> Hi Ivan,
>
> I'm fearing that only a few mailing list users have actually deeper
> Beam experience. I only used it briefly 3 years ago. Most users here
> are using Flink directly to avoid these kinds of double-abstraction
> issues.
>
> It might be better to switch to the Beam mailing list if you have
> Beam-specific questions including how the Flink runner actually
> translates the Beam program to Flink.
>
> On Tue, May 19, 2020 at 11:38 AM Ivan San Jose <
>
[hidden email]> wrote:
> > Actually I'm also thinking about how Beam coders are related with
> > runner's serialization... I mean, on Beam you specify a coder per
> > each
> > Java type in order to Beam be able to serialize/deserialize that
> > type,
> > but then, is the runner used under the hood
> > serializing/deserializing
> > again the result, so that is doing a double serialization, does it
> > make
> > sense? Or how does it work?
> >
> > On Tue, 2020-05-19 at 08:54 +0000, Ivan San Jose wrote:
> > > Yep, sorry if I'm bothering you but I think I'm still not getting
> > > this,
> > > how could I tell Beam to tell Flink to use that serializer
> > instead of
> > > Java standard one, because I think Beam is abstracting us from
> > Flink
> > > checkpointing mechanism, so I'm afraid that if we use Flink API
> > > directly we might break other things that Beam is hidding for
> > us...
> > >
> > > On Tue, 2020-05-19 at 10:44 +0200, Arvid Heise wrote:
> > > > Hi Ivan,
> > > >
> > > > The easiest way is to use some implementation that's already
> > there
> > > > [1]. I already mentioned Avro and would strongly recommend
> > giving
> > > > it
> > > > a go. If you make sure to provide a default value for as many
> > > > fields
> > > > as possible, you can always remove them later giving you great
> > > > flexibility. I can give you more hints if you decide to go this
> > > > route.
> > > >
> > > > If you want to have a custom implementation, I'd start at
> > looking
> > > > of
> > > > one of the simpler implementations like MapSerializerSnapshot
> > [2].
> > > >
> > > > [1]
> > > >
> >
https://ci.apache.org/projects/flink/flink-docs-master/api/java/org/apache/flink/api/common/typeutils/TypeSerializerSnapshot.html> > > > (see known implementing classes).
> > > > [2]
> > > >
> >
https://github.com/apache/flink/blob/master/flink-core/src/main/java/org/apache/flink/api/common/typeutils/base/MapSerializerSnapshot.java> > > >
> > > > On Tue, May 19, 2020 at 10:31 AM Ivan San Jose <
> > > >
[hidden email]> wrote:
> > > > > Thanks for your complete answer Arvid, we will try to
> > approach
> > > > > all
> > > > > things you mentioned, but take into account we are using Beam
> > on
> > > > > top of
> > > > > Flink, so, to be honest, I don't know how could we implement
> > the
> > > > > custom
> > > > > serialization thing (
> > > > >
> >
https://ci.apache.org/projects/flink/flink-docs-release-1.10/dev/stream/state/custom_serialization.html#the-typeserializersnapshot-abstraction> > > > > ) there. Could you please give us some hints? Thanks
> > > > >
> > > > > On Mon, 2020-05-18 at 12:41 +0200, Arvid Heise wrote:
> > > > > > Hi Ivan,
> > > > > >
> > > > > > First let's address the issue with idle partitions. The
> > > > > > solution
> > > > > is
> > > > > > to use a watermark assigner that also emits a watermark
> > with
> > > > > > some
> > > > > > idle timeout [1].
> > > > > >
> > > > > > Now the second question, on why Kafka commits are committed
> > for
> > > > > in-
> > > > > > flight, checkpointed data. The basic idea is that you are
> > not
> > > > > losing
> > > > > > data while avoiding replicated output.
> > > > > > So if you commit offsets only after data has been fully
> > > > > processed,
> > > > > > upon crash the same data point would be reprocessed jointly
> > > > > > with
> > > > > the
> > > > > > restored in-flight data, so you get duplicate messages in
> > your
> > > > > > system.
> > > > > > To avoid duplicates data needs to be more or less
> > completely
> > > > > flushed
> > > > > > out the system before a checkpoint is performed. That would
> > > > > produce a
> > > > > > huge downtime.
> > > > > > Instead, we assume that we can always resume from the
> > > > > checkpoints.
> > > > > > Which leads to the last question on what to do when your
> > > > > > pipeline
> > > > > has
> > > > > > breaking changes.
> > > > > > First strategy is to avoid breaking changes as much as
> > > > > > possible.
> > > > > > State could for example also be stored as Avro to allow
> > schema
> > > > > > evolution. Minor things like renamed operators will not
> > happen
> > > > > with a
> > > > > > bit more expertise.
> > > > > > Second strategy is to use state migration [2].
> > Alternatively,
> > > > > > you
> > > > > can
> > > > > > manually convert state with state processor API [3].
> > > > > > Last option is to do a full reprocessing of data. This can
> > be
> > > > > done on
> > > > > > a non-production cluster and then a savepoint can be used
> > to
> > > > > > bootstrap the production cluster quickly. This option needs
> > to
> > > > > > be
> > > > > > available anyways for the case that you find any logic
> > error.
> > > > > > But
> > > > > of
> > > > > > course, this option has the highest implications (may need
> > to
> > > > > purge
> > > > > > sink beforehand).
> > > > > >
> > > > > > [1]
> > > > > >
> > > > >
> >
https://github.com/ververica/flink-training-troubleshooting/blob/master/src/main/java/com/ververica/flinktraining/solutions/troubleshoot/TroubledStreamingJobSolution2.java#L129-L187> > > > > > [2]
> > > > > >
> > > > >
> >
https://ci.apache.org/projects/flink/flink-docs-release-1.10/dev/stream/state/custom_serialization.html#the-typeserializersnapshot-abstraction> > > > > > [3]
> > > > > >
> > > > >
> >
https://ci.apache.org/projects/flink/flink-docs-stable/dev/libs/state_processor_api.html> > > > > > On Fri, May 15, 2020 at 2:58 PM Ivan San Jose <
> > > > > >
[hidden email]> wrote:
> > > > > > > Hi, we are starting to use Beam with Flink as runner on
> > our
> > > > > > > applications, and recently we would like to get
> > advantages
> > > > > > > that
> > > > > > > Flink
> > > > > > > checkpoiting provides, but it seems we are not
> > understanding
> > > > > > > it
> > > > > > > clearly.
> > > > > > >
> > > > > > > Simplifying, our application does the following:
> > > > > > > - Read meesages from a couple of Kafka topics
> > > > > > > - Combine them
> > > > > > > - Write combination result to a sink (Exasol DB)
> > > > > > >
> > > > > > > As application is processing messages using event time,
> > and
> > > > > > > one
> > > > > of
> > > > > > > the
> > > > > > > topics is almost idle, the first time application is
> > started
> > > > > > > messages
> > > > > > > are stuck in the combiner because watermark don't advance
> > > > > > > until
> > > > > we
> > > > > > > have
> > > > > > > messages arriving onto idled topic (we know this and is
> > not a
> > > > > > > problem
> > > > > > > for us though).
> > > > > > >
> > > > > > > The problem is that we've observed, if a checkpoint is
> > > > > triggered
> > > > > > > when
> > > > > > > messages are still stuck in the combiner, surprisingly
> > for
> > > > > > > us,
> > > > > the
> > > > > > > checkpoint finishes successfully (and offsets committed
> > to
> > > > > Kafka)
> > > > > > > even
> > > > > > > messages haven't progressed to the sink yet. Is this
> > > > > > > expected?
> > > > > > >
> > > > > > > The thing is that, if in the future, we make not state
> > > > > compatible
> > > > > > > changes in application source code, checkpoint taken
> > couldn't
> > > > > be
> > > > > > > restored. So we would like to start the application
> > without
> > > > > using
> > > > > > > any
> > > > > > > checkpoint but without losing data.
> > > > > > > Problem here would be that data loss would happen because
> > > > > messages
> > > > > > > stuck in combiner are already committed to Kafka and
> > > > > application
> > > > > > > would
> > > > > > > start to read from latest commited offset in Kafka if we
> > > > > > > don't
> > > > > use
> > > > > > > any
> > > > > > > checkpoint, thus those messages are not going to be read
> > from
> > > > > the
> > > > > > > source again.
> > > > > > >
> > > > > > > So, I guess our question is how are you doing in order to
> > not
> > > > > lose
> > > > > > > data
> > > > > > > when developing applications, because sooner or later you
> > are
> > > > > going
> > > > > > > to
> > > > > > > add breaking changes...
> > > > > > >
> > > > > > > For example, we've seen those two errors so far:
> > > > > > > - After changing an operator name:
> > > > > > >
> > > > > > > 2020-05-13 07:23:52,248 ERROR Fatal error occurred in the
> > > > > cluster
> > > > > > > entrypoint.
> > > > > > > org.apache.flink.runtime.dispatcher.DispatcherException:
> > > > > > > Failed
> > > > > to
> > > > > > > take
> > > > > > > leadership with session id 00000000-0000-0000-0000-
> > > > > 000000000000.
> > > > > > > ...
> > > > > > > Caused by:
> > > > > org.apache.flink.runtime.client.JobExecutionException:
> > > > > > > Could
> > > > > > > not set up JobManager
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.runtime.jobmaster.JobManagerRunner.<init>(JobMan
> > > > > ag
> > > > > > > erRu
> > > > > > > nner.java:152)
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.runtime.dispatcher.DefaultJobManagerRunnerFactor
> > > > > y.
> > > > > > > crea
> > > > > > >
> > teJobManagerRunner(DefaultJobManagerRunnerFactory.java:83)
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.runtime.dispatcher.Dispatcher.lambda$createJobMa
> > > > > na
> > > > > > > gerR
> > > > > > > unner$5(Dispatcher.java:375)
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.util.function.CheckedSupplier.lambda$unchecked$0
> > > > > (C
> > > > > > > heck
> > > > > > > edSupplier.java:34)
> > > > > > > ... 7 more
> > > > > > > Caused by: java.lang.IllegalStateException: Failed to
> > > > > > > rollback
> > > > > to
> > > > > > > checkpoint/savepoint
> > > > > hdfs://RTDWLTDEV/data/lake/processing/flink-
> > > > > > > savepoints/holly-reconciliation-fact/savepoint-90ab28-
> > > > > bcc1f65a0986.
> > > > > > > Cannot map checkpoint/savepoint state for operator
> > > > > > > f476451c6210bd2783f36fa331b9da5e to the new program,
> > because
> > > > > the
> > > > > > > operator is not available in the new program. If you want
> > to
> > > > > allow
> > > > > > > to
> > > > > > > skip this, you can set the --allowNonRestoredState option
> > on
> > > > > the
> > > > > > > CLI.
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.runtime.checkpoint.Checkpoints.loadAndValidateCh
> > > > > ec
> > > > > > > kpoi
> > > > > > > nt(Checkpoints.java:205)
> > > > > > > ...
> > > > > > >
> > > > > > > - After modifying a Java model class involved in a
> > combine:
> > > > > > > org.apache.flink.runtime.state.BackendBuildingException:
> > > > > > > Failed
> > > > > > > when
> > > > > > > trying to restore heap backend
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.runtime.state.heap.HeapKeyedStateBackendBuilder.
> > > > > bu
> > > > > > > ild(
> > > > > > > HeapKeyedStateBackendBuilder.java:116)
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.runtime.state.filesystem.FsStateBackend.createKe
> > > > > ye
> > > > > > > dSta
> > > > > > > teBackend(FsStateBackend.java:529)
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.streaming.api.operators.StreamTaskStateInitializ
> > > > > er
> > > > > > > Impl
> > > > > > >
> > > > >
> > .lambda$keyedStatedBackend$1(StreamTaskStateInitializerImpl.java:
> > > > > 29
> > > > > > > 1)
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.streaming.api.operators.BackendRestorerProcedure
> > > > > .a
> > > > > > > ttem
> > > > > > > ptCreateAndRestore(BackendRestorerProcedure.java:142)
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.streaming.api.operators.BackendRestorerProcedure
> > > > > .c
> > > > > > > reat
> > > > > > > eAndRestore(BackendRestorerProcedure.java:121)
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.streaming.api.operators.StreamTaskStateInitializ
> > > > > er
> > > > > > > Impl
> > > > > > >
> > .keyedStatedBackend(StreamTaskStateInitializerImpl.java:307)
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.streaming.api.operators.StreamTaskStateInitializ
> > > > > er
> > > > > > > Impl
> > > > > > >
> > > > >
> > .streamOperatorStateContext(StreamTaskStateInitializerImpl.java:1
> > > > > 35
> > > > > > > )
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.streaming.api.operators.AbstractStreamOperator.i
> > > > > ni
> > > > > > > tial
> > > > > > > izeState(AbstractStreamOperator.java:253)
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.streaming.runtime.tasks.StreamTask.initializeSta
> > > > > te
> > > > > > > (Str
> > > > > > > eamTask.java:881)
> > > > > > > at
> > > > > > >
> > > > >
> > org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(Stream
> > > > > Ta
> > > > > > > sk.j
> > > > > > > ava:395)
> > > > > > > at
> > > > > > >
> > org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:705
> > > > > > > )
> > > > > > > at
> > > > > org.apache.flink.runtime.taskmanager.Task.run(Task.java:530)
> > > > > > > at java.lang.Thread.run(Thread.java:748)
> > > > > > > Caused by: java.io.InvalidClassException:
> > > > > > >
> > internal.holly.beatrix.wallet.walletfact.model.WalletMetadata
> > > > > > > ;
> > > > > > > local
> > > > > > > class incompatible: stream classdesc serialVersionUID =
> > > > > > > 8366890161513008789, local class serialVersionUID =
> > > > > > > 174312384610985998
> > > > > > > at
> > > > > > >
> > > > >
> > java.io.ObjectStreamClass.initNonProxy(ObjectStreamClass.java:699
> > > > > )
> > > > > > >
> > > > > > > Apologies in advance as we are new to Flink, so may be we
> > are
> > > > > > > missing
> > > > > > > something obvious here.
> > > > > > >
> > > > > > > Thanks
> > > > > > >
> > > > > > >
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