer and Flink Committer Ruan Hang at Flink Forward Asia 2024, covering Flink CDC, CDC‑YAML core functions, typical scenarios, and future plans.

Flink CDC Overview

Flink CDC has evolved from a simple MySQL CDC source to a distributed data integration tool that supports stream‑batch processing and can be described with YAML.

Real‑time Flink Integration

In the latest Flink version the full functionality of open‑source Flink CDC is integrated. Users define data ingestion jobs with a YAML file, which lowers the entry barrier and provides built‑in templates. The platform automatically adds connectors for common lake/warehouse sinks such as Paimon, Hologres and StarRocks.

CDC‑YAML Core Features

Supported sync links include MySQL, Kafka (binlog), and downstream targets Paimon, StarRocks, Hologres and Kafka. The YAML format also supports schema evolution, full‑load synchronization, and automatic connector discovery.

Transform and Route

The Transform module allows adding computed columns, metadata columns, custom UDFs, and filtering. It also supports redefining primary or partition keys. The Route module configures one‑to‑one or many‑to‑one table mappings, batch naming of sink tables, and versioned prefixes.

Data‑Sync Metrics

Metrics expose the current phase (full load vs incremental), processed shard counts, table‑level row counts, latest timestamp, lag, and per‑job read counts, enabling precise monitoring of synchronization progress.

Other Features

Fine‑grained schema‑change strategies (IGNORE, EVOLVE, EXCEPTION, LENIENT, TRY_EVOLVE) allow users to control how dangerous operations such as DROP TABLE or TRUNCATE are handled. A wide‑tolerance mode for Hologres reduces schema‑change frequency by mapping MySQL types to a limited set of target types.

Typical Scenarios

Full‑database sync (e.g., MySQL → Paimon) is demonstrated with a simple YAML job that defines the source, tables to sync, and sink connection. Binlog‑to‑Kafka sync preserves original change events using Debezium or Canal JSON formats. Partitioned table aggregation is achieved via many‑to‑one routing in the Route module.

Performance Optimizations for MySQL CDC

Four enterprise‑level optimizations—tuned Debezium parameters, filtering of irrelevant tables, parallel binlog parsing, and parallel serialization—yield up to 80 % throughput improvement when only one table is involved and even higher gains with many tables.

Demo & Future Plans

Demo jobs show full‑database sync to Paimon and binlog sync to Kafka, including schema changes and monitoring. Future work includes dirty‑data handling, upstream throttling, and expanding source/target connectors.

References

Data Ingestion Beta: https://x.sm.cn/5bwU6P3

Open‑source Flink CDC: https://x.sm.cn/VMLEvp