Just a quick ping, that regardless of the name of the thing, I'm still
interested in answers to my questions :)
On Tue, Jul 28, 2015 at 3:07 PM, Gwen Shapira <gshap...@cloudera.com> wrote:
> Thanks Guazhang! Much clearer now, at least for me.
>
> Few comments / questions:
>
> 1. Perhaps punctuate(int numRecords) will be a nice API addition, some
> use-cases have record-count based windows, rather than time-based..
> 2. The diagram for "Flexible partition distribution" shows two joins.
> Is the idea to implement two Processors and string them together?
> 3. Is the local state persistent? Can you talk a bit about how local
> state works with high availability?
>
> Gwen
>
> On Tue, Jul 28, 2015 at 12:57 AM, Guozhang Wang <wangg...@gmail.com> wrote:
>> I have updated the wiki page incorporating people's comments, please feel
>> free to take another look before today's meeting.
>>
>> On Mon, Jul 27, 2015 at 11:19 PM, Yi Pan <nickpa...@gmail.com> wrote:
>>
>>> 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
>>> > > > > >
>>> > > > >
>>> > > >
>>> > >
>>> >
>>>
>>
>>
>>
>> --
>> -- Guozhang
