Hi Kurt,
Answer your questions:
a) Sorry, I just updated the Google doc, still have no time update the
FLIP, will update FLIP as soon as possible.
About your description at this point, I have a question, what does it mean:
how do we combine with
`AggregateFunction`?
I have shown you the examples which Flink has supported:
- input.localKeyBy(0).aggregate()
- input.localKeyBy(0).window().aggregate()
You can show me a example about how do we combine with `AggregateFuncion`
through your localAggregate API.
About the example, how to do the local aggregation for AVG, consider this
code:
*DataStream<Tuple2<String, Long>> source = null; source .localKeyBy(0)
.timeWindow(Time.seconds(60)) .aggregate(agg1, new
WindowFunction<Tuple2<Long, Long>, Tuple3<String, Long, Long>, String,
TimeWindow>() {}) .keyBy(0) .timeWindow(Time.seconds(60)) .aggregate(agg2,
new WindowFunction<Tuple2<Long, Long>, Tuple2<String, Long>, String,
TimeWindow>());*
*agg1:*
*signature : new AggregateFunction<Tuple2<String, Long>, Tuple2<Long,
Long>, Tuple2<Long, Long>>() {}*
*input param type: Tuple2<String, Long> f0: key, f1: value*
*intermediate result type: Tuple2<Long, Long>, f0: local aggregated sum;
f1: local aggregated count*
*output param type: Tuple2<Long, Long>, f0: local aggregated sum; f1:
local aggregated count*
*agg2:*
*signature: new AggregateFunction<Tuple3<String, Long, Long>, Long,
Tuple2<String, Long>>() {},*
*input param type: Tuple3<String, Long, Long>, f0: key, f1: local
aggregated sum; f2: local aggregated count*
*intermediate result type: Long avg result*
*output param type: Tuple2<String, Long> f0: key, f1 avg result*
For sliding window, we just need to change the window type if users want to
do.
Again, we try to give the design and implementation in the DataStream
level. So I believe we can match all the requirements(It's just that the
implementation may be different) comes from the SQL level.
b) Yes, Theoretically, your thought is right. But in reality, it cannot
bring many benefits.
If we want to get the benefits from the window API, while we do not reuse
the window operator? And just copy some many duplicated code to another
operator?
c) OK, I agree to let the state backend committers join this discussion.
Best,
Vino
Kurt Young <ykt...@gmail.com> 于2019年6月24日周一 下午6:53写道：
> Hi vino,
>
> One thing to add, for a), I think use one or two examples like how to do
> local aggregation on a sliding window,
> and how do we do local aggregation on an unbounded aggregate, will do a lot
> help.
>
> Best,
> Kurt
>
>
> On Mon, Jun 24, 2019 at 6:06 PM Kurt Young <ykt...@gmail.com> wrote:
>
> > Hi vino,
> >
> > I think there are several things still need discussion.
> >
> > a) We all agree that we should first go with a unified abstraction, but
> > the abstraction is not reflected by the FLIP.
> > If your answer is "locakKeyBy" API, then I would ask how do we combine
> > with `AggregateFunction`, and how do
> > we do proper local aggregation for those have different intermediate
> > result type, like AVG. Could you add these
> > to the document?
> >
> > b) From implementation side, reusing window operator is one of the
> > possible solutions, but not we base on window
> > operator to have two different implementations. What I understanding is,
> > one of the possible implementations should
> > not touch window operator.
> >
> > c) 80% of your FLIP content is actually describing how do we support
> local
> > keyed state. I don't know if this is necessary
> > to introduce at the first step and we should also involve committers work
> > on state backend to share their thoughts.
> >
> > Best,
> > Kurt
> >
> >
> > On Mon, Jun 24, 2019 at 5:17 PM vino yang <yanghua1...@gmail.com> wrote:
> >
> >> Hi Kurt,
> >>
> >> You did not give more further different opinions, so I thought you have
> >> agreed with the design after we promised to support two kinds of
> >> implementation.
> >>
> >> In API level, we have answered your question about pass an
> >> AggregateFunction to do the aggregation. No matter introduce localKeyBy
> >> API
> >> or not, we can support AggregateFunction.
> >>
> >> So what's your different opinion now? Can you share it with us?
> >>
> >> Best,
> >> Vino
> >>
> >> Kurt Young <ykt...@gmail.com> 于2019年6月24日周一 下午4:24写道：
> >>
> >> > Hi vino,
> >> >
> >> > Sorry I don't see the consensus about reusing window operator and keep
> >> the
> >> > API design of localKeyBy. But I think we should definitely more
> thoughts
> >> > about this topic.
> >> >
> >> > I also try to loop in Stephan for this discussion.
> >> >
> >> > Best,
> >> > Kurt
> >> >
> >> >
> >> > On Mon, Jun 24, 2019 at 3:26 PM vino yang <yanghua1...@gmail.com>
> >> wrote:
> >> >
> >> > > Hi all,
> >> > >
> >> > > I am happy we have a wonderful discussion and received many valuable
> >> > > opinions in the last few days.
> >> > >
> >> > > Now, let me try to summarize what we have reached consensus about
> the
> >> > > changes in the design.
> >> > >
> >> > > - provide a unified abstraction to support two kinds of
> >> > implementation;
> >> > > - reuse WindowOperator and try to enhance it so that we can make
> >> the
> >> > > intermediate result of the local aggregation can be buffered and
> >> > > flushed to
> >> > > support two kinds of implementation;
> >> > > - keep the API design of localKeyBy, but declare the disabled
> some
> >> > APIs
> >> > > we cannot support currently, and provide a configurable API for
> >> users
> >> > to
> >> > > choose how to handle intermediate result;
> >> > >
> >> > > The above three points have been updated in the design doc. Any
> >> > > questions, please let me know.
> >> > >
> >> > > @Aljoscha Krettek <aljos...@apache.org> What do you think? Any
> >> further
> >> > > comments?
> >> > >
> >> > > Best,
> >> > > Vino
> >> > >
> >> > > vino yang <yanghua1...@gmail.com> 于2019年6月20日周四 下午2:02写道：
> >> > >
> >> > > > Hi Kurt,
> >> > > >
> >> > > > Thanks for your comments.
> >> > > >
> >> > > > It seems we come to a consensus that we should alleviate the
> >> > performance
> >> > > > degraded by data skew with local aggregation. In this FLIP, our
> key
> >> > > > solution is to introduce local keyed partition to achieve this
> goal.
> >> > > >
> >> > > > I also agree that we can benefit a lot from the usage of
> >> > > > AggregateFunction. In combination with localKeyBy, We can easily
> >> use it
> >> > > to
> >> > > > achieve local aggregation:
> >> > > >
> >> > > > - input.localKeyBy(0).aggregate()
> >> > > > - input.localKeyBy(0).window().aggregate()
> >> > > >
> >> > > >
> >> > > > I think the only problem here is the choices between
> >> > > >
> >> > > > - (1) Introducing a new primitive called localKeyBy and
> implement
> >> > > > local aggregation with existing operators, or
> >> > > > - (2) Introducing an operator called localAggregation which is
> >> > > > composed of a key selector, a window-like operator, and an
> >> aggregate
> >> > > > function.
> >> > > >
> >> > > >
> >> > > > There may exist some optimization opportunities by providing a
> >> > composited
> >> > > > interface for local aggregation. But at the same time, in my
> >> opinion,
> >> > we
> >> > > > lose flexibility (Or we need certain efforts to achieve the same
> >> > > > flexibility).
> >> > > >
> >> > > > As said in the previous mails, we have many use cases where the
> >> > > > aggregation is very complicated and cannot be performed with
> >> > > > AggregateFunction. For example, users may perform windowed
> >> aggregations
> >> > > > according to time, data values, or even external storage.
> Typically,
> >> > they
> >> > > > now use KeyedProcessFunction or customized triggers to implement
> >> these
> >> > > > aggregations. It's not easy to address data skew in such cases
> with
> >> a
> >> > > > composited interface for local aggregation.
> >> > > >
> >> > > > Given that Data Stream API is exactly targeted at these cases
> where
> >> the
> >> > > > application logic is very complicated and optimization does not
> >> > matter, I
> >> > > > think it's a better choice to provide a relatively low-level and
> >> > > canonical
> >> > > > interface.
> >> > > >
> >> > > > The composited interface, on the other side, may be a good choice
> in
> >> > > > declarative interfaces, including SQL and Table API, as it allows
> >> more
> >> > > > optimization opportunities.
> >> > > >
> >> > > > Best,
> >> > > > Vino
> >> > > >
> >> > > >
> >> > > > Kurt Young <ykt...@gmail.com> 于2019年6月20日周四 上午10:15写道：
> >> > > >
> >> > > >> Hi all,
> >> > > >>
> >> > > >> As vino said in previous emails, I think we should first discuss
> >> and
> >> > > >> decide
> >> > > >> what kind of use cases this FLIP want to
> >> > > >> resolve, and what the API should look like. From my side, I think
> >> this
> >> > > is
> >> > > >> probably the root cause of current divergence.
> >> > > >>
> >> > > >> My understand is (from the FLIP title and motivation section of
> the
> >> > > >> document), we want to have a proper support of
> >> > > >> local aggregation, or pre aggregation. This is not a very new
> idea,
> >> > most
> >> > > >> SQL engine already did this improvement. And
> >> > > >> the core concept about this is, there should be an
> >> AggregateFunction,
> >> > no
> >> > > >> matter it's a Flink runtime's AggregateFunction or
> >> > > >> SQL's UserDefinedAggregateFunction. Both aggregation have concept
> >> of
> >> > > >> intermediate data type, sometimes we call it ACC.
> >> > > >> I quickly went through the POC piotr did before [1], it also
> >> directly
> >> > > uses
> >> > > >> AggregateFunction.
> >> > > >>
> >> > > >> But the thing is, after reading the design of this FLIP, I can't
> >> help
> >> > > >> myself feeling that this FLIP is not targeting to have a proper
> >> > > >> local aggregation support. It actually want to introduce another
> >> > > concept:
> >> > > >> LocalKeyBy, and how to split and merge local key groups,
> >> > > >> and how to properly support state on local key. Local aggregation
> >> just
> >> > > >> happened to be one possible use case of LocalKeyBy.
> >> > > >> But it lacks supporting the essential concept of local
> aggregation,
> >> > > which
> >> > > >> is intermediate data type. Without this, I really don't thing
> >> > > >> it is a good fit of local aggregation.
> >> > > >>
> >> > > >> Here I want to make sure of the scope or the goal about this
> FLIP,
> >> do
> >> > we
> >> > > >> want to have a proper local aggregation engine, or we
> >> > > >> just want to introduce a new concept called LocalKeyBy?
> >> > > >>
> >> > > >> [1]: https://github.com/apache/flink/pull/4626
> >> > > >>
> >> > > >> Best,
> >> > > >> Kurt
> >> > > >>
> >> > > >>
> >> > > >> On Wed, Jun 19, 2019 at 5:13 PM vino yang <yanghua1...@gmail.com
> >
> >> > > wrote:
> >> > > >>
> >> > > >> > Hi Hequn,
> >> > > >> >
> >> > > >> > Thanks for your comments!
> >> > > >> >
> >> > > >> > I agree that allowing local aggregation reusing window API and
> >> > > refining
> >> > > >> > window operator to make it match both requirements (come from
> our
> >> > and
> >> > > >> Kurt)
> >> > > >> > is a good decision!
> >> > > >> >
> >> > > >> > Concerning your questions:
> >> > > >> >
> >> > > >> > 1. The result of input.localKeyBy(0).sum(1).keyBy(0).sum(1) may
> >> be
> >> > > >> > meaningless.
> >> > > >> >
> >> > > >> > Yes, it does not make sense in most cases. However, I also want
> >> to
> >> > > note
> >> > > >> > users should know the right semantics of localKeyBy and use it
> >> > > >> correctly.
> >> > > >> > Because this issue also exists for the global keyBy, consider
> >> this
> >> > > >> example:
> >> > > >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is also
> >> > meaningless.
> >> > > >> >
> >> > > >> > 2. About the semantics of
> >> > > >> > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
> >> > > >> >
> >> > > >> > Good catch! I agree with you that it's not good to enable all
> >> > > >> > functionalities for localKeyBy from KeyedStream.
> >> > > >> > Currently, We do not support some APIs such as
> >> > > >> > connect/join/intervalJoin/coGroup. This is due to that we force
> >> the
> >> > > >> > operators on LocalKeyedStreams chained with the inputs.
> >> > > >> >
> >> > > >> > Best,
> >> > > >> > Vino
> >> > > >> >
> >> > > >> >
> >> > > >> > Hequn Cheng <chenghe...@gmail.com> 于2019年6月19日周三 下午3:42写道：
> >> > > >> >
> >> > > >> > > Hi,
> >> > > >> > >
> >> > > >> > > Thanks a lot for your great discussion and great to see that
> >> some
> >> > > >> > agreement
> >> > > >> > > has been reached on the "local aggregate engine"!
> >> > > >> > >
> >> > > >> > > ===> Considering the abstract engine,
> >> > > >> > > I'm thinking is it valuable for us to extend the current
> >> window to
> >> > > >> meet
> >> > > >> > > both demands raised by Kurt and Vino? There are some benefits
> >> we
> >> > can
> >> > > >> get:
> >> > > >> > >
> >> > > >> > > 1. The interfaces of the window are complete and clear. With
> >> > > windows,
> >> > > >> we
> >> > > >> > > can define a lot of ways to split the data and perform
> >> different
> >> > > >> > > computations.
> >> > > >> > > 2. We can also leverage the window to do miniBatch for the
> >> global
> >> > > >> > > aggregation, i.e, we can use the window to bundle data belong
> >> to
> >> > the
> >> > > >> same
> >> > > >> > > key, for every bundle we only need to read and write once
> >> state.
> >> > > This
> >> > > >> can
> >> > > >> > > greatly reduce state IO and improve performance.
> >> > > >> > > 3. A lot of other use cases can also benefit from the window
> >> base
> >> > on
> >> > > >> > memory
> >> > > >> > > or stateless.
> >> > > >> > >
> >> > > >> > > ===> As for the API,
> >> > > >> > > I think it is good to make our API more flexible. However, we
> >> may
> >> > > >> need to
> >> > > >> > > make our API meaningful.
> >> > > >> > >
> >> > > >> > > Take my previous reply as an example,
> >> > > >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The result may be
> >> > > >> > meaningless.
> >> > > >> > > Another example I find is the intervalJoin, e.g.,
> >> > > >> > > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In
> >> this
> >> > > >> case, it
> >> > > >> > > will bring problems if input1 and input2 share different
> >> > > parallelism.
> >> > > >> We
> >> > > >> > > don't know which input should the join chained with? Even if
> >> they
> >> > > >> share
> >> > > >> > the
> >> > > >> > > same parallelism, it's hard to tell what the join is doing.
> >> There
> >> > > are
> >> > > >> > maybe
> >> > > >> > > some other problems.
> >> > > >> > >
> >> > > >> > > From this point of view, it's at least not good to enable all
> >> > > >> > > functionalities for localKeyBy from KeyedStream?
> >> > > >> > >
> >> > > >> > > Great to also have your opinions.
> >> > > >> > >
> >> > > >> > > Best, Hequn
> >> > > >> > >
> >> > > >> > >
> >> > > >> > >
> >> > > >> > >
> >> > > >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <
> >> yanghua1...@gmail.com
> >> > >
> >> > > >> > wrote:
> >> > > >> > >
> >> > > >> > > > Hi Kurt and Piotrek,
> >> > > >> > > >
> >> > > >> > > > Thanks for your comments.
> >> > > >> > > >
> >> > > >> > > > I agree that we can provide a better abstraction to be
> >> > compatible
> >> > > >> with
> >> > > >> > > two
> >> > > >> > > > different implementations.
> >> > > >> > > >
> >> > > >> > > > First of all, I think we should consider what kind of
> >> scenarios
> >> > we
> >> > > >> need
> >> > > >> > > to
> >> > > >> > > > support in *API* level?
> >> > > >> > > >
> >> > > >> > > > We have some use cases which need to a customized
> aggregation
> >> > > >> through
> >> > > >> > > > KeyedProcessFunction, (in the usage of our
> localKeyBy.window
> >> > they
> >> > > >> can
> >> > > >> > use
> >> > > >> > > > ProcessWindowFunction).
> >> > > >> > > >
> >> > > >> > > > Shall we support these flexible use scenarios?
> >> > > >> > > >
> >> > > >> > > > Best,
> >> > > >> > > > Vino
> >> > > >> > > >
> >> > > >> > > > Kurt Young <ykt...@gmail.com> 于2019年6月18日周二 下午8:37写道：
> >> > > >> > > >
> >> > > >> > > > > Hi Piotr,
> >> > > >> > > > >
> >> > > >> > > > > Thanks for joining the discussion. Make “local
> aggregation"
> >> > > >> abstract
> >> > > >> > > > enough
> >> > > >> > > > > sounds good to me, we could
> >> > > >> > > > > implement and verify alternative solutions for use cases
> of
> >> > > local
> >> > > >> > > > > aggregation. Maybe we will find both solutions
> >> > > >> > > > > are appropriate for different scenarios.
> >> > > >> > > > >
> >> > > >> > > > > Starting from a simple one sounds a practical way to go.
> >> What
> >> > do
> >> > > >> you
> >> > > >> > > > think,
> >> > > >> > > > > vino?
> >> > > >> > > > >
> >> > > >> > > > > Best,
> >> > > >> > > > > Kurt
> >> > > >> > > > >
> >> > > >> > > > >
> >> > > >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
> >> > > >> pi...@ververica.com>
> >> > > >> > > > > wrote:
> >> > > >> > > > >
> >> > > >> > > > > > Hi Kurt and Vino,
> >> > > >> > > > > >
> >> > > >> > > > > > I think there is a trade of hat we need to consider for
> >> the
> >> > > >> local
> >> > > >> > > > > > aggregation.
> >> > > >> > > > > >
> >> > > >> > > > > > Generally speaking I would agree with Kurt about local
> >> > > >> > > aggregation/pre
> >> > > >> > > > > > aggregation not using Flink's state flush the operator
> >> on a
> >> > > >> > > checkpoint.
> >> > > >> > > > > > Network IO is usually cheaper compared to Disks IO.
> This
> >> has
> >> > > >> > however
> >> > > >> > > > > couple
> >> > > >> > > > > > of issues:
> >> > > >> > > > > > 1. It can explode number of in-flight records during
> >> > > checkpoint
> >> > > >> > > barrier
> >> > > >> > > > > > alignment, making checkpointing slower and decrease the
> >> > actual
> >> > > >> > > > > throughput.
> >> > > >> > > > > > 2. This trades Disks IO on the local aggregation
> machine
> >> > with
> >> > > >> CPU
> >> > > >> > > (and
> >> > > >> > > > > > Disks IO in case of RocksDB) on the final aggregation
> >> > machine.
> >> > > >> This
> >> > > >> > > is
> >> > > >> > > > > > fine, as long there is no huge data skew. If there is
> >> only a
> >> > > >> > handful
> >> > > >> > > > (or
> >> > > >> > > > > > even one single) hot keys, it might be better to keep
> the
> >> > > >> > persistent
> >> > > >> > > > > state
> >> > > >> > > > > > in the LocalAggregationOperator to offload final
> >> aggregation
> >> > > as
> >> > > >> > much
> >> > > >> > > as
> >> > > >> > > > > > possible.
> >> > > >> > > > > > 3. With frequent checkpointing local aggregation
> >> > effectiveness
> >> > > >> > would
> >> > > >> > > > > > degrade.
> >> > > >> > > > > >
> >> > > >> > > > > > I assume Kurt is correct, that in your use cases
> >> stateless
> >> > > >> operator
> >> > > >> > > was
> >> > > >> > > > > > behaving better, but I could easily see other use cases
> >> as
> >> > > well.
> >> > > >> > For
> >> > > >> > > > > > example someone is already using RocksDB, and his job
> is
> >> > > >> > bottlenecked
> >> > > >> > > > on
> >> > > >> > > > > a
> >> > > >> > > > > > single window operator instance because of the data
> >> skew. In
> >> > > >> that
> >> > > >> > > case
> >> > > >> > > > > > stateful local aggregation would be probably a better
> >> > choice.
> >> > > >> > > > > >
> >> > > >> > > > > > Because of that, I think we should eventually provide
> >> both
> >> > > >> versions
> >> > > >> > > and
> >> > > >> > > > > in
> >> > > >> > > > > > the initial version we should at least make the “local
> >> > > >> aggregation
> >> > > >> > > > > engine”
> >> > > >> > > > > > abstract enough, that one could easily provide
> different
> >> > > >> > > implementation
> >> > > >> > > > > > strategy.
> >> > > >> > > > > >
> >> > > >> > > > > > Piotrek
> >> > > >> > > > > >
> >> > > >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <
> ykt...@gmail.com
> >> >
> >> > > >> wrote:
> >> > > >> > > > > > >
> >> > > >> > > > > > > Hi,
> >> > > >> > > > > > >
> >> > > >> > > > > > > For the trigger, it depends on what operator we want
> to
> >> > use
> >> > > >> under
> >> > > >> > > the
> >> > > >> > > > > > API.
> >> > > >> > > > > > > If we choose to use window operator,
> >> > > >> > > > > > > we should also use window's trigger. However, I also
> >> think
> >> > > >> reuse
> >> > > >> > > > window
> >> > > >> > > > > > > operator for this scenario may not be
> >> > > >> > > > > > > the best choice. The reasons are the following:
> >> > > >> > > > > > >
> >> > > >> > > > > > > 1. As a lot of people already pointed out, window
> >> relies
> >> > > >> heavily
> >> > > >> > on
> >> > > >> > > > > state
> >> > > >> > > > > > > and it will definitely effect performance. You can
> >> > > >> > > > > > > argue that one can use heap based statebackend, but
> >> this
> >> > > will
> >> > > >> > > > introduce
> >> > > >> > > > > > > extra coupling. Especially we have a chance to
> >> > > >> > > > > > > design a pure stateless operator.
> >> > > >> > > > > > > 2. The window operator is *the most* complicated
> >> operator
> >> > > >> Flink
> >> > > >> > > > > currently
> >> > > >> > > > > > > have. Maybe we only need to pick a subset of
> >> > > >> > > > > > > window operator to achieve the goal, but once the
> user
> >> > wants
> >> > > >> to
> >> > > >> > > have
> >> > > >> > > > a
> >> > > >> > > > > > deep
> >> > > >> > > > > > > look at the localAggregation operator, it's still
> >> > > >> > > > > > > hard to find out what's going on under the window
> >> > operator.
> >> > > >> For
> >> > > >> > > > > > simplicity,
> >> > > >> > > > > > > I would also recommend we introduce a dedicated
> >> > > >> > > > > > > lightweight operator, which also much easier for a
> >> user to
> >> > > >> learn
> >> > > >> > > and
> >> > > >> > > > > use.
> >> > > >> > > > > > >
> >> > > >> > > > > > > For your question about increasing the burden in
> >> > > >> > > > > > > `StreamOperator::prepareSnapshotPreBarrier()`, the
> only
> >> > > thing
> >> > > >> > this
> >> > > >> > > > > > function
> >> > > >> > > > > > > need
> >> > > >> > > > > > > to do is output all the partial results, it's purely
> >> cpu
> >> > > >> > workload,
> >> > > >> > > > not
> >> > > >> > > > > > > introducing any IO. I want to point out that even if
> we
> >> > have
> >> > > >> this
> >> > > >> > > > > > > cost, we reduced another barrier align cost of the
> >> > operator,
> >> > > >> > which
> >> > > >> > > is
> >> > > >> > > > > the
> >> > > >> > > > > > > sync flush stage of the state, if you introduced
> state.
> >> > This
> >> > > >> > > > > > > flush actually will introduce disk IO, and I think
> it's
> >> > > >> worthy to
> >> > > >> > > > > > exchange
> >> > > >> > > > > > > this cost with purely CPU workload. And we do have
> some
> >> > > >> > > > > > > observations about these two behavior (as i said
> >> before,
> >> > we
> >> > > >> > > actually
> >> > > >> > > > > > > implemented both solutions), the stateless one
> actually
> >> > > >> performs
> >> > > >> > > > > > > better both in performance and barrier align time.
> >> > > >> > > > > > >
> >> > > >> > > > > > > Best,
> >> > > >> > > > > > > Kurt
> >> > > >> > > > > > >
> >> > > >> > > > > > >
> >> > > >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
> >> > > >> yanghua1...@gmail.com
> >> > > >> > >
> >> > > >> > > > > wrote:
> >> > > >> > > > > > >
> >> > > >> > > > > > >> Hi Kurt,
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> Thanks for your example. Now, it looks more clearly
> >> for
> >> > me.
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> From your example code snippet, I saw the
> >> localAggregate
> >> > > API
> >> > > >> has
> >> > > >> > > > three
> >> > > >> > > > > > >> parameters:
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> 1. key field
> >> > > >> > > > > > >> 2. PartitionAvg
> >> > > >> > > > > > >> 3. CountTrigger: Does this trigger comes from
> window
> >> > > >> package?
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> I will compare our and your design from API and
> >> operator
> >> > > >> level:
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> *From the API level:*
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> As I replied to @dianfu in the old email thread,[1]
> >> the
> >> > > >> Window
> >> > > >> > API
> >> > > >> > > > can
> >> > > >> > > > > > >> provide the second and the third parameter right
> now.
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> If you reuse specified interface or class, such as
> >> > > *Trigger*
> >> > > >> or
> >> > > >> > > > > > >> *CounterTrigger* provided by window package, but do
> >> not
> >> > use
> >> > > >> > window
> >> > > >> > > > > API,
> >> > > >> > > > > > >> it's not reasonable.
> >> > > >> > > > > > >> And if you do not reuse these interface or class,
> you
> >> > would
> >> > > >> need
> >> > > >> > > to
> >> > > >> > > > > > >> introduce more things however they are looked
> similar
> >> to
> >> > > the
> >> > > >> > > things
> >> > > >> > > > > > >> provided by window package.
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> The window package has provided several types of the
> >> > window
> >> > > >> and
> >> > > >> > > many
> >> > > >> > > > > > >> triggers and let users customize it. What's more,
> the
> >> > user
> >> > > is
> >> > > >> > more
> >> > > >> > > > > > familiar
> >> > > >> > > > > > >> with Window API.
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> This is the reason why we just provide localKeyBy
> API
> >> and
> >> > > >> reuse
> >> > > >> > > the
> >> > > >> > > > > > window
> >> > > >> > > > > > >> API. It reduces unnecessary components such as
> >> triggers
> >> > and
> >> > > >> the
> >> > > >> > > > > > mechanism
> >> > > >> > > > > > >> of buffer (based on count num or time).
> >> > > >> > > > > > >> And it has a clear and easy to understand semantics.
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> *From the operator level:*
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> We reused window operator, so we can get all the
> >> benefits
> >> > > >> from
> >> > > >> > > state
> >> > > >> > > > > and
> >> > > >> > > > > > >> checkpoint.
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> From your design, you named the operator under
> >> > > localAggregate
> >> > > >> > API
> >> > > >> > > > is a
> >> > > >> > > > > > >> *stateless* operator. IMO, it is still a state, it
> is
> >> > just
> >> > > >> not
> >> > > >> > > Flink
> >> > > >> > > > > > >> managed state.
> >> > > >> > > > > > >> About the memory buffer (I think it's still not very
> >> > clear,
> >> > > >> if
> >> > > >> > you
> >> > > >> > > > > have
> >> > > >> > > > > > >> time, can you give more detail information or answer
> >> my
> >> > > >> > > questions),
> >> > > >> > > > I
> >> > > >> > > > > > have
> >> > > >> > > > > > >> some questions:
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> - if it just a raw JVM heap memory buffer, how to
> >> > support
> >> > > >> > fault
> >> > > >> > > > > > >> tolerance, if the job is configured EXACTLY-ONCE
> >> > semantic
> >> > > >> > > > guarantee?
> >> > > >> > > > > > >> - if you thought the memory buffer(non-Flink
> state),
> >> > has
> >> > > >> > better
> >> > > >> > > > > > >> performance. In our design, users can also config
> >> HEAP
> >> > > >> state
> >> > > >> > > > backend
> >> > > >> > > > > > to
> >> > > >> > > > > > >> provide the performance close to your mechanism.
> >> > > >> > > > > > >> - `StreamOperator::prepareSnapshotPreBarrier()`
> >> related
> >> > > to
> >> > > >> the
> >> > > >> > > > > timing
> >> > > >> > > > > > of
> >> > > >> > > > > > >> snapshot. IMO, the flush action should be a
> >> > synchronized
> >> > > >> > action?
> >> > > >> > > > (if
> >> > > >> > > > > > >> not,
> >> > > >> > > > > > >> please point out my mistake) I still think we
> should
> >> > not
> >> > > >> > depend
> >> > > >> > > on
> >> > > >> > > > > the
> >> > > >> > > > > > >> timing of checkpoint. Checkpoint related
> operations
> >> are
> >> > > >> > inherent
> >> > > >> > > > > > >> performance sensitive, we should not increase its
> >> > burden
> >> > > >> > > anymore.
> >> > > >> > > > > Our
> >> > > >> > > > > > >> implementation based on the mechanism of Flink's
> >> > > >> checkpoint,
> >> > > >> > > which
> >> > > >> > > > > can
> >> > > >> > > > > > >> benefit from the asnyc snapshot and incremental
> >> > > checkpoint.
> >> > > >> > IMO,
> >> > > >> > > > the
> >> > > >> > > > > > >> performance is not a problem, and we also do not
> >> find
> >> > the
> >> > > >> > > > > performance
> >> > > >> > > > > > >> issue
> >> > > >> > > > > > >> in our production.
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> [1]:
> >> > > >> > > > > > >>
> >> > > >> > > > > > >>
> >> > > >> > > > > >
> >> > > >> > > > >
> >> > > >> > > >
> >> > > >> > >
> >> > > >> >
> >> > > >>
> >> > >
> >> >
> >>
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> Best,
> >> > > >> > > > > > >> Vino
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> Kurt Young <ykt...@gmail.com> 于2019年6月18日周二
> 下午2:27写道：
> >> > > >> > > > > > >>
> >> > > >> > > > > > >>> Yeah, sorry for not expressing myself clearly. I
> will
> >> > try
> >> > > to
> >> > > >> > > > provide
> >> > > >> > > > > > more
> >> > > >> > > > > > >>> details to make sure we are on the same page.
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> For DataStream API, it shouldn't be optimized
> >> > > automatically.
> >> > > >> > You
> >> > > >> > > > have
> >> > > >> > > > > > to
> >> > > >> > > > > > >>> explicitly call API to do local aggregation
> >> > > >> > > > > > >>> as well as the trigger policy of the local
> >> aggregation.
> >> > > Take
> >> > > >> > > > average
> >> > > >> > > > > > for
> >> > > >> > > > > > >>> example, the user program may look like this (just
> a
> >> > > draft):
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> assuming the input type is DataStream<Tupl2<String,
> >> > Int>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> ds.localAggregate(
> >> > > >> > > > > > >>> 0, //
> >> The
> >> > > local
> >> > > >> > key,
> >> > > >> > > > > which
> >> > > >> > > > > > >> is
> >> > > >> > > > > > >>> the String from Tuple2
> >> > > >> > > > > > >>> PartitionAvg(1), // The
> >> partial
> >> > > >> > > aggregation
> >> > > >> > > > > > >>> function, produces Tuple2<Long, Int>, indicating
> sum
> >> and
> >> > > >> count
> >> > > >> > > > > > >>> CountTrigger.of(1000L) // Trigger policy,
> >> note
> >> > > >> this
> >> > > >> > > > should
> >> > > >> > > > > be
> >> > > >> > > > > > >>> best effort, and also be composited with time based
> >> or
> >> > > >> memory
> >> > > >> > > size
> >> > > >> > > > > > based
> >> > > >> > > > > > >>> trigger
> >> > > >> > > > > > >>> ) //
> The
> >> > > return
> >> > > >> > type
> >> > > >> > > > is
> >> > > >> > > > > > >> local
> >> > > >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
> >> > > >> > > > > > >>> .keyBy(0) // Further
> >> > keyby
> >> > > it
> >> > > >> > with
> >> > > >> > > > > > >> required
> >> > > >> > > > > > >>> key
> >> > > >> > > > > > >>> .aggregate(1) // This will
> >> merge
> >> > > all
> >> > > >> > the
> >> > > >> > > > > > partial
> >> > > >> > > > > > >>> results and get the final average.
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> (This is only a draft, only trying to explain what
> it
> >> > > looks
> >> > > >> > > like. )
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> The local aggregate operator can be stateless, we
> can
> >> > > keep a
> >> > > >> > > memory
> >> > > >> > > > > > >> buffer
> >> > > >> > > > > > >>> or other efficient data structure to improve the
> >> > aggregate
> >> > > >> > > > > performance.
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> Let me know if you have any other questions.
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> Best,
> >> > > >> > > > > > >>> Kurt
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
> >> > > >> > yanghua1...@gmail.com
> >> > > >> > > >
> >> > > >> > > > > > wrote:
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>>> Hi Kurt,
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> Thanks for your reply.
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> Actually, I am not against you to raise your
> design.
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> From your description before, I just can imagine
> >> your
> >> > > >> > high-level
> >> > > >> > > > > > >>>> implementation is about SQL and the optimization
> is
> >> > inner
> >> > > >> of
> >> > > >> > the
> >> > > >> > > > > API.
> >> > > >> > > > > > >> Is
> >> > > >> > > > > > >>> it
> >> > > >> > > > > > >>>> automatically? how to give the configuration
> option
> >> > about
> >> > > >> > > trigger
> >> > > >> > > > > > >>>> pre-aggregation?
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> Maybe after I get more information, it sounds more
> >> > > >> reasonable.
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> IMO, first of all, it would be better to make your
> >> user
> >> > > >> > > interface
> >> > > >> > > > > > >>> concrete,
> >> > > >> > > > > > >>>> it's the basis of the discussion.
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> For example, can you give an example code snippet
> to
> >> > > >> introduce
> >> > > >> > > how
> >> > > >> > > > > to
> >> > > >> > > > > > >>> help
> >> > > >> > > > > > >>>> users to process data skew caused by the jobs
> which
> >> > built
> >> > > >> with
> >> > > >> > > > > > >> DataStream
> >> > > >> > > > > > >>>> API?
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> If you give more details we can discuss further
> >> more. I
> >> > > >> think
> >> > > >> > if
> >> > > >> > > > one
> >> > > >> > > > > > >>> design
> >> > > >> > > > > > >>>> introduces an exact interface and another does
> not.
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> The implementation has an obvious difference. For
> >> > > example,
> >> > > >> we
> >> > > >> > > > > > introduce
> >> > > >> > > > > > >>> an
> >> > > >> > > > > > >>>> exact API in DataStream named localKeyBy, about
> the
> >> > > >> > > > pre-aggregation
> >> > > >> > > > > we
> >> > > >> > > > > > >>> need
> >> > > >> > > > > > >>>> to define the trigger mechanism of local
> >> aggregation,
> >> > so
> >> > > we
> >> > > >> > find
> >> > > >> > > > > > reused
> >> > > >> > > > > > >>>> window API and operator is a good choice. This is
> a
> >> > > >> reasoning
> >> > > >> > > link
> >> > > >> > > > > > from
> >> > > >> > > > > > >>>> design to implementation.
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> What do you think?
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> Best,
> >> > > >> > > > > > >>>> Vino
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> Kurt Young <ykt...@gmail.com> 于2019年6月18日周二
> >> 上午11:58写道：
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>>> Hi Vino,
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>> Now I feel that we may have different
> >> understandings
> >> > > about
> >> > > >> > what
> >> > > >> > > > > kind
> >> > > >> > > > > > >> of
> >> > > >> > > > > > >>>>> problems or improvements you want to
> >> > > >> > > > > > >>>>> resolve. Currently, most of the feedback are
> >> focusing
> >> > on
> >> > > >> *how
> >> > > >> > > to
> >> > > >> > > > > do a
> >> > > >> > > > > > >>>>> proper local aggregation to improve performance
> >> > > >> > > > > > >>>>> and maybe solving the data skew issue*. And my
> gut
> >> > > >> feeling is
> >> > > >> > > > this
> >> > > >> > > > > is
> >> > > >> > > > > > >>>>> exactly what users want at the first place,
> >> > > >> > > > > > >>>>> especially those +1s. (Sorry to try to summarize
> >> here,
> >> > > >> please
> >> > > >> > > > > correct
> >> > > >> > > > > > >>> me
> >> > > >> > > > > > >>>> if
> >> > > >> > > > > > >>>>> i'm wrong).
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>> But I still think the design is somehow diverged
> >> from
> >> > > the
> >> > > >> > goal.
> >> > > >> > > > If
> >> > > >> > > > > we
> >> > > >> > > > > > >>>> want
> >> > > >> > > > > > >>>>> to have an efficient and powerful way to
> >> > > >> > > > > > >>>>> have local aggregation, supporting intermedia
> >> result
> >> > > type
> >> > > >> is
> >> > > >> > > > > > >> essential
> >> > > >> > > > > > >>>> IMO.
> >> > > >> > > > > > >>>>> Both runtime's `AggregateFunction` and
> >> > > >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction` have a
> proper
> >> > > >> support of
> >> > > >> > > > > > >>>> intermediate
> >> > > >> > > > > > >>>>> result type and can do `merge` operation
> >> > > >> > > > > > >>>>> on them.
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>> Now, we have a lightweight alternatives which
> >> performs
> >> > > >> well,
> >> > > >> > > and
> >> > > >> > > > > > >> have a
> >> > > >> > > > > > >>>>> nice fit with the local aggregate requirements.
> >> > > >> > > > > > >>>>> Mostly importantly, it's much less complex
> because
> >> > it's
> >> > > >> > > > stateless.
> >> > > >> > > > > > >> And
> >> > > >> > > > > > >>>> it
> >> > > >> > > > > > >>>>> can also achieve the similar multiple-aggregation
> >> > > >> > > > > > >>>>> scenario.
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>> I still not convinced why we shouldn't consider
> it
> >> as
> >> > a
> >> > > >> first
> >> > > >> > > > step.
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>> Best,
> >> > > >> > > > > > >>>>> Kurt
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino yang <
> >> > > >> > > > yanghua1...@gmail.com>
> >> > > >> > > > > > >>>> wrote:
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>>> Hi Kurt,
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> Thanks for your comments.
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> It seems we both implemented local aggregation
> >> > feature
> >> > > to
> >> > > >> > > > optimize
> >> > > >> > > > > > >>> the
> >> > > >> > > > > > >>>>>> issue of data skew.
> >> > > >> > > > > > >>>>>> However, IMHO, the API level of optimizing
> >> revenue is
> >> > > >> > > different.
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> *Your optimization benefits from Flink SQL and
> >> it's
> >> > not
> >> > > >> > user's
> >> > > >> > > > > > >>>> faces.(If
> >> > > >> > > > > > >>>>> I
> >> > > >> > > > > > >>>>>> understand it incorrectly, please correct
> this.)*
> >> > > >> > > > > > >>>>>> *Our implementation employs it as an
> optimization
> >> > tool
> >> > > >> API
> >> > > >> > for
> >> > > >> > > > > > >>>>> DataStream,
> >> > > >> > > > > > >>>>>> it just like a local version of the keyBy API.*
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> Based on this, I want to say support it as a
> >> > DataStream
> >> > > >> API
> >> > > >> > > can
> >> > > >> > > > > > >>> provide
> >> > > >> > > > > > >>>>>> these advantages:
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> - The localKeyBy API has a clear semantic and
> >> it's
> >> > > >> > flexible
> >> > > >> > > > not
> >> > > >> > > > > > >>> only
> >> > > >> > > > > > >>>>> for
> >> > > >> > > > > > >>>>>> processing data skew but also for implementing
> >> some
> >> > > >> user
> >> > > >> > > > cases,
> >> > > >> > > > > > >>> for
> >> > > >> > > > > > >>>>>> example, if we want to calculate the
> >> multiple-level
> >> > > >> > > > aggregation,
> >> > > >> > > > > > >>> we
> >> > > >> > > > > > >>>>> can
> >> > > >> > > > > > >>>>>> do
> >> > > >> > > > > > >>>>>> multiple-level aggregation in the local
> >> > aggregation:
> >> > > >> > > > > > >>>>>>
> >> > > input.localKeyBy("a").sum(1).localKeyBy("b").window();
> >> > > >> //
> >> > > >> > > here
> >> > > >> > > > > > >> "a"
> >> > > >> > > > > > >>>> is
> >> > > >> > > > > > >>>>> a
> >> > > >> > > > > > >>>>>> sub-category, while "b" is a category, here we
> >> do
> >> > not
> >> > > >> need
> >> > > >> > > to
> >> > > >> > > > > > >>>> shuffle
> >> > > >> > > > > > >>>>>> data
> >> > > >> > > > > > >>>>>> in the network.
> >> > > >> > > > > > >>>>>> - The users of DataStream API will benefit
> from
> >> > this.
> >> > > >> > > > Actually,
> >> > > >> > > > > > >> we
> >> > > >> > > > > > >>>>> have
> >> > > >> > > > > > >>>>>> a lot of scenes need to use DataStream API.
> >> > > Currently,
> >> > > >> > > > > > >> DataStream
> >> > > >> > > > > > >>>> API
> >> > > >> > > > > > >>>>> is
> >> > > >> > > > > > >>>>>> the cornerstone of the physical plan of Flink
> >> SQL.
> >> > > >> With a
> >> > > >> > > > > > >>> localKeyBy
> >> > > >> > > > > > >>>>>> API,
> >> > > >> > > > > > >>>>>> the optimization of SQL at least may use this
> >> > > optimized
> >> > > >> > API,
> >> > > >> > > > > > >> this
> >> > > >> > > > > > >>>> is a
> >> > > >> > > > > > >>>>>> further topic.
> >> > > >> > > > > > >>>>>> - Based on the window operator, our state
> would
> >> > > benefit
> >> > > >> > from
> >> > > >> > > > > > >> Flink
> >> > > >> > > > > > >>>>> State
> >> > > >> > > > > > >>>>>> and checkpoint, we do not need to worry about
> >> OOM
> >> > and
> >> > > >> job
> >> > > >> > > > > > >> failed.
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> Now, about your questions:
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> 1. About our design cannot change the data type
> >> and
> >> > > about
> >> > > >> > the
> >> > > >> > > > > > >>>>>> implementation of average:
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> Just like my reply to Hequn, the localKeyBy is
> an
> >> API
> >> > > >> > provides
> >> > > >> > > > to
> >> > > >> > > > > > >> the
> >> > > >> > > > > > >>>>> users
> >> > > >> > > > > > >>>>>> who use DataStream API to build their jobs.
> >> > > >> > > > > > >>>>>> Users should know its semantics and the
> difference
> >> > with
> >> > > >> > keyBy
> >> > > >> > > > API,
> >> > > >> > > > > > >> so
> >> > > >> > > > > > >>>> if
> >> > > >> > > > > > >>>>>> they want to the average aggregation, they
> should
> >> > carry
> >> > > >> > local
> >> > > >> > > > sum
> >> > > >> > > > > > >>>> result
> >> > > >> > > > > > >>>>>> and local count result.
> >> > > >> > > > > > >>>>>> I admit that it will be convenient to use keyBy
> >> > > directly.
> >> > > >> > But
> >> > > >> > > we
> >> > > >> > > > > > >> need
> >> > > >> > > > > > >>>> to
> >> > > >> > > > > > >>>>>> pay a little price when we get some benefits. I
> >> think
> >> > > >> this
> >> > > >> > > price
> >> > > >> > > > > is
> >> > > >> > > > > > >>>>>> reasonable. Considering that the DataStream API
> >> > itself
> >> > > >> is a
> >> > > >> > > > > > >> low-level
> >> > > >> > > > > > >>>> API
> >> > > >> > > > > > >>>>>> (at least for now).
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> 2. About stateless operator and
> >> > > >> > > > > > >>>>>> `StreamOperator::prepareSnapshotPreBarrier()`:
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> Actually, I have discussed this opinion with
> >> @dianfu
> >> > in
> >> > > >> the
> >> > > >> > > old
> >> > > >> > > > > > >> mail
> >> > > >> > > > > > >>>>>> thread. I will copy my opinion from there:
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> - for your design, you still need somewhere to
> >> give
> >> > > the
> >> > > >> > > users
> >> > > >> > > > > > >>>>> configure
> >> > > >> > > > > > >>>>>> the trigger threshold (maybe memory
> >> availability?),
> >> > > >> this
> >> > > >> > > > design
> >> > > >> > > > > > >>>> cannot
> >> > > >> > > > > > >>>>>> guarantee a deterministic semantics (it will
> >> bring
> >> > > >> trouble
> >> > > >> > > for
> >> > > >> > > > > > >>>> testing
> >> > > >> > > > > > >>>>>> and
> >> > > >> > > > > > >>>>>> debugging).
> >> > > >> > > > > > >>>>>> - if the implementation depends on the timing
> of
> >> > > >> > checkpoint,
> >> > > >> > > > it
> >> > > >> > > > > > >>>> would
> >> > > >> > > > > > >>>>>> affect the checkpoint's progress, and the
> >> buffered
> >> > > data
> >> > > >> > may
> >> > > >> > > > > > >> cause
> >> > > >> > > > > > >>>> OOM
> >> > > >> > > > > > >>>>>> issue. In addition, if the operator is
> >> stateless,
> >> > it
> >> > > >> can
> >> > > >> > not
> >> > > >> > > > > > >>> provide
> >> > > >> > > > > > >>>>>> fault
> >> > > >> > > > > > >>>>>> tolerance.
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> Best,
> >> > > >> > > > > > >>>>>> Vino
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> Kurt Young <ykt...@gmail.com> 于2019年6月18日周二
> >> > 上午9:22写道：
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>>> Hi Vino,
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>> Thanks for the proposal, I like the general
> idea
> >> and
> >> > > IMO
> >> > > >> > it's
> >> > > >> > > > > > >> very
> >> > > >> > > > > > >>>>> useful
> >> > > >> > > > > > >>>>>>> feature.
> >> > > >> > > > > > >>>>>>> But after reading through the document, I feel
> >> that
> >> > we
> >> > > >> may
> >> > > >> > > over
> >> > > >> > > > > > >>>> design
> >> > > >> > > > > > >>>>>> the
> >> > > >> > > > > > >>>>>>> required
> >> > > >> > > > > > >>>>>>> operator for proper local aggregation. The main
> >> > reason
> >> > > >> is
> >> > > >> > we
> >> > > >> > > > want
> >> > > >> > > > > > >>> to
> >> > > >> > > > > > >>>>>> have a
> >> > > >> > > > > > >>>>>>> clear definition and behavior about the "local
> >> keyed
> >> > > >> state"
> >> > > >> > > > which
> >> > > >> > > > > > >>> in
> >> > > >> > > > > > >>>> my
> >> > > >> > > > > > >>>>>>> opinion is not
> >> > > >> > > > > > >>>>>>> necessary for local aggregation, at least for
> >> start.
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>> Another issue I noticed is the local key by
> >> operator
> >> > > >> cannot
> >> > > >> > > > > > >> change
> >> > > >> > > > > > >>>>>> element
> >> > > >> > > > > > >>>>>>> type, it will
> >> > > >> > > > > > >>>>>>> also restrict a lot of use cases which can be
> >> > benefit
> >> > > >> from
> >> > > >> > > > local
> >> > > >> > > > > > >>>>>>> aggregation, like "average".
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>> We also did similar logic in SQL and the only
> >> thing
> >> > > >> need to
> >> > > >> > > be
> >> > > >> > > > > > >> done
> >> > > >> > > > > > >>>> is
> >> > > >> > > > > > >>>>>>> introduce
> >> > > >> > > > > > >>>>>>> a stateless lightweight operator which is
> >> *chained*
> >> > > >> before
> >> > > >> > > > > > >>> `keyby()`.
> >> > > >> > > > > > >>>>> The
> >> > > >> > > > > > >>>>>>> operator will flush all buffered
> >> > > >> > > > > > >>>>>>> elements during
> >> > > >> > `StreamOperator::prepareSnapshotPreBarrier()`
> >> > > >> > > > and
> >> > > >> > > > > > >>>> make
> >> > > >> > > > > > >>>>>>> himself stateless.
> >> > > >> > > > > > >>>>>>> By the way, in the earlier version we also did
> >> the
> >> > > >> similar
> >> > > >> > > > > > >> approach
> >> > > >> > > > > > >>>> by
> >> > > >> > > > > > >>>>>>> introducing a stateful
> >> > > >> > > > > > >>>>>>> local aggregation operator but it's not
> >> performed as
> >> > > >> well
> >> > > >> > as
> >> > > >> > > > the
> >> > > >> > > > > > >>>> later
> >> > > >> > > > > > >>>>>> one,
> >> > > >> > > > > > >>>>>>> and also effect the barrie
> >> > > >> > > > > > >>>>>>> alignment time. The later one is fairly simple
> >> and
> >> > > more
> >> > > >> > > > > > >> efficient.
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>> I would highly suggest you to consider to have
> a
> >> > > >> stateless
> >> > > >> > > > > > >> approach
> >> > > >> > > > > > >>>> at
> >> > > >> > > > > > >>>>>> the
> >> > > >> > > > > > >>>>>>> first step.
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>> Best,
> >> > > >> > > > > > >>>>>>> Kurt
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark Wu <
> >> > > >> imj...@gmail.com>
> >> > > >> > > > > > >> wrote:
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>>> Hi Vino,
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>> Thanks for the proposal.
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>> Regarding to the "input.keyBy(0).sum(1)" vs
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
> >> > > >> > > > > > >> have
> >> > > >> > > > > > >>>> you
> >> > > >> > > > > > >>>>>>> done
> >> > > >> > > > > > >>>>>>>> some benchmark?
> >> > > >> > > > > > >>>>>>>> Because I'm curious about how much performance
> >> > > >> improvement
> >> > > >> > > can
> >> > > >> > > > > > >> we
> >> > > >> > > > > > >>>> get
> >> > > >> > > > > > >>>>>> by
> >> > > >> > > > > > >>>>>>>> using count window as the local operator.
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>> Best,
> >> > > >> > > > > > >>>>>>>> Jark
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino yang <
> >> > > >> > > > yanghua1...@gmail.com
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>>>> wrote:
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>>> Hi Hequn,
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> Thanks for your reply.
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to provide a
> >> tool
> >> > > >> which
> >> > > >> > > can
> >> > > >> > > > > > >>> let
> >> > > >> > > > > > >>>>>> users
> >> > > >> > > > > > >>>>>>> do
> >> > > >> > > > > > >>>>>>>>> pre-aggregation in the local. The behavior of
> >> the
> >> > > >> > > > > > >>> pre-aggregation
> >> > > >> > > > > > >>>>> is
> >> > > >> > > > > > >>>>>>>>> similar to keyBy API.
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> So the three cases are different, I will
> >> describe
> >> > > them
> >> > > >> > one
> >> > > >> > > by
> >> > > >> > > > > > >>>> one:
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> *In this case, the result is event-driven,
> each
> >> > > event
> >> > > >> can
> >> > > >> > > > > > >>> produce
> >> > > >> > > > > > >>>>> one
> >> > > >> > > > > > >>>>>>> sum
> >> > > >> > > > > > >>>>>>>>> aggregation result and it is the latest one
> >> from
> >> > the
> >> > > >> > source
> >> > > >> > > > > > >>>> start.*
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> *In this case, the semantic may have a
> >> problem, it
> >> > > >> would
> >> > > >> > do
> >> > > >> > > > > > >> the
> >> > > >> > > > > > >>>>> local
> >> > > >> > > > > > >>>>>>> sum
> >> > > >> > > > > > >>>>>>>>> aggregation and will produce the latest
> partial
> >> > > result
> >> > > >> > from
> >> > > >> > > > > > >> the
> >> > > >> > > > > > >>>>>> source
> >> > > >> > > > > > >>>>>>>>> start for every event. *
> >> > > >> > > > > > >>>>>>>>> *These latest partial results from the same
> key
> >> > are
> >> > > >> > hashed
> >> > > >> > > to
> >> > > >> > > > > > >>> one
> >> > > >> > > > > > >>>>>> node
> >> > > >> > > > > > >>>>>>> to
> >> > > >> > > > > > >>>>>>>>> do the global sum aggregation.*
> >> > > >> > > > > > >>>>>>>>> *In the global aggregation, when it received
> >> > > multiple
> >> > > >> > > partial
> >> > > >> > > > > > >>>>> results
> >> > > >> > > > > > >>>>>>>> (they
> >> > > >> > > > > > >>>>>>>>> are all calculated from the source start) and
> >> sum
> >> > > them
> >> > > >> > will
> >> > > >> > > > > > >> get
> >> > > >> > > > > > >>>> the
> >> > > >> > > > > > >>>>>>> wrong
> >> > > >> > > > > > >>>>>>>>> result.*
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> 3.
> >> > > >> > > input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> *In this case, it would just get a partial
> >> > > aggregation
> >> > > >> > > result
> >> > > >> > > > > > >>> for
> >> > > >> > > > > > >>>>>> the 5
> >> > > >> > > > > > >>>>>>>>> records in the count window. The partial
> >> > aggregation
> >> > > >> > > results
> >> > > >> > > > > > >>> from
> >> > > >> > > > > > >>>>> the
> >> > > >> > > > > > >>>>>>>> same
> >> > > >> > > > > > >>>>>>>>> key will be aggregated globally.*
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> So the first case and the third case can get
> >> the
> >> > > >> *same*
> >> > > >> > > > > > >> result,
> >> > > >> > > > > > >>>> the
> >> > > >> > > > > > >>>>>>>>> difference is the output-style and the
> latency.
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> Generally speaking, the local key API is just
> >> an
> >> > > >> > > optimization
> >> > > >> > > > > > >>>> API.
> >> > > >> > > > > > >>>>> We
> >> > > >> > > > > > >>>>>>> do
> >> > > >> > > > > > >>>>>>>>> not limit the user's usage, but the user has
> to
> >> > > >> > understand
> >> > > >> > > > > > >> its
> >> > > >> > > > > > >>>>>>> semantics
> >> > > >> > > > > > >>>>>>>>> and use it correctly.
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> Best,
> >> > > >> > > > > > >>>>>>>>> Vino
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> Hequn Cheng <chenghe...@gmail.com>
> >> 于2019年6月17日周一
> >> > > >> > 下午4:18写道：
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>> Hi Vino,
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>> Thanks for the proposal, I think it is a
> very
> >> > good
> >> > > >> > > feature!
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>> One thing I want to make sure is the
> semantics
> >> > for
> >> > > >> the
> >> > > >> > > > > > >>>>>> `localKeyBy`.
> >> > > >> > > > > > >>>>>>>> From
> >> > > >> > > > > > >>>>>>>>>> the document, the `localKeyBy` API returns
> an
> >> > > >> instance
> >> > > >> > of
> >> > > >> > > > > > >>>>>>> `KeyedStream`
> >> > > >> > > > > > >>>>>>>>>> which can also perform sum(), so in this
> case,
> >> > > what's
> >> > > >> > the
> >> > > >> > > > > > >>>>> semantics
> >> > > >> > > > > > >>>>>>> for
> >> > > >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will the
> >> following
> >> > > code
> >> > > >> > share
> >> > > >> > > > > > >>> the
> >> > > >> > > > > > >>>>> same
> >> > > >> > > > > > >>>>>>>>> result?
> >> > > >> > > > > > >>>>>>>>>> and what're the differences between them?
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
> >> > > >> > > > > > >>>>>>>>>> 2.
> input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> >> > > >> > > > > > >>>>>>>>>> 3.
> >> > > >> > > > > > >>
> >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>> Would also be great if we can add this into
> >> the
> >> > > >> > document.
> >> > > >> > > > > > >>> Thank
> >> > > >> > > > > > >>>>> you
> >> > > >> > > > > > >>>>>>>> very
> >> > > >> > > > > > >>>>>>>>>> much.
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>> Best, Hequn
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM vino yang <
> >> > > >> > > > > > >>>>> yanghua1...@gmail.com>
> >> > > >> > > > > > >>>>>>>>> wrote:
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>> Hi Aljoscha,
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>> I have looked at the "*Process*" section of
> >> FLIP
> >> > > >> wiki
> >> > > >> > > > > > >>>> page.[1]
> >> > > >> > > > > > >>>>>> This
> >> > > >> > > > > > >>>>>>>>> mail
> >> > > >> > > > > > >>>>>>>>>>> thread indicates that it has proceeded to
> the
> >> > > third
> >> > > >> > step.
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>> When I looked at the fourth step(vote
> step),
> >> I
> >> > > >> didn't
> >> > > >> > > > > > >> find
> >> > > >> > > > > > >>>> the
> >> > > >> > > > > > >>>>>>>>>>> prerequisites for starting the voting
> >> process.
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>> Considering that the discussion of this
> >> feature
> >> > > has
> >> > > >> > been
> >> > > >> > > > > > >>> done
> >> > > >> > > > > > >>>>> in
> >> > > >> > > > > > >>>>>>> the
> >> > > >> > > > > > >>>>>>>>> old
> >> > > >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me when should
> I
> >> > start
> >> > > >> > > > > > >> voting?
> >> > > >> > > > > > >>>> Can
> >> > > >> > > > > > >>>>> I
> >> > > >> > > > > > >>>>>>>> start
> >> > > >> > > > > > >>>>>>>>>> now?
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>> Best,
> >> > > >> > > > > > >>>>>>>>>>> Vino
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>> [1]:
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>
> >> > > >> > > > > >
> >> > > >> > > > >
> >> > > >> > > >
> >> > > >> > >
> >> > > >> >
> >> > > >>
> >> > >
> >> >
> >>
> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
> >> > > >> > > > > > >>>>>>>>>>> [2]:
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>
> >> > > >> > > > > >
> >> > > >> > > > >
> >> > > >> > > >
> >> > > >> > >
> >> > > >> >
> >> > > >>
> >> > >
> >> >
> >>
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>> leesf <leesf0...@gmail.com> 于2019年6月13日周四
> >> > > 上午9:19写道：
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for your
> >> efforts.
> >> > > >> > > > > > >>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>> Best,
> >> > > >> > > > > > >>>>>>>>>>>> Leesf
> >> > > >> > > > > > >>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>> vino yang <yanghua1...@gmail.com>
> >> > 于2019年6月12日周三
> >> > > >> > > > > > >>> 下午5:46写道：
> >> > > >> > > > > > >>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> Hi folks,
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP discussion
> >> > thread
> >> > > >> > > > > > >> about
> >> > > >> > > > > > >>>>>>> supporting
> >> > > >> > > > > > >>>>>>>>>> local
> >> > > >> > > > > > >>>>>>>>>>>>> aggregation in Flink.
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> In short, this feature can effectively
> >> > alleviate
> >> > > >> data
> >> > > >> > > > > > >>>> skew.
> >> > > >> > > > > > >>>>>>> This
> >> > > >> > > > > > >>>>>>>> is
> >> > > >> > > > > > >>>>>>>>>> the
> >> > > >> > > > > > >>>>>>>>>>>>> FLIP:
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>
> >> > > >> > > > > >
> >> > > >> > > > >
> >> > > >> > > >
> >> > > >> > >
> >> > > >> >
> >> > > >>
> >> > >
> >> >
> >>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are widely used
> to
> >> > > >> perform
> >> > > >> > > > > > >>>>>> aggregating
> >> > > >> > > > > > >>>>>>>>>>>> operations
> >> > > >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) on the
> >> elements
> >> > > >> that
> >> > > >> > > > > > >>> have
> >> > > >> > > > > > >>>>> the
> >> > > >> > > > > > >>>>>>> same
> >> > > >> > > > > > >>>>>>>>>> key.
> >> > > >> > > > > > >>>>>>>>>>>> When
> >> > > >> > > > > > >>>>>>>>>>>>> executed at runtime, the elements with
> the
> >> > same
> >> > > >> key
> >> > > >> > > > > > >>> will
> >> > > >> > > > > > >>>> be
> >> > > >> > > > > > >>>>>>> sent
> >> > > >> > > > > > >>>>>>>> to
> >> > > >> > > > > > >>>>>>>>>> and
> >> > > >> > > > > > >>>>>>>>>>>>> aggregated by the same task.
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> The performance of these aggregating
> >> > operations
> >> > > is
> >> > > >> > > > > > >> very
> >> > > >> > > > > > >>>>>>> sensitive
> >> > > >> > > > > > >>>>>>>>> to
> >> > > >> > > > > > >>>>>>>>>>> the
> >> > > >> > > > > > >>>>>>>>>>>>> distribution of keys. In the cases where
> >> the
> >> > > >> > > > > > >>> distribution
> >> > > >> > > > > > >>>>> of
> >> > > >> > > > > > >>>>>>> keys
> >> > > >> > > > > > >>>>>>>>>>>> follows a
> >> > > >> > > > > > >>>>>>>>>>>>> powerful law, the performance will be
> >> > > >> significantly
> >> > > >> > > > > > >>>>>> downgraded.
> >> > > >> > > > > > >>>>>>>>> More
> >> > > >> > > > > > >>>>>>>>>>>>> unluckily, increasing the degree of
> >> > parallelism
> >> > > >> does
> >> > > >> > > > > > >>> not
> >> > > >> > > > > > >>>>> help
> >> > > >> > > > > > >>>>>>>> when
> >> > > >> > > > > > >>>>>>>>> a
> >> > > >> > > > > > >>>>>>>>>>> task
> >> > > >> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> Local aggregation is a widely-adopted
> >> method
> >> > to
> >> > > >> > > > > > >> reduce
> >> > > >> > > > > > >>>> the
> >> > > >> > > > > > >>>>>>>>>> performance
> >> > > >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can decompose
> the
> >> > > >> > > > > > >> aggregating
> >> > > >> > > > > > >>>>>>>> operations
> >> > > >> > > > > > >>>>>>>>>> into
> >> > > >> > > > > > >>>>>>>>>>>> two
> >> > > >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we aggregate
> >> the
> >> > > >> elements
> >> > > >> > > > > > >>> of
> >> > > >> > > > > > >>>>> the
> >> > > >> > > > > > >>>>>>> same
> >> > > >> > > > > > >>>>>>>>> key
> >> > > >> > > > > > >>>>>>>>>>> at
> >> > > >> > > > > > >>>>>>>>>>>>> the sender side to obtain partial
> results.
> >> > Then
> >> > > at
> >> > > >> > > > > > >> the
> >> > > >> > > > > > >>>>> second
> >> > > >> > > > > > >>>>>>>>> phase,
> >> > > >> > > > > > >>>>>>>>>>>> these
> >> > > >> > > > > > >>>>>>>>>>>>> partial results are sent to receivers
> >> > according
> >> > > to
> >> > > >> > > > > > >>> their
> >> > > >> > > > > > >>>>> keys
> >> > > >> > > > > > >>>>>>> and
> >> > > >> > > > > > >>>>>>>>> are
> >> > > >> > > > > > >>>>>>>>>>>>> combined to obtain the final result.
> Since
> >> the
> >> > > >> number
> >> > > >> > > > > > >>> of
> >> > > >> > > > > > >>>>>>> partial
> >> > > >> > > > > > >>>>>>>>>>> results
> >> > > >> > > > > > >>>>>>>>>>>>> received by each receiver is limited by
> the
> >> > > >> number of
> >> > > >> > > > > > >>>>>> senders,
> >> > > >> > > > > > >>>>>>>> the
> >> > > >> > > > > > >>>>>>>>>>>>> imbalance among receivers can be reduced.
> >> > > >> Besides, by
> >> > > >> > > > > > >>>>>> reducing
> >> > > >> > > > > > >>>>>>>> the
> >> > > >> > > > > > >>>>>>>>>>> amount
> >> > > >> > > > > > >>>>>>>>>>>>> of transferred data the performance can
> be
> >> > > further
> >> > > >> > > > > > >>>>> improved.
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> *More details*:
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> Design documentation:
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>
> >> > > >> > > > > >
> >> > > >> > > > >
> >> > > >> > > >
> >> > > >> > >
> >> > > >> >
> >> > > >>
> >> > >
> >> >
> >>
> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> Old discussion thread:
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>
> >> > > >> > > > > >
> >> > > >> > > > >
> >> > > >> > > >
> >> > > >> > >
> >> > > >> >
> >> > > >>
> >> > >
> >> >
> >>
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
> >> > > >> > > > > > >>>>>>>>>
> >> https://issues.apache.org/jira/browse/FLINK-12786
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> We are looking forwards to your feedback!
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> Best,
> >> > > >> > > > > > >>>>>>>>>>>>> Vino
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>
> >> > > >> > > > > >
> >> > > >> > > > > >
> >> > > >> > > > >
> >> > > >> > > >
> >> > > >> > >
> >> > > >> >
> >> > > >>
> >> > > >
> >> > >
> >> >
> >>
> >
>
