大数据下一代变革之必研究数据湖技术Hudi原理实战双管齐下-后续

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目录

• 集成Flink
  • 编程示例
  • 打包运行
  • CDC入湖
    • 概述
    • MySQL 启用 binlog
    • 初始化MySQL 源数据表
    • 准备Jar包依赖
    • flink读取mysql binlog写入kafka
    • flink读取kafka数据并写入hudi数据湖
  • 调优
    • Memory
    • Parallelism
    • Compaction
• 集成Hive
  • 集成步骤
  • Flink同步Hive
  • Hive Catalog

编程示例

本节通过一个简单Flink写入Hudi表的编程示例，后续可结合自身业务拓展，先创建一个Maven项目，这次就使用Java来编写Flink程序。

由于*仓库没有scala2.12版本的资源，前面文章已经编译好相关jar，那这里就将hudi-flink1.15-bundle-0.12.1.jar手动安装到本地maven仓库

mvn install:install-file -DgroupId=org.apache.hudi -DartifactId=hudi-flink_2.12 -Dversion=0.12.1 -Dpackaging=jar -Dfile=./hudi-flink1.15-bundle-0.12.1.jar

Pom文件内容添加如下内容：

<?xml version="1.0" encoding="UTF-8"?>

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>

  <groupId>cn.itxs</groupId>
  <artifactId>hudi-flink-demo</artifactId>
  <version>1.0</version>

  <name>hudi-flink-demo</name>

  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <scala.version>2.12.10</scala.version>
    <scala.binary.version>2.12</scala.binary.version>
    <hoodie.version>0.12.1</hoodie.version>
    <hadoop.version>3.3.4</hadoop.version>
    <flink.version>1.15.1</flink.version>
    <slf4j.version>2.0.5</slf4j.version>
  </properties>
  <dependencies>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-java</artifactId>
      <version>${flink.version}</version>
      <scope>provided</scope>
    </dependency>

    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-streaming-java</artifactId>
      <version>${flink.version}</version>
      <scope>provided</scope>
    </dependency>

    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-clients</artifactId>
      <version>${flink.version}</version>
      <scope>provided</scope>
    </dependency>

    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-runtime-web</artifactId>
      <version>${flink.version}</version>
      <scope>provided</scope>
    </dependency>

    <dependency>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-api</artifactId>
      <version>${slf4j.version}</version>
    </dependency>

    <dependency>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-log4j12</artifactId>
      <version>${slf4j.version}</version>
    </dependency>

    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-table-planner_${scala.binary.version}</artifactId>
      <version>${flink.version}</version>
      <scope>provided</scope>
    </dependency>

    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-statebackend-rocksdb</artifactId>
      <version>${flink.version}</version>
    </dependency>

    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>${hadoop.version}</version>
      <scope>provided</scope>
    </dependency>
    <dependency>
      <groupId>org.apache.hudi</groupId>
      <artifactId>hudi-flink_${scala.binary.version}</artifactId>
      <version>${hoodie.version}</version>
    </dependency>
  </dependencies>

  <build>
    <plugins>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-compiler-plugin</artifactId>
        <version>3.10.1</version>
        <configuration>
          <source>1.8</source>
          <target>1.8</target>
          <encoding>${project.build.sourceEncoding}</encoding>
        </configuration>
      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-shade-plugin</artifactId>
        <version>3.4.1</version>
        <executions>
          <execution>
            <phase>package</phase>
            <goals>
              <goal>shade</goal>
            </goals>
            <configuration>
              <filters>
                <filter>
                  <artifact>*:*</artifact>
                  <excludes>
                    <exclude>META-INF/*.SF</exclude>
                    <exclude>META-INF/*.DSA</exclude>
                    <exclude>META-INF/*.RSA</exclude>
                  </excludes>
                </filter>
              </filters>
            </configuration>
          </execution>
        </executions>
      </plugin>
    </plugins>
  </build>
</project>

创建一个HudiDemo的Java文件实现一个简单写入hudi表流程

package cn.itxs;

import org.apache.flink.configuration.Configuration;
import org.apache.flink.contrib.streaming.state.EmbeddedRocksDBStateBackend;
import org.apache.flink.contrib.streaming.state.PredefinedOptions;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

import java.util.concurrent.TimeUnit;

public class HudiDemo
{
    public static void main( String[] args )
    {
        //StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 本地启动flink的web页面
        StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(new Configuration());

        EmbeddedRocksDBStateBackend embeddedRocksDBStateBackend = new EmbeddedRocksDBStateBackend(true);
        embeddedRocksDBStateBackend.setDbStoragePath("file:///D:/rocksdb");
        embeddedRocksDBStateBackend.setPredefinedOptions(PredefinedOptions.SPINNING_DISK_OPTIMIZED_HIGH_MEM);
        env.setStateBackend(embeddedRocksDBStateBackend);

        env.enableCheckpointing(TimeUnit.SECONDS.toMillis(5), CheckpointingMode.EXACTLY_ONCE);
        CheckpointConfig checkpointConfig = env.getCheckpointConfig();
        checkpointConfig.setCheckpointStorage("hdfs://hadoop1:9000/checkpoints/flink");
        checkpointConfig.setMinPauseBetweenCheckpoints(TimeUnit.SECONDS.toMillis(2));
        checkpointConfig.setTolerableCheckpointFailureNumber(5);
        checkpointConfig.setCheckpointTimeout(TimeUnit.MINUTES.toMillis(1));
        checkpointConfig.setExternalizedCheckpointCleanup(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);

        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
        tableEnv.executeSql("CREATE TABLE source_a2 (\n" +
                " uuid varchar(20),\n" +
                " name varchar(10),\n" +
                " age int,\n" +
                " ts timestamp(3),\n" +
                " `partition` varchar(20),\n" +
                " PRIMARY KEY(uuid) NOT ENFORCED\n" +
                " ) WITH (\n" +
                " 'connector' = 'datagen',\n" +
                " 'rows-per-second' = '1'\n" +
                ")"
        );

        tableEnv.executeSql("CREATE TABLE a2 (\n" +
                " uuid varchar(20),\n" +
                " name varchar(10),\n" +
                " age int,\n" +
                " ts timestamp(3),\n" +
                " `partition` varchar(20),\n" +
                "PRIMARY KEY(uuid) NOT ENFORCED\n" +
                " ) WITH (\n" +
                " 'connector' = 'hudi',\n" +
                " 'path' = 'hdfs://hadoop1:9000/tmp/hudi_flink/a2',\n" +
                " 'table.type' = 'MERGE_ON_READ'\n" +
                ")"
        );

        tableEnv.executeSql("insert into a2 select * from source_a2");

    }
}

通过使用createLocalEnvironmentWithWebUI开启动FlinkWebUI,也即是可以在本地上查看flink的web页面

本地rocksdb状态后端也有对应的存储数据

HDFS上也可以查看到刚刚创建的hudi表信息

打包运行

对上面小修改一下代码，将最前面的环境中注释createLocalEnvironmentWithWebUI和setDbStoragePath，放开getExecutionEnvironment;将表名改为a3，执行mvn package编译打包，将打包的文件上传

flink run -t yarn-per-job -c cn.itxs.HudiDemo /home/commons/flink-1.15.1/otherjars/hudi-flink-demo-1.0.jar

运行日志如下

查看Yarn的application_1669357770610_0019

查看HDFS也可以查看到刚刚创建的hudi表信息

CDC入湖

概述

CDC 即 Change Data Capture 变更数据捕获，可以通过 CDC 得知数据源表的更新内容（包含Insert Update 和 Delete），并将这些更新内容作为数据流发送到下游系统。捕获到的数据操作具有一个标识符，分别对应数据的增加，修改和删除。

• +I：新增数据。
• -U：一条数据的修改会产生两个U标识符数据。其中-U含义为修改前数据。
• +U：修改之后的数据。
• -D：删除的数据。

CDC数据保存了完整的数据库变更，可以通过以下任意一种方式将数据导入Hudi：

• 对接CDC Format，消费Kafka数据的同时导入Hudi。支持debezium-json、canal-json和maxwell-json三种格式，该方式优点是可扩展性强，缺点是需要依赖Kafka和Debezium数据同步工具。
• 通过Flink-CDC-Connector直接对接DB的Binlog，将数据导入Hudi。该方式优点是轻量化组件依赖少。

说明

• 如果无法保证上游数据顺序，则需要指定write.precombine.field字段。
• 在CDC场景下，需要开启changelog模式，即changelog.enabled设为true。

下面则演示上面第一种方式方式的使用

MySQL 启用 binlog

下面以 MySQL 5.7 版本为例说明。修改my.cnf文件，增加：

server_id=1
log_bin=mysql-bin
binlog_format=ROW
expire_logs_days=30

初始化MySQL 源数据表

先创建演示数据库 test和一张 student 表

create database test;
use test;
CREATE TABLE `student` (
	`id` INT NOT NULL AUTO_INCREMENT,
	`name` varchar(10) NOT NULL,
	`age` int NOT NULL,
	`class` varchar(10) DEFAULT NULL,
	PRIMARY KEY (`id`)
) ENGINE = InnoDB CHARSET = utf8;

准备Jar包依赖

将flink-sql-connector-mysql-cdc-2.3.0.jar和flink-sql-connector-kafka-1.15.1.jar上传到flink的lib目录下

flink-sql-connector-mysql-cdc-2.3.0.jar可以从github上下载 https://github.com/ververica/flink-cdc-connectors

flink-sql-connector-kafka-1.15.1.jar直接在maven仓库下

flink读取mysql binlog写入kafka

• 创建mysql表

CREATE TABLE student_binlog (
 id INT NOT NULL,
 name STRING,
 age INT,
 class STRING,
 PRIMARY KEY (`id`) NOT ENFORCED
) WITH (
 'connector' = 'mysql-cdc',
 'hostname' = 'mysqlserver',
 'port' = '3308',
 'username' = 'root',
 'password' = '123456',
 'database-name' = 'test',
 'table-name' = 'student'
);

• 创建kafka表

create table student_binlog_sink_kafka(
 id INT NOT NULL,
 name STRING,
 age INT,
 class STRING,
 PRIMARY KEY (`id`) NOT ENFORCED
) with (
  'connector'='upsert-kafka',
  'topic'='data_test',
  'properties.bootstrap.servers' = 'kafka1:9092',
  'properties.group.id' = 'testGroup',
  'key.format'='json',
  'value.format'='json'
);

• 将mysql binlog日志写入kafka

insert into student_binlog_sink_kafka select * from student_binlog;

查看Flink的Web UI，可以看到刚才提交的job

开启tableau方式查询表

set 'sql-client.execution.result-mode' = 'tableau';select * from student_binlog_sink_kafka;

往mysql的student表插入和更新数据测试下

INSERT INTO student VALUES(1,'张三',16,'高一3班');
COMMIT;
INSERT INTO student VALUES(2,'李四',18,'高三3班');
COMMIT;
UPDATE student SET NAME='李四四' WHERE id = 2;
COMMIT;

flink读取kafka数据并写入hudi数据湖

• 创建Kafka源表

CREATE TABLE student_binlog_source_kafka (
 id INT NOT NULL,
 name STRING,
 age INT,
 class STRING
)
WITH(
    'connector' = 'kafka',
    'topic'='data_test',
    'properties.bootstrap.servers' = 'kafka1:9092',
    'properties.group.id' = 'testGroup',
    'scan.startup.mode' = 'earliest-offset',
    'format' = 'json'
);

• 创建hudi目标表

CREATE TABLE student_binlog_sink_hudi (
 id INT NOT NULL,
 name STRING,
 age INT,
 class STRING,
 PRIMARY KEY (`id`) NOT ENFORCED
)
PARTITIONED BY (`class`)
WITH (
  'connector' = 'hudi',
  'path' = 'hdfs://hadoop1:9000/tmp/hudi_flink/student_binlog_sink_hudi',
  'table.type' = 'MERGE_ON_READ',
  'write.option' = 'insert',
  'write.precombine.field' = 'class'
);

• 将kafka数据写入hudi表

insert into student_binlog_sink_hudi select * from student_binlog_source_kafka;

mysql中student表新增加2条数据

INSERT INTO student VALUES(3,'韩梅梅',16,'高二2班');
INSERT INTO student VALUES(4,'李雷',16,'高二2班');
COMMIT;

查看HDFS中已经有相应的分区和数据了

调优

Memory

参数名称	描述	默认值	备注
write.task.max.size	每个write task使用的最大内存，超过则对数据进行flush	1024MB	write buffer使用的内存 = write.task.max.size - compaction.max_memory，当write buffer总共使用的内存超过限制，则将最大的buffer进行flush
write.batch.size	数据写入batch的大小	64MB	推荐使用默认配置
write.log_block.size	Hudi的log writer将数据进行缓存，等达到该参数限制，才将数据flush到disk形成LogBlock	128MB	推荐使用默认配置
write.merge.max_memory	COW类型的表，进行incremental data和data file能使用的最大heap size	100MB	推荐使用默认配置
compaction.max_memory	每个write task进行compaction能使用的最大heap size	100MB	如果是online compaction，且资源充足，可以调大该值，如1024MB

Parallelism

参数名称	描述	默认值	备注
write.tasks	write task的并行度，每一个write task写入1~N个顺序buckets	4	增加该值，对小文件的数据没有影响
write.bucket_assign.tasks	bucket assigner operators的并行度	Flink的parallelism.default参数	增加该值，会增加bucket的数量，所以也会增加小文件的数量
write.index_boostrap.tasks	index bootstrap的并行度	Flink的parallelism.default参数
read.tasks	read operators的并行度	4
compaction.tasks	online compaction的并行度	4	推荐使用offline compaction

Compaction

只适用于online compaction

参数名称	描述	默认值	备注
compaction.schedule.enabled	是否定期生成compaction plan	true	即使compaction.async.enabled = false，也推荐开启该值
compaction.async.enabled	MOR类型表默认开启Async Compaction	true	false表示关闭online compaction
compaction.trigger.strategy	触发compaction的Strategy	num_commits	可选参数值：1. num_commits：delta commits数量达到多少；2. time_elapsed：上次compaction过后多少秒；3. num_and_time：同时满足num_commits和time_elapsed；4. num_or_time：满足num_commits或time_elapsed
compaction.delta_commits		5
compaction.delta_seconds		3600
compaction.target_io	每个compaction读写合计的目标IO，默认500GB	512000

集成Hive

hudi源表对应一份hdfs数据，可以通过spark，flink 组件或者hudi客户端将hudi表的数据映射为hive外部表，基于该外部表， hive可以方便的进行实时视图，读优化视图以及增量视图的查询。

集成步骤

这里以hive3.1.3(关于hive可以详细查看前面的文章)、 hudi 0.12.1为例， 其他版本类似

将hudi-hadoop-mr-bundle-0.9.0xxx.jar , hudi-hive-sync-bundle-0.9.0xx.jar 放到hiveserver 节点的lib目录下

cd /home/commons/apache-hive-3.1.3-bin
cp -rf /home/commons/hudi-release-0.12.1/packaging/hudi-hadoop-mr-bundle/target/hudi-hadoop-mr-bundle-0.12.1.jar lib/
cp -rf /home/commons/hudi-release-0.12.1/packaging/hudi-hive-sync-bundle/target/hudi-hive-sync-bundle-0.12.1.jar lib/

按照需求选择合适的方式并重启hive

nohup hive --service metastore &
nohup hive --service hiveserver2 &

连接jdbc hive2测试,显示所有数据库

Flink同步Hive

Flink hive sync 现在支持两种 hive sync mode, 分别是 hms 和 jdbc 模式。 其中 hms 只需要配置 metastore uris；而 jdbc 模式需要同时配置 jdbc 属性 和 metastore uris，具体配置示例如下

CREATE TABLE t7(
  id int,
  num int,
  ts int,
  primary key (id) not enforced
)
PARTITIONED BY (num)
with(
  'connector'='hudi',
  'path' = 'hdfs://hadoop1:9000/tmp/hudi_flink/t7',
  'table.type'='COPY_ON_WRITE', 
  'hive_sync.enable'='true', 
  'hive_sync.table'='h7', 
  'hive_sync.db'='default', 
  'hive_sync.mode' = 'hms',
  'hive_sync.metastore.uris' = 'thrift://hadoop2:9083'
);
insert into t7 values(1,1,1);

Hive Catalog

Flink官网的找到对应文档版本找到connector-hive，下载flink-sql-connector-hive-3.1.2_2.12-1.15.1.jar,上传到flink的lib目录下，建表示例

CREATE CATALOG hive_catalog WITH (
    'type' = 'hive',
    'default-database' = 'default',
    'hive-conf-dir' = '/home/commons/apache-hive-3.1.3-bin/conf/'
);

use catalog hive_catalog;
CREATE TABLE t8(
  id int,
  num int,
  ts int,
  primary key (id) not enforced
)
PARTITIONED BY (num)
with(
  'connector'='hudi',
  'path' = 'hdfs://hadoop1:9000/tmp/hudi_flink/t8',
  'table.type'='COPY_ON_WRITE', 
  'hive_sync.enable'='true', 
  'hive_sync.table'='h8', 
  'hive_sync.db'='default', 
  'hive_sync.mode' = 'hms',
  'hive_sync.metastore.uris' = 'thrift://hadoop2:9083'
);

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