It works after calling through() before countByKey, so many 0.10.0.1 examples on the web missing the `through()` call and it will fail to get the right output when running with input topic > 1 partitions.
Thanks very much all ! Finally got the correct results. On Thu, Sep 1, 2016 at 4:52 AM, Matthias J. Sax <matth...@confluent.io> wrote: > Hi Tommy, > > I did checkout your github project and can verify the "issue". As you > are using Kafka 0.10.0.1 the automatic repartitioning step is not > available. > > If you use "trunk" version, your program will run as expected. If you > want to stay with 0.10.0.1, you need to repartition the data after map() > explicitly, via a call to through(): > > > val wordCounts: KStream[String, JLong] = textLines > > .flatMapValues(_.toLowerCase.split("\\W+").toIterable.asJava) > > .map((key: String, word: String) => new KeyValue(word, word)) > > .through("my-repartitioing-topic") > > .countByKey("counts") > > .toStream > > Keep in mind, that it is recommended to create all user topics manually. > Thus, you should create your repartitioning topic you specify in > through() before you start your Kafka Streams application. > > > -Matthias > > > On 08/31/2016 09:07 PM, Guozhang Wang wrote: > > Hello Tommy, > > > > Which version of Kafka are you using? > > > > Guozhang > > > > On Wed, Aug 31, 2016 at 4:41 AM, Tommy Q <deeplam...@gmail.com> wrote: > > > >> I cleaned up all the zookeeper & kafka states and run the WordCountDemo > >> again, the results in wc-out is still wrong: > >> > >> a 1 > >>> b 1 > >>> a 1 > >>> b 1 > >>> c 1 > >> > >> > >> > >> On Wed, Aug 31, 2016 at 5:32 PM, Michael Noll <mich...@confluent.io> > >> wrote: > >> > >>> Can you double-check whether the results in wc-out are not rather: > >>> > >>> a 1 > >>> b 1 > >>> a 2 > >>> b 2 > >>> c 1 > >>> > >>> ? > >>> > >>> On Wed, Aug 31, 2016 at 5:47 AM, Tommy Q <deeplam...@gmail.com> wrote: > >>> > >>>> Tried the word count example as discussed, the result in wc-out is > >> wrong: > >>>> > >>>> a 1 > >>>>> b 1 > >>>>> a 1 > >>>>> b 1 > >>>>> c 1 > >>>> > >>>> > >>>> The expected result should be: > >>>> > >>>> a 2 > >>>>> b 2 > >>>>> c 1 > >>>> > >>>> > >>>> Kafka version is 0.10.0.1 > >>>> > >>>> > >>>> On Tue, Aug 30, 2016 at 10:29 PM, Matthias J. Sax < > >> matth...@confluent.io > >>>> > >>>> wrote: > >>>> > >>>>> No. It does not support hidden topics. > >>>>> > >>>>> The only explanation might be, that there is no repartitioning step. > >>> But > >>>>> than the question would be, if there is a bug in Kafka Streams, > >> because > >>>>> between map() and countByKey() repartitioning is required. > >>>>> > >>>>> Can you verify that the result is correct? > >>>>> > >>>>> -Matthias > >>>>> > >>>>> On 08/30/2016 03:24 PM, Tommy Q wrote: > >>>>>> Does Kafka support hidden topics ? (Since all the topics infos are > >>>> stored > >>>>>> in ZK, this probably not the case ) > >>>>>> > >>>>>> On Tue, Aug 30, 2016 at 5:58 PM, Matthias J. Sax < > >>>> matth...@confluent.io> > >>>>>> wrote: > >>>>>> > >>>>>>> Hi Tommy, > >>>>>>> > >>>>>>> yes, you do understand Kafka Streams correctly. And yes, for > >>>> shuffling, > >>>>>>> na internal topic will be created under the hood. It should be > >> named > >>>>>>> "<application-id>-something-repartition". I am not sure, why it > >> is > >>>> not > >>>>>>> listed via bin/kafka-topics.sh > >>>>>>> > >>>>>>> The internal topic "<application-id>-counts-changelog" you see is > >>>>>>> created to back the state of countByKey() operator. > >>>>>>> > >>>>>>> See > >>>>>>> https://cwiki.apache.org/confluence/display/KAFKA/ > >>>>>>> Kafka+Streams%3A+Internal+Data+Management > >>>>>>> > >>>>>>> and > >>>>>>> > >>>>>>> http://www.confluent.io/blog/data-reprocessing-with-kafka- > >>>>>>> streams-resetting-a-streams-application > >>>>>>> > >>>>>>> > >>>>>>> -Matthias > >>>>>>> > >>>>>>> > >>>>>>> On 08/30/2016 06:55 AM, Tommy Q wrote: > >>>>>>>> Michael, Thanks for your help. > >>>>>>>> > >>>>>>>> Take the word count example, I am trying to walk through the code > >>>> based > >>>>>>> on > >>>>>>>> your explanation: > >>>>>>>> > >>>>>>>> val textLines: KStream[String, String] = > >>>>>>> builder.stream("input-topic") > >>>>>>>> val wordCounts: KStream[String, JLong] = textLines > >>>>>>>> .flatMapValues(_.toLowerCase.split("\\W+").toIterable. > >>> asJava) > >>>>>>>> .map((key: String, word: String) => new KeyValue(word, > >> word)) > >>>>>>>> .countByKey("counts") > >>>>>>>> .toStream > >>>>>>>> > >>>>>>>> wordCounts.to(stringSerde, longSerde, "wc-out") > >>>>>>>> > >>>>>>>> Suppose the input-topic has two partitions and each partition > >> has a > >>>>>>> string > >>>>>>>> record produced into: > >>>>>>>> > >>>>>>>> input-topic_0 : "a b" > >>>>>>>>> input-topic_1 : "a b c" > >>>>>>>> > >>>>>>>> > >>>>>>>> Suppose we started two instance of the stream topology ( task_0 > >> and > >>>>>>>> task_1). So after flatMapValues & map executed, they should have > >>> the > >>>>>>>> following task state: > >>>>>>>> > >>>>>>>> task_0 : [ (a, "a"), (b, "b") ] > >>>>>>>>> task_1 : [ (a, "a"), (b: "b"), (c: "c") ] > >>>>>>>> > >>>>>>>> > >>>>>>>> Before the execution of countByKey, the kafka-stream framework > >>>> should > >>>>>>>> insert a invisible shuffle phase internally: > >>>>>>>> > >>>>>>>> shuffled across the network : > >>>>>>>>> > >>>>>>>> > >>>>>>>> > >>>>>>>>> _internal_topic_shuffle_0 : [ (a, "a"), (a, "a") ] > >>>>>>>>> _internal_topic_shuffle_1 : [ (b, "b"), (b: "b"), (c: "c") ] > >>>>>>>> > >>>>>>>> > >>>>>>>> countByKey (reduce) : > >>>>>>>> > >>>>>>>> task_0 (counts-changelog_0) : [ (a, 2) ] > >>>>>>>> > >>>>>>>> task_1 (counts-changelog_1): [ (b, 2), (c, 1) ] > >>>>>>>> > >>>>>>>> > >>>>>>>> And after the execution of `wordCounts.to(stringSerde, longSerde, > >>>>>>>> "wc-out")`, we get the word count output in wc-out topic: > >>>>>>>> > >>>>>>>> task_0 (wc-out_0) : [ (a, 2) ] > >>>>>>>> > >>>>>>>> task_1 (wc-out_1): [ (b, 2), (c, 1) ] > >>>>>>>> > >>>>>>>> > >>>>>>>> > >>>>>>>> According the steps list above, do I understand the internals of > >>>>> kstream > >>>>>>>> word count correctly ? > >>>>>>>> Another question is does the shuffle across the network work by > >>>>> creating > >>>>>>>> intermediate topics ? If so, why can't I find the intermediate > >>> topics > >>>>>>> using > >>>>>>>> `bin/kafka-topics.sh --list --zookeeper localhost:2181` ? I can > >>> only > >>>>> see > >>>>>>>> the counts-changelog got created by the kstream framework. > >>>>>>>> > >>>>>>>> > >>>>>>>> > >>>>>>>> On Tue, Aug 30, 2016 at 2:25 AM, Michael Noll < > >>> mich...@confluent.io> > >>>>>>> wrote: > >>>>>>>> > >>>>>>>>> In Kafka Streams, data is partitioned according to the keys of > >> the > >>>>>>>>> key-value records, and operations such as countByKey operate on > >>>> these > >>>>>>>>> stream partitions. When reading data from Kafka, these stream > >>>>>>> partitions > >>>>>>>>> map to the partitions of the Kafka input topic(s), but these may > >>>>> change > >>>>>>>>> once you add processing operations. > >>>>>>>>> > >>>>>>>>> To your question: The first step, if the data isn't already > >> keyed > >>>> as > >>>>>>>>> needed, is to select the key you want to count by, which results > >>> in > >>>> 1+ > >>>>>>>>> output stream partitions. Here, data may get shuffled across > >> the > >>>>>>> network > >>>>>>>>> (but if won't if there's no need to, e.g. when the data is > >> already > >>>>>>> keyed as > >>>>>>>>> needed). Then the count operation is performed for each stream > >>>>>>> partition, > >>>>>>>>> which is similar to the sort-and-reduce phase in Hadoop. > >>>>>>>>> > >>>>>>>>> On Mon, Aug 29, 2016 at 5:31 PM, Tommy <deeplam...@gmail.com> > >>>> wrote: > >>>>>>>>> > >>>>>>>>>> Hi, > >>>>>>>>>> > >>>>>>>>>> For "word count" example in Hadoop, there are > >>>> shuffle-sort-and-reduce > >>>>>>>>>> phases that handles outputs from different mappers, how does it > >>>> work > >>>>> in > >>>>>>>>>> KStream ? > >>>>>>>>>> > >>>>>>>>> > >>>>>>>> > >>>>>>> > >>>>>>> > >>>>>> > >>>>> > >>>>> > >>>> > >>> > >> > > > > > > > >