Alexander Fedulov walks us through window options with Apache Flink:
In the previous articles of the series, we described how you can achieve flexible stream partitioning based on dynamically-updated configurations (a set of fraud-detection rules) and how you can utilize Flink’s Broadcast mechanism to distribute processing configuration at runtime among the relevant operators.
Following up directly where we left the discussion of the end-to-end solution last time, in this article we will describe how you can use the “Swiss knife” of Flink – the Process Function to create an implementation that is tailor-made to match your streaming business logic requirements. Our discussion will continue in the context of the Fraud Detection engine. We will also demonstrate how you can implement your own custom replacement for time windows for cases where the out-of-the-box windowing available from the DataStream API does not satisfy your requirements. In particular, we will look at the trade-offs that you can make when designing a solution which requires low-latency reactions to individual events.
This article will describe some high-level concepts that can be applied independently, but it is recommended that you review the material in part one and part two of the series as well as checkout the code base in order to make it easier to follow along.
It’s worth giving this a careful read.Comments closed