Redis Dimension Table

Function

Create a Redis table to connect to source streams for wide table generation.

Prerequisites

• An enhanced datasource connection with Redis has been established, so that you can configure security group rules as required.
  • For details about how to set up 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 Overview 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 you create a Flink OpenSource SQL job, set Flink Version to 1.12 in the Running Parameters tab. Select Save Job Log, and specify 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.
• If 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, there can be only one non-primary key in addition to the primary key and the non-primary key must be of the map type. The map values of the non-primary key must be of the double type, indicating the score. The keys in the map are the values 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

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

Read data from a Kafka source table, use a Redis table as the dimension table. Write wide table information generated by the source and dimension tables to a Kafka result table. The procedure is as follows:

1. Create an enhanced datasource connection in the VPC and subnet where Redis and Kafka locates, and bind the connection to the required Flink elastic resource pool. For details, see Enhanced Datasource Connections.
2. Set Redis and Kafka 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 passes the test, it is bound to the queue.
3. Run the following commands on the Redis client to send data to Redis:

   HMSET 330102  area_province_name a1 area_province_name b1 area_county_name c1 area_street_name d1 region_name e1
   
   HMSET 330106  area_province_name a1 area_province_name b1 area_county_name c2 area_street_name d2 region_name e1
   
   HMSET 330108  area_province_name a1 area_province_name b1 area_county_name c3 area_street_name d3 region_name e1
   
   HMSET 330110  area_province_name a1 area_province_name b1 area_county_name c4 area_street_name d4 region_name e1

4. Create a Flink OpenSource SQL job Enter the following job script and submit the job. The job script uses Kafka as the data source and a Redis table as the dimension table. Data is output to a Kafka result table.
   When you create a job, set Flink Version to 1.12 in the Running Parameters tab. Select Save Job Log, and specify the OBS bucket for saving job logs. Set the values of the parameters in bold in the following script as needed.

   CREATE TABLE orders (
     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,
     proctime as Proctime()
   ) WITH (
     'connector' = 'kafka',
     'topic' = 'kafkaSourceTopic',
     'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
     'properties.group.id' = 'GroupId',
     'scan.startup.mode' = 'latest-offset',
     'format' = 'json'
   );
   
   -- Create an address dimension table
   create table area_info (
       area_id string, 
       area_province_name string,
       area_city_name string,
       area_county_name string, 
       area_street_name string, 
       region_name string, 
       primary key (area_id) not enforced -- Redis key
   ) WITH (
     'connector' = 'redis',
     'host' = 'RedisIP',
     'password' = 'RedisPassword',
     'data-type' = 'hash',
     'deploy-mode' = 'master-replica'
   );
   
   -- Generate a wide table based on the address dimension table containing detailed order information.
   create table order_detail(
       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,
       area_province_name string,
       area_city_name string,
       area_county_name string,
       area_street_name string,
       region_name string
   ) with (
     'connector' = 'kafka',
     'topic' = 'kafkaSinkTopic',
     'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
     'format' = 'json'
   );
   
   insert into order_detail
       select orders.order_id, orders.order_channel, orders.order_time, orders.pay_amount, orders.real_pay, orders.pay_time, orders.user_id, orders.user_name,
              area.area_id, area.area_province_name, area.area_city_name, area.area_county_name,
              area.area_street_name, area.region_name  from orders
       left join area_info for system_time as of orders.proctime as area on orders.area_id = area.area_id;

5. Connect to the Kafka cluster and insert the following test data into the source topic in Kafka:

   {"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"}
   
   {"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"}
   
   {"order_id":"202103251505050001", "order_channel":"qqShop", "order_time":"2021-03-25 15:05:05", "pay_amount":"500.00", "real_pay":"400.00", "pay_time":"2021-03-25 15:10:00", "user_id":"0003", "user_name":"Cindy", "area_id":"330108"}

6. Connect to the Kafka cluster and read data from the sink topic of Kafka. The result data is as follows:

   {"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","area_province_name":"a1","area_city_name":"b1","area_county_name":"c2","area_street_name":"d2","region_name":"e1"}
   
   {"order_id":"202103251202020001","order_channel":"miniAppShop","order_time":"2021-03-25 12:02:02","pay_amount":60.0,"real_pay":60.0,"pay_time":"2021-03-25 12:03:00","user_id":"0002","user_name":"Bob","area_id":"330110","area_province_name":"a1","area_city_name":"b1","area_county_name":"c4","area_street_name":"d4","region_name":"e1"}
   
   {"order_id":"202103251505050001","order_channel":"qqShop","order_time":"2021-03-25 15:05:05","pay_amount":500.0,"real_pay":400.0,"pay_time":"2021-03-25 15:10:00","user_id":"0003","user_name":"Cindy","area_id":"330108","area_province_name":"a1","area_city_name":"b1","area_county_name":"c3","area_street_name":"d3","region_name":"e1"}
   

FAQs

If Chinese characters are written to the Redis in the Windows environment, an exception will occur during data writing.