HBase Dimension Table

Function

Create a Hbase dimension table to connect to the source streams for wide table generation.

Prerequisites

• An enhanced datasource connection has been created for DLI to connect to HBase, so that jobs can run on the dedicated queue of DLI and you can set the security group rules as required.
• If MRS HBase is used, IP addresses of all hosts in the MRS cluster have been added to host information of the enhanced datasource connection.

  .

  For details, see section "Modifying the Host Information" in the Data Lake Insight User Guide.

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.
• All the column families in HBase table must be declared as ROW type, the field name maps to the column family name, and the nested field names map to the column qualifier names. There is no need to declare all the families and qualifiers in the schema, users can declare what is used in the query. Except the ROW type fields, the single atomic type field (for example, STRING, BIGINT) will be recognized as HBase rowkey. The rowkey field can be an arbitrary name, but should be quoted using backticks if it is a reserved keyword.

Syntax

create table hbaseSource (
  attr_name attr_type 
  (',' attr_name attr_type)* 
 )
with (
  'connector' = 'hbase-2.2',
  'table-name' = '',
  'zookeeper.quorum' = ''
);

Parameters

Data Type Mapping

HBase stores all data as byte arrays. The data needs to be serialized and deserialized during read and write operation.

When serializing and de-serializing, Flink HBase connector uses utility class org.apache.hadoop.hbase.util.Bytes provided by HBase (Hadoop) to convert Flink data types to and from byte arrays.

Flink HBase connector encodes null values to empty bytes, and decode empty bytes to null values for all data types except string type. For string type, the null literal is determined by null-string-literal option.

Example

In this example, data is read from a Kafka data source, an HBase table is used as a dimension table to generate a wide table, and the result is written to a Kafka result table. The procedure is as follows (the HBase versions in this example are 1.3.1 and 2.2.3):

1. Create an enhanced datasource connection in the VPC and subnet where HBase and Kafka locate, and bind the connection to the required Flink elastic resource pool. Add MRS host information for the enhanced datasource connection..
2. Set HBase and Kafka security groups and add inbound rules to allow access from the Flink queue. Test the connectivity using the HBase and Kafka address. If the connection passes the test, it is bound to the queue.
3. Create a HBase table and name it area_info using the HBase shell. The table has only one column family detail. The creation statement is as follows:

   create 'area_info', {NAME => 'detail'}

4. Run the following statement in the HBase shell to insert dimension table data:

   put 'area_info', '330106', 'detail:area_province_name', 'a1'
   put 'area_info', '330106', 'detail:area_city_name', 'b1'
   put 'area_info', '330106', 'detail:area_county_name', 'c2'
   put 'area_info', '330106', 'detail:area_street_name', 'd2'
   put 'area_info', '330106', 'detail:region_name', 'e1'
   
   put 'area_info', '330110', 'detail:area_province_name', 'a1'
   put 'area_info', '330110', 'detail:area_city_name', 'b1'
   put 'area_info', '330110', 'detail:area_county_name', 'c4'
   put 'area_info', '330110', 'detail:area_street_name', 'd4'
   put 'area_info', '330110', 'detail:region_name', 'e1'

5. 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 an HBase 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,   
     detail row(
       area_province_name string, 
       area_city_name string, 
       area_county_name string, 
       area_street_name string, 
       region_name string) 
   ) WITH (
     'connector' = 'hbase-2.2',
     'table-name' = 'area_info',
     'zookeeper.quorum' = 'ZookeeperAddress:ZookeeperPort',
     'lookup.async' = 'true',
     'lookup.cache.max-rows' = '10000',
     'lookup.cache.ttl' = '2h'
   );
   
   -- 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' = '<yourSinkTopic>',
     '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;

6. Connect to the Kafka cluster and insert the following test data into the source topic in 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"}
   
   {"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"}

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

   {"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","area_province_name":"a1","area_city_name":"b1","area_county_name":"c2","area_street_name":"d2","region_name":"e1"}
   
   {"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"}

FAQs

Q: What should I do if Flink job logs contain the following error information?

org.apache.zookeeper.ClientCnxn$SessionTimeoutException: Client session timed out, have not heard from server in 90069ms for connection id 0x0

A: The datasource connection is not bound or the binding fails. Configure the datasource connection or configure the security group of the Kafka cluster to allow access from the DLI queue.