Hmm, it seems I left out the Dev ML in my mail. Looping that back in..
On 28.08.20 13:54, Dawid Wysakowicz wrote:
@Aljoscha Let me bring back to the ML some of the points we discussed
offline.
Ad. 1 Yes I agree it's not just about scheduling. It includes more
changes to the runtime. We might need to make it more prominent in the
write up.
Ad. 2 You have a good point here that switching the default value for
TimeCharacteristic to INGESTION time might not be the best option as it
might hide problems if we assign ingestion time, which is rarely a right
choice for user programs. Maybe we could just go with the EVENT_TIME as
the default?
Ad. 4 That's a very good point! I do agree with you it would be better
to change the behaviour of said methods for batch-style execution. Even
though it changes the behaviour, the overall logic is still correct.
Moreover I'd also recommend deprecating some of the relational-like
methods, which we should rather redirect to the Table API. I added a
section about it to the FLIP (mostly copying over your message). Let me
know what you think about it.
Best,
Dawid
On 25/08/2020 11:39, Aljoscha Krettek wrote:
Thanks for creating this FLIP! I think the general direction is very
good but I think there are some specifics that we should also put in
there and that we may need to discuss here as well.
## About batch vs streaming scheduling
I think we shouldn't call it "scheduling", because the decision
between bounded and unbounded affects more than just scheduling. It
affects how we do network transfers and the semantics of time, among
other things. So maybe we should differentiate between batch-style and
streaming-style execution, though I'm not sure I like those terms either.
## About processing-time support in batch
It's not just about "batch" changing the default to ingestion time is
a change for stream processing as well. Actually, I don't know if
ingestion time even makes sense for batch processing. IIRC, with the
new sources we actually always have a timestamp, so this discussion
might be moot. Maybe Becket and/or Stephan (cc'ed) could chime in on
this.
Also, I think it's right that we currently ignore processing-time
timers at the end of input in streaming jobs, but this has been a
source of trouble for users. See [1] and several discussions on the
ML. I'm also cc'ing Flavio here who also ran into this problem. I
think we should solve this quickly after laying the foundations of
bounded processing on the DataStream API.
## About broadcast state support
I think as a low-hanging fruit we could just read the broadcast side
first and then switch to the regular input. We do need to be careful
with creating distributed deadlocks, though, so this might be trickier
than it seems at first.
## Loose ends and weird semantics
There are some operations in the DataStream API that have semantics
that might make sense for stream processing but should behave
differently for batch. For example, KeyedStream.reduce() is
essentially a reduce on a GlobalWindow with a Trigger that fires on
every element. In DB terms it produces an UPSERT stream as an output,
if you get ten input elements for a key you also get ten output
records. For batch processing it might make more sense to instead only
produce one output record per key with the result of the aggregation.
This would be correct for downstream consumers that expect an UPSERT
stream but it would change the actual physical output stream that they
see.
There might be other such operations in the DataStream API that have
slightly weird behaviour that doesn't make much sense when you do
bounded processing.
Best,
Aljoscha
[1] https://issues.apache.org/jira/browse/FLINK-18647
On 24.08.20 11:29, Kostas Kloudas wrote:
Thanks a lot for the discussion!
I will open a voting thread shortly!
Kostas
On Mon, Aug 24, 2020 at 9:46 AM Kostas Kloudas <kklou...@apache.org>
wrote:
Hi Guowei,
Thanks for the insightful comment!
I agree that this can be a limitation of the current runtime, but I
think that this FLIP can go on as it discusses mainly the semantics
that the DataStream API will expose when applied on bounded data.
There will definitely be other FLIPs that will actually handle the
runtime-related topics.
But it is good to document them nevertheless so that we start soon
ironing out the remaining rough edges.
Cheers,
Kostas
On Mon, Aug 24, 2020 at 9:16 AM Guowei Ma <guowei....@gmail.com> wrote:
Hi, Klou
Thanks for your proposal. It's a very good idea.
Just a little comment about the "Batch vs Streaming Scheduling".
In the AUTOMATIC execution mode maybe we could not pick BATCH
execution mode even if all sources are bounded. For example some
applications would use the `CheckpointListener`, which is not
available in the BATCH mode in current implementation.
So maybe we need more checks in the AUTOMATIC execution mode.
Best,
Guowei
On Thu, Aug 20, 2020 at 10:27 PM Kostas Kloudas
<kklou...@apache.org> wrote:
Hi all,
Thanks for the comments!
@Dawid: "execution.mode" can be a nice alternative and from a quick
look it is not used currently by any configuration option. I will
update the FLIP accordingly.
@David: Given that having the option to allow timers to fire at the
end of the job is already in the FLIP, I will leave it as is and I
will update the default policy to be "ignore processing time timers
set by the user". This will allow existing dataStream programs to run
on bounded inputs. This update will affect point 2 in the "Processing
Time Support in Batch" section.
If these changes cover your proposals, then I would like to start a
voting thread tomorrow evening if this is ok with you.
Please let me know until then.
Kostas
On Tue, Aug 18, 2020 at 3:54 PM David Anderson
<da...@alpinegizmo.com> wrote:
Being able to optionally fire registered processing time timers
at the end of a job would be interesting, and would help in (at
least some of) the cases I have in mind. I don't have a better idea.
David
On Mon, Aug 17, 2020 at 8:24 PM Kostas Kloudas
<kklou...@apache.org> wrote:
Hi Kurt and David,
Thanks a lot for the insightful feedback!
@Kurt: For the topic of checkpointing with Batch Scheduling, I
totally
agree with you that it requires a lot more work and careful
thinking
on the semantics. This FLIP was written under the assumption
that if
the user wants to have checkpoints on bounded input, he/she will
have
to go with STREAMING as the scheduling mode. Checkpointing for
BATCH
can be handled as a separate topic in the future.
In the case of MIXED workloads and for this FLIP, the scheduling
mode
should be set to STREAMING. That is why the AUTOMATIC option sets
scheduling to BATCH only if all the sources are bounded. I am
not sure
what are the plans there at the scheduling level, as one could
imagine
in the future that in mixed workloads, we schedule first all the
bounded subgraphs in BATCH mode and we allow only one UNBOUNDED
subgraph per application, which is going to be scheduled after all
Bounded ones have finished. Essentially the bounded subgraphs
will be
used to bootstrap the unbounded one. But, I am not aware of any
plans
towards that direction.
@David: The processing time timer handling is a topic that has also
been discussed in the community in the past, and I do not
remember any
final conclusion unfortunately.
In the current context and for bounded input, we chose to favor
reproducibility of the result, as this is expected in batch
processing
where the whole input is available in advance. This is why this
proposal suggests to not allow processing time timers. But I
understand your argument that the user may want to be able to
run the
same pipeline on batch and streaming this is why we added the two
options under future work, namely (from the FLIP):
```
Future Work: In the future we may consider adding as options the
capability of:
* firing all the registered processing time timers at the end of
a job
(at close()) or,
* ignoring all the registered processing time timers at the end
of a job.
```
Conceptually, we are essentially saying that we assume that batch
execution is assumed to be instantaneous and refers to a single
"point" in time and any processing-time timers for the future
may fire
at the end of execution or be ignored (but not throw an
exception). I
could also see ignoring the timers in batch as the default, if this
makes more sense.
By the way, do you have any usecases in mind that will help us
better
shape our processing time timer handling?
Kostas
On Mon, Aug 17, 2020 at 2:52 PM David Anderson
<da...@alpinegizmo.com> wrote:
Kostas,
I'm pleased to see some concrete details in this FLIP.
I wonder if the current proposal goes far enough in the
direction of recognizing the need some users may have for
"batch" and "bounded streaming" to be treated differently. If
I've understood it correctly, the section on scheduling allows
me to choose STREAMING scheduling even if I have bounded
sources. I like that approach, because it recognizes that even
though I have bounded inputs, I don't necessarily want batch
processing semantics. I think it makes sense to extend this
idea to processing time support as well.
My thinking is that sometimes in development and testing it's
reasonable to run exactly the same job as in production, except
with different sources and sinks. While it might be a
reasonable default, I'm not convinced that switching a
processing time streaming job to read from a bounded source
should always cause it to fail.
David
On Wed, Aug 12, 2020 at 5:22 PM Kostas Kloudas
<kklou...@apache.org> wrote:
Hi all,
As described in FLIP-131 [1], we are aiming at deprecating the
DataSet
API in favour of the DataStream API and the Table API. After
this work
is done, the user will be able to write a program using the
DataStream
API and this will execute efficiently on both bounded and
unbounded
data. But before we reach this point, it is worth discussing and
agreeing on the semantics of some operations as we transition
from the
streaming world to the batch one.
This thread and the associated FLIP [2] aim at discussing
these issues
as these topics are pretty important to users and can lead to
unpleasant surprises if we do not pay attention.
Let's have a healthy discussion here and I will be updating
the FLIP
accordingly.
Cheers,
Kostas
[1]
https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=158866741
[2]
https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=158871522
