[DISUCSS] When timestamp - offset + windowSize < 0, elements cannot be assigned to the correct window

[邓子琦]

I have created an issue on jira
https://issues.apache.org/jira/projects/FLINK/issues/FLINK-26334
issue

        Hello!

        When we were studying the flink source code, we found that there
was a problem with its algorithm for calculating the window start time.
When *timestamp - offset + windowSize < 0* , the element will be
incorrectly allocated to a window with a WindowSize larger than its own
timestamp.

        The problem is in
*org.apache.flink.streaming.api.windowing.windows.TimeWindow*

public static long getWindowStartWithOffset(long timestamp, long
offset, long windowSize) {
    return timestamp - (timestamp - offset + windowSize) % windowSize;
}

        We believe that this violates the constraints between time and
window. *That is, an element should fall within a window whose start time
is less than its own timestamp and whose end time is greater than its own
timestamp.* However, the current situation is when *timestamp - offset +
windowSize < 0*, *the element falls into a future time window.*

       *You can reproduce the bug with the code at the end of the post.*
Solution

        In fact, the original algorithm is no problem in python, the key to
this problem is the processing of the remainder operation by the
programming language.

        We finally think that it should be modified to the following
algorithm.

public static long getWindowStartWithOffset(long timestamp, long
offset, long windowSize) {
    return timestamp
            - (timestamp - offset) % windowSize
            - (windowSize & (timestamp - offset) >> 63);
}

        *windowSize & (timestamp - offset) >> 63* The function of this
formula is to subtract windowSize from the overall operation result
when *timestamp
- offset<0*, otherwise do nothing. This way we can handle both positive and
negative timestamps.

        Finally, the element can be assigned to the correct window.

        This code can pass current unit tests.
getWindowStartWithOffset methods in other packages

        I think that there should be many places in
*getWindowStartWithOffset*. We searched for this method in the project and
found that the problem of negative timestamps is handled in *flink.table.*

        Below is their source code.


*org.apache.flink.table.runtime.operators.window.grouping.WindowsGrouping*

private long getWindowStartWithOffset(long timestamp, long offset,
long windowSize) {
    long remainder = (timestamp - offset) % windowSize;
    // handle both positive and negative cases    if (remainder < 0) {
        return timestamp - (remainder + windowSize);
    } else {
        return timestamp - remainder;
    }
}

Can we make a pull request?

        If the community deems it necessary to revise it, hopefully this
task can be handed over to us. Our members are all students who have just
graduated from school, and it is a great encouragement for us to contribute
code to flink.

        Thank you so much!

        From Deng Ziqi & Lin Wanni & Guo Yuanfang


 ===========================================
reproduce

/* output
WindowStart: -15000    ExactSize:1    (a,-17000)
WindowStart: -10000    ExactSize:1    (b,-12000)
WindowStart: -5000 ExactSize:2    (c,-7000)
WindowStart: -5000 ExactSize:2    (d,-2000)
WindowStart: 0 ExactSize:1    (e,3000)
WindowStart: 5000  ExactSize:1    (f,8000)
WindowStart: 10000 ExactSize:1    (g,13000)
WindowStart: 15000 ExactSize:1    (h,18000)
 */public class Example {
    public static void main(String[] args) throws Exception {

        final TimeZone timeZone = TimeZone.getTimeZone("GTM+0");
        TimeZone.setDefault(timeZone);
        StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
        env
                .setParallelism(1)
                .fromElements(
                        Tuple2.of("a",-17*1000L),
                        Tuple2.of("b",-12*1000L),
                        Tuple2.of("c",-7*1000L),
                        Tuple2.of("d",-2*1000L),
                        Tuple2.of("e",3*1000L),
                        Tuple2.of("f",8*1000L),
                        Tuple2.of("g",13*1000L),
                        Tuple2.of("h",18*1000L)
                )
                .assignTimestampsAndWatermarks(

WatermarkStrategy.<Tuple2<String,Long>>forMonotonousTimestamps()
                                .withTimestampAssigner(
                                        new
SerializableTimestampAssigner<Tuple2<String, Long>>() {
                                            @Override
                                            public long
extractTimestamp(Tuple2<String, Long> element, long l) {
                                                return element.f1;
                                            }
                                        }
                                )
                )
                .keyBy(r->1)
                .window(TumblingEventTimeWindows.of(Time.seconds(5)))
                .process(
                        new ProcessWindowFunction<Tuple2<String,
Long>, String, Integer, TimeWindow>() {
                            @Override
                            public void process(Integer integer,
ProcessWindowFunction<Tuple2<String, Long>, String, Integer,
TimeWindow>.Context context, Iterable<Tuple2<String, Long>> elements,
Collector<String> out) throws Exception {
                                for (Tuple2<String, Long> element : elements) {
                                    out.collect("WindowStart:
"+context.window().getStart()
                                            + "\tExactSize:" +
elements.spliterator().getExactSizeIfKnown()+"\t"
                                            + element
                                    );
                                }
                            }
                        }
                )
                .print();
        env.execute();
    }
}