TD, it looks like your instincts were correct. I misunderstood what you meant.
If I force an eval on the inputstream using foreachRDD, the windowing will
work correctly. If I don’t do that, lazy eval somehow screws with window
batches I eventually receive. Any reason the bug is categorized as minor? It
seems that anyone who uses the windowing functionality would run into this bug.
I imagine this would include anyone who wants to use spark streaming to
aggregate data in fixed time batches, which seems like a fairly common use case.
Alan
On Jul 22, 2014, at 11:30 PM, Alan Ngai <a...@opsclarity.com> wrote:
> foreachRDD is how I extracted values in the first place, so that’s not going
> to make a difference. I don’t think it’s related to SPARK-1312 because I’m
> generating data every second in the first place and I’m using foreachRDD
> right after the window operation. The code looks something like
>
> val batchInterval = 5
> val windowInterval = 25
> val slideInterval = 15
>
> val windowedStream = inputStream.window(Seconds(windowInterval),
> Seconds(slideInterval))
>
> val outputFunc = (r: RDD[MetricEvent], t: Time) => {
> println("======================================== %s".format(t.milliseconds
> / 1000))
> r.foreach{metric =>
> val timeKey = metric.timeStamp / batchInterval * batchInterval
> println("%s %s %s %s".format(timeKey, metric.timeStamp, metric.name,
> metric.value))
> }
> }
> testWindow.foreachRDD(outputFunc)
>
> On Jul 22, 2014, at 10:13 PM, Tathagata Das <tathagata.das1...@gmail.com>
> wrote:
>
>> It could be related to this bug that is currently open.
>> https://issues.apache.org/jira/browse/SPARK-1312
>>
>> Here is a workaround. Can you put a inputStream.foreachRDD(rdd => { }) and
>> try these combos again?
>>
>> TD
>>
>>
>> On Tue, Jul 22, 2014 at 6:01 PM, Alan Ngai <a...@opsclarity.com> wrote:
>> I have a sample application pumping out records 1 per second. The batch
>> interval is set to 5 seconds. Here’s a list of “observed window intervals”
>> vs what was actually set
>>
>> window=25, slide=25 : observed-window=25, overlapped-batches=0
>> window=25, slide=20 : observed-window=20, overlapped-batches=0
>> window=25, slide=15 : observed-window=15, overlapped-batches=0
>> window=25, slide=10 : observed-window=20, overlapped-batches=2
>> window=25, slide=5 : observed-window=25, overlapped-batches=3
>>
>> can someone explain this behavior to me? I’m trying to aggregate metrics by
>> time batches, but want to skip partial batches. Therefore, I’m trying to
>> find a combination which results in 1 overlapped batch, but no combination I
>> tried gets me there.
>>
>> Alan
>>
>>
>
