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From "Nong Li (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-12546) Writing to partitioned parquet table can fail with OOM
Date Tue, 29 Dec 2015 00:52:49 GMT
Nong Li created SPARK-12546:
-------------------------------

             Summary: Writing to partitioned parquet table can fail with OOM
                 Key: SPARK-12546
                 URL: https://issues.apache.org/jira/browse/SPARK-12546
             Project: Spark
          Issue Type: Bug
    Affects Versions: 1.6.0
            Reporter: Nong Li


It is possible to have jobs fail with OOM when writing to a partitioned parquet table. While
this was probably always possible, it is more likely in 1.6 due to the memory manager changes.
The unified memory manager enables Spark to use more of the process memory (in particular,
for execution) which gets us in this state more often. This issue can happen for libraries
that consume a lot of memory, such as parquet. Prior to 1.6, these libraries would more likely
use memory that spark was not using (i.e. from the storage pool). In 1.6, this storage memory
can now be used for execution.

There are a couple of configs that can help with this issue.
  - parquet.memory.pool.ratio: This is a parquet config on how much of the heap the parquet
writers should use. This default to .95. Consider a much lower value (e.g. 0.1)
  - spark.memory.faction: This is a spark config to control how much of the memory should
be allocated to spark. Consider setting this to 0.6.

This should cause jobs to potentially spill more but require less memory. More aggressive
tuning will control this trade off.



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