Thanks for the explanation. Agree, we might talk about different things using
the same wording.
I'm also struggling to understand the use case (for a generic DoFn).
Regards
JB
On 11/17/2017 07:40 AM, Eugene Kirpichov wrote:
To avoid spending a lot of time pursuing a false path, I'd like to say
straight up that SDF is definitely not going to help here, despite the fact
that its API includes the term "checkpoint". In SDF, the "checkpoint"
captures the state of processing within a single element. If you're
applying an SDF to 1000 elements, it will, like any other DoFn, be applied
to each of them independently and in parallel, and you'll have 1000
checkpoints capturing the state of processing each of these elements, which
is probably not what you want.
I'm afraid I still don't understand what kind of checkpoint you need, if it
is not just deterministic grouping into batches. "Checkpoint" is a very
broad term and it's very possible that everybody in this thread is talking
about different things when saying it. So it would help if you could give a
more concrete example: for example, take some IO that you think could be
easier to write with your proposed API, give the contents of a hypothetical
PCollection being written to this IO, give the code of a hypothetical DoFn
implementing the write using your API, and explain what you'd expect to
happen at runtime.
On Thu, Nov 16, 2017 at 10:33 PM Romain Manni-Bucau <[email protected]>
wrote:
@Eugene: yes and the other alternative of Reuven too but it is still
1. relying on timers, 2. not really checkpointed
In other words it seems all solutions are to create a chunk of size 1
and replayable to fake the lack of chunking in the framework. This
always implies a chunk handling outside the component (typically
before for an output). My point is I think IO need it in their own
"internal" or at least control it themselves since the chunk size is
part of the IO handling most of the time.
I think JB spoke of the same "group before" trick using restrictions
which can work I have to admit if SDF are implemented by runners. Is
there a roadmap/status on that? Last time I checked SDF was a great
API without support :(.
Romain Manni-Bucau
@rmannibucau | Blog | Old Blog | Github | LinkedIn
2017-11-17 7:25 GMT+01:00 Eugene Kirpichov <[email protected]>:
JB, not sure what you mean? SDFs and triggers are unrelated, and the post
doesn't mention the word. Did you mean something else, e.g. restriction
perhaps? Either way I don't think SDFs are the solution here; SDFs have
to
do with the ability to split the processing of *a single element* over
multiple calls, whereas Romain I think is asking for repeatable grouping
of
*multiple* elements.
Romain - does
https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/GroupIntoBatches.java
do what
you want?
On Thu, Nov 16, 2017 at 10:19 PM Jean-Baptiste Onofré <[email protected]>
wrote:
It sounds like the "Trigger" in the Splittable DoFn, no ?
https://beam.apache.org/blog/2017/08/16/splittable-do-fn.html
Regards
JB
On 11/17/2017 06:56 AM, Romain Manni-Bucau wrote:
it gives the fn/transform the ability to save a state - it can get
back on "restart" / whatever unit we can use, probably runner
dependent? Without that you need to rewrite all IO usage with
something like the previous pattern which makes the IO not self
sufficient and kind of makes the entry cost and usage of beam way
further.
In my mind it is exactly what jbatch/spring-batch uses but adapted to
beam (stream in particular) case.
Romain Manni-Bucau
@rmannibucau | Blog | Old Blog | Github | LinkedIn
2017-11-17 6:49 GMT+01:00 Reuven Lax <[email protected]>:
Romain,
Can you define what you mean by checkpoint? What are the semantics,
what
does it accomplish?
Reuven
On Fri, Nov 17, 2017 at 1:40 PM, Romain Manni-Bucau <
[email protected]>
wrote:
Yes, what I propose earlier was:
I. checkpoint marker:
@AnyBeamAnnotation
@CheckpointAfter
public void someHook(SomeContext ctx);
II. pipeline.apply(ParDo.of(new MyFn()).withCheckpointAlgorithm(new
CountingAlgo()))
III. (I like this one less)
// in the dofn
@CheckpointTester
public boolean shouldCheckpoint();
IV. @Checkpointer Serializable getCheckpoint(); in the dofn per
element
Romain Manni-Bucau
@rmannibucau | Blog | Old Blog | Github | LinkedIn
2017-11-17 6:06 GMT+01:00 Raghu Angadi <[email protected]
:
How would you define it (rough API is fine)?. Without more details,
it is
not easy to see wider applicability and feasibility in runners.
On Thu, Nov 16, 2017 at 1:13 PM, Romain Manni-Bucau <
[email protected]>
wrote:
This is a fair summary of the current state but also where beam
can
have a
very strong added value and make big data great and smooth.
Instead of this replay feature isnt checkpointing willable? In
particular
with SDF no?
Le 16 nov. 2017 19:50, "Raghu Angadi" <[email protected]>
a
écrit :
Core issue here is that there is no explicit concept of
'checkpoint'
in
Beam (UnboundedSource has a method 'getCheckpointMark' but that
refers to
the checkoint on external source). Runners do checkpoint
internally
as
implementation detail. Flink's checkpoint model is entirely
different
from
Dataflow's and Spark's.
@StableReplay helps, but it does not explicitly talk about a
checkpoint
by
design.
If you are looking to achieve some guarantees with a sink/DoFn, I
think
it
is better to start with the requirements. I worked on
exactly-once
sink
for
Kafka (see KafkaIO.write().withEOS()), where we essentially
reshard
the
elements and assign sequence numbers to elements with in each
shard.
Duplicates in replays are avoided based on these sequence
numbers.
DoFn
state API is used to buffer out-of order replays. The
implementation
strategy works in Dataflow but not in Flink which has a
horizontal
checkpoint. KafkaIO checks for compatibility.
On Wed, Nov 15, 2017 at 12:38 AM, Romain Manni-Bucau <
[email protected]>
wrote:
Hi guys,
The subject is a bit provocative but the topic is real and
coming
again and again with the beam usage: how a dofn can handle some
"chunking".
The need is to be able to commit each N records but with N not
too
big.
The natural API for that in beam is the bundle one but bundles
are
not
reliable since they can be very small (flink) - we can say it is
"ok"
even if it has some perf impacts - or too big (spark does full
size
/
#workers).
The workaround is what we see in the ES I/O: a maxSize which
does
an
eager flush. The issue is that then the checkpoint is not
respected
and you can process multiple times the same records.
Any plan to make this API reliable and controllable from a beam
point
of view (at least in a max manner)?
Thanks,
Romain Manni-Bucau
@rmannibucau | Blog | Old Blog | Github | LinkedIn
--
Jean-Baptiste Onofré
[email protected]
http://blog.nanthrax.net
Talend - http://www.talend.com
--
Jean-Baptiste Onofré
[email protected]
http://blog.nanthrax.net
Talend - http://www.talend.com
