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From Soumya Simanta <>
Subject Spark cluster memory configuration for spark-shell
Date Tue, 29 Oct 2013 19:40:28 GMT
I'm new to Spark. I want to try out a few simple example from the Spark
shell. However, I'm not sure how to configure it so that I can make the
max. use of memory on my workers.

On average I've around 48 GB of RAM on each node on my cluster. I've around
10 nodes.

Based on the documentation I could find memory based configuration in two

*1. $SPARK_INSTALL_DIR/dist/conf/ *

*SPARK_WORKER_MEMORY*Total amount of memory to allow Spark applications to
use on the machine, e.g. 1000m, 2g (default: total memory minus 1 GB); note
that each application's *individual* memory is configured using its
spark.executor.memory property.
*2. spark.executor.memory JVM flag. *
spark.executor.memory512mAmount of memory to use per executor process, in
the same format as JVM memory strings (e.g. 512m, 2g).

In my case I want to use the max. memory possible on each node. My
understanding is that I don't have to change *SPARK_WORKER_MEMORY *and I
will have to increase spark.executor.memory to something big (e.g., 24g or
32g). Is this correct? If yes, what is the correct way of setting this
property if I just want to use the spark-shell.


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