Interconnecting FlinkServer with Hudi

Scenario

This section describes how to interconnect FlinkServer with Hudi through Flink SQL jobs.

Prerequisites

• The HDFS, Yarn, Hive, Spark, Flink, and Kafka services have been installed in a cluster.
• The client that contains the Flink and Kafka services has been installed in a directory, for example, /opt/client.
• 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.

Flink Support for Read and Write Operations on Hudi Tables

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

Procedure

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 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. This operation is required for all jobs.

   Enable the fault recovery policy to improve job reliability. For example, set Failure Recovery Policy to fixed-delay, Retry Times to 3, and Retry Interval to 30. You can set the latter two parameters based on service requirements.

   Wait until the job is started and its status is Running, choose More > Job Monitoring to go to the native UI of Flink, and view the job status.

   

   • 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.
   • By default, writing data to a Hudi table is to save Flink's state indexes to the backend. To use bucket indexes, add the following parameters to the Hudi table:

     '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.
   • For a Hudi table, bucket indexes of Flink and Spark can be saved to the backend together.
   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 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 
      kafka 
      select 
      * 
      from 
      stream_mor union all select * from stream_write_cow;

   

   • The IP address and port number of the Kafka broker instance are as follows:
     • To obtain the instance IP address, log in to FusionInsight Manager, choose Cluster > Services > Kafka, click Instance, and query the instance IP address on the instance list page.
     • If Kerberos authentication is enabled for the cluster (the cluster is in security mode), the Broker port number is the value of sasl.port. The default value is 21007.
     • If Kerberos authentication is disabled for the cluster (the cluster is in normal mode), the broker port number is the value of port. The default value is 9092. 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. Click Configurations then All Configurations. On the page that is displayed, 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 reside:Kafka port number --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 located:Kafka port number ----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 Kafka instances reside:Kafka port number --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 Kafka role instance: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"}

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.

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 Job. On the job development page, configure the job. Enter the SQL statement. After the SQL statement passes the verification, start the job.

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

   CREATE TABLE stream_mor2(
   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_mor2',
   'table.type' = 'MERGE_ON_READ',
   'hoodie.datasource.write.recordkey.field' = 'uuid',
   'write.precombine.field' = 'ts',
   'write.tasks' = '4',
   'hive_sync.enable' = 'true',
   'hive_sync.table' = 'Name of the table to be synchronized to Hive, for example, stream_mor2',
   'hive_sync.db' = 'Name of the database to be synchronized to Hive, for example, default',
   'hive_sync.metastore.uris' = 'Value of hive.metastore.uris in the hive-site.xml file on the Hive client',
   'hive_sync.jdbc_url' = 'Value of CLIENT_HIVE_URI in the component_env file on the Hive client'
   );
   CREATE TABLE datagen (
   uuid varchar(20), name varchar(10), age int, ts INT, p varchar(20)
   ) WITH (
   'connector' = 'datagen',
   'rows-per-second' = '1',
   'fields.p.length' = '1'
   );insert into stream_mor2 select * from datagen;

3. Wait for the Flink job to run for a period of time and continuously write the random test data generated by datagen to the Hudi table. You can click More > Job Monitoring to go to the native UI of Flink and view the job status.
4. Log in to the node where the client is deployed, load environment variables, run the beeline command to log in to the Hive client, and run SQL statements to check whether the Hudi Sink table is successfully created on Hive and whether data can be read from the table.

   cd /opt/hadoopclient

   source bigdata_env

   beeline

   desc formatted default.stream_mor2;

   select * from default.stream_mor2 limit 5;

   show partitions default.stream_mor2;