FLINK基础（93）: DS算子与窗口（7）单流算子(6) 窗口算子 Window/WindowAll/Window Apply/WindowReduce

Window 

KeyedStream → WindowedStream 

Windows can be defined on already partitioned KeyedStreams. Windows group the data in each key according to some characteristic (e.g., the data that arrived within the last 5 seconds). See windows for a complete description of windows.

java

dataStream
  .keyBy(value -> value.f0)
  .window(TumblingEventTimeWindows.of(Time.seconds(5))); 

WindowAll #

DataStreamStream → AllWindowedStream #

Windows can be defined on regular DataStreams. Windows group all the stream events according to some characteristic (e.g., the data that arrived within the last 5 seconds). See windows for a complete description of windows.

This is in many cases a non-parallel transformation. All records will be gathered in one task for the windowAll operator.

java

dataStream
  .windowAll(TumblingEventTimeWindows.of(Time.seconds(5)));

Window Apply #

WindowedStream → DataStream #

AllWindowedStream → DataStream #

Applies a general function to the window as a whole. Below is a function that manually sums the elements of a window.

If you are using a windowAll transformation, you need to use an AllWindowFunction instead.

windowedStream.apply(new WindowFunction<Tuple2<String,Integer>, Integer, Tuple, Window>() {
    public void apply (Tuple tuple,
            Window window,
            Iterable<Tuple2<String, Integer>> values,
            Collector<Integer> out) throws Exception {
        int sum = 0;
        for (value t: values) {
            sum += t.f1;
        }
        out.collect (new Integer(sum));
    }
});

// applying an AllWindowFunction on non-keyed window stream
allWindowedStream.apply (new AllWindowFunction<Tuple2<String,Integer>, Integer, Window>() {
    public void apply (Window window,
            Iterable<Tuple2<String, Integer>> values,
            Collector<Integer> out) throws Exception {
        int sum = 0;
        for (value t: values) {
            sum += t.f1;
        }
        out.collect (new Integer(sum));
    }
});

WindowReduce #

WindowedStream → DataStream #

Applies a functional reduce function to the window and returns the reduced value.

windowedStream.reduce (new ReduceFunction<Tuple2<String,Integer>>() {
    public Tuple2<String, Integer> reduce(Tuple2<String, Integer> value1, Tuple2<String, Integer> value2) throws Exception {
        return new Tuple2<String,Integer>(value1.f0, value1.f1 + value2.f1);
    }
});