If I change it to a Tumbling window some of the results will be lost since the pattern I'm matching has a temporal extent, so if the pattern starts in one tumbling window and ends in the next, it won't be reported. Based on the temporal length of the query, you can set the sliding window and the window lengths to capture all the patterns, though as you note, you will get duplicates.
On Mon, Oct 8, 2018 at 7:46 AM Hequn Cheng <chenghe...@gmail.com> wrote: > Hi Eric, > > Can you change Sliding window to Tumbling window? The data of different > sliding window are likely overlap. > > Best, Hequn > > On Mon, Oct 8, 2018 at 3:35 PM Dominik Wosiński <wos...@gmail.com> wrote: > >> Hey, >> IMHO, the simplest way in your case would be to use the Evictor to evict >> duplicate values after the window is generated. Have look at it here: >> https://ci.apache.org/projects/flink/flink-docs-release-1.6/api/java/org/apache/flink/streaming/api/windowing/evictors/Evictor.html >> >> Best Regards, >> Dominik. >> >> pon., 8 paź 2018 o 08:00 Eric L Goodman <eric.good...@colorado.edu> >> napisał(a): >> >>> What is the best way to avoid or remove duplicates when joining a stream >>> with itself? I'm performing a streaming temporal triangle computation and >>> the first part is to find triads of two edges of the form vertexA->vertexB >>> and vertexB->vertexC (and there are temporal constraints where the first >>> edge occurs before the second edge). To do that, I have the following code: >>> >>> DataStream<Triad> triads = edges.join(edges) >>> .where(new DestKeySelector()) >>> .equalTo(new SourceKeySelector()) >>> .window(SlidingEventTimeWindows.of(Time.milliseconds(windowSizeMs), >>> Time.milliseconds(slideSizeMs))) >>> .apply(new EdgeJoiner(queryWindow)); >>> >>> However, when I look at the triads being built, there are two copies of >>> each triad. >>> >>> For example, if I create ten edges (time, source, target): >>> >>> 0.0, 4, 0 >>> >>> 0.01, 1, 5 >>> >>> 0.02, 3, 7 >>> >>> 0.03, 0, 8 >>> >>> 0.04, 0, 9 >>> >>> 0.05, 4, 8 >>> >>> 0.06, 4, 3 >>> >>> 0.07, 5, 9 >>> >>> 0.08, 7, 1 >>> >>> 0.09, 9, 6 >>> >>> >>> It creates the following triads (time1, source1, target1, time2, >>> source2, targe2). Note there are two copies of each. >>> >>> 0.0, 4, 0 0.03, 0, 8 >>> >>> 0.0, 4, 0 0.03, 0, 8 >>> >>> 0.0, 4, 0 0.04, 0, 9 >>> >>> 0.0, 4, 0 0.04, 0, 9 >>> >>> 0.01, 1, 5 0.07, 5, 9 >>> >>> 0.01, 1, 5 0.07, 5, 9 >>> >>> 0.02, 3, 7 0.08, 7, 1 >>> >>> 0.02, 3, 7 0.08, 7, 1 >>> >>> 0.04, 0, 9 0.09, 9, 6 >>> >>> 0.04, 0, 9 0.09, 9, 6 >>> >>> 0.07, 5, 9 0.09, 9, 6 >>> >>> 0.07, 5, 9 0.09, 9, 6 >>> >>> I'm assuming this behavior has something to do with the joining of "edges" >>> with itself. >>> >>> I can provide more code if that would be helpful, but I believe I've >>> captured the most salient portion. >>> >>> >>> >>> >>> >>>
