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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