Thanks TD! Is it possible to perhaps add another window method that doesn't not generate partial windows? Or, Is it possible to remove the first few partial windows? I'm thinking of using an accumulator to count how many windows there are.
-A -----Original Message----- From: Tathagata Das [mailto:tathagata.das1...@gmail.com] Sent: March-24-14 6:55 PM To: user@spark.apache.org Cc: u...@spark.incubator.apache.org Subject: Re: [bug?] streaming window unexpected behaviour Yes, I believe that is current behavior. Essentially, the first few RDDs will be partial windows (assuming window duration > sliding interval). TD On Mon, Mar 24, 2014 at 1:12 PM, Adrian Mocanu <amoc...@verticalscope.com> wrote: > I have what I would call unexpected behaviour when using window on a stream. > > I have 2 windowed streams with a 5s batch interval. One window stream > is (5s,5s)=smallWindow and the other (10s,5s)=bigWindow > > What I've noticed is that the 1st RDD produced by bigWindow is > incorrect and is of the size 5s not 10s. So instead of waiting 10s and > producing 1 RDD with size 10s, Spark produced the 1st 10s RDD of size 5s. > > Why is this happening? To me it looks like a bug; Matei or TD can you > verify that this is correct behaviour? > > > > > > I have the following code > > val ssc = new StreamingContext(conf, Seconds(5)) > > > > val smallWindowStream = ssc.queueStream(smallWindowRddQueue) > > val bigWindowStream = ssc.queueStream(bigWindowRddQueue) > > > > val smallWindow = smallWindowReshapedStream.window(Seconds(5), > Seconds(5)) > > .reduceByKey((t1, t2) => (t1._1, t1._2, t1._3 + t2._3)) > > val bigWindow = bigWindowReshapedStream.window(Seconds(10), > Seconds(5)) > > .reduceByKey((t1, t2) => (t1._1, t1._2, t1._3 + t2._3)) > > > > -Adrian > >
