Redis Source Table

Function

Create a source stream to obtain data from Redis as input for jobs.

Prerequisites

• An enhanced datasource connection has been created for DLI to connect to the Redis database, so that you can configure security group rules as required.
  • For details about how to create an enhanced datasource connection, see Enhanced Datasource Connections in the Data Lake Insight User Guide.
  • For details about how to configure security group rules, see Security Group in the Virtual Private Cloud User Guide.
• In Flink cross-source development scenarios, there is a risk of password leakage if datasource authentication information is directly configured. You are advised to use the datasource authentication provided by DLI.

  For details about datasource authentication, see Introduction to Datasource Authentication.

Precautions

• When creating a Flink OpenSource SQL job, you need to set Flink Version to 1.12 on the Running Parameters tab of the job editing page, select Save Job Log, and set the OBS bucket for saving job logs.
• To obtain the key values, you can set the primary key in Flink. The primary key maps to the Redis key.
• The primary key cannot be a composite primary key, and only can be one field.
• Constraints on schema-syntax:
  • If schema-syntax is map or array, there can be only one non-primary key and it must be of the same map or array type.
  • If schema-syntax is fields-scores, the number of non-primary keys must be an even number, and the second key of every two keys except the primary key must be of the double type. The double value is the score of the previous key. The following is an example:

    CREATE TABLE redisSource (
      redisKey string,
      order_id string,
      score1 double,
      order_channel string,
      score2 double,
      order_time string,
      score3 double,
      pay_amount double,
      score4 double,
      real_pay double,
      score5 double,
      pay_time string,
      score6 double,
      user_id string,
      score7 double,
      user_name string,
      score8 double,
      area_id string,
      score9 double,
      primary key (redisKey) not enforced
    ) WITH (
      'connector' = 'redis',
      'host' = 'RedisIP',
      'password' = 'RedisPassword',
      'data-type' = 'sorted-set',
      'deploy-mode' = 'master-replica',
      'schema-syntax' = 'fields-scores'
    );

• Restrictions on data-type:
  • When data-type is set, the types of non-primary keys defined in Flink must be the same.
  • If data-type is sorted-set and schema-syntax is fields or array, only sorted-set values can be read from Redis, and the score value cannot be read.
  • If data-type is string, only one non-primary key field is allowed.
  • If data-type is sorted-set and schema-syntax is map, only one non-primary key field is allowed besides the primary key field.

    This non-primary key field must be of the map type. The map value of the field must be of the double type, indicating the score. The map key of the field indicates the value in the Redis set.

  • If data-type is sorted-set and schema-syntax is array-scores, only two non-primary keys are allowed and must be of the array type.
    The first key indicates values in the Redis set. The second key is of the array<double> type, indicating index scores. The following is an example:

    CREATE TABLE redisSink (
      order_id string,
      arrayField Array<String>,
      arrayScore array<double>,
      primary key (order_id) not enforced
    ) WITH (
      'connector' = 'redis',
      'host' = 'RedisIP',
      'password' = 'RedisPassword',
      'data-type' = 'sorted-set',
      "default-score" = '3',
      'deploy-mode' = 'master-replica',
      'schema-syntax' = 'array-scores'
    );

Syntax

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create table dwsSource (
  attr_name attr_type 
  (',' attr_name attr_type)* 
  (',' watermark for rowtime_column_name as watermark-strategy_expression)
  ,PRIMARY KEY (attr_name, ...) NOT ENFORCED
)
with (
  'connector' = 'redis',
  'host' = ''
);

Parameters

Example

In this example, data is read from the DCS Redis data source and written to the Print result table. The procedure is as follows:

1. Create an enhanced datasource connection in the VPC and subnet where Redis locates, and bind the connection to the required Flink elastic resource pool. For details, see Enhanced Datasource Connections.
2. Set Redis security groups and add inbound rules to allow access from the Flink queue. Test the connectivity using the Redis address by referring to Testing Address Connectivity. If the connection is successful, the datasource is bound to the queue. Otherwise, the binding fails.
3. Run the following commands on the Redis client to insert data into different keys and store the data in hash format:

   HMSET redisSource 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
   
   HMSET redisSource1 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
   
   HMSET redisSource2 order_id 202103251202020001 order_channel miniAppShop order_time "2021-03-25 12:02:02" pay_amount 60.00 real_pay 60.00 pay_time "2021-03-25 12:03:00" user_id 0002 user_name Bob area_id 330110

4. Create a Flink OpenSource SQL job. Enter the following job script to read data in hash format from Redis.
   When you create a job, set Flink Version to 1.12 on 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 redisSource (
     redisKey string,
     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 (redisKey) not enforced  --Obtains the key value from Redis.
   ) WITH (
     'connector' = 'redis',
     'host' = 'RedisIP',
     'password' = 'RedisPassword',
     'data-type' = 'hash',
     'deploy-mode' = 'master-replica'
   );
   
   CREATE TABLE printSink (
     redisKey string,
     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 redisSource;

5. 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 taskmanager.out file, and view result logs.

   The data result is as follows:

   +I(redisSource1,202103241606060001,appShop,2021-03-24 16:06:06,200.0,180.0,2021-03-24 16:10:06,0001,Alice,330106)
   +I(redisSource,202103241000000001,webShop,2021-03-24 10:00:00,100.0,100.0,2021-03-24 10:02:03,0001,Alice,330106)
   +I(redisSource2,202103251202020001,miniAppShop,2021-03-25 12:02:02,60.0,60.0,2021-03-25 12:03:00,0002,Bob,330110)

FAQ

• Q: What should I do if the Flink job execution fails and the log contains the following error information?

  Caused by: org.apache.flink.client.program.ProgramInvocationException: The main method caused an error: RealLine:36;Usage of 'set' data-type and 'fields' schema syntax in source Redis connector with multiple non-key column types. As 'set' in Redis is not sorted, it's not possible to map 'set's values to table schema with different types.

  A: If data-type is set, the data types of non-primary key fields in Flink are different. As a result, this error is reported. When data-type is set, the types of non-primary keys defined in Flink must be the same.

• Q: If data-type is hash, what are the differences between schema-syntax set to fields and that to map?

  A: When schema-syntax is set to fields, the hash value in the Redis key is assigned to the field with the same name in Flink. When schema-syntax is set to map, the hash key and hash value of each hash in Redis are put into a map, which represents the value of the corresponding Flink field. Specifically, this map contains all hash keys and hash values of a key in Redis.

  • For fields:
    1. Insert the following data into Redis:

       HMSET redisSource 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

    2. When schema-syntax is set to fields, use the following job script:

       CREATE TABLE redisSource (
         redisKey string,
         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 (redisKey) not enforced
       ) WITH (
         'connector' = 'redis',
         'host' = 'RedisIP',
         'password' = 'RedisPassword',
         'data-type' = 'hash',
         'deploy-mode' = 'master-replica'
       );
       
       CREATE TABLE printSink (
         redisKey string,
         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 redisSource;

    3. The job execution result is as follows:

       +I(redisSource,202103241000000001,webShop,2021-03-24 10:00:00,100.0,100.0,2021-03-24 10:02:03,0001,Alice,330106)

  • For map:
    1. Insert the following data into Redis:

       HMSET redisSource 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

    2. When schema-syntax is set to map, use the following job script:

       CREATE TABLE redisSource (
         redisKey string,
         order_result map<string, string>,
         primary key (redisKey) not enforced
       ) WITH (
         'connector' = 'redis',
         'host' = 'RedisIP',
         'password' = 'RedisPassword',
         'data-type' = 'hash',
         'deploy-mode' = 'master-replica',
         'schema-syntax' = 'map'
       );
       
       CREATE TABLE printSink (
         redisKey string,
         order_result map<string, string>
       ) WITH (
         'connector' = 'print'
       );
       
       insert into printSink select * from redisSource;

    3. The job execution result is as follows:

       +I(redisSource,{user_id=0001, user_name=Alice, pay_amount=100.00, real_pay=100.00, order_time=2021-03-24 10:00:00, area_id=330106, order_id=202103241000000001, order_channel=webShop, pay_time=2021-03-24 10:02:03})