Contributions
Tutorial
There are many great libraries for stream processing in Scala. A common use case is to create complex streaming pipelines and maintain data aggregations with it.
However, sometimes the tooling supplied by streaming libraries are not enough to create truly scalable solutions. In this story, I describe how probabilistic data structures can help in better controlling the memory footprint of your streaming applications. I use KafkaStreams and Algebird in a step-by-step tutorial with full examples.
However, sometimes the tooling supplied by streaming libraries are not enough to create truly scalable solutions. In this story, I describe how probabilistic data structures can help in better controlling the memory footprint of your streaming applications. I use KafkaStreams and Algebird in a step-by-step tutorial with full examples.