Hudi第三章：集成Flink

系列文章目录

Hudi第一章：编译安装
Hudi第二章：集成Spark
Hudi第二章：集成Spark(二)
Hudi第三章：集成Flink

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前言

之前的两次博客学习了hudi和spark的集成，现在我们来学习hudi和flink的集成。

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一、环境准备

1.上传并解压

2.修改配置文件

vim /opt/module/flink-1.13.6/conf/flink-conf.yaml
直接在最后追加即可。

classloader.check-leaked-classloader: false
taskmanager.numberOfTaskSlots: 4

state.backend: rocksdb
execution.checkpointing.interval: 30000
state.checkpoints.dir: hdfs://hadoop102:8020/ckps
state.backend.incremental: true

sudo vim /etc/profile.d/my_env.sh

export HADOOP_CLASSPATH=`hadoop classpath`
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop

source /etc/profile.d/my_env.sh

3.拷贝jar包

cp /opt/software/hudi-0.12.0/packaging/hudi-flink-bundle/target/hudi-flink1.13-bundle-0.12.0.jar  /opt/module/flink-1.13.6/lib/
cp /opt/module/hadoop/share/hadoop/common/lib/guava-27.0-jre.jar /opt/module/flink-1.13.6/lib/
cp /opt/module/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-client-core-3.1.3.jar /opt/module/flink-1.13.6/lib/

4.启动sql-client

1.启动hadoop

2.启动session

/opt/module/flink-1.13.6/bin/yarn-session.sh -d

3.启动sql-client

bin/sql-client.sh embedded -s yarn-session

启动成功后可以在web端看一下。

也可以跳转到flink的webui。


现在我们就可以在终端写代码了。

二、sql-client编码

1.创建表

CREATE TABLE t1(
  uuid VARCHAR(20) PRIMARY KEY NOT ENFORCED,
  name VARCHAR(10),
  age INT,
  ts TIMESTAMP(3),
  `partition` VARCHAR(20)
)
PARTITIONED BY (`partition`)
WITH (
  'connector' = 'hudi',
  'path' = 'hdfs://hadoop102:8020/tmp/hudi_flink/t1',
  'table.type' = 'MERGE_ON_READ'
);

2.插入数据

INSERT INTO t1 VALUES
  ('id1','Danny',23,TIMESTAMP '1970-01-01 00:00:01','par1'),
  ('id2','Stephen',33,TIMESTAMP '1970-01-01 00:00:02','par1'),
  ('id3','Julian',53,TIMESTAMP '1970-01-01 00:00:03','par2'),
  ('id4','Fabian',31,TIMESTAMP '1970-01-01 00:00:04','par2'),
  ('id5','Sophia',18,TIMESTAMP '1970-01-01 00:00:05','par3'),
  ('id6','Emma',20,TIMESTAMP '1970-01-01 00:00:06','par3'),
  ('id7','Bob',44,TIMESTAMP '1970-01-01 00:00:07','par4'),
  ('id8','Han',56,TIMESTAMP '1970-01-01 00:00:08','par4');

3.查询数据

我们先更改一下表格式，默认的看得可能不习惯。

set sql-client.execution.result-mode=tableau;
select * from t1;

4.更新数据

前面说过hudi的更新操作就是插入一条主键相同的新数据，由更新的ts来覆盖旧的。

insert into t1 values
  ('id1','Danny',27,TIMESTAMP '1970-01-02 00:00:01','par1');

可以看到数据已经完成了更新。

5.流式插入

flink最常用的还是流式数据的处理。

CREATE TABLE sourceT (
  uuid varchar(20),
  name varchar(10),
  age int,
  ts timestamp(3),
  `partition` varchar(20)
) WITH (
  'connector' = 'datagen',
  'rows-per-second' = '1'
);

create table t2(
  uuid varchar(20),
  name varchar(10),
  age int,
  ts timestamp(3),
  `partition` varchar(20)
)
with (
  'connector' = 'hudi',
  'path' = '/tmp/hudi_flink/t2',
  'table.type' = 'MERGE_ON_READ'
);

我们创建两张表，第一张的连接器是datagen可以用来流式的生产数据。第二张表是正常的hudi表。

insert into t2 select * from sourceT;

我们可以在webui看一下。

因为是流式处理，所以这个进程是不会停止的。

select * from t2 limit 10;

再查看一次

我们会发现是不断有数据产生。

三、IDEA编码

我们需要将编译好的一个包拉到本地。

然后将他倒入maven仓库

mvn install:install-file -DgroupId=org.apache.hudi -DartifactId=hudi-flink_2.12 -Dversion=0.12.0 -Dpackaging=jar -Dfile=./hudi-flink1.13-bundle-0.12.0.jar

1.编写pom.xml

<?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>com.atguigu.hudi</groupId>
    <artifactId>flink-hudi-demo</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <flink.version>1.13.6</flink.version>
        <hudi.version>0.12.0</hudi.version>
        <java.version>1.8</java.version>
        <scala.binary.version>2.12</scala.binary.version>
        <slf4j.version>1.7.30</slf4j.version>
    </properties>

    <dependencies>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>   <!--不会打包到依赖中，只参与编译，不参与运行 -->
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

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

        <!--idea运行时也有webui-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-runtime-web_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-api</artifactId>
            <version>${slf4j.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
            <version>${slf4j.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-to-slf4j</artifactId>
            <version>2.14.0</version>
            <scope>provided</scope>
        </dependency>


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

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>3.1.3</version>
            <scope>provided</scope>
        </dependency>
        <!--手动install到本地maven仓库-->
        <dependency>
            <groupId>org.apache.hudi</groupId>
            <artifactId>hudi-flink_2.12</artifactId>
            <version>${hudi.version}</version>
            <scope>provided</scope>
        </dependency>

    </dependencies>


    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>3.2.4</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <artifactSet>
                                <excludes>
                                    <exclude>com.google.code.findbugs:jsr305</exclude>
                                    <exclude>org.slf4j:*</exclude>
                                    <exclude>log4j:*</exclude>
                                    <exclude>org.apache.hadoop:*</exclude>
                                </excludes>
                            </artifactSet>
                            <filters>
                                <filter>
                                    <!-- Do not copy the signatures in the META-INF folder.
                                    Otherwise, this might cause SecurityExceptions when using the JAR. -->
                                    <artifact>*:*</artifact>
                                    <excludes>
                                        <exclude>META-INF/*.SF</exclude>
                                        <exclude>META-INF/*.DSA</exclude>
                                        <exclude>META-INF/*.RSA</exclude>
                                    </excludes>
                                </filter>
                            </filters>
                            <transformers combine.children="append">
                                <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer">
                                </transformer>
                            </transformers>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

2.编写demo

HudiDemo.java
一个简单的流式数据处理和刚刚一样。

package com.atguigu.hudi.flink;

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) {
        System.setProperty("HADOOP_USER_NAME", "atguigu");

        StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(new Configuration());
        // 设置状态后端RocksDB
        EmbeddedRocksDBStateBackend embeddedRocksDBStateBackend = new EmbeddedRocksDBStateBackend(true);
        embeddedRocksDBStateBackend.setDbStoragePath("/home/chaoge/Downloads/hudi");
        embeddedRocksDBStateBackend.setPredefinedOptions(PredefinedOptions.SPINNING_DISK_OPTIMIZED_HIGH_MEM);
        env.setStateBackend(embeddedRocksDBStateBackend);

        // checkpoint配置
        env.enableCheckpointing(TimeUnit.SECONDS.toMillis(30), CheckpointingMode.EXACTLY_ONCE);
        CheckpointConfig checkpointConfig = env.getCheckpointConfig();
        checkpointConfig.setCheckpointStorage("hdfs://hadoop102:8020/ckps");
        checkpointConfig.setMinPauseBetweenCheckpoints(TimeUnit.SECONDS.toMillis(20));
        checkpointConfig.setTolerableCheckpointFailureNumber(5);
        checkpointConfig.setCheckpointTimeout(TimeUnit.MINUTES.toMillis(1));
        checkpointConfig.enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);

        StreamTableEnvironment sTableEnv = StreamTableEnvironment.create(env);

        sTableEnv.executeSql("CREATE TABLE sourceT (\n" +
                "  uuid varchar(20),\n" +
                "  name varchar(10),\n" +
                "  age int,\n" +
                "  ts timestamp(3),\n" +
                "  `partition` varchar(20)\n" +
                ") WITH (\n" +
                "  'connector' = 'datagen',\n" +
                "  'rows-per-second' = '1'\n" +
                ")");

        sTableEnv.executeSql("create table t2(\n" +
                "  uuid varchar(20),\n" +
                "  name varchar(10),\n" +
                "  age int,\n" +
                "  ts timestamp(3),\n" +
                "  `partition` varchar(20)\n" +
                ")\n" +
                "with (\n" +
                "  'connector' = 'hudi',\n" +
                "  'path' = 'hdfs://hadoop102:8020/tmp/hudi_idea/t2',\n" +
                "  'table.type' = 'MERGE_ON_READ'\n" +
                ")");

        sTableEnv.executeSql("insert into t2 select * from sourceT");

    }
}

当我们运行的时候，可以再本地webui查看。
127.0.0.1:8081/

也可以在hdfs路径看一下。

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总结

flink第一次就先写到这里剩下的还要在写一次。