Function

The Avro Schema Registry (avro-confluent) format allows you to read records that were serialized by the io.confluent.kafka.serializers.KafkaAvroSerializer and to write records that can in turn be read by the io.confluent.kafka.serializers.KafkaAvroDeserializer.

When reading (deserializing) a record with this format the Avro writer schema is fetched from the configured Confluent Schema Registry based on the schema version ID encoded in the record while the reader schema is inferred from table schema.

When writing (serializing) a record with this format the Avro schema is inferred from the table schema and used to retrieve a schema ID to be encoded with the data The lookup is performed with in the configured Confluent Schema Registry under the subject. The subject is specified by avro-confluent.schema-registry.subject.

Supported Connectors

kafka
upsert kafka

Parameters

**Table 1** Parameter description
Parameter	Mandatory	Default Value	Type	Description
format	Yes	None	String	Format to be used. Set this parameter to avro-confluent.
avro-confluent.schema-registry.subject	No	None	String	The Confluent Schema Registry subject under which to register the schema used by this format during serialization. By default, kafka and upsert-kafka connectors use <topic_name>-value or <topic_name>-key as the default subject name if this format is used as the value or key format.
avro-confluent.schema-registry.url	Yes	None	String	URL of the Confluent Schema Registry to fetch/register schemas.

Example

1. Read JSON data from the source topic in Kafka and write the data in Confluent Avro format to the sink topic.

Create a datasource connection for the communication with the VPC and subnet where Kafka and ECS locate 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 and ECS IP addresses. 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.
Purchase an ECS cluster, download Confluent 5.5.2 (https://packages.confluent.io/archive/5.5/) and jdk1.8.0_232, and upload them to the ECS cluster. Run the following command to decompress the packages (assume that the decompression directories are confluent-5.5.2 and jdk1.8.0_232):
```
tar zxvf confluent-5.5.2-2.11.tar.gz
tar zxvf jdk1.8.0_232.tar.gz
```
Run the following commands to install jdk1.8.0_232 in the current ECS cluster. You can run the pwd command in the jdk1.8.0_232 folder to view the value of yourJdkPath.
```
export JAVA_HOME=<yourJdkPath>
export PATH=$JAVA_HOME/bin:$PATH
export CLASSPATH=.:$JAVA_HOME/lib:$JAVA_HOME/jre/lib
```
Go to the confluent-5.5.2/etc/schema-registry/ directory and modify the following configuration items in the schema-registry.properties file:
```
listeners=http://<yourEcsIp>:8081  
kafkastore.bootstrap.servers=<yourKafkaAddress1>:<yourKafkaPort>,<yourKafkaAddress2>:<yourKafkaPort>
```
Switch to the confluent-5.5.2 directory and run the following command to start Confluent:
```
bin/schema-registry-start etc/schema-registry/schema-registry.properties
```

Create a Flink opensource SQL job, select the Flink 1.12 version, and allow DLI to save job logs in OBS. Add the following statement to the job and submit it:

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',
  'properties.bootstrap.servers' = '<yourKafkaAddress1>:<yourKafkaPort>,<yourKafkaAddress2>:<yourKafkaPort>',
  'topic' = '<yourSourceTopic>',
  'properties.group.id' = '<yourGroupId>',
  'scan.startup.mode' = 'latest-offset',
  'format' = 'json'
);
CREATE TABLE kafkaSink (
  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',
  'properties.bootstrap.servers' = '<yourKafkaAddress1>:<yourKafkaPort>,<yourKafkaAddress2>:<yourKafkaPort>',
  'topic' = '<yourSinkTopic>',
  'format' = 'avro-confluent',
  'avro-confluent.schema-registry.url' = 'http://<yourEcsIp>:8081',
  'avro-confluent.schema-registry.subject' = '<yourSubject>'
);
insert into kafkaSink select * from kafkaSource;

Insert the following data into Kafka:

{"order_id":"202103241000000001", "order_channel":"webShop", "order_time":"2021-03-24 10:00:00", "pay_amount":"100.00", "real_pay":"100.00", "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":"200.00", "real_pay":"180.00", "pay_time":"2021-03-24 16:10:06", "user_id":"0001", "user_name":"Alice", "area_id":"330106"}

Read the data of the sink Kafka topic. You will find that the data has been written and the schema has been saved to the _schema topic of Kafka.