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
