Yea, exactly.

On Wed, Aug 9, 2017 at 9:40 AM, Reuven Lax <re...@google.com.invalid> wrote:

> Oh, I understand now. This DoFn is saying "make my input deterministically
> replayable." If it turns out the input already is deterministically
> replayable, then nothing needs to be done.
>
>
>
> On Wed, Aug 9, 2017 at 9:10 AM, Kenneth Knowles <k...@google.com.invalid>
> wrote:
>
> > The term "determinism" refers to a property of the input PCollection, not
> > any transform or DoFn. What we mean by it is that the PCollection has
> > well-defined contents, so any transform consuming it will see consistent
> > PCollection contents across retries.
> >
> > Illustrated, I think we are talking about the same situation, where we
> hope
> > for an execution like this:
> >
> >     Transform(s) -> Checkpoint -> WriteTransform
> >
> > In every case I know of the purpose of the Checkpoint is so that
> > WriteTransform sees the same input across retries, even if the upstream
> > Transform(s) are not deterministic.
> >
> > So "marking DoFns as having side effects, and having the runner
> > automatically insert such a Checkpoint in front of them" is precisely
> what
> > what you get from "Requires deterministic input". Of course, there are
> lots
> > of kinds of side effects and they don't all require deterministic input,
> so
> > that's how the vocabulary developed.
> >
> > Kenn
> >
> > On Wed, Aug 9, 2017 at 8:48 AM, Reuven Lax <re...@google.com.invalid>
> > wrote:
> >
> > > Is determinism the right thing for this? One thing to keep in mind, is
> > that
> > > most inputs will not be deterministic. If any upstream aggregation is
> > done
> > > and allowed_lateness > 0, then that aggregation is non deterministic
> > > (basically, if it is retried it might get a slightly different set of
> > input
> > > elements to aggregate) and so are downstream dependent values.
> Similarly
> > if
> > > an upstream aggregation uses count or processing-time triggers, the
> > result
> > > of that aggregation will be non deterministic.
> > >
> > > In the above cases, the property of the DoFn that requires this
> > > checkpointing is not determinism, it's the fact that the DoFn has side
> > > effects.
> > >
> > > BTW, nothing prevents us from allowing automatic inference, but _also_
> > > adding a checkpoint operator (which will be a noop operator for runners
> > > such as Dataflow).
> > >
> > > Reuven
> > >
> > > On Wed, Aug 9, 2017 at 8:32 AM, Kenneth Knowles <k...@google.com.invalid
> >
> > > wrote:
> > >
> > > > We've had a few threads related to this. There was one proposal that
> > > seemed
> > > > to achieve consensus [1]. The TL;DR is that we have to assume any
> DoFn
> > > > might have side effects (in the broadest sense of the term where
> > anything
> > > > other than a pure mathematical function is a side effect) and when we
> > > want
> > > > deterministic input we use a special DoFn parameter like distinct
> from
> > > > ProcessContext to request it, something like:
> > > >
> > > > @ProcessElement
> > > > public void process(DeterministicInput elem, OutputReceiver
> > mainOutput) {
> > > >  ... elem.get() instead of context.element() ...
> > > >  ... mainOutput.output() instead of context.output() ...
> > > > }
> > > >
> > > > A runner can then add checkpointing if needed or elide it if not
> > needed.
> > > It
> > > > depends on the runner's inherent checkpointing behavior and the
> ability
> > > to
> > > > analyze a pipeline to know whether intervening transforms are
> > > deterministic
> > > > functions.
> > > >
> > > > I started some work on breaking down
> > > > (StartBundle|Process|FinishBundle)Context to transition towards
> this,
> > > but
> > > > development has stalled in favor of other priorities. I'd be happy to
> > > chat
> > > > with anyone who wants to pick this up.
> > > >
> > > > Kenn
> > > >
> > > > [1]
> > > > https://lists.apache.org/thread.html/ae3c838df060e47148439d1dad818d
> > > > 5e927b2a25ff00cc4153221dff@%3Cdev.beam.apache.org%3E
> > > >
> > > > On Wed, Aug 9, 2017 at 2:07 AM, Aljoscha Krettek <
> aljos...@apache.org>
> > > > wrote:
> > > >
> > > > > Yes, I think making this explicit would be good. Having a
> > > transformation
> > > > > that makes assumptions about how the runner implements certain
> things
> > > is
> > > > > not optimal. Also, I think that most people probably don't use
> Kafka
> > > with
> > > > > the Dataflow Runner (because GCE has Pubsub, but I'm guest guessing
> > > > here).
> > > > > This would mean that the intersection of "people who would benefit
> > from
> > > > an
> > > > > exactly-once Kafka sink" and "people who use Beam on Dataflow" is
> > > rather
> > > > > small, and therefore not many people would benefit from such a
> > > Transform.
> > > > >
> > > > > This is all just conjecture, of course.
> > > > >
> > > > > Best,
> > > > > Aljoscha
> > > > >
> > > > > > On 8. Aug 2017, at 23:34, Reuven Lax <re...@google.com.INVALID>
> > > wrote:
> > > > > >
> > > > > > I think the issue we're hitting is how to write this in Beam.
> > > > > >
> > > > > > Dataflow historically guaranteed checkpointing at every GBK
> (which
> > > due
> > > > to
> > > > > > the design of Dataflow's streaming shuffle was reasonably
> > efficient).
> > > > In
> > > > > > Beam we never formalized these semantics, leaving these syncs in
> a
> > > gray
> > > > > > area. I believe the Spark runner currently checkpoints the RDD on
> > > every
> > > > > > GBK, so these unwritten semantics currently work for Dataflow and
> > for
> > > > > Spark.
> > > > > >
> > > > > > We need someway to express this operation in Beam, whether it be
> > via
> > > an
> > > > > > explicit Checkpoint() operation or via marking DoFns as having
> side
> > > > > > effects, and having the runner automatically insert such a
> > Checkpoint
> > > > in
> > > > > > front of them. In Flink, this operation can be implemented using
> > what
> > > > > > Aljoscha posted.
> > > > > >
> > > > > > Reuven
> > > > > >
> > > > > > On Tue, Aug 8, 2017 at 8:22 AM, Aljoscha Krettek <
> > > aljos...@apache.org>
> > > > > > wrote:
> > > > > >
> > > > > >> Hi,
> > > > > >>
> > > > > >> In Flink, there is a TwoPhaseCommit SinkFunction that can be
> used
> > > for
> > > > > such
> > > > > >> cases: [1]. The PR for a Kafka 0.11 exactly once producer builds
> > on
> > > > > that:
> > > > > >> [2]
> > > > > >>
> > > > > >> Best,
> > > > > >> Aljoscha
> > > > > >>
> > > > > >> [1] https://github.com/apache/flink/blob/
> > > > 62e99918a45b7215c099fbcf160d45
> > > > > >> aa02d4559e/flink-streaming-java/src/main/java/org/apache/
> > > > > >> flink/streaming/api/functions/sink/TwoPhaseCommitSinkFunction.
> > > > java#L55
> > > > > <
> > > > > >> https://github.com/apache/flink/blob/
> > 62e99918a45b7215c099fbcf160d45
> > > > > >> aa02d4559e/flink-streaming-java/src/main/java/org/apache/
> > > > > >> flink/streaming/api/functions/sink/TwoPhaseCommitSinkFunction.
> > > > java#L55>
> > > > > >> [2] https://github.com/apache/flink/pull/4239
> > > > > >>> On 3. Aug 2017, at 04:03, Raghu Angadi
> > <rang...@google.com.INVALID
> > > >
> > > > > >> wrote:
> > > > > >>>
> > > > > >>> Kafka 0.11 added support for transactions[1], which allows
> > > end-to-end
> > > > > >>> exactly-once semantics. Beam's KafkaIO users can benefit from
> > these
> > > > > while
> > > > > >>> using runners that support exactly-once processing.
> > > > > >>>
> > > > > >>> I have an implementation of EOS support for Kafka sink :
> > > > > >>> https://github.com/apache/beam/pull/3612
> > > > > >>> It has two shuffles and builds on Beam state-API and checkpoint
> > > > barrier
> > > > > >>> between stages (as in Dataflow). Pull request has a longer
> > > > description.
> > > > > >>>
> > > > > >>> - What other runners in addition to Dataflow would be
> compatible
> > > with
> > > > > >> such
> > > > > >>> a strategy?
> > > > > >>> - I think it does not quite work for Flink (as it has a global
> > > > > >> checkpoint,
> > > > > >>> not between the stages). How would one go about implementing
> > such a
> > > > > sink.
> > > > > >>>
> > > > > >>> Any comments on the pull request are also welcome.
> > > > > >>>
> > > > > >>> Thanks,
> > > > > >>> Raghu.
> > > > > >>>
> > > > > >>> [1]
> > > > > >>> https://www.confluent.io/blog/exactly-once-semantics-are-
> > > > > >> possible-heres-how-apache-kafka-does-it/
> > > > > >>
> > > > > >>
> > > > >
> > > > >
> > > >
> > >
> >
>

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