Hi, Jay,
{quote}
1. Yeah we are going to try to generalize the partition management stuff.
We'll get a wiki/JIRA up for that. I think that gives what you want in
terms of moving partitioning to the client side.
{quote}
Great! I am looking forward to that.
{quote}
I think the key observation is that the whole reason
LinkedIn split data over clusters to begin with was because of the lack of
quotas, which are in any case getting implemented.
{quote}
I am not sure that I followed this point. Is your point that with quota, it
is possible to host all data in a single cluster?
-Yi
On Mon, Jul 27, 2015 at 8:53 AM, Jay Kreps <j...@confluent.io> wrote:
> Hey Yi,
>
> Great points. I think for some of this the most useful thing would be to
> get a wip prototype out that we could discuss concretely. I think Yasuhiro
> and Guozhang took that prototype I had done, and had some improvements.
> Give us a bit to get that into understandable shape so we can discuss.
>
> To address a few of your other points:
> 1. Yeah we are going to try to generalize the partition management stuff.
> We'll get a wiki/JIRA up for that. I think that gives what you want in
> terms of moving partitioning to the client side.
> 2. I think consuming from a different cluster you produce to will be easy.
> More than that is more complex, though I agree the pluggable partitioning
> makes it theoretically possible. Let's try to get something that works for
> the first case, it sounds like that solves the use case you describe of
> wanting to directly transform from a given cluster but produce back to a
> different cluster. I think the key observation is that the whole reason
> LinkedIn split data over clusters to begin with was because of the lack of
> quotas, which are in any case getting implemented.
>
> -Jay
>
> On Sun, Jul 26, 2015 at 11:31 PM, Yi Pan <nickpa...@gmail.com> wrote:
>
> > Hi, Jay and all,
> >
> > Thanks for all your quick responses. I tried to summarize my thoughts
> here:
> >
> > - ConsumerRecord as stream processor API:
> >
> > * This KafkaProcessor API is targeted to receive the message from
> Kafka.
> > So, to Yasuhiro's join/transformation example, any join/transformation
> > results that are materialized in Kafka should have ConsumerRecord format
> > (i.e. w/ topic and offsets). Any non-materialized join/transformation
> > results should not be processed by this KafkaProcessor API. One example
> is
> > the in-memory operators API in Samza, which is designed to handle the
> > non-materialzied join/transformation results. And yes, in this case, a
> more
> > abstract data model is needed.
> >
> > * Just to support Jay's point of a general
> > ConsumerRecord/ProducerRecord, a general stream processing on more than
> one
> > data sources would need at least the following info: data source
> > description (i.e. which topic/table), and actual data (i.e. key-value
> > pairs). It would make sense to have the data source name as part of the
> > general metadata in stream processing (think about it as the table name
> for
> > records in standard SQL).
> >
> > - SQL/DSL
> >
> > * I think that this topic itself is worthy of another KIP discussion.
> I
> > would prefer to leave it out of scope in KIP-28.
> >
> > - Client-side pluggable partition manager
> >
> > * Given the use cases we have seen with large-scale deployment of
> > Samza/Kafka in LinkedIn, I would argue that we should make it as the
> > first-class citizen in this KIP. The use cases include:
> >
> > * multi-cluster Kafka
> >
> > * host-affinity (i.e. local-state associated w/ certain partitions
> on
> > client)
> >
> > - Multi-cluster scenario
> >
> > * Although I originally just brought it up as a use case that requires
> > client-side partition manager, reading Jay’s comments, I realized that I
> > have one fundamental issue w/ the current copycat + transformation model.
> > If I interpret Jay’s comment correctly, the proposed
> copycat+transformation
> > plays out in the following way: i) copycat takes all data from sources
> (no
> > matter it is Kafka or non-Kafka) into *one single Kafka cluster*; ii)
> > transformation is only restricted to take data sources in *this single
> > Kafka cluster* to perform aggregate/join etc. This is different from my
> > original understanding of the copycat. The main issue I have with this
> > model is: huge data-copy between Kafka clusters. In LinkedIn, we used to
> > follow this model that uses MirrorMaker to map topics from tracking
> > clusters to Samza-specific Kafka cluster and only do stream processing in
> > the Samza-specific Kafka cluster. We moved away from this model and
> started
> > allowing users to directly consume from tracking Kafka clusters due to
> the
> > overhead of copying huge amount of traffic between Kafka clusters. I
> agree
> > that the initial design of KIP-28 would probably need a smaller scope of
> > problem to solve, hence, limiting to solving partition management in a
> > single cluster. However, I would really hope the design won’t prevent the
> > use case of processing data directly from multiple clusters. In my
> opinion,
> > making the partition manager as a client-side pluggable logic would allow
> > us to achieve these goals.
> >
> > Thanks a lot in advance!
> >
> > -Yi
> >
> > On Fri, Jul 24, 2015 at 11:13 AM, Jay Kreps <j...@confluent.io> wrote:
> >
> > > Hey Yi,
> > >
> > > For your other two points:
> > >
> > > - This definitely doesn't cover any kind of SQL or anything like this.
> > >
> > > - The prototype we started with just had process() as a method but
> > Yasuhiro
> > > had some ideas of adding additional filter/aggregate convenience
> methods.
> > > We should discuss how this would fit with the operator work you were
> > doing
> > > in Samza. Probably the best way is just get the code out there in
> current
> > > state and start talking about it?
> > >
> > > - Your point about multiple clusters. We actually have a proposed
> > extension
> > > for the Kafka group management protocol that would allow it to cover
> > > multiple clusters but actually I think that use case is not the focus.
> I
> > > think in scope would be consuming from one cluster and producing to
> > > another.
> > >
> > > One of the assumptions we are making is that we will split into two
> > > categories:
> > > a. Ingress/egress which is handled by copycat
> > > b. Transformation which would be handled by this api
> > >
> > > I think there are a number of motivations for this
> > > - It is really hard to provide hard guarantees if you allow non-trivial
> > > aggregation coupled with the ingress/egress. So if you want to be able
> to
> > > do something that provides a kind of end-to-end "exactly once"
> guarantee
> > > (that's not really the right term but what people use) I think it will
> be
> > > really hard to do this across multiple systems (hello two-phase commit)
> > > - The APIs for ingest/egress end up needing to be really different for
> a
> > > first-class ingestion framework
> > >
> > > So the case where you have data coming from many systems including many
> > > Kafka clusters is just about how easy/hard it is to use copycat with
> the
> > > transformer api in the same program. I think this is something we
> should
> > > work out as part of the prototyping.
> > >
> > > -Jay
> > >
> > > On Fri, Jul 24, 2015 at 12:57 AM, Yi Pan <nickpa...@gmail.com> wrote:
> > >
> > > > Hi, Guozhang,
> > > >
> > > > Thanks for starting this. I took a quick look and had the following
> > > > thoughts to share:
> > > >
> > > > - In the proposed KafkaProcessor API, there is no interface like
> > > Collector
> > > > that allows users to send messages to. Why is that? Is the idea to
> > > > initialize the producer once and re-use it in the processor? And if
> > there
> > > > are many KStreamThreads in the process, are there going to be many
> > > > instances of KafkaProducer although all outputs are sending to the
> same
> > > > Kafka cluster?
> > > >
> > > > - Won’t it be simpler if the process() API just takes in the
> > > ConsumerRecord
> > > > as the input instead of a tuple of (topic, key, value)?
> > > >
> > > > - Also, the input only indicates the topic of a message. What if the
> > > stream
> > > > task needs to consume and produce messages from/to multiple Kafka
> > > clusters?
> > > > To support that case, there should be a system/cluster name in both
> > input
> > > > and output as well.
> > > >
> > > > - How are the output messages handled? There does not seem to have an
> > > > interface that allows user to send an output messages to multiple
> > output
> > > > Kafka clusters.
> > > >
> > > > - It seems the proposed model also assumes one thread per processor.
> > What
> > > > becomes thread-local and what are shared among processors? Is the
> > > proposed
> > > > model targeting to have the consumers/producers become thread-local
> > > > instances within each KafkaProcessor? What’s the cost associated with
> > > this
> > > > model?
> > > >
> > > > - One more important issue: how do we plug-in client-side partition
> > > > management logic? Considering about the use case where the stream
> task
> > > > needs to consume from multiple Kafka clusters, I am not even sure
> that
> > we
> > > > can rely on Kafka broker to maintain the consumer group membership?
> > Maybe
> > > > we still can get the per cluster consumer group membership and
> > > partitions.
> > > > However, in this case, we truly need a client-side plugin partition
> > > > management logic to determine how to assign partitions in different
> > Kafka
> > > > clusters to consumers (i.e. consumers for cluster1.topic1.p1 and
> > > > cluster2.topic2.p1 has to be assigned together to one KafkaProcessor
> > for
> > > > processing). Based on the full information about (group members, all
> > > topic
> > > > partitions) in all Kafka clusters with input topics, there should be
> > two
> > > > levels of partition management policies: a) how to group all topic
> > > > partitions in all Kafka clusters to processor groups (i.e. the same
> > > concept
> > > > as Task group in Samza); b) how to assign the processor groups to
> group
> > > > members. Note if a processor group includes topic partitions from
> more
> > > than
> > > > one Kafka clusters, it has to be assigned to the common group members
> > in
> > > > all relevant Kafka clusters. This can not be done just by the brokers
> > in
> > > a
> > > > single Kafka cluster.
> > > >
> > > > - It seems that the intention of this KIP is also trying to put
> SQL/DSL
> > > > libraries into Kafka. Why is it? Shouldn't Kafka be more focused on
> > > hiding
> > > > system-level integration details and leave it open for any additional
> > > > modules outside the Kafka core to enrich the functionality that are
> > > > user-facing?
> > > >
> > > > Just a few quick cents. Thanks a lot!
> > > >
> > > > -Yi
> > > >
> > > > On Fri, Jul 24, 2015 at 12:12 AM, Neha Narkhede <n...@confluent.io>
> > > wrote:
> > > >
> > > > > Ewen:
> > > > >
> > > > > * I think trivial filtering and aggregation on a single stream
> > usually
> > > > work
> > > > > > fine with this model.
> > > > >
> > > > >
> > > > > The way I see this, the process() API is an abstraction for
> > > > > message-at-a-time computations. In the future, you could imagine
> > > > providing
> > > > > a simple DSL layer on top of the process() API that provides a set
> of
> > > > APIs
> > > > > for stream processing operations on sets of messages like joins,
> > > windows
> > > > > and various aggregations.
> > > > >
> > > > > * Spark (and presumably
> > > > > > spark streaming) is supposed to get a big win by handling
> shuffles
> > > such
> > > > > > that the data just stays in cache and never actually hits disk,
> or
> > at
> > > > > least
> > > > > > hits disk in the background. Will we take a hit because we always
> > > write
> > > > > to
> > > > > > Kafka?
> > > > >
> > > > >
> > > > > The goal isn't so much about forcing materialization of
> intermediate
> > > > > results into Kafka but designing the API to integrate with Kafka to
> > > allow
> > > > > such materialization, wherever that might be required. The downside
> > > with
> > > > > other stream processing frameworks is that they have weak
> integration
> > > > with
> > > > > Kafka where interaction with Kafka is only at the endpoints of
> > > processing
> > > > > (first input, final output). Any intermediate operations that might
> > > > benefit
> > > > > from persisting intermediate results into Kafka are forced to be
> > broken
> > > > up
> > > > > into 2 separate topologies/plans/stages of processing that lead to
> > more
> > > > > jobs. The implication is that now the set of stream processing
> > > operations
> > > > > that should really have lived in one job per application is now
> split
> > > up
> > > > > across several piecemeal jobs that need to be monitored, managed
> and
> > > > > operated separately. The APIs should still allows in-memory storage
> > of
> > > > > intermediate results where they make sense.
> > > > >
> > > > > Jiangjie,
> > > > >
> > > > > I just took a quick look at the KIP, is it very similar to mirror
> > maker
> > > > > > with message handler?
> > > > >
> > > > >
> > > > > Not really. I wouldn't say it is similar, but mirror maker is a
> > special
> > > > > instance of using copycat with Kafka source, sink + optionally the
> > > > > process() API. I can imagine replacing the MirrorMaker, in the due
> > > course
> > > > > of time, with copycat + process().
> > > > >
> > > > > Thanks,
> > > > > Neha
> > > > >
> > > > > On Thu, Jul 23, 2015 at 11:32 PM, Jiangjie Qin
> > > <j...@linkedin.com.invalid
> > > > >
> > > > > wrote:
> > > > >
> > > > > > Hey Guozhang,
> > > > > >
> > > > > > I just took a quick look at the KIP, is it very similar to mirror
> > > maker
> > > > > > with message handler?
> > > > > >
> > > > > > Thanks,
> > > > > >
> > > > > > Jiangjie (Becket) Qin
> > > > > >
> > > > > > On Thu, Jul 23, 2015 at 10:25 PM, Ewen Cheslack-Postava <
> > > > > e...@confluent.io
> > > > > > >
> > > > > > wrote:
> > > > > >
> > > > > > > Just some notes on the KIP doc itself:
> > > > > > >
> > > > > > > * It'd be useful to clarify at what point the plain consumer +
> > > custom
> > > > > > code
> > > > > > > + producer breaks down. I think trivial filtering and
> aggregation
> > > on
> > > > a
> > > > > > > single stream usually work fine with this model. Anything where
> > you
> > > > > need
> > > > > > > more complex joins, windowing, etc. are where it breaks down. I
> > > think
> > > > > > most
> > > > > > > interesting applications require that functionality, but it's
> > > helpful
> > > > > to
> > > > > > > make this really clear in the motivation -- right now, Kafka
> only
> > > > > > provides
> > > > > > > the lowest level plumbing for stream processing applications,
> so
> > > most
> > > > > > > interesting apps require very heavyweight frameworks.
> > > > > > > * I think the feature comparison of plain producer/consumer,
> > stream
> > > > > > > processing frameworks, and this new library is a good start,
> but
> > we
> > > > > might
> > > > > > > want something more thorough and structured, like a feature
> > matrix.
> > > > > Right
> > > > > > > now it's hard to figure out exactly how they relate to each
> > other.
> > > > > > > * I'd personally push the library vs. framework story very
> > strongly
> > > > --
> > > > > > the
> > > > > > > total buy-in and weak integration story of stream processing
> > > > frameworks
> > > > > > is
> > > > > > > a big downside and makes a library a really compelling (and
> > > currently
> > > > > > > unavailable, as far as I am aware) alternative.
> > > > > > > * Comment about in-memory storage of other frameworks is
> > > interesting
> > > > --
> > > > > > it
> > > > > > > is specific to the framework, but is supposed to also give
> > > > performance
> > > > > > > benefits. The high-level functional processing interface would
> > > allow
> > > > > for
> > > > > > > combining multiple operations when there's no shuffle, but when
> > > there
> > > > > is
> > > > > > a
> > > > > > > shuffle, we'll always be writing to Kafka, right? Spark (and
> > > > presumably
> > > > > > > spark streaming) is supposed to get a big win by handling
> > shuffles
> > > > such
> > > > > > > that the data just stays in cache and never actually hits disk,
> > or
> > > at
> > > > > > least
> > > > > > > hits disk in the background. Will we take a hit because we
> always
> > > > write
> > > > > > to
> > > > > > > Kafka?
> > > > > > > * I really struggled with the structure of the KIP template
> with
> > > > > Copycat
> > > > > > > because the flow doesn't work well for proposals like this.
> They
> > > > aren't
> > > > > > as
> > > > > > > concrete changes as the KIP template was designed for. I'd
> > > completely
> > > > > > > ignore that template in favor of optimizing for clarity if I
> were
> > > > you.
> > > > > > >
> > > > > > > -Ewen
> > > > > > >
> > > > > > > On Thu, Jul 23, 2015 at 5:59 PM, Guozhang Wang <
> > wangg...@gmail.com
> > > >
> > > > > > wrote:
> > > > > > >
> > > > > > > > Hi all,
> > > > > > > >
> > > > > > > > I just posted KIP-28: Add a transform client for data
> > processing
> > > > > > > > <
> > > > > > > >
> > > > > > >
> > > > > >
> > > > >
> > > >
> > >
> >
> https://cwiki.apache.org/confluence/display/KAFKA/KIP-28+-+Add+a+transform+client+for+data+processing
> > > > > > > > >
> > > > > > > > .
> > > > > > > >
> > > > > > > > The wiki page does not yet have the full design /
> > implementation
> > > > > > details,
> > > > > > > > and this email is to kick-off the conversation on whether we
> > > should
> > > > > add
> > > > > > > > this new client with the described motivations, and if yes
> what
> > > > > > features
> > > > > > > /
> > > > > > > > functionalities should be included.
> > > > > > > >
> > > > > > > > Looking forward to your feedback!
> > > > > > > >
> > > > > > > > -- Guozhang
> > > > > > > >
> > > > > > >
> > > > > > >
> > > > > > >
> > > > > > > --
> > > > > > > Thanks,
> > > > > > > Ewen
> > > > > > >
> > > > > >
> > > > >
> > > > >
> > > > >
> > > > > --
> > > > > Thanks,
> > > > > Neha
> > > > >
> > > >
> > >
> >
>
