Updated on 2024-08-19 GMT+08:00

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,

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 and so on

Flink also supports to encode the INSERT/UPDATE/DELETE messages in Flink SQL as Maxwell JSON 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 Maxwell messages.

For details, see Maxwell Format.

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

Table 1 Parameters

Parameter

Mandatory

Default Value

Type

Description

format

Yes

None

String

Format to be used. Set this parameter to maxwell-json.

maxwell-json.ignore-parse-errors

No

false

Boolean

Whether fields and rows with parse errors will be skipped or failed. Fields are set to null in case of errors.

maxwell-json.timestamp-format.standard

No

'SQL'

String

Specify the input and output timestamp format. Currently supported values are SQL and ISO-8601:

  • SQL will parse input timestamp in "yyyy-MM-dd HH:mm:ss.s{precision}" format, e.g '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, e.g '2020-12-30T12:13:14.123' and output timestamp in the same format.

maxwell-json.map-null-key.mode

No

'FAIL'

String

Specify the handling mode when serializing null keys for map data. Currently supported values are FAIL, DROP, and LITERAL:

  • FAIL will throw exception when encountering map with null key.
  • DROP will drop null key entries for map data.
  • LITERAL will replace null key with string literal. The string literal is defined by maxwell-json.map-null-key.literal.

maxwell-json.map-null-key.literal

No

'null'

String

Specify string literal to replace null key when maxwell-json.map-null-key.mode is LITERAL.

maxwell-json.encode.decimal-as-plain-number

No

false

Boolean

Encode all decimals as plain numbers instead of possible scientific notations. By default, decimals may be written using scientific notation. 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.

Table 2 Metadata

Key

Data Type

Description

database

STRING NULL

The originating database. Corresponds to the database field in the Maxwell record if available.

table

STRING NULL

The originating database table. Corresponds to the table field in the Maxwell record if available.

primary-key-columns

ARRAY<STRING> NULL

Array of primary key names. Corresponds to the primary_key_columns field in the Maxwell record if available.

ingestion-timestamp

TIMESTAMP_LTZ(3) NULL

The timestamp at which the connector processed the event. Corresponds to the ts field in the Maxwell record.

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]