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