On Mon, May 20, 2019 at 8:24 AM Jan Lukavský <[email protected]
<mailto:[email protected]>> wrote:
This discussion brings many really interesting questions for
me. :-)
> I don't see batch vs. streaming as part of the model. One
can have
microbatch, or even a runner that alternates between
different modes.
Although I understand motivation of this statement, this
project name is
"Apache Beam: An advanced unified programming model". What
does the
model unify, if "streaming vs. batch" is not part of the model?
Using microbatching, chaining of batch jobs, or pure
streaming are
exactly the "runtime conditions/characteristics" I refer to.
All these
define several runtime parameters, which in turn define how
well/badly
will the pipeline perform and how many resources might be
needed. From
my point of view, pure streaming should be the most resource
demanding
(if not, why bother with batch? why not run everything in
streaming
only? what will there remain to "unify"?).
> Fortunately, for batch, only the state for a single key
needs to be
preserved at a time, rather than the state for all keys
across the range
of skew. Of course if you have few or hot keys, one can still
have
issues (and this is not specific to StatefulDoFns).
Yes, but here is still the presumption that my stateful DoFn can
tolerate arbitrary shuffling of inputs. Let me explain the
use case in
more detail.
Suppose you have input stream consisting of 1s and 0s (and
some key for
each element, which is irrelevant for the demonstration).
Your task is
to calculate in running global window the actual number of
changes
between state 0 and state 1 and vice versa. When the state
doesn't
change, you don't calculate anything. If input (for given
key) would be
(tN denotes timestamp N):
t1: 1
t2: 0
t3: 0
t4: 1
t5: 1
t6: 0
then the output should yield (supposing that default state is
zero):
t1: (one: 1, zero: 0)
t2: (one: 1, zero: 1)
t3: (one: 1, zero: 1)
t4: (one: 2, zero: 1)
t5: (one: 2, zero: 1)
t6: (one: 2, zero: 2)
How would you implement this in current Beam semantics?
I think your saying here that I know that my input is ordered in
a specific way and since I assume the order when writing my
pipeline I can perform this optimization. But there is nothing
preventing a runner from noticing that your processing in the
global window with a specific type of trigger and re-ordering
your inputs/processing to get better performance (since you can't
use an AfterWatermark trigger for your pipeline in streaming for
the GlobalWindow).
Today, if you must have a strict order, you must guarantee that
your StatefulParDo implements the necessary "buffering & sorting"
into state. I can see why you would want an annotation that says
I must have timestamp ordered elements, since it makes writing
certain StatefulParDos much easier. StatefulParDo is a low-level
function, it really is the "here you go and do whatever you need
to but here be dragons" function while windowing and triggering
is meant to keep many people from writing StatefulParDo in the
first place.
> Pipelines that fail in the "worst case" batch scenario are
likely to
degrade poorly (possibly catastrophically) when the watermark
falls
behind in streaming mode as well.
But the worst case is defined by input of size (available
resources +
single byte) -> pipeline fail. Although it could have
finished, given
the right conditions.
> This might be reasonable, implemented by default by buffering
everything and releasing elements as the watermark
(+lateness) advances,
but would likely lead to inefficient (though *maybe* easier
to reason
about) code.
Sure, the pipeline will be less efficient, because it would
have to
buffer and sort the inputs. But at least it will produce
correct results
in cases where updates to state are order-sensitive.
> Would it be roughly equivalent to GBK + FlatMap(lambda
(key, values):
[(key, value) for value in values])?
I'd say roughly yes, but difference would be in the trigger.
The trigger
should ideally fire as soon as watermark (+lateness) crosses
element
with lowest timestamp in the buffer. Although this could be
somehow
emulated by fixed trigger each X millis.
> Or is the underlying desire just to be able to hint to the
runner
that the code may perform better (e.g. require less
resources) as skew
is reduced (and hence to order by timestamp iff it's cheap)?
No, the sorting would have to be done in streaming case as
well. That is
an imperative of the unified model. I think it is possible to
sort by
timestamp only in batch case (and do it for *all* batch
stateful pardos
without annotation), or introduce annotation, but then make
the same
guarantees for streaming case as well.
Jan
On 5/20/19 4:41 PM, Robert Bradshaw wrote:
> On Mon, May 20, 2019 at 1:19 PM Jan Lukavský
<[email protected] <mailto:[email protected]>> wrote:
>> Hi Robert,
>>
>> yes, I think you rephrased my point - although no
*explicit* guarantees
>> of ordering are given in either mode, there is *implicit*
ordering in
>> streaming case that is due to nature of the processing -
the difference
>> between watermark and timestamp of elements flowing
through the pipeline
>> are generally low (too high difference leads to the
overbuffering
>> problem), but there is no such bound on batch.
> Fortunately, for batch, only the state for a single key
needs to be
> preserved at a time, rather than the state for all keys
across the
> range of skew. Of course if you have few or hot keys, one
can still
> have issues (and this is not specific to StatefulDoFns).
>
>> As a result, I see a few possible solutions:
>>
>> - the best and most natural seems to be extension of
the model, so
>> that it defines batch as not only "streaming pipeline
executed in batch
>> fashion", but "pipeline with at least as good runtime
characteristics as
>> in streaming case, executed in batch fashion", I really
don't think that
>> there are any conflicts with the current model, or that
this could
>> affect performance, because the required sorting (as
pointed by
>> Aljoscha) is very probably already done during translation
of stateful
>> pardos. Also note that this definition only affects user
defined
>> stateful pardos
> I don't see batch vs. streaming as part of the model. One
can have
> microbatch, or even a runner that alternates between
different modes.
> The model describes what the valid outputs are given a
(sometimes
> partial) set of inputs. It becomes really hard to define
things like
> "as good runtime characteristics." Once you allow any
> out-of-orderedness, it is not very feasible to try and
define (and
> more cheaply implement) a "upper bound" of acceptable
> out-of-orderedness.
>
> Pipelines that fail in the "worst case" batch scenario are
likely to
> degrade poorly (possibly catastrophically) when the
watermark falls
> behind in streaming mode as well.
>
>> - another option would be to introduce annotation for
DoFns (e.g.
>> @RequiresStableTimeCharacteristics), which would result in
the sorting
>> in batch case - but - this extension would have to ensure
the sorting in
>> streaming mode also - it would require definition of
allowed lateness,
>> and triggger (essentially similar to window)
> This might be reasonable, implemented by default by buffering
> everything and releasing elements as the watermark (+lateness)
> advances, but would likely lead to inefficient (though
*maybe* easier
> to reason about) code. Not sure about the semantics of
triggering
> here, especially data-driven triggers. Would it be roughly
equivalent
> to GBK + FlatMap(lambda (key, values): [(key, value) for
value in
> values])?
>
> Or is the underlying desire just to be able to hint to the
runner that
> the code may perform better (e.g. require less resources)
as skew is
> reduced (and hence to order by timestamp iff it's cheap)?
>
>> - last option would be to introduce these "higher order
guarantees" in
>> some extension DSL (e.g. Euphoria), but that seems to be
the worst
>> option to me
>>
>> I see the first two options quite equally good, although
the letter one
>> is probably more time consuming to implement. But it would
bring
>> additional feature to streaming case as well.
>>
>> Thanks for any thoughts.
>>
>> Jan
>>
>> On 5/20/19 12:41 PM, Robert Bradshaw wrote:
>>> On Fri, May 17, 2019 at 4:48 PM Jan Lukavský
<[email protected] <mailto:[email protected]>> wrote:
>>>> Hi Reuven,
>>>>
>>>>> How so? AFAIK stateful DoFns work just fine in batch
runners.
>>>> Stateful ParDo works in batch as far, as the logic
inside the state works for absolutely unbounded
out-of-orderness of elements. That basically (practically)
can work only for cases, where the order of input elements
doesn't matter. But, "state" can refer to "state machine",
and any time you have a state machine involved, then the
ordering of elements would matter.
>>> No guarantees on order are provided in *either* streaming
or batch
>>> mode by the model. However, it is the case that in order
to make
>>> forward progress most streaming runners attempt to limit
the amount of
>>> out-of-orderedness of elements (in terms of event time
vs. processing
>>> time) to make forward progress, which in turn could help
cap the
>>> amount of state that must be held concurrently, whereas a
batch runner
>>> may not allow any state to be safely discarded until the
whole
>>> timeline from infinite past to infinite future has been
observed.
>>>
>>> Also, as pointed out, state is not preserved "batch to
batch" in batch mode.
>>>
>>>
>>> On Thu, May 16, 2019 at 3:59 PM Maximilian Michels
<[email protected] <mailto:[email protected]>> wrote:
>>>
>>>>> batch semantics and streaming semantics differs only
in that I can have GlobalWindow with default trigger on batch
and cannot on stream
>>>> You can have a GlobalWindow in streaming with a default
trigger. You
>>>> could define additional triggers that do early firings.
And you could
>>>> even trigger the global window by advancing the
watermark to +inf.
>>> IIRC, as a pragmatic note, we prohibited global window
with default
>>> trigger on unbounded PCollections in the SDK because this
is more
>>> likely to be user error than an actual desire to have no
output until
>>> drain. But it's semantically valid in the model.
