I agree with Dawid.
Maybe one thing to add is that reusing parts of the pipeline is possible
via StatementSets in TableEnvironment. They allow you to add multiple
queries that consume from a common part of the pipeline (for example a
common source). But all of that is compiled into one big job and static
during runtime, not isolated.
One option is to introduce an additional Flink job that multiplexes the
source Kafka topic into more Kafka topics such that isolated jobs can
access this intermediate storage.
I hope this helps.
Regards,
Timo
On 24.11.20 16:54, Dawid Wysakowicz wrote:
Hi,
Really sorry for a late reply.
To the best of my knowledge there is no such possibility to "attach" to
a source/reader of a different job. Every job would read the source
separately.
`The GenericInMemoryCatalog is an in-memory
implementation of a catalog. All objects will be available only for the
lifetime of the session.`. I presume, in session mode, we can share Kafka
source for multiple SQL jobs?
Unfortunately this is wrong assumption. Catalogs store "metadata of
Tables, such as connetion parameters, schema etc. Not the data itself,
or parts of the graph. The information from a catalog can be used to
create an execution graph that can be submitted to a cluster. It has
nothing to do with a session cluster. The session here means a job/the
lifetime of the GenericInMemoryCatalog.
Both queries will share the same reader as they are part of a single job
graph. Can we somehow take a snapshot of this and submit another query with
them again under the same job graph?
Again unfortunately there is no guarantees this will work. As of now it
is a limitation of SQL that it does not support stateful upgrades of a
Graph or Flink version. As Till said, if the plan will contain the same
sub plans they should be able to match. However with such an extensive
changes to the graph I would not count that it happens. It can work for
rather simpler changes such as e.g. changing a predicate (but still it
can greatly affect the plan if the predicate could've been optimized).
There were and there are some discussions going on to improve the
situation here.
A proper solution for the problem for a STREAMING job would be rather
hard in my opinion. As we would have to somehow keep the state of the
shared source between multiple different jobs. We would need to know
e.g. the offsets that a certain job consumed up to a certain checkpoint.
What to do if e.g. a particular query requests to start reading from
offsets in the past etc.
There is some ongoing effort to support caching a queries that could be
reused between jobs in the same cluster as a better support for
Interactive programming[1], but I don't think it will support a
STREAMING mode.
Just as a side. I am not a Spark expert and I might be completely wrong,
but as far as I am familiar with Spark, it also does not support
dynamically reusing streaming sources. It does have the caching of
intermediate shuffles implemented, something that the FLIP-36 resembles.
Best regards,
Dawid
[1] https://cwiki.apache.org/confluence/x/8hclBg
On 23/11/2020 21:09, lalala wrote:
Hi Till,
Thank you for your comment. I am looking forward to hearing from Timo and
Dawid as well.
Best regards,
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