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 ? > >>>>> > >>>> > >>> > >> > >> > > > >
