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Flink CDC Components

· 7 min read
tanzhuo

While recently mapping out our real-time data pipelines, I revisited everything I know about Flink CDC. The need to "stream database changes out in real time" comes up on virtually every data team, so it deserves its own write-up.

Background​

The most basic approach to data synchronization is scheduled batch jobs: pull a full copy of the business database every night and write it into the warehouse. It's simple to build, but latency is measured in hours or even days, and once data volumes grow, the load on the source database is significant. The moment the business asks for "real-time dashboards," batch jobs can't keep up. CDC is the technical path that emerged to solve this, and Flink CDC is one of the most thorough integrations of CDC capability with a stream processing engine.

Flink CDC (Change Data Capture) is an important component in the Apache Flink ecosystem, used to capture database changes in real time and turn them into data streams for processing. In modern data architectures, CDC is widely used to build real-time data synchronization, real-time data warehouses, and event-driven systems.

Compared with traditional synchronization approaches (such as scheduled full syncs), CDC captures data changes by reading the database's change logs—Binlog, WAL, and the like—achieving low-latency, low-intrusion synchronization.

What Is CDC​

CDC stands for Change Data Capture. The core idea: whenever data in the database undergoes an INSERT / UPDATE / DELETE, record those changes and deliver them to downstream systems in real time.

It's worth unpacking the implementation mechanics a bit. CDC comes in two broad flavors. One is query-based: it polls timestamp or auto-increment key columns to discover new data—simple to implement, but it can't catch DELETEs and easily misses records modified multiple times between two polls. The other is log-based: it consumes the change log the database already maintains for primary-replica replication—MySQL's Binlog and PostgreSQL's WAL both fall into this category. The log naturally records the complete before and after state of every row change, so log-based CDC captures the full set of insert/update/delete events with virtually no extra query load on the source database. Flink CDC takes the log-based path.

In real data architectures, CDC is commonly used for:

  • Database → data warehouse (real-time warehousing)
  • Database → Kafka (event streams)
  • Database → search engines (e.g. Elasticsearch)
  • Database → caching systems (e.g. Redis)

CDC is therefore one of the key foundational capabilities of a modern real-time data platform.

Flink CDC works by capturing the source system's change logs, converting them into data streams, and processing them in real time. This happens without affecting the source system, because the CDC component only reads the source system's logs and never writes to it. From the database's point of view, a CDC client is essentially indistinguishable from an ordinary replica—it masquerades as a replication client, subscribes to the change log from the primary, and parses log events into structured change records for Flink to process.

A strength of Flink CDC is its support for many data sources, including relational databases (MySQL, Oracle, etc.) and NoSQL databases (MongoDB, Cassandra, etc.). It also supports multiple data formats, such as JSON and CSV.

Another easily underrated strength is how deeply it integrates with Flink itself. Once the change stream enters Flink, it's just an ordinary data stream: Flink's windowing, multi-stream joins, state management, and checkpoint-based fault tolerance all apply directly. In other words, "capturing changes" and "processing changes" happen in the same engine—no separate Kafka Connect cluster to maintain as a relay, a shorter pipeline, and fewer moving parts to operate.

With Flink CDC you get real-time data synchronization and stream processing in one. For example, you can use the CDC component to sync data from a MySQL database into Kafka, then process it in real time with Flink—achieving real-time synchronization and processing that improve both the timeliness and the accuracy of your data.

At a high level, the division of labor in a Flink CDC pipeline is: the Source handles "in," the Sink handles "out," and Flink's operators and state mechanisms do the computation in between. Flink CDC ships with the following built-in components:

  1. Source: reads change logs from the data source and converts them into a Flink data stream. A CDC Source typically takes a snapshot of the existing data first, then seamlessly hands off to incremental log consumption, so downstream consumers receive the complete "full plus incremental" dataset.
  2. Debezium Connector: an open source CDC tool that connects to many data sources (MySQL, PostgreSQL, MongoDB, etc.) and captures their change logs. Flink CDC reuses Debezium's log-parsing capability under the hood, running it embedded inside the Flink job and eliminating the cost of deploying a standalone Debezium service.
  3. Sink: writes the Flink data stream into a target system, such as Kafka, HDFS, or Elasticsearch. Working with Flink's checkpoint mechanism, the Sink avoids data loss when a job fails and restarts.
  4. State: handles stateful stream processing—for example, merging two data streams requires the State component to store intermediate state. State is especially important in CDC scenarios: dimension-table joins, deduplication by primary key, and reconstructing the latest image from UPDATE events all depend on state storage.
  5. Table API: provides a SQL-like API for convenient stream processing and querying. For most synchronization needs, declaring a CDC source table and a target table in Flink SQL and writing a single INSERT INTO gets the whole pipeline running—no Java code required.

Pitfalls and Caveats​

A few things worth checking up front in practice:

  1. Confirm the source database's log configuration first. Taking MySQL as an example, Binlog must be enabled with format ROW, or you won't get row-level before/after images; the connection account also needs the replication privileges required to read the Binlog.

  2. Don't set the log retention too short. If the job is stopped for a while and the Binlog for that period has already been purged by the database, the job can't find its resume position and has to redo a full snapshot.

  3. The downstream must be able to handle UPDATE and DELETE. A CDC stream is a changelog stream with retraction semantics; if the Sink side (say, an append-only store) can't digest deletes and updates, the pipeline design needs extra handling.

warning

A CDC job is fundamentally a long-running streaming job—configure checkpointing properly. A CDC job without checkpoints, once it fails, loses its resume position and possibly data too.

Wrap-up​

CDC answers the question of how changes inside a database can flow out with low latency and low intrusion, and the log-based approach is today's mainstream path. Flink CDC's value is pulling log capture (via Debezium) and stream computation (the Flink engine itself) into a single framework: the Source reads changes, the Sink writes to targets, State powers stateful computation, and the Table API lowers the barrier to entry. For teams building a real-time warehouse or syncing heterogeneous data, it's a short-pipeline, few-component option well worth considering first.

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