Spark Streaming实现实时WordCount，DStream的使用，updateStateByKey(func)实现累计计算单词出现频率

一、 实战

1．用Spark Streaming实现实时WordCount
架构图：

说明：在hadoop1:9999下的nc上发送消息，消费端接收消息，然后并进行单词统计计算。

* 2.安装并启动生成者 *
首先在一台Linux（ip：192.168.10.101）上用YUM安装nc工具
yum install -y nc

启动一个服务端并监听9999端口
nc -lk 9999

2.编写Spark Streaming程序
编写Pom文件

<?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>cn.toto.spark</groupId>    <artifactId>bigdata</artifactId>    <version>1.0-SNAPSHOT</version>    <properties>        <maven.compiler.source>1.7</maven.compiler.source>        <maven.compiler.target>1.7</maven.compiler.target>        <encoding>UTF-8</encoding>        <scala.version>2.10.6</scala.version>        <spark.version>1.6.2</spark.version>        <hadoop.version>2.6.4</hadoop.version>    </properties>    <dependencies>        <dependency>            <groupId>org.scala-lang</groupId>            <artifactId>scala-library</artifactId>            <version>${scala.version}</version>        </dependency>        <dependency>            <groupId>org.scala-lang</groupId>            <artifactId>scala-compiler</artifactId>            <version>${scala.version}</version>        </dependency>        <dependency>            <groupId>org.scala-lang</groupId>            <artifactId>scala-reflect</artifactId>            <version>${scala.version}</version>        </dependency>        <dependency>            <groupId>org.apache.spark</groupId>            <artifactId>spark-core_2.10</artifactId>            <version>${spark.version}</version>        </dependency>        <dependency>            <groupId>org.apache.hadoop</groupId>            <artifactId>hadoop-client</artifactId>            <version>${hadoop.version}</version>        </dependency>        <dependency>            <groupId>mysql</groupId>            <artifactId>mysql-connector-java</artifactId>            <version>5.1.38</version>        </dependency>        <dependency>            <groupId>org.apache.spark</groupId>            <artifactId>spark-sql_2.10</artifactId>            <version>${spark.version}</version>        </dependency>        <dependency>            <groupId>org.apache.spark</groupId>            <artifactId>spark-mllib_2.10</artifactId>            <version>${spark.version}</version>        </dependency>        <dependency>            <groupId>org.apache.spark</groupId>            <artifactId>spark-hive_2.10</artifactId>            <version>${spark.version}</version>        </dependency>        <dependency>            <groupId>org.apache.spark</groupId>            <artifactId>spark-streaming_2.10</artifactId>            <version>${spark.version}</version>        </dependency>        <dependency>            <groupId>org.apache.spark</groupId>            <artifactId>spark-streaming-flume_2.10</artifactId>            <version>${spark.version}</version>        </dependency>        <dependency>            <groupId>org.apache.spark</groupId>            <artifactId>spark-streaming-kafka_2.10</artifactId>            <version>${spark.version}</version>        </dependency>    </dependencies>    <build>        <sourceDirectory>src/main/scala</sourceDirectory>        <testSourceDirectory>src/test/scala</testSourceDirectory>        <plugins>            <plugin>                <groupId>net.alchim31.maven</groupId>                <artifactId>scala-maven-plugin</artifactId>                <version>3.2.2</version>                <executions>                    <execution>                        <goals>                            <goal>compile</goal>                            <goal>testCompile</goal>                        </goals>                        <configuration>                            <args>                                <arg>-make:transitive</arg>                                <arg>-dependencyfile</arg>                                <arg>${project.build.directory}/.scala_dependencies</arg>                            </args>                        </configuration>                    </execution>                </executions>            </plugin>            <plugin>                <groupId>org.apache.maven.plugins</groupId>                <artifactId>maven-surefire-plugin</artifactId>                <version>2.18.1</version>                <configuration>                    <useFile>false</useFile>                    <disableXmlReport>true</disableXmlReport>                    <includes>                        <include>**/*Test.*</include>                        <include>**/*Suite.*</include>                    </includes>                </configuration>            </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>                            <transformers>                                <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">                                    <mainClass>cn.toto.spark.JdbcRDDDemo</mainClass>                                </transformer>                            </transformers>                        </configuration>                    </execution>                </executions>            </plugin>        </plugins>    </build></project>

package cn.toto.spark.streamsimport org.apache.log4j.{Level, Logger}import org.apache.spark.Loggingimport org.apache.log4j.{Logger, Level}import org.apache.spark.Loggingobject LoggerLevels extends Logging {  def setStreamingLogLevels() {    val log4jInitialized = Logger.getRootLogger.getAllAppenders.hasMoreElements    if (!log4jInitialized) {      logInfo("Setting log level to [WARN] for streaming example." +        " To override add a custom log4j.properties to the classpath.")      Logger.getRootLogger.setLevel(Level.WARN)    }  }}package cn.toto.sparkimport cn.toto.spark.streams.LoggerLevelsimport org.apache.spark.SparkConfimport org.apache.spark.streaming.{Seconds, StreamingContext}/**  * Created by toto on 2017/7/13.  */object NetworkWordCount {  def main(args: Array[String]) {    //设置日志级别    LoggerLevels.setStreamingLogLevels()    //创建SparkConf并设置为本地模式运行    //注意local[2]代表开两个线程    val conf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount")    //设置DStream批次时间间隔为5秒    val ssc = new StreamingContext(conf, Seconds(5))    //通过网络读取数据    val lines = ssc.socketTextStream("hadoop1", 9999)    //将读到的数据用空格切成单词    val words = lines.flatMap(_.split(" "))    //将单词和1组成一个pair    val pairs = words.map(word => (word, 1))    //按单词进行分组求相同单词出现的次数    val wordCounts = pairs.reduceByKey(_ + _)    //打印结果到控制台    wordCounts.print()    //开始计算    ssc.start()    //等待停止    ssc.awaitTermination()  }}

3.启动Spark Streaming程序：由于使用的是本地模式”local[2]”所以可以直接在本地运行该程序
注意： 要指定并行度，如在本地运行设置setMaster(“local[2]”)，相当于启动两个线程，一个给receiver，一个给computer。如果是在集群中运行，必须要求集群中可用core数大于1

4.在Linux端命令行中输入单词

5.在IDEA控制台中查看结果

二、DStream的使用

package cn.toto.sparkimport org.apache.spark.SparkConfimport org.apache.spark.streaming.dstream.{DStream, ReceiverInputDStream}import org.apache.spark.streaming.{Seconds, StreamingContext}/**  * Created by toto on 2017/7/13.  */object StreamingWordCount {  def main(args: Array[String]): Unit = {    val conf = new SparkConf().setAppName("StreamingWordCount").setMaster("local[2]")    //创建StreamingContext并设置产生批次的间隔时间    val ssc = new StreamingContext(conf, Seconds(5))    //从Socket端口中创建RDD    val lines:ReceiverInputDStream[String] = ssc.socketTextStream("hadoop1",9999)    val words: DStream[String] = lines.flatMap(_.split(" "))    val wordAndOne: DStream[(String, Int)] = words.map((_, 1))    val result: DStream[(String, Int)] = wordAndOne.reduceByKey(_+_)    //打印    result.print()    //开启程序    ssc.start()    //等待结束    ssc.awaitTermination()  }}

运行结果：

---

上面的案例中，所有的都是临时计算，然后获得到结果内容，第二次计算的时候结果值不是在上一次基础上进行累加的。下面的案例中将实现累加的效果：

在上述的wordCount案例中，每次在Linux端输入的单词次数都被正确的统计出来，但是结果不能累加，如果需要累加需要使用updateStateByKey(func)来更新状态

package cn.toto.sparkimport cn.toto.spark.streams.LoggerLevelsimport org.apache.spark.{HashPartitioner, SparkConf}import org.apache.spark.streaming.{Seconds, StreamingContext}object NetworkUpdateStateWordCount {  /**    * String : 单词    * Seq[Int] ：单词在当前批次出现的次数    * Option[Int] ： 历史结果    */  val updateFunc = (iter: Iterator[(String, Seq[Int], Option[Int])]) => {    //iter.flatMap(it=>Some(it._2.sum + it._3.getOrElse(0)).map(x=>(it._1,x)))    iter.flatMap{case(x,y,z)=>Some(y.sum + z.getOrElse(0)).map(m=>(x, m))}  }  def main(args: Array[String]) {    LoggerLevels.setStreamingLogLevels()    val conf = new SparkConf().setMaster("local[2]").setAppName("NetworkUpdateStateWordCount")    val ssc = new StreamingContext(conf, Seconds(5))    //做checkpoint 写入共享存储中    ssc.checkpoint("E://workspace//netresult")    val lines = ssc.socketTextStream("hadoop1", 9999)    //reduceByKey 结果不累加    //val result = lines.flatMap(_.split(" ")).map((_, 1)).reduceByKey(_+_)    //updateStateByKey结果可以累加但是需要传入一个自定义的累加函数：updateFunc    val results = lines.flatMap(_.split(" "))                       .map((_,1)).updateStateByKey(                            updateFunc,new HashPartitioner(ssc.sparkContext.defaultParallelism),true)    results.print()    ssc.start()    ssc.awaitTermination()  }}

在nc上输入内容：

运行结果如下：