Re: [BEAM-135] Utilities for "batching" elements in a DoFn

[Etienne Chauchot]

Hi Kenn,

I have started using state and timer APIs, they seem awesome!
Please take a look at 
https://github.com/echauchot/beam/tree/BEAM-135-BATCHING-PARDO

It contains a PTransform that does the batching trans-bundles and 
respecting the windows (even if tests are not finished yet, see @Ignore 
and TODOs)

 I have some questions:

- I use the timer to detect the end of the window like you suggested. 
But the timer can only be set in @ProcessElement and @Ontimer. Javadoc 
says that timers are implicitly scoped to a key/window and that a timer 
can be set only for a single time per scope. I noticed that if I call 
timer.setForNowPlus in the @ProcessElement method, it seems that the 
timer is set n times for n elements. So I just created a state with 
boolean to prevent setting the timer more than once per key/window.
=> Would it be good maybe to have a end user way of indicating that the 
timer will be set only once per key/window. Something analogous to 
@Setup, to avoid the user having to use a state boolean?

- I understand that state and timers need to be per-key, but if the end 
user does not need a key (lets say he just needs a PCollection<String>). 
Then, do we tell him to use a PCollection<KV> anyway like I wrote in the 
javadoc of BatchingParDo?

WDYT?

Thanks,

Etienne


Le 26/01/2017 à 17:28, Etienne Chauchot a écrit :
Wonderful !

Thanks Kenn !

Etienne


Le 26/01/2017 à 15:34, Kenneth Knowles a écrit :
Hi Etienne,

I was drafting a proposal about @OnWindowExpiration when this email
arrived. I thought I would try to quickly unblock you by responding 
with a
TL;DR: you can achieve your goals with state & timers as they currently
exist. You'll set a timer for 
window.maxTimestamp().plus(allowedLateness)
precisely - when this timer fires, you are guaranteed that the input
watermark has exceeded this point (so all new data is droppable) 
while the
output timestamp is held to this point (so you can safely output into 
the
window).

@OnWindowExpiration is (1) a convenience to save you from needing a 
handle
on the allowed lateness (not a problem in your case) and (2) actually
meaningful and potentially less expensive to implement in the absence of
state (this is why it needs a design discussion at all, really).

Caveat: these APIs are new and not supported in every runner and 
windowing
configuration.

Kenn

On Thu, Jan 26, 2017 at 1:48 AM, Etienne Chauchot <echauc...@gmail.com>
wrote:

Hi,

I have started to implement this ticket. For now it is implemented as a
PTransform that simply does ParDo.of(new DoFn) and all the processing
related to batching is done in the DoFn.

I'm starting to deal with windows and bundles (starting to take a 
look at
the State API to process trans-bundles, more questions about this to 
come).
My comments/questions are inline:


Le 17/01/2017 à 18:41, Ben Chambers a écrit :

We should start by understanding the goals. If elements are in 
different
windows can they be out in the same batch? If they have different
timestamps what timestamp should the batch have?

Regarding timestamps: currently design is as so: the transform does not
group elements in the PCollection, so the "batch" does not exist as an
element in the PCollection. There is only a user defined function
(perBatchFn) that gets called when batchSize elements have been 
processed.
This function takes an ArrayList as parameter. So elements keep their
original timestamps


Regarding windowing: I guess that if elements are not in the same 
window,
they are not expected to be in the same batch.
I'm just starting to work on these subjects, so I might lack a bit of
information;
what I am currently thinking about is that I need a way to know in the
DoFn that the window has expired so that I can call the perBatchFn 
even if
batchSize is not reached.  This is the @OnWindowExpiration callback 
that
Kenneth mentioned in an email about bundles.
Lets imagine that we have a collection of elements artificially
timestamped every 10 seconds (for simplicity of the example) and a 
fixed
windowing of 1 minute. Then each window contains 6 elements. If we 
were to
buffer the elements by batches of 5 elements, then for each window we
expect to get 2 batches (one of 5 elements, one of 1 element). For 
that to
append, we need a @OnWindowExpiration on the DoFn where we call 
perBatchFn

As a composite transform this will likely require a group by key 
which may
affect performance. Maybe within a dofn is better.

Yes, the processing is done with a DoFn indeed.

Then it could be some annotation or API that informs the runner. 
Should
batch sizes be fixed in the annotation (element count or size) or 
should
the user have some method that lets them decide when to process a 
batch
based on the contents?

For now, the user passes batchSize as an argument to 
BatchParDo.via() it
is a number of elements. But batch based on content might be useful 
for the
user. Give hint to the runner might be more flexible for the runner. 
Thanks.

Another thing to think about is whether this should be connected to 
the
ability to run parts of the bundle in parallel.

Yes!

Maybe each batch is an RPC
and you just want to start an async RPC for each batch. Then in 
addition
to
start the final RPC in finishBundle, you also need to wait for all the
RPCs
to complete.

Actually, currently each batch processing is whatever the user wants
(perBatchFn user defined function). If the user decides to issue an 
async
RPC in that function (call with the arrayList of input elements), 
IMHO he
is responsible for waiting for the response in that method if he 
needs the
response, but he can also do a send and forget, depending on his use 
case.

Besides, I have also included a perElementFn user function to allow the
user to do some processing on the elements before adding them to the 
batch
(example use case: convert a String to a DTO object to invoke an 
external
service)

Etienne

On Tue, Jan 17, 2017, 8:48 AM Etienne Chauchot<echauc...@gmail.com>
wrote:

Hi JB,

I meant jira vote but discussion on the ML works also :)

As I understand the need (see stackoverflow links in jira ticket) the
aim is to avoid the user having to code the batching logic in his own
DoFn.processElement() and DoFn.finishBundle() regardless of the 
bundles.
For example, possible use case is to batch a call to an external 
service
(for performance).

I was thinking about providing a PTransform that implements the 
batching
in its own DoFn and that takes user defined functions for 
customization.

Etienne

Le 17/01/2017 à 17:30, Jean-Baptiste Onofré a écrit :

Hi

I guess you mean discussion on the mailing list about that, right ?

AFAIR the ide⁣​a is to provide a utility class to deal with

pooling/batching. However not sure it's required as with 
@StartBundle etc
in DoFn and batching depends of the end user "logic".

Regards
JB

On Jan 17, 2017, 08:26, at 08:26, Etienne 
Chauchot<echauc...@gmail.com>

wrote:

Hi all,
I have started to work on this ticket
https://issues.apache.org/jira/browse/BEAM-135

As there where no vote since March 18th, is the issue still
relevant/needed?

Regards,

Etienne