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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