window是处理数据的核心。按需选择你需要的窗口类型后，它会将传入的原始数据流切分成多个buckets，所有计算都在window中进行。
flink本身提供的实例程序TopSpeedWindowing.java
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.tuple.Tuple4;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.streaming.api.functions.timestamps.AscendingTimestampExtractor;
import org.apache.flink.streaming.api.functions.windowing.delta.DeltaFunction;
import org.apache.flink.streaming.api.windowing.assigners.GlobalWindows;
import org.apache.flink.streaming.api.windowing.evictors.TimeEvictor;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.triggers.DeltaTrigger;import java.util.Arrays;
import java.util.Random;
import java.util.concurrent.TimeUnit;/**
* An example of grouped stream windowing where different eviction and trigger
* policies can be used. A source fetches events from cars every 100 msec
* containing their id, their current speed (kmh), overall elapsed distance (m)
* and a timestamp. The streaming example triggers the top speed of each car
* every x meters elapsed for the last y seconds.
*/
public class TopSpeedWindowing { // *************************************************************************
// PROGRAM
// ************************************************************************* public static void main(String[] args) throws Exception { final ParameterTool params = ParameterTool.fromArgs(args); final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
env.getConfig().setGlobalJobParameters(params); @SuppressWarnings({"rawtypes", "serial"})
DataStream<Tuple4<Integer, Integer, Double, Long>> carData;
if (params.has("input")) {
carData = env.readTextFile(params.get("input")).map(new ParseCarData());
} else {
System.out.println("Executing TopSpeedWindowing example with default input data set.");
System.out.println("Use --input to specify file input.");
carData = env.addSource(CarSource.create(2));
} int evictionSec = 10;
double triggerMeters = 50;
DataStream<Tuple4<Integer, Integer, Double, Long>> topSpeeds = carData
.assignTimestampsAndWatermarks(new CarTimestamp()) //1
.keyBy(0)
.window(GlobalWindows.create()) //2
.evictor(TimeEvictor.of(Time.of(evictionSec, TimeUnit.SECONDS))) //3
.trigger(DeltaTrigger.of(triggerMeters,
new DeltaFunction<Tuple4<Integer, Integer, Double, Long>>() {//4
private static final long serialVersionUID = 1L; @Override
public double getDelta(
Tuple4<Integer, Integer, Double, Long> oldDataPoint,
Tuple4<Integer, Integer, Double, Long> newDataPoint) {
return newDataPoint.f2 - oldDataPoint.f2;
}
}, carData.getType().createSerializer(env.getConfig())))//4
.maxBy(1); if (params.has("output")) {
topSpeeds.writeAsText(params.get("output"));
} else {
System.out.println("Printing result to stdout. Use --output to specify output path.");
topSpeeds.print();
} env.execute("CarTopSpeedWindowingExample");
} // *************************************************************************
// USER FUNCTIONS
// ************************************************************************* private static class CarSource implements SourceFunction<Tuple4<Integer, Integer, Double, Long>> { private static final long serialVersionUID = 1L;
private Integer[] speeds;
private Double[] distances; private Random rand = new Random(); private volatile boolean isRunning = true; private CarSource(int numOfCars) {
speeds = new Integer[numOfCars];
distances = new Double[numOfCars];
Arrays.fill(speeds, 50);
Arrays.fill(distances, 0d);
} public static CarSource create(int cars) {
return new CarSource(cars);
} @Override
public void run(SourceContext<Tuple4<Integer, Integer, Double, Long>> ctx) throws Exception { while (isRunning) {
Thread.sleep(100);
for (int carId = 0; carId < speeds.length; carId++) {
if (rand.nextBoolean()) {
speeds[carId] = Math.min(100, speeds[carId] + 5);
} else {
speeds[carId] = Math.max(0, speeds[carId] - 5);
}
distances[carId] += speeds[carId] / 3.6d;
Tuple4<Integer, Integer, Double, Long> record = new Tuple4<>(carId,
speeds[carId], distances[carId], System.currentTimeMillis());
ctx.collect(record);
}
}
} @Override
public void cancel() {
isRunning = false;
}
} private static class ParseCarData extends RichMapFunction<String, Tuple4<Integer, Integer, Double, Long>> {
private static final long serialVersionUID = 1L; @Override
public Tuple4<Integer, Integer, Double, Long> map(String record) {
String rawData = record.substring(1, record.length() - 1);
String[] data = rawData.split(",");
return new Tuple4<>(Integer.valueOf(data[0]), Integer.valueOf(data[1]), Double.valueOf(data[2]), Long.valueOf(data[3]));
}
} private static class CarTimestamp extends AscendingTimestampExtractor<Tuple4<Integer, Integer, Double, Long>> {
private static final long serialVersionUID = 1L; @Override
public long extractAscendingTimestamp(Tuple4<Integer, Integer, Double, Long> element) {
return element.f3;
}
}}
其中，
1. 定义时间戳，上篇文章<flink中的时间戳如何使用？—Watermark使用及原理>上进行了介绍，本篇不做赘述。
2.窗口类型，Windows Assigner定义如何将数据流分配到一个或者多个窗口；其层次结构如下：
evictor：用于数据剔除，其层次结构如下：
3. trigger：窗口触发器，其层次结构如下：
4. Window function定义窗口内数据的计算逻辑，其层次结构如下：
参考资料
