Do you know about Kafka Streams? It's DSL gives you exactly what you want to do.
Check out the documentation and WordCount example: http://docs.confluent.io/current/streams/index.html https://github.com/confluentinc/examples/blob/3.1.x/kafka-streams/src/main/java/io/confluent/examples/streams/WordCountLambdaExample.java Let us know if you have further questions. -Matthias On 1/4/17 12:48 PM, Benjamin Black wrote: > Hello, > > I'm looking for guidance on how to approach a counting problem. We want to > consume a stream of data that consists of IDs and generate an output of the > aggregated count with a window size of X seconds using processing time and > a hopping time window. For example, using a window size of 1 second, if we > get IDs 1, 2, 2, 2 in the 1st second, then the output would be 1=1, 2=3. If > we get IDs 1, 3, 3 in the 2nd second then the output would be 1=1, 3=2. The > aggregated count will then be turned into increment commands to a cache and > a database. > > Obviously we will need some state to be stored during the count of a > window, but we only need to keep it for the time period of the window (i.e. > a second). I was thinking this could be achieved by using a persistent > store, where the counts are reset during the punctuate and the store topic > uses log compression. Alternatively, we could simple have an in memory > store that is reset during the punctuate. My concern with the in memory > store is that I don't know when the input topic offset is committed or when > the output data is written and therefore we could lose data. Ultimately, at > the end of the second, the input offset and output data should be written > at the same time, reducing the likelihood of lost data. We would rather > lose data, than have duplicate counts. What is the correct approach? Is > there a better way of tackling the problem? > > I have put together some code, but it doesn't do exactly what I expect. I'm > happy to share if it helps. > > Thanks, > Ben >
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