Whoops, I was mistaken in my original post last year. By default, there is one executor per node per Spark Context, as you said. "spark.executor.memory" is the amount of memory that the application requests for each of its executors. SPARK_WORKER_MEMORY is the amount of memory a Spark Worker is willing to allocate in executors.So if you were to set SPARK_WORKER_MEMORY to 8g everywhere on your cluster, and spark.executor.memory to 4g, you would be able to run 2 simultaneous Spark Contexts who get 4g per node. Similarly, if spark.executor.memory were 8g, you could only run 1 Spark Context at a time on the cluster, but it would get all the cluster's memory.On Thu, Jul 24, 2014 at 7:25 AM, Martin Goodson <firstname.lastname@example.org> wrote:
Thank you Nishkam,I have read your code. So, for the sake of my understanding, it seems that for each spark context there is one executor per node? Can anyone confirm this?
--Martin Goodson | VP Data Science
(0)20 3397 1240On Thu, Jul 24, 2014 at 6:12 AM, Nishkam Ravi <email@example.com> wrote:
See if this helps: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 <firstname.lastname@example.org> 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
On Wed, Jul 23, 2014 at 4:19 PM, Andrew Ash <email@example.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?AndrewOn Wed, Jul 23, 2014 at 6:10 AM, Martin Goodson <firstname.lastname@example.org> 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:
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:
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