Avro

Function

Apache Avro is supported for you to read and write Avro data based on an Avro schema with Flink. The Avro schema is derived from the table schema.

Supported Connectors

• Kafka
• Upsert Kafka

Parameters

Data Type Mapping

Currently, the Avro schema is derived from the table schema and cannot be explicitly defined. The following table lists mappings between Flink to Avro types.

In addition to the following types, Flink supports reading/writing nullable types. Flink maps nullable types to Avro union(something, null), where something is an Avro type converted from Flink type.

You can refer to Apache Avro 1.11.0 Specification for more information about Avro types.

Example

Read data from Kafka, deserialize the data to the Avro format, and outputs the data to print.

1. Create a datasource connection for access to 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 1.12. Copy the following statement and submit the job:

   CREATE TABLE kafkaSource (
     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
   ) WITH (
     'connector' = 'kafka',
     'topic' = '<yourTopic>',
     'properties.bootstrap.servers' = '<yourKafkaAddress1>:<yourKafkaPort>,<yourKafkaAddress2>:<yourKafkaPort>,<yourKafkaAddress3>:<yourKafkaPort>',
     'properties.group.id' = '<yourGroupId>',
     'scan.startup.mode' = 'latest-offset',
     "format" = "avro"
   );
   
   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
   ) WITH (
     'connector' = 'print'
   );
   
   insert into printSink select * from kafkaSource;

3. Insert the following data to Kafka using Avro data serialization:

   {"order_id":"202103241000000001","order_channel":"webShop","order_time":"2021-03-24 10:00:00","pay_amount":100.0,"real_pay":100.0,"pay_time":"2021-03-24 10:02:03","user_id":"0001","user_name":"Alice","area_id":"330106"}
   
   {"order_id":"202103241606060001","order_channel":"appShop","order_time":"2021-03-24 16:06:06","pay_amount":200.0,"real_pay":180.0,"pay_time":"2021-03-24 16:10:06","user_id":"0001","user_name":"Alice","area_id":"330106"}

4. Perform the following operations to view the output:
   • Method 1: Locate the job and click More > FlinkUI. Choose Task Managers > Stdout.
   • Method 2: If you allow DLI to save job logs in OBS, view the output in the taskmanager.out file.

     +I(202103241000000001,webShop,2021-03-2410:00:00,100.0,100.0,2021-03-2410:02:03,0001,Alice,330106)
     +I(202103241606060001,appShop,2021-03-2416:06:06,200.0,180.0,2021-03-2416:10:06,0001,Alice,330106)