Flink——入門WordCount程序

Flink是什麼？

Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. （官網原文）

Flink是一個框架、分佈式的處理引擎。主要用於對無限制和有限制的數據流進行有狀態的計算。在Flink中，一切都是由流組成：離線數據是有限制的流；實時數據時無限制的流。Flink最大的優點就是低延遲、高吞吐且可以保證精準一次性的狀態一致性，他是一個真真正正的流式處理框架。

下面使用scala編寫flink批處理版本和flink流式處理版本的WordCount程序：

 1、新建maven工程，導入依賴

 <dependencies>
        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-scala -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-scala_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-streaming-scala -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-clients -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>
    </dependencies>
    <build>
        <plugins>
            <!-- 該插件用於將Scala代碼編譯成class文件 -->
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.4.6</version>
                <executions>
                    <execution>
                        <!-- 聲明綁定到maven的compile階段 -->
                        <goals>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-assembly-plugin</artifactId>
                <version>3.0.0</version>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>

2、編寫批處理版本wordcount

package com.wei.scala

import org.apache.flink.api.scala._

object WordCount {
  /**
   *  批處理
   * @param args
   */
  def main(args: Array[String]): Unit = {

    //創建執行環境
    val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment

    //讀取數據文件
    val dataSet = env.readTextFile("C:\\idea\\Flink\\src\\main\\resources\\hello.txt")

    //切分文本內容
    val result =dataSet.flatMap(_.split(" "))
      .map((_,1))
      .groupBy(0)
      .sum(1)

    result.print()
  }
}

3、編寫流式處理版本wordcount

package com.wei.scala

import org.apache.flink.streaming.api.scala._
object WordCount2 {
  def main(args: Array[String]): Unit = {

    //創建執行環境
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment

    //接收一個socket文本流
    val dataStream: DataStream[String] = env.socketTextStream("hdp-1",9999)

    //對流入的數據進行處理
    val result: DataStream[(String, Int)] = dataStream.flatMap(_.split(" ")).filter(_.nonEmpty).map((_,1)).keyBy(0).sum(1)

    //輸出結果
    result.print()

    //啓動executor
    env.execute("my first wordCount job")

  }

}

在虛擬機上啓動一個netcat端口，用於發送數據，程序利用socketTextStream接收一個文本流，經過處理並且輸出結果：