基于Kafka+SparkStreaming+HBase实时点击流案例

原文地址：https://www.jianshu.com/p/ccba410462ba

前言

最近在专注Spark开发，记录下自己的工作和学习路程，希望能跟大家互相交流成长
本文章更倾向于实战案例，涉及框架原理及基本应用还请读者自行阅读相关文章，相关在本文章最后参考资料中
关于Zookeeper/Kafka/HBase/Hadoop相关集群环境搭建作者会陆续更新
本文章发布后会及时更新文章中出现的错误及增加内容，欢迎大家订阅
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背景

Kafka实时记录从数据采集工具Flume或业务系统实时接口收集数据，并作为消息缓冲组件为上游实时计算框架提供可靠数据支撑，Spark 1.3版本后支持两种整合Kafka机制（Receiver-based Approach 和 Direct Approach），具体细节请参考文章最后官方文档链接，数据存储使用HBase

实现思路

1. 实现Kafka消息生产者模拟器
2. Spark-Streaming采用Direct Approach方式实时获取Kafka中数据
3. Spark-Streaming对数据进行业务计算后数据存储到HBase

本地虚拟机集群环境配置

由于笔者机器性能有限，hadoop/zookeeper/kafka集群都搭建在一起主机名分别为hadoop1,hadoop2,hadoop3; hbase为单节点 在hadoop1

缺点及不足

由于笔者技术有限，代码设计上有部分缺陷，比如spark-streaming计算后数据保存hbase逻辑性能很低，希望大家多提意见以便小编及时更正

代码实现

Kafka消息模拟器

package clickstream
import java.util.{Properties, Random, UUID}
import kafka.producer.{KeyedMessage, Producer, ProducerConfig}
import org.codehaus.jettison.json.JSONObject

/**  * 
Created by 郭飞 on 2016/5/31.  
*/
object KafkaMessageGenerator {
  private val random = new Random()
  private var pointer = -1
  private val os_type = Array(
    "Android", "IPhone OS",
    "None", "Windows Phone")

  def click() : Double = {
    random.nextInt(10)
  }

  def getOsType() : String = {
    pointer = pointer + 1
    if(pointer >= os_type.length) {
      pointer = 0
      os_type(pointer)
    } else {
      os_type(pointer)
    }
  }

  def main(args: Array[String]): Unit = {
    val topic = "user_events"
    //本地虚拟机ZK地址
    val brokers = "hadoop1:9092,hadoop2:9092,hadoop3:9092"
    val props = new Properties()
    props.put("metadata.broker.list", brokers)
    props.put("serializer.class", "kafka.serializer.StringEncoder")

    val kafkaConfig = new ProducerConfig(props)
    val producer = new Producer[String, String](kafkaConfig)

    while(true) {
      // prepare event data
      val event = new JSONObject()
      event
        .put("uid", UUID.randomUUID())//随机生成用户id
        .put("event_time", System.currentTimeMillis.toString) //记录时间发生时间
        .put("os_type", getOsType) //设备类型
        .put("click_count", click) //点击次数

      // produce event message
      producer.send(new KeyedMessage[String, String](topic, event.toString))
      println("Message sent: " + event)

      Thread.sleep(200)
    }
  }
}

Spark-Streaming主类

package clickstream
import kafka.serializer.StringDecoder
import net.sf.json.JSONObject
import org.apache.hadoop.hbase.client.{HTable, Put}
import org.apache.hadoop.hbase.util.Bytes
import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
  * Created by 郭飞 on 2016/5/31.
  */
object PageViewStream {
  def main(args: Array[String]): Unit = {
    var masterUrl = "local[2]"
    if (args.length > 0) {
      masterUrl = args(0)
    }

    // Create a StreamingContext with the given master URL
    val conf = new SparkConf().setMaster(masterUrl).setAppName("PageViewStream")
    val ssc = new StreamingContext(conf, Seconds(5))

    // Kafka configurations
    val topics = Set("PageViewStream")
    //本地虚拟机ZK地址
    val brokers = "hadoop1:9092,hadoop2:9092,hadoop3:9092"
    val kafkaParams = Map[String, String](
      "metadata.broker.list" -> brokers,
      "serializer.class" -> "kafka.serializer.StringEncoder")

    // Create a direct stream
    val kafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topics)

    val events = kafkaStream.flatMap(line => {
      val data = JSONObject.fromObject(line._2)
      Some(data)
    })
    // Compute user click times
    val userClicks = events.map(x => (x.getString("uid"), x.getInt("click_count"))).reduceByKey(_ + _)
    userClicks.foreachRDD(rdd => {
      rdd.foreachPartition(partitionOfRecords => {
        partitionOfRecords.foreach(pair => {
          //Hbase配置
          val tableName = "PageViewStream"
          val hbaseConf = HBaseConfiguration.create()
          hbaseConf.set("hbase.zookeeper.quorum", "hadoop1:9092")
          hbaseConf.set("hbase.zookeeper.property.clientPort", "2181")
          hbaseConf.set("hbase.defaults.for.version.skip", "true")
          //用户ID
          val uid = pair._1
          //点击次数
          val click = pair._2
          //组装数据
          val put = new Put(Bytes.toBytes(uid))
          put.add("Stat".getBytes, "ClickStat".getBytes, Bytes.toBytes(click))
          val StatTable = new HTable(hbaseConf, TableName.valueOf(tableName))
          StatTable.setAutoFlush(false, false)
          //写入数据缓存
          StatTable.setWriteBufferSize(3*1024*1024)
          StatTable.put(put)
          //提交
          StatTable.flushCommits()
        })
      })
    })
    ssc.start()
    ssc.awaitTermination()

  }

}

作者：MichaelFly
链接：https://www.jianshu.com/p/ccba410462ba
來源：简书
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