flink第一个应用

去年华为大佬就开始在用flink,今天刚有空就稍微跟着写了个demo玩起来（就不用java了 spark和flink还是用scala玩）

package flink.test
import org.apache.flink.api.java.utils.ParameterTool
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.streaming.api.windowing.time.Time

object StreamingWindowWordCount {
  def main(args:Array[String]):Unit={
    //get port param
    val port:Int = try {
      ParameterTool.fromArgs(args).getInt("port")
    }catch{
      case e:Exception=> {
        System.err.println("no port")
      }
        9876（启动linux的NC -l 9876端口进行监听）
    }

    //获取运行环境
    val env:StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment;
    //解析参数
    val text = env.socketTextStream("rhel071",port,'\n')
    //解析数据，分组，窗口操作，聚合求sum
    //注意：在这需要做一个隐式转换，否则使用flatmap会报错
    import org.apache.flink.api.scala._
    val windowCount = text.flatMap(line=>line.split("\\s"))
      .map(word=>WordWithCount(word,1L))
      .keyBy("word")
      .timeWindow(Time.seconds(2),Time.seconds(1))
        .reduce((a,b)=>WordWithCount(a.word,a.count + b.count))//key 一样获取相同的数据进行汇总（scala逻辑基本和spark没什么两样,都是进行数据的算子操作,需要action算子才能触发动作）
    //.sum("count")

      //使用一个单线程打印结果
      windowCount.print().setParallelism(1)
    env.execute("streaming word count")
  }
  case class WordWithCount(word:String,count:Long)
}

maven项目（这种东西不适合自己找jar包,本地测试需要的jar包量实在太多,特别项目牵扯上hadoop hbase的时候）
这里是小例子应用到的maven,记录下

<?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>finkDemo_20180918</groupId>
    <artifactId>finkDemo</artifactId>
    <version>1.0-SNAPSHOT</version>
    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.4.2</version>
            <!--<scope>provided</scope>-->
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.11</artifactId>
            <version>1.4.2</version>
            <!--<scope>provided</scope>-->
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-scala_2.11</artifactId>
            <version>1.4.2</version>
            <!--<scope>provided</scope>-->
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.11</artifactId>
            <version>1.4.2</version>
            <!--<scope>provided</scope>-->
        </dependency>
    </dependencies>
</project>

flink的离线操作

package flink.test

import org.apache.flink.api.scala.ExecutionEnvironment

object BatchWordCountScala {
   def main(args:Array[String]):Unit = {
     val inputPath:String = "D:\\flink\\batch\\file";
     val outputPath:String = "D:\\flink\\data\\result";
     val env:ExecutionEnvironment  = ExecutionEnvironment.getExecutionEnvironment;
     val text = env.readTextFile(inputPath)
     import org.apache.flink.api.scala._
     val counts = text.flatMap(line => line.split(" ")).map(word => WordWithCount(word,1L)).groupBy(0).sum(1)
     counts.writeAsCsv(outputPath,"\n"," ")
     env.execute("batch word count")
   }
  case class WordWithCount(word:String,count:Long)
}

把目录下的text文件word解析统计后存入result目录