Understand Flink CDC API
This documentation is for an unreleased version of Apache Flink CDC. We recommend you use the latest stable version.

Understand Flink CDC API #

If you are planning to build your own Flink CDC connectors, or considering contributing to Flink CDC, you might want to hava a deeper look at the APIs of Flink CDC. This document will go through some important concepts and interfaces in order to help you with your development.

Event #

An event under the context of Flink CDC is a special kind of record in Flink’s data stream. It describes the captured changes in the external system on source side, gets processed and transformed by internal operators built by Flink CDC, and finally passed to data sink then write or applied to the external system on sink side.

Each change event contains the table ID it belongs to, and the payload that the event carries. Based on the type of payload, we categorize events into these kinds:

DataChangeEvent #

DataChangeEvent describes data changes in the source. It consists of 5 fields

  • Table ID: table ID it belongs to
  • Before: pre-image of the data
  • After: post-image of the data
  • Operation type: type of the change operation
  • Meta: metadata of the change

For the operation type field, we pre-define 4 operation types:

  • Insert: new data entry, with before = null and after = new data
  • Delete: removal of data, with before = removed data and after = null
  • Update: update of existed data, with before = data before change and after = data after change
  • Replace:

SchemaChangeEvent #

SchemaChangeEvent describes schema changes in the source. Compared to DataChangeEvent, the payload of SchemaChangeEvent describes changes in the table structure in the external system, including:

  • AddColumnEvent: new column in the table
  • AlterColumnTypeEvent: type change of a column
  • CreateTableEvent: creation of a new table. Also used to describe the schema of a pre-emitted DataChangeEvent
  • DropColumnEvent: removal of a column
  • RenameColumnEvent: name change of a column

Flow of Events #

As you may have noticed, data change event doesn’t have its schema bound with it. This reduces the size of data change event and the overhead of serialization, but makes it not self-descriptive Then how does the framework know how to interpret the data change event?

To resolve the problem, the framework adds a requirement to the flow of events: a CreateTableEvent must be emitted before any DataChangeEvent if a table is new to the framework, and SchemaChangeEvent must be emitted before any DataChangeEvent if the schema of a table is changed. This requirement makes sure that the framework has been aware of the schema before processing any data changes.

Flow of Events

Data Source #

Data source works as a factory of EventSource and MetadataAccessor, constructing runtime implementations of source that captures changes from external system and provides metadata.

EventSource is a Flink source that reads changes, converts them to events , then emits to downstream Flink operators. You can refer to Flink documentation to learn internals and how to implement a Flink source.

MetadataAccessor serves as the metadata reader of the external system, by listing namespaces, schemas and tables, and provide the table schema (table structure) of the given table ID.

Data Sink #

Symmetrical with data source, data sink consists of EventSink and MetadataApplier, which writes data change events and apply schema changes (metadata changes) to external system.

EventSink is a Flink sink that receives change event from upstream operator, and apply them to the external system. Currently we only support Flink’s Sink V2 API.

MetadataApplier will be used to handle schema changes. When the framework receives schema change event from source, after making some internal synchronizations and flushes, it will apply the schema change to external system via this applier.