Thanks Fabian, Kostas, Here is what I had in the Trigger - idea is to run bitwise OR until a threshold is reached or a timeout is reached (nothing too fancy here). Let me know what you guys think. Like I said, I moved this logic to Process Function and I haven't seen the same issue I was with this.
@PublicEvolvingpublic class BitwiseOrTrigger<W extends Window> extends Trigger<FactoredEvent, W> { private static final long serialVersionUID = 1L; private final int threshold; private final long epocDelta; private final ReducingStateDescriptor<Tuple2<Integer, Long>> stateDesc = new ReducingStateDescriptor<>("bitwiseOr", new BitwiseOr(), TypeInformation.of(new TypeHint<Tuple2<Integer,Long>>() {})); private BitwiseOrTrigger(int threshold, long allowedLateness) { this.threshold = threshold; this.epocDelta = allowedLateness; } @Override public TriggerResult onElement(FactoredEvent event, long timestamp, W window, TriggerContext ctx) throws Exception { ReducingState<Tuple2<Integer,Long>> currState = ctx.getPartitionedState(stateDesc); if (this.epocDelta>0) { ctx.registerProcessingTimeTimer(System.currentTimeMillis() + this.epocDelta); } currState.add(new Tuple2<Integer,Long>(event.getFactor(), this.epocDelta)); if (currState.get().f0 >= threshold) { currState.clear(); return TriggerResult.FIRE_AND_PURGE; } return TriggerResult.CONTINUE; } @Override public TriggerResult onEventTime(long time, W window, TriggerContext ctx) { return TriggerResult.FIRE_AND_PURGE; } @Override public TriggerResult onProcessingTime(long time, W window, TriggerContext ctx) throws Exception { return TriggerResult.FIRE_AND_PURGE; } @Override public void clear(W window, TriggerContext ctx) throws Exception { ctx.getPartitionedState(stateDesc).clear(); } @Override public boolean canMerge() { return true; } @Override public void onMerge(W window, OnMergeContext ctx) throws Exception { ctx.mergePartitionedState(stateDesc); } @Override public String toString() { return "BitwiseOrTrigger(" + threshold + ")"; } public static <W extends Window> BitwiseOrTrigger<W> of(int threshold, long expirationEpoc) { return new BitwiseOrTrigger<>(threshold, expirationEpoc); } private static class BitwiseOr implements ReduceFunction<Tuple2<Integer, Long>> { private static final long serialVersionUID = 1L; @Override public Tuple2<Integer, Long> reduce(Tuple2<Integer, Long> tup1, Tuple2<Integer, Long> tup2) throws Exception { Tuple2<Integer, Long> retTup = tup1; retTup.f0 = tup1.f0 | tup2.f0; return retTup; } }} On Monday, May 14, 2018, 6:00:11 AM EDT, Fabian Hueske <fhue...@gmail.com> wrote: Hi Ashish, Did you use per-window state (also called partitioned state) in your Trigger? If yes, you need to make sure that it is completely removed in the clear() method because processing time timers won't fire once a window was purged. So you cannot (fully) rely on timers to clean up per-window state. Best, Fabian 2018-05-14 9:34 GMT+02:00 Kostas Kloudas <k.klou...@data-artisans.com>: Hi Ashish, It would be helpful to share the code of your custom trigger for the first case.Without that, we cannot tell what state you create and how/when you update/clear it. Cheers,Kostas On May 14, 2018, at 1:04 AM, ashish pok <ashish...@yahoo.com> wrote: Hi Till, Thanks for getting back. I am sure that will fix the issue but I feel like that would potentially mask an issue. I have been going back and forth with Fabian on a use case where for some of our highly transient datasets, it might make sense to just use memory based state (except of course data loss becomes an issue when apps occasionally hit a problem and whole job restarts or app has to be taken down etc - ie. handling graceful shutdowns / restarts better essentially). I was on the hook to create a business case and post it back to this forum (which I am hoping I can get around to at some point soon). Long story short, this is one of those datasets. States in this case are either fired and cleared normally or on processing timeout. So technically, unless there is a memory leak in app code, memory usage should plateau out at a high-point. What I was noticing was memory would start to creep up ever so slowly. I couldn't tell exactly why heap utilization kept on growing (ever so slowly but it had upward trend for sure) because the states should technically be cleared if not as part of a reducing function then on timeout. App after running for couple of days would then run into Java Heap issues. So changing to RocksDB probably will fix the issue but not necessarily leak of states that should be cleared IMO. Interestingly, I switched my app from using something like this: WindowedStream<BasicFactTuple, String, GlobalWindow> windowedStats = statsStream .keyBy(BasicFactTuple::getKey) .window(GlobalWindows.create() ) .trigger(BitwiseOrTrigger.of( 60, AppConfigs.getWindowSize(5*60* 1000))) ; To DataStream<PlatformEvent> processStats = pipStatsStream .keyBy(BasicFactTuple::getKey) .process(new IfStatsReduceProcessFn( AppConfigs.getWindowSize(5*60* 1000), 60)) I basically moved logic of trigger to process function over the weekend. Once I did that, heap is completely stabilized. In trigger implementation, I was using FIRE_AND_PURGE on trigger condition or onProcessingTime and in process implementation I am using .clear() method for same. I seem to have solved the problem by using process but I'd be interested to understand the cause of why heap would creep up in trigger scenario. Hope this makes sense, Ashish On Sunday, May 13, 2018, 4:06:59 PM EDT, Till Rohrmann <till.rohrm...@gmail.com> wrote: Hi Ashish, have you tried using Flink's RocksDBStateBackend? If your job accumulates state exceeding the available main memory, then you have to use a state backend which can spill to disk. The RocksDBStateBackend offers you exactly this functionality. Cheers,Till On Mon, Apr 30, 2018 at 3:54 PM, ashish pok <ashish...@yahoo.com> wrote: All, I am using noticing heap utilization creeping up slowly in couple of apps which eventually lead to OOM issue. Apps only have 1 process function that cache state. I did make sure I have a clear method invoked when events are collected normally, on exception and on timeout. Are any other best practices others follow for memory backed states? Thanks, -- Ashish