Flink1.8中如何進(jìn)行流處理SocketWordCount

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package com.hadoop.ljs.flink.streaming;
import org.apache.flink.api.common.functions.FlatMapFunction;import org.apache.flink.api.common.functions.ReduceFunction;import org.apache.flink.api.java.utils.ParameterTool;import org.apache.flink.streaming.api.datastream.DataStream;import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;import org.apache.flink.streaming.api.windowing.time.Time;import org.apache.flink.util.Collector;
/** * Implements a streaming windowed version of the "WordCount" program. * * <p>This program connects to a server socket and reads strings from the socket. * The easiest way to try this out is to open a text server (at port 12345) * using the <i>netcat</i> tool via * <pre> * nc -l 12345 on Linux or nc -l -p 12345 on Windows * </pre> * and run this example with the hostname and the port as arguments. */@SuppressWarnings("serial")public class SocketWordCount {
  public static void main(String[] args) throws Exception {
    // the host and the port to connect to    final String hostname;    final int port;    try {      final ParameterTool params = ParameterTool.fromArgs(args);      hostname = params.has("hostname") ? params.get("hostname") : "localhost";      port = params.getInt("port");
      /*hostname = "10.124.165.98";      port = 9999;*/    } catch (Exception e) {      System.err.println("No port specified. Please run 'SocketWordCount " +        "--hostname <hostname> --port <port>', where hostname (localhost by default) " +        "and port is the address of the text server");      System.err.println("To start a simple text server, run 'netcat -l <port>' and " +        "type the input text into the command line");      return;    }
    // get the execution environment    final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    // get input data by connecting to the socket    DataStream<String> text = env.socketTextStream(hostname, port, "\n");
    // parse the data, group it, window it, and aggregate the counts    DataStream<WordWithCount> windowCounts = text
        .flatMap(new FlatMapFunction<String, WordWithCount>() {          @Override          public void flatMap(String value, Collector<WordWithCount> out) {            for (String word : value.split("\\s")) {              out.collect(new WordWithCount(word, 1L));            }          }        })
        .keyBy("word")        .timeWindow(Time.seconds(5))
        .reduce(new ReduceFunction<WordWithCount>() {          @Override          public WordWithCount reduce(WordWithCount a, WordWithCount b) {            return new WordWithCount(a.word, a.count + b.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.   */  public static class WordWithCount {
    public String word;    public long count;
    public WordWithCount() {}
    public WordWithCount(String word, long count) {      this.word = word;      this.count = count;    }
    @Override    public String toString() {      return word + " : " + count;    }  }}