Well, I can tell you this: Performance is good initially but then it degrades massively after processing a few billion records in a few hours. It becomes quite “choppy”. A little bit of data is processed, then absolute silence, then another small chunk processed, and so on. I did enable rocksdb logs and there’s no write stalling happening.
Note that this is different from what we saw in the previous version. In that version turning on caching would slow down 5x the whole thing right from the beginning. So it seems to me KAFKA-5172 can cause such a behavior? Ara. On May 5, 2017, at 5:22 PM, Guozhang Wang <wangg...@gmail.com<mailto:wangg...@gmail.com>> wrote: Could KAFKA-5172 cause similar observations? Guozhang On Thu, May 4, 2017 at 1:30 AM, Damian Guy <damian....@gmail.com<mailto:damian....@gmail.com>> wrote: It is odd as the person that originally reported the problem has verified that it is fixed. On Thu, 4 May 2017 at 08:31 Guozhang Wang <wangg...@gmail.com<mailto:wangg...@gmail.com>> wrote: Ara, That is a bit weird, I double checked and agreed with Eno that this commit is in both trunk and 0.10.2, so I suspect the same issue still persists in trunk, hence there might be another issue that is not fixed in 2645. Could you help verify if that is the case? In which we can re-open https://issues.apache.org/jira/browse/KAFKA-4851 and investigate further. Guozhang On Tue, May 2, 2017 at 1:02 PM, Ara Ebrahimi < ara.ebrah...@argyledata.com<mailto:ara.ebrah...@argyledata.com>> wrote: No errors. But if I enable caching I see performance drop considerably. The workaround was to disable caching. The same thing is still true in 10.2.1. Ara. On May 2, 2017, at 12:55 PM, Eno Thereska <eno.there...@gmail.com<mailto:eno.there...@gmail.com>> wrote: Hi Ara, The PR https://github.com/apache/kafka/pull/2645 has gone to both trunk and 0.10.2.1, I just checked. What error are you seeing, could you give us an update? Thanks Eno On Fri, Apr 28, 2017 at 7:10 PM, Ara Ebrahimi < ara.ebrah...@argyledata.com<mailto:ara.ebrah...@argyledata.com>> wrote: Hi, I upgraded to 0.10.2.1 yesterday, enabled caching for session windows and tested again. It doesn’t seem to be fixed? Ara. On Mar 27, 2017, at 2:10 PM, Damian Guy <damian....@gmail.com<mailto:damian....@gmail.com>> wrote: Hi Ara, There is a performance issue in the 0.10.2 release of session windows. It is fixed with this PR: https://github.com/apache/kafka/pull/2645 You can work around this on 0.10.2 by calling the aggregate(..), reduce(..) etc methods and supplying StateStoreSupplier<SessionStore> with caching disabled, i.e, by doing something like: final StateStoreSupplier<SessionStore> sessionStore = Stores.create(*"session-store-name"*) .withKeys(Serdes.String()) .withValues(Serdes.String()) .persistent() .sessionWindowed(TimeUnit.MINUTES.toMillis(7)) .build(); The fix has also been cherry-picked to the 0.10.2 branch, so you could build from source and not have to create the StateStoreSupplier. Thanks, Damian On Mon, 27 Mar 2017 at 21:56 Ara Ebrahimi < ara.ebrah...@argyledata.com<mailto:ara.ebrah...@argyledata.com> wrote: Thanks for the response Mathias! The reason we want this exact task assignment to happen is that a critical part of our pipeline involves grouping relevant records together (that’s what the aggregate function in the topology is for). And for hot keys this can lead to sometimes 100s of records to get grouped together. Even worse, these records are session bound, we use session windows. Hence we see lots of activity around the store backing the aggregate function and even though we use SSD drives we’re not seeing the kind of performance we want to see. It seems like the aggregate function leads to lots of updates to these hot keys which lead to lots of rocksdb activity. Now there are many ways to fix this problem: - just don’t aggregate, create an algorithm which is not reliant on grouping/aggregating records. Not what we can do with our tight schedule right now. - do grouping/aggregating but employ n instances and rely on uniform distribution of these tasks. This is the easiest solution and what we expected to work but didn’t work as you can tell from this thread. We threw 4 instances at it but only 2 got used. - tune rocksdb? I tried this actually but it didn’t really help us much, aside from the fact that tuning rocksdb is very tricky. - use in-memory store instead? Unfortunately we have to use session windows for this aggregate function and apparently there’s no in-memory session store impl? I tried to create one but soon realized it’s too much work :) I looked at default PartitionAssigner code too, but that ain’t trivial either. So I’m a bit hopeless :( Ara. On Mar 27, 2017, at 1:35 PM, Matthias J. Sax < matth...@confluent.io<mailto:matth...@confluent.io> <mailto: matth...@confluent.io>> wrote: ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ From: "Matthias J. Sax" <matth...@confluent.io<mailto: matth...@confluent.io Subject: Re: more uniform task assignment across kafka stream nodes Date: March 27, 2017 at 1:35:30 PM PDT To: users@kafka.apache.org<mailto:users@kafka.apache.org> Reply-To: <users@kafka.apache.org<mailto:users@kafka.apache.org>> Ara, thanks for the detailed information. If I parse this correctly, both instances run the same number of tasks (12 each). That is all Streams promises. To come back to your initial question: Is there a way to tell kafka streams to uniformly assign partitions across instances? If I have n kafka streams instances running, I want each to handle EXACTLY 1/nth number of partitions. No dynamic task assignment logic. Just dumb 1/n assignment. That is exactly what you get: each of you two instances get 24/2 = 12 tasks assigned. That is dump 1/n assignment, isn't it? So my original response was correct. However, I now understand better what you are actually meaning by your question. Note that Streams does not distinguish "type" of tasks -- it only sees 24 tasks and assigns those in a balanced way. Thus, currently there is no easy way to get the assignment you want to have, except, you implement you own `PartitionAssignor`. This is the current implementation for 0.10.2 https://github.com/apache/kafka/blob/0.10.2/streams/src/ main/java/org/apache/kafka/streams/processor/internals/ StreamPartitionAssignor.java You can, if you wish write your own assignor and set it via StreamsConfig. However, be aware that this might be tricky to get right and also might have runtime implications with regard to rebalancing and state store recovery. We recently improve the current implementation to avoid costly task movements: https://issues.apache.org/jira/browse/KAFKA-4677 Thus, I would not recommend to implement an own `PartitionAssignor`. However, the root question is, why do you need this exact assignment you are looking for in the first place? Why is it "bad" if "types" of tasks are not distinguished? I would like to understand your requirement better -- it might be worth to improve Streams here. -Matthias On 3/27/17 12:57 PM, Ara Ebrahimi wrote: Hi, So, I simplified the topology by making sure we have only 1 source topic. Now I have 1 source topic, 8 partitions, 2 instances. And here’s how the topology looks like: instance 1: KafkaStreams processID: 48b58bc0-f600-4ec8-bc92-8cb3ea081aac StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-1 Active tasks: StreamsTask taskId: 0_3 ProcessorTopology: KSTREAM-SOURCE-0000000000: topics: [activities-avro-or] children: [KSTREAM-FILTER-0000000001] KSTREAM-FILTER-0000000001: children: [KSTREAM-MAP-0000000002] KSTREAM-MAP-0000000002: children: [KSTREAM-BRANCH-0000000003] KSTREAM-BRANCH-0000000003: children: [KSTREAM-BRANCHCHILD-0000000004, KSTREAM-BRANCHCHILD- 0000000005] KSTREAM-BRANCHCHILD-0000000004: children: [KSTREAM-MAPVALUES-0000000006] KSTREAM-MAPVALUES-0000000006: children: [KSTREAM-FLATMAPVALUES-0000000007] KSTREAM-FLATMAPVALUES-0000000007: children: [KSTREAM-MAP-0000000008] KSTREAM-MAP-0000000008: children: [KSTREAM-FILTER-0000000011] KSTREAM-FILTER-0000000011: children: [KSTREAM-SINK-0000000010] KSTREAM-SINK-0000000010: topic: activities-by-phone-store-or-repartition KSTREAM-BRANCHCHILD-0000000005: Partitions [activities-avro-or-3] Standby tasks: StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-2 Active tasks: StreamsTask taskId: 1_2 ProcessorTopology: KSTREAM-SOURCE-0000000012: topics: [activities-by-phone-store-or-repartition] children: [KSTREAM-AGGREGATE-0000000009] KSTREAM-AGGREGATE-0000000009: states: [activities-by-phone-store-or] children: [KTABLE-TOSTREAM-0000000013] KTABLE-TOSTREAM-0000000013: children: [KSTREAM-FILTER-0000000014] KSTREAM-FILTER-0000000014: children: [KSTREAM-FILTER-0000000015] KSTREAM-FILTER-0000000015: children: [KSTREAM-MAP-0000000016] KSTREAM-MAP-0000000016: children: [KSTREAM-MAP-0000000017] KSTREAM-MAP-0000000017: children: [KSTREAM-SINK-0000000018] KSTREAM-SINK-0000000018: topic: ml-features-avro-or Partitions [activities-by-phone-store-or-repartition-2] Standby tasks: StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-3 Active tasks: StreamsTask taskId: 1_1 ProcessorTopology: KSTREAM-SOURCE-0000000012: topics: [activities-by-phone-store-or-repartition] children: [KSTREAM-AGGREGATE-0000000009] KSTREAM-AGGREGATE-0000000009: states: [activities-by-phone-store-or] children: [KTABLE-TOSTREAM-0000000013] KTABLE-TOSTREAM-0000000013: children: [KSTREAM-FILTER-0000000014] KSTREAM-FILTER-0000000014: children: [KSTREAM-FILTER-0000000015] KSTREAM-FILTER-0000000015: children: [KSTREAM-MAP-0000000016] KSTREAM-MAP-0000000016: children: [KSTREAM-MAP-0000000017] KSTREAM-MAP-0000000017: children: [KSTREAM-SINK-0000000018] KSTREAM-SINK-0000000018: topic: ml-features-avro-or Partitions [activities-by-phone-store-or-repartition-1] StreamsTask taskId: 2_7 ProcessorTopology: KSTREAM-SOURCE-0000000019: topics: [ml-features-avro-or] Partitions [ml-features-avro-or-7] Standby tasks: StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-4 Active tasks: StreamsTask taskId: 2_0 ProcessorTopology: KSTREAM-SOURCE-0000000019: topics: [ml-features-avro-or] Partitions [ml-features-avro-or-0] StreamsTask taskId: 2_6 ProcessorTopology: KSTREAM-SOURCE-0000000019: topics: [ml-features-avro-or] Partitions [ml-features-avro-or-6] Standby tasks: StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-5 Active tasks: StreamsTask taskId: 0_0 ProcessorTopology: KSTREAM-SOURCE-0000000000: topics: [activities-avro-or] children: [KSTREAM-FILTER-0000000001] KSTREAM-FILTER-0000000001: children: [KSTREAM-MAP-0000000002] KSTREAM-MAP-0000000002: children: [KSTREAM-BRANCH-0000000003] KSTREAM-BRANCH-0000000003: children: [KSTREAM-BRANCHCHILD-0000000004, KSTREAM-BRANCHCHILD- 0000000005] KSTREAM-BRANCHCHILD-0000000004: children: [KSTREAM-MAPVALUES-0000000006] KSTREAM-MAPVALUES-0000000006: children: [KSTREAM-FLATMAPVALUES-0000000007] KSTREAM-FLATMAPVALUES-0000000007: children: [KSTREAM-MAP-0000000008] KSTREAM-MAP-0000000008: children: [KSTREAM-FILTER-0000000011] KSTREAM-FILTER-0000000011: children: [KSTREAM-SINK-0000000010] KSTREAM-SINK-0000000010: topic: activities-by-phone-store-or-repartition KSTREAM-BRANCHCHILD-0000000005: Partitions [activities-avro-or-0] StreamsTask taskId: 1_6 ProcessorTopology: KSTREAM-SOURCE-0000000012: topics: [activities-by-phone-store-or-repartition] children: [KSTREAM-AGGREGATE-0000000009] KSTREAM-AGGREGATE-0000000009: states: [activities-by-phone-store-or] children: [KTABLE-TOSTREAM-0000000013] KTABLE-TOSTREAM-0000000013: children: [KSTREAM-FILTER-0000000014] KSTREAM-FILTER-0000000014: children: [KSTREAM-FILTER-0000000015] KSTREAM-FILTER-0000000015: children: [KSTREAM-MAP-0000000016] KSTREAM-MAP-0000000016: children: [KSTREAM-MAP-0000000017] KSTREAM-MAP-0000000017: children: [KSTREAM-SINK-0000000018] KSTREAM-SINK-0000000018: topic: ml-features-avro-or Partitions [activities-by-phone-store-or-repartition-6] Standby tasks: StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-6 Active tasks: StreamsTask taskId: 1_0 ProcessorTopology: KSTREAM-SOURCE-0000000012: topics: [activities-by-phone-store-or-repartition] children: [KSTREAM-AGGREGATE-0000000009] KSTREAM-AGGREGATE-0000000009: states: [activities-by-phone-store-or] children: [KTABLE-TOSTREAM-0000000013] KTABLE-TOSTREAM-0000000013: children: [KSTREAM-FILTER-0000000014] KSTREAM-FILTER-0000000014: children: [KSTREAM-FILTER-0000000015] KSTREAM-FILTER-0000000015: children: [KSTREAM-MAP-0000000016] KSTREAM-MAP-0000000016: children: [KSTREAM-MAP-0000000017] KSTREAM-MAP-0000000017: children: [KSTREAM-SINK-0000000018] KSTREAM-SINK-0000000018: topic: ml-features-avro-or Partitions [activities-by-phone-store-or-repartition-0] StreamsTask taskId: 0_7 ProcessorTopology: KSTREAM-SOURCE-0000000000: topics: [activities-avro-or] children: [KSTREAM-FILTER-0000000001] KSTREAM-FILTER-0000000001: children: [KSTREAM-MAP-0000000002] KSTREAM-MAP-0000000002: children: [KSTREAM-BRANCH-0000000003] KSTREAM-BRANCH-0000000003: children: [KSTREAM-BRANCHCHILD-0000000004, KSTREAM-BRANCHCHILD- 0000000005] KSTREAM-BRANCHCHILD-0000000004: children: [KSTREAM-MAPVALUES-0000000006] KSTREAM-MAPVALUES-0000000006: children: [KSTREAM-FLATMAPVALUES-0000000007] KSTREAM-FLATMAPVALUES-0000000007: children: [KSTREAM-MAP-0000000008] KSTREAM-MAP-0000000008: children: [KSTREAM-FILTER-0000000011] KSTREAM-FILTER-0000000011: children: [KSTREAM-SINK-0000000010] KSTREAM-SINK-0000000010: topic: activities-by-phone-store-or-repartition KSTREAM-BRANCHCHILD-0000000005: Partitions [activities-avro-or-7] Standby tasks: StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-7 Active tasks: StreamsTask taskId: 2_4 ProcessorTopology: KSTREAM-SOURCE-0000000019: topics: [ml-features-avro-or] Partitions [ml-features-avro-or-4] Standby tasks: StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-8 Active tasks: StreamsTask taskId: 1_3 ProcessorTopology: KSTREAM-SOURCE-0000000012: topics: [activities-by-phone-store-or-repartition] children: [KSTREAM-AGGREGATE-0000000009] KSTREAM-AGGREGATE-0000000009: states: [activities-by-phone-store-or] children: [KTABLE-TOSTREAM-0000000013] KTABLE-TOSTREAM-0000000013: children: [KSTREAM-FILTER-0000000014] KSTREAM-FILTER-0000000014: children: [KSTREAM-FILTER-0000000015] KSTREAM-FILTER-0000000015: children: [KSTREAM-MAP-0000000016] KSTREAM-MAP-0000000016: children: [KSTREAM-MAP-0000000017] KSTREAM-MAP-0000000017: children: [KSTREAM-SINK-0000000018] KSTREAM-SINK-0000000018: topic: ml-features-avro-or Partitions [activities-by-phone-store-or-repartition-3] Standby tasks: instance 2: KafkaStreams processID: 092072f8-87be-4989-a94f-0ed544f5ca44 StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-1 Active tasks: StreamsTask taskId: 2_1 ProcessorTopology: KSTREAM-SOURCE-0000000019: topics: [ml-features-avro-or] Partitions [ml-features-avro-or-1] StreamsTask taskId: 2_5 ProcessorTopology: KSTREAM-SOURCE-0000000019: topics: [ml-features-avro-or] Partitions [ml-features-avro-or-5] Standby tasks: StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-2 Active tasks: StreamsTask taskId: 0_4 ProcessorTopology: KSTREAM-SOURCE-0000000000: topics: [activities-avro-or] children: [KSTREAM-FILTER-0000000001] KSTREAM-FILTER-0000000001: children: [KSTREAM-MAP-0000000002] KSTREAM-MAP-0000000002: children: [KSTREAM-BRANCH-0000000003] KSTREAM-BRANCH-0000000003: children: [KSTREAM-BRANCHCHILD-0000000004, KSTREAM-BRANCHCHILD- 0000000005] KSTREAM-BRANCHCHILD-0000000004: children: [KSTREAM-MAPVALUES-0000000006] KSTREAM-MAPVALUES-0000000006: children: [KSTREAM-FLATMAPVALUES-0000000007] KSTREAM-FLATMAPVALUES-0000000007: children: [KSTREAM-MAP-0000000008] KSTREAM-MAP-0000000008: children: [KSTREAM-FILTER-0000000011] KSTREAM-FILTER-0000000011: children: [KSTREAM-SINK-0000000010] KSTREAM-SINK-0000000010: topic: activities-by-phone-store-or-repartition KSTREAM-BRANCHCHILD-0000000005: Partitions [activities-avro-or-4] Standby tasks: StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-3 Active tasks: StreamsTask taskId: 2_2 ProcessorTopology: KSTREAM-SOURCE-0000000019: topics: [ml-features-avro-or] Partitions [ml-features-avro-or-2] StreamsTask taskId: 1_7 ProcessorTopology: KSTREAM-SOURCE-0000000012: topics: [activities-by-phone-store-or-repartition] children: [KSTREAM-AGGREGATE-0000000009] KSTREAM-AGGREGATE-0000000009: states: [activities-by-phone-store-or] children: [KTABLE-TOSTREAM-0000000013] KTABLE-TOSTREAM-0000000013: children: [KSTREAM-FILTER-0000000014] KSTREAM-FILTER-0000000014: children: [KSTREAM-FILTER-0000000015] KSTREAM-FILTER-0000000015: children: [KSTREAM-MAP-0000000016] KSTREAM-MAP-0000000016: children: [KSTREAM-MAP-0000000017] KSTREAM-MAP-0000000017: children: [KSTREAM-SINK-0000000018] KSTREAM-SINK-0000000018: topic: ml-features-avro-or Partitions [activities-by-phone-store-or-repartition-7] Standby tasks: StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-4 Active tasks: StreamsTask taskId: 2_3 ProcessorTopology: KSTREAM-SOURCE-0000000019: topics: [ml-features-avro-or] Partitions [ml-features-avro-or-3] Standby tasks: StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-5 Active tasks: StreamsTask taskId: 0_1 ProcessorTopology: KSTREAM-SOURCE-0000000000: topics: [activities-avro-or] children: [KSTREAM-FILTER-0000000001] KSTREAM-FILTER-0000000001: children: [KSTREAM-MAP-0000000002] KSTREAM-MAP-0000000002: children: [KSTREAM-BRANCH-0000000003] KSTREAM-BRANCH-0000000003: children: [KSTREAM-BRANCHCHILD-0000000004, KSTREAM-BRANCHCHILD- 0000000005] KSTREAM-BRANCHCHILD-0000000004: children: [KSTREAM-MAPVALUES-0000000006] KSTREAM-MAPVALUES-0000000006: children: [KSTREAM-FLATMAPVALUES-0000000007] KSTREAM-FLATMAPVALUES-0000000007: children: [KSTREAM-MAP-0000000008] KSTREAM-MAP-0000000008: children: [KSTREAM-FILTER-0000000011] KSTREAM-FILTER-0000000011: children: [KSTREAM-SINK-0000000010] KSTREAM-SINK-0000000010: topic: activities-by-phone-store-or-repartition KSTREAM-BRANCHCHILD-0000000005: Partitions [activities-avro-or-1] StreamsTask taskId: 1_5 ProcessorTopology: KSTREAM-SOURCE-0000000012: topics: [activities-by-phone-store-or-repartition] children: [KSTREAM-AGGREGATE-0000000009] KSTREAM-AGGREGATE-0000000009: states: [activities-by-phone-store-or] children: [KTABLE-TOSTREAM-0000000013] KTABLE-TOSTREAM-0000000013: children: [KSTREAM-FILTER-0000000014] KSTREAM-FILTER-0000000014: children: [KSTREAM-FILTER-0000000015] KSTREAM-FILTER-0000000015: children: [KSTREAM-MAP-0000000016] KSTREAM-MAP-0000000016: children: [KSTREAM-MAP-0000000017] KSTREAM-MAP-0000000017: children: [KSTREAM-SINK-0000000018] KSTREAM-SINK-0000000018: topic: ml-features-avro-or Partitions [activities-by-phone-store-or-repartition-5] Standby tasks: StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-6 Active tasks: StreamsTask taskId: 1_4 ProcessorTopology: KSTREAM-SOURCE-0000000012: topics: [activities-by-phone-store-or-repartition] children: [KSTREAM-AGGREGATE-0000000009] KSTREAM-AGGREGATE-0000000009: states: [activities-by-phone-store-or] children: [KTABLE-TOSTREAM-0000000013] KTABLE-TOSTREAM-0000000013: children: [KSTREAM-FILTER-0000000014] KSTREAM-FILTER-0000000014: children: [KSTREAM-FILTER-0000000015] KSTREAM-FILTER-0000000015: children: [KSTREAM-MAP-0000000016] KSTREAM-MAP-0000000016: children: [KSTREAM-MAP-0000000017] KSTREAM-MAP-0000000017: children: [KSTREAM-SINK-0000000018] KSTREAM-SINK-0000000018: topic: ml-features-avro-or Partitions [activities-by-phone-store-or-repartition-4] Standby tasks: StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-7 Active tasks: StreamsTask taskId: 0_2 ProcessorTopology: KSTREAM-SOURCE-0000000000: topics: [activities-avro-or] children: [KSTREAM-FILTER-0000000001] KSTREAM-FILTER-0000000001: children: [KSTREAM-MAP-0000000002] KSTREAM-MAP-0000000002: children: [KSTREAM-BRANCH-0000000003] KSTREAM-BRANCH-0000000003: children: [KSTREAM-BRANCHCHILD-0000000004, KSTREAM-BRANCHCHILD- 0000000005] KSTREAM-BRANCHCHILD-0000000004: children: [KSTREAM-MAPVALUES-0000000006] KSTREAM-MAPVALUES-0000000006: children: [KSTREAM-FLATMAPVALUES-0000000007] KSTREAM-FLATMAPVALUES-0000000007: children: [KSTREAM-MAP-0000000008] KSTREAM-MAP-0000000008: children: [KSTREAM-FILTER-0000000011] KSTREAM-FILTER-0000000011: children: [KSTREAM-SINK-0000000010] KSTREAM-SINK-0000000010: topic: activities-by-phone-store-or-repartition KSTREAM-BRANCHCHILD-0000000005: Partitions [activities-avro-or-2] StreamsTask taskId: 0_6 ProcessorTopology: KSTREAM-SOURCE-0000000000: topics: [activities-avro-or] children: [KSTREAM-FILTER-0000000001] KSTREAM-FILTER-0000000001: children: [KSTREAM-MAP-0000000002] KSTREAM-MAP-0000000002: children: [KSTREAM-BRANCH-0000000003] KSTREAM-BRANCH-0000000003: children: [KSTREAM-BRANCHCHILD-0000000004, KSTREAM-BRANCHCHILD- 0000000005] KSTREAM-BRANCHCHILD-0000000004: children: [KSTREAM-MAPVALUES-0000000006] KSTREAM-MAPVALUES-0000000006: children: [KSTREAM-FLATMAPVALUES-0000000007] KSTREAM-FLATMAPVALUES-0000000007: children: [KSTREAM-MAP-0000000008] KSTREAM-MAP-0000000008: children: [KSTREAM-FILTER-0000000011] KSTREAM-FILTER-0000000011: children: [KSTREAM-SINK-0000000010] KSTREAM-SINK-0000000010: topic: activities-by-phone-store-or-repartition KSTREAM-BRANCHCHILD-0000000005: Partitions [activities-avro-or-6] Standby tasks: StreamsThread appId: mar-23-modular StreamsThread clientId: mar-23-modular StreamsThread threadId: StreamThread-8 Active tasks: StreamsTask taskId: 0_5 ProcessorTopology: KSTREAM-SOURCE-0000000000: topics: [activities-avro-or] children: [KSTREAM-FILTER-0000000001] KSTREAM-FILTER-0000000001: children: [KSTREAM-MAP-0000000002] KSTREAM-MAP-0000000002: children: [KSTREAM-BRANCH-0000000003] KSTREAM-BRANCH-0000000003: children: [KSTREAM-BRANCHCHILD-0000000004, KSTREAM-BRANCHCHILD- 0000000005] KSTREAM-BRANCHCHILD-0000000004: children: [KSTREAM-MAPVALUES-0000000006] KSTREAM-MAPVALUES-0000000006: children: [KSTREAM-FLATMAPVALUES-0000000007] KSTREAM-FLATMAPVALUES-0000000007: children: [KSTREAM-MAP-0000000008] KSTREAM-MAP-0000000008: children: [KSTREAM-FILTER-0000000011] KSTREAM-FILTER-0000000011: children: [KSTREAM-SINK-0000000010] KSTREAM-SINK-0000000010: topic: activities-by-phone-store-or-repartition KSTREAM-BRANCHCHILD-0000000005: Partitions [activities-avro-or-5] Standby tasks: activities-avro-or is input topic. ml-features-avro-or is output topic. In the middle we have an aggregate (activities-by-phone-store-or- repartition). On instance 1 I see 3 tasks for activities-avro-or and on instance 2 I see 5. Bad. On instance 1 see 4 tasks for ml-features-avro-or. And 4 on instance 2. Good. On instance 1 see 5 tasks for activities-by-phone-store-or- repartition. And 3 on instance 2. Bad. As I said in terms of offsets for all these partitions I see uniform distribution, so we’re not dealing with a bad key scenario. Ara. On Mar 25, 2017, at 6:43 PM, Matthias J. Sax < matth...@confluent.io <mailto: matth...@confluent.io>> wrote: ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ From: "Matthias J. Sax" <matth...@confluent.io<mailto: matth...@confluent.io Subject: Re: more uniform task assignment across kafka stream nodes Date: March 25, 2017 at 6:43:12 PM PDT To: users@kafka.apache.org<mailto:users@kafka.apache.org> Reply-To: <users@kafka.apache.org<mailto:users@kafka.apache.org>> Please share the rest of your topology code (without any UDFs / business logic). Otherwise, I cannot give further advice. -Matthias On 3/25/17 6:08 PM, Ara Ebrahimi wrote: Via: builder.stream("topic1"); builder.stream("topic2"); builder.stream("topic3”); These are different kinds of topics consuming different avro objects. Ara. On Mar 25, 2017, at 6:04 PM, Matthias J. Sax < matth...@confluent.io <mailto: matth...@confluent.io><mailto:matth...@confluent.io>> wrote: ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ From: "Matthias J. Sax" <matth...@confluent.io<mailto: matth...@confluent.io <mailto:matth...@confluent.io>> Subject: Re: more uniform task assignment across kafka stream nodes Date: March 25, 2017 at 6:04:30 PM PDT To: users@kafka.apache.org<mailto:users@kafka.apache.org><mailto: users@kafka.apache.org> Reply-To: <users@kafka.apache.org<mailto:users@kafka.apache.org> <mailto: users@kafka.apache.org>> Ara, How do you consume your topics? Via builder.stream("topic1", "topic2", "topic3); or via builder.stream("topic1"); builder.stream("topic2"); builder.stream("topic3"); Both and handled differently with regard to creating tasks (partition to task assignment also depends on you downstream code though). If this does not help, can you maybe share the structure of processing? To dig deeper, we would need to know the topology DAG. -Matthias On 3/25/17 5:56 PM, Ara Ebrahimi wrote: Mathias, This apparently happens because we have more than 1 source topic. We have 3 source topics in the same application. So it seems like the task assignment algorithm creates topologies not for one specific topic at a time but the total partitions across all source topics consumed in an application instance. Because we have some code dependencies between these 3 source topics we can’t separate them into 3 applications at this time. Hence the reason I want to get the task assignment algorithm basically do a uniform and simple task assignment PER source topic. Ara. On Mar 25, 2017, at 5:21 PM, Matthias J. Sax < matth...@confluent.io <mailto: matth...@confluent.io><mailto:matth...@confluent.io><mailto: matth...@confluent.io>> wrote: ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ From: "Matthias J. Sax" <matth...@confluent.io<mailto: matth...@confluent.io <mailto:matth...@confluent.io><mailto:matth...@confluent.io>> Subject: Re: more uniform task assignment across kafka stream nodes Date: March 25, 2017 at 5:21:47 PM PDT To: users@kafka.apache.org<mailto:users@kafka.apache.org><mailto: users@kafka.apache.org><mailto:users@kafka.apache.org> Reply-To: <users@kafka.apache.org<mailto:users@kafka.apache.org> <mailto: users@kafka.apache.org><mailto:users@kafka.apache.org>> Hi, I am wondering why this happens in the first place. Streams, load-balanced over all running instances, and each instance should be the same number of tasks (and thus partitions) assigned. What is the overall assignment? Do you have StandyBy tasks configured? What version do you use? -Matthias On 3/24/17 8:09 PM, Ara Ebrahimi wrote: Hi, Is there a way to tell kafka streams to uniformly assign partitions across instances? If I have n kafka streams instances running, I want each to handle EXACTLY 1/nth number of partitions. No dynamic task assignment logic. Just dumb 1/n assignment. Here’s our scenario. Lets say we have an “source" topic with 8 partitions. We also have 2 kafka streams instances. Each instances get assigned to handle 4 “source" topic partitions. BUT then we do a few maps and an aggregate. So data gets shuffled around. The map function uniformly distributes these across all partitions (I can verify that by looking at the partition offsets). After the map what I notice by looking at the topology is that one kafka streams instance get assigned to handle say 2 aggregate repartition topics and the other one gets assigned 6. Even worse, on bigger clusters (say 4 instances) we see say 2 nodes gets assigned downstream aggregate repartition topics and 2 other nodes assigned NOTHING to handle. Ara. ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ -- -- Guozhang -- -- Guozhang ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________
