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

**Table 1** Parameters
Parameter	Mandatory	Default value	Type	Description
format	Yes	None	String	Format to be used. Set the value to avro.
avro.codec	No	None	String	Avro compression codec for the file system only The codec is disabled by default. Available values are deflate, snappy, bzip2, and xz.

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.

**Table 2** Data type mapping
Flink SQL Type	Avro Type	Avro Logical Type
CHAR / VARCHAR / STRING	string	-
BOOLEAN	boolean	-
BINARY / VARBINARY	bytes	-
DECIMAL	fixed	decimal
TINYINT	int	-
SMALLINT	int	-
INT	int	-
BIGINT	long	-
FLOAT	float	-
DOUBLE	double	-
DATE	int	date
TIME	int	time-millis
TIMESTAMP	long	timestamp-millis
ARRAY	array	-
MAP (keys must be of the string, char, or varchar type.)	map	-
MULTISET (elements must be of the string, char, or varchar type.)	map	-
ROW	record	-

Example

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

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.

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;

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"}

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)
```