I am new to Kafka but I think I have a good use case for it. I am trying to build daily counts of requests based on a number of different attributes in a high throughput system (~1 million requests/sec. across all 8 servers). The different attributes are unbounded in terms of values, and some will spread across 100's of millions values. This is my current through process, let me know where I could be more efficient or if there is a better way to do it.
I'll create an AVRO object "Impression" which has all the attributes of the inbound request. My application servers then will on each request create and send this to a single kafka topic. I'll then have a consumer which creates a stream from the topic. From there I'll use the windowed timeframes and groupBy to group by the attributes on each given day. At the end of the day I'd need to read out the data store to an external system for storage. Since I won't know all the values I'd need something similar to the KVStore.all() but for WindowedKV Stores. This appears that it'd be possible in 1.1 with this commit: https://github.com/apache/kafka/commit/1d1c8575961bf6bce7decb049be7f10ca76bd0c5 . Is this the best approach to doing this? Or would I be better using the stream to listen and then an external DB like Aerospike to store the counts and read out of it directly end of day. Thanks for the help! Daum
