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本地jvm執(zhí)行flink程序帶web ui的操作

更新時間：2021年08月14日 08:50:24 作者：iFence

這篇文章主要介紹了本地jvm執(zhí)行flink程序帶web ui的操作，具有很好的參考價值，希望對大家有所幫助。如有錯誤或未考慮完全的地方，望不吝賜教

本地jvm執(zhí)行flink帶web ui

使用

StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment();

可以獲取flink執(zhí)行環(huán)境。但是本地jvm執(zhí)行的時候是不帶web ui的。有時候出于監(jiān)控的考慮，需要帶著監(jiān)控頁面查看。任務(wù)運行狀況，可以使用下面方式獲取flink本地執(zhí)行環(huán)境，并帶有web ui。

Configuration config = new Configuration();
config.setInteger(RestOptions.PORT,9998);
StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(config);

Flink 本地執(zhí)行入門

一、maven依賴

<properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <flink.version>1.6.3</flink.version>
    <java.version>1.8</java.version>
    <scala.version>2.11.8</scala.version>
    <hbase.version>1.2.4</hbase.version>
    <scala.binary.version>2.11</scala.binary.version>
    <maven.compiler.source>${java.version}</maven.compiler.source>
    <maven.compiler.target>${java.version}</maven.compiler.target>
</properties>
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-clients_${scala.binary.version}</artifactId>
    <version>${flink.version}</version>
</dependency>

二、本地執(zhí)行

import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.common.JobExecutionResult;
import org.apache.flink.api.java.ExecutionEnvironment;
public class FlinkReadTextFile {
    public static void main(String[] args) throws Exception {
        ExecutionEnvironment env = ExecutionEnvironment.createLocalEnvironment();
        DataSet<String> data = env.readTextFile("file:///Users/***/Documents/test.txt");
        data.filter(new FilterFunction<String>() {
            @Override
            public boolean filter(String value) throws Exception {
                return value.startsWith("五芳齋美");
            }
        })
                .writeAsText("file:///Users/***/Documents/test01.txt");
        JobExecutionResult res = env.execute();
    }
}

三、實例

import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.api.scala._
 
object SocketWindowWordCount {
  /** Main program method */
  def main(args: Array[String]): Unit ={ // the port to connect to
//  val port: Int = try {
//    ParameterTool.fromArgs(args).getInt("port")
//  } catch {
//    case e: Exception => {
//      System.err.println("No port specified. Please run 'SocketWindowWordCount --port <port>'")
//      return
//    }
//  }
  // get the execution environment
  val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
  // get input data by connecting to the socket
  val text = env.socketTextStream("localhost", 9000, '\n')
  // parse the data, group it, window it, and aggregate the counts
  val windowCounts = text
    .flatMap { w => w.split("\\s") }
    .map { w => WordWithCount(w, 1) }
    .keyBy("word")
    .timeWindow(Time.seconds(5), Time.seconds(1))
    .sum("count")
  // print the results with a single thread, rather than in parallel
  windowCounts.print().setParallelism(1)
  env.execute("Socket Window WordCount")
}
// Data type for words with count
case class WordWithCount(word: String, count: Long)
}

以上為個人經(jīng)驗，希望能給大家一個參考，也希望大家多多支持腳本之家。

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