[jira] [Created] (FLINK-10809) Using DataStreamUtils.reinterpretAsKeyedStream produces corrupted keyed state after restore

["Dawid Wysakowicz (JIRA)" <ji...@apache.org>]

Dawid Wysakowicz created FLINK-10809:
----------------------------------------

             Summary: Using DataStreamUtils.reinterpretAsKeyedStream produces corrupted keyed state after restore
                 Key: FLINK-10809
                 URL: https://issues.apache.org/jira/browse/FLINK-10809
             Project: Flink
          Issue Type: Bug
          Components: DataStream API, State Backends, Checkpointing
    Affects Versions: 1.7.0
            Reporter: Dawid Wysakowicz


I've tried using {{DataStreamUtils.reinterpretAsKeyedStream}} for results of windowed aggregation:
{code}
		DataStream<Tuple2<Integer, List<Event>>> eventStream4 = eventStream2.keyBy(Event::getKey)
			.window(SlidingEventTimeWindows.of(Time.milliseconds(150 * 3), Time.milliseconds(150)))
			.apply(new WindowFunction<Event, Tuple2<Integer, List<Event>>, Integer, TimeWindow>() {
				private static final long serialVersionUID = 3166250579972849440L;

				@Override
				public void apply(
					Integer key, TimeWindow window, Iterable<Event> input,
					Collector<Tuple2<Integer, List<Event>>> out) throws Exception {

					out.collect(Tuple2.of(key, StreamSupport.stream(input.spliterator(), false).collect(Collectors.toList())));
				}
			});

		DataStreamUtils.reinterpretAsKeyedStream(eventStream4, events-> events.f0)
			.flatMap(createSlidingWindowCheckMapper(pt))
			.addSink(new PrintSinkFunction<>());
{code}

and then in the createSlidingWindowCheckMapper I verify that each event belongs to 3 consecutive windows, for which I keep contents of last window in ValueState. In a non-failure setup this check runs fine, but it misses few windows after restore at the beginning.

{code}
public class SlidingWindowCheckMapper extends RichFlatMapFunction<Tuple2<Integer, List<Event>>, String> {

	private static final long serialVersionUID = -744070793650644485L;

	/** This value state tracks previously seen events with the number of windows they appeared in. */
	private transient ValueState<List<Tuple2<Event, Integer>>> previousWindow;

	private final int slideFactor;

	SlidingWindowCheckMapper(int slideFactor) {
		this.slideFactor = slideFactor;
	}

	@Override
	public void open(Configuration parameters) throws Exception {
		ValueStateDescriptor<List<Tuple2<Event, Integer>>> previousWindowDescriptor =
			new ValueStateDescriptor<>("previousWindow",
				new ListTypeInfo<>(new TupleTypeInfo<>(TypeInformation.of(Event.class), BasicTypeInfo.INT_TYPE_INFO)));

		previousWindow = getRuntimeContext().getState(previousWindowDescriptor);
	}

	@Override
	public void flatMap(Tuple2<Integer, List<Event>> value, Collector<String> out) throws Exception {
		List<Tuple2<Event, Integer>> previousWindowValues = Optional.ofNullable(previousWindow.value()).orElseGet(
			Collections::emptyList);

		List<Event> newValues = value.f1;
		newValues.stream().reduce(new BinaryOperator<Event>() {
			@Override
			public Event apply(Event event, Event event2) {
				if (event2.getSequenceNumber() - 1 != event.getSequenceNumber()) {
					out.collect("Alert: events in window out ouf order!");
				}

				return event2;
			}
		});

		List<Tuple2<Event, Integer>> newWindow = new ArrayList<>();
		for (Tuple2<Event, Integer> windowValue : previousWindowValues) {
			if (!newValues.contains(windowValue.f0)) {
				out.collect(String.format("Alert: event %s did not belong to %d consecutive windows. Event seen so far %d times.Current window: %s",
					windowValue.f0,
					slideFactor,
					windowValue.f1,
					value.f1));
			} else {
				newValues.remove(windowValue.f0);
				if (windowValue.f1 + 1 != slideFactor) {
					newWindow.add(Tuple2.of(windowValue.f0, windowValue.f1 + 1));
				}
			}
		}

		newValues.forEach(e -> newWindow.add(Tuple2.of(e, 1)));

		previousWindow.update(newWindow);
	}
}
{code}



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