Hey Jan, Thanks for the enthusiasm in improving Kafka's design. Now that I have read through your discussion with Jun, here are my thoughts:
- The latest proposal should with log compacted topics by properly deleting old messages after a new message with the same key is produced. So it is probably not a concern anymore. Could you comment if there is still issue? - I wrote the SEP-5 and I am pretty familiar with the motivation and the design of SEP-5. SEP-5 is probably orthornal to the motivation of this KIP. The goal of SEP-5 is to allow user to increase task number of an existing Samza job. But if we increase the partition number of input topics, messages may still be consumed out-of-order by tasks in Samza which cause incorrect result. Similarly, the approach you proposed does not seem to ensure that the messages can be delivered in order, even if we can make sure that each consumer instance is assigned the set of new partitions covering the same set of keys. - I am trying to understand why it is better to copy the data instead of copying the change log topic for streaming use-case. For core Kafka use-case, and for the stream use-case that does not need to increase consumers, the current KIP already supports in-order delivery without the overhead of copying the data. For stream use-case that needs to increase consumer number, the existing consumer can backfill the existing data in the change log topic to the same change log topic with the new partition number, before the new set of consumers bootstrap state from the new partitions of the change log topic. If this solution works, then could you summarize the advantage of copying the data of input topic as compared to copying the change log topic? For example, does it enable more use-case, simplify the implementation of Kafka library, or reduce the operation overhead etc? Thanks, Dong On Wed, Mar 21, 2018 at 6:57 AM, Jan Filipiak <jan.filip...@trivago.com> wrote: > Hi Jun, > > I was really seeing progress in our conversation but your latest reply is > just devastating. > I though we were getting close being on the same page now it feels like we > are in different libraries. > > I just quickly slam my answers in here. If they are to brief I am sorry > give me a ping and try to go into details more. > Just want to show that your pro/cons listing is broken. > > Best Jan > > and want to get rid of this horrible compromise > > > On 19.03.2018 05:48, Jun Rao wrote: > >> Hi, Jan, >> >> Thanks for the discussion. Great points. >> >> Let me try to summarize the approach that you are proposing. On the broker >> side, we reshuffle the existing data in a topic from current partitions to >> the new partitions. Once the reshuffle fully catches up, switch the >> consumers to start consuming from the new partitions. If a consumer needs >> to rebuild its local state (due to partition changes), let the consumer >> rebuild its state by reading all existing data from the new partitions. >> Once all consumers have switches over, cut over the producer to the new >> partitions. >> >> The pros for this approach are that : >> 1. There is just one way to rebuild the local state, which is simpler. >> > true thanks > >> >> The cons for this approach are: >> 1. Need to copy existing data. >> > Very unfair and not correct. It does not require you to copy over existing > data. It _allows_ you to copy all existing data. > > 2. The cutover of the producer is a bit complicated since it needs to >> coordinate with all consumer groups. >> > Also not true. I explicitly tried to make clear that there is only one > special consumer (in the case of actually copying data) coordination is > required. > >> 3. The rebuilding of the state in the consumer is from the input topic, >> which can be more expensive than rebuilding from the existing state. >> > true, but rebuilding state is only required if you want to increase > processing power, so we assume this is at hand. > >> 4. The broker potentially has to know the partitioning function. If this >> needs to be customized at the topic level, it can be a bit messy. >> > I would argue against having the operation being performed by the broker. > This was not discussed yet but if you see my original email i suggested > otherwise from the beginning. > >> >> Here is an alternative approach by applying your idea not in the broker, >> but in the consumer. When new partitions are added, we don't move existing >> data. In KStreams, we first reshuffle the new input data to a new topic T1 >> with the old number of partitions and feed T1's data to the rest of the >> pipeline. In the meantime, KStreams reshuffles all existing data of the >> change capture topic to another topic C1 with the new number of >> partitions. >> We can then build the state of the new tasks from C1. Once the new states >> have been fully built, we can cut over the consumption to the input topic >> and delete T1. This approach works with compacted topic too. If an >> application reads from the beginning of a compacted topic, the consumer >> will reshuffle the portion of the input when the number of partitions >> doesn't match the number of tasks. >> > We all wipe this idea from our heads instantly. Mixing Ideas from an > argument is not a resolution strategy > just leads to horrible horrible software. > > >> The pros of this approach are: >> 1. No need to copy existing data. >> 2. Each consumer group can cut over to the new partitions independently. >> 3. The state is rebuilt from the change capture topic, which is cheaper >> than rebuilding from the input topic. >> 4. Only the KStreams job needs to know the partitioning function. >> >> The cons of this approach are: >> 1. Potentially the same input topic needs to be reshuffled more than once >> in different consumer groups during the transition phase. >> >> What do you think? >> >> Thanks, >> >> Jun >> >> >> >> On Thu, Mar 15, 2018 at 1:04 AM, Jan Filipiak <jan.filip...@trivago.com> >> wrote: >> >> Hi Jun, >>> >>> thank you for following me on these thoughts. It was important to me to >>> feel that kind of understanding for my arguments. >>> >>> What I was hoping for (I mentioned this earlier) is that we can model the >>> case where we do not want to copy the data the exact same way as the case >>> when we do copy the data. Maybe you can peek into the mails before to see >>> more details for this. >>> >>> This means we have the same mechanism to transfer consumer groups to >>> switch topic. The offset mapping that would be generated would even be >>> simpler End Offset of the Old topic => offset 0 off all the partitions of >>> the new topic. Then we could model the transition of a non-copy expansion >>> the exact same way as a copy-expansion. >>> >>> I know this only works when topic growth by a factor. But the benefits of >>> only growing by a factor are to strong anyways. See Clemens's hint and >>> remember that state reshuffling is entirely not needed if one doesn't >>> want >>> to grow processing power. >>> >>> I think these benefits should be clear, and that there is basically no >>> downside to what is currently at hand but just makes everything easy. >>> >>> One thing you need to know is. that if you do not offer rebuilding a log >>> compacted topic like i suggest that even if you have consumer state >>> reshuffling. The topic is broken and can not be used to bootstrap new >>> consumers. They don't know if they need to apply a key from and old >>> partition or not. This is a horrible downside I haven't seen a solution >>> for >>> in the email conversation. >>> >>> I argue to: >>> >>> Only grow topic by a factor always. >>> Have the "no copy consumer" transition as the trivial case of the "copy >>> consumer transition". >>> If processors needs to be scaled, let them rebuild from the new topic and >>> leave the old running in the mean time. >>> Do not implement key shuffling in streams. >>> >>> I hope I can convince you especially with the fact how I want to handle >>> consumer transition. I think >>> you didn't quite understood me there before. I think the term "new topic" >>> intimidated you a little. >>> How we solve this on disc doesn't really matter, If the data goes into >>> the >>> same Dir or a different Dir or anything. I do think that it needs to >>> involve at least rolling a new segment for the existing partitions. >>> But most of the transitions should work without restarting consumers. >>> (newer consumers with support for this). But with new topic i just meant >>> the topic that now has a different partition count. Plenty of ways to >>> handle that (versions, aliases) >>> >>> Hope I can further get my idea across. >>> >>> Best Jan >>> >>> >>> >>> >>> >>> >>> On 14.03.2018 02:45, Jun Rao wrote: >>> >>> Hi, Jan, >>>> >>>> Thanks for sharing your view. >>>> >>>> I agree with you that recopying the data potentially makes the state >>>> management easier since the consumer can just rebuild its state from >>>> scratch (i.e., no need for state reshuffling). >>>> >>>> On the flip slide, I saw a few disadvantages of the approach that you >>>> suggested. (1) Building the state from the input topic from scratch is >>>> in >>>> general less efficient than state reshuffling. Let's say one computes a >>>> count per key from an input topic. The former requires reading all >>>> existing >>>> records in the input topic whereas the latter only requires reading data >>>> proportional to the number of unique keys. (2) The switching of the >>>> topic >>>> needs modification to the application. If there are many applications >>>> on a >>>> topic, coordinating such an effort may not be easy. Also, it's not clear >>>> how to enforce exactly-once semantic during the switch. (3) If a topic >>>> doesn't need any state management, recopying the data seems wasteful. In >>>> that case, in place partition expansion seems more desirable. >>>> >>>> I understand your concern about adding complexity in KStreams. But, >>>> perhaps >>>> we could iterate on that a bit more to see if it can be simplified. >>>> >>>> Jun >>>> >>>> >>>> On Mon, Mar 12, 2018 at 11:21 PM, Jan Filipiak < >>>> jan.filip...@trivago.com> >>>> wrote: >>>> >>>> Hi Jun, >>>> >>>>> I will focus on point 61 as I think its _the_ fundamental part that I >>>>> cant >>>>> get across at the moment. >>>>> >>>>> Kafka is the platform to have state materialized multiple times from >>>>> one >>>>> input. I emphasize this: It is the building block in architectures that >>>>> allow you to >>>>> have your state maintained multiple times. You put a message in once, >>>>> and >>>>> you have it pop out as often as you like. I believe you understand >>>>> this. >>>>> >>>>> Now! The path of thinking goes the following: I am using apache kafka >>>>> and >>>>> I _want_ my state multiple times. What am I going todo? >>>>> >>>>> A) Am I going to take my state that I build up, plunge some sort of RPC >>>>> layer ontop of it, use that RPC layer to throw my records across >>>>> instances? >>>>> B) Am I just going to read the damn message twice? >>>>> >>>>> Approach A is fundamentally flawed and a violation of all that is good >>>>> and >>>>> holy in kafka deployments. I can not understand how this Idea can come >>>>> in >>>>> the first place. >>>>> (I do understand: IQ in streams, they polluted the kafka streams >>>>> codebase >>>>> really bad already. It is not funny! I think they are equally flawed as >>>>> A) >>>>> >>>>> I say, we do what Kafka is good at. We repartition the topic once. We >>>>> switch the consumers. >>>>> (Those that need more partitions are going to rebuild their state in >>>>> multiple partitions by reading the new topic, those that don't just >>>>> assign >>>>> the new partitions properly) >>>>> We switch producers. Done! >>>>> >>>>> The best thing! It is trivial, hipster stream processor will have an >>>>> easy >>>>> time with that aswell. Its so super simple. And simple IS good! >>>>> It is what kafka was build todo. It is how we do it today. All I am >>>>> saying >>>>> is that a little broker help doing the producer swap is super useful. >>>>> >>>>> For everyone interested in why kafka is so powerful with approach B, >>>>> please watch https://youtu.be/bEbeZPVo98c?t=1633 >>>>> I already looked up a good point in time, I think after 5 minutes the >>>>> "state" topic is handled and you should be able to understand me >>>>> and inch better. >>>>> >>>>> Please do not do A to the project, it deserves better! >>>>> >>>>> Best Jan >>>>> >>>>> >>>>> >>>>> On 13.03.2018 02:40, Jun Rao wrote: >>>>> >>>>> Hi, Jan, >>>>> >>>>>> Thanks for the reply. A few more comments below. >>>>>> >>>>>> 50. Ok, we can think a bit harder for supporting compacted topics. >>>>>> >>>>>> 51. This is a fundamental design question. In the more common case, >>>>>> the >>>>>> reason why someone wants to increase the number of partitions is that >>>>>> the >>>>>> consumer application is slow and one wants to run more consumer >>>>>> instances >>>>>> to increase the degree of parallelism. So, fixing the number of >>>>>> running >>>>>> consumer instances when expanding the partitions won't help this case. >>>>>> If >>>>>> we do need to increase the number of consumer instances, we need to >>>>>> somehow >>>>>> reshuffle the state of the consumer across instances. What we have >>>>>> been >>>>>> discussing in this KIP is whether we can do this more effectively >>>>>> through >>>>>> the KStream library (e.g. through a 2-phase partition expansion). This >>>>>> will >>>>>> add some complexity, but it's probably better than everyone doing this >>>>>> in >>>>>> the application space. The recopying approach that you mentioned >>>>>> doesn't >>>>>> seem to address the consumer state management issue when the consumer >>>>>> switches from an old to a new topic. >>>>>> >>>>>> 52. As for your example, it depends on whether the join key is the >>>>>> same >>>>>> between (A,B) and (B,C). If the join key is the same, we can do a >>>>>> 2-phase >>>>>> partition expansion of A, B, and C together. If the join keys are >>>>>> different, one would need to repartition the data on a different key >>>>>> for >>>>>> the second join, then the partition expansion can be done >>>>>> independently >>>>>> between (A,B) and (B,C). >>>>>> >>>>>> 53. If you always fix the number of consumer instances, we you >>>>>> described >>>>>> works. However, as I mentioned in #51, I am not sure how your proposal >>>>>> deals with consumer states when the number of consumer instances >>>>>> grows. >>>>>> Also, it just seems that it's better to avoid re-copying the existing >>>>>> data. >>>>>> >>>>>> 60. "just want to throw in my question from the longer email in the >>>>>> other >>>>>> Thread here. How will the bloom filter help a new consumer to decide >>>>>> to >>>>>> apply the key or not?" Not sure that I fully understood your question. >>>>>> The >>>>>> consumer just reads whatever key is in the log. The bloom filter just >>>>>> helps >>>>>> clean up the old keys. >>>>>> >>>>>> 61. "Why can we afford having a topic where its apparently not >>>>>> possible >>>>>> to >>>>>> start a new application on? I think this is an overall flaw of the >>>>>> discussed idea here. Not playing attention to the overall >>>>>> architecture." >>>>>> Could you explain a bit more when one can't start a new application? >>>>>> >>>>>> Jun >>>>>> >>>>>> >>>>>> >>>>>> On Sat, Mar 10, 2018 at 1:40 AM, Jan Filipiak < >>>>>> jan.filip...@trivago.com >>>>>> wrote: >>>>>> >>>>>> Hi Jun, thanks for your mail. >>>>>> >>>>>> Thank you for your questions! >>>>>>> I think they are really good and tackle the core of the problem I >>>>>>> see. >>>>>>> >>>>>>> I will answer inline, mostly but still want to set the tone here. >>>>>>> >>>>>>> The core strength of kafka is what Martin once called the >>>>>>> kappa-Architecture. How does this work? >>>>>>> You have everything as a log as in kafka. When you need to change >>>>>>> something. >>>>>>> You create the new version of your application and leave it running >>>>>>> in >>>>>>> parallel. >>>>>>> Once the new version is good you switch your users to use the new >>>>>>> application. >>>>>>> >>>>>>> The online reshuffling effectively breaks this architecture and I >>>>>>> think >>>>>>> the switch in thinking here is more harmful >>>>>>> than any details about the partitioning function to allow such a >>>>>>> change. >>>>>>> I >>>>>>> feel with my suggestion we are the closest to >>>>>>> the original and battle proven architecture and I can only warn to >>>>>>> move >>>>>>> away from it. >>>>>>> >>>>>>> I might have forgotten something, sometimes its hard for me to >>>>>>> getting >>>>>>> all >>>>>>> the thoughts captured in a mail, but I hope the comments inline will >>>>>>> further make my concern clear, and put some emphasis on why I prefer >>>>>>> my >>>>>>> solution ;) >>>>>>> >>>>>>> One thing we should all be aware of when discussing this, and I think >>>>>>> Dong >>>>>>> should have mentioned it (maybe he did). >>>>>>> We are not discussing all of this out of thin air but there is an >>>>>>> effort >>>>>>> in the Samza project. >>>>>>> >>>>>>> https://cwiki.apache.org/confluence/display/SAMZA/SEP-5%3A+ >>>>>>> Enable+partition+expansion+of+input+streams >>>>>>> https://issues.apache.org/jira/browse/SAMZA-1293 >>>>>>> >>>>>>> To be clear. I think SEP-5 (state of last week, dont know if it >>>>>>> adapted >>>>>>> to >>>>>>> this discussion) is on a way better path than KIP-253, and I can't >>>>>>> really >>>>>>> explain why. >>>>>>> >>>>>>> Best Jan, >>>>>>> >>>>>>> nice weekend everyone >>>>>>> >>>>>>> >>>>>>> On 09.03.2018 03:36, Jun Rao wrote: >>>>>>> >>>>>>> Hi, Jan, >>>>>>> >>>>>>> Thanks for the feedback. Just some comments on the earlier points >>>>>>>> that >>>>>>>> you >>>>>>>> mentioned. >>>>>>>> >>>>>>>> 50. You brought up the question of whether existing data needs to be >>>>>>>> copied >>>>>>>> during partition expansion. My understand of your view is that avoid >>>>>>>> copying existing data will be more efficient, but it doesn't work >>>>>>>> well >>>>>>>> with >>>>>>>> compacted topics since some keys in the original partitions will >>>>>>>> never >>>>>>>> be >>>>>>>> cleaned. It would be useful to understand your use case of compacted >>>>>>>> topics >>>>>>>> a bit more. In the common use case, the data volume in a compacted >>>>>>>> topic >>>>>>>> may not be large. So, I am not sure if there is a strong need to >>>>>>>> expand >>>>>>>> partitions in a compacted topic, at least initially. >>>>>>>> >>>>>>>> I do agree. State is usually smaller. Update rates might be also >>>>>>>> >>>>>>>> competitively high. >>>>>>> Doing Log-compaction (even beeing very efficient and configurable) is >>>>>>> also >>>>>>> a more expensive operation than >>>>>>> just discarding old segments. Further if you want to use more >>>>>>> consumers >>>>>>> processing the events >>>>>>> you also have to grow the number of partitions. Especially for >>>>>>> use-cases >>>>>>> we do (KIP-213) a tiny state full >>>>>>> table might be very expensive to process if it joins against a huge >>>>>>> table. >>>>>>> >>>>>>> I can just say we have been in the spot of needing to grow log >>>>>>> compacted >>>>>>> topics. Mainly for processing power we can bring to the table. >>>>>>> >>>>>>> Further i am not at all concerned about the extra spaced used by >>>>>>> "garbage >>>>>>> keys". I am more concerned about the correctness of innocent >>>>>>> consumers. >>>>>>> The >>>>>>> logic becomes complicated. Say for streams one would need to load the >>>>>>> record into state but not forward it the topology ( to have it >>>>>>> available >>>>>>> for shuffeling). I rather have it simple and a topic clean regardless >>>>>>> if >>>>>>> it >>>>>>> still has its old partition count. Especially with multiple >>>>>>> partitions >>>>>>> growth's I think it becomes insanely hard to to this shuffle correct. >>>>>>> Maybe >>>>>>> Streams and Samza can do it. Especially if you do "hipster stream >>>>>>> processing" <https://www.confluent.io/blog >>>>>>> /introducing-kafka-streams- >>>>>>> stream-processing-made-simple/>. This makes kafka way to >>>>>>> complicated. >>>>>>> With my approach I think its way simpler because the topic has no >>>>>>> "history" >>>>>>> in terms of partitioning but is always clean. >>>>>>> >>>>>>> >>>>>>> 51. "Growing the topic by an integer factor does not require any >>>>>>> state >>>>>>> >>>>>>> redistribution at all." Could you clarify this a bit more? Let's say >>>>>>>> you >>>>>>>> have a consumer app that computes the windowed count per key. If you >>>>>>>> double >>>>>>>> the number of partitions from 1 to 2 and grow the consumer instances >>>>>>>> from >>>>>>>> 1 >>>>>>>> to 2, we would need to redistribute some of the counts to the new >>>>>>>> consumer >>>>>>>> instance. Regarding to linear hashing, it's true that it won't solve >>>>>>>> the >>>>>>>> problem with compacted topics. The main benefit is that it >>>>>>>> redistributes >>>>>>>> the keys in one partition to no more than two partitions, which >>>>>>>> could >>>>>>>> help >>>>>>>> redistribute the state. >>>>>>>> >>>>>>>> You don't need to spin up a new consumer in this case. every >>>>>>>> consumer >>>>>>>> >>>>>>>> would just read every partition with the (partition % num_task) >>>>>>> task. >>>>>>> it >>>>>>> will still have the previous data right there and can go on. >>>>>>> >>>>>>> This sounds contradictory to what I said before, but please bear with >>>>>>> me. >>>>>>> >>>>>>> 52. Good point on coordinating the expansion of 2 topics that need to >>>>>>> be >>>>>>> >>>>>>> joined together. This is where the 2-phase partition expansion could >>>>>>>> potentially help. In the first phase, we could add new partitions to >>>>>>>> the 2 >>>>>>>> topics one at a time but without publishing to the new patitions. >>>>>>>> Then, >>>>>>>> we >>>>>>>> can add new consumer instances to pick up the new partitions. In >>>>>>>> this >>>>>>>> transition phase, no reshuffling is needed since no data is coming >>>>>>>> from >>>>>>>> the >>>>>>>> new partitions. Finally, we can enable the publishing to the new >>>>>>>> partitions. >>>>>>>> >>>>>>>> I think its even worse than you think. I would like to introduce the >>>>>>>> >>>>>>>> Term >>>>>>> transitive copartitioning. Imagine >>>>>>> 2 streams application. One joins (A,B) the other (B,C) then there is >>>>>>> a >>>>>>> transitive copartition requirement for >>>>>>> (A,C) to be copartitioned aswell. This can spread significantly and >>>>>>> require many consumers to adapt at the same time. >>>>>>> >>>>>>> It is also not entirely clear to me how you not need reshuffling in >>>>>>> this >>>>>>> case. If A has a record that never gets updated after the expansion >>>>>>> and >>>>>>> the >>>>>>> coresponding B record moves to a new partition. How shall they meet >>>>>>> w/o >>>>>>> shuffle? >>>>>>> >>>>>>> 53. "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." Not sure that I fully understand this. I think >>>>>>>> you >>>>>>>> mean >>>>>>>> that a consumer application can run more instances than the number >>>>>>>> of >>>>>>>> partitions. In that case, the consumer can just repartitioning the >>>>>>>> input >>>>>>>> data according to the number of instances. This is possible, but >>>>>>>> just >>>>>>>> has >>>>>>>> the overhead of reshuffling the data. >>>>>>>> >>>>>>>> No what I meant is ( that is also your question i think Mathias) >>>>>>>> that >>>>>>>> if >>>>>>>> >>>>>>>> you grow a topic by a factor. >>>>>>> Even if your processor is statefull you can can just assign all the >>>>>>> multiples of the previous partition to >>>>>>> this consumer and the state to keep processing correctly will be >>>>>>> present >>>>>>> w/o any shuffling. >>>>>>> >>>>>>> Say you have an assignment >>>>>>> Statefull consumer => partition >>>>>>> 0 => 0 >>>>>>> 1 => 1 >>>>>>> 2 => 2 >>>>>>> >>>>>>> and you grow you topic by 4 you get, >>>>>>> >>>>>>> 0 => 0,3,6,9 >>>>>>> 1 => 1,4,7,10 >>>>>>> 2 => 2,5,8,11 >>>>>>> >>>>>>> Say your hashcode is 8. 8%3 => 2 before so consumer for partition 2 >>>>>>> has >>>>>>> it. >>>>>>> Now you you have 12 partitions so 8%12 => 8, so it goes into >>>>>>> partition >>>>>>> 8 >>>>>>> which is assigned to the same consumer >>>>>>> who had 2 before and therefore knows the key. >>>>>>> >>>>>>> Userland reshuffeling is there as an options. And it does exactly >>>>>>> what >>>>>>> I >>>>>>> suggest. And I think its the perfect strategie. All I am suggestion >>>>>>> is >>>>>>> broker side support to switch the producers to the newly partitioned >>>>>>> topic. >>>>>>> Then the old (to few partition topic) can go away. Remember the list >>>>>>> of >>>>>>> steps in the beginning of this thread. If one has broker support for >>>>>>> all >>>>>>> where its required and streams support for those that aren’t >>>>>>> necessarily. >>>>>>> Then one has solved the problem. >>>>>>> I repeat it because I think its important. I am really happy that you >>>>>>> brought that up! because its 100% what I want just with the >>>>>>> differences >>>>>>> to >>>>>>> have an option to discard the to small topic later (after all >>>>>>> consumers >>>>>>> adapted). And to have order correct there. I need broker support >>>>>>> managing >>>>>>> the copy process + the produces and fence them against each other. I >>>>>>> also >>>>>>> repeat. the copy process can run for weeks in the worst case. Copying >>>>>>> the >>>>>>> data is not the longest task migrating consumers might very well be. >>>>>>> Once all consumers switched and copying is really up to date (think >>>>>>> ISR >>>>>>> like up to date) only then we stop the producer, wait for the copy to >>>>>>> finish and use the new topic for producing. >>>>>>> >>>>>>> After this the topic is perfect in shape. and no one needs to worry >>>>>>> about >>>>>>> complicated stuff. (old keys hanging around might arrive in some >>>>>>> other >>>>>>> topic later.....). can only imagine how many tricky bugs gonna arrive >>>>>>> after >>>>>>> someone had grown and shrunken is topic 10 times. >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> 54. "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." I am >>>>>>>> not >>>>>>>> sure a customized partitioner itself solves the problem. We probably >>>>>>>> need >>>>>>>> some broker side support to enforce when the new partitions can be >>>>>>>> used. >>>>>>>> We >>>>>>>> also need some support on the consumer/kstream side to preserve the >>>>>>>> per >>>>>>>> key >>>>>>>> ordering and potentially migrate the processing state. This is not >>>>>>>> trivial >>>>>>>> and I am not sure if it's ideal to fully push to the application >>>>>>>> space. >>>>>>>> >>>>>>>> Broker support is defenitly the preferred way here. I have nothing >>>>>>>> >>>>>>>> against >>>>>>> broker support. >>>>>>> I tried to say that for what I would preffer - copying the data over, >>>>>>> at >>>>>>> least for log compacted topics - >>>>>>> I would require more broker support than the KIP currently offers. >>>>>>> >>>>>>> >>>>>>> >>>>>>> Jun >>>>>>> >>>>>>> On Tue, Mar 6, 2018 at 10:33 PM, Jan Filipiak < >>>>>>>> jan.filip...@trivago.com >>>>>>>> wrote: >>>>>>>> >>>>>>>> Hi Dong, >>>>>>>> >>>>>>>> are you actually reading my emails, or are you just using the >>>>>>>> thread I >>>>>>>> >>>>>>>>> started for general announcements regarding the KIP? >>>>>>>>> >>>>>>>>> I tried to argue really hard against linear hashing. Growing the >>>>>>>>> topic >>>>>>>>> by >>>>>>>>> an integer factor does not require any state redistribution at >>>>>>>>> all. I >>>>>>>>> fail >>>>>>>>> to see completely where linear hashing helps on log compacted >>>>>>>>> topics. >>>>>>>>> >>>>>>>>> If you are not willing to explain to me what I might be >>>>>>>>> overlooking: >>>>>>>>> that >>>>>>>>> is fine. >>>>>>>>> But I ask you to not reply to my emails then. Please understand my >>>>>>>>> frustration with this. >>>>>>>>> >>>>>>>>> Best Jan >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> On 06.03.2018 19:38, Dong Lin 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+de >>>>>>>>>>>>>>>>> livery+with+partition+expansio >>>>>>>>>>>>>>>>> n >>>>>>>>>>>>>>>>> . >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> 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 >>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> >>> > >
