Interconnecting FlinkServer with ClickHouse

Scenario

Flink interconnects with the ClickHouseBalancer instance of ClickHouse to read and write data, preventing ClickHouse traffic distribution problems.

Prerequisites

• Services such as ClickHouse, HDFS, Yarn, Flink, and Kafka have been installed in the cluster.
• The client has been installed, for example, in /opt/Bigdata/client.

Mapping Between Flink SQL and ClickHouse Data Types

Procedure

1. Log in to the node where the client is installed as user root.
2. Run the following command to go to the client installation directory:

   cd /opt/Bigdata/client

3. Run the following command to configure environment variables:

   source bigdata_env

4. If Kerberos authentication is enabled for the current cluster, run the following command to authenticate the current user. The current user must have the permission to create ClickHouse tables. If Kerberos authentication is disabled for the current cluster, skip this step:

   kinit Component service user

   Example: kinit clickhouseuser

5. Connect to the ClickHouse client. For details, see Using ClickHouse from Scratch.
   • Normal mode:

     clickhouse client --host IP address of the ClickHouse instance --user Username --password 'Password' --port ClickHouse port number

   • Security mode:

     clickhouse client --host IP address of the ClickHouse instance --user Username --password 'Password'--port ClickHouse port number --secure --multiline

6. Run the following statements to create a replication table and a distributed table.
   1. Create a replication table default.test1.

      CREATE TABLE default.test1 on cluster default_cluster
      (
      `pid` Int8,
      `uid` UInt8,
      `Int_16` Int16,
      `Int_32` Int32,
      `Int_64` Int64,
      `String_x` String,
      `String_y` String,
      `float_32` Float32,
      `float_64` Float64,
      `Decimal_x` Decimal32(2),
      `Date_x` Date,
      `DateTime_x` DateTime
      )
      ENGINE = ReplicatedReplacingMergeTree('/clickhouse/tables/{shard}/test1','{replica}')
      PARTITION BY pid
      ORDER BY (pid, DateTime_x);

   2. Create a distributed table test1_all.

      CREATE TABLE test1_all ON CLUSTER default_cluster
      (
      `pid` Int8,
      `uid` UInt8,
      `Int_16` Int16,
      `Int_32` Int32,
      `Int_64` Int64,
      `String_x` String,
      `String_y` String,
      `float_32` Float32,
      `float_64` Float64,
      `Decimal_x` Decimal32(2),
      `Date_x` Date,
      `DateTime_x` DateTime
      )
      ENGINE = Distributed(default_cluster, default, test1, rand());

7. Log in to Manager and choose Cluster > Services > Flink. In the Basic Information area, click the link on the right of Flink WebUI to access the Flink web UI.
8. Create a Flink SQL job and set Task Type to Stream job. For details, see Creating a Job. On the job development page, configure the job parameters as follows and start the job. Select Enable CheckPoint in Running Parameter and set Time Interval (ms) to 60000.
   • If the current MRS cluster is in security mode, perform the following operations:

     create table kafkasource(
     `pid` TINYINT,
     `uid` BOOLEAN,
     `Int_16` SMALLINT,
     `Int_32` INTEGER,
     `Int_64` BIGINT,
     `String_x` CHAR,
     `String_y` VARCHAR(10),
     `float_32` FLOAT,
     `float_64` DOUBLE,
     `Decimal_x` DECIMAL(9,2),
     `Date_x` DATE,
     `DateTime_x` TIMESTAMP
     ) with(
       'connector' = 'kafka',
       'topic' = 'input',
       'properties.bootstrap.servers' = 'IP address of the Kafka broker instance:Kafka port number',
       'properties.group.id' = 'group1',
       'scan.startup.mode' = 'earliest-offset',
       'format' = 'json',
       'properties.sasl.kerberos.service.name' = 'kafka',
       'properties.security.protocol' = 'SASL_PLAINTEXT',
       'properties.kerberos.domain.name' = 'hadoop.System domain name'
     );
     CREATE TABLE cksink (
     `pid` TINYINT,
     `uid` BOOLEAN,
     `Int_16` SMALLINT,
     `Int_32` INTEGER,
     `Int_64` BIGINT,
     `String_x` CHAR,
     `String_y` VARCHAR(10),
     `float_32` FLOAT,
     `float_64` DOUBLE,
     `Decimal_x` DECIMAL(9,2),
     `Date_x` DATE,
     `DateTime_x` TIMESTAMP
     ) WITH (
     'connector' = 'jdbc',
     'url' = 'jdbc:clickhouse://ClickHouseBalancer instance IP address: ClickHouseBalancer port number/default?ssl=true&sslmode=none',
     'username' = 'ClickHouse user. For details, see the note below.',
     'password' = 'ClickHouse user password. For details, see the note below.',
     'table-name' = 'test1_all',
     'driver' = 'ru.yandex.clickhouse.ClickHouseDriver',
     'sink.buffer-flush.max-rows' = '0',
     'sink.buffer-flush.interval' = '60s'
     );
     Insert into cksink 
     select
     * 
     from 
     kafkasource;

   • If the current MRS cluster is in normal mode, perform the following operations:

     create table kafkasource(
     `pid` TINYINT,
     `uid` BOOLEAN,
     `Int_16` SMALLINT,
     `Int_32` INTEGER,
     `Int_64` BIGINT,
     `String_x` CHAR,
     `String_y` VARCHAR(10),
     `float_32` FLOAT,
     `float_64` DOUBLE,
     `Decimal_x` DECIMAL(9,2),
     `Date_x` DATE,
     `DateTime_x` TIMESTAMP
     ) with(
       'connector' = 'kafka',
       'topic' = 'kinput',
       'properties.bootstrap.servers' = 'IP address of the Kafka broker instance:Kafka port number',
       'properties.group.id' = 'kafka_test',
       'scan.startup.mode' = 'earliest-offset',
       'format' = 'json'
     );
     CREATE TABLE cksink (
     `pid` TINYINT,
     `uid` BOOLEAN,
     `Int_16` SMALLINT,
     `Int_32` INTEGER,
     `Int_64` BIGINT,
     `String_x` CHAR,
     `String_y` VARCHAR(10),
     `float_32` FLOAT,
     `float_64` DOUBLE,
     `Decimal_x` DECIMAL(9,2),
     `Date_x` DATE,
     `DateTime_x` TIMESTAMP
     ) WITH (
     'connector' = 'jdbc',
     'url' = 'jdbc:clickhouse://ClickHouseBalancer instance IP address: ClickHouseBalancer port number/default',
     'table-name' = 'test1_all',
     'driver' = 'ru.yandex.clickhouse.ClickHouseDriver',
     'sink.buffer-flush.max-rows' = '0',
     'sink.buffer-flush.interval' = '60s'
     );
     Insert into cksink 
     select
     * 
     from 
     kafkasource;

   

   • If an MRS cluster is in the security mode, the user in the cksink table must have related permissions on the ClickHouse tables. For details, see ClickHouse User and Permission Management.
   • Kafka port number
     • In security mode, the port number is the value of sasl.port (21007 by default).
     • In non-security mode, the port is the value of port (9092 by default). If the port number is set to 9092, set allow.everyone.if.no.acl.found to true. The procedure is as follows:

       Log in to FusionInsight Manager and choose Cluster > Services > Kafka. On the displayed page, click Configurations and then All Configurations, search for allow.everyone.if.no.acl.found, set its value to true, and click Save.

   • System domain name: You can log in to FusionInsight Manager, choose System > Permission > Domain and Mutual Trust, and check the value of Local Domain. hadoop.<System domain name> indicates the username. All letters in the system domain name are lowercase letters.
   • ClickHouseBalancer port number
     • In security mode, the value is the value of lb_https_port. The default value is 21426.
     • In common mode, the value is the value of lb_tcp_secure_port. The default value is 21428.
   • For details about other JDBC connector parameters, visit the Flink official website.
   • Parameters for batch write: Flink stores data in the memory and then flushes the data to the database table when the trigger condition is met. The configurations are as follows:

     sink.buffer-flush.max-rows: Number of rows written to ClickHouse. The default value is 100.

     sink.buffer-flush.interval: Interval for batch write. The default value is 1s.

     If either of the two conditions is met, a sink operation is triggered. That is, data will be flushed to the database table.

     • Scenario 1: sink every 60s

       'sink.buffer-flush.max-rows' = '0',

       'sink.buffer-flush.interval' = '60s'

     • Scenario 2: sink every 100 rows

       'sink.buffer-flush.max-rows' = '100',

       'sink.buffer-flush.interval' = '0s'

     • Scenario 3: no sink

       'sink.buffer-flush.max-rows' = '0',

       'sink.buffer-flush.interval' = '0s'

9. On the job management page, check whether the job status is Running.
10. Execute the following script to write data to Kafka. For details, see Managing Messages in Kafka Topics.

    sh kafka-console-producer.sh --broker-list IP address of the node where the Kafka instance locates:Kafka port number --topicTopic name --producer.config ../config/producer.properties

    For example, if the topic name is kinput, the script is sh kafka-console-producer.sh --broker-list IP address of the node where the Kafka instance locates:Kafka port number --topic kinput --producer.config ../config/producer.properties.

    Enter the message content.

    {"pid": "3","uid":false,"Int_16": "6533","Int_32": "429496294","Int_64": "1844674407370955614","String_x": "abc1","String_y": "abc1defghi","float_32": "0.1234","float_64": "95.1","Decimal_x": "0.451236414","Date_x": "2021-05-29","DateTime_x": "2021-05-21 10:05:10"},
    {"pid": "4","uid":false,"Int_16": "6533","Int_32": "429496294","Int_64": "1844674407370955614","String_x": "abc1","String_y": "abc1defghi","float_32": "0.1234","float_64": "95.1","Decimal_x": "0.4512314","Date_x": "2021-05-29","DateTime_x": "2021-05-21 10:05:10"}

    Press Enter to send the message.

11. Interconnect with ClickHouse to query the table data.

    clickhouse client --host IP address of the ClickHouse instance --user Username --password 'Password'--port ClickHouse port number --secure --multiline

    Run the following command to check whether data is written to a specified ClickHouse table, for example, test1_all.

    select * from test1_all;