Debezium

Updated on 2025-01-10 GMT+08:00

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Function

Debezium is a Changelog Data Capture (CDC) tool that can stream changes in real-time from MySQL, PostgreSQL, Oracle, Microsoft SQL Server and many other databases into Kafka. Debezium provides a unified format schema for changelog and supports to serialize messages using JSON and Apache Avro.

Flink supports to interpret Debezium JSON and Avro messages as INSERT/UPDATE/DELETE messages into Flink SQL system. This is useful in many cases to leverage this feature, such as

Synchronizing incremental data from databases to other systems
Auditing logs
Real-time materialized views on databases
Temporal join changing history of a database table

Flink also supports to encode the INSERT/UPDATE/DELETE messages in Flink SQL as Debezium JSON or Avro messages, and emit to external systems like Kafka. However, currently Flink cannot combine UPDATE_BEFORE and UPDATE_AFTER into a single UPDATE message. Therefore, Flink encodes UPDATE_BEFORE and UDPATE_AFTER as DELETE and INSERT Debezium messages.

For details, see Debezium Format.

Supported Connectors

Kafka
FileSystem

Caveats

Duplicate change events
Under normal operating scenarios, the Debezium application delivers every change event exactly-once. Flink works pretty well when consuming Debezium produced events in this situation. However, Debezium application works in at-least-once delivery if any failover happens. That means, in the abnormal situations, Debezium may deliver duplicate change events to Kafka and Flink will get the duplicate events. This may cause Flink query to get wrong results or unexpected exceptions.

Solution: Set table.exec.source.cdc-events-duplicate to true and define a primary key on this source.

Framework will generate an additional stateful operator, and use the primary key to deduplicate the change events and produce a normalized changelog stream.

For more information, see Debezium documentation.
Consuming data produced by Debezium Postgres Connector
If you are using Debezium Connector for PostgreSQL to capture the changes to Kafka, please make sure the REPLICA IDENTITY configuration of the monitored PostgreSQL table has been set to FULL which is by default DEFAULT. Otherwise, Flink SQL currently will fail to interpret the Debezium data.

In FULL strategy, the UPDATE and DELETE events will contain the previous values of all the table's columns.

In other strategies, the before field of UPDATE and DELETE events will only contain primary key columns or null if no primary key.

You can change the REPLICA IDENTITY by running ALTER TABLE <your-table-name> REPLICA IDENTITY FULL.

Parameter Description

Flink provides debezium-avro-confluent and debezium-json formats to interpret Avro or Json messages produced by Debezium. Use format debezium-avro-confluent to interpret Debezium Avro messages and format debezium-json to interpret Debezium Json messages.

**Table 1** Debezium Avro parameters
Parameter	Mandatory	Default Value	Data Type	Description
format	Yes	None	String	Format to be used. Set this parameter to 'debezium-avro-confluent'.
debezium-avro-confluent.basic-auth.credentials-source	No	None	String	Basic auth credentials source for Schema Registry
debezium-avro-confluent.basic-auth.user-info	No	None	String	Basic auth user info for schema registry
debezium-avro-confluent.bearer-auth.credentials-source	No	None	String	Bearer auth credentials source for Schema Registry
debezium-avro-confluent.bearer-auth.token	No	None	String	Bearer auth token for Schema Registry
debezium-avro-confluent.properties	No	None	Map	Properties map that is forwarded to the underlying Schema Registry. This is useful for options that are not officially exposed via Flink config options. However, note that Flink options have higher precedence.
debezium-avro-confluent.ssl.keystore.location	No	None	String	Location/File of SSL keystore
debezium-avro-confluent.ssl.keystore.password	No	None	String	Password for SSL keystore
debezium-avro-confluent.ssl.truststore.location	No	None	String	Location/File of SSL truststore
debezium-avro-confluent.ssl.truststore.password	No	None	String	Password for SSL truststore
debezium-avro-confluent.subject	No	None	String	The Confluent Schema Registry subject under which to register the schema used by this format during serialization. By default, 'kafka' and 'upsert-kafka' connectors use '<topic_name>-value' or '<topic_name>-key' as the default subject name if this format is used as the value or key format. But for other connectors (e.g. 'filesystem'), the subject option is required when used as sink.
debezium-avro-confluent.url	No	None	String	The URL of the Confluent Schema Registry to fetch/register schemas.

**Table 2** Debezium JSON parameters
Parameter	Mandatory	Default Value	Mandatory	Description
format	Yes	None	String	Format to be used. In this example.Set this parameter to debezium-json.
debezium-json.schema-include	No	false	Boolean	Whether the Debezium JSON messages contain the schema. When setting up Debezium Kafka Connect, enable the Kafka configuration value.converter.schemas.enable to include the schema in the message.
debezium-json.ignore-parse-errors	No	false	Boolean	Whether fields and rows with parse errors will be skipped or failed. The default value is false, indicating that an error will be thrown. Fields are set to null in case of errors.
debezium-json.timestamp-format.standard	No	'SQL'	String	Input and output timestamp formats. Currently supported values are SQL and ISO-8601. SQL will parse input timestamp in "yyyy-MM-dd HH:mm:ss.s{precision}" format, for example 2020-12-30 12:13:14.123 and output timestamp in the same format. ISO-8601 will parse input timestamp in "yyyy-MM-ddTHH:mm:ss.s{precision}" format, for example 2020-12-30T12:13:14.123 and output timestamp in the same format.
debezium-json.map-null-key.mode	No	'FAIL'	String	Handling mode when serializing null keys for map data. Available values are as follows: FAIL will throw exception when encountering map value with null key. DROP will drop null key entries for map data. LITERAL replaces the empty key value in the map with a string constant. The string literal is defined by debezium-json.map-null-key.literal option.
debezium-json.map-null-key.literal	No	'null'	String	String literal to replace null key when debezium-json.map-null-key.mode is LITERAL.
debezium-json.encode.decimal-as-plain-number	No	false	Boolean	Encode all decimals as plain numbers instead of possible scientific notations. For example, 0.000000027 is encoded as 2.7E-8 by default, and will be written as 0.000000027 if set this parameter to true.

Metadata

The following format metadata can be exposed as read-only (VIRTUAL) columns in DDL.

Format metadata fields are only available if the corresponding connector forwards format metadata. Currently, only the Kafka connector is able to expose metadata fields for its value format.

**Table 3** Metadata
Key	Data Type	Description
schema	STRING NULL	JSON string describing the schema of the payload. Null if the schema is not included in the Debezium record.
ingestion-timestamp	TIMESTAMP_LTZ(3) NULL	The timestamp at which the connector processed the event. Corresponds to the ts_ms field in the Debezium record.
source.timestamp	TIMESTAMP_LTZ(3) NULL	The timestamp at which the source system created the event. Corresponds to the source.ts_ms field in the Debezium record.
source.database	STRING NULL	The originating database. Corresponds to the source.db field in the Debezium record if available.
source.schema	STRING NULL	The originating database schema. Corresponds to the source.schema field in the Debezium record if available.
source.table	STRING NULL	The originating database table. Corresponds to the source.table or source.collection field in the Debezium record if available.
source.properties	MAP<STRING, STRING> NULL	Map of various source properties. Corresponds to the source field in the Debezium record.

The following example shows how to access Canal metadata fields in Kafka:

CREATE TABLE KafkaTable (
  origin_ts TIMESTAMP(3) METADATA FROM 'value.ingestion-timestamp' VIRTUAL,
  event_time TIMESTAMP(3) METADATA FROM 'value.source.timestamp' VIRTUAL,
  origin_database STRING METADATA FROM 'value.source.database' VIRTUAL,
  origin_schema STRING METADATA FROM 'value.source.schema' VIRTUAL,
  origin_table STRING METADATA FROM 'value.source.table' VIRTUAL,
  origin_properties MAP<STRING, STRING> METADATA FROM 'value.source.properties' VIRTUAL,
  user_id BIGINT,
  item_id BIGINT,
  behavior STRING
) WITH (
  'connector' = 'kafka',
  'topic' = 'kafkaTopic',
  'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
  'properties.group.id' = 'GroupId',
  'scan.startup.mode' = 'earliest-offset',
  'value.format' = 'debezium-json'
);

Example

Use Kafka to parse Debezium JSON data and output the result to Print.

Create a datasource connection for the communication with the VPC and subnet where Kafka locates and bind the connection to the queue. Set a security group and inbound rule to allow access of the queue and test the connectivity of the queue using the Kafka IP address. For example, locate a general-purpose queue where the job runs and choose More > Test Address Connectivity in the Operation column. If the connection is successful, the datasource is bound to the queue. Otherwise, the binding fails.

Create a Flink OpenSource SQL job and select Flink 1.15. Copy the following statement and submit the job:

CREATE TABLE kafkaSource (
  id bigint,
  name string,
  description string,  
  weight DECIMAL(10, 2)
) WITH (
  'connector' = 'kafka',
  'topic' = 'kafkaTopic',
  'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
  'properties.group.id' = 'GroupId',
  'scan.startup.mode' = 'latest-offset',
  'format' = 'debezium-json'
);
CREATE TABLE printSink (
  id bigint,
  name string,
  description string,  
  weight DECIMAL(10, 2)
) WITH (
  'connector' = 'print'
);
insert into printSink select * from kafkaSource;

Insert the data below into the appropriate Kafka topics. The data shows that the MySQL products table has four columns: id, name, description, and weight. This JSON message represents an update event on the products table, where the weight value of the row with id = 111 has been changed from 5.18 to 5.15.

{
  "before": {
    "id": 111,
    "name": "scooter",
    "description": "Big 2-wheel scooter",
    "weight": 5.18
  },
  "after": {
    "id": 111,
    "name": "scooter",
    "description": "Big 2-wheel scooter",
    "weight": 5.15
  },
  "source": {
    "version": "0.9.5.Final",
	"connector": "mysql",
	"name": "fullfillment",
	"server_id" :1,
	"ts_sec": 1629607909,
	"gtid": "mysql-bin.000001",
	"pos": 2238,"row": 0,
	"snapshot": false,
	"thread": 7,
	"db": "inventory",
	"table": "test",
	"query": null},
  "op": "u",
  "ts_ms": 1589362330904,
  "transaction": null
}

Perform the following operations to view the data result in the taskmanager.out file:
1. Log in to the DLI console. In the navigation pane, choose Job Management > Flink Jobs.
2. Click the name of the corresponding Flink job, choose Run Log, click OBS Bucket, and locate the folder of the log you want to view according to the date.
3. Go to the folder of the date, find the folder whose name contains taskmanager, download the .out file, and view result logs.
```
-U[111, scooter, Big 2-wheel scooter, 5.18]
+U[111, scooter, Big 2-wheel scooter, 5.15]
```