Hi,
the window contents are stored in state managed by the window operator at all
times until they are purged by a Trigger returning PURGE from one of its on*()
methods.
Out of the box, Flink does not have something akin to the lateness and cleanup
of Google Dataflow. You can, however implement it yourself using a custom
Trigger. This is an example that mimics Google Dataflow:
public class EventTimeTrigger implements Trigger<Object, TimeWindow> {
private static final long serialVersionUID = 1L;
private final boolean accumulating;
private final long allowedLateness;
private EventTimeTrigger(boolean accumulating, long allowedLateness) {
this.accumulating = accumulating;
this.allowedLateness = allowedLateness;
}
@Override
public TriggerResult onElement(Object element, long timestamp, TimeWindow
window, TriggerContext ctx) throws Exception {
ctx.registerEventTimeTimer(window.maxTimestamp());
return TriggerResult.CONTINUE;
}
@Override
public TriggerResult onEventTime(long time, TimeWindow window,
TriggerContext ctx) {
if (time == window.maxTimestamp()) {
if (accumulating) {
// register the cleanup timer if we are accumulating (and allow
lateness)
if (allowedLateness > 0) {
ctx.registerEventTimeTimer(window.maxTimestamp() +
allowedLateness);
}
return TriggerResult.FIRE;
} else {
return TriggerResult.FIRE_AND_PURGE;
}
} else if (time == window.maxTimestamp() + allowedLateness) {
return TriggerResult.PURGE;
}
return TriggerResult.CONTINUE;
}
@Override
public TriggerResult onProcessingTime(long time, TimeWindow window,
TriggerContext ctx) throws Exception {
return TriggerResult.CONTINUE;
}
@Override
public String toString() {
return "EventTimeTrigger()";
}
/**
* Creates an event-time trigger that fires once the watermark passes the
end of the window.
*
* <p>
* Once the trigger fires all elements are discarded. Elements that arrive
late immediately
* trigger window evaluation with just this one element.
*/
public static EventTimeTrigger discarding() {
return new EventTimeTrigger(false, 0L);
}
/**
* Creates an event-time trigger that fires once the watermark passes the
end of the window.
*
* <p>
* This trigger will not immediately discard all elements once it fires.
Only after the
* watermark passes the specified lateness are the window elements
discarded, without
* emitting a new result. If a late element arrives within the specified
lateness
* the window is computed again and a new result is emitted.
*/
public static EventTimeTrigger accumulating(AbstractTime allowedLateness) {
return new EventTimeTrigger(true, allowedLateness.toMilliseconds());
}
}
You can specify a lateness and while that time is not yet reached the windows
will remain and late arriving elements will trigger window emission with the
complete window contents.
Cheers,
Aljoscha
> On 13 Jan 2016, at 15:12, Andrew Coates <[email protected]> wrote:
>
> Hi,
>
> I'm trying to understand how the lifecycle of messages / state is managed by
> Flink, but I'm failing to find any documentation.
>
> Specially, if I'm using a windowed stream and a type of trigger that retain
> the elements of the window to allow for processing of late data e.g.
> ContinousEventTimeTrigger, then where are the contents of the windows, or
> their intermediate computation results, stored, and when is the data removed?
>
> I'm thinking in terms of Google's Dataflow API, setting a windows the
> withAllowedLateness option allows the caller to control how long past the end
> of a window the data should be maintained. Does Flink have anything similar?
>
> Thanks,
>
> Andy
