This is actually simpler than you think, you can just use the Time.of(...)
helper:
ds.window(Time.of(long windowSize, Timestamp yourTimeStampExtractor, long
startTime))...
Gyula
Martin Neumann ezt írta (időpont: 2015. szept. 8., K,
20:20):
> Hej,
>
> I want to give TimeTriggerPolicy a try and
Hej,
I want to give TimeTriggerPolicy a try and see how much of a problem it
will be in this use case. Is there any example on how to use it? I looked
at the API descriptions but I'm confused now.
cheers Martin
On Fri, Aug 28, 2015 at 5:35 PM, Martin Neumann wrote:
> The stream consists of log
The stream consists of logs from different machines with synchronized
clocks. As a result timestamps are not strictly increasing but there is a
bound on how much out of order they can be. (One aim is to detect events go
out of order more then a certain amount indication some problem in the
system s
Hi Martin,
the answer depends, because the current windowing implementation has some
problems. We are working on improving it in the 0.10 release, though.
If your elements arrive with strictly increasing timestamps and you have
parallelism=1 or don't perform any re-partitioning of data (which a
gr
Hi Martin,
you need to implement you own policy. However, this should be be
complicated. Have a look at "TimeTriggerPolicy". You just need to
provide a "Timestamp" implementation that extracts you ts-attribute from
the tuples.
-Matthias
On 08/28/2015 03:58 PM, Martin Neumann wrote:
> Hej,
>
> I
Hej,
I have a stream of timestamped events I want to process in Flink streaming.
Di I have to write my own policies to do so, or can define time based
windows to use the timestamps instead of the system time?
cheers Martin
