【flink学习笔记】【2】本地模式-流处理wordcount_本地模式flink wordcount

一、有界流wordcount

package com.shinho.wc;

import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import org.apache.flink.util.Collector;


public class BoundryWordCount {
    public static void main(String[] args) throws Exception {
        //1创建流式执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStreamSource<String> lineDS = env.readTextFile("input/words.txt");

        SingleOutputStreamOperator<Tuple2<String, Long>> wordAndOne = lineDS.flatMap((String line, Collector<Tuple2<String, Long>> out) -> {
            String[] words = line.split(" ");
            for (String word : words) {
                out.collect(Tuple2.of(word, 1L));
            }
        }).returns(Types.TUPLE(Types.STRING, Types.LONG));

        //分组
        KeyedStream<Tuple2<String, Long>, Tuple> keyBy = wordAndOne.keyBy(0);

        //求和
        SingleOutputStreamOperator<Tuple2<String, Long>> sum = keyBy.sum(1);

        //
        sum.print();

        //启动执行
        env.execute();
    }
}

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控制台输出结果

1> (xx,1)
7> (kaikai,1)
3> (hello,1)
6> (word,1)
2> (gez,1)
7> (count,1)
3> (hello,2)
3> (hello,3)
3> (hello,4)
6> (word,2)

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前面是并行子任务的编码，子任务个数取决于并行度（电脑CPU核数）。同一个任务上才能进行词频叠加。

二、无界流wordcount

监听事件

yum install -y nc
nc -lk 7777
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package com.shinho.wc;

import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

public class NoBoundryWordCount {
    public static void main(String[] args) throws Exception {
        // 创建流式环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //2.读取文本流
        DataStreamSource<String> lineDS = env.socketTextStream("192.168.10.132", 7777);

        SingleOutputStreamOperator<Tuple2<String, Long>> wordAndOne = lineDS.flatMap((String line, Collector<Tuple2<String, Long>> out) -> {
            String[] words = line.split(" ");
            for (String word : words) {
                out.collect(Tuple2.of(word, 1L));
            }
        }).returns(Types.TUPLE(Types.STRING, Types.LONG));

        //分组
        KeyedStream<Tuple2<String, Long>, Tuple> keyBy = wordAndOne.keyBy(0);

        //求和
        SingleOutputStreamOperator<Tuple2<String, Long>> sum = keyBy.sum(1);

        //
        sum.print();

        //启动执行
        env.execute();
    }
}

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