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
