We are proud to announce that a full working public demo of the framework is now available!
BDWatchdog has now been published! If you like or if you are interested in our work, the citation is available in the Cite Us section.
BDWatchdog is a framework to assist in tasks of in-depth and real-time analysis of the execution of Big Data frameworks and applications. With BDWatchdog two approaches are used in order to get an accurate picture of what an application is doing with the resources it has available (e.g., CPU, memory, disk and network), 1) per-process resource monitoring using timeseries and 2) mixed system and JVM profiling using flame graphs. By using this approach, we put the focus on applications rather than on hosts and with BDWatchdog it is possible to perform richer queries that, for example, show how much CPU is using a Spark job across a cluster.
With monitoring and profiling, used individually or combined, it is also possible to easily identify both resources and code bottlenecks as well as account for resource utilization or spot certain patterns that frameworks or applications may have.
This frameworks has been tested on both Docker containers and virtual machines, although we encourage the serverless paradigm and thus strongly focus on containers as a light form of virtualization.