Hi Jay, thanks a lot for the comments.
I think this solution is better. We probably don’t need data channel
anymore. It can be replaced with a list of producer if we need more sender
thread.
I’ll update the KIP page.
The reasoning about message handler is mainly for efficiency purpose. I’m
thinking that if something can be done in pipeline for all the clients
such as filtering/reformatting, it is probably better to do it in the
pipeline than asking 100 clients do the same thing for 100 times.
―Jiangjie (Becket) Qin
On 2/8/15, 4:59 PM, "Jay Kreps" <jay.kr...@gmail.com> wrote:
>Yeah, I second Neha's comments. The current mm code has taken something
>pretty simple and made it pretty scary with callbacks and wait/notify
>stuff. Do we believe this works? I can't tell by looking at it which is
>kind of bad for something important like this. I don't mean this as
>criticism, I know the history: we added in memory queues to help with
>other
>performance problems without thinking about correctness, then we added
>stuff to work around the in-memory queues not lose data, and so on.
>
>Can we instead do the opposite exercise and start with the basics of what
>mm should do and think about what deficiencies prevents this approach from
>working? Then let's make sure the currently in-flight work will remove
>these deficiencies. After all mm is kind of the prototypical kafka use
>case
>so if we can't make our clients to this probably no one else can.
>
>I think mm should just be N independent threads each of which has their
>own
>consumer but share a producer and each of which looks like this:
>
>while(true) {
> val recs = consumer.poll(Long.MaxValue);
> for (rec <- recs)
> producer.send(rec, logErrorCallback)
> if(System.currentTimeMillis - lastCommit > commitInterval) {
> producer.flush()
> consumer.commit()
> lastCommit = System.currentTimeMillis
> }
>}
>
>This will depend on setting the retry count in the producer to something
>high with a largish backoff so that a failed send attempt doesn't drop
>data.
>
>We will need to use the callback to force a flush and offset commit on
>rebalance.
>
>This approach may have a few more TCP connections due to using multiple
>consumers but I think it is a lot easier to reason about and the total
>number of mm instances is always going to be small.
>
>Let's talk about where this simple approach falls short, I think that will
>help us understand your motivations for additional elements.
>
>Another advantage of this is that it is so simple I don't think we really
>even need to both making mm extensible because writing your own code that
>does custom processing or transformation is just ten lines and no plug in
>system is going to make it simpler.
>
>-Jay
>
>
>On Sun, Feb 8, 2015 at 2:40 PM, Neha Narkhede <n...@confluent.io> wrote:
>
>> Few comments -
>>
>> 1. Why do we need the message handler? Do you have concrete use cases in
>> mind? If not, we should consider adding it in the future when/if we do
>>have
>> use cases for it. The purpose of the mirror maker is a simple tool for
>> setting up Kafka cluster replicas. I don't see why we need to include a
>> message handler for doing stream transformations or filtering. You can
>> always write a simple process for doing that once the data is copied as
>>is
>> in the target cluster
>> 2. Why keep both designs? We should prefer the simpler design unless it
>>is
>> not feasible due to the performance issue that we previously had. Did
>>you
>> get a chance to run some tests to see if that is really still a problem
>>or
>> not? It will be easier to think about the design and also make the KIP
>> complete if we make a call on the design first.
>> 3. Can you explain the need for keeping a list of unacked offsets per
>> partition? Consider adding a section on retries and how you plan to
>>handle
>> the case when the producer runs out of all retries.
>>
>> Thanks,
>> Neha
>>
>> On Sun, Feb 8, 2015 at 2:06 PM, Jiangjie Qin <j...@linkedin.com.invalid>
>> wrote:
>>
>> > Hi Neha,
>> >
>> > Yes, I’ve updated the KIP so the entire KIP is based on new consumer
>>now.
>> > I’ve put both designs with and without data channel in the KIP as I
>>still
>> > feel we might need the data channel to provide more flexibility,
>> > especially after message handler is introduced. I’ve put my thinking
>>of
>> > the pros and cons of the two designs in the KIP as well. It’ll be
>>great
>> if
>> > you can give a review and comment.
>> >
>> > Thanks.
>> >
>> > Jiangjie (Becket) Qin
>> >
>> > On 2/6/15, 7:30 PM, "Neha Narkhede" <n...@confluent.io> wrote:
>> >
>> > >Hey Becket,
>> > >
>> > >What are the next steps on this KIP. As per your comment earlier on
>>the
>> > >thread -
>> > >
>> > >I do agree it makes more sense
>> > >> to avoid duplicate effort and plan based on new consumer. I’ll
>>modify
>> > >>the
>> > >> KIP.
>> > >
>> > >
>> > >Did you get a chance to think about the simplified design that we
>> proposed
>> > >earlier? Do you plan to update the KIP with that proposal?
>> > >
>> > >Thanks,
>> > >Neha
>> > >
>> > >On Wed, Feb 4, 2015 at 12:12 PM, Jiangjie Qin
>><j...@linkedin.com.invalid
>> >
>> > >wrote:
>> > >
>> > >> In mirror maker we do not do de-serialization on the messages.
>>Mirror
>> > >> maker use source TopicPartition hash to chose a producer to send
>> > >>messages
>> > >> from the same source partition. The partition those messages end up
>> with
>> > >> are decided by Partitioner class in KafkaProducer (assuming you are
>> > >>using
>> > >> the new producer), which uses hash code of bytes[].
>> > >>
>> > >> If deserialization is needed, it has to be done in message handler.
>> > >>
>> > >> Thanks.
>> > >>
>> > >> Jiangjie (Becket) Qin
>> > >>
>> > >> On 2/4/15, 11:33 AM, "Bhavesh Mistry" <mistry.p.bhav...@gmail.com>
>> > >>wrote:
>> > >>
>> > >> >Hi Jiangjie,
>> > >> >
>> > >> >Thanks for entertaining my question so far. Last question, I
>>have is
>> > >> >about
>> > >> >serialization of message key. If the key de-serialization
>>(Class) is
>> > >>not
>> > >> >present at the MM instance, then does it use raw byte hashcode to
>> > >> >determine
>> > >> >the partition ? How are you going to address the situation where
>>key
>> > >> >needs
>> > >> >to be de-serialization and get actual hashcode needs to be
>>computed
>> ?.
>> > >> >
>> > >> >
>> > >> >Thanks,
>> > >> >
>> > >> >Bhavesh
>> > >> >
>> > >> >On Fri, Jan 30, 2015 at 1:41 PM, Jiangjie Qin
>> > >><j...@linkedin.com.invalid>
>> > >> >wrote:
>> > >> >
>> > >> >> Hi Bhavesh,
>> > >> >>
>> > >> >> Please see inline comments.
>> > >> >>
>> > >> >> Jiangjie (Becket) Qin
>> > >> >>
>> > >> >> On 1/29/15, 7:00 PM, "Bhavesh Mistry"
>><mistry.p.bhav...@gmail.com>
>> > >> >>wrote:
>> > >> >>
>> > >> >> >Hi Jiangjie,
>> > >> >> >
>> > >> >> >Thanks for the input.
>> > >> >> >
>> > >> >> >a) Is MM will producer ack will be attach to Producer
>>Instance or
>> > >>per
>> > >> >> >topic. Use case is that one instance of MM
>> > >> >> >needs to handle both strong ack and also ack=0 for some topic.
>> Or
>> > >>it
>> > >> >> >would
>> > >> >> >be better to set-up another instance of MM.
>> > >> >> The acks setting is producer level setting instead of topic
>>level
>> > >> >>setting.
>> > >> >> In this case you probably need to set up another instance.
>> > >> >> >
>> > >> >> >b) Regarding TCP connections, Why does #producer instance
>>attach
>> to
>> > >>TCP
>> > >> >> >connection. Is it possible to use Broker Connection TCP Pool,
>> > >>producer
>> > >> >> >will just checkout TCP connection to Broker. So, # of
>>Producer
>> > >> >>Instance
>> > >> >> >does not correlation to Brokers Connection. Is this possible ?
>> > >> >> In new producer, each producer maintains a connection to each
>> broker
>> > >> >> within the producer instance. Making producer instances to share
>> the
>> > >>TCP
>> > >> >> connections is a very big change to the current design, so I
>> suppose
>> > >>we
>> > >> >> won’t be able to do that.
>> > >> >> >
>> > >> >> >
>> > >> >> >Thanks,
>> > >> >> >
>> > >> >> >Bhavesh
>> > >> >> >
>> > >> >> >On Thu, Jan 29, 2015 at 11:50 AM, Jiangjie Qin
>> > >> >><j...@linkedin.com.invalid
>> > >> >> >
>> > >> >> >wrote:
>> > >> >> >
>> > >> >> >> Hi Bhavesh,
>> > >> >> >>
>> > >> >> >> I think it is the right discussion to have when we are
>>talking
>> > >>about
>> > >> >>the
>> > >> >> >> new new design for MM.
>> > >> >> >> Please see the inline comments.
>> > >> >> >>
>> > >> >> >> Jiangjie (Becket) Qin
>> > >> >> >>
>> > >> >> >> On 1/28/15, 10:48 PM, "Bhavesh Mistry"
>> > >><mistry.p.bhav...@gmail.com>
>> > >> >> >>wrote:
>> > >> >> >>
>> > >> >> >> >Hi Jiangjie,
>> > >> >> >> >
>> > >> >> >> >I just wanted to let you know about our use case and stress
>>the
>> > >> >>point
>> > >> >> >>that
>> > >> >> >> >local data center broker cluster have fewer partitions than
>>the
>> > >> >> >> >destination
>> > >> >> >> >offline broker cluster. Just because we do the batch pull
>>from
>> > >>CAMUS
>> > >> >> >>and
>> > >> >> >> >in
>> > >> >> >> >order to drain data faster than the injection rate (from
>>four
>> DCs
>> > >> >>for
>> > >> >> >>same
>> > >> >> >> >topic).
>> > >> >> >> Keeping the same partition number in source and target
>>cluster
>> > >>will
>> > >> >>be
>> > >> >> >>an
>> > >> >> >> option but will not be enforced by default.
>> > >> >> >> >
>> > >> >> >> >We are facing following issues (probably due to
>>configuration):
>> > >> >> >> >
>> > >> >> >> >1) We occasionally loose data due to message batch
>>size is
>> > >>too
>> > >> >> >>large
>> > >> >> >> >(2MB) on target data (we are using old producer but I think
>>new
>> > >> >> >>producer
>> > >> >> >> >will solve this problem to some extend).
>> > >> >> >> We do see this issue in LinkedIn as well. New producer also
>> might
>> > >> >>have
>> > >> >> >> this issue. There are some proposal of solutions, but no real
>> work
>> > >> >> >>started
>> > >> >> >> yet. For now, as a workaround, setting a more aggressive
>>batch
>> > >>size
>> > >> >>on
>> > >> >> >> producer side should work.
>> > >> >> >> >2) Since only one instance is set to MM data, we are
>>not
>> > >>able
>> > >> >>to
>> > >> >> >> >set-up ack per topic instead ack is attached to producer
>> > >>instance.
>> > >> >> >> I don’t quite get the question here.
>> > >> >> >> >3) How are you going to address two phase commit
>>problem
>> if
>> > >> >>ack is
>> > >> >> >> >set
>> > >> >> >> >to strongest, but auto commit is on for consumer (meaning
>> > >>producer
>> > >> >>does
>> > >> >> >> >not
>> > >> >> >> >get ack, but consumer auto committed offset that message).
>> Is
>> > >> >>there
>> > >> >> >> >transactional (Kafka transaction is in process) based ack
>>and
>> > >>commit
>> > >> >> >> >offset
>> > >> >> >> >?
>> > >> >> >> Auto offset commit should be turned off in this case. The
>>offset
>> > >>will
>> > >> >> >>only
>> > >> >> >> be committed once by the offset commit thread. So there is no
>> two
>> > >> >>phase
>> > >> >> >> commit.
>> > >> >> >> >4) How are you planning to avoid duplicated message?
>>( Is
>> > >> >> >> >brokergoing
>> > >> >> >> >have moving window of message collected and de-dupe ?)
>> > >>Possibly, we
>> > >> >> >>get
>> > >> >> >> >this from retry set to 5…?
>> > >> >> >> We are not trying to completely avoid duplicates. The
>>duplicates
>> > >>will
>> > >> >> >> still be there if:
>> > >> >> >> 1. Producer retries on failure.
>> > >> >> >> 2. Mirror maker is hard killed.
>> > >> >> >> Currently, dedup is expected to be done by user if necessary.
>> > >> >> >> >5) Last, is there any warning or any thing you can
>>provide
>> > >> >>insight
>> > >> >> >> >from MM component about data injection rate into destination
>> > >> >> >>partitions is
>> > >> >> >> >NOT evenly distributed regardless of keyed or non-keyed
>> message
>> > >> >> >>(Hence
>> > >> >> >> >there is ripple effect such as data not arriving late, or
>>data
>> is
>> > >> >> >>arriving
>> > >> >> >> >out of order in intern of time stamp and early some time,
>>and
>> > >> >>CAMUS
>> > >> >> >> >creates huge number of file count on HDFS due to uneven
>> injection
>> > >> >>rate
>> > >> >> >>.
>> > >> >> >> >Camus Job is configured to run every 3 minutes.)
>> > >> >> >> I think uneven data distribution is typically caused by
>>server
>> > >>side
>> > >> >> >> unbalance, instead of something mirror maker could control.
>>In
>> new
>> > >> >> >>mirror
>> > >> >> >> maker, however, there is a customizable message handler, that
>> > >>might
>> > >> >>be
>> > >> >> >> able to help a little bit. In message handler, you can
>> explicitly
>> > >> >>set a
>> > >> >> >> partition that you want to produce the message to. So if you
>> know
>> > >>the
>> > >> >> >> uneven data distribution in target cluster, you may offset it
>> > >>here.
>> > >> >>But
>> > >> >> >> that probably only works for non-keyed messages.
>> > >> >> >> >
>> > >> >> >> >I am not sure if this is right discussion form to bring
>>these
>> to
>> > >> >> >> >your/kafka
>> > >> >> >> >Dev team attention. This might be off track,
>> > >> >> >> >
>> > >> >> >> >
>> > >> >> >> >Thanks,
>> > >> >> >> >
>> > >> >> >> >Bhavesh
>> > >> >> >> >
>> > >> >> >> >On Wed, Jan 28, 2015 at 11:07 AM, Jiangjie Qin
>> > >> >> >><j...@linkedin.com.invalid
>> > >> >> >> >
>> > >> >> >> >wrote:
>> > >> >> >> >
>> > >> >> >> >> I’ve updated the KIP page. Feedbacks are welcome.
>> > >> >> >> >>
>> > >> >> >> >> Regarding the simple mirror maker design. I thought over
>>it
>> and
>> > >> >>have
>> > >> >> >> >>some
>> > >> >> >> >> worries:
>> > >> >> >> >> There are two things that might worth thinking:
>> > >> >> >> >> 1. One of the enhancement to mirror maker is adding a
>>message
>> > >> >> >>handler to
>> > >> >> >> >> do things like reformatting. I think we might potentially
>> want
>> > >>to
>> > >> >> >>have
>> > >> >> >> >> more threads processing the messages than the number of
>> > >>consumers.
>> > >> >> >>If we
>> > >> >> >> >> follow the simple mirror maker solution, we lose this
>> > >>flexibility.
>> > >> >> >> >> 2. This might not matter too much, but creating more
>> consumers
>> > >> >>means
>> > >> >> >> >>more
>> > >> >> >> >> footprint of TCP connection / memory.
>> > >> >> >> >>
>> > >> >> >> >> Any thoughts on this?
>> > >> >> >> >>
>> > >> >> >> >> Thanks.
>> > >> >> >> >>
>> > >> >> >> >> Jiangjie (Becket) Qin
>> > >> >> >> >>
>> > >> >> >> >> On 1/26/15, 10:35 AM, "Jiangjie Qin" <j...@linkedin.com>
>> > wrote:
>> > >> >> >> >>
>> > >> >> >> >> >Hi Jay and Neha,
>> > >> >> >> >> >
>> > >> >> >> >> >Thanks a lot for the reply and explanation. I do agree it
>> > >>makes
>> > >> >>more
>> > >> >> >> >>sense
>> > >> >> >> >> >to avoid duplicate effort and plan based on new consumer.
>> I’ll
>> > >> >> >>modify
>> > >> >> >> >>the
>> > >> >> >> >> >KIP.
>> > >> >> >> >> >
>> > >> >> >> >> >To Jay’s question on message ordering - The data channel
>> > >> >>selection
>> > >> >> >> >>makes
>> > >> >> >> >> >sure that the messages from the same source partition
>>will
>> > >>sent
>> > >> >>by
>> > >> >> >>the
>> > >> >> >> >> >same producer. So the order of the messages is guaranteed
>> with
>> > >> >> >>proper
>> > >> >> >> >> >producer settings
>> > >> >>(MaxInFlightRequests=1,retries=Integer.MaxValue,
>> > >> >> >> >>etc.)
>> > >> >> >> >> >For keyed messages, because they come from the same
>>source
>> > >> >>partition
>> > >> >> >> >>and
>> > >> >> >> >> >will end up in the same target partition, as long as they
>> are
>> > >> >>sent
>> > >> >> >>by
>> > >> >> >> >>the
>> > >> >> >> >> >same producer, the order is guaranteed.
>> > >> >> >> >> >For non-keyed messages, the messages coming from the same
>> > >>source
>> > >> >> >> >>partition
>> > >> >> >> >> >might go to different target partitions. The order is
>>only
>> > >> >> >>guaranteed
>> > >> >> >> >> >within each partition.
>> > >> >> >> >> >
>> > >> >> >> >> >Anyway, I’ll modify the KIP and data channel will be
>>away.
>> > >> >> >> >> >
>> > >> >> >> >> >Thanks.
>> > >> >> >> >> >
>> > >> >> >> >> >Jiangjie (Becket) Qin
>> > >> >> >> >> >
>> > >> >> >> >> >
>> > >> >> >> >> >On 1/25/15, 4:34 PM, "Neha Narkhede" <n...@confluent.io>
>> > >>wrote:
>> > >> >> >> >> >
>> > >> >> >> >> >>I think there is some value in investigating if we can
>>go
>> > >>back
>> > >> >>to
>> > >> >> >>the
>> > >> >> >> >> >>simple mirror maker design, as Jay points out. Here you
>> have
>> > >>N
>> > >> >> >> >>threads,
>> > >> >> >> >> >>each has a consumer and a producer.
>> > >> >> >> >> >>
>> > >> >> >> >> >>The reason why we had to move away from that was a
>> > >>combination
>> > >> >>of
>> > >> >> >>the
>> > >> >> >> >> >>difference in throughput between the consumer and the
>>old
>> > >> >>producer
>> > >> >> >>and
>> > >> >> >> >> >>the
>> > >> >> >> >> >>deficiency of the consumer rebalancing that limits the
>> total
>> > >> >> >>number of
>> > >> >> >> >> >>mirror maker threads. So the only option available was
>>to
>> > >> >>increase
>> > >> >> >>the
>> > >> >> >> >> >>throughput of the limited # of mirror maker threads that
>> > >>could
>> > >> >>be
>> > >> >> >> >> >>deployed.
>> > >> >> >> >> >>Now that queuing design may not make sense, if the new
>> > >> >>producer's
>> > >> >> >> >> >>throughput is almost similar to the consumer AND the
>>fact
>> > >>that
>> > >> >>the
>> > >> >> >>new
>> > >> >> >> >> >>round-robin based consumer rebalancing can allow a very
>> high
>> > >> >> >>number of
>> > >> >> >> >> >>mirror maker instances to exist.
>> > >> >> >> >> >>
>> > >> >> >> >> >>This is the end state that the mirror maker should be in
>> once
>> > >> >>the
>> > >> >> >>new
>> > >> >> >> >> >>consumer is complete, so it wouldn't hurt to see if we
>>can
>> > >>just
>> > >> >> >>move
>> > >> >> >> >>to
>> > >> >> >> >> >>that right now.
>> > >> >> >> >> >>
>> > >> >> >> >> >>On Fri, Jan 23, 2015 at 8:40 PM, Jay Kreps
>> > >><jay.kr...@gmail.com
>> > >> >
>> > >> >> >> >>wrote:
>> > >> >> >> >> >>
>> > >> >> >> >> >>> QQ: If we ever use a different technique for the data
>> > >>channel
>> > >> >> >> >>selection
>> > >> >> >> >> >>> than for the producer partitioning won't that break
>> > >>ordering?
>> > >> >>How
>> > >> >> >> >>can
>> > >> >> >> >> >>>we
>> > >> >> >> >> >>> ensure these things stay in sync?
>> > >> >> >> >> >>>
>> > >> >> >> >> >>> With respect to the new consumer--I really do want to
>> > >> >>encourage
>> > >> >> >> >>people
>> > >> >> >> >> >>>to
>> > >> >> >> >> >>> think through how MM will work with the new consumer.
>>I
>> > >>mean
>> > >> >>this
>> > >> >> >> >>isn't
>> > >> >> >> >> >>> very far off, maybe a few months if we hustle? I could
>> > >> >>imagine us
>> > >> >> >> >> >>>getting
>> > >> >> >> >> >>> this mm fix done maybe sooner, maybe in a month? So I
>> guess
>> > >> >>this
>> > >> >> >> >>buys
>> > >> >> >> >> >>>us an
>> > >> >> >> >> >>> extra month before we rip it out and throw it away?
>>Maybe
>> > >>two?
>> > >> >> >>This
>> > >> >> >> >>bug
>> > >> >> >> >> >>>has
>> > >> >> >> >> >>> been there for a while, though, right? Is it worth it?
>> > >> >>Probably
>> > >> >> >>it
>> > >> >> >> >>is,
>> > >> >> >> >> >>>but
>> > >> >> >> >> >>> it still kind of sucks to have the duplicate effort.
>> > >> >> >> >> >>>
>> > >> >> >> >> >>> So anyhow let's definitely think about how things will
>> work
>> > >> >>with
>> > >> >> >>the
>> > >> >> >> >> >>>new
>> > >> >> >> >> >>> consumer. I think we can probably just have N threads,
>> each
>> > >> >> >>thread
>> > >> >> >> >>has
>> > >> >> >> >> >>>a
>> > >> >> >> >> >>> producer and consumer and is internally single
>>threaded.
>> > >>Any
>> > >> >> >>reason
>> > >> >> >> >> >>>this
>> > >> >> >> >> >>> wouldn't work?
>> > >> >> >> >> >>>
>> > >> >> >> >> >>> -Jay
>> > >> >> >> >> >>>
>> > >> >> >> >> >>>
>> > >> >> >> >> >>> On Wed, Jan 21, 2015 at 5:29 PM, Jiangjie Qin
>> > >> >> >> >> >>><j...@linkedin.com.invalid>
>> > >> >> >> >> >>> wrote:
>> > >> >> >> >> >>>
>> > >> >> >> >> >>> > Hi Jay,
>> > >> >> >> >> >>> >
>> > >> >> >> >> >>> > Thanks for comments. Please see inline responses.
>> > >> >> >> >> >>> >
>> > >> >> >> >> >>> > Jiangjie (Becket) Qin
>> > >> >> >> >> >>> >
>> > >> >> >> >> >>> > On 1/21/15, 1:33 PM, "Jay Kreps"
>><jay.kr...@gmail.com>
>> > >> >>wrote:
>> > >> >> >> >> >>> >
>> > >> >> >> >> >>> > >Hey guys,
>> > >> >> >> >> >>> > >
>> > >> >> >> >> >>> > >A couple questions/comments:
>> > >> >> >> >> >>> > >
>> > >> >> >> >> >>> > >1. The callback and user-controlled commit offset
>> > >> >> >>functionality
>> > >> >> >> >>is
>> > >> >> >> >> >>> already
>> > >> >> >> >> >>> > >in the new consumer which we are working on in
>> parallel.
>> > >> >>If we
>> > >> >> >> >> >>> accelerated
>> > >> >> >> >> >>> > >that work it might help concentrate efforts. I
>>admit
>> > >>this
>> > >> >> >>might
>> > >> >> >> >>take
>> > >> >> >> >> >>> > >slightly longer in calendar time but could still
>> > >>probably
>> > >> >>get
>> > >> >> >> >>done
>> > >> >> >> >> >>>this
>> > >> >> >> >> >>> > >quarter. Have you guys considered that approach?
>> > >> >> >> >> >>> > Yes, I totally agree that ideally we should put
>>efforts
>> > >>on
>> > >> >>new
>> > >> >> >> >> >>>consumer.
>> > >> >> >> >> >>> > The main reason for still working on the old
>>consumer
>> is
>> > >> >>that
>> > >> >> >>we
>> > >> >> >> >> >>>expect
>> > >> >> >> >> >>> it
>> > >> >> >> >> >>> > would still be used in LinkedIn for quite a while
>> before
>> > >>the
>> > >> >> >>new
>> > >> >> >> >> >>>consumer
>> > >> >> >> >> >>> > could be fully rolled out. And we recently
>>suffering a
>> > >>lot
>> > >> >>from
>> > >> >> >> >> >>>mirror
>> > >> >> >> >> >>> > maker data loss issue. So our current plan is making
>> > >> >>necessary
>> > >> >> >> >> >>>changes to
>> > >> >> >> >> >>> > make current mirror maker stable in production.
>>Then we
>> > >>can
>> > >> >> >>test
>> > >> >> >> >>and
>> > >> >> >> >> >>> > rollout new consumer gradually without getting
>>burnt.
>> > >> >> >> >> >>> > >
>> > >> >> >> >> >>> > >2. I think partitioning on the hash of the topic
>> > >>partition
>> > >> >>is
>> > >> >> >> >>not a
>> > >> >> >> >> >>>very
>> > >> >> >> >> >>> > >good idea because that will make the case of going
>> from
>> > >>a
>> > >> >> >>cluster
>> > >> >> >> >> >>>with
>> > >> >> >> >> >>> > >fewer partitions to one with more partitions not
>> work. I
>> > >> >> >>think an
>> > >> >> >> >> >>> > >intuitive
>> > >> >> >> >> >>> > >way to do this would be the following:
>> > >> >> >> >> >>> > >a. Default behavior: Just do what the producer
>>does.
>> > >>I.e.
>> > >> >>if
>> > >> >> >>you
>> > >> >> >> >> >>> specify a
>> > >> >> >> >> >>> > >key use it for partitioning, if not just partition
>>in
>> a
>> > >> >> >> >>round-robin
>> > >> >> >> >> >>> > >fashion.
>> > >> >> >> >> >>> > >b. Add a --preserve-partition option that will
>> > >>explicitly
>> > >> >> >> >>inherent
>> > >> >> >> >> >>>the
>> > >> >> >> >> >>> > >partition from the source irrespective of whether
>> there
>> > >>is
>> > >> >>a
>> > >> >> >>key
>> > >> >> >> >>or
>> > >> >> >> >> >>> which
>> > >> >> >> >> >>> > >partition that key would hash to.
>> > >> >> >> >> >>> > Sorry that I did not explain this clear enough. The
>> hash
>> > >>of
>> > >> >> >>topic
>> > >> >> >> >> >>> > partition is only used when decide which mirror
>>maker
>> > >>data
>> > >> >> >>channel
>> > >> >> >> >> >>>queue
>> > >> >> >> >> >>> > the consumer thread should put message into. It only
>> > >>tries
>> > >> >>to
>> > >> >> >>make
>> > >> >> >> >> >>>sure
>> > >> >> >> >> >>> > the messages from the same partition is sent by the
>> same
>> > >> >> >>producer
>> > >> >> >> >> >>>thread
>> > >> >> >> >> >>> > to guarantee the sending order. This is not at all
>> > >>related
>> > >> >>to
>> > >> >> >> >>which
>> > >> >> >> >> >>> > partition in target cluster the messages end up.
>>That
>> is
>> > >> >>still
>> > >> >> >> >> >>>decided by
>> > >> >> >> >> >>> > producer.
>> > >> >> >> >> >>> > >
>> > >> >> >> >> >>> > >3. You don't actually give the
>> ConsumerRebalanceListener
>> > >> >> >> >>interface.
>> > >> >> >> >> >>>What
>> > >> >> >> >> >>> > >is
>> > >> >> >> >> >>> > >that going to look like?
>> > >> >> >> >> >>> > Good point! I should have put it in the wiki. I just
>> > >>added
>> > >> >>it.
>> > >> >> >> >> >>> > >
>> > >> >> >> >> >>> > >4. What is MirrorMakerRecord? I think ideally the
>> > >> >> >> >> >>> > >MirrorMakerMessageHandler
>> > >> >> >> >> >>> > >interface would take a ConsumerRecord as input and
>> > >>return a
>> > >> >> >> >> >>> > >ProducerRecord,
>> > >> >> >> >> >>> > >right? That would allow you to transform the key,
>> value,
>> > >> >> >> >>partition,
>> > >> >> >> >> >>>or
>> > >> >> >> >> >>> > >destination topic...
>> > >> >> >> >> >>> > MirrorMakerRecord is introduced in KAFKA-1650,
>>which is
>> > >> >>exactly
>> > >> >> >> >>the
>> > >> >> >> >> >>>same
>> > >> >> >> >> >>> > as ConsumerRecord in KAFKA-1760.
>> > >> >> >> >> >>> > private[kafka] class MirrorMakerRecord (val
>> sourceTopic:
>> > >> >> >>String,
>> > >> >> >> >> >>> > val sourcePartition: Int,
>> > >> >> >> >> >>> > val sourceOffset: Long,
>> > >> >> >> >> >>> > val key: Array[Byte],
>> > >> >> >> >> >>> > val value: Array[Byte]) {
>> > >> >> >> >> >>> > def size = value.length + {if (key == null) 0 else
>> > >> >> >>key.length}
>> > >> >> >> >> >>> > }
>> > >> >> >> >> >>> >
>> > >> >> >> >> >>> > However, because source partition and offset is
>>needed
>> in
>> > >> >> >>producer
>> > >> >> >> >> >>>thread
>> > >> >> >> >> >>> > for consumer offsets bookkeeping, the record
>>returned
>> by
>> > >> >> >> >> >>> > MirrorMakerMessageHandler needs to contain those
>> > >> >>information.
>> > >> >> >> >> >>>Therefore
>> > >> >> >> >> >>> > ProducerRecord does not work here. We could probably
>> let
>> > >> >> >>message
>> > >> >> >> >> >>>handler
>> > >> >> >> >> >>> > take ConsumerRecord for both input and output.
>> > >> >> >> >> >>> > >
>> > >> >> >> >> >>> > >5. Have you guys thought about what the
>>implementation
>> > >>will
>> > >> >> >>look
>> > >> >> >> >> >>>like in
>> > >> >> >> >> >>> > >terms of threading architecture etc with the new
>> > >>consumer?
>> > >> >> >>That
>> > >> >> >> >>will
>> > >> >> >> >> >>>be
>> > >> >> >> >> >>> > >soon so even if we aren't starting with that let's
>> make
>> > >> >>sure
>> > >> >> >>we
>> > >> >> >> >>can
>> > >> >> >> >> >>>get
>> > >> >> >> >> >>> > >rid
>> > >> >> >> >> >>> > >of a lot of the current mirror maker accidental
>> > >>complexity
>> > >> >>in
>> > >> >> >> >>terms
>> > >> >> >> >> >>>of
>> > >> >> >> >> >>> > >threads and queues when we move to that.
>> > >> >> >> >> >>> > I haven¹t thought about it throughly. The quick
>>idea is
>> > >> >>after
>> > >> >> >> >> >>>migration
>> > >> >> >> >> >>> to
>> > >> >> >> >> >>> > the new consumer, it is probably better to use a
>>single
>> > >> >> >>consumer
>> > >> >> >> >> >>>thread.
>> > >> >> >> >> >>> > If multithread is needed, decoupling consumption and
>> > >> >>processing
>> > >> >> >> >>might
>> > >> >> >> >> >>>be
>> > >> >> >> >> >>> > used. MirrorMaker definitely needs to be changed
>>after
>> > >>new
>> > >> >> >> >>consumer
>> > >> >> >> >> >>>get
>> > >> >> >> >> >>> > checked in. I¹ll document the changes and can submit
>> > >>follow
>> > >> >>up
>> > >> >> >> >> >>>patches
>> > >> >> >> >> >>> > after the new consumer is available.
>> > >> >> >> >> >>> > >
>> > >> >> >> >> >>> > >-Jay
>> > >> >> >> >> >>> > >
>> > >> >> >> >> >>> > >On Tue, Jan 20, 2015 at 4:31 PM, Jiangjie Qin
>> > >> >> >> >> >>><j...@linkedin.com.invalid
>> > >> >> >> >> >>> >
>> > >> >> >> >> >>> > >wrote:
>> > >> >> >> >> >>> > >
>> > >> >> >> >> >>> > >> Hi Kafka Devs,
>> > >> >> >> >> >>> > >>
>> > >> >> >> >> >>> > >> We are working on Kafka Mirror Maker
>>enhancement. A
>> > >>KIP
>> > >> >>is
>> > >> >> >> >>posted
>> > >> >> >> >> >>>to
>> > >> >> >> >> >>> > >> document and discuss on the followings:
>> > >> >> >> >> >>> > >> 1. KAFKA-1650: No Data loss mirror maker change
>> > >> >> >> >> >>> > >> 2. KAFKA-1839: To allow partition aware mirror.
>> > >> >> >> >> >>> > >> 3. KAFKA-1840: To allow message filtering/format
>> > >> >>conversion
>> > >> >> >> >> >>> > >> Feedbacks are welcome. Please let us know if you
>> have
>> > >>any
>> > >> >> >> >> >>>questions or
>> > >> >> >> >> >>> > >> concerns.
>> > >> >> >> >> >>> > >>
>> > >> >> >> >> >>> > >> Thanks.
>> > >> >> >> >> >>> > >>
>> > >> >> >> >> >>> > >> Jiangjie (Becket) Qin
>> > >> >> >> >> >>> > >>
>> > >> >> >> >> >>> >
>> > >> >> >> >> >>> >
>> > >> >> >> >> >>>
>> > >> >> >> >> >>
>> > >> >> >> >> >>
>> > >> >> >> >> >>
>> > >> >> >> >> >>--
>> > >> >> >> >> >>Thanks,
>> > >> >> >> >> >>Neha
>> > >> >> >> >> >
>> > >> >> >> >>
>> > >> >> >> >>
>> > >> >> >>
>> > >> >> >>
>> > >> >>
>> > >> >>
>> > >>
>> > >>
>> > >
>> > >
>> > >--
>> > >Thanks,
>> > >Neha
>> >
>> >
>>
>>
>> --
>> Thanks,
>> Neha
>>
