I think it's fair to assume that topics will always be increased by an integer factor - e.g. from 2 partitions to 4 partitions. Then the mapping is much easier.
Why anyone would increase partitions by lass than x2 is a mystery to me. If your two partitions cannot handle the load, then with three partitions each one will still get 67% of that load which is still way too dangerous. So in your case we go from part1: A B C D part2: E F G H to part1: A C part2: B D part3: E F part4: G H ________________________________ From: Matthias J. Sax <matth...@confluent.io> Sent: 09 March 2018 07:53 To: dev@kafka.apache.org Subject: Re: [DISCUSS] KIP-253: Support in-order message delivery with partition expansion @Jan: You suggest to copy the data from one topic to a new topic, and provide an "offset mapping" from the old to the new topic for the consumers. I don't quite understand how this would work. Let's say there are 2 partitions in the original topic and 3 partitions in the new topic. If we assume that we don't use linear hashing as you suggest, there is no guarantee how data will be distributed in the new topic and also no guarantee about ordering of records in the new topic. Example (I hope I got it right -- please correct me if it's wrong) A B C D E F G H could be copied to: A C H B E F D G If the consumer was at offset 1 and 2 in the first topic how would the mapping be computed? We need to enures that B C D as well as G H are read after the switch. Thus, offset would need to be 1 0 0. I am not sure how this would be computed? Furthermore, I want to point out that the new offsets would imply that E is consumed a second time by the consumer. E and F were consumed originally, but E is copied after B that was not yet consumed. Or is there a way that we can ensure that this "flip" does never happen while we copy the data? -Matthias On 3/8/18 10:32 PM, Matthias J. Sax wrote: > As I just mentioned joins: > > For Kafka Streams it might also be required to change the partition > count for multiple topics in a coordinated way that allows to maintain > the co-partitioning property that Kafka Streams uses to computed joins. > > Any thoughts how this could be handled? > > > -Matthias > > On 3/8/18 10:08 PM, Matthias J. Sax wrote: >> Jun, >> >> There is one more case: non-windowed aggregations. For windowed >> aggregation, the changelog topic will be compact+delete. However, for >> non-windowed aggregation the policy is compact only. >> >> Even if we assume that windowed aggregations are dominant and >> non-windowed aggregation are used rarely, it seems to be bad to not >> support the feature is a non-windowed aggregation is used. Also, >> non-windowed aggregation volume depends on input-stream volume that >> might be high. >> >> Furthermore, we support stream-table join and this requires that the >> stream and the table are co-partitioned. Thus, even if the table would >> have lower volume but the stream must be scaled out, we also need to >> scale out the table to preserve co-partitioning. >> >> >> -Matthias >> >> On 3/8/18 6:44 PM, Jun Rao wrote: >>> Hi, Matthis, >>> >>> My understanding is that in KStream, the only case when a changelog topic >>> needs to be compacted is when the corresponding input is a KTable. In all >>> other cases, the changelog topics are of compacted + deletion. So, if most >>> KTables are not high volume, there may not be a need to expand its >>> partitions and therefore the partitions of the corresponding changelog >>> topic. >>> >>> Thanks, >>> >>> Jun >>> >>> On Wed, Mar 7, 2018 at 2:34 PM, Matthias J. Sax <matth...@confluent.io> >>> wrote: >>> >>>> Jun, >>>> >>>> thanks for your comment. This should actually work for Streams, because >>>> we don't rely on producer "hashing" but specify the partition number >>>> explicitly on send(). >>>> >>>> About not allowing to change the number of partition for changelog >>>> topics: for Streams, this seems to imply that we need to create a second >>>> changelog topic for each store with the new partition count. However, it >>>> would be unclear when/if we can delete the old topic. Thus, it moves the >>>> "problem" into the application layer. It's hard to judge for me atm what >>>> the impact would be, but it's something we should pay attention to. >>>> >>>> >>>> -Matthias >>>> >>>> On 3/6/18 3:45 PM, Jun Rao wrote: >>>>> Hi, Mattias, >>>>> >>>>> Regarding your comment "If it would be time-delay based, it might be >>>>> problematic >>>>> for Kafka Streams: if we get the information that the new input >>>> partitions >>>>> are available for producing, we need to enable the new changelog >>>> partitions >>>>> for producing, too. If those would not be available yet, because the >>>>> time-delay did not trigger yet, it would be problematic to avoid >>>>> crashing.", could you just enable the changelog topic to write to its new >>>>> partitions immediately? The input topic can be configured with a delay >>>> in >>>>> writing to the new partitions. Initially, there won't be new data >>>> produced >>>>> into the newly added partitions in the input topic. However, we could >>>>> prebuild the state for the new input partition and write the state >>>> changes >>>>> to the corresponding new partitions in the changelog topic. >>>>> >>>>> Hi, Jan, >>>>> >>>>> For a compacted topic, garbage collecting the old keys in the existing >>>>> partitions after partition expansion can be tricky as your pointed out. A >>>>> few options here. (a) Let brokers exchange keys across brokers during >>>>> compaction. This will add complexity on the broker side. (b) Build an >>>>> external tool that scans the compacted topic and drop the prefix of a >>>>> partition if all records in the prefix are removable. The admin can then >>>>> run this tool when the unneeded space needs to be reclaimed. (c) Don't >>>>> support partition change in a compacted topic. This might be ok since >>>> most >>>>> compacted topics are not high volume. >>>>> >>>>> Thanks, >>>>> >>>>> Jun >>>>> >>>>> >>>>> On Tue, Mar 6, 2018 at 10:38 AM, Dong Lin <lindon...@gmail.com> wrote: >>>>> >>>>>> Hi everyone, >>>>>> >>>>>> Thanks for all the comments! It appears that everyone prefers linear >>>>>> hashing because it reduces the amount of state that needs to be moved >>>>>> between consumers (for stream processing). The KIP has been updated to >>>> use >>>>>> linear hashing. >>>>>> >>>>>> Regarding the migration endeavor: it seems that migrating producer >>>> library >>>>>> to use linear hashing should be pretty straightforward without >>>>>> much operational endeavor. If we don't upgrade client library to use >>>> this >>>>>> KIP, we can not support in-order delivery after partition is changed >>>>>> anyway. Suppose we upgrade client library to use this KIP, if partition >>>>>> number is not changed, the key -> partition mapping will be exactly the >>>>>> same as it is now because it is still determined using murmur_hash(key) >>>> % >>>>>> original_partition_num. In other words, this change is backward >>>> compatible. >>>>>> >>>>>> Regarding the load distribution: if we use linear hashing, the load may >>>> be >>>>>> unevenly distributed because those partitions which are not split may >>>>>> receive twice as much traffic as other partitions that are split. This >>>>>> issue can be mitigated by creating topic with partitions that are >>>> several >>>>>> times the number of consumers. And there will be no imbalance if the >>>>>> partition number is always doubled. So this imbalance seems acceptable. >>>>>> >>>>>> Regarding storing the partition strategy as per-topic config: It seems >>>> not >>>>>> necessary since we can still use murmur_hash as the default hash >>>> function >>>>>> and additionally apply the linear hashing algorithm if the partition >>>> number >>>>>> has increased. Not sure if there is any use-case for producer to use a >>>>>> different hash function. Jason, can you check if there is some use-case >>>>>> that I missed for using the per-topic partition strategy? >>>>>> >>>>>> Regarding how to reduce latency (due to state store/load) in stream >>>>>> processing consumer when partition number changes: I need to read the >>>> Kafka >>>>>> Stream code to understand how Kafka Stream currently migrate state >>>> between >>>>>> consumers when the application is added/removed for a given job. I will >>>>>> reply after I finish reading the documentation and code. >>>>>> >>>>>> >>>>>> Thanks, >>>>>> Dong >>>>>> >>>>>> >>>>>> On Mon, Mar 5, 2018 at 10:43 AM, Jason Gustafson <ja...@confluent.io> >>>>>> wrote: >>>>>> >>>>>>> Great discussion. I think I'm wondering whether we can continue to >>>> leave >>>>>>> Kafka agnostic to the partitioning strategy. The challenge is >>>>>> communicating >>>>>>> the partitioning logic from producers to consumers so that the >>>>>> dependencies >>>>>>> between each epoch can be determined. For the sake of discussion, >>>> imagine >>>>>>> you did something like the following: >>>>>>> >>>>>>> 1. The name (and perhaps version) of a partitioning strategy is stored >>>> in >>>>>>> topic configuration when a topic is created. >>>>>>> 2. The producer looks up the partitioning strategy before writing to a >>>>>>> topic and includes it in the produce request (for fencing). If it >>>> doesn't >>>>>>> have an implementation for the configured strategy, it fails. >>>>>>> 3. The consumer also looks up the partitioning strategy and uses it to >>>>>>> determine dependencies when reading a new epoch. It could either fail >>>> or >>>>>>> make the most conservative dependency assumptions if it doesn't know >>>> how >>>>>> to >>>>>>> implement the partitioning strategy. For the consumer, the new >>>> interface >>>>>>> might look something like this: >>>>>>> >>>>>>> // Return the partition dependencies following an epoch bump >>>>>>> Map<Integer, List<Integer>> dependencies(int >>>>>> numPartitionsBeforeEpochBump, >>>>>>> int numPartitionsAfterEpochBump) >>>>>>> >>>>>>> The unordered case then is just a particular implementation which never >>>>>> has >>>>>>> any epoch dependencies. To implement this, we would need some way for >>>> the >>>>>>> consumer to find out how many partitions there were in each epoch, but >>>>>>> maybe that's not too unreasonable. >>>>>>> >>>>>>> Thanks, >>>>>>> Jason >>>>>>> >>>>>>> >>>>>>> On Mon, Mar 5, 2018 at 4:51 AM, Jan Filipiak <jan.filip...@trivago.com >>>>> >>>>>>> wrote: >>>>>>> >>>>>>>> Hi Dong >>>>>>>> >>>>>>>> thank you very much for your questions. >>>>>>>> >>>>>>>> regarding the time spend copying data across: >>>>>>>> It is correct that copying data from a topic with one partition >>>> mapping >>>>>>> to >>>>>>>> a topic with a different partition mapping takes way longer than we >>>> can >>>>>>>> stop producers. Tens of minutes is a very optimistic estimate here. >>>>>> Many >>>>>>>> people can not afford copy full steam and therefore will have some >>>> rate >>>>>>>> limiting in place, this can bump the timespan into the day's. The good >>>>>>> part >>>>>>>> is that the vast majority of the data can be copied while the >>>> producers >>>>>>> are >>>>>>>> still going. One can then, piggyback the consumers ontop of this >>>>>>> timeframe, >>>>>>>> by the method mentioned (provide them an mapping from their old >>>> offsets >>>>>>> to >>>>>>>> new offsets in their repartitioned topics. In that way we separate >>>>>>>> migration of consumers from migration of producers (decoupling these >>>> is >>>>>>>> what kafka is strongest at). The time to actually swap over the >>>>>> producers >>>>>>>> should be kept minimal by ensuring that when a swap attempt is started >>>>>>> the >>>>>>>> consumer copying over should be very close to the log end and is >>>>>> expected >>>>>>>> to finish within the next fetch. The operation should have a time-out >>>>>> and >>>>>>>> should be "reattemtable". >>>>>>>> >>>>>>>> Importance of logcompaction: >>>>>>>> If a producer produces key A, to partiton 0, its forever gonna be >>>>>> there, >>>>>>>> unless it gets deleted. The record might sit in there for years. A new >>>>>>>> producer started with the new partitions will fail to delete the >>>> record >>>>>>> in >>>>>>>> the correct partition. Th record will be there forever and one can not >>>>>>>> reliable bootstrap new consumers. I cannot see how linear hashing can >>>>>>> solve >>>>>>>> this. >>>>>>>> >>>>>>>> Regarding your skipping of userland copying: >>>>>>>> 100%, copying the data across in userland is, as far as i can see, >>>>>> only a >>>>>>>> usecase for log compacted topics. Even for logcompaction + retentions >>>>>> it >>>>>>>> should only be opt-in. Why did I bring it up? I think log compaction >>>>>> is a >>>>>>>> very important feature to really embrace kafka as a "data plattform". >>>>>> The >>>>>>>> point I also want to make is that copying data this way is completely >>>>>>>> inline with the kafka architecture. it only consists of reading and >>>>>>> writing >>>>>>>> to topics. >>>>>>>> >>>>>>>> I hope it clarifies more why I think we should aim for more than the >>>>>>>> current KIP. I fear that once the KIP is done not much more effort >>>> will >>>>>>> be >>>>>>>> taken. >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> On 04.03.2018 02:28, Dong Lin wrote: >>>>>>>> >>>>>>>>> Hey Jan, >>>>>>>>> >>>>>>>>> In the current proposal, the consumer will be blocked on waiting for >>>>>>> other >>>>>>>>> consumers of the group to consume up to a given offset. In most >>>> cases, >>>>>>> all >>>>>>>>> consumers should be close to the LEO of the partitions when the >>>>>>> partition >>>>>>>>> expansion happens. Thus the time waiting should not be long e.g. on >>>>>> the >>>>>>>>> order of seconds. On the other hand, it may take a long time to wait >>>>>> for >>>>>>>>> the entire partition to be copied -- the amount of time is >>>>>> proportional >>>>>>> to >>>>>>>>> the amount of existing data in the partition, which can take tens of >>>>>>>>> minutes. So the amount of time that we stop consumers may not be on >>>>>> the >>>>>>>>> same order of magnitude. >>>>>>>>> >>>>>>>>> If we can implement this suggestion without copying data over in >>>> purse >>>>>>>>> userland, it will be much more valuable. Do you have ideas on how >>>> this >>>>>>> can >>>>>>>>> be done? >>>>>>>>> >>>>>>>>> Not sure why the current KIP not help people who depend on log >>>>>>> compaction. >>>>>>>>> Could you elaborate more on this point? >>>>>>>>> >>>>>>>>> Thanks, >>>>>>>>> Dong >>>>>>>>> >>>>>>>>> On Wed, Feb 28, 2018 at 10:55 PM, Jan Filipiak<Jan.Filipiak@trivago. >>>>>> com >>>>>>>> >>>>>>>>> wrote: >>>>>>>>> >>>>>>>>> Hi Dong, >>>>>>>>>> >>>>>>>>>> I tried to focus on what the steps are one can currently perform to >>>>>>>>>> expand >>>>>>>>>> or shrink a keyed topic while maintaining a top notch semantics. >>>>>>>>>> I can understand that there might be confusion about "stopping the >>>>>>>>>> consumer". It is exactly the same as proposed in the KIP. there >>>> needs >>>>>>> to >>>>>>>>>> be >>>>>>>>>> a time the producers agree on the new partitioning. The extra >>>>>>> semantics I >>>>>>>>>> want to put in there is that we have a possibility to wait until all >>>>>>> the >>>>>>>>>> existing data >>>>>>>>>> is copied over into the new partitioning scheme. When I say stopping >>>>>> I >>>>>>>>>> think more of having a memory barrier that ensures the ordering. I >>>> am >>>>>>>>>> still >>>>>>>>>> aming for latencies on the scale of leader failovers. >>>>>>>>>> >>>>>>>>>> Consumers have to explicitly adapt the new partitioning scheme in >>>> the >>>>>>>>>> above scenario. The reason is that in these cases where you are >>>>>>> dependent >>>>>>>>>> on a particular partitioning scheme, you also have other topics that >>>>>>> have >>>>>>>>>> co-partition enforcements or the kind -frequently. Therefore all >>>> your >>>>>>>>>> other >>>>>>>>>> input topics might need to grow accordingly. >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> What I was suggesting was to streamline all these operations as best >>>>>> as >>>>>>>>>> possible to have "real" partition grow and shrinkage going on. >>>>>>> Migrating >>>>>>>>>> the producers to a new partitioning scheme can be much more >>>>>> streamlined >>>>>>>>>> with proper broker support for this. Migrating consumer is a step >>>>>> that >>>>>>>>>> might be made completly unnecessary if - for example streams - takes >>>>>>> the >>>>>>>>>> gcd as partitioning scheme instead of enforcing 1 to 1. Connect >>>>>>> consumers >>>>>>>>>> and other consumers should be fine anyways. >>>>>>>>>> >>>>>>>>>> I hope this makes more clear where I was aiming at. The rest needs >>>> to >>>>>>> be >>>>>>>>>> figured out. The only danger i see is that when we are introducing >>>>>> this >>>>>>>>>> feature as supposed in the KIP, it wont help any people depending on >>>>>>> log >>>>>>>>>> compaction. >>>>>>>>>> >>>>>>>>>> The other thing I wanted to mention is that I believe the current >>>>>>>>>> suggestion (without copying data over) can be implemented in pure >>>>>>>>>> userland >>>>>>>>>> with a custom partitioner and a small feedbackloop from >>>>>> ProduceResponse >>>>>>>>>> => >>>>>>>>>> Partitionier in coorporation with a change management system. >>>>>>>>>> >>>>>>>>>> Best Jan >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> On 28.02.2018 07:13, Dong Lin wrote: >>>>>>>>>> >>>>>>>>>> Hey Jan, >>>>>>>>>>> >>>>>>>>>>> I am not sure if it is acceptable for producer to be stopped for a >>>>>>>>>>> while, >>>>>>>>>>> particularly for online application which requires low latency. I >>>> am >>>>>>>>>>> also >>>>>>>>>>> not sure how consumers can switch to a new topic. Does user >>>>>>> application >>>>>>>>>>> needs to explicitly specify a different topic for producer/consumer >>>>>> to >>>>>>>>>>> subscribe to? It will be helpful for discussion if you can provide >>>>>>> more >>>>>>>>>>> detail on the interface change for this solution. >>>>>>>>>>> >>>>>>>>>>> Thanks, >>>>>>>>>>> Dong >>>>>>>>>>> >>>>>>>>>>> On Mon, Feb 26, 2018 at 12:48 AM, Jan >>>> Filipiak<Jan.Filipiak@trivago. >>>>>>> com >>>>>>>>>>>> >>>>>>>>>>> wrote: >>>>>>>>>>> >>>>>>>>>>> Hi, >>>>>>>>>>> >>>>>>>>>>>> just want to throw my though in. In general the functionality is >>>>>> very >>>>>>>>>>>> usefull, we should though not try to find the architecture to hard >>>>>>>>>>>> while >>>>>>>>>>>> implementing. >>>>>>>>>>>> >>>>>>>>>>>> The manual steps would be to >>>>>>>>>>>> >>>>>>>>>>>> create a new topic >>>>>>>>>>>> the mirrormake from the new old topic to the new topic >>>>>>>>>>>> wait for mirror making to catch up. >>>>>>>>>>>> then put the consumers onto the new topic >>>>>>>>>>>> (having mirrormaker spit out a mapping from old offsets to >>>>>> new >>>>>>>>>>>> offsets: >>>>>>>>>>>> if topic is increased by factor X there is gonna be a >>>>>> clean >>>>>>>>>>>> mapping from 1 offset in the old topic to X offsets in the new >>>>>> topic, >>>>>>>>>>>> if there is no factor then there is no chance to >>>>>> generate a >>>>>>>>>>>> mapping that can be reasonable used for continuing) >>>>>>>>>>>> make consumers stop at appropriate points and continue >>>>>>>>>>>> consumption >>>>>>>>>>>> with offsets from the mapping. >>>>>>>>>>>> have the producers stop for a minimal time. >>>>>>>>>>>> wait for mirrormaker to finish >>>>>>>>>>>> let producer produce with the new metadata. >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> Instead of implementing the approach suggest in the KIP which will >>>>>>>>>>>> leave >>>>>>>>>>>> log compacted topic completely crumbled and unusable. >>>>>>>>>>>> I would much rather try to build infrastructure to support the >>>>>>>>>>>> mentioned >>>>>>>>>>>> above operations more smoothly. >>>>>>>>>>>> Especially having producers stop and use another topic is >>>> difficult >>>>>>> and >>>>>>>>>>>> it would be nice if one can trigger "invalid metadata" exceptions >>>>>> for >>>>>>>>>>>> them >>>>>>>>>>>> and >>>>>>>>>>>> if one could give topics aliases so that their produces with the >>>>>> old >>>>>>>>>>>> topic >>>>>>>>>>>> will arrive in the new topic. >>>>>>>>>>>> >>>>>>>>>>>> The downsides are obvious I guess ( having the same data twice for >>>>>>> the >>>>>>>>>>>> transition period, but kafka tends to scale well with datasize). >>>> So >>>>>>>>>>>> its a >>>>>>>>>>>> nicer fit into the architecture. >>>>>>>>>>>> >>>>>>>>>>>> I further want to argument that the functionality by the KIP can >>>>>>>>>>>> completely be implementing in "userland" with a custom partitioner >>>>>>> that >>>>>>>>>>>> handles the transition as needed. I would appreciate if someone >>>>>> could >>>>>>>>>>>> point >>>>>>>>>>>> out what a custom partitioner couldn't handle in this case? >>>>>>>>>>>> >>>>>>>>>>>> With the above approach, shrinking a topic becomes the same steps. >>>>>>>>>>>> Without >>>>>>>>>>>> loosing keys in the discontinued partitions. >>>>>>>>>>>> >>>>>>>>>>>> Would love to hear what everyone thinks. >>>>>>>>>>>> >>>>>>>>>>>> Best Jan >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> On 11.02.2018 00:35, Dong Lin wrote: >>>>>>>>>>>> >>>>>>>>>>>> Hi all, >>>>>>>>>>>> >>>>>>>>>>>>> I have created KIP-253: Support in-order message delivery with >>>>>>>>>>>>> partition >>>>>>>>>>>>> expansion. See >>>>>>>>>>>>> https://cwiki.apache.org/confluence/display/KAFKA/KIP-253% >>>>>>>>>>>>> 3A+Support+in-order+message+delivery+with+partition+expansion >>>>>>>>>>>>> . >>>>>>>>>>>>> >>>>>>>>>>>>> This KIP provides a way to allow messages of the same key from >>>> the >>>>>>>>>>>>> same >>>>>>>>>>>>> producer to be consumed in the same order they are produced even >>>>>> if >>>>>>> we >>>>>>>>>>>>> expand partition of the topic. >>>>>>>>>>>>> >>>>>>>>>>>>> Thanks, >>>>>>>>>>>>> Dong >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>>> >>> >> >
