Re: Interpreter behavior

2018-11-08 Thread tecgie88
Hi We use zeppelin in multi-user environment, the interpreter scope mode seems to allow notebook execution in serial only. If multiple users are running their notebooks concurrently, these notebooks are queued for serial execution. If one notebook takes a long time to complete, it basically bl

Re: Interpreter behavior

2018-11-08 Thread Jeff Zhang
Which version do you use ? This seems a bug. Each note should have its own scheduler in scoped per note mode. 于2018年11月9日周五 上午1:56写道: > Hi > > We use zeppelin in multi-user environment, the interpreter scope mode > seems to allow notebook execution in serial only. If multiple users are > running

Re: Interpreter behavior

2018-11-08 Thread Ajay Viswanathan
This is an issue even I am facing in my project currently. By running the spark interpreter in Scoped + Per Note mode, I do manage to execute paragraphs in parallel, but it becomes very resource intensive and times out if I run more than 3-4 jobs in parallel on a 4-core cloud instance. Typically a

Re: Interpreter behavior

2018-11-08 Thread Jeff Zhang
HI Ajay, Thanks for the reporting, which version do you use ? One know issue of spark scoped mode is that each spark repl will occupy large memory and won't be released, one workaround is to increase the driver memory. https://jira.apache.org/jira/browse/ZEPPELIN-3389 Ajay Viswanathan 于2018年11月

Re: Interpreter behavior

2018-11-08 Thread Ajay Viswanathan
I use version 0.7.3. I have been trying to investigate the reasons for the timeouts. I was trying to tune the number of cores available. Now that you've mentioned the driver memory issue, I'll try it out again and let you know if that solves the problem. What would be a back-of-the-envelope calcula

Re: Interpreter behavior

2018-11-08 Thread Jeff Zhang
There's no standard way to calculate the memory requirement for driver. It depends on your app, e.g if you want to fetch large data into driver, then you'd better to set a large value for driver memory. Regarding running paragraphs simultaneously, for scala/python/r code, the execution in one para