Upsert Kafka

Function

Apache Kafka is a fast, scalable, and fault-tolerant distributed message publishing and subscription system. It delivers high throughput and built-in partitions and provides data replicas and fault tolerance. Apache Kafka is applicable to scenarios of handling massive messages. The Upsert Kafka connector allows for reading data from and writing data into Kafka topics in the upsert fashion. Source tables and result tables are supported.

• As a source, the upsert-kafka connector produces a changelog stream, where each data record represents an update or delete event.

  The value in a data record is interpreted as an UPDATE of the last value for the same key, if any (if a corresponding key does not exist yet, the UPDATE will be considered an INSERT). Using the table analogy, a data record in a changelog stream is interpreted as an UPSERT, also known as INSERT/UPDATE, because any existing row with the same key is overwritten. Also, null values are interpreted in a special way: A record with a null value represents a DELETE.

• As a sink, the upsert-kafka connector can consume a changelog stream. It will write INSERT/UPDATE_AFTER data as normal Kafka messages value, and write DELETE data as Kafka messages with null values (indicate tombstone for the key). Flink will guarantee the message ordering on the primary key by partition data on the values of the primary key columns, so the UPDATE/DELETE messages on the same key will fall into the same partition.

Prerequisites

Caveats

• When you create a Flink OpenSource SQL job, set Flink Version to 1.15 in the Running Parameters tab. Select Save Job Log, and specify the OBS bucket for saving job logs.
• Storing authentication credentials such as usernames and passwords in code or plaintext poses significant security risks. It is recommended using DEW to manage credentials instead. Storing encrypted credentials in configuration files or environment variables and decrypting them when needed ensures security. For details, see .
• The Upsert Kafka always works in the upsert fashion and requires to define the primary key in the DDL. With the assumption that records with the same key should be ordered in the same partition, the primary key semantic on the changelog source means the materialized changelog is unique on the primary keys. The primary key definition will also control which fields should end up in Kafka's key.
• Because the connector is working in upsert mode, the last record on the same key will take effect when reading back as a source.
• For details about how to use data types, see Format.

Syntax

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create table kafkaTable(
  attr_name attr_type 
  (',' attr_name attr_type)* 
  (','PRIMARY KEY (attr_name, ...) NOT ENFORCED)
)
with (
  'connector' = 'upsert-kafka',
  'topic' = '',
  'properties.bootstrap.servers' = '',
  'key.format' = '',
  'value.format' = ''
);

Parameter Description

Metadata

For a list of available metadata fields, see Kafka Connector.

Example

• Example 1: This example reads data from a DMS Kafka data source and writes it to the Print result table.
  1. Create an enhanced datasource connection in the VPC and subnet where Kafka locates, and bind the connection to the required Flink elastic resource pool.
  2. Set Kafka security groups and add inbound rules to allow access from the Flink queue. Test the connectivity using the Kafka address. If the connection passes the test, it is bound to the queue.
  3. Create a Flink OpenSource SQL job. Enter the following job script and submit the job.
     When you create a job, set Flink Version to 1.15 in the Running Parameters tab. Select Save Job Log, and specify the OBS bucket for saving job logs. Change the values of the parameters in bold as needed in the following script.

     CREATE TABLE upsertKafkaSource (
       order_id string,
       order_channel string,
       order_time string,
       pay_amount double,
       real_pay double,
       pay_time string,
       user_id string,
       user_name string,  
       area_id string,
       PRIMARY KEY (order_id) NOT ENFORCED
     ) WITH (
       'connector' = 'upsert-kafka',
       'topic' = 'KafkaTopic',
       'properties.bootstrap.servers' =  'KafkaAddress1:KafkaPort,KafkAddress2:KafkaPort',
       'key.format' = 'csv',
       'value.format' = 'json'
     );
     
     CREATE TABLE printSink (
       order_id string,
       order_channel string,
       order_time string,
       pay_amount double,
       real_pay double,
       pay_time string,
       user_id string,
       user_name string,  
       area_id string,
       PRIMARY KEY (order_id) NOT ENFORCED
     ) WITH (
       'connector' = 'print'
     );
     
     INSERT INTO printSink SELECT * FROM upsertKafkaSource;

  4. Insert the following data to the specified topics in Kafka. (Note: Specify the key when inserting data to Kafka.)

     {"order_id":"202303251202020001", "order_channel":"miniAppShop", "order_time":"2023-03-25 12:02:02", "pay_amount":"60.00", "real_pay":"60.00", "pay_time":"2023-03-25 12:03:00", "user_id":"0002", "user_name":"Bob", "area_id":"330110"}
     
     {"order_id":"202303251505050001", "order_channel":"appshop", "order_time":"2023-03-25 15:05:05", "pay_amount":"500.00", "real_pay":"400.00", "pay_time":"2023-03-25 15:10:00", "user_id":"0003", "user_name":"Cindy", "area_id":"330108"}
     
     {"order_id":"202303251202020001", "order_channel":"miniAppShop", "order_time":"2023-03-25 12:02:02", "pay_amount":"60.00", "real_pay":"60.00", "pay_time":"2023-03-25 12:03:00", "user_id":"0002", "user_name":"Bob", "area_id":"330111"}

  5. View the out file of the TaskManager. The data results are as follows:

     +I(202303251202020001,miniAppShop,2023-03-2512:02:02,60.0,60.0,2023-03-2512:03:00,0002,Bob,330110)
     +I(202303251505050001,appshop,2023-03-25 15:05:05,500.0,400.0,2023-03-2515:10:00,0003,Cindy,330108)
     -U(202303251202020001,miniAppShop,2023-03-2512:02:02,60.0,60.0,2023-03-2512:03:00,0002,Bob,330110)
     +U(202303251202020001,miniAppShop,2023-03-2512:02:02,60.0,60.0,2023-03-2512:03:00,0002,Bob,330111)

FAQ

None