Creating a FlinkServer Job to Write Data to a Hudi Table

Scenario

This section describes how to interconnect FlinkServer with Hudi through Flink SQL jobs. When you use Flink SQL to read data from or write data to Hudi, the TINYINT, SMALLINT, and TIME types cannot be defined.

Table 1 lists the read and write operations supported by Flink on Hudi COW and MOR tables.

Prerequisites

• The HDFS, Yarn, Flink, and Hudi services have been installed in a cluster.
• The client that contains the Hudi service has been installed, for example, in the /opt/client directory.
• Flink 1.12.2 or later and Hudi 0.9.0 or later are required.
• You have created a user assigned with the FlinkServer Admin Privilege (for example, flink_admin) for accessing the Flink web UI by referring to Creating a FlinkServer Role. The user has been added to the hadoop, hive, and kafkaadmin user groups and granted the Manager_administrator role.

Creating a Job

1. Log in to Manager as user flink_admin 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.
2. Create a Flink SQL job by referring to Creating a FlinkServer Job. On the job development page, configure the job as follows: Enter the SQL statement. After the SQL statement passes the verification, start the job. The following SQL examples are added as three jobs and run in sequence.

   In Basic Parameter, select Enable CheckPoint, set Time Interval(ms) to 60000, and retain the default value for Mode.

   

   • CheckPoint should be enabled on the Flink web UI because data is written to a Hudi table only when a Flink SQL job triggers CheckPoint. Adjust the CheckPoint interval based on service requirements. You are advised to set the interval to a large number.
   • If the CheckPoint interval is too short, job exceptions may occur due to untimely data updates. It is recommended that the CheckPoint interval be configured at the minute level.
   • Asynchronous compaction is required when a Flink SQL job writes an MOR table. For details about the parameter for controlling the compaction interval, visit Hudi official website https://hudi.apache.org/docs/configurations.html.
   • The following content is available in MRS 3.2.1 or later. By default, Hudi table write statements use Flink status index. To use the bucket index, add the following parameters to the statement:

     'index.type'='BUCKET',
     'hoodie.bucket.index.num.buckets'='Number of buckets in each partition of a Hudi table'
     'hoodie.bucket.index.hash.field'='recordkey.field'

     • hoodie.bucket.index.num.buckets: Number of buckets in each partition of a Hudi table. Data in each partition is stored in each bucket in hash mode. This parameter cannot be modified after being set during table creation or data writing for the first time. Otherwise, an exception occurs during data update.
     • hoodie.bucket.index.hash.field: Field for calculating the hash value during bucketing. The field must be a subset of the primary key. The default value is the primary key of the Hudi table. If this parameter is left blank, the default value recordkey.field is used.
   • The following content is available in MRS 3.2.1 or later. The same Hudi table can be written by the bucket indexes across the Flink and Spark engines.
   1. Job 1: This Flink SQL job writes data to an MOR table in streams.

      CREATE TABLE stream_mor(
      uuid VARCHAR(20),
      name VARCHAR(10),
      age INT,
      ts INT,
      `p` VARCHAR(20)
      ) PARTITIONED BY (`p`) WITH (
      'connector' = 'hudi',
      'path' = 'hdfs://hacluster/tmp/hudi/stream_mor',
      'table.type' = 'MERGE_ON_READ',
      'hoodie.datasource.write.recordkey.field' = 'uuid',
      'write.precombine.field' = 'ts',
      'write.tasks' = '4'
      );
      
      CREATE TABLE kafka(
      uuid VARCHAR(20),
      name VARCHAR(10),
      age INT,
      ts INT,
      `p` VARCHAR(20)
      ) WITH (
      'connector' = 'kafka',
      'topic' = 'writehudi',
      'properties.bootstrap.servers' = 'IP address of the Kafka broker instance:Kafka port number',
      'properties.group.id' = 'testGroup1',
      'scan.startup.mode' = 'latest-offset',
      'format' = 'json',
      'properties.sasl.kerberos.service.name' = 'kafka',--This parameter is not required for clusters in normal mode. Delete the comma (,) in the previous line.
      'properties.security.protocol' = 'SASL_PLAINTEXT',--This parameter is not required for clusters in normal mode.
      'properties.kerberos.domain.name' = 'hadoop.System domain name'--This parameter is not required for clusters in normal mode.
      );
      
      insert into
      stream_mor
      select
      *
      from
      kafka;

   2. Job 2: This Flink SQL job writes data to a COW table in streams.

      CREATE TABLE stream_write_cow(
      uuid VARCHAR(20),
      name VARCHAR(10),
      age INT,
      ts INT,
      `p` VARCHAR(20)
      ) PARTITIONED BY (`p`) WITH (
      'connector' = 'hudi',
      'path' = 'hdfs://hacluster/tmp/hudi/stream_cow',
      'hoodie.datasource.write.recordkey.field' = 'uuid',
      'write.precombine.field' = 'ts',
      'write.tasks' = '4'
      );
      
      CREATE TABLE kafka(
      uuid VARCHAR(20),
      name VARCHAR(10),
      age INT,
      ts INT,
      `p` VARCHAR(20)
      ) WITH (
      'connector' = 'kafka',
      'topic' = 'writehudi',
      'properties.bootstrap.servers' = 'IP address of the Kafka broker instance:Kafka port number',
      'properties.group.id' = 'testGroup1',
      'scan.startup.mode' = 'latest-offset',
      'format' = 'json',
      'properties.sasl.kerberos.service.name' = 'kafka',--This parameter is not required for clusters in normal mode. Delete the comma (,) in the previous line.
      'properties.security.protocol' = 'SASL_PLAINTEXT',--This parameter is not required for clusters in normal mode.
      'properties.kerberos.domain.name' = 'hadoop.System domain name'--This parameter is not required for clusters in normal mode.
      );
      
      insert into
      stream_write_cow
      select
      *
      from
      kafka;

   3. Job 3: This Flink SQL job reads MOR and COW tables in streams, merges data, and outputs the merged data to Kafka. Verify the SQL statement of job 3 and start it after job 1 and job 2 are started and their status is running. Otherwise, an error message may be displayed during SQL verification, indicating that the Hudi table directory cannot be found.

      CREATE TABLE stream_mor(
      uuid VARCHAR(20),
      name VARCHAR(10),
      age INT,
      ts INT,
      `p` VARCHAR(20)
      ) PARTITIONED BY (`p`) WITH (
      'connector' = 'hudi',  
      'path' = 'hdfs://hacluster/tmp/hudi/stream_mor',  
      'table.type' = 'MERGE_ON_READ',
      'hoodie.datasource.write.recordkey.field' = 'uuid',
      'write.precombine.field' = 'ts',
      'read.tasks' = '4',
      'read.streaming.enabled' = 'true',
      'read.streaming.check-interval' = '5',
      'read.streaming.start-commit' = 'earliest'
      );
      CREATE TABLE stream_write_cow(
      uuid VARCHAR(20),
      name VARCHAR(10),
      age INT,
      ts INT,
      `p` VARCHAR(20)
      ) PARTITIONED BY (`p`) WITH (
      'connector' = 'hudi',
      'path' = 'hdfs://hacluster/tmp/hudi/stream_cow',
      'hoodie.datasource.write.recordkey.field' = 'uuid',
      'write.precombine.field' = 'ts',
      'read.tasks' = '4',
      'read.streaming.enabled' = 'true',
      'read.streaming.check-interval' = '5',
      'read.streaming.start-commit' = 'earliest'
      );
      
      CREATE TABLE kafka(
      uuid VARCHAR(20),
      name VARCHAR(10),
      age INT,
      ts INT,
      `p` VARCHAR(20)
      ) WITH (
      'connector' = 'kafka',
      'topic' = 'readhudi',
      'properties.bootstrap.servers' = 'IP address of the Kafka broker instance:Kafka port',
      'properties.group.id' = 'testGroup1',
      'scan.startup.mode' = 'latest-offset',
      'format' = 'json',
      'properties.sasl.kerberos.service.name' = 'kafka',--This parameter is not required for clusters in normal mode. Delete the comma (,) in the previous line.
      'properties.security.protocol' = 'SASL_PLAINTEXT',--This parameter is not required for clusters in normal mode.
      'properties.kerberos.domain.name' = 'hadoop.System domain name'--This parameter is not required for clusters in normal mode.
      );
      
      insert into 
      kafka 
      select 
      * 
      from 
      stream_mor union all select * from stream_write_cow;

   

   • Kafka port
     • Value of sasl.port when Authentication Mode of the cluster is Security Mode, 21007 by default.
     • Value of port when Authentication Mode of the cluster is Normal Mode, 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 page that is displayed, click the Configurations tab then the All Configurations sub-tab. On the displayed page, search for allow.everyone.if.no.acl.found, set it 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.

3. 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 Kafka instances are deployed:Kafka port --topic Topic name --producer.config Client directory/Kafka/kafka/config/producer.properties

   In this example, the topic name is writehudi.

   sh kafka-console-producer.sh --broker-list IP address of the node where the Kafka instance is deployed:Kafka port ----topic writehudi --producer.config /opt/client/Kafka/kafka/config/producer.properties

   Enter the message content.

   {"uuid": "1","name":"a01","age":10,"ts":10,"p":"1"}
   {"uuid": "2","name":"a02","age":20,"ts":20,"p":"2"}

   Press Enter to send the message.

4. Consumes Kafka topic data and reads the result of reading the Hudi table from Flink streams.

   sh kafka-console-consumer.sh --bootstrap-server IP address of the node where the Kafka Broker instance is deployed:Kafka port --topic Topic name --consumer.config Client directory/Kafka/kafka/config/consumer.properties --from-beginning

   In this example, the topic name is readhudi.

   sh kafka-console-consumer.sh --bootstrap-server IP address of the node where the Kafka Broker instance is deployed:Kafka port --topic readhudi --consumer.config /opt/client/Kafka/kafka/config/consumer.properties --from-beginning

   The read result is as follows (the sequence is not fixed):

   {"uuid": "1","name":"a01","age":10,"ts":10,"p":"1"}
   {"uuid": "2","name":"a02","age":20,"ts":20,"p":"2"}
   {"uuid": "1","name":"a01","age":10,"ts":10,"p":"1"}
   {"uuid": "2","name":"a02","age":20,"ts":20,"p":"2"}

Precautions for Using FlinkSQL Lookup Join Hudi

(This topic is available for MRS 3.5.0 and later.)

• The lookup.join.cache.ttl parameter is used to control the loading period of dimension table data. The default value is 60 minutes.
• Do not use a Hudi table with over 100,000 rows of records as a dimension table, as the data will be loaded to Flink TaskManager Heap memory.
• The new and updated data in the dimension table can be loaded for calculation only after the next loading period.

CREATE TABLE hudimor(
  uuid VARCHAR(20),
  name VARCHAR(10),
  age INT,
  ts INT,
  `p` VARCHAR(20),
  PRIMARY KEY (uuid) NOT ENFORCED
) PARTITIONED BY (`p`) WITH (
  'connector' = 'hudi',
  'path' = 'hdfs://hacluster/tmp/hudimor',
  'table.type' = 'MERGE_ON_READ',
  'hoodie.datasource.write.recordkey.field' = 'uuid',
  'write.precombine.field' = 'ts',
  'lookup.join.cache.ttl' = '60min'
);
CREATE TABLE datagen(uuid varchar(20), proctime as PROCTIME()) WITH (
  'connector' = 'datagen',
  'rows-per-second' = '1'
);
CREATE TABLE blackhole (
  uuid VARCHAR(20),
  name VARCHAR(10),
  age INT,
  ts INT,
  `p` VARCHAR(20)
) WITH ('connector' = 'blackhole');
insert into
  blackhole
select
  t1.uuid as uuid,
  t2.name as name,
  t2.age as age,
  t2.ts as ts,
  t2.p as p
FROM
  datagen AS t1
  left JOIN hudimor FOR SYSTEM_TIME AS OF t1.proctime AS t2 ON t1.uuid = t2.uuid;

WITH Parameters

Synchronizing Metadata from Flink On Hudi to Hive

After this feature is enabled, Flink automatically creates a Hudi table on Hive and adds partitions to the Hudi table when writing data to it. Then services such as SparkSQL and Hive can read data form the Hudi table.

The metadata can be synchronized with either of the following methods. The JDBC mode is used as an example in the following steps.