Maxwell

Function

Maxwell is a Changelog Data Capture (CDC) tool that can stream changes in real-time from MySQL into Kafka and other streaming connectors. Maxwell provides a unified format schema for changelog and supports to serialize messages using JSON.

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

Supported Connectors

• Kafka
• FileSystem

Caveats

The Maxwell application allows to deliver every change event exactly-once. Flink works pretty well when consuming Maxwell produced events in this situation. If Maxwell application works in at-least-once delivery, it 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. Thus, it is recommended setting job configuration table.exec.source.cdc-events-duplicate to true and define PRIMARY KEY on the source in this situation. Framework will generate an additional stateful operator, and use the primary key to deduplicate the change events and produce a normalized changelog stream.

Parameters

Metadata

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

The following is an example of using metadata:

CREATE TABLE KafkaTable (
  origin_database STRING METADATA FROM 'value.database' VIRTUAL,
  origin_table STRING METADATA FROM 'value.table' VIRTUAL,
  origin_primary_key_columns ARRAY<STRING> METADATA FROM 'value.primary-key-columns' VIRTUAL,
  origin_ts TIMESTAMP(3) METADATA FROM 'value.ingestion-timestamp' 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' = 'maxwell-json'
);

Example

Use Kafka to send data and output the data 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' = 'maxwell-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 (for details about the meaning of each field, see Maxwell documentation):

   {
      "database":"test",
      "table":"e",
      "type":"insert",
      "ts":1477053217,
      "xid":23396,
      "commit":true,
      "position":"master.000006:800911",
      "server_id":23042,
      "thread_id":108,
      "primary_key": [1, "2016-10-21 05:33:37.523000"],
      "primary_key_columns": ["id", "c"],
      "data":{
        "id":111,
        "name":"scooter",
        "description":"Big 2-wheel scooter",
        "weight":5.15
      },
      "old":{
        "weight":5.18
      }
   }

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.

   +I[111, scooter, Big 2-wheel scooter, 5.15]