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CommunityData:Hyak Spark
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=== Slurm kills my job! Encountering Memory Limits === In theory, Spark enables you to run computations on data of any size without memory limitations. In practice, memory management issues occur. We are trying to understand these issues and to learn how to write Spark scripts that don't overuse memory. ==== Things to try if you run out of memory ==== # The 'out of memory' may be ephemeral or due to memory management issues in some layer other than your code -- try starting up a new cluster and running the same job unchanged. # Repartition your data: increasing the number of partitions should make it easier for the Spark scheduler to avoid exceeding its memory limits. # Increase the number of nodes. This can solve the problem essentially by giving Spark more Ram to work with. # Be careful moving data out of distributed Spark objects into normal python objects. Work your way up from a small sample to a larger sample. # You might try tweaking memory management options in <code>$SPARK_HOME/conf/spark-env.sh</code> and <$SPARK_HOME/conf/spark-defaults.conf</code>. Decreasing the number of executors, and the total memory allocated to executors should make Spark more resilient at the cost of performance.
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