Master Flink CDC YAML: Real‑Time Data Integration Best Practices in 10 Minutes

Introduction

This article, contributed by the Alibaba Cloud Open‑Source Big Data Platform Data Channel team, presents best practices for using Flink CDC YAML in real‑time Flink jobs. It is organized into five parts: an overview of CDC YAML, its core capabilities, typical use cases, Alibaba Cloud Flink CDC enterprise features, and a quick ten‑minute demo.

What Is CDC YAML?

CDC YAML is a simple, user‑friendly data integration API provided by Flink CDC. It enables rapid construction of powerful data synchronization pipelines that continuously capture both data changes and schema changes from source databases and sync them to data warehouses, data lakes, or other downstream systems. Even users without Flink or development experience can quickly set up real‑time data ingestion and ETL processing.

Core Capabilities of CDC YAML

End‑to‑end Data Pipeline : Supports second‑level synchronization of data and schema changes to downstream systems, enabling fast data lake and warehouse construction.

Fine‑grained Schema Evolution : Allows selective synchronization of schema changes, preventing unwanted operations such as table drops.

Unified Full‑and‑Incremental Reading : Automatically switches from snapshot to incremental reading without user intervention.

Rich Transform Support : Users can add computed columns, metadata, filter fields, rename keys, and develop custom UDFs compatible with Flink.

Flexible Routing Strategies : Supports one‑to‑one, one‑to‑many, and many‑to‑one table mappings, as well as sharding and merging scenarios.

Comprehensive Job Metrics : Provides metrics such as processed table count, shard count, and latest data timestamp.

Alibaba Cloud Flink integrates additional connectors, including MySQL, Kafka, Hologres, StarRocks, Paimon, and Print.

Comparison with SQL and DataStream Jobs

Compared with SQL jobs, CDC YAML automatically discovers schemas, supports whole‑database sync, fine‑grained schema changes, and preserves original changelog structures. Compared with DataStream jobs, CDC YAML is designed for users of all skill levels, hides low‑level details, uses an easy‑to‑read YAML format, and enables reuse of existing jobs.

Typical Application Scenarios

Whole‑Database Sync to Build Data Lakes : Sync an entire MySQL database to Paimon with a simple YAML job.

source:
  type: mysql
  name: MySQL Source
  hostname: ${secret_values.mysql-hostname}
  port: 3306
  username: flink
  password: ${secret_values.mysql-password}
  tables: app_db.*
  server-id: 18601-18604

sink:
  type: paimon
  name: Paimon Sink
  catalog.properties.metastore: filesystem
  catalog.properties.warehouse: oss://test-bucket/warehouse
  catalog.properties.fs.oss.endpoint: oss-cn-beijing-internal.aliyuncs.com
  catalog.properties.fs.oss.accessKeyId: ${secret_values.test_ak}
  catalog.properties.fs.oss.accessKeySecret: ${secret_values.test_sk}

Sharding Merging : Merge multiple sharded tables into a single target table.

source:
  type: mysql
  tables: app_db.customers.*

route:
  - source-table: app_db.customers.*
    sink-table: app_db.customers

Raw Binlog Sync to Kafka : Preserve original changelog events for audit or replay.

source:
  type: mysql
  metadata-column.include-list: op_ts

sink:
  type: kafka
  properties.bootstrap.servers: ${secret_values.bootstraps-server}

Fine‑Grained Schema Change Strategies

LENIENT (default) : Converts unsupported changes to a compatible form.

EXCEPTION : Throws an error on any schema change.

IGNORE : Skips all schema changes.

EVOLVE : Applies all changes, failing only on unsupported ones.

TRY_EVOLVE : Attempts changes and ignores unsupported ones.

Additional per‑connector options (e.g., include.schema.changes, exclude.schema.changes) allow precise control.

Enterprise‑Grade Features of Alibaba Cloud Flink CDC

MySQL CDC Performance Optimizations : Debezium parameter tuning (+11%), filter irrelevant tables, parallel binlog parsing (+14%), and parallel serialization (+42%). Overall performance can improve by up to 80% for single‑table streams and up to 10× for multi‑table workloads.

OSS‑Based Binlog Persistence : Enables replay of expired binlog data by storing logs in OSS.

Rich Monitoring Metrics : Includes job phase, processed tables/shards, latest timestamp, latency, and message counts.

Quick Ten‑Minute CDC YAML Demo on Alibaba Cloud

The demo walks through preparing resources (OSS bucket, RDS MySQL instance, Flink compute instance, AccessKey), creating a secret‑managed variable set, building a full‑library sync job to Paimon, deploying and starting the job, and finally querying the synchronized data via a Session cluster.

After deployment, the job enters the incremental phase, synchronizing two tables and two shards, each with five records. Users can verify the data in Paimon through the ETL debugging interface.

Related Links

Flink CDC Documentation: https://nightlies.apache.org/flink/flink-cdc-docs-stable/

Develop a YAML Draft: https://help.aliyun.com/zh/flink/user-guide/develop-a-yaml-draft

Alibaba Cloud Free Trial: https://free.aliyun.com/

Create an AccessKey Pair: https://help.aliyun.com/zh/ram/user-guide/create-an-accesskey-pair