Hi, it is possible that the number of processing time timers can grow, because internal timers are scoped by time, key, and namespace (typically this means „window“, because each key can be part of multiple windows). So if the number of keys in your application is steadily growing this can happen.
To analyse the heap dump, I usually take the following approach: - obviously include only reachable objects. If dumps are very big, try limit the size or to trigger the OOM earlier by configuring a lower heap size. It should still give you the problematic object accumulation, if there is one. - like at the statistics of „heavy hitter“ classes, i.e. classes for which the instances contribute the most to the overall heap consumption. Sometimes this will show you classes that are also part of classes that rank higher up, e.g. 1st place could be string, and second place char[]. But you can figure that out in the next step. - explore the instances of the top heavy hitter class(es). If there is a leak, if you just randomly sample into some objects, the likelihood is usually *very* high that you catch an object that is part of the leak (as determined in the next step). Otherwise just repeat and sample another object. - inspect the object instance and follow the reference links to the parent objects in the object graph that hold a reference to the leak object candidate. You will typically end up in some array where the leak accumulates. Inspect the object holding references to the leaking objects. You can see the field values and this can help to determine if the collection of objects is justified or if data is actually leaking. So in your case, you can start from some InternalTimer or Window object, backwards through the reference chain to see what class is holding onto them and why (e.g. should they already be gone w.r.t. to their timestamp). Walking through the references should be supported by all major heap analysis tools, including JVisualVM that comes with your JDK. You can also use OQL[1] to query for timers or windows that should already be gone. Overall I think it could at least be helpful to see the statistics for heavy hitter classes and screenshots of representative reference chains to objects to figure out the problem cause. If it is not possible to share heap dumps, unfortunately I think giving you this strategy is currently the best I can offer to help. Best, Stefan [1] https://blogs.oracle.com/sundararajan/querying-java-heap-with-oql <https://blogs.oracle.com/sundararajan/querying-java-heap-with-oql> > Am 20.06.2018 um 02:33 schrieb ashish pok <ashish...@yahoo.com>: > > All, > > I took a few heap dumps (when app restarts and at 2 hour intervals) using > jmap, they are 5GB to 8GB. I did some compares and what I can see is heap > shows data tuples (basically instances of object that is maintained as > states) counts going up slowly. > > Only thing I could possibly relate that to were > streaming.api.operators.InternalTimer and > streaming.api.windowing.windows.TimeWindow both were trending up as well. > There are definitely lot more windows created than the increments I could > notice but nevertheless those objects are trending up. Input stream has a > very consistent sin wave throughput. So it really doesn't make sense for > windows and tuples to keep trending up. There is also no event storm or > anything of that sort (ie. source stream has been very steady as far as > throughput is concerned). > > Here is a plot of heap utilization: > > <1529454480422blob.jpg> > So it has a typical sin wave pattern which is definitely expected as input > stream has the same pattern but source doesnt have a trend upwards like heap > utilization shown above. Screenshot above is showing spike from 60% > utilization to 80% and trend keeps going up until an issue occurs that resets > the app. > > Since processing is based on ProcessingTime, I really would have expected > memory to reach a steady state and remain sort of flat from a trending > perspective. > > Appreciate any pointers anyone might have. > > Thanks, Ashish > > On Monday, June 18, 2018, 12:54:03 PM EDT, ashish pok <ashish...@yahoo.com> > wrote: > > > Right, thats where I am headed now but was wondering there are any “gochas” I > am missing before I try and dig into a few gigs of heap dump. > > > Thanks, Ashish > > > Sent from Yahoo Mail for iPhone <https://overview.mail.yahoo.com/?.src=iOS> > > On Monday, June 18, 2018, 3:37 AM, Stefan Richter > <s.rich...@data-artisans.com> wrote: > > Hi, > > can you take a heap dump from a JVM that runs into the problem and share it > with us? That would make finding the cause a lot easier. > > Best, > Stefan > >> Am 15.06.2018 um 23:01 schrieb ashish pok <ashish...@yahoo.com >> <mailto:ashish...@yahoo.com>>: >> >> All, >> >> I have another slow Memory Leak situation using basic TimeSession Window >> (earlier it was GlobalWindow related that Fabian helped clarify). >> >> I have a very simple data pipeline: >> >> DataStream<PlatformEvent> processedData = rawTuples >> >> .window(ProcessingTimeSessionWindows.withGap(Time.seconds(AppConfigs.getWindowSize(780)))) >> >> .trigger(new ProcessingTimePurgeTrigger()) >> .apply(new IPSLAMetricWindowFn()) >> .name("windowFunctionTuple") >> .map(new TupleToPlatformEventMapFn()) >> .name("mapTupleEvent") >> ; >> >> >> I initially didnt even have ProcessingTmePurgeTrigger and it was using >> default Trigger. In an effort to fix this issue, I created my own Trigger >> from default ProcessingTimeTrigger with simple override to onProcessingTime >> method (essentially replacing FIRE with FIRE_AND_PURGE) >> >> @Override >> public TriggerResult onProcessingTime(long time, TimeWindow window, >> TriggerContext ctx) { >> return TriggerResult.FIRE_AND_PURGE; >> } >> >> This seems to have done nothing (may have delayed issue by couple of hours - >> not certain). But, I still see heap utilization creep up slowly and >> eventually reaches a point when GC starts to take too long and then the >> dreaded OOM. >> >> For completeness here is my Window Function (still using old function >> interface). It creates few metrics for reporting and applies logic by >> looping over the Iterable. NO states are explicitly kept in this function, >> needed RichWindowFunction to generate metrics basically. >> >> public class IPSLAMetricWindowFn extends >> RichWindowFunction<NumericFactTuple, BasicFactTuple, String, TimeWindow> { >> >> private static final long serialVersionUID = 1L; >> >> private static Logger logger = >> LoggerFactory.getLogger(IPSLAMetricWindowFn.class); >> >> private Meter in; >> >> private Meter out; >> >> private Meter error; >> >> @Override >> public void open(Configuration conf) throws Exception { >> this.in <http://this.in/> = getRuntimeContext() >> .getMetricGroup() >> .addGroup(AppConstants.APP_METRICS.PROCESS) >> .meter(AppConstants.APP_METRICS.IN, new >> MeterView(AppConstants.APP_METRICS.INTERVAL_30)); >> this.out = getRuntimeContext() >> .getMetricGroup() >> .addGroup(AppConstants.APP_METRICS.PROCESS) >> .meter(AppConstants.APP_METRICS.OUT, new >> MeterView(AppConstants.APP_METRICS.INTERVAL_30)); >> this.error = getRuntimeContext() >> .getMetricGroup() >> .addGroup(AppConstants.APP_METRICS.PROCESS) >> .meter(AppConstants.APP_METRICS.ERROR, new >> MeterView(AppConstants.APP_METRICS.INTERVAL_30)); >> super.open(conf); >> } >> >> @Override >> public void apply(String key, TimeWindow window, >> Iterable<NumericFactTuple> events, Collector<BasicFactTuple> collector) >> throws Exception { >> } >> } >> >> >> Appreciate any pointers on what could be causing leaks here. This seems >> pretty straight-forward. >> >> Thanks, Ashish >> > > <1529454480422blob.jpg>
