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

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.

For details, see Avro Format.

Supported Connectors

  • Kafka
  • Upsert Kafka
  • FileSystem

Parameters

Table 1 Parameter

Parameter

Mandatory

Default value

Type

Description

format

Yes

None

String

Format to be used. Set the value to avro.

avro.codec

No

None

String

For Filesystem only, the compression codec for avro. Snappy compression as default. The valid enumerations are: null, 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.

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

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.

  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 1.15. 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' = 'kafkaTopic',
      'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
      'properties.group.id' = 'GroupId',
      '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 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[202103241000000001, webShop, 2021-03-24 10:00:00, 100.0, 100.0, 2021-03-24 10:02:03, 0001, Alice, 330106]
    +I[202103241606060001, appShop, 2021-03-24 16:06:06, 200.0, 180.0, 2021-03-24 16:10:06, 0001, Alice, 330106]