Comparative Overview of Open‑Source CDC Solutions: Debezium, Flink CDC, and Canal

Data‑in‑motion replication (CDC) is widely used for real‑time data needs, and the article compares three common open‑source CDC solutions—Canal, Debezium, and Flink CDC—by describing their principles and applicable scenarios.

Debezium

Debezium is an open‑source distributed platform for change data capture that works as a Kafka Connect source plugin. It extracts database changes from binlog, providing durable, low‑latency event streams. The core Kafka Connect interface requires implementing List<SourceRecord> in the poll method, which is delivered to Kafka with at‑least‑once semantics.

The Debezium MySQL architecture includes SnapshotReader for full‑load and BinlogReader for incremental changes, both extending AbstractReader. AbstractReader decouples record production from Kafka delivery using a thread‑safe BlockingQueue, enabling responsibility separation, thread isolation, and conversion between single and batch records.

Deployment

Typical deployment uses Apache Kafka Connect, where Debezium Source Connectors push records to Kafka topics and Sink Connectors forward them to downstream systems. Alternative deployments include Debezium Server (streaming to various message middleware) and an embedded engine that runs Debezium as a library inside a custom Java application.

Flink CDC

Flink CDC, started in July 2020, builds on Debezium and provides a two‑stage synchronization process: a full‑load phase that queries the entire table and an incremental phase that consumes binlog events. Early versions suffered from global locking, limited horizontal scalability, and lack of checkpointing during the snapshot phase.

Version 2.0 introduces concurrent reading, lock‑free operation, and checkpoint support for the snapshot stage, addressing the three major pain points of earlier releases.

Canal

Canal, originally developed by Alibaba, simulates a MySQL slave to receive binlog events. It parses binary logs, supports MySQL versions 5.1‑8.0, and provides a server/instance architecture with HA managed via ZooKeeper EPHEMERAL nodes. The server ensures only one instance runs per table at a time, while the client connects to the active server.

canalServer.setCanalInstanceGenerator(new CanalInstanceGenerator() {
    public CanalInstance generate(String destination) {
        Canal canal = canalConfigClient.findCanal(destination);
        // ... configure canal properties ...
        CanalInstanceWithManager instance = new CanalInstanceWithManager(canal, filter) {
            protected CanalHAController initHaController() {
                HA... // HA initialization logic
            }
            protected void startEventParserInternal(CanalEventParser parser, boolean isGroup) {
                // parser setup logic
            }
        };
        return instance;
    }
});
canalServer.start();
canalServer.start(destination);
canalServer.subscribe(clientIdentity);

Canal’s design includes EventParser (binlog dump, parsing), EventSink (filtering, routing, merging), and EventStore (currently in‑memory RingBuffer with future file‑based support). HA mechanisms rely on ZooKeeper watchers and EPHEMERAL nodes to coordinate server and client failover.

Summary

CDC solutions fall into two categories: query‑based (batch) and log‑based (streaming). Log‑based approaches like Debezium, Flink CDC, and Canal provide real‑time, consistent change capture. Among them, Flink CDC, Debezium, and Oracle GoldenGate excel at full‑load + incremental sync, while Canal lacks snapshot support. Flink CDC also offers strong integration with distributed storage systems (Hive, Iceberg, Hudi) and flexible connector ecosystem.