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From Shannon Carey <sca...@expedia.com>
Subject Re: Firing windows multiple times
Date Tue, 30 Aug 2016 21:49:14 GMT
I appreciate your suggestion!

However, the main problem with your approach is the amount of time that goes by without an
updated value from minuteAggregate and hourlyAggregate (lack of a continuously updated aggregate).

For example, if we use a tumbling window of 1 month duration, then we only get an update for
that value once a month! The values from that stream will be on average 0.5 months stale.
A year-long window is even worse.

-Shannon

From: Aljoscha Krettek <aljoscha@apache.org<mailto:aljoscha@apache.org>>
Date: Tuesday, August 30, 2016 at 9:08 AM
To: Shannon Carey <scarey@expedia.com<mailto:scarey@expedia.com>>, "user@flink.apache.org<mailto:user@flink.apache.org>"
<user@flink.apache.org<mailto:user@flink.apache.org>>
Subject: Re: Firing windows multiple times

Hi,
I think this can be neatly expressed by using something like a tree of windowed aggregations,
i.e. you specify your smallest window computation first and then specify larger window computations
based smaller windows. I've written an example that showcases this approach: https://gist.github.com/aljoscha/728ac69361f75c3ca87053b1a6f91fcd

The basic idea in pseudo code is this:

DataStream input = ...
dailyAggregate = input.keyBy(...).window(Time.days(1)).reduce(new Sum())
weeklyAggregate = dailyAggregate.keyBy(...).window(Time.days(7)).reduce(new Sum())
monthlyAggregate = weeklyAggregate(...).window(Time.days(30)).reduce(new Sum())

the benefit of this approach is that you don't duplicate computation and that you can have
incremental aggregation using a reduce function. When manually keeping elements and evicting
them based on time the amount of state that would have to be kept would be much larger.

Does that make sense and would it help your use case?

Cheers,
Aljoscha

On Mon, 29 Aug 2016 at 23:18 Shannon Carey <scarey@expedia.com<mailto:scarey@expedia.com>>
wrote:
Yes, let me describe an example use-case that I'm trying to implement efficiently within Flink.

We've been asked to aggregate per-user data on a daily level, and from there produce aggregates
on a variety of time frames. For example, 7 days, 30 days, 180 days, and 365 days.

We can talk about the hardest one, the 365 day window, with the knowledge that adding the
other time windows magnifies the problem.

I can easily use tumbling time windows of 1-day size for the first aggregation. However, for
the longer aggregation, if I take the naive approach and use a sliding window, the window
size would be 365 days and the slide would be one day. If a user comes back every day, I run
the risk of magnifying the size of the data by up to 365 because each day of data will be
included in up to 365 year-long window panes. Also, if I want to fire the aggregate information
more rapidly than once a day, then I have to worry about getting 365 different windows fired
at the same time & trying to figure out which one to pay attention to, or coming up with
a hare-brained custom firing trigger. We tried emitting each day-aggregate into a time series
database and doing the final 365 day aggregation as a query, but that was more complicated
than we wanted: in particular we'd like to have all the logic in the Flink job not split across
different technology & infrastructure.

The work-around I'm thinking of is to use a single window that contains 365 days of data (relative
to the current watermark) on an ongoing basis. The windowing function would be responsible
for evicting old data based on the current watermark.

Does that make sense? Does it seem logical, or am I misunderstanding something about how Flink
works?

-Shannon


From: Aljoscha Krettek <aljoscha@apache.org<mailto:aljoscha@apache.org>>
Date: Monday, August 29, 2016 at 3:56 AM

To: "user@flink.apache.org<mailto:user@flink.apache.org>" <user@flink.apache.org<mailto:user@flink.apache.org>>
Subject: Re: Firing windows multiple times

Hi,
that would certainly be possible? What do you think can be gained by having knowledge about
the current watermark in the WindowFunction, in a specific case, possibly?

Cheers,
Aljoscha

On Wed, 24 Aug 2016 at 23:21 Shannon Carey <scarey@expedia.com<mailto:scarey@expedia.com>>
wrote:
What do you think about adding the current watermark to the window function metadata in FLIP-2?

From: Shannon Carey <scarey@expedia.com<mailto:scarey@expedia.com>>
Date: Friday, August 12, 2016 at 6:24 PM
To: Aljoscha Krettek <aljoscha@apache.org<mailto:aljoscha@apache.org>>, "user@flink.apache.org<mailto:user@flink.apache.org>"
<user@flink.apache.org<mailto:user@flink.apache.org>>

Subject: Re: Firing windows multiple times

Thanks Aljoscha, I didn't know about those. Yes, they look like handy changes, especially
to enable flexible approaches for eviction. In particular, having the current watermark available
to the evictor via EvictorContext is helpful: it will be able to evict the old data more easily
without needing to rely on Window#maxTimestamp().

However, I think you might still be missing a piece. Specifically, it would still not be possible
for the window function to choose which items to aggregate based on the current watermark.
In particular, it is desirable to be able to aggregate only the items below the watermark,
omitting items which have come in with timestamps larger than the watermark. Does that make
sense?

-Shannon

From: Aljoscha Krettek <aljoscha@apache.org<mailto:aljoscha@apache.org>>
Date: Friday, August 12, 2016 at 4:25 AM
To: "user@flink.apache.org<mailto:user@flink.apache.org>" <user@flink.apache.org<mailto:user@flink.apache.org>>
Subject: Re: Firing windows multiple times

Hi,
there is already this FLIP: https://cwiki.apache.org/confluence/display/FLINK/FLIP-4+%3A+Enhance+Window+Evictor
which also links to a mailing list discussion. And this FLIP: https://cwiki.apache.org/confluence/display/FLINK/FLIP-2+Extending+Window+Function+Metadata.
The former proposes to enhance the Evictor API a bit, among other things we propose to give
the evictor access to the current watermark. The other FLIP proposes to extend the amount
of meta-data we give to the window function. The first to things we propose to add is a "firing
reason" that would tell you whether this was an early firing, an on time firing or a late
firing. The second thing is a firing counter that would tell you how many times the trigger
has fired so far for the current window.

Would a combination of these help with your use case?

Cheers,
Aljoscha

On Thu, 11 Aug 2016 at 19:19 Shannon Carey <scarey@expedia.com<mailto:scarey@expedia.com>>
wrote:
"If Window B is a Folding Window and does not have an evictor then it should not keep the
list of all received elements."

Agreed! Upon closer inspection, the behavior I'm describing is only present when using EvictingWindowOperator,
not when using WindowOperator. I misread line 382 of WindowOperator which calls windowState.add():
in actuality, the windowState is a FoldingState which incorporates the user-provided fold
function in order to eagerly fold the data. In contrast, if you use an evictor, EvictingWindowOperator
has the behavior I describe.

I am already using a custom Trigger which uses a processing timer to FIRE a short time after
a new event comes in, and an event timer to FIRE_AND_PURGE.

It seems that I can achieve the desired effect by avoiding use of an evictor so that the intermediate
events are not retained in an EvictingWindowOperator's state, and perform any necessary eviction
within my fold function. This has the aforementioned drawbacks of the windowed fold function
not knowing about watermarks, and therefore it is difficult to be precise about choosing which
items to evict. However, this seems to be the best choice within the current framework.

Interestingly, it appears that TimeEvictor doesn't really know about watermarks either. When
a window emits an event, regardless of how it was fired, it is assigned the timestamp given
by its window's maxTimestamp(), which might be much greater than the processing time that
actually fired the event. Then, TimeEvictor compares the max timestamp of all items in the
window against the other ones in order to determine which ones to evict. Basically, it assumes
that the events were emitted due to the window terminating with FIRE_AND_PURGE. What if we
gave more information (specifically, the current watermark) to the evictor in order to allow
it to deal with a mix of intermediate events (fired by processing time) and final events (fired
by event time when the watermark reaches the window)? That value is already available in the
WindowOperator & could be passed to the Evictor very easily. It would be an API change,
of course.

Other than that, is it worth considering a change to EvictingWindowOperator to allow user-supplied
functions to reduce the size of its state when people fire upstream windows repeatedly? From
what I see when I monitor the state with debugger print statements, the EvictingWindowOperator
is definitely holding on to all the elements ever received, not just the aggregated result.
You can see this clearly because EvictingWindowOperator holds a ListState instead of a FoldingState.
The user-provided fold function is only applied upon fire().

-Shannon


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