基於Kafka+SparkStreaming+HBase實時點擊流案例

時間  2019-11-06
標籤 基於 kafka+sparkstreaming+hbase kafka sparkstreaming hbase 實時 點擊 案例 欄目 Kafka 简体版
原文   原文鏈接

前言

最近在專一Spark開發，記錄下本身的工做和學習路程，但願能跟你們互相交流成長
本文章更傾向於實戰案例，涉及框架原理及基本應用還請讀者自行閱讀相關文章，相關在本文章最後參考資料中
關於Zookeeper/Kafka/HBase/Hadoop相關集羣環境搭建做者會陸續更新
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背景

Kafka實時記錄從數據採集工具Flume或業務系統實時接口收集數據，並做爲消息緩衝組件爲上游實時計算框架提供可靠數據支撐，Spark 1.3版本後支持兩種整合Kafka機制（Receiver-based Approach 和 Direct Approach），具體細節請參考文章最後官方文檔連接，數據存儲使用HBasejava

實現思路

  1. 實現Kafka消息生產者模擬器
  2. Spark-Streaming採用Direct Approach方式實時獲取Kafka中數據
  3. Spark-Streaming對數據進行業務計算後數據存儲到HBase

本地虛擬機集羣環境配置

因爲筆者機器性能有限，hadoop/zookeeper/kafka集羣都搭建在一塊兒主機名分別爲hadoop1,hadoop2,hadoop3; hbase爲單節點 在hadoop1git

缺點及不足

因爲筆者技術有限，代碼設計上有部分缺陷，好比spark-streaming計算後數據保存hbase邏輯性能很低，但願你們多提意見以便小編及時更正github

代碼實現

Kafka消息模擬器redis

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

/**  * 
Created by 郭飛 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"
    //本地虛擬機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())//隨機生成用戶id
        .put("event_time", System.currentTimeMillis.toString) //記錄時間發生時間
        .put("os_type", getOsType) //設備類型
        .put("click_count", click) //點擊次數

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

      Thread.sleep(200)
    }
  }
}

Spark-Streaming主類apache

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 郭飛 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")
    //本地虛擬機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")
          //用戶ID
          val uid = pair._1
          //點擊次數
          val click = pair._2
          //組裝數據
          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)
          //寫入數據緩存
          StatTable.setWriteBufferSize(3*1024*1024)
          StatTable.put(put)
          //提交
          StatTable.flushCommits()
        })
      })
    })
    ssc.start()
    ssc.awaitTermination()

  }

}

Maven POM文件

<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.guofei.spark</groupId>
  <artifactId>RiskControl</artifactId>
  <version>1.0-SNAPSHOT</version>
  <packaging>jar</packaging>

  <name>RiskControl</name>
  <url>http://maven.apache.org</url>

  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
  </properties>

  <dependencies>
    <!--Spark core 及 streaming -->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_2.10</artifactId>
      <version>1.3.0</version>
    </dependency>
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-streaming_2.10</artifactId>
      <version>1.3.0</version>
    </dependency>
    <!-- Spark整合Kafka-->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-streaming-kafka_2.10</artifactId>
      <version>1.3.0</version>
    </dependency>

    <!-- 整合Hbase-->
    <dependency>
      <groupId>org.apache.hbase</groupId>
      <artifactId>hbase</artifactId>
      <version>0.96.2-hadoop2</version>
      <type>pom</type>
    </dependency>
    <!--Hbase依賴 -->
    <dependency>
      <groupId>org.apache.hbase</groupId>
      <artifactId>hbase-server</artifactId>
      <version>0.96.2-hadoop2</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hbase</groupId>
      <artifactId>hbase-client</artifactId>
      <version>0.96.2-hadoop2</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hbase</groupId>
      <artifactId>hbase-common</artifactId>
      <version>0.96.2-hadoop2</version>
    </dependency>
    <dependency>
      <groupId>commons-io</groupId>
      <artifactId>commons-io</artifactId>
      <version>1.3.2</version>
    </dependency>
    <dependency>
      <groupId>commons-logging</groupId>
      <artifactId>commons-logging</artifactId>
      <version>1.1.3</version>
    </dependency>
    <dependency>
      <groupId>log4j</groupId>
      <artifactId>log4j</artifactId>
      <version>1.2.17</version>
    </dependency>

    <dependency>
      <groupId>com.google.protobuf</groupId>
      <artifactId>protobuf-java</artifactId>
      <version>2.5.0</version>
    </dependency>
    <dependency>
      <groupId>io.netty</groupId>
      <artifactId>netty</artifactId>
      <version>3.6.6.Final</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hbase</groupId>
      <artifactId>hbase-protocol</artifactId>
      <version>0.96.2-hadoop2</version>
    </dependency>
    <dependency>
      <groupId>org.apache.zookeeper</groupId>
      <artifactId>zookeeper</artifactId>
      <version>3.4.5</version>
    </dependency>
    <dependency>
      <groupId>org.cloudera.htrace</groupId>
      <artifactId>htrace-core</artifactId>
      <version>2.01</version>
    </dependency>
    <dependency>
      <groupId>org.codehaus.jackson</groupId>
      <artifactId>jackson-mapper-asl</artifactId>
      <version>1.9.13</version>
    </dependency>
    <dependency>
      <groupId>org.codehaus.jackson</groupId>
      <artifactId>jackson-core-asl</artifactId>
      <version>1.9.13</version>
    </dependency>
    <dependency>
      <groupId>org.codehaus.jackson</groupId>
      <artifactId>jackson-jaxrs</artifactId>
      <version>1.9.13</version>
    </dependency>
    <dependency>
      <groupId>org.codehaus.jackson</groupId>
      <artifactId>jackson-xc</artifactId>
      <version>1.9.13</version>
    </dependency>
    <dependency>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-api</artifactId>
      <version>1.6.4</version>
    </dependency>
    <dependency>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-log4j12</artifactId>
      <version>1.6.4</version>
    </dependency>

    <!-- Hadoop依賴包-->
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>2.6.4</version>
    </dependency>
    <dependency>
      <groupId>commons-configuration</groupId>
      <artifactId>commons-configuration</artifactId>
      <version>1.6</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-auth</artifactId>
      <version>2.6.4</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-common</artifactId>
      <version>2.6.4</version>
    </dependency>

    <dependency>
      <groupId>net.sf.json-lib</groupId>
      <artifactId>json-lib</artifactId>
      <version>2.4</version>
      <classifier>jdk15</classifier>
    </dependency>

    <dependency>
      <groupId>org.codehaus.jettison</groupId>
      <artifactId>jettison</artifactId>
      <version>1.1</version>
    </dependency>

    <dependency>
      <groupId>redis.clients</groupId>
      <artifactId>jedis</artifactId>
      <version>2.5.2</version>
    </dependency>
    <dependency>
      <groupId>org.apache.commons</groupId>
      <artifactId>commons-pool2</artifactId>
      <version>2.2</version>
    </dependency>
  </dependencies>

  <build>
    <sourceDirectory>src/main/scala</sourceDirectory>
    <testSourceDirectory>src/test/scala</testSourceDirectory>
    <plugins>
      <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>
            <configuration>
              <args>
                <arg>-make:transitive</arg>
                <arg>-dependencyfile</arg>
                <arg>${project.build.directory}/.scala_dependencies</arg>
              </args>
            </configuration>
          </execution>
        </executions>
      </plugin>

      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-shade-plugin</artifactId>
        <version>2.4.3</version>
        <executions>
          <execution>
            <phase>package</phase>
            <goals>
              <goal>shade</goal>
            </goals>
            <configuration>
              <filters>
                <filter>
                  <artifact>*:*</artifact>
                  <excludes>
                    <exclude>META-INF/*.SF</exclude>
                    <exclude>META-INF/*.DSA</exclude>
                    <exclude>META-INF/*.RSA</exclude>
                  </excludes>
                </filter>
              </filters>
            </configuration>
          </execution>
        </executions>
      </plugin>
    </plugins>
  </build>
</project>

FAQ

  1. Maven導入json-lib報錯
    Failure to find net.sf.json-lib:json-lib:jar:2.3 in
    http://repo.maven.apache.org/maven2 was cached in the local
    repository
    解決：
    http://stackoverflow.com/questions/4173214/maven-missing-net-sf-json-lib
    <dependency>
    <groupId>net.sf.json-lib</groupId>
    <artifactId>json-lib</artifactId>
    <version>2.4</version>
    <classifier>jdk15</classifier>
    </dependency>
  2. 執行Spark-Streaming程序報錯
    org.apache.spark.SparkException: Task not serializable
userClicks.foreachRDD(rdd => { 
rdd.foreachPartition(partitionOfRecords => { 
partitionOfRecords.foreach(
這裏面的代碼中所包含的對象必須是序列化的
這裏面的代碼中所包含的對象必須是序列化的
這裏面的代碼中所包含的對象必須是序列化的
}) 
}) 
})
  1. 執行Maven打包報錯，找不到依賴的jar包
    error：not found: object kafka
    ERROR import kafka.javaapi.producer.Producer
    解決：win10本地系統 用戶/郭飛/.m2/ 目錄含有中文

參考文檔



做者：MichaelFly
連接：https://www.jianshu.com/p/ccba410462ba
來源：簡書
簡書著做權歸做者全部，任何形式的轉載都請聯繫做者得到受權並註明出處。json

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