Spark Streaming的核心DStream案例

文章目录

pom.xml

<?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>com.SparkStream</groupId>
    <artifactId>SparkStreamspace</artifactId>
    <version>1.0-SNAPSHOT</version>

    <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>-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>
                            <transformers>
                                <transformer implementation=
                                                     "org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                    <mainClass></mainClass>
                                </transformer>
                            </transformers>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <configuration>
                    <source>6</source>
                    <target>6</target>
                </configuration>
            </plugin>
        </plugins>
    </build>


    <properties>
        <scala.version>2.11.8</scala.version>
        <hadoop.version>2.7.4</hadoop.version>
        <spark.version>2.3.2</spark.version>
    </properties>
    <dependencies>
        <!--Scala-->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>
        <!--Spark-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <!--Hadoop-->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>2.3.2</version>
        </dependency>

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.46</version>
        </dependency>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>2.0.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-streams</artifactId>
            <version>2.0.0</version>
        </dependency>




        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>2.11.8</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>2.0.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>2.0.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
            <version>2.3.2</version>
        </dependency>

    </dependencies>

</project>

1、transform()方法

TransformTest.scala

import org.apache.spark.streaming.dstream.{DStream, ReceiverInputDStream}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}

object TransformTest {
  def main(args: Array[String]): Unit = {
    //1.创建SparkConf对象
    val sparkConf: SparkConf = new SparkConf()
      .setAppName("TransformTest ").setMaster("local[2]")
    //2.创建SparkContext对象，它是所有任务计算的源头
    val sc: SparkContext = new SparkContext(sparkConf)
    //3.设置日志级别
    sc.setLogLevel("WARN")
    //4.创建StreamingContext，需要两个参数，分别为SparkContext和批处理时间间隔
    val ssc: StreamingContext = new StreamingContext(sc, Seconds(5))
    //5.连接socket服务，需要socket服务地址、端口号及存储级别(默认的)
    val dstream: ReceiverInputDStream[String] =
      ssc.socketTextStream("192.168.121.134", 9999)
    //6.使用RDD-to-RDD函数，返回新的DStream对象（即words），并空格切分每行
    val words: DStream[String] = dstream.transform(rdd => rdd
      .flatMap(_.split(" ")))
    //7.打印输出结果
    words.print()
    //8.开启流式计算
    ssc.start()
    //9.用于保持程序一直运行，除非人为干预停止
    ssc.awaitTermination()
  }
}

执行命令nc -lk 9999启动服务端监听Socket服务，并输入内容。

2、updateStateByKey()方法

UpdateStateByKeyTest.scala

import org.apache.spark.streaming.dstream.{DStream, ReceiverInputDStream}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}

object UpdateStateByKeyTest {
  //newValues 表示当前批次汇总成的(K,V)中相同K的所有V
  //runningCount 表示历史的所有相同key的value总和
  def updateFunction(newValues: Seq[Int], runningCount: Option[Int]):
  Option[Int] = {
    val newCount = runningCount.getOrElse(0) + newValues.sum
    Some(newCount)
  }

  def main(args: Array[String]): Unit = {
    //1.创建SparkConf对象
    val sparkConf: SparkConf = new SparkConf()
      .setAppName("UpdateStateByKeyTest ").setMaster("local[2]")
    //2.创建SparkContext对象,它是所有任务计算的源头
    val sc: SparkContext = new SparkContext(sparkConf)
    //3.设置日志级别
    sc.setLogLevel("WARN")
    //4.创建StreamingContext，需要两个参数，分别为SparkContext和批处理时间间隔
    val ssc: StreamingContext = new StreamingContext(sc, Seconds(5))
    //5.配置检查点目录，使用updateStateByKey方法必须配置检查点目录
    ssc.checkpoint("./")
    //6. 连接socket服务，需要socket服务地址、端口号及存储级别(默认的)
    val dstream: ReceiverInputDStream[String] = ssc
      .socketTextStream("192.168.121.134", 9999)
    //7.按空格进行切分每一行,并将切分出来的单词出现的次数记录为1
    val wordAndOne: DStream[(String, Int)] = dstream.flatMap(_.split(" "))
      .map(word => (word, 1))
    //8.调用updateStateByKey操作，统计单词在全局中出现的次数
    var result: DStream[(String, Int)] = wordAndOne
      .updateStateByKey(updateFunction)
    //9.打印输出结果
    result.print()
    //10.开启流式计算
    ssc.start()
    //11.用于保持程序运行，除非被干预停止
    ssc.awaitTermination()
  }
}

执行命令nc -lk 9999启动服务端监听Socket服务，并输入内容。

3、window()方法

WindowTest.scala

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.dstream.{DStream, ReceiverInputDStream}

object WindowTest {
  def main(args: Array[String]): Unit = {
    //1.创建SparkConf对象
    val sparkConf: SparkConf = new SparkConf()
      .setAppName("WindowTest ").setMaster("local[2]")
    //2.创建SparkContext对象，它是所有任务计算的源头
    val sc: SparkContext = new SparkContext(sparkConf)
    //3.设置日志级别
    sc.setLogLevel("WARN")
    //4.创建StreamingContext，需要两个参数，分别为SparkContext和批处理时间间隔
    val ssc: StreamingContext = new StreamingContext(sc, Seconds(1))
    //5.连接socket服务，需要socket服务地址、端口号及存储级别(默认的)
    val dstream: ReceiverInputDStream[String] = ssc
      .socketTextStream("192.168.121.134", 9999)
    //6.按空格进行切分每一行
    val words: DStream[String] = dstream.flatMap(_.split(" "))
    //7.调用window操作，需要两个参数，窗口长度和滑动时间间隔
    val windowWords: DStream[String] = words.window(Seconds(3), Seconds(1))
    //8.打印输出结果
    windowWords.print()
    //9.开启流式计算
    ssc.start()
    //10.让程序一直运行，除非人为干预停止
    ssc.awaitTermination()
  }
}

执行命令nc -lk 9999启动服务端监听Socket服务，并输入内容。

4、reduceByKeyAndWindow()方法

ReduceByKeyAndWindowTest.scala

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.dstream.{DStream, ReceiverInputDStream}

object ReduceByKeyAndWindowTest {
  def main(args: Array[String]): Unit = {
    //1.创建SparkConf对象
    val sparkConf: SparkConf = new SparkConf()
      .setAppName("ReduceByKeyAndWindowTest ").setMaster("local[2]")
    //2.创建SparkContext对象，它是所有任务计算的源头
    val sc: SparkContext = new SparkContext(sparkConf)
    //3.设置日志级别
    sc.setLogLevel("WARN")
    //4.创建StreamingContext，需要两个参数，分别为SparkContext和批处理时间间隔
    val ssc: StreamingContext = new StreamingContext(sc, Seconds(1))
    //5.连接socket服务，需要socket服务地址、端口号及存储级别(默认的)
    val dstream: ReceiverInputDStream[String] = ssc
      .socketTextStream("192.168.121.134", 9999)
    //6.按空格进行切分每一行, 并将切分的单词出现次数记录为1
    val wordAndOne: DStream[(String, Int)] = dstream.flatMap(_.split(" "))
      .map(word => (word, 1))
    //7.调用reduceByKeyAndWindow操作
    val windowWords: DStream[(String, Int)] = wordAndOne
      .reduceByKeyAndWindow((a: Int, b: Int) => (a + b), Seconds(3), Seconds(1))
    //8.打印输出结果
    windowWords.print()
    //9.开启流式计算
    ssc.start()
    //10.让程序一直运行，除非人为干预停止
    ssc.awaitTermination()
  }
}

执行命令nc -lk 9999启动服务端监听Socket服务，并输入内容。

5、SaveAsTextFilesTest()方法

SaveAsTextFilesTest.scala

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.dstream.ReceiverInputDStream

object SaveAsTextFilesTest {
  def main(args: Array[String]): Unit = {
    //1.设置本地测试环境
    System.setProperty("HADOOP_USER_NAME", "root")
    //2.创建SparkConf对象
    val sparkConf: SparkConf = new SparkConf()
      .setAppName("SaveAsTextFilesTest ").setMaster("local[2]")
    //3.创建SparkContext对象，它是所有任务计算的源头
    val sc: SparkContext = new SparkContext(sparkConf)
    //4.设置日志级别
    sc.setLogLevel("WARN")
    //5.创建StreamingContext，需要两个参数，分别为SparkContext和批处理时间间隔
    val ssc: StreamingContext = new StreamingContext(sc, Seconds(5))
    //6.连接socket服务，需要socket服务地址、端口号及存储级别(默认的)
    val dstream: ReceiverInputDStream[String] = ssc
      .socketTextStream("192.168.121.134", 9999)
    //7.调用saveAsTextFiles操作，将nc交互界面输出的内容保存到HDFS上
    dstream.saveAsTextFiles("hdfs://hadoop02:9000/data/root/saveAsTextFiles/satf","txt")
      ssc.start ()
      ssc.awaitTermination ()
      }
      }

开启zookeeper和Hadoop集群，查看active在哪一个节点，下图就改成哪一个。

执行命令nc -lk 9999启动服务端监听Socket服务，并输入内容。

内容会保存到hdfs中，并以satf开头以txt结尾的文件形式保存。