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
> > > > > > >
> > > > > >
> > > > >
> > > >
> > >
> >
>
