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本文主要研究一下flink的Tumbling Window

WindowAssigner

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/windowing/assigners/WindowAssigner.java

@PublicEvolving
public abstract class WindowAssigner<T, W extends Window> implements Serializable {private static final long serialVersionUID = 1L;/*** Returns a {@code Collection} of windows that should be assigned to the element.** @param element The element to which windows should be assigned.* @param timestamp The timestamp of the element.* @param context The {@link WindowAssignerContext} in which the assigner operates.*/public abstract Collection<W> assignWindows(T element, long timestamp, WindowAssignerContext context);/*** Returns the default trigger associated with this {@code WindowAssigner}.*/public abstract Trigger<T, W> getDefaultTrigger(StreamExecutionEnvironment env);/*** Returns a {@link TypeSerializer} for serializing windows that are assigned by* this {@code WindowAssigner}.*/public abstract TypeSerializer<W> getWindowSerializer(ExecutionConfig executionConfig);/*** Returns {@code true} if elements are assigned to windows based on event time,* {@code false} otherwise.*/public abstract boolean isEventTime();/*** A context provided to the {@link WindowAssigner} that allows it to query the* current processing time.** <p>This is provided to the assigner by its containing* {@link org.apache.flink.streaming.runtime.operators.windowing.WindowOperator},* which, in turn, gets it from the containing* {@link org.apache.flink.streaming.runtime.tasks.StreamTask}.*/public abstract static class WindowAssignerContext {/*** Returns the current processing time.*/public abstract long getCurrentProcessingTime();}
}

• WindowAssigner定义了assignWindows、getDefaultTrigger、getWindowSerializer、isEventTime这几个抽象方法，同时定义了抽象静态类WindowAssignerContext；它有两个泛型，其中T为元素类型，而W为窗口类型

Window

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/windowing/windows/Window.java

@PublicEvolving
public abstract class Window {/*** Gets the largest timestamp that still belongs to this window.** @return The largest timestamp that still belongs to this window.*/public abstract long maxTimestamp();
}

• Window对象代表把无限流数据划分为有限buckets的集合，它有一个maxTimestamp，代表该窗口数据在该时间点内到达；它有两个子类，一个是GlobalWindow，一个是TimeWindow

TimeWindow

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/windowing/windows/TimeWindow.java

@PublicEvolving
public class TimeWindow extends Window {private final long start;private final long end;public TimeWindow(long start, long end) {this.start = start;this.end = end;}/*** Gets the starting timestamp of the window. This is the first timestamp that belongs* to this window.** @return The starting timestamp of this window.*/public long getStart() {return start;}/*** Gets the end timestamp of this window. The end timestamp is exclusive, meaning it* is the first timestamp that does not belong to this window any more.** @return The exclusive end timestamp of this window.*/public long getEnd() {return end;}/*** Gets the largest timestamp that still belongs to this window.** <p>This timestamp is identical to {@code getEnd() - 1}.** @return The largest timestamp that still belongs to this window.** @see #getEnd()*/@Overridepublic long maxTimestamp() {return end - 1;}@Overridepublic boolean equals(Object o) {if (this == o) {return true;}if (o == null || getClass() != o.getClass()) {return false;}TimeWindow window = (TimeWindow) o;return end == window.end && start == window.start;}@Overridepublic int hashCode() {return MathUtils.longToIntWithBitMixing(start + end);}@Overridepublic String toString() {return "TimeWindow{" +"start=" + start +", end=" + end +'}';}/*** Returns {@code true} if this window intersects the given window.*/public boolean intersects(TimeWindow other) {return this.start <= other.end && this.end >= other.start;}/*** Returns the minimal window covers both this window and the given window.*/public TimeWindow cover(TimeWindow other) {return new TimeWindow(Math.min(start, other.start), Math.max(end, other.end));}// ------------------------------------------------------------------------// Serializer// ------------------------------------------------------------------------//......// ------------------------------------------------------------------------//  Utilities// ------------------------------------------------------------------------/*** Merge overlapping {@link TimeWindow}s. For use by merging* {@link org.apache.flink.streaming.api.windowing.assigners.WindowAssigner WindowAssigners}.*/public static void mergeWindows(Collection<TimeWindow> windows, MergingWindowAssigner.MergeCallback<TimeWindow> c) {// sort the windows by the start time and then merge overlapping windowsList<TimeWindow> sortedWindows = new ArrayList<>(windows);Collections.sort(sortedWindows, new Comparator<TimeWindow>() {@Overridepublic int compare(TimeWindow o1, TimeWindow o2) {return Long.compare(o1.getStart(), o2.getStart());}});List<Tuple2<TimeWindow, Set<TimeWindow>>> merged = new ArrayList<>();Tuple2<TimeWindow, Set<TimeWindow>> currentMerge = null;for (TimeWindow candidate: sortedWindows) {if (currentMerge == null) {currentMerge = new Tuple2<>();currentMerge.f0 = candidate;currentMerge.f1 = new HashSet<>();currentMerge.f1.add(candidate);} else if (currentMerge.f0.intersects(candidate)) {currentMerge.f0 = currentMerge.f0.cover(candidate);currentMerge.f1.add(candidate);} else {merged.add(currentMerge);currentMerge = new Tuple2<>();currentMerge.f0 = candidate;currentMerge.f1 = new HashSet<>();currentMerge.f1.add(candidate);}}if (currentMerge != null) {merged.add(currentMerge);}for (Tuple2<TimeWindow, Set<TimeWindow>> m: merged) {if (m.f1.size() > 1) {c.merge(m.f1, m.f0);}}}/*** Method to get the window start for a timestamp.** @param timestamp epoch millisecond to get the window start.* @param offset The offset which window start would be shifted by.* @param windowSize The size of the generated windows.* @return window start*/public static long getWindowStartWithOffset(long timestamp, long offset, long windowSize) {return timestamp - (timestamp - offset + windowSize) % windowSize;}
}

• TimeWindow有start及end属性，其中start为inclusive，而end为exclusive，所以maxTimestamp返回的是end-1；这里重写了equals及hashcode方法
• TimeWindow提供了intersects方法用于表示本窗口与指定窗口是否有交叉；而cover方法用于返回本窗口与指定窗口的重叠窗口
• TimeWindow还提供了mergeWindows及getWindowStartWithOffset静态方法；前者用于合并重叠的时间窗口，后者用于获取指定timestamp、offset、windowSize的window start

TumblingEventTimeWindows

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/windowing/assigners/TumblingEventTimeWindows.java

@PublicEvolving
public class TumblingEventTimeWindows extends WindowAssigner<Object, TimeWindow> {private static final long serialVersionUID = 1L;private final long size;private final long offset;protected TumblingEventTimeWindows(long size, long offset) {if (offset < 0 || offset >= size) {throw new IllegalArgumentException("TumblingEventTimeWindows parameters must satisfy 0 <= offset < size");}this.size = size;this.offset = offset;}@Overridepublic Collection<TimeWindow> assignWindows(Object element, long timestamp, WindowAssignerContext context) {if (timestamp > Long.MIN_VALUE) {// Long.MIN_VALUE is currently assigned when no timestamp is presentlong start = TimeWindow.getWindowStartWithOffset(timestamp, offset, size);return Collections.singletonList(new TimeWindow(start, start + size));} else {throw new RuntimeException("Record has Long.MIN_VALUE timestamp (= no timestamp marker). " +"Is the time characteristic set to 'ProcessingTime', or did you forget to call " +"'DataStream.assignTimestampsAndWatermarks(...)'?");}}@Overridepublic Trigger<Object, TimeWindow> getDefaultTrigger(StreamExecutionEnvironment env) {return EventTimeTrigger.create();}@Overridepublic String toString() {return "TumblingEventTimeWindows(" + size + ")";}public static TumblingEventTimeWindows of(Time size) {return new TumblingEventTimeWindows(size.toMilliseconds(), 0);}public static TumblingEventTimeWindows of(Time size, Time offset) {return new TumblingEventTimeWindows(size.toMilliseconds(), offset.toMilliseconds());}@Overridepublic TypeSerializer<TimeWindow> getWindowSerializer(ExecutionConfig executionConfig) {return new TimeWindow.Serializer();}@Overridepublic boolean isEventTime() {return true;}
}

• TumblingEventTimeWindows继承了Window，其中元素类型为Object，而窗口类型为TimeWindow；它有两个参数，一个是size，一个是offset，其中offset必须大于等于0，size必须大于offset
• assignWindows方法获取的窗口为start及start+size，而start=TimeWindow.getWindowStartWithOffset(timestamp, offset, size)；getDefaultTrigger方法返回的是EventTimeTrigger；getWindowSerializer方法返回的是TimeWindow.Serializer()；isEventTime返回true
• TumblingEventTimeWindows提供了of静态工厂方法，可以指定size及offset参数

TumblingProcessingTimeWindows

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/windowing/assigners/TumblingProcessingTimeWindows.java

public class TumblingProcessingTimeWindows extends WindowAssigner<Object, TimeWindow> {private static final long serialVersionUID = 1L;private final long size;private final long offset;private TumblingProcessingTimeWindows(long size, long offset) {if (offset < 0 || offset >= size) {throw new IllegalArgumentException("TumblingProcessingTimeWindows parameters must satisfy  0 <= offset < size");}this.size = size;this.offset = offset;}@Overridepublic Collection<TimeWindow> assignWindows(Object element, long timestamp, WindowAssignerContext context) {final long now = context.getCurrentProcessingTime();long start = TimeWindow.getWindowStartWithOffset(now, offset, size);return Collections.singletonList(new TimeWindow(start, start + size));}public long getSize() {return size;}@Overridepublic Trigger<Object, TimeWindow> getDefaultTrigger(StreamExecutionEnvironment env) {return ProcessingTimeTrigger.create();}@Overridepublic String toString() {return "TumblingProcessingTimeWindows(" + size + ")";}public static TumblingProcessingTimeWindows of(Time size) {return new TumblingProcessingTimeWindows(size.toMilliseconds(), 0);}public static TumblingProcessingTimeWindows of(Time size, Time offset) {return new TumblingProcessingTimeWindows(size.toMilliseconds(), offset.toMilliseconds());}@Overridepublic TypeSerializer<TimeWindow> getWindowSerializer(ExecutionConfig executionConfig) {return new TimeWindow.Serializer();}@Overridepublic boolean isEventTime() {return false;}
}

• TumblingProcessingTimeWindows继承了WindowAssigner，其中元素类型为Object，而窗口类型为TimeWindow；它有两个参数，一个是size，一个是offset，其中offset必须大于等于0，size必须大于offset
• assignWindows方法获取的窗口为start及start+size，而start=TimeWindow.getWindowStartWithOffset(now, offset, size)，而now值则为context.getCurrentProcessingTime()，则是与TumblingEventTimeWindows的不同之处，TumblingProcessingTimeWindows不使用timestamp参数来计算，它使用now值替代；getDefaultTrigger方法返回的是ProcessingTimeTrigger，而isEventTime方法返回的为false
• TumblingProcessingTimeWindows也提供了of静态工厂方法，可以指定size及offset参数

小结

• flink的Tumbling Window分为TumblingEventTimeWindows及TumblingProcessingTimeWindows，它们都继承了WindowAssigner，其中元素类型为Object，而窗口类型为TimeWindow；它有两个参数，一个是size，一个是offset，其中offset必须大于等于0，size必须大于offset
• WindowAssigner定义了assignWindows、getDefaultTrigger、getWindowSerializer、isEventTime这几个抽象方法，同时定义了抽象静态类WindowAssignerContext；它有两个泛型，其中T为元素类型，而W为窗口类型；TumblingEventTimeWindows及TumblingProcessingTimeWindows的窗口类型为TimeWindow，它有start及end属性，其中start为inclusive，而end为exclusive，maxTimestamp返回的是end-1，它还提供了mergeWindows及getWindowStartWithOffset静态方法；前者用于合并重叠的时间窗口，后者用于获取指定timestamp、offset、windowSize的window start
• TumblingEventTimeWindows及TumblingProcessingTimeWindows的不同在于assignWindows、getDefaultTrigger、isEventTime方法；前者assignWindows使用的是参数中的timestamp，而后者使用的是now值；前者的getDefaultTrigger返回的是EventTimeTrigger，而后者返回的是ProcessingTimeTrigger；前者isEventTime方法返回的为true，而后者返回的为false

doc

• Tumbling Windows