Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.
Published on
http://flink-forward.org/kb_sessions/flinkspector-taming-the-squirrel/
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
Login to see the comments