Have you tried taking thread dumps via the UI? There is a link to do so on the Executors' page (typically under http://driver IP:4040/exectuors.
By visualizing the thread call stack of the executors with slow running tasks, you can see exactly what code is executing at an instant in time. If you sample the executor several times in a short time period, you can identify 'hot spots' or expensive sections in the user code. On Tue, Dec 2, 2014 at 3:03 PM, Steve Lewis <lordjoe2...@gmail.com> wrote: > I am working on a problem which will eventually involve many millions of > function calls. A have a small sample with several thousand calls working > but when I try to scale up the amount of data things stall. I use 120 > partitions and 116 finish in very little time. The remaining 4 seem to do > all the work and stall after a fixed number (about 1000) calls and even > after hours make no more progress. > > This is my first large and complex job with spark and I would like any > insight on how to debug the issue or even better why it might exist. The > cluster has 15 machines and I am setting executor memory at 16G. > > Also what other questions are relevant to solving the issue >
