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I'm trying to build a recommender using Spark and just ran out of memory:

Exception in thread "dag-scheduler-event-loop" java.lang.OutOfMemoryError: Java heap space


I'd like to increase the memory available to Spark by modifying the spark.executor.memory property, in PySpark, at runtime.

Is that possible? If so, how?

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You can simply do one thing just modify the settings for Spark Context. And for that you need to exist the current running context and then create a new one.

Once a SparkConf object is passed to Spark, it is cloned and can no longer be modified by the user. Spark does not support modifying the configuration at runtime.

So, after the shell is started you have to stop the existing context before creating a new one.

For pySpark:

from pyspark import SparkContext

SparkContext.setSystemProperty('spark.executor.memory', '2g')

sc = SparkContext("local", "App Name")


Refence: https://spark.apache.org/docs/0.8.1/python-programming-guide.html

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