The local mode still parallelizes calculations and it is useful for debugging as it goes through the steps of serialization/deserialization as a cluster would.
On Fri, Jan 23, 2015 at 5:44 PM, olegshirokikh <[email protected]> wrote: > I'm trying to understand the basics of Spark internals and Spark > documentation for submitting applications in local mode says for > spark-submit --master setting: > > local[K] Run Spark locally with K worker threads (ideally, set this to the > number of cores on your machine). > > local[*] Run Spark locally with as many worker threads as logical cores on > your machine. > Since all the data is stored on a single local machine, it does not benefit > from distributed operations on RDDs. > > How does it benefit and what internally is going on when Spark utilizes > several logical cores? > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Apache-Spark-standalone-mode-number-of-cores-tp21342.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: [email protected] > For additional commands, e-mail: [email protected] > >
