I see -- sorry for miss-understanding initially. I agree that it would be possible to detect. Feel free to file a Jira for this improvement and maybe pick it up by yourself :)
-Matthias On 7/17/18 3:01 PM, Vasily Sulatskov wrote: > Hi, > > I do understand that in a general case it's not possible to guarantee > that newValue and oldValue parts of a Change message arrive to the > same partitions, and I guess that's not really in the plans, but if I > correctly understand how it works, it should be possible to detect if > both newValue and oldValue go to the same partition and keep them > together, thus improving kafka-streams consistency guarantees. Right? > > For example right now I have such a usecase that when I perform > groupBy on a table, my new keys are computed purely from old keys, and > not from the value. And handling of such cases (not a general case) > can be improved. > On Tue, Jul 17, 2018 at 1:48 AM Matthias J. Sax <matth...@confluent.io> wrote: >> >> It is not possible to use a single message, because both messages may go >> to different partitions and may be processed by different applications >> instances. >> >> Note, that the overall KTable state is sharded. Updating a single >> upstream shard, might required to update two different downstream shards. >> >> >> -Matthias >> >> On 7/16/18 2:50 PM, Vasily Sulatskov wrote: >>> Hi, >>> >>> It seems that it wouldn't be that difficult to address: just don't >>> break Change(newVal, oldVal) into Change(newVal, null) / >>> Change(oldVal, null) and update aggregator value in one .process() >>> call. >>> >>> Would this change make sense? >>> On Mon, Jul 16, 2018 at 10:34 PM Matthias J. Sax <matth...@confluent.io> >>> wrote: >>>> >>>> Vasily, >>>> >>>> yes, it can happen. As you noticed, both messages might be processed on >>>> different machines. Thus, Kafka Streams provides 'eventual consistency' >>>> guarantees. >>>> >>>> >>>> -Matthias >>>> >>>> On 7/16/18 6:51 AM, Vasily Sulatskov wrote: >>>>> Hi John, >>>>> >>>>> Thanks a lot for you explanation. It does make much more sense now. >>>>> >>>>> The Jira issue I think is pretty well explained (with a reference to >>>>> this thread). And I've lest my 2 cents in the pull request. >>>>> >>>>> You are right I didn't notice that repartition topic contains the same >>>>> message effectively twice, and 0/1 bytes are non-visible, so when I >>>>> used kafka-console-consumer I didn't notice that. So I have a quick >>>>> suggestion here, wouldn't it make sense to change 0 and 1 bytes to >>>>> something that has visible corresponding ascii characters, say + and >>>>> -, as these messages are effectively commands to reducer to execute >>>>> either an addition or subtraction? >>>>> >>>>> On a more serious, side, can you please explain temporal aspects of >>>>> how change messages are handled? More specifically, is it guaranteed >>>>> that both Change(newValue, null) and Change(null, oldValue) are >>>>> handled before a new aggregated value is comitted to an output topic? >>>>> Change(newValue, null) and Change(null, oldValue) are delivered as two >>>>> separate messages via a kafka topic, and when they are read from a >>>>> topic (possibly on a different machine where a commit interval is >>>>> asynchronous to a machine that's put these changes into a topic) can >>>>> it happen so a Change(newValue, null) is processed by a >>>>> KTableReduceProcessor, the value of the aggregator is updated, and >>>>> committed to the changelog topic, and a Change(null, oldValue) is >>>>> processed only in the next commit interval? If I am understand this >>>>> correctly that would mean that in an aggregated table an incorrect >>>>> aggregated value will be observed briefly, before being eventually >>>>> corrected. >>>>> >>>>> Can that happen? Or I can't see something that would make it impossible? >>>>> On Fri, Jul 13, 2018 at 8:05 PM John Roesler <j...@confluent.io> wrote: >>>>>> >>>>>> Hi Vasily, >>>>>> >>>>>> I'm glad you're making me look at this; it's good homework for me! >>>>>> >>>>>> This is very non-obvious, but here's what happens: >>>>>> >>>>>> KStreamsReduce is a Processor of (K, V) => (K, Change<V>) . I.e., it >>>>>> emits >>>>>> new/old Change pairs as the value. >>>>>> >>>>>> Next is the Select (aka GroupBy). In the DSL code, this is the >>>>>> KTableRepartitionMap (we call it a repartition when you select a new key, >>>>>> since the new keys may belong to different partitions). >>>>>> KTableRepartitionMap is a processor that does two things: >>>>>> 1. it maps K => K1 (new keys) and V => V1 (new values) >>>>>> 2. it "explodes" Change(new, old) into [ Change(null, old), Change(new, >>>>>> null)] >>>>>> In other words, it turns each Change event into two events: a retraction >>>>>> and an update >>>>>> >>>>>> Next comes the reduce operation. In building the processor node for this >>>>>> operation, we create the sink, repartition topic, and source, followed by >>>>>> the actual Reduce node. So if you want to look at how the changes get >>>>>> serialized and desesrialized, it's in KGroupedTableImpl#buildAggregate. >>>>>> You'll see that sink and source a ChangedSerializer and >>>>>> ChangedDeserializer. >>>>>> >>>>>> By looking into those implementations, I found that they depend on each >>>>>> Change containing just one of new OR old. They serialize the underlying >>>>>> value using the serde you provide, along with a single byte that >>>>>> signifies >>>>>> if the serialized value is the new or old value, which the deserializer >>>>>> uses on the receiving end to turn it back into a Change(new, null) or >>>>>> Change(null, old) as appropriate. This is why the repartition topic looks >>>>>> like it's just the raw data. It basically is, except for the magic byte. >>>>>> >>>>>> Does that make sense? >>>>>> >>>>>> Also, I've created https://issues.apache.org/jira/browse/KAFKA-7161 and >>>>>> https://github.com/apache/kafka/pull/5366 . Do you mind taking a look and >>>>>> leaving any feedback you have? >>>>>> >>>>>> Thanks, >>>>>> -John >>>>>> >>>>>> On Fri, Jul 13, 2018 at 12:00 PM Vasily Sulatskov <vas...@sulatskov.net> >>>>>> wrote: >>>>>> >>>>>>> Hi John, >>>>>>> >>>>>>> Thanks for your explanation. >>>>>>> >>>>>>> I have an answer to the practical question, i.e. a null aggregator >>>>>>> value should be interpreted as a fatal application error. >>>>>>> >>>>>>> On the other hand, looking at the app topology, I see that a message >>>>>>> from KSTREAM-REDUCE-0000000002 / "table" goes goes to >>>>>>> KTABLE-SELECT-0000000006 which in turn forwards data to >>>>>>> KSTREAM-SINK-0000000007 (topic: aggregated-table-repartition), and at >>>>>>> this point I assume that data goes back to kafka into a *-repartition >>>>>>> topic, after that the message is read from kafka by >>>>>>> KSTREAM-SOURCE-0000000008 (topics: [aggregated-table-repartition]), >>>>>>> and finally gets to Processor: KTABLE-REDUCE-0000000009 (stores: >>>>>>> [aggregated-table]), where the actual aggregation takes place. What I >>>>>>> don't get is where this Change value comes from, I mean if it's been >>>>>>> produced by KSTREAM-REDUCE-0000000002, but it shouldn't matter as the >>>>>>> message goes through kafka where it gets serialized, and looking at >>>>>>> kafka "repartition" topic, it contains regular values, not a pair of >>>>>>> old/new. >>>>>>> >>>>>>> As far as I understand, Change is a purely in-memory representation of >>>>>>> the state for a particular key, and at no point it's serialized back >>>>>>> to kafka, yet somehow this Change values makes it to reducer. I feel >>>>>>> like I am missing something here. Could you please clarify this? >>>>>>> >>>>>>> Can you please point me to a place in kafka-streams sources where a >>>>>>> Change of newValue/oldValue is produced, so I could take a look? I >>>>>>> found KTableReduce implementation, but can't find who makes these >>>>>>> Change value. >>>>>>> On Fri, Jul 13, 2018 at 6:17 PM John Roesler <j...@confluent.io> wrote: >>>>>>>> >>>>>>>> Hi again Vasily, >>>>>>>> >>>>>>>> Ok, it looks to me like this behavior is the result of the un-clean >>>>>>>> topology change. >>>>>>>> >>>>>>>> Just in case you're interested, here's what I think happened. >>>>>>>> >>>>>>>> 1. Your reduce node in subtopology1 (KSTREAM-REDUCE-0000000002 / >>>>>>>> "table" >>>>>>> ) >>>>>>>> internally emits pairs of "oldValue"/"newValue" . (side-note: It's by >>>>>>>> forwarding both the old and new value that we are able to maintain >>>>>>>> aggregates using the subtractor/adder pairs) >>>>>>>> >>>>>>>> 2. In the full topology, these old/new pairs go through some >>>>>>>> transformations, but still in some form eventually make their way down >>>>>>>> to >>>>>>>> the reduce node (KTABLE-REDUCE-0000000009/"aggregated-table"). >>>>>>>> >>>>>>>> 3. The reduce processor logic looks like this: >>>>>>>> final V oldAgg = store.get(key); >>>>>>>> V newAgg = oldAgg; >>>>>>>> >>>>>>>> // first try to add the new value >>>>>>>> if (value.newValue != null) { >>>>>>>> if (newAgg == null) { >>>>>>>> newAgg = value.newValue; >>>>>>>> } else { >>>>>>>> newAgg = addReducer.apply(newAgg, value.newValue); >>>>>>>> } >>>>>>>> } >>>>>>>> >>>>>>>> // then try to remove the old value >>>>>>>> if (value.oldValue != null) { >>>>>>>> // Here's where the assumption breaks down... >>>>>>>> newAgg = removeReducer.apply(newAgg, value.oldValue); >>>>>>>> } >>>>>>>> >>>>>>>> 4. Here's what I think happened. This processor saw an event like >>>>>>>> {new=null, old=(key2, 732, 10:50:40)}. This would skip the first block, >>>>>>> and >>>>>>>> (since "oldValue != null") would go into the second block. I think that >>>>>>> in >>>>>>>> the normal case we can rely on the invariant that any value we get as >>>>>>>> an >>>>>>>> "oldValue" is one that we've previously seen ( as "newValue" ). >>>>>>>> Consequently, we should be able to assume that if we get a non-null >>>>>>>> "oldValue", "newAgg" will also not be null (because we would have >>>>>>>> written >>>>>>>> it to the store back when we saw it as "newValue" and then retrieved it >>>>>>>> just now as "newAgg = oldAgg"). >>>>>>>> >>>>>>>> However, if subtopology2, along with KTABLE-SELECT-0000000006 >>>>>>>> and KSTREAM-SINK-0000000013 get added after (KSTREAM-REDUCE-0000000002 >>>>>>>> / >>>>>>>> "table") has already emitted some values, then we might in fact receive >>>>>>> an >>>>>>>> event with some "oldValue" that we have in fact never seen before >>>>>>> (because ( >>>>>>>> KTABLE-REDUCE-0000000009/"aggregated-table") wasn't in the topology >>>>>>>> when >>>>>>> it >>>>>>>> was first emitted as a "newValue"). >>>>>>>> >>>>>>>> This would violate our assumption, and we would unintentionally send a >>>>>>>> "null" as the "newAgg" parameter to the "removeReducer" (aka >>>>>>>> subtractor). >>>>>>>> If you want to double-check my reasoning, you should be able to do so >>>>>>>> in >>>>>>>> the debugger with a breakpoint in KTableReduce. >>>>>>>> >>>>>>>> >>>>>>>> tl;dr: Supposing you reset the app when the topology changes, I think >>>>>>> that >>>>>>>> you should be able to rely on non-null aggregates being passed in to >>>>>>> *both* >>>>>>>> the adder and subtractor in a reduce. >>>>>>>> >>>>>>>> I would be in favor, as you suggested, of adding an explicit check and >>>>>>>> throwing an exception if the aggregate is ever null at those points. >>>>>>>> This >>>>>>>> would actually help us detect if the topology has changed unexpectedly >>>>>>> and >>>>>>>> shut down, hopefully before any damage is done. I'll send a PR and see >>>>>>> what >>>>>>>> everyone thinks. >>>>>>>> >>>>>>>> Does this all seem like it adds up to you? >>>>>>>> -John >>>>>>>> >>>>>>>> >>>>>>>> On Fri, Jul 13, 2018 at 4:06 AM Vasily Sulatskov <vas...@sulatskov.net> >>>>>>>> wrote: >>>>>>>> >>>>>>>>> Hi John, >>>>>>>>> >>>>>>>>> Thanks for your reply. I am not sure if this behavior I've observed is >>>>>>>>> a bug or not, as I've not been resetting my application properly. On >>>>>>>>> the other hand if the subtractor or adder in the reduce operation are >>>>>>>>> never supposed to be called with null aggregator value, perhaps it >>>>>>>>> would make sense to put a null check in the table reduce >>>>>>>>> implementation to detect an application entering an invalid state. A >>>>>>>>> bit like a check for topics having the same number of partitions when >>>>>>>>> doing a join. >>>>>>>>> >>>>>>>>> Here's some information about my tests. Hope that can be useful: >>>>>>>>> >>>>>>>>> Topology at start: >>>>>>>>> >>>>>>>>> 2018-07-13 10:29:48 [main] INFO TableAggregationTest - Topologies: >>>>>>>>> Sub-topology: 0 >>>>>>>>> Source: KSTREAM-SOURCE-0000000000 (topics: [slope]) >>>>>>>>> --> KSTREAM-MAP-0000000001 >>>>>>>>> Processor: KSTREAM-MAP-0000000001 (stores: []) >>>>>>>>> --> KSTREAM-FILTER-0000000004 >>>>>>>>> <-- KSTREAM-SOURCE-0000000000 >>>>>>>>> Processor: KSTREAM-FILTER-0000000004 (stores: []) >>>>>>>>> --> KSTREAM-SINK-0000000003 >>>>>>>>> <-- KSTREAM-MAP-0000000001 >>>>>>>>> Sink: KSTREAM-SINK-0000000003 (topic: table-repartition) >>>>>>>>> <-- KSTREAM-FILTER-0000000004 >>>>>>>>> >>>>>>>>> Sub-topology: 1 >>>>>>>>> Source: KSTREAM-SOURCE-0000000005 (topics: [table-repartition]) >>>>>>>>> --> KSTREAM-REDUCE-0000000002 >>>>>>>>> Processor: KSTREAM-REDUCE-0000000002 (stores: [table]) >>>>>>>>> --> KTABLE-TOSTREAM-0000000006 >>>>>>>>> <-- KSTREAM-SOURCE-0000000005 >>>>>>>>> Processor: KTABLE-TOSTREAM-0000000006 (stores: []) >>>>>>>>> --> KSTREAM-SINK-0000000007 >>>>>>>>> <-- KSTREAM-REDUCE-0000000002 >>>>>>>>> Sink: KSTREAM-SINK-0000000007 (topic: slope-table) >>>>>>>>> <-- KTABLE-TOSTREAM-0000000006 >>>>>>>>> >>>>>>>>> This topology just takes data from the source topic "slope" which >>>>>>>>> produces messages like this: >>>>>>>>> >>>>>>>>> key1 >>>>>>>>> {"value":187,"timestamp":"2018-07-13T10:28:14.188+02:00[Europe/Paris]"} >>>>>>>>> key3 >>>>>>>>> {"value":187,"timestamp":"2018-07-13T10:28:14.188+02:00[Europe/Paris]"} >>>>>>>>> key2 >>>>>>>>> {"value":187,"timestamp":"2018-07-13T10:28:14.188+02:00[Europe/Paris]"} >>>>>>>>> key3 >>>>>>>>> {"value":188,"timestamp":"2018-07-13T10:28:15.187+02:00[Europe/Paris]"} >>>>>>>>> key1 >>>>>>>>> {"value":188,"timestamp":"2018-07-13T10:28:15.187+02:00[Europe/Paris]"} >>>>>>>>> key2 >>>>>>>>> {"value":188,"timestamp":"2018-07-13T10:28:15.187+02:00[Europe/Paris]"} >>>>>>>>> >>>>>>>>> Every second, there are 3 new messages arrive from "slope" topic for >>>>>>>>> keys key1, key2 and key3, with constantly increasing value. >>>>>>>>> Data is transformed so that the original key is also tracked in the >>>>>>>>> message value, grouped by key, and windowed with a custom window, and >>>>>>>>> reduced with a dummy reduce operation to make a KTable. >>>>>>>>> KTable is converted back to a stream, and sent to a topic (just for >>>>>>>>> debugging purposes). >>>>>>>>> >>>>>>>>> Here's the source (it's kafka-streams-scala for the most part). Also >>>>>>>>> please ignore silly classes, it's obviously a test: >>>>>>>>> >>>>>>>>> val slopeTable = builder >>>>>>>>> .stream[String, TimedValue]("slope") >>>>>>>>> .map( >>>>>>>>> (key, value) => >>>>>>>>> ( >>>>>>>>> StringWrapper(key), >>>>>>>>> TimedValueWithKey(value = value.value, timestamp = >>>>>>>>> value.timestamp, key = key) >>>>>>>>> ) >>>>>>>>> ) >>>>>>>>> .groupByKey >>>>>>>>> .windowedBy(new RoundedWindows(ChronoUnit.MINUTES, 1)) >>>>>>>>> .reduceMat((aggValue, newValue) => newValue, "table") >>>>>>>>> >>>>>>>>> slopeTable.toStream >>>>>>>>> .to("slope-table") >>>>>>>>> >>>>>>>>> Topology after change without a proper reset: >>>>>>>>> >>>>>>>>> 2018-07-13 10:38:32 [main] INFO TableAggregationTest - Topologies: >>>>>>>>> Sub-topology: 0 >>>>>>>>> Source: KSTREAM-SOURCE-0000000000 (topics: [slope]) >>>>>>>>> --> KSTREAM-MAP-0000000001 >>>>>>>>> Processor: KSTREAM-MAP-0000000001 (stores: []) >>>>>>>>> --> KSTREAM-FILTER-0000000004 >>>>>>>>> <-- KSTREAM-SOURCE-0000000000 >>>>>>>>> Processor: KSTREAM-FILTER-0000000004 (stores: []) >>>>>>>>> --> KSTREAM-SINK-0000000003 >>>>>>>>> <-- KSTREAM-MAP-0000000001 >>>>>>>>> Sink: KSTREAM-SINK-0000000003 (topic: table-repartition) >>>>>>>>> <-- KSTREAM-FILTER-0000000004 >>>>>>>>> >>>>>>>>> Sub-topology: 1 >>>>>>>>> Source: KSTREAM-SOURCE-0000000005 (topics: [table-repartition]) >>>>>>>>> --> KSTREAM-REDUCE-0000000002 >>>>>>>>> Processor: KSTREAM-REDUCE-0000000002 (stores: [table]) >>>>>>>>> --> KTABLE-SELECT-0000000006, KTABLE-TOSTREAM-0000000012 >>>>>>>>> <-- KSTREAM-SOURCE-0000000005 >>>>>>>>> Processor: KTABLE-SELECT-0000000006 (stores: []) >>>>>>>>> --> KSTREAM-SINK-0000000007 >>>>>>>>> <-- KSTREAM-REDUCE-0000000002 >>>>>>>>> Processor: KTABLE-TOSTREAM-0000000012 (stores: []) >>>>>>>>> --> KSTREAM-SINK-0000000013 >>>>>>>>> <-- KSTREAM-REDUCE-0000000002 >>>>>>>>> Sink: KSTREAM-SINK-0000000007 (topic: >>>>>>>>> aggregated-table-repartition) >>>>>>>>> <-- KTABLE-SELECT-0000000006 >>>>>>>>> Sink: KSTREAM-SINK-0000000013 (topic: slope-table) >>>>>>>>> <-- KTABLE-TOSTREAM-0000000012 >>>>>>>>> >>>>>>>>> Sub-topology: 2 >>>>>>>>> Source: KSTREAM-SOURCE-0000000008 (topics: >>>>>>>>> [aggregated-table-repartition]) >>>>>>>>> --> KTABLE-REDUCE-0000000009 >>>>>>>>> Processor: KTABLE-REDUCE-0000000009 (stores: [aggregated-table]) >>>>>>>>> --> KTABLE-TOSTREAM-0000000010 >>>>>>>>> <-- KSTREAM-SOURCE-0000000008 >>>>>>>>> Processor: KTABLE-TOSTREAM-0000000010 (stores: []) >>>>>>>>> --> KSTREAM-SINK-0000000011 >>>>>>>>> <-- KTABLE-REDUCE-0000000009 >>>>>>>>> Sink: KSTREAM-SINK-0000000011 (topic: slope-aggregated-table) >>>>>>>>> <-- KTABLE-TOSTREAM-0000000010 >>>>>>>>> >>>>>>>>> Here's the source of the sub-topology that does table aggregation: >>>>>>>>> >>>>>>>>> slopeTable >>>>>>>>> .groupBy( >>>>>>>>> (key, value) => (new Windowed(StringWrapper("dummykey"), >>>>>>>>> key.window()), value) >>>>>>>>> ) >>>>>>>>> .reduceMat(adder = (aggValue, newValue) => { >>>>>>>>> log.info(s"Called ADD: newValue=$newValue aggValue=$aggValue") >>>>>>>>> val agg = Option(aggValue) >>>>>>>>> TimedValueWithKey( >>>>>>>>> value = agg.map(_.value).getOrElse(0) + newValue.value, >>>>>>>>> timestamp = >>>>>>>>> >>>>>>>>> Utils.latestTimestamp(agg.map(_.timestamp).getOrElse(zeroTimestamp), >>>>>>>>> newValue.timestamp), >>>>>>>>> key = "reduced" >>>>>>>>> ) >>>>>>>>> }, subtractor = (aggValue, newValue) => { >>>>>>>>> log.info(s"Called SUB: newValue=$newValue aggValue=$aggValue") >>>>>>>>> val agg = Option(aggValue) >>>>>>>>> TimedValueWithKey( >>>>>>>>> value = agg.map(_.value).getOrElse(0) - newValue.value, >>>>>>>>> timestamp = >>>>>>>>> >>>>>>>>> Utils.latestTimestamp(agg.map(_.timestamp).getOrElse(zeroTimestamp), >>>>>>>>> newValue.timestamp), >>>>>>>>> key = "reduced" >>>>>>>>> ) >>>>>>>>> }, "aggregated-table") >>>>>>>>> .toStream >>>>>>>>> .to("slope-aggregated-table") >>>>>>>>> >>>>>>>>> I log all calls to adder and subtractor, so I am able to see what's >>>>>>>>> going on there, as well as I track the original keys of the aggregated >>>>>>>>> values and their timestamps, so it's relatively easy to see how the >>>>>>>>> data goes through this topology >>>>>>>>> >>>>>>>>> In order to reproduce this behavior I need to: >>>>>>>>> 1. Start a full topology (with table aggregation) >>>>>>>>> 2. Start without table aggregation (no app reset) >>>>>>>>> 3. Start with table aggregation (no app reset) >>>>>>>>> >>>>>>>>> Bellow is an interpretation of the adder/subtractor logs for a given >>>>>>>>> key/window in the chronological order >>>>>>>>> >>>>>>>>> SUB: newValue=(key2, 732, 10:50:40) aggValue=null >>>>>>>>> ADD: newValue=(key2, 751, 10:50:59) aggValue=(-732, 10:50:40) >>>>>>>>> SUB: newValue=(key1, 732, 10:50:40) aggValue=(19, 10:50:59) >>>>>>>>> ADD: newValue=(key1, 751, 10:50:59) aggValue=(-713, 10:50:59) >>>>>>>>> SUB: newValue=(key3, 732, 10:50:40) aggValue=(38, 10:50:59) >>>>>>>>> ADD: newValue=(key3, 751, 10:50:59) aggValue=(-694, 10:50:59) >>>>>>>>> >>>>>>>>> And in the end the last value that's materialized for that window >>>>>>>>> (i.e. windowed key) in the kafka topic is 57, i.e. a increase in value >>>>>>>>> for a single key between some point in the middle of the window and at >>>>>>>>> the end of the window, times 3. As opposed to the expected value of >>>>>>>>> 751 * 3 = 2253 (sum of last values in a time window for all keys being >>>>>>>>> aggregated). >>>>>>>>> >>>>>>>>> It's clear to me that I should do an application reset, but I also >>>>>>>>> would like to understand, should I expect adder/subtractor being >>>>>>>>> called with null aggValue, or is it a clear sign that something went >>>>>>>>> horribly wrong? >>>>>>>>> >>>>>>>>> On Fri, Jul 13, 2018 at 12:19 AM John Roesler <j...@confluent.io> >>>>>>> wrote: >>>>>>>>>> >>>>>>>>>> Hi Vasily, >>>>>>>>>> >>>>>>>>>> Thanks for the email. >>>>>>>>>> >>>>>>>>>> To answer your question: you should reset the application basically >>>>>>> any >>>>>>>>>> time you change the topology. Some transitions are safe, but others >>>>>>> will >>>>>>>>>> result in data loss or corruption. Rather than try to reason about >>>>>>> which >>>>>>>>> is >>>>>>>>>> which, it's much safer just to either reset the app or not change it >>>>>>> (if >>>>>>>>> it >>>>>>>>>> has important state). >>>>>>>>>> >>>>>>>>>> Beyond changes that you make to the topology, we spend a lot of >>>>>>> effort to >>>>>>>>>> try and make sure that different versions of Streams will produce the >>>>>>>>> same >>>>>>>>>> topology, so unless the release notes say otherwise, you should be >>>>>>> able >>>>>>>>> to >>>>>>>>>> upgrade without a reset. >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> I can't say right now whether those wacky behaviors are bugs or the >>>>>>>>> result >>>>>>>>>> of changing the topology without a reset. Or if they are correct but >>>>>>>>>> surprising behavior somehow. I'll look into it tomorrow. Do feel >>>>>>> free to >>>>>>>>>> open a Jira ticket if you think you have found a bug, especially if >>>>>>> you >>>>>>>>> can >>>>>>>>>> describe a repro. Knowing your topology before and after the change >>>>>>> would >>>>>>>>>> also be immensely helpful. You can print it with Topology.describe(). >>>>>>>>>> >>>>>>>>>> Regardless, I'll make a note to take a look at the code tomorrow and >>>>>>> try >>>>>>>>> to >>>>>>>>>> decide if you should expect these behaviors with "clean" topology >>>>>>>>> changes. >>>>>>>>>> >>>>>>>>>> Thanks, >>>>>>>>>> -John >>>>>>>>>> >>>>>>>>>> On Thu, Jul 12, 2018 at 11:51 AM Vasily Sulatskov < >>>>>>> vas...@sulatskov.net> >>>>>>>>>> wrote: >>>>>>>>>> >>>>>>>>>>> Hi, >>>>>>>>>>> >>>>>>>>>>> I am doing some experiments with kafka-streams KGroupedTable >>>>>>>>>>> aggregation, and admittedly I am not wiping data properly on each >>>>>>>>>>> restart, partially because I also wonder what would happen if you >>>>>>>>>>> change a streams topology without doing a proper reset. >>>>>>>>>>> >>>>>>>>>>> I've noticed that from time to time, kafka-streams >>>>>>>>>>> KGroupedTable.reduce() can call subtractor function with null >>>>>>>>>>> aggregator value, and if you try to work around that, by >>>>>>> interpreting >>>>>>>>>>> null aggregator value as zero for numeric value you get incorrect >>>>>>>>>>> aggregation result. >>>>>>>>>>> >>>>>>>>>>> I do understand that the proper way of handling this is to do a >>>>>>> reset >>>>>>>>>>> on topology changes, but I'd like to understand if there's any >>>>>>>>>>> legitmate case when kafka-streams can call an adder or a >>>>>>> substractor >>>>>>>>>>> with null aggregator value, and should I plan for this, or should I >>>>>>>>>>> interpret this as an invalid state, and terminate the application, >>>>>>> and >>>>>>>>>>> do a proper reset? >>>>>>>>>>> >>>>>>>>>>> Also, I can't seem to find a guide which explains when application >>>>>>>>>>> reset is necessary. Intuitively it seems that it should be done >>>>>>> every >>>>>>>>>>> time a topology changes. Any other cases? >>>>>>>>>>> >>>>>>>>>>> I tried to debug where the null value comes from and it seems that >>>>>>>>>>> KTableReduce.process() is getting called with Change<V> value with >>>>>>>>>>> newValue == null, and some non-null oldValue. Which leads to and to >>>>>>>>>>> subtractor being called with null aggregator value. I wonder how >>>>>>> it is >>>>>>>>>>> possible to have an old value for a key without a new value (does >>>>>>> it >>>>>>>>>>> happen because of the auto commit interval?). >>>>>>>>>>> >>>>>>>>>>> I've also noticed that it's possible for an input value from a >>>>>>> topic >>>>>>>>>>> to bypass aggregation function entirely and be directly >>>>>>> transmitted to >>>>>>>>>>> the output in certain cases: oldAgg is null, newValue is not null >>>>>>> and >>>>>>>>>>> oldValue is null - in that case newValue will be transmitted >>>>>>> directly >>>>>>>>>>> to the output. I suppose it's the correct behaviour, but feels a >>>>>>> bit >>>>>>>>>>> weird nonetheless. And I've actually been able to observe this >>>>>>>>>>> behaviour in practice. I suppose it's also caused by this happening >>>>>>>>>>> right before a commit happens, and the message is sent to a >>>>>>> changelog >>>>>>>>>>> topic. >>>>>>>>>>> >>>>>>>>>>> Please can someone with more knowledge shed some light on these >>>>>>> issues? >>>>>>>>>>> >>>>>>>>>>> -- >>>>>>>>>>> Best regards, >>>>>>>>>>> Vasily Sulatskov >>>>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> -- >>>>>>>>> Best regards, >>>>>>>>> Vasily Sulatskov >>>>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> Best regards, >>>>>>> Vasily Sulatskov >>>>>>> >>>>> >>>>> >>>>> >>>> >>> >>> >> > >
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