Correct.
On Sun, Jan 26, 2014 at 6:30 PM, Manoj Samel <manojsamelt...@gmail.com>wrote: > Yes, that's what I meant (thanks for the correction). > > From the tests run, it seems best is to start workers with default mem (or > bit higher) and give much more memory/most of the memory to executors; > since most of the work will be done in executor jvm and the worker jvm > seems more like node manager for that node. > > > On Sat, Jan 25, 2014 at 6:32 AM, Archit Thakur > <archit279tha...@gmail.com>wrote: > >> >> >> >> On Fri, Jan 24, 2014 at 11:29 PM, Manoj Samel >> <manojsamelt...@gmail.com>wrote: >> >>> On cluster with HDFS + Spark (in standalone deploy mode), there is a >>> master node + 4 worker nodes. When a spark-shell connects to master, it >>> creates 4 executor JVMs on each of the 4 worker nodes. >>> >> >> No, It creates 1 (4 in total) executor JVM on each of the 4 worker nodes. >> >>> >>> When the application reads a HDFS files and does computations in RDDs, >>> what work gets done on master, worker, executor and driver ? >>> >>> Thanks, >>> >> >> >
