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.
