Abstract

The costs of logic errors in production for streaming applications are higher than for batch processing systems. Depending on the setup, errors cannot be rectified or have already influenced important decisions. The goal of Flinkspector is to improve the test process of Apache Flink streaming applications in order to detect streaming application logic errors early during development. It features dedicated mechanics for test setup, execution, and evaluation. While Flinkspector’s streamlined API keeps testing overhead small. The framework is able to handle non-terminating and parallelized data flows involving windowing. The lightweight integration-tests enabled by Flinkspector allow Flink applications to be included into the continuous integration and deployment process. The talk introduces the core functionality of Flinkspector. In addition, background concepts of the runtime and the evaluation algorithms are presented. https://github.com/ottogroup/flink-spector

Slides: Alexander Kolb – Flinkspector – Taming the squirrel

Video on YouTube

Speaker

Alexander Kolb
Junior Data Architect, Otto group

Details