Separate reply because I'm switching topics (no pun intended). :-) One impact of the Streams EOS design is how we handle failures in Streams. In the EOS design we have effectively three main failure categories, as far as I understand:
1. Transient failures (which we now e.g. handle with infinite retries on the side of producers) 2. Fatal errors aka "stop the world" (nothing we can do about those -- in the worst case, these might bring down the full app) 3. Producer fence errors (happens when tasks have been migrated out of a processing thread) My question is: how are failures handled that are caused by corrupt messages, think: "poison pills"? These typically manifest themselves as exceptions that are thrown by the serdes. Into which of the three failure "categories" above would such "poison pill" exceptions fall? For the record, today users need to take extra steps to handle such poison pills (see e.g. [1]), but that isn't very elegant or convenient. Since we have already begun discussing how to improve the status quo, I am interested in understanding the impact of this type of failure on/in the EOS design. -Michael [1] http://docs.confluent.io/current/streams/faq.html#handling-corrupted-records-and-deserialization-errors-poison-pill-messages On Wed, Mar 22, 2017 at 3:12 PM, Michael Noll <mich...@confluent.io> wrote: > I second Eno's concern regarding the impact of Streams EOS on state stores. > > > We do a full recovery today and the EOS proposal will not make this > any worse. > > Yes, today we do a full state store recovery under certain failures. > However, I think the point (or perhaps: open question) is that, with the > EOS design, there's now an *increased likelihood* of such failures that > trigger full state store recovery. If this increase is significant, then I > would consider this to be a regression that we should address. > > As Eno said: > > > currently we pay the recovery price for a Kafka Streams instance > failure. > > Now we might pay it for a transaction failure. Will transaction failures > be > > more or less common than the previous types of failures? > > Damian voiced similar concerns at the very beginning of this discussion, > not sure what his current opinion is here. > > -Michael > > > > > > On Wed, Mar 22, 2017 at 1:04 AM, Sriram Subramanian <r...@confluent.io> > wrote: > >> To add to this discussion, I do think we should think about this in >> increments. We do a full recovery today and the EOS proposal will not make >> this any worse. Using store snapshot is a good option to avoid store >> recovery in the future but as Eno points out, all pluggable stores would >> need to have this ability. W.r.t transaction failures, this should not be >> an issue. We should be simply retrying. There is one optimization we can >> do >> for clean shutdowns. We could store a clean shutdown file that contains >> the >> input offsets. This file gets written when you close the streams instance. >> On start, you could can check the offsets from the shutdown file and >> compare it with the offsets we get from the consumer and ensure they >> match. >> If they do, you could use the same store instead of recovering. However, >> if >> we go with the snapshot approach, this will not be required. My vote would >> be to implement V1 and solve the bootstrap problem which exist today in >> the >> future versions. >> >> On Tue, Mar 21, 2017 at 4:43 PM, Matthias J. Sax <matth...@confluent.io> >> wrote: >> >> > Thanks for your feedback Eno. >> > >> > For now, I still think that the KIP itself does not need to talk about >> > this in more detail, because we apply the same strategy for EoS as for >> > non-EoS (as of 0.10.2). >> > >> > Thus, in case of a clean shutdown, we write the checkpoint file for a >> > store and thus know we can reuse the store. In case of failure, we need >> > to recreate the store from the changelog. >> > >> > > Will a V1 design that relies on plain store recovery from Kafka for >> > > each transaction abort be good enough, or usable? >> > >> > Why should it not be usable? It's the same strategy as used in 0.10.2 >> > and it runs in production in many companies already. >> > >> > > however it seems to me we might have a regression of sorts >> > > Now we might pay it for a transaction failure. >> > >> > I would assume transaction failures to be quite rare. Maybe the core EoS >> > folks can comment here, too. >> > >> > >> > >> > -Matthias >> > >> > >> > >> > On 3/20/17 3:16 PM, Eno Thereska wrote: >> > > Hi Matthias, >> > > >> > > I'd like to see some more info on how you propose to handle >> transactions >> > that involve state stores in the KIP itself. The design doc has info >> about >> > various optimisations like RocksDb snapshots and transactions and such, >> but >> > will there be a user-visible interface that indicates whether a store >> has >> > snapshot and/or transactional capabilities? If a user plugs in another >> > store, what guarantees are they expected to get? >> > > >> > > Will a V1 design that relies on plain store recovery from Kafka for >> each >> > transaction abort be good enough, or usable? If your dataset is large >> > (e.g., 200GB) the recovery time might be so large as to effectively >> render >> > that Kafka Streams instance unavailable for tens of minutes. You mention >> > that is not a regression to what we currently have, however it seems to >> me >> > we might have a regression of sorts: currently we pay the recovery price >> > for a Kafka Streams instance failure. Now we might pay it for a >> transaction >> > failure. Will transaction failures be more or less common than the >> previous >> > types of failures? I'd like to see this addressed. >> > > >> > > Thanks >> > > Eno >> > > >> > > >> > > >> > >> On 15 Mar 2017, at 22:09, Matthias J. Sax <matth...@confluent.io> >> > wrote: >> > >> >> > >> Just a quick follow up: >> > >> >> > >> Our overall proposal is, to implement KIP-129 as is as a “Stream EoS >> > >> 1.0” version. The raised concerns are all valid, but hard to >> quantify at >> > >> the moment. Implementing KIP-129, that provides a clean design, >> allows >> > >> us to gain more insight in the performance implications. This enables >> > >> us, to make an educated decision, if the “producer per task” model >> > >> perform wells or not, and if a switch to a “producer per thread” >> model >> > >> is mandatory. >> > >> >> > >> We also want to point out, that we can move incrementally from >> "producer >> > >> per task" to "producer per thread" design or apply some incremental >> > >> improvements to "producer per task" (as discussed in the doc). Thus, >> > >> there is not issue with regard to upgrading. >> > >> >> > >> >> > >> -Matthias >> > >> >> > >> >> > >> >> > >> On 3/15/17 2:36 PM, Matthias J. Sax wrote: >> > >>> Hi, >> > >>> >> > >>> I want to pick up this thread again. As there are some concerns >> about >> > >>> the "producer per task" design, we did write up an alternative >> > "producer >> > >>> per thread" design and discuss pros/cons of both approaches: >> > >>> >> > >>> https://docs.google.com/document/d/1CfOJaa6mdg5o7pLf_ >> > zXISV4oE0ZeMZwT_sG1QWgL4EE >> > >>> >> > >>> >> > >>> Looking forward to your feedback. >> > >>> >> > >>> >> > >>> -Matthias >> > >>> >> > >>> >> > >>> On 3/10/17 3:24 AM, Damian Guy wrote: >> > >>>> Hi Matthias, >> > >>>> >> > >>>> Thanks for the response. I agree with you regarding the use of >> > >>>> PartitionGrouper to reduce the number of tasks. It would be good to >> > have an >> > >>>> idea of any additional load on the brokers as we increase the >> number >> > of >> > >>>> tasks and therefore producers. >> > >>>> >> > >>>> Thanks, >> > >>>> Damian >> > >>>> >> > >>>> On Wed, 8 Mar 2017 at 01:45 Matthias J. Sax <matth...@confluent.io >> > >> > wrote: >> > >>>> >> > >>>>> Damian, Jun, >> > >>>>> >> > >>>>> Thanks for your input. >> > >>>>> >> > >>>>> >> > >>>>> About Performance test: >> > >>>>> >> > >>>>> I can follow up with more performance tests using more partitions >> and >> > >>>>> also collecting broker metrics. >> > >>>>> >> > >>>>> However, I want to highlight again, that even if 1000+ partitions >> > would >> > >>>>> be problematic, one can simply implement PartitionGrouper >> interface >> > and >> > >>>>> reduce the number of tasks to 250 or 100... So I am not sure, if >> we >> > >>>>> should block this KIP, even if there might be some performance >> > penalty >> > >>>>> for currently single partitioned tasks. >> > >>>>> >> > >>>>> About memory usage. JXM max-heap and max-off-heap did report 256MB >> > and >> > >>>>> 133MB for all experiments (thus I did not put it in the >> spreadsheet). >> > >>>>> Thus, using 100 producers (each using a max of 32MB of memory) was >> > not >> > >>>>> an issue with regard to memory consumption. I did not track >> "current >> > >>>>> head/off-heap" memory as this would require a more advance test >> > setup to >> > >>>>> monitor it over time. If you think this would be required, we can >> do >> > >>>>> some tests though. >> > >>>>> >> > >>>>> However, as 256 MB was enough memory, and there are other >> components >> > >>>>> next to the producers using memory, I don't expect a severely >> > increased >> > >>>>> memory usage. Producer allocate memory on-demand, and if load is >> > shared >> > >>>>> over multiple producers, overall memory usage should stay the same >> > as a >> > >>>>> single producer should allocate less memory. >> > >>>>> >> > >>>>> >> > >>>>> About Batching: >> > >>>>> >> > >>>>> As you can see from the benchmarks (in the detailed view -- I also >> > added >> > >>>>> some graphs to the summary now) the average batch size gets >> slightly >> > >>>>> decrease with an increased number of partitions. However, there >> is no >> > >>>>> big difference between "producer per thread" and "producer per >> task" >> > >>>>> scenario. >> > >>>>> >> > >>>>> >> > >>>>> About acks: >> > >>>>> >> > >>>>> This is covered by KIP-98 already. If idempotent producer is use, >> > it's >> > >>>>> required to set max.in.flight.requests.per.connection=1 and >> retries >> > > 0 >> > >>>>> -- otherwise a config exception will be thrown. For transactions, >> > it's >> > >>>>> further required that acks=-1 to avoid a config exception. >> > >>>>> >> > >>>>> Other bits, like min.isr, replication.factor, etc. (ie, all >> > broker/topic >> > >>>>> configs) are out of scope, and it's user responsibility to set >> those >> > >>>>> values correctly to ensure transactionality and idempotency. >> > >>>>> >> > >>>>> >> > >>>>> >> > >>>>> -Matthias >> > >>>>> >> > >>>>> >> > >>>>> On 3/7/17 9:32 AM, Jun Rao wrote: >> > >>>>>> Hi, Guozhang, >> > >>>>>> >> > >>>>>> Thanks for the KIP. A couple of comments. >> > >>>>>> >> > >>>>>> 1. About the impact on producer batching. My understanding is >> that >> > >>>>>> typically different sub-topologies in the same task are >> publishing >> > to >> > >>>>>> different topics. Since the producer batching happens at the >> > >>>>>> topic/partition level, using a producer per task may not impact >> > batching >> > >>>>>> much. >> > >>>>>> >> > >>>>>> 2. When processing.guarantee is set to exactly_once, do we want >> to >> > >>>>> enforce >> > >>>>>> acks to all in the producer? The default acks is 1 and may cause >> > acked >> > >>>>> data >> > >>>>>> to be lost later when the leader changes. >> > >>>>>> >> > >>>>>> Thanks, >> > >>>>>> >> > >>>>>> Jun >> > >>>>>> >> > >>>>>> On Tue, Mar 7, 2017 at 3:42 AM, Damian Guy <damian....@gmail.com >> > >> > wrote: >> > >>>>>> >> > >>>>>>> Hi Matthias, >> > >>>>>>> >> > >>>>>>> Thanks. The perf test is a good start but I don't think it goes >> far >> > >>>>> enough. >> > >>>>>>> 100 partitions is not a lot. What happens when there are >> thousands >> > of >> > >>>>>>> partitions? What is the load on the brokers? How much more >> memory >> > is >> > >>>>> used >> > >>>>>>> by the Streams App etc? >> > >>>>>>> >> > >>>>>>> Thanks, >> > >>>>>>> Damian >> > >>>>>>> >> > >>>>>>> On Tue, 7 Mar 2017 at 03:02 Matthias J. Sax < >> matth...@confluent.io >> > > >> > >>>>> wrote: >> > >>>>>>> >> > >>>>>>>> Hi, >> > >>>>>>>> >> > >>>>>>>> I want to give a first respond: >> > >>>>>>>> >> > >>>>>>>> >> > >>>>>>>> >> > >>>>>>>> 1. Producer per task: >> > >>>>>>>> >> > >>>>>>>> First, we did some performance tests, indicating that the >> > performance >> > >>>>>>>> penalty is small. Please have a look here: >> > >>>>>>>> >> > >>>>>>>> https://docs.google.com/spreadsheets/d/18aGOB13- >> > >>>>>>> ibwHJl5VE27HnHBlNkZAIeLF9yQ99FDxkuM/edit?usp=sharing >> > >>>>>>>> >> > >>>>>>>> For the test, we ran with a trunk version and a modified >> version >> > that >> > >>>>>>>> uses a producer per task (of course, no transactions, but >> > at-least-once >> > >>>>>>>> semantics). The scaling factor indicates the number of brokers >> and >> > >>>>>>>> (single threaded) Streams instances. We used SimpleBenchmark >> that >> > is >> > >>>>>>>> part of AK code base. >> > >>>>>>>> >> > >>>>>>>> >> > >>>>>>>> Second, as the design is "producer per task" (and not "producer >> > per >> > >>>>>>>> partition") it is possible to specify a custom PartitionGrouper >> > that >> > >>>>>>>> assigns multiple partitions to a single task. Thus, it allows >> to >> > reduce >> > >>>>>>>> the number of tasks for scenarios with many partitions. Right >> > now, this >> > >>>>>>>> interface must be implemented solely by the user, but we could >> > also add >> > >>>>>>>> a new config parameter that specifies the max.number.of.tasks >> or >> > >>>>>>>> partitions.per.task so that the user can configure this >> instead of >> > >>>>>>>> implementing the interface. >> > >>>>>>>> >> > >>>>>>>> Third, there is the idea of a "Producer Pool" that would allow >> to >> > share >> > >>>>>>>> resources (network connections, memory, etc) over multiple >> > producers. >> > >>>>>>>> This would allow to separate multiple transaction on the >> producer >> > >>>>> level, >> > >>>>>>>> while resources are shared. There is no detailed design >> document >> > yet >> > >>>>> and >> > >>>>>>>> there would be a KIP for this feature. >> > >>>>>>>> >> > >>>>>>>> Thus, if there should be any performance problems for high >> scale >> > >>>>>>>> scenarios, there are multiple ways to tackle them while keeping >> > the >> > >>>>>>>> "producer per task" design. >> > >>>>>>>> >> > >>>>>>>> Additionally, a "producer per thread" design would be way more >> > complex >> > >>>>>>>> and I summarized the issues in a separate document. I will >> share >> > a link >> > >>>>>>>> to the document soon. >> > >>>>>>>> >> > >>>>>>>> >> > >>>>>>>> >> > >>>>>>>> 2. StateStore recovery: >> > >>>>>>>> >> > >>>>>>>> Streams EoS will in the first design not allow to exploit the >> > >>>>>>>> improvements that are added for 0.11 at the moment. However, as >> > 0.10.2 >> > >>>>>>>> faces the same issues of potentially long recovery, there is no >> > >>>>>>>> regression with this regard. Thus, I see those improvements as >> > >>>>>>>> orthogonal or add-ons. Nevertheless, we should try to explore >> > those >> > >>>>>>>> options and if possible get them into 0.11 such that Streams >> with >> > EoS >> > >>>>>>>> gets the same improvements as at-least-once scenario. >> > >>>>>>>> >> > >>>>>>>> >> > >>>>>>>> >> > >>>>>>>> 3. Caching: >> > >>>>>>>> >> > >>>>>>>> We might need to do some experiments to quantify the impact on >> > caching. >> > >>>>>>>> If it's severe, the suggested default commit interval of 100ms >> > could >> > >>>>>>>> also be increased. Also, EoS will not enforce any commit >> > interval, but >> > >>>>>>>> only change the default value. Thus, a user can freely >> trade-off >> > >>>>> latency >> > >>>>>>>> vs. caching-effect. >> > >>>>>>>> >> > >>>>>>>> Last but not least, there is the idea to allow >> "read_uncommitted" >> > for >> > >>>>>>>> intermediate topic. This would be an advance design for Streams >> > EoS >> > >>>>> that >> > >>>>>>>> allows downstream sub-topologies to read uncommitted data >> > >>>>>>>> optimistically. In case of failure, a cascading abort of >> > transactions >> > >>>>>>>> would be required. This change will need another KIP. >> > >>>>>>>> >> > >>>>>>>> >> > >>>>>>>> >> > >>>>>>>> 4. Idempotent Producer: >> > >>>>>>>> >> > >>>>>>>> The transactional part automatically leverages the idempotent >> > >>>>> properties >> > >>>>>>>> of the producer. Idempotency is a requirement: >> > >>>>>>>> >> > >>>>>>>>> Note that enable.idempotence must be enabled if a >> > TransactionalId is >> > >>>>>>>> configured. >> > >>>>>>>> >> > >>>>>>>> See >> > >>>>>>>> >> > >>>>>>>> >> > >>>>> https://docs.google.com/document/d/11Jqy_ >> > GjUGtdXJK94XGsEIK7CP1SnQGdp2eF >> > >>>>>>> 0wSw9ra8/edit#bookmark=id.g2xsf9n49puh >> > >>>>>>>> >> > >>>>>>>> All idempotent retries, are handled by the producer internally >> > (with or >> > >>>>>>>> without transaction) if enable.idempotence is set to true. >> > >>>>>>>> >> > >>>>>>>> >> > >>>>>>>> >> > >>>>>>>> -Matthias >> > >>>>>>>> >> > >>>>>>>> >> > >>>>>>>> >> > >>>>>>>> On 3/3/17 3:34 AM, Eno Thereska wrote: >> > >>>>>>>>> Another question: >> > >>>>>>>>> >> > >>>>>>>>> The KIP doesn’t exactly spell out how it uses the idempotence >> > >>>>> guarantee >> > >>>>>>>> from KIP-98. It seems that only the transactional part is >> needed. >> > Or is >> > >>>>>>> the >> > >>>>>>>> idempotence guarantee working behind the scenes and helping for >> > some >> > >>>>>>>> scenarios for which it is not worthwhile aborting a transaction >> > (e.g., >> > >>>>>>>> retransmitting a record after a temporary network glitch)? >> > >>>>>>>>> >> > >>>>>>>>> Thanks >> > >>>>>>>>> Eno >> > >>>>>>>>> >> > >>>>>>>>>> On Mar 2, 2017, at 4:56 PM, Jay Kreps <j...@confluent.io> >> wrote: >> > >>>>>>>>>> >> > >>>>>>>>>> I second the concern on with the one producer per task >> > approach. At a >> > >>>>>>>>>> high-level it seems to make sense but I think Damian is >> exactly >> > right >> > >>>>>>>> that >> > >>>>>>>>>> that cuts against the general design of the producer. Many >> > people >> > >>>>> have >> > >>>>>>>> high >> > >>>>>>>>>> input partition counts and will have high task counts as a >> > result. I >> > >>>>>>>> think >> > >>>>>>>>>> processing 1000 partitions should not be an unreasonable >> thing >> > to >> > >>>>> want >> > >>>>>>>> to >> > >>>>>>>>>> do. >> > >>>>>>>>>> >> > >>>>>>>>>> The tricky bits will be: >> > >>>>>>>>>> >> > >>>>>>>>>> - Reduced effectiveness of batching (or more latency and >> > memory to >> > >>>>>>> get >> > >>>>>>>>>> equivalent batching). This doesn't show up in simple >> benchmarks >> > >>>>>>>> because >> > >>>>>>>>>> much of the penalty is I/O and CPU on the broker and the >> > additional >> > >>>>>>>> threads >> > >>>>>>>>>> from all the producers can make a single-threaded benchmark >> > seem >> > >>>>>>>> faster. >> > >>>>>>>>>> - TCP connection explosion. We maintain one connection per >> > broker. >> > >>>>>>>> This >> > >>>>>>>>>> is already high since each app instance does this. This >> design >> > >>>>>>> though >> > >>>>>>>> will >> > >>>>>>>>>> add an additional multiplicative factor based on the >> partition >> > >>>>> count >> > >>>>>>>> of the >> > >>>>>>>>>> input. >> > >>>>>>>>>> - Connection and metadata request storms. When an instance >> with >> > >>>>> 1000 >> > >>>>>>>>>> tasks starts up it is going to try to create many thousands >> of >> > >>>>>>>> connections >> > >>>>>>>>>> and issue a thousand metadata requests all at once. >> > >>>>>>>>>> - Memory usage. We currently default to 64MB per producer. >> > This can >> > >>>>>>> be >> > >>>>>>>>>> tuned down, but the fact that we are spreading the batching >> > over >> > >>>>>>> more >> > >>>>>>>>>> producers will fundamentally mean we need a lot more memory >> to >> > get >> > >>>>>>>> good >> > >>>>>>>>>> perf and the memory usage will change as your task >> assignment >> > >>>>>>> changes >> > >>>>>>>> so it >> > >>>>>>>>>> will be hard to set correctly unless it is done >> automatically. >> > >>>>>>>>>> - Metrics explosion (1000 producer instances, each with >> their >> > own >> > >>>>>>>>>> metrics to monitor). >> > >>>>>>>>>> - Thread explosion, 1000 background threads, one per >> producer, >> > each >> > >>>>>>>>>> sending data. >> > >>>>>>>>>> >> > >>>>>>>>>> -Jay >> > >>>>>>>>>> >> > >>>>>>>>>> On Wed, Mar 1, 2017 at 3:05 AM, Damian Guy < >> > damian....@gmail.com> >> > >>>>>>>> wrote: >> > >>>>>>>>>> >> > >>>>>>>>>>> Hi Guozhang, >> > >>>>>>>>>>> >> > >>>>>>>>>>> Thanks for the KIP! This is an important feature for Kafka >> > Streams >> > >>>>>>> and >> > >>>>>>>> will >> > >>>>>>>>>>> help to unlock a bunch of use cases. >> > >>>>>>>>>>> >> > >>>>>>>>>>> I have some concerns/questions: >> > >>>>>>>>>>> >> > >>>>>>>>>>> 1. Producer per task: I'm worried about the overhead this >> is >> > going >> > >>>>>>> to >> > >>>>>>>>>>> put on both the streams app and the Kafka Brokers. You can >> > easily >> > >>>>>>>>>>> imagine >> > >>>>>>>>>>> an app consuming thousands of partitions. What load will >> this >> > put >> > >>>>>>> on >> > >>>>>>>> the >> > >>>>>>>>>>> brokers? Am i correct in assuming that there will be >> metadata >> > >>>>>>>> requests >> > >>>>>>>>>>> per >> > >>>>>>>>>>> Producer? The memory overhead in the streams app will also >> > >>>>> increase >> > >>>>>>>>>>> fairly >> > >>>>>>>>>>> significantly. Should we adjust >> ProducerConfig.BUFFER_MEMORY_ >> > >>>>>>> CONFIG? >> > >>>>>>>>>>> 2. State Store recovery: As we already know, restoring the >> > entire >> > >>>>>>>>>>> changelog can take an extremely long time. Even with a >> fairly >> > >>>>> small >> > >>>>>>>>>>> dataset >> > >>>>>>>>>>> and an inappropriately tuned segment size, this can take >> way >> > too >> > >>>>>>>> long. >> > >>>>>>>>>>> My >> > >>>>>>>>>>> concern is that failures happen and then recovery takes >> > "forever" >> > >>>>>>>> and we >> > >>>>>>>>>>> end up in a situation where we need to change the >> > >>>>> max.poll.interval >> > >>>>>>>> to >> > >>>>>>>>>>> be >> > >>>>>>>>>>> some very large number or else we end up in "rebalance >> hell". >> > I >> > >>>>>>> don't >> > >>>>>>>>>>> think >> > >>>>>>>>>>> this provides a very good user experience. You mention >> RocksDB >> > >>>>>>>>>>> checkpointing in the doc - should we explore this idea some >> > more? >> > >>>>>>>> i.e., >> > >>>>>>>>>>> understand the penalty for checkpointing. Maybe checkpoint >> > every >> > >>>>>>> *n* >> > >>>>>>>>>>> commits? >> > >>>>>>>>>>> 3. What does EoS mean for Caching? If we set the commit >> > interval >> > >>>>> to >> > >>>>>>>>>>> 100ms then the cache is not going to be very effective. >> > Should it >> > >>>>>>>> just >> > >>>>>>>>>>> be >> > >>>>>>>>>>> disabled? >> > >>>>>>>>>>> >> > >>>>>>>>>>> Thanks, >> > >>>>>>>>>>> Damian >> > >>>>>>>>>>> >> > >>>>>>>>>>> On Tue, 28 Feb 2017 at 21:54 Guozhang Wang < >> wangg...@gmail.com >> > > >> > >>>>>>> wrote: >> > >>>>>>>>>>> >> > >>>>>>>>>>>> Hi all, >> > >>>>>>>>>>>> >> > >>>>>>>>>>>> I have just created KIP-129 to leverage KIP-98 in Kafka >> > Streams and >> > >>>>>>>>>>> provide >> > >>>>>>>>>>>> exactly-once processing semantics: >> > >>>>>>>>>>>> >> > >>>>>>>>>>>> >> > >>>>>>>>>>>> https://cwiki.apache.org/confluence/display/KAFKA/KIP- >> > >>>>>>>>>>> 129%3A+Streams+Exactly-Once+Semantics >> > >>>>>>>>>>>> >> > >>>>>>>>>>>> This KIP enables Streams users to optionally turn on >> > exactly-once >> > >>>>>>>>>>>> processing semantics without changing their app code at >> all by >> > >>>>>>>> leveraging >> > >>>>>>>>>>>> the transactional messaging features provided in KIP-98. >> > >>>>>>>>>>>> >> > >>>>>>>>>>>> The above wiki page provides a high-level view of the >> proposed >> > >>>>>>>> changes, >> > >>>>>>>>>>>> while detailed implementation design can be found in this >> > Google >> > >>>>>>> doc: >> > >>>>>>>>>>>> >> > >>>>>>>>>>>> >> > >>>>>>>>>>>> https://docs.google.com/document/d/ >> > 1pGZ8xtOOyGwDYgH5vA6h19zOMMadu >> > >>>>>>>>>>> FK1DAB8_gBYA2c >> > >>>>>>>>>>>> >> > >>>>>>>>>>>> We would love to hear your comments and suggestions. >> > >>>>>>>>>>>> >> > >>>>>>>>>>>> Thanks, >> > >>>>>>>>>>>> -- Guozhang >> > >>>>>>>>>>>> >> > >>>>>>>>>>> >> > >>>>>>>>> >> > >>>>>>>> >> > >>>>>>>> >> > >>>>>>> >> > >>>>>> >> > >>>>> >> > >>>>> >> > >>>> >> > >>> >> > >> >> > > >> > >> > >> > >
