本地jvm执行flink程序带web ui的操作

目录

• 本地jvm执行flink带web ui
• 使用
• Flink 本地执行入门
• 一、maven依赖
• 二、本地执行
• 三、实例

本地jvm执行flink带web ui

使用

StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment();

可以获取flink执行环境。但是本地jvm执行的时候是不带web ui的。有时候出于监控的考虑，需要带着监控页面查看。任务运行状况，可以使用下面方式获取flink本地执行环境，并带有web ui。

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

Flink 本地执行入门

一、maven依赖

<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、本地执行

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("五芳斋美");
            }
        })
                .writeAsText("file:///Users/***/Documents/test01.txt");
        JobExecutionResult res = env.execute();
    }
}

三、实例

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)
}

以上为个人经验，希望能给大家一个参考，也希望大家多多支持我们。