SparkStreaming wordCountDemo基础案例

体现sparkStreaming的秒级准实时性，所以我们需要一个能够持续输入数据的东东

1.CentOS上下载nc

创建一个scala工程,导入相关pom依赖

<?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.shiao</groupId>
    <artifactId>spark-01</artifactId>
    <version>1.0</version>

    <packaging>jar</packaging>

    <properties>
        <scala.version>2.11.8</scala.version>
        <hadoop.version>2.7.4</hadoop.version>
        <spark.version>2.0.2</spark.version>
    </properties>

    <dependencies>
        <!--scala依赖-->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>
        <!--spark依赖-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <!--hadoop依赖-->
        <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>5.1.30</version>
        </dependency>


        <!--引入spark-streaming依赖-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>2.0.2</version>
        </dependency>

    </dependencies>




    <!--配置插件-->
    <build>
        <plugins>
            <!--scala编译插件-->
            <plugin>
                <groupId>org.scala-tools</groupId>
                <artifactId>maven-scala-plugin</artifactId>
                <version>2.15.2</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>

            <!--项目打包插件-->
            <plugin>
                <artifactId>maven-assembly-plugin</artifactId>
                <configuration>
                    <archive>
                        <manifest>
                            <mainClass>WordCount</mainClass>
                        </manifest>
                    </archive>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
            </plugin>
        </plugins>

    </build>

    
</project>

　　创建一个object

编写代码

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

object SparkStreamingWordCount {
  def main(args: Array[String]): Unit = {


    //创建sparkContext
    val configStr = new SparkConf().setAppName("SparkStreamingWordCount").setMaster("local[2]")
    val sc = new SparkContext(configStr)

    //创建streamingContext
    val scc = new StreamingContext(sc, Seconds(5))

    //去掉多余的日志,影响观看
    sc.setLogLevel("WARN")

    //创建receive获取socket数据
    val lines: ReceiverInputDStream[String] = scc.socketTextStream("192.168.52.110", 9999)

    //计数处理,以逗号划分,分成一个个字符串;对每个字符串进行处理成值为1的元组;对相同单词进行相加;进行打印
    val value: DStream[(String, Int)] = lines.flatMap(_.split("\,")).map((_, 1)).reduceByKey(_ + _)
    value.print()

    //开启并阻塞线程，以保持不断获取
    scc.start()
    scc.awaitTermination()
  }
}

跑起来

使用scoket nc打开9999端口发送数据

 测试