[Flink]Flink三種運行模式安裝部署以及實現WordCount

前言

Flink三種運行方式：Local、Standalone、On Yarn。成功部署後分別用Scala和Java實現wordcount

環境

版本：Flink 1.6.2
集群環境：Hadoop2.6
開發工具: IntelliJ IDEA

一.Local模式

解壓：tar -zxvf flink-1.6.2-bin-hadoop26-scala_2.11.tgz
cd flink-1.6.2
啟動：./bin/start-cluster.sh
停止：./bin/stop-cluster.sh

可以通過master：8081監控集群狀態

二.Standalone模式

集群安裝
1：修改conf/flink-conf.yaml
jobmanager.rpc.address: hadoop100
2：修改conf/slaves
hadoop101
hadoop102
3：拷貝到其他節點
scp -rq /usr/local/flink-1.6.2 hadoop101:/usr/local
scp -rq /usr/local/flink-1.6.2 hadoop102:/usr/local
4：在hadoop100(master)節點啟動
bin/start-cluster.sh
5：訪問http://hadoop100:8081

三.Flink On Yarn模式

On Yarn實現邏輯

第一種【yarn-session.sh(開辟資源)+flink run(提交任務)】

啟動一個一直運行的flink集群
./bin/yarn-session.sh -n 2 -jm 1024 -tm 1024 [-d]
附著到一個已存在的flink yarn session
./bin/yarn-session.sh -id application_1463870264508_0029
執行任務
./bin/flink run ./examples/batch/WordCount.jar -input hdfs://hadoop100:9000/LICENSE -output hdfs://hadoop100:9000/wordcount-result.txt

第二種【flink run -m yarn-cluster(開辟資源+提交任務)】

啟動集群，執行任務
./bin/flink run -m yarn-cluster -yn 2 -yjm 1024 -ytm 1024 ./examples/batch/WordCount.jar
註意：client端必須要設置YARN_CONF_DIR或者HADOOP_CONF_DIR或者HADOOP_HOME環境變量，通過這個環境變量來讀取YARN和HDFS的配置信息，否則啟動會失敗

四.WordCount

代碼

Scala實現代碼

package com.skyell

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

/**
  * 滑動窗口計算
  *
  * 每隔1秒統計最近2秒數據，打印到控制臺
  */
object SocketWindowWordCountScala {
  def main(args: Array[String]): Unit = {

    // 獲取socket端口號
    val port: Int = try{
      ParameterTool.fromArgs(args).getInt("port")
    }catch {
      case e: Exception => {
        System.err.println("No port set use default port 9002--scala")
      }
        9002
    }

    // 獲取運行環境
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment

    // 連接socket獲取數據
    val text = env.socketTextStream("master", port, ‘\n‘)

    //添加隱式轉換,否則會報錯
    import org.apache.flink.api.scala._

    // 解析數據(把數據打平），分組,窗口計算，並且聚合求sum
    val windowCount = text.flatMap(line => line.split("\\s"))
      .map(w => WordWithCount(w, 1))
      .keyBy("word") // 針對相同word進行分組
      .timeWindow(Time.seconds(2), Time.seconds(1))// 窗口時間函數
      .sum("count")

    windowCount.print().setParallelism(1)  // 設置並行度為1

    env.execute("Socket window count")

  }
  // case 定義的類可以直接調用，不用new
  case class WordWithCount(word:String,count: Long)

}

Java實現代碼

package com.skyell;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;


public class BatchWordCountJava {
    public static void main(String[] args) throws Exception{

        String inputPath = "D:\\DATA\\file";
        String outPath = "D:\\DATA\\result";

        // 獲取運行環境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
        // 讀取本地文件中內容
        DataSource<String> text = env.readTextFile(inputPath);
        // groupBy(0):從0聚合  sum(1):以第二個字段加和計算
        DataSet<Tuple2<String, Integer>> counts = text.flatMap(new Tokenizer()).groupBy(0).sum(1);

        counts.writeAsCsv(outPath, "\n", " ").setParallelism(1);

        env.execute("batch word count");
    }

    public static class Tokenizer implements FlatMapFunction<String, Tuple2<String,Integer>>{
        public void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception {
            String[] tokens = value.toLowerCase().split("\\W+");
            for (String token: tokens
                 ) {
                if(token.length()>0){
                    out.collect(new Tuple2<String, Integer>(token, 1));
                }
            }
        }
    }
}

pom依賴配置

    <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.6.2</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.11</artifactId>
            <version>1.6.2</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-scala_2.11</artifactId>
            <version>1.6.2</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.11</artifactId>
            <version>1.6.2</version>
            <scope>provided</scope>
        </dependency>

hadoop記錄-[Flink]Flink三種運行模式安裝部署以及實現WordCount（轉載）