联系我们
简单又实用的WordPress网站制作教学
当前位置：网站首页 > 程序开发学习 > 正文

Hudi第二章：集成Spark(二)

作者：小教学发布时间：2023-10-03分类：程序开发学习浏览：77

导读：系列文章目录Hudi第一章：编译安装Hudi第二章：集成SparkHudi第二章：集成Spark(二)文章目录系列文章目录前言一、IDEA1.环境准备2...

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)

文章目录

  • 系列文章目录
  • 前言
  • 一、IDEA
    • 1.环境准备
    • 2.代码编写
      • 1.插入数据
      • 2.查询数据
      • 3.更新数据
      • 4.指定时间点查询
      • 5.增量查询
      • 6.删除数据
      • 7.覆盖数据
  • 二、DeltaStreamer
    • 1.安装Kafka
    • 2.准备数据源
    • 3.编写配置文件
    • 4.运行代码
  • 三、并发控制
    • 1.Spark DataFrame写入
    • 2.elta Streamer
  • 总结

前言

这次我们将hudi集成Spark补充完整。

一、IDEA

之前我们使用了spark-shell和spark-sql进行操作，现在我们使用IDEA进行数据处理。

1.环境准备

创建项目啥的不说了
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>spark-hudi-demo</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <scala.version>2.12.10</scala.version>
        <scala.binary.version>2.12</scala.binary.version>
        <spark.version>3.2.2</spark.version>
        <hadoop.version>3.1.3</hadoop.version>
        <hudi.version>0.12.0</hudi.version>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
    </properties>

    <dependencies>
        <!-- 依赖Scala语言 -->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>
        <!-- Spark Core 依赖 -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_${scala.binary.version}</artifactId>
            <version>${spark.version}</version>
            <scope>provided</scope>
        </dependency>
        <!-- Spark SQL 依赖 -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_${scala.binary.version}</artifactId>
            <version>${spark.version}</version>
            <scope>provided</scope>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_${scala.binary.version}</artifactId>
            <version>${spark.version}</version>
            <scope>provided</scope>
        </dependency>

        <!-- Hadoop Client 依赖 -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
            <scope>provided</scope>
        </dependency>

        <!-- hudi-spark3.2 -->
        <dependency>
            <groupId>org.apache.hudi</groupId>
            <artifactId>hudi-spark3.2-bundle_${scala.binary.version}</artifactId>
            <version>${hudi.version}</version>
            <scope>provided</scope>
        </dependency>

    </dependencies>

    <build>
        <plugins>
            <!-- assembly打包插件 -->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-assembly-plugin</artifactId>
                <version>3.0.0</version>
                <executions>
                    <execution>
                        <id>make-assembly</id>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
                <configuration>
                    <archive>
                        <manifest>
                        </manifest>
                    </archive>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
            </plugin>

            <!--Maven编译scala所需依赖-->
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.2.2</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>

</project>

2.代码编写

因为idea编写方法和spark-shell几乎一样，所以就做一个最简单的例子。

1.插入数据

package com.atguigu.hudi.spark

import org.apache.hudi.QuickstartUtils._
import org.apache.spark.SparkConf
import org.apache.spark.sql._
import scala.collection.JavaConversions._
import org.apache.spark.sql.SaveMode._
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.config.HoodieWriteConfig._


object InsertDemo {
  def main( args: Array[String] ): Unit = {
    // 创建 SparkSession
    val sparkConf = new SparkConf()
      .setAppName(this.getClass.getSimpleName)
      .setMaster("local[*]")
      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    val sparkSession = SparkSession.builder()
      .config(sparkConf)
      .enableHiveSupport()
      .getOrCreate()

    val tableName = "hudi_trips_cow"
    val basePath = "hdfs://hadoop1:8020/tmp/hudi_trips_cow"
    val dataGen = new DataGenerator

    val inserts = convertToStringList(dataGen.generateInserts(10))
    val df = sparkSession.read.json(sparkSession.sparkContext.parallelize(inserts, 2))
    df.write.format("hudi").
      options(getQuickstartWriteConfigs).
      option(PRECOMBINE_FIELD.key(), "ts").
      option(RECORDKEY_FIELD.key(), "uuid").
      option(PARTITIONPATH_FIELD.key(), "partitionpath").
      option(TBL_NAME.key(), tableName).
      mode(Overwrite).
      save(basePath)
  }
}

如果出现这个错误，需要对idea做一些设置。
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
我得版本比较新，其它版本可能不太一样。
在这里插入图片描述
如果出现这个问题，是因为我们在idea链接集群用的是本地的用户名，我们需要更改一下
加一行代码

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

在这里插入图片描述
执行成功后，我们在hdfs路径查看一下有没有新表。
在这里插入图片描述
其它的都类似，所以只放代码，不运行了。

2.查询数据

package com.atguigu.hudi.spark

import org.apache.spark.SparkConf
import org.apache.spark.sql._


object QueryDemo {
  def main( args: Array[String] ): Unit = {
    // 创建 SparkSession
    val sparkConf = new SparkConf()
      .setAppName(this.getClass.getSimpleName)
      .setMaster("local[*]")
      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    val sparkSession = SparkSession.builder()
      .config(sparkConf)
      .enableHiveSupport()
      .getOrCreate()

    val basePath = "hdfs://hadoop1:8020/tmp/hudi_trips_cow"

    val tripsSnapshotDF = sparkSession.
      read.
      format("hudi").
      load(basePath)

    //    时间旅行查询写法一
    //    sparkSession.read.
    //      format("hudi").
    //      option("as.of.instant", "20210728141108100").
    //      load(basePath)
    //
    //    时间旅行查询写法二
    //    sparkSession.read.
    //      format("hudi").
    //      option("as.of.instant", "2021-07-28 14:11:08.200").
    //      load(basePath)
    //
    //    时间旅行查询写法三：等价于"as.of.instant = 2021-07-28 00:00:00"
    //    sparkSession.read.
    //      format("hudi").
    //      option("as.of.instant", "2021-07-28").
    //      load(basePath)

    tripsSnapshotDF.createOrReplaceTempView("hudi_trips_snapshot")

    sparkSession
      .sql("select fare, begin_lon, begin_lat, ts from  hudi_trips_snapshot where fare > 20.0")
      .show()

  }
}

3.更新数据

package com.atguigu.hudi.spark

import org.apache.hudi.QuickstartUtils._
import org.apache.spark.SparkConf
import org.apache.spark.sql._
import scala.collection.JavaConversions._
import org.apache.spark.sql.SaveMode._
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.config.HoodieWriteConfig._


object UpdateDemo {
  def main( args: Array[String] ): Unit = {
    // 创建 SparkSession
    val sparkConf = new SparkConf()
      .setAppName(this.getClass.getSimpleName)
      .setMaster("local[*]")
      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    val sparkSession = SparkSession.builder()
      .config(sparkConf)
      .enableHiveSupport()
      .getOrCreate()

    val tableName = "hudi_trips_cow"
    val basePath = "hdfs://hadoop1:8020/tmp/hudi_trips_cow"

    val dataGen = new DataGenerator
    val updates = convertToStringList(dataGen.generateUpdates(10))
    val df = sparkSession.read.json(sparkSession.sparkContext.parallelize(updates, 2))
    df.write.format("hudi").
      options(getQuickstartWriteConfigs).
      option(PRECOMBINE_FIELD.key(), "ts").
      option(RECORDKEY_FIELD.key(), "uuid").
      option(PARTITIONPATH_FIELD.key(), "partitionpath").
      option(TBL_NAME.key(), tableName).
      mode(Append).
      save(basePath)


//    val tripsSnapshotDF = sparkSession.
//      read.
//      format("hudi").
//      load(basePath)
//    tripsSnapshotDF.createOrReplaceTempView("hudi_trips_snapshot")
//
//    sparkSession
//      .sql("select fare, begin_lon, begin_lat, ts from  hudi_trips_snapshot where fare > 20.0")
//      .show()

  }
}

4.指定时间点查询

package com.atguigu.hudi.spark

import org.apache.hudi.DataSourceReadOptions._
import org.apache.spark.SparkConf
import org.apache.spark.sql._


object PointInTimeQueryDemo {
  def main( args: Array[String] ): Unit = {
    // 创建 SparkSession
    val sparkConf = new SparkConf()
      .setAppName(this.getClass.getSimpleName)
      .setMaster("local[*]")
      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    val sparkSession = SparkSession.builder()
      .config(sparkConf)
      .enableHiveSupport()
      .getOrCreate()

    val basePath = "hdfs://hadoop1:8020/tmp/hudi_trips_cow"

    import sparkSession.implicits._
    val commits = sparkSession.sql("select distinct(_hoodie_commit_time) as commitTime from  hudi_trips_snapshot order by commitTime").map(k => k.getString(0)).take(50)
    val beginTime = "000"
    val endTime = commits(commits.length - 2)

    val tripsIncrementalDF = sparkSession.read.format("hudi").
      option(QUERY_TYPE.key(), QUERY_TYPE_INCREMENTAL_OPT_VAL).
      option(BEGIN_INSTANTTIME.key(), beginTime).
      option(END_INSTANTTIME.key(), endTime).
      load(basePath)

    tripsIncrementalDF.createOrReplaceTempView("hudi_trips_point_in_time")

    sparkSession.
      sql("select `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts from hudi_trips_point_in_time where fare > 20.0").
      show()

  }
}

5.增量查询

package com.atguigu.hudi.spark

import org.apache.hudi.DataSourceReadOptions._
import org.apache.spark.SparkConf
import org.apache.spark.sql._


object IncrementalQueryDemo {
  def main( args: Array[String] ): Unit = {
    // 创建 SparkSession
    val sparkConf = new SparkConf()
      .setAppName(this.getClass.getSimpleName)
      .setMaster("local[*]")
      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    val sparkSession = SparkSession.builder()
      .config(sparkConf)
      .enableHiveSupport()
      .getOrCreate()

    val basePath = "hdfs://hadoop1:8020/tmp/hudi_trips_cow"

    import sparkSession.implicits._
    val commits = sparkSession.sql("select distinct(_hoodie_commit_time) as commitTime from  hudi_trips_snapshot order by commitTime").map(k => k.getString(0)).take(50)
    val beginTime = commits(commits.length - 2)

    val tripsIncrementalDF = sparkSession.read.format("hudi").
      option(QUERY_TYPE.key(), QUERY_TYPE_INCREMENTAL_OPT_VAL).
      option(BEGIN_INSTANTTIME.key(), beginTime).
      load(basePath)

    tripsIncrementalDF.createOrReplaceTempView("hudi_trips_incremental")

    sparkSession.sql("select `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts from  hudi_trips_incremental where fare > 20.0").show()

  }
}

6.删除数据

package com.atguigu.hudi.spark

import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.QuickstartUtils._
import org.apache.hudi.config.HoodieWriteConfig._
import org.apache.spark.SparkConf
import org.apache.spark.sql.SaveMode._
import org.apache.spark.sql._

import scala.collection.JavaConversions._


object DeleteDemo {
  def main( args: Array[String] ): Unit = {
    // 创建 SparkSession
    val sparkConf = new SparkConf()
      .setAppName(this.getClass.getSimpleName)
      .setMaster("local[*]")
      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    val sparkSession = SparkSession.builder()
      .config(sparkConf)
      .enableHiveSupport()
      .getOrCreate()

    val tableName = "hudi_trips_cow"
    val basePath = "hdfs://hadoop1:8020/tmp/hudi_trips_cow"
    val dataGen = new DataGenerator

    sparkSession.
      read.
      format("hudi").
      load(basePath).
      createOrReplaceTempView("hudi_trips_snapshot")

    sparkSession.sql("select uuid, partitionpath from hudi_trips_snapshot").count()

    val ds = sparkSession.sql("select uuid, partitionpath from hudi_trips_snapshot").limit(2)

    val deletes = dataGen.generateDeletes(ds.collectAsList())
    val df = sparkSession.read.json(sparkSession.sparkContext.parallelize(deletes, 2))

    df.write.format("hudi").
      options(getQuickstartWriteConfigs).
      option(OPERATION.key(),"delete").
      option(PRECOMBINE_FIELD.key(), "ts").
      option(RECORDKEY_FIELD.key(), "uuid").
      option(PARTITIONPATH_FIELD.key(), "partitionpath").
      option(TBL_NAME.key(), tableName).
      mode(Append).
      save(basePath)

    val roAfterDeleteViewDF = sparkSession.
      read.
      format("hudi").
      load(basePath)

    roAfterDeleteViewDF.createOrReplaceTempView("hudi_trips_snapshot")

    // 返回的总行数应该比原来少2行
    sparkSession.sql("select uuid, partitionpath from hudi_trips_snapshot").count()

  }
}

7.覆盖数据

package com.atguigu.hudi.spark

import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.QuickstartUtils._
import org.apache.hudi.config.HoodieWriteConfig._
import org.apache.spark.SparkConf
import org.apache.spark.sql.SaveMode._
import org.apache.spark.sql._

import scala.collection.JavaConversions._


object InsertOverwriteDemo {
  def main( args: Array[String] ): Unit = {
    // 创建 SparkSession
    val sparkConf = new SparkConf()
      .setAppName(this.getClass.getSimpleName)
      .setMaster("local[*]")
      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    val sparkSession = SparkSession.builder()
      .config(sparkConf)
      .enableHiveSupport()
      .getOrCreate()

    val tableName = "hudi_trips_cow"
    val basePath = "hdfs://hadoop1:8020/tmp/hudi_trips_cow"
    val dataGen = new DataGenerator

    sparkSession.
      read.format("hudi").
      load(basePath).
      select("uuid","partitionpath").
      sort("partitionpath","uuid").
      show(100, false)


    val inserts = convertToStringList(dataGen.generateInserts(10))
    val df = sparkSession.read.json(sparkSession.sparkContext.parallelize(inserts, 2)).
      filter("partitionpath = 'americas/united_states/san_francisco'")

    df.write.format("hudi").
      options(getQuickstartWriteConfigs).
      option(OPERATION.key(),"insert_overwrite").
      option(PRECOMBINE_FIELD.key(), "ts").
      option(RECORDKEY_FIELD.key(), "uuid").
      option(PARTITIONPATH_FIELD.key(), "partitionpath").
      option(TBL_NAME.key(), tableName).
      mode(Append).
      save(basePath)

    sparkSession.
      read.format("hudi").
      load(basePath).
      select("uuid","partitionpath").
      sort("partitionpath","uuid").
      show(100, false)
  }
}

二、DeltaStreamer

这是一个hudi自带的导入工具，可以从一些数据源将数据快速导入hudi，这里我们用kafka做数据源。

1.安装Kafka

zk安装
kafka安装

2.准备数据源

我们可以新创建任务，也可以直接在之前的idea项目上编写。
pom.xml

       <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>2.4.1</version>
        </dependency>

        <!--fastjson <= 1.2.80 存在安全漏洞,-->
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.83</version>
        </dependency>

TestKafkaProducer.java

package com.atguigu.util;

import com.alibaba.fastjson.JSONObject;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;

import java.util.Properties;
import java.util.Random;

public class TestKafkaProducer {
    public static void main(String[] args) {
        Properties props = new Properties();
        props.put("bootstrap.servers", "hadoop102:9092,hadoop103:9092,hadoop104:9092");
        props.put("acks", "-1");
        props.put("batch.size", "1048576");
        props.put("linger.ms", "5");
        props.put("compression.type", "snappy");
        props.put("buffer.memory", "33554432");
        props.put("key.serializer",
                "org.apache.kafka.common.serialization.StringSerializer");
        props.put("value.serializer",
                "org.apache.kafka.common.serialization.StringSerializer");
        KafkaProducer<String, String> producer = new KafkaProducer<String, String>(props);
        Random random = new Random();
        for (int i = 0; i < 1000; i++) {
            JSONObject model = new JSONObject();
            model.put("userid", i);
            model.put("username", "name" + i);
            model.put("age", 18);
            model.put("partition", random.nextInt(100));
            producer.send(new ProducerRecord<String, String>("hudi_test", model.toJSONString()));
        }
        producer.flush();
        producer.close();
    }
}

启动zk和kafka
然后创建一个消费者，要与代码中对应。

bin/kafka-topics.sh --bootstrap-server hadoop102:9092 --create --topic hudi_test

然后先消费数据。

bin/kafka-console-consumer.sh --bootstrap-server hadoop102:9092 --from-beginning --topic hudi_test

因为我们还没有生产数据，所以现在应该什么都没有。
在这里插入图片描述
运行idea里的代码。
在这里插入图片描述
当Kafka消费到数据时，我们的数据源准备完成。

3.编写配置文件

我们为其创建一个单独的文件夹。

mkdir /opt/module/hudi-props

cp /opt/software/hudi-0.12.0/hudi-utilities/src/test/resources/delta-streamer-config/kafka-source.properties /opt/module/hudi-props/
cp /opt/software/hudi-0.12.0/hudi-utilities/src/test/resources/delta-streamer-config/base.properties /opt/module/hudi-props/
touch /opt/module/hudi-props/source-schema-json.avsc

编写source-schema-json.avsc

{        
  "type": "record",
  "name": "Profiles",   
  "fields": [
    {
      "name": "userid",
      "type": [ "null", "string" ],
      "default": null
    },
    {
      "name": "username",
      "type": [ "null", "string" ],
      "default": null
    },
    {
      "name": "age",
      "type": [ "null", "string" ],
      "default": null
    },
    {
      "name": "partition",
      "type": [ "null", "string" ],
      "default": null
    }
  ]
}

然后复制一份

cp source-schema-json.avsc target-schema-json.avsc

编写kafka-source.properties

include=hdfs://hadoop102:8020/hudi-props/base.properties
# Key fields, for kafka example
hoodie.datasource.write.recordkey.field=userid
hoodie.datasource.write.partitionpath.field=partition
# schema provider configs
#hoodie.deltastreamer.schemaprovider.registry.url=http://localhost:8081/subjects/impressions-value/versions/latest
hoodie.deltastreamer.schemaprovider.source.schema.file=hdfs://hadoop102:8020/hudi-props/source-schema-json.avsc
hoodie.deltastreamer.schemaprovider.target.schema.file=hdfs://hadoop102:8020/hudi-props/target-schema-json.avsc
# Kafka Source
#hoodie.deltastreamer.source.kafka.topic=uber_trips
hoodie.deltastreamer.source.kafka.topic=hudi_test
#Kafka props
bootstrap.servers=hadoop102:9092,hadoop103:9092,hadoop104:9092
auto.offset.reset=earliest
schema.registry.url=http://localhost:8081
group.id=test-group

我把要修改的地方都画出来。
在这里插入图片描述
然后将其上传到hdfs

hadoop fs -put /opt/module/hudi-props/ /

在这里插入图片描述
将需要的jar包拷入spark

cp /opt/software/hudi-0.12.0/packaging/hudi-utilities-bundle/target/hudi-utilities-bundle_2.12-0.12.0.jar /opt/module/spark-3.2.2/jars/

4.运行代码

spark-submit \
--class org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer  \
/opt/module/spark-3.2.2/jars/hudi-utilities-bundle_2.12-0.12.0.jar \
--props hdfs://hadoop102:8020/hudi-props/kafka-source.properties \
--schemaprovider-class org.apache.hudi.utilities.schema.FilebasedSchemaProvider  \
--source-class org.apache.hudi.utilities.sources.JsonKafkaSource  \
--source-ordering-field userid \
--target-base-path hdfs://hadoop102:8020/tmp/hudi/hudi_test  \
--target-table hudi_test \
--op BULK_INSERT \
--table-type MERGE_ON_READ

在这里插入图片描述
可以看到hdfs上已经出现了表信息。
现在我们用spark-sql验证一下内容。

spark-sql \
  --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer' \
  --conf 'spark.sql.catalog.spark_catalog=org.apache.spark.sql.hudi.catalog.HoodieCatalog' \
  --conf 'spark.sql.extensions=org.apache.spark.sql.hudi.HoodieSparkSessionExtension'

use spark_hudi;

create table hudi_test using hudi
location 'hdfs://hadoop102:8020/tmp/hudi/hudi_test';

select * from hudi_test;

在这里插入图片描述

三、并发控制

之前我们都是用单个用户来写入，这很明显不符合生产环境，所以下边说一下并发写入。
当并发写入的时候，我们就需要使用到锁，这里我们选择zk来进行辅助。

1.Spark DataFrame写入

先登录spark-shell

spark-shell \
  --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer' \
  --conf 'spark.sql.catalog.spark_catalog=org.apache.spark.sql.hudi.catalog.HoodieCatalog' \
  --conf 'spark.sql.extensions=org.apache.spark.sql.hudi.HoodieSparkSessionExtension'

创建并发表

import org.apache.hudi.QuickstartUtils._
import scala.collection.JavaConversions._
import org.apache.spark.sql.SaveMode._
import org.apache.hudi.DataSourceReadOptions._
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.config.HoodieWriteConfig._

val tableName = "hudi_trips_cow"
val basePath = "file:///tmp/hudi_trips_cow"
val dataGen = new DataGenerator

val inserts = convertToStringList(dataGen.generateInserts(10))
val df = spark.read.json(spark.sparkContext.parallelize(inserts, 2))
df.write.format("hudi").
  options(getQuickstartWriteConfigs).
  option(PRECOMBINE_FIELD_OPT_KEY, "ts").
  option(RECORDKEY_FIELD_OPT_KEY, "uuid").
  option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").

  option("hoodie.write.concurrency.mode", "optimistic_concurrency_control").
  option("hoodie.cleaner.policy.failed.writes", "LAZY").
option("hoodie.write.lock.provider", "org.apache.hudi.client.transaction.lock.ZookeeperBasedLockProvider").
  option("hoodie.write.lock.zookeeper.url", "hadoop102,hadoop103,hadoop104").
  option("hoodie.write.lock.zookeeper.port", "2181").
  option("hoodie.write.lock.zookeeper.lock_key", "test_table").
  option("hoodie.write.lock.zookeeper.base_path", "/multiwriter_test").

  option(TABLE_NAME, tableName).
  mode(Append).
  save(basePath)

在这里我把和之前不同的地方单独隔离了出来。
因为我们这里会用到zk，所以提前先打开一个zk窗口

bin/zkCli.sh

在这里插入图片描述
当数据导入时，zk生成一个新节点作为锁，结束后，自动释放，所以要快一点。
开始导入数据，并在zk查看。
在这里插入图片描述
在这里插入图片描述
结束后，锁会被释放。

2.elta Streamer

elta Streamer使用kafka作为数据源，所以要先把kafka打开。
创建kafka-multiwriter-source.propertis文件，直接kafka-source.properties上复制一份然后修改。
cp kafka-source.properties kafka-multiwriter-source.propertis
在这里插入图片描述
修改内容,在后边追加即可。

hoodie.write.concurrency.mode=optimistic_concurrency_control
hoodie.cleaner.policy.failed.writes=LAZY
hoodie.write.lock.provider=org.apache.hudi.client.transaction.lock.ZookeeperBasedLockProvider
hoodie.write.lock.zookeeper.url=hadoop102,hadoop103,hadoop104
hoodie.write.lock.zookeeper.port=2181
hoodie.write.lock.zookeeper.lock_key=test_table2
hoodie.write.lock.zookeeper.base_path=/multiwriter_test2

在这里插入图片描述
然后上传到hdfs

hadoop fs -put /opt/module/hudi-props/kafka-multiwriter-source.propertis /hudi-props

在这里插入图片描述
然后提前打开zk，之后进行数据插入。

spark-submit \
--class org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer  \
/opt/module/spark-3.2.2/jars/hudi-utilities-bundle_2.12-0.12.0.jar \
--props hdfs://hadoop102:8020/hudi-props/kafka-multiwriter-source.propertis \
--schemaprovider-class org.apache.hudi.utilities.schema.FilebasedSchemaProvider  \
--source-class org.apache.hudi.utilities.sources.JsonKafkaSource  \
--source-ordering-field userid \
--target-base-path hdfs://hadoop102:8020/tmp/hudi/hudi_test_multi  \
--target-table hudi_test_multi \
--op INSERT \
--table-type MERGE_ON_READ

在这里插入图片描述
在这里插入图片描述

总结

课程后边其实还有一些调优的内容，想了想还是没有写下了，用的时候再说吧。




标签：Hudi第二章：集成Spark(二)_超哥--的博客

相关推荐

程序开发学习排行
最近发表
网站分类
标签列表
琼ICP备20000547号-3