Mayur, I don't know if I exactly understand the context of what you are asking, but let me just mention issues I had with deploying.
* As my application is a streaming application, it doesn't read any files from disk, so therefore I have no Hadoop/HDFS in place and I there is no need for it, either. There should be no dependency on Hadoop or HDFS, since you can perfectly run Spark applications without it. * I use Mesos and so far I always had the downloaded Spark distribution accessible for all machines (e.g., via HTTP) and then added my application code by uploading a jar built with `sbt assembly`. As the Spark code itself must not be contained in that jar file, I had to add '% "provided"' in the sbt file, which in turn prevented me from running the application locally from IntelliJ IDEA (it would not find the libraries marked with "provided"), I always had to use `sbt run`. * When using Mesos, on the Spark slaves the Spark jar is loaded before the application jar, and so the log4j file from the Spark jar is used instead of my custom one (that is different when running locally), so I had to edit that file in the Spark distribution jar to customize logging of my Spark nodes. I wonder if the two latter problems would vanish if the Spark libraries were bundled together with the application. (That would be your approach #1, I guess.) Tobias
