See if this helps: https://github.com/nishkamravi2/SparkAutoConfig/
It's a very simple tool for auto-configuring default parameters in Spark. Takes as input high-level parameters (like number of nodes, cores per node, memory per node, etc) and spits out default configuration, user advice and command line. Compile (javac SparkConfigure.java) and run (java SparkConfigure). Also cc'ing dev in case others are interested in helping evolve this over time (by refining the heuristics and adding more parameters). On Wed, Jul 23, 2014 at 8:31 AM, Martin Goodson <mar...@skimlinks.com> wrote: > Thanks Andrew, > > So if there is only one SparkContext there is only one executor per > machine? This seems to contradict Aaron's message from the link above: > > "If each machine has 16 GB of RAM and 4 cores, for example, you might set > spark.executor.memory between 2 and 3 GB, totaling 8-12 GB used by Spark.)" > > Am I reading this incorrectly? > > Anyway our configuration is 21 machines (one master and 20 slaves) each > with 60Gb. We would like to use 4 cores per machine. This is pyspark so we > want to leave say 16Gb on each machine for python processes. > > Thanks again for the advice! > > > > -- > Martin Goodson | VP Data Science > (0)20 3397 1240 > [image: Inline image 1] > > > On Wed, Jul 23, 2014 at 4:19 PM, Andrew Ash <and...@andrewash.com> wrote: > >> Hi Martin, >> >> In standalone mode, each SparkContext you initialize gets its own set of >> executors across the cluster. So for example if you have two shells open, >> they'll each get two JVMs on each worker machine in the cluster. >> >> As far as the other docs, you can configure the total number of cores >> requested for the SparkContext, the amount of memory for the executor JVM >> on each machine, the amount of memory for the Master/Worker daemons (little >> needed since work is done in executors), and several other settings. >> >> Which of those are you interested in? What spec hardware do you have and >> how do you want to configure it? >> >> Andrew >> >> >> On Wed, Jul 23, 2014 at 6:10 AM, Martin Goodson <mar...@skimlinks.com> >> wrote: >> >>> We are having difficulties configuring Spark, partly because we still >>> don't understand some key concepts. For instance, how many executors are >>> there per machine in standalone mode? This is after having closely read >>> the documentation several times: >>> >>> *http://spark.apache.org/docs/latest/configuration.html >>> <http://spark.apache.org/docs/latest/configuration.html>* >>> *http://spark.apache.org/docs/latest/spark-standalone.html >>> <http://spark.apache.org/docs/latest/spark-standalone.html>* >>> *http://spark.apache.org/docs/latest/tuning.html >>> <http://spark.apache.org/docs/latest/tuning.html>* >>> *http://spark.apache.org/docs/latest/cluster-overview.html >>> <http://spark.apache.org/docs/latest/cluster-overview.html>* >>> >>> The cluster overview has some information here about executors but is >>> ambiguous about whether there are single executors or multiple executors on >>> each machine. >>> >>> This message from Aaron Davidson implies that the executor memory >>> should be set to total available memory on the machine divided by the >>> number of cores: >>> *http://mail-archives.apache.org/mod_mbox/spark-user/201312.mbox/%3CCANGvG8o5K1SxgnFMT_9DK=vj_plbve6zh_dn5sjwpznpbcp...@mail.gmail.com%3E >>> <http://mail-archives.apache.org/mod_mbox/spark-user/201312.mbox/%3CCANGvG8o5K1SxgnFMT_9DK=vj_plbve6zh_dn5sjwpznpbcp...@mail.gmail.com%3E>* >>> >>> But other messages imply that the executor memory should be set to the >>> *total* available memory of each machine. >>> >>> We would very much appreciate some clarity on this and the myriad of >>> other memory settings available (daemon memory, worker memory etc). Perhaps >>> a worked example could be added to the docs? I would be happy to provide >>> some text as soon as someone can enlighten me on the technicalities! >>> >>> Thank you >>> >>> -- >>> Martin Goodson | VP Data Science >>> (0)20 3397 1240 >>> [image: Inline image 1] >>> >> >> >
