【Flink1.14实战】Flink DataStream 算子Reduce&Window

Reduce

**KeyedStream→ DataStream **

一个分组数据流的聚合操作，合并当前的元素和上次聚合的结果，产生一个新的值，返回的流中包含每一次聚合的结果，而不是只返回最后一次聚合的最终结果。

如下例子和sum的效果一样

package quick;

import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

public class ReduceExample  {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        DataStream<Tuple2<Integer, Integer>> dataStream = env
                .fromElements(Tuple2.of(1,1),Tuple2.of(1,2),Tuple2.of(2,2),Tuple2.of(2,2))
                .keyBy(value -> value.f0)
                .reduce(new ReduceFunction<Tuple2<Integer, Integer>>() {
                    @Override
                    public Tuple2<Integer, Integer> reduce(Tuple2<Integer, Integer> value1, Tuple2<Integer, Integer> value2)
                            throws Exception {
                        return new Tuple2(value1.f0 , value1.f1+value2.f1);
                    }
                });

        dataStream.print();

        env.execute("ReduceExample job");


    }


}

修改pom

<version>1.0.1</version>
<mainClass>quick.ReduceExample  </mainClass>

然后，将打包应用程序提交，Flink 的Web UI来提交作业监控集群的状态和正在运行的作业。

Window

KeyedStream → WindowedStream

可以在已分区的 KeyedStreams 上定义 Windows。Windows 根据某些特征（例如，最近 5 秒内到达的数据）对每个键中的数据进行分组。

import org.apache.flink.api.common.functions.FlatMapFunction;
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 WindowExample {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        DataStream<Tuple2<String, Integer>> dataStream = env
                .socketTextStream("localhost", 9999)
                .flatMap(new Splitter())
                .keyBy(value -> value.f0)
                .window(TumblingEventTimeWindows.of(Time.seconds(5)))
                .sum(1);

        dataStream.print();

        env.execute("WindowExample");
    }

    public static class Splitter implements FlatMapFunction<String, Tuple2<String, Integer>> {
        @Override
        public void flatMap(String sentence, Collector<Tuple2<String, Integer>> out) throws Exception {
            for (String word: sentence.split(" ")) {
                out.collect(new Tuple2<String, Integer>(word, 1));
            }
        }
    }

}

要运行示例程序，首先从终端使用 netcat 启动输入流：

nc -lk 9999

然后输入word回车

源码地址：