102.Spark2Streaming在Kafka和HBase下的读写

CM版本：5.14.3CDH版本：5.14.2Apache Kafka版本：0.10.2SPARK版本：2.2.0Redhat版本：7.3已启用Kerberos,用root用户进行操作 102.2 操作演示

1.准备环境

导出keytab文件

[root@cdh01 ~]# kadmin.local 

Authenticating as principal hbase/admin@FAYSON.COM with password.

kadmin.local:  xst -norandkey -k fayson.keytab fayson@FAYSON.COM

检查导出的keytab文件是否正确

[root@cdh01 ~]# klist -ek fayson.keytab

jaas.cof文件：

把fayson.keytab和jaas.conf文件拷贝至集群的所有节点统一的/data/disk1/0286-kafka-shell/conf目录下

KafkaClient {

  com.sun.security.auth.module.Krb5LoginModule required

  useKeyTab=true

  keyTab="/data/disk1/0286-kafka-shell/conf/fayson.keytab"

  principal="fayson@FAYSON.COM";

};



Client {

  com.sun.security.auth.module.Krb5LoginModule required

  useKeyTab=true

  storeKey=true

  keyTab="/data/disk1/0286-kafka-shell/conf/fayson.keytab"

  principal="fayson@FAYSON.COM";

};

根据需求将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": "劳动者、运输工作和部分体力生产工作",
   "address": "山东东三路18号-6-6",
   "city": "长江",
   "marriage": "1",
   "sex": "1",
   "name": "魏淑芬",
   "mobile_phone_num": "13508268580",
   "bank_name": "广发银行32",
   "id": "510105198906185189",
   "child_num": "1",
   "fix_phone_num": "16004180180"
}

把spark_kafka_version的kafka版本修改为0.10通过CM下载Hbase客户端配置文件
把Spark2访问Hbase的依赖包添加到集群的/opt/cloudera/parcels/SPARK2/lib/spark2/jars目录下，依赖的jar包如下:

需要将依赖包拷贝至集群所有节点

hbase-client-1.2.0-cdh5.14.2.jar
hbase-common-1.2.0-cdh5.14.2.jar
hbase-protocol-1.2.0-cdh5.14.2.jar
htrace-core-3.2.0-incubating.jar

2.开发示例

pom.xml依赖如下

在maven创建scala语言的spark2demo

    org.apache.hbase
    hbase-client
    1.2.0-cdh5.14.2

添加访问Hbase的集群配置信息hdfs-site.xml/core-stie.xml/hbase-site.xml文件在resources下创建0289.properties配置文件：

kafka.brokers=cdh02.fayson.com:9092,cdh03.fayson.com:9092,cdh04.fayson.com:9092
kafka.topics=kafka_hbase_topic
principal.account=fayson@FAYSON.COM
keytab.filepath=/data/disk1/spark2streaming-kafka-hbase/conf/fayson.keytab

创建HbaseUtils.scala类，主要用于创建Hbase的Connection

package utils

import java.io.File
import java.security.PrivilegedAction
import org.apache.hadoop.hbase.{HbaseConfiguration}
import org.apache.hadoop.hbase.client.{Connection, ConnectionFactory}
import org.apache.hadoop.security.UserGroupInformation


object HbaseUtil {

  
  def getHbaseConn(confPath: String, principal: String, keytabPath: String): Connection = {
    val configuration = HbaseConfiguration.create
    val coreFile = new File(confPath + File.separator + "core-site.xml")
    if(!coreFile.exists()) {
      val in = HbaseUtil.getClass.getClassLoader.getResourceAsStream("hbase-conf/core-site.xml")
      configuration.addResource(in)
    }
    val hdfsFile = new File(confPath + File.separator + "hdfs-site.xml")
    if(!hdfsFile.exists()) {
      val in = HbaseUtil.getClass.getClassLoader.getResourceAsStream("hbase-conf/hdfs-site.xml")
      configuration.addResource(in)
    }
    val hbaseFile = new File(confPath + File.separator + "hbase-site.xml")
    if(!hbaseFile.exists()) {
      val in = HbaseUtil.getClass.getClassLoader.getResourceAsStream("hbase-conf/hbase-site.xml")
      configuration.addResource(in)
    }

    UserGroupInformation.setConfiguration(configuration)
    UserGroupInformation.loginUserFromKeytab(principal, keytabPath)
    val loginUser = UserGroupInformation.getLoginUser
    loginUser.doAs(new PrivilegedAction[Connection] {
      override def run(): Connection = ConnectionFactory.createConnection(configuration)
    })
  }
}

创建Kafka2Spark2Hbase.scala文件：

package com.cloudera.streaming

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

import org.apache.commons.lang.StringUtils
import org.apache.hadoop.hbase.TableName
import org.apache.hadoop.hbase.client.Put
import org.apache.hadoop.hbase.util.Bytes
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import utils.HbaseUtil

import scala.util.Try
import scala.util.parsing.json.JSON


object Kafka2Spark2Hbase {

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

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

  def main(args: Array[String]): Unit = {
    //加载配置文件
    val properties = new Properties()
    val file = new File(confPath + File.separator + "0288.properties")
    if(!file.exists()) {
      val in = Kafka2Spark2Kudu.getClass.getClassLoader.getResourceAsStream("0289.properties")
      properties.load(in);
    } else {
      properties.load(new FileInputStream(confPath))
    }

    val brokers = properties.getProperty("kafka.brokers")
    val topics = properties.getProperty("kafka.topics")
    val principal = properties.getProperty("principal.account")
    val keytabFilePath = properties.getProperty("keytab.filepath")
    println("kafka.brokers:" + brokers)
    println("kafka.topics:" + topics)

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

    val spark = SparkSession.builder().appName("Kafka2Spark2Hbase-kerberos").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"
      , "security.protocol" -> "SASL_PLAINTEXT"
      , "sasl.kerberos.service.name" -> "kafka"
      , "key.deserializer" -> classOf[StringDeserializer]
      , "value.deserializer" -> classOf[StringDeserializer]
      , "group.id" -> "testgroup"
    )

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

    dStream.foreachRDD(rdd => {
      rdd.foreachPartition(partitionRecords => {
        val connection = HbaseUtil.getHbaseConn(confPath, principal, keytabFilePath) // 获取Hbase连接
        partitionRecords.foreach(line => {
          //将Kafka的每一条消息解析为JSON格式数据
          val jsonObj =  JSON.parseFull(line.value())
          println(line.value())
          val map:Map[String,Any] = jsonObj.get.asInstanceOf[Map[String, Any]]
          val rowkey = map.get("id").get.asInstanceOf[String]
          val name = map.get("name").get.asInstanceOf[String]
          val sex = map.get("sex").get.asInstanceOf[String]
          val city = map.get("city").get.asInstanceOf[String]
          val occupation = map.get("occupation").get.asInstanceOf[String]
          val mobile_phone_num = map.get("mobile_phone_num").get.asInstanceOf[String]
          val fix_phone_num = map.get("fix_phone_num").get.asInstanceOf[String]
          val bank_name = map.get("bank_name").get.asInstanceOf[String]
          val address = map.get("address").get.asInstanceOf[String]
          val marriage = map.get("marriage").get.asInstanceOf[String]
          val child_num = map.get("child_num").get.asInstanceOf[String]

          val tableName = TableName.valueOf("user_info")
          val table = connection.getTable(tableName)
          val put = new Put(Bytes.toBytes(rowkey))
          put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("name"), Bytes.toBytes(name))
          put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("sex"), Bytes.toBytes(sex))
          put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("city"), Bytes.toBytes(city))
          put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("occupation"), Bytes.toBytes(occupation))
          put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("mobile_phone_num"), Bytes.toBytes(mobile_phone_num))
          put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("fix_phone_num"), Bytes.toBytes(fix_phone_num))
          put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("bank_name"), Bytes.toBytes(bank_name))
          put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("address"), Bytes.toBytes(address))
          put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("marriage"), Bytes.toBytes(marriage))
          put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("child_num"), Bytes.toBytes(child_num))

          Try(table.put(put)).getOrElse(table.close())//将数据写入Hbase，若出错关闭table
          table.close()//分区数据写入Hbase后关闭连接
        })
        connection.close()

      })
    })

    ssc.start()
    ssc.awaitTermination()
  }

}

使用mvn命令编译工程

scala工程编译时mvn命令要加scala:compile

mvn clean scala:compile package7.将编译好的spark2-demo-1.0-SNAPSHOT.jar包上传至服务器

编译好的spark2-demo-1.0-SNAPSHOT.jar包上传至服务器

Spark2应用的配置文件放在conf目录下 spark2streaming-kafka-hbase目录拷贝至集群的所有节点

3.运行示例

spark2-submit命令向集群提交Spark2Streaming作业

spark2-submit --class com.cloudera.streaming.Kafka2Spark2Hbase 
--master yarn 
--deploy-mode client 
--executor-memory 2g 
--executor-cores 2 
--driver-memory 2g 
--num-executors 2 
--queue default  
--principal fayson@FAYSON.COM 
--keytab /data/disk1/spark2streaming-kafka-hbase/conf/fayson.keytab 
--files "/data/disk1/spark2streaming-kafka-hbase/conf/jaas.conf#jaas.conf" 
--driver-java-options "-Djava.security.auth.login.config=/data/disk1/spark2streaming-kafka-hbase/conf/jaas.conf" 
--conf "spark.executor.extraJavaOptions=-Djava.security.auth.login.config=/data/disk1/spark2streaming-kafka-hbase/conf/jaas.conf" 
spark2-demo-1.0-SNAPSHOT.jar

运行脚本向Kafka的Kafka_hbase_topic生产消息用hbase shell命令查看数据是否入库成功

4.总结

在访问Kerberos环境的Hbase，需要加载Hbase的客户端配置文件，为了方便直接将三个配置文件加载在0289.properties配置文件中，如果指定的为相对路径可能会出现Kerberos认证失败，所以要指定了keytab文件的绝对路径Spark2默认的kafka版本为0.9需要通过CM将默认的Kafka版本修改为0.10jaas.conf文件及keytab需要在集群的所有节点存在，因为Driver和Executor是随机在集群的节点上启动的在选择依赖包时需要注意与Spark版本的兼容性问题

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