This is the demo code for the article Kafka Streams - Working with Time (medium.com)
When we use time-based operations in a Kafka Streams application, then time is usually extracted from records in a stream. This event-time differs from processing-time (wallclock-time).
In this sample application we use a custom transformation to suppress event emission of windowed operations based on wallclock time, while still working with event-time in our windowing operation.
Our Spring Boot App has a producer bean which creates random page ratings (1-5) in a fixed interval. We aggregate these ratings per PageId in time windows. A custom processor is used to suppress updates as long as new ratings arrive for a time window.
- Start docker compose with
docker-compose up -d - Test build with
./mvnw clean package - Then start the app using the
mainfunction inPageRatingAplication.kt