ClickHouse

Function

DLI has the capability to export data from Flink jobs to the ClickHouse database. However, it only supports exporting data to result tables.

ClickHouse is a column-based database oriented to online analysis and processing. It supports SQL query and provides good query performance. The aggregation analysis and query performance based on large and wide tables is excellent, which is one order of magnitude faster than other analytical databases. For details, see Using ClickHouse from Scratch.

Prerequisites

• Your jobs are running on a dedicated queue of DLI.
• You have established an enhanced datasource connection to ClickHouse and set the port in the security group rule of the ClickHouse cluster as needed.

  For details about how to set up an enhanced datasource connection, see Enhanced Datasource Connections.

  For details about how to configure security group rules, see Security Group Overview.

Caveats

• When you create a Flink OpenSource SQL job, set Flink Version to 1.15 in the Running Parameters tab. Select Save Job Log, and specify the OBS bucket for saving job logs.
• Storing authentication credentials such as usernames and passwords in code or plaintext poses significant security risks. It is recommended using DEW to manage credentials instead. Storing encrypted credentials in configuration files or environment variables and decrypting them when needed ensures security. For details, see Flink OpenSource SQL Jobs Using DEW to Manage Access Credentials.
• When you create a ClickHouse cluster for MRS, set the cluster version to MRS 3.1.0 or later.
• The ClickHouse result table does not support table data deletion.
• Flink supports the following data types: string, tinyint, smallint, int, bigint, float, double, date, timestamp, decimal, and array.

  The array supports only the int, bigint, string, float, and double data types.

Syntax

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create table clickhouseSink (
  attr_name attr_type 
  (',' attr_name attr_type)* 
)
with (
  'type' = 'clickhouse',
  'url' = '',
  'table-name' = ''
);

Parameter Description

Example

• Example 1: Read data from Kafka and insert the data into ClickHouse. (The ClickHouse version is 21.3.4.25 of MRS, and Kerberos authentication is not enabled for the MRS cluster):
  1. Create an enhanced datasource connection in the VPC and subnet where ClickHouse and Kafka clusters locate, and bind the connection to the required Flink elastic resource pool. For details, see Enhanced Datasource Connections.
  2. Set ClickHouse and Kafka cluster security groups and add inbound rules to allow access from the Flink queue. Test the connectivity using the ClickHouse address by referring to Testing Address Connectivity. If the connection passes the test, it is bound to the queue.
  3. Use the ClickHouse client to connect to the ClickHouse server and run the following command to query other environment parameters such as the cluster identifier:

     For details, see Using ClickHouse from Scratch.

     select cluster,shard_num,replica_num,host_name from system.clusters;

     The following information is displayed:

     ┌─cluster────┬────┬─shard_num─┐
     │ default_cluster │    1   │           1 │
     │ default_cluster │    1   │           2 │
     └──────── ┴────┴────── ┘

     Run the following command to create database flink on a node of the ClickHouse cluster based on the obtained cluster ID, for example, default_cluster:

     CREATE DATABASE flink ON CLUSTER default_cluster;

  4. Run the following command to create the ReplicatedMergeTree table named order on the node of cluster default_cluster and on database flink:

     CREATE TABLE flink.order ON CLUSTER default_cluster(order_id String,order_channel String,order_time String,pay_amount Float64,real_pay Float64,pay_time String,user_id String,user_name String,area_id String) ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/flink/order', '{replica}')ORDER BY order_id;

  5. Create a Flink OpenSource SQL job. Enter the following job script and submit the job. The job script uses the DMS Kafka data source and the ClickHouse result table.

     Change the values of the parameters in bold as needed in the following script.

     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
     ) WITH (
       'connector' = 'kafka',
       'topic' = 'KafkaTopic',
       'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
       'properties.group.id' = 'GroupId',
       'scan.startup.mode' = 'latest-offset',
       'format' = 'json'
     );
     
     create table clickhouseSink(
       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' = 'clickhouse',
       'url' = 'jdbc:clickhouse://ClickhouseAddress1:ClickhousePort,ClickhouseAddress2:ClickhousePort/flink',
       'table-name' = 'order',
       'sink.buffer-flush.max-rows' = '10',
       'sink.buffer-flush.interval' = '3s'
     );
     
     insert into clickhouseSink select * from orders;

  6. Connect to the Kafka cluster and insert the following test data into DMS 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. Use the ClickHouse client to connect to the ClickHouse and run the following command to query the data written to table order in database flink:

     select * from flink.order;

     The query result is as follows:

     202103241000000001 webShop 2021-03-24 10:00:00 100 100 2021-03-24 10:02:03 0001 Alice 330106
     
     202103241606060001 appShop 2021-03-24 16:06:06 200 180 2021-03-24 16:10:06 0001 Alice 330106 
     
     202103251202020001 miniAppShop 2021-03-25 12:02:02 60 60 2021-03-25 12:03:00 0002 Bob 330110 

• Example 2: Read data from Kafka and insert the data into ClickHouse. The procedure is as follows (The ClickHouse version is 21.3.4.25 of MRS, and Kerberos authentication is enabled for the MRS cluster):
  1. Create an enhanced datasource connection in the VPC and subnet where ClickHouse and Kafka clusters locate, and bind the connection to the required Flink elastic resource pool. For details, see Enhanced Datasource Connections.
  2. Set ClickHouse and Kafka cluster security groups and add inbound rules to allow access from the Flink queue. Test the connectivity using the ClickHouse address by referring to Testing Address Connectivity. If the connection passes the test, it is bound to the queue.
  3. Use the ClickHouse client to connect to the ClickHouse server and run the following command to query other environment parameters such as the cluster identifier:

     Refer to Using ClickHouse from Scratch for more details.

     select cluster,shard_num,replica_num,host_name from system.clusters;

     The following information is displayed:

     ┌─cluster────┬────┬─shard_num─┐
     │ default_cluster │    1   │           1 │
     │ default_cluster │    1   │           2 │
     └──────── ┴────┴────── ┘

     Run the following command to create database flink on a node of the ClickHouse cluster based on the obtained cluster ID, for example, default_cluster:

     CREATE DATABASE flink ON CLUSTER default_cluster;

  4. Run the following command to create the ReplicatedMergeTree table named order on the node of cluster default_cluster and on database flink:

     CREATE TABLE flink.order ON CLUSTER default_cluster(order_id String,order_channel String,order_time String,pay_amount Float64,real_pay Float64,pay_time String,user_id String,user_name String,area_id String) ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/flink/order', '{replica}')ORDER BY order_id;

  5. Create a Flink OpenSource SQL job. Enter the following job script and submit the job. The job script uses the Kafka data source and the ClickHouse result table.

     Change the values of the parameters in bold as needed in the following script.

     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
     ) WITH (
       'connector' = 'kafka',
       'topic' = 'KafkaTopic',
       'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
       'properties.group.id' = 'GroupId',
       'scan.startup.mode' = 'latest-offset',
       'format' = 'json'
     );
     
     create table clickhouseSink(
       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' = 'clickhouse',
       'url' = 'jdbc:clickhouse://ClickhouseAddress1:ClickhousePort,ClickhouseAddress2:ClickhousePort/flink?ssl=true&sslmode=none',
       'table-name' = 'order',
       'username' = 'username',
       'password' = 'password', --Key in the DEW secret
       'sink.buffer-flush.max-rows' = '10',
       'sink.buffer-flush.interval' = '3s',
       'dew.endpoint'='kms.xx.myhuaweicloud.com', --Endpoint information for the DEW service being used
       'dew.csms.secretName'='xx', --Name of the DEW shared secret
       'dew.csms.decrypt.fields'='password', --The password field value must be decrypted and replaced using DEW secret management.
       'dew.csms.version'='v1'
     );
     
     insert into clickhouseSink select * from orders;

  6. Connect to the Kafka cluster and insert the following test data into 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. Use the ClickHouse client to connect to the ClickHouse and run the following command to query the data written to table order in database flink:

     select * from flink.order;

     The query result is as follows:

     202103241000000001 webShop 2021-03-24 10:00:00 100 100 2021-03-24 10:02:03 0001 Alice 330106
     
     202103241606060001 appShop 2021-03-24 16:06:06 200 180 2021-03-24 16:10:06 0001 Alice 330106 
     
     202103251202020001 miniAppShop 2021-03-25 12:02:02 60 60 2021-03-25 12:03:00 0002 Bob 330110