Debezium

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.

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.

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.

1. 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.
2. 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;

3. 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
   }

4. 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]