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 > >>>>> > >>> > >>> > >>> > >> > > > > > -- Best regards, Vasily Sulatskov
