Spark2Streaming读非Kerberos环境的Kafka并写数据到Kudu

Fayson

发布于 2018-08-17 17:28:35

9700

发布于 2018-08-17 17:28:35

文章被收录于专栏：Hadoop实操

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Fayson的github： https://github.com/fayson/cdhproject
提示：代码块部分可以左右滑动查看噢

1.文档编写目的

在前面的文章Fayson介绍了在Kerberos环境下《Spark2Streaming读Kerberos环境的Kafka并写数据到Kudu》，本篇文章Fayson主要介绍如何使用Spark2 Streaming访问非Kerberos环境的Kafka并将接收到的数据写入Kudu。

文章概述

1.环境准备

2.Spark2Streaming示例开发

3.示例运行

4.总结

测试环境

1.CM和CDH版本为5.15

2.CDK2.2.0（Apache Kafka0.10.2）

3.Spark2.2.0

4.操作系统版本为RedHat7.4

2.环境准备

1.准备向Kakfa发送数据的脚本，关于脚本这里就不在过多的介绍前面很多文章都有介绍，具体可以参考Fayson的GitHub：

https://github.com/fayson/cdhproject/tree/master/kafkademo/0283-kafka-shell

根据需要将conf下面的配置文件修改为自己集群的环境即可，发送至Kafka的JSON数据示例如下：

{
   "occupation": "生产工作、运输工作和部分体力劳动者",
   "address": "台东东二路16号-8-8",
   "city": "长治",
   "marriage": "1",
   "sex": "1",
   "name": "仲淑兰",
   "mobile_phone_num": "13607268580",
   "bank_name": "广州银行31",
   "id": "510105197906185179",
   "child_num": "1",
   "fix_phone_num": "15004170180"
}

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2.登录CM进入SPARK2服务的配置项将spark_kafka_version的kafka版本修改为0.10

修改完成后并部署客户端配置

3.Spark2Streaming示例代码

1.使用maven创建scala语言的spark2demo工程，pom.xml依赖如下

<dependency>
    <groupId>org.apache.kudu</groupId>
    <artifactId>kudu-spark2_2.11</artifactId>
    <version>1.6.0-cdh5.14.2</version>
</dependency>
<dependency>
    <groupId>org.apache.kudu</groupId>
    <artifactId>kudu-client</artifactId>
    <version>1.6.0-cdh5.14.2</version>
</dependency>
<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-core_2.11</artifactId>
    <version>2.2.0.cloudera2</version>
</dependency>
<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-sql_2.11</artifactId>
    <version>2.2.0.cloudera2</version>
</dependency>
<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-streaming_2.11</artifactId>
    <version>2.2.0.cloudera2</version>
</dependency>
<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
    <version>2.2.0.cloudera2</version>
</dependency>
<dependency>
    <groupId>org.scala-lang</groupId>
    <artifactId>scala-library</artifactId>
    <version>2.11.8</version>
</dependency>

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具体需要的依赖包，可以参考Fayson前面的文章《Spark2Streaming读Kerberos环境的Kafka并写数据到Kudu》

2.在resources下创建0294.properties配置文件，内容如下：

kafka.brokers=cdh02.fayson.com:9092,cdh03.fayson.com:9092,cdh04.fayson.com:9092
kafka.topics=kafka_kudu_topic
kudumaster.list=cdh01.fayson.com,cdh02.fayson.com,cdh03.fayson.com

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3.创建Kafka2Spark2Kudu.scala类

package com.cloudera.streaming.nokerberos

import java.io.{File, FileInputStream}
import java.util.Properties

import org.apache.commons.lang.StringUtils
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.kudu.client.CreateTableOptions
import org.apache.kudu.spark.kudu.KuduContext
import org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{StringType, StructField, StructType}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, HasOffsetRanges, KafkaUtils, LocationStrategies}

import scala.util.parsing.json.JSON
import scala.collection.JavaConverters._

/**
  * package: com.cloudera.streaming.nokerberos
  * 使用spark2-submit的方式提交作业
    spark2-submit --class com.fayson.streaming.nokerberos.Kafka2Spark2Kudu \
    --master yarn \
    --deploy-mode client \
    --executor-memory 2g \
    --executor-cores 2 \
    --driver-memory 2g \
    --num-executors 2 \
    spark2-demo-1.0-SNAPSHOT.jar
  * creat_user: Fayson
  * email: htechinfo@163.com
  * creat_date: 2018/8/6
  * creat_time: 下午5:05
  * 公众号：Hadoop实操
  */
object Kafka2Spark2Kudu {

  Logger.getLogger("com").setLevel(Level.ERROR) //设置日志级别

  var confPath: String = System.getProperty("user.dir") + File.separator + "conf/0294.properties"

  /**
    * 建表Schema定义
    */
  val userInfoSchema = StructType(
    //         col name   type     nullable?
    StructField("id", StringType , false) ::
      StructField("name" , StringType, true ) ::
      StructField("sex" , StringType, true ) ::
      StructField("city" , StringType, true ) ::
      StructField("occupation" , StringType, true ) ::
      StructField("tel" , StringType, true ) ::
      StructField("fixPhoneNum" , StringType, true ) ::
      StructField("bankName" , StringType, true ) ::
      StructField("address" , StringType, true ) ::
      StructField("marriage" , StringType, true ) ::
      StructField("childNum", StringType , true ) :: Nil
  )

  /**
    * 定义一个UserInfo对象
    */
  case class UserInfo (
                        id: String,
                        name: String,
                        sex: String,
                        city: String,
                        occupation: String,
                        tel: String,
                        fixPhoneNum: String,
                        bankName: String,
                        address: String,
                        marriage: String,
                        childNum: String
                      )

  def main(args: Array[String]): Unit = {
    //加载配置文件
    val properties = new Properties()
    val file = new File(confPath)
    if(!file.exists()) {
      System.out.println(Kafka2Spark2Kudu.getClass.getClassLoader.getResource("0294.properties"))
      val in = Kafka2Spark2Kudu.getClass.getClassLoader.getResourceAsStream("0294.properties")
      properties.load(in);
    } else {
      properties.load(new FileInputStream(confPath))
    }

    val brokers = properties.getProperty("kafka.brokers")
    val topics = properties.getProperty("kafka.topics")
    val kuduMaster = properties.getProperty("kudumaster.list")
    println("kafka.brokers:" + brokers)
    println("kafka.topics:" + topics)
    println("kudu.master:" + kuduMaster)

    if(StringUtils.isEmpty(brokers)|| StringUtils.isEmpty(topics) || StringUtils.isEmpty(kuduMaster)) {
      println("未配置Kafka和KuduMaster信息")
      System.exit(0)
    }
    val topicsSet = topics.split(",").toSet

    val spark = SparkSession.builder().appName("Kafka2Spark2Kudu-nokerberos").config(new SparkConf()).getOrCreate()
    val ssc = new StreamingContext(spark.sparkContext, Seconds(5)) //设置Spark时间窗口，每5s处理一次
    val kafkaParams = Map[String, Object]("bootstrap.servers" -> brokers
      , "auto.offset.reset" -> "latest"
      , "key.deserializer" -> classOf[StringDeserializer]
      , "value.deserializer" -> classOf[StringDeserializer]
      , "group.id" -> properties.getProperty("group.id")
    )

    val dStream = KafkaUtils.createDirectStream[String, String](ssc,
      LocationStrategies.PreferConsistent,
      ConsumerStrategies.Subscribe[String, String](topicsSet, kafkaParams))

    //引入隐式
    import spark.implicits._
    val kuduContext = new KuduContext(kuduMaster, spark.sparkContext)

    //判断表是否存在
    if(!kuduContext.tableExists("user_info")) {
      println("create Kudu Table :{user_info}")
      val createTableOptions = new CreateTableOptions()
      createTableOptions.addHashPartitions(List("id").asJava, 8).setNumReplicas(3)
      kuduContext.createTable("user_info", userInfoSchema, Seq("id"), createTableOptions)
    }

    dStream.foreachRDD(rdd => {
      //将rdd数据重新封装为Rdd[UserInfo]
      val newrdd = rdd.map(line => {
        val jsonObj =  JSON.parseFull(line.value())
        val map:Map[String,Any] = jsonObj.get.asInstanceOf[Map[String, Any]]
        new UserInfo(
          map.get("id").get.asInstanceOf[String],
          map.get("name").get.asInstanceOf[String],
          map.get("sex").get.asInstanceOf[String],
          map.get("city").get.asInstanceOf[String],
          map.get("occupation").get.asInstanceOf[String],
          map.get("mobile_phone_num").get.asInstanceOf[String],
          map.get("fix_phone_num").get.asInstanceOf[String],
          map.get("bank_name").get.asInstanceOf[String],
          map.get("address").get.asInstanceOf[String],
          map.get("marriage").get.asInstanceOf[String],
          map.get("child_num").get.asInstanceOf[String]
        )
      })
      //将RDD转换为DataFrame
      val userinfoDF = spark.sqlContext.createDataFrame(newrdd)
      kuduContext.upsertRows(userinfoDF, "user_info")
    })
    ssc.start()
    ssc.awaitTermination()
  }
}

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4.使用mvn命令编译工程，注意由于是scala工程编译时mvn命令要加scala:compile

mvn clean scala:compile package

5.将编译好的spark2-demo-1.0-SNAPSHOT.jar包及配置文件上传至服务器

0294.properties配置文件内容如下：

4.示例运行

1.使用spark2-submit命令向集群提交Spark2Streaming作业

spark2-submit --class com.cloudera.streaming.nokerberos.Kafka2Spark2Kudu \
    --master yarn \
    --deploy-mode client \
    --executor-memory 2g \
    --executor-cores 2 \
    --driver-memory 2g \
    --num-executors 2 \
    spark2-demo-1.0-SNAPSHOT.jar

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通过CM查看作业是否提交成功

Spark2的UI界面

2.查看Kudu Master的UI界面，Tables列表可以看到user_info表已被创建

找到Kudu向Impala的建表语句

CREATE EXTERNAL TABLE `user_info` STORED AS KUDU
TBLPROPERTIES(
    'kudu.table_name' = 'user_info',
    'kudu.master_addresses' = 'cdh01.fayson.com:7051,cdh02.fayson.com:7051,cdh03.fayson.com:7051')

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