Flink_WordCount_代码实例

import org.apache.flink.api.common.functions.FlatMapFunction;import org.apache.flink.api.common.functions.MapFunction;import org.apache.flink.api.java.tuple.Tuple2;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;public class WindowWordCount {    public static void main(String [] args) throws Exception{        // get env        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();        DataStream<Tuple2<String, Long>> dataStream = env                // source                .socketTextStream("localhost", 9999)                // flatmap DataStream->DataStream 将读入的一份数据，转换成0到n个。这里就是拆分。                .flatMap(new FlatMapFunction<String, String>() {                    @Override                    public void flatMap(String s, Collector<String> collector) throws Exception {                        String [] words = s.split(" ");                        for(String word : words){                            collector.collect(word);                        }                    }                })                // map  DataStream->DataStream 读入一份，转换成一份，这里是组装成tuple对                .map(new MapFunction<String, Tuple2<String, Long>>() {                    @Override                    public Tuple2<String, Long> map(String s) throws Exception {                        return new Tuple2<String, Long>(s, 1l);                    }                })                // keyBy DataStream->KeyedStream 逻辑上将数据根据key进行分区，保证相同的key分到一起。默认是hash分区                .keyBy(0)                // window                .timeWindow(Time.seconds(2))                // sum WindowedStream->DataStream 聚合窗口内容。另外还有min,max等                .sum(1);        //sink        dataStream.print();        env.execute("Word Count!");    }}