Re: Strategy regarding maximum number of executor's failure for log running jobs/ spark streaming jobs

2015-04-07 Thread twinkle sachdeva
Hi, One of the rational behind killing the app can be to avoid skewness in data. I have created this issue (https://issues.apache.org/jira/browse/SPARK-6735) to provide options for disabling this behaviour, as well as making the number of executor's failure to be relative with respect to a

Re: Strategy regarding maximum number of executor's failure for log running jobs/ spark streaming jobs

2015-04-06 Thread Sandy Ryza
What's the advantage of killing an application for lack of resources? I think the rationale behind killing an app based on executor failures is that, if we see a lot of them in a short span of time, it means there's probably something going wrong in the app or on the cluster. On Wed, Apr 1, 2015

Re: Strategy regarding maximum number of executor's failure for log running jobs/ spark streaming jobs

2015-04-01 Thread twinkle sachdeva
Hi, Thanks Sandy. Another way to look at this is that would we like to have our long running application to die? So let's say, we create a window of around 10 batches, and we are using incremental kind of operations inside our application, as restart here is a relatively more costlier, so

Re: Strategy regarding maximum number of executor's failure for log running jobs/ spark streaming jobs

2015-04-01 Thread Sandy Ryza
That's a good question, Twinkle. One solution could be to allow a maximum number of failures within any given time span. E.g. a max failures per hour property. -Sandy On Tue, Mar 31, 2015 at 11:52 PM, twinkle sachdeva twinkle.sachd...@gmail.com wrote: Hi, In spark over YARN, there is a