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author | Matei Zaharia <matei@eecs.berkeley.edu> | 2013-06-30 15:38:58 -0700 |
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committer | Matei Zaharia <matei@eecs.berkeley.edu> | 2013-06-30 15:46:46 -0700 |
commit | 03d0b858c807339b4221bedffa29ac76eef5352e (patch) | |
tree | 3235e3d155dfc6eb0b55a36046492f653ab41346 /docs/tuning.md | |
parent | ccfe953a4db25c920157554a2cd820f8afb41ca3 (diff) | |
download | spark-03d0b858c807339b4221bedffa29ac76eef5352e.tar.gz spark-03d0b858c807339b4221bedffa29ac76eef5352e.tar.bz2 spark-03d0b858c807339b4221bedffa29ac76eef5352e.zip |
Made use of spark.executor.memory setting consistent and documented it
Conflicts:
core/src/main/scala/spark/SparkContext.scala
Diffstat (limited to 'docs/tuning.md')
-rw-r--r-- | docs/tuning.md | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/docs/tuning.md b/docs/tuning.md index 32c7ab86e9..5ffca54481 100644 --- a/docs/tuning.md +++ b/docs/tuning.md @@ -157,9 +157,9 @@ their work directories), *not* on your driver program. **Cache Size Tuning** -One important configuration parameter for GC is the amount of memory that should be used for -caching RDDs. By default, Spark uses 66% of the configured memory (`SPARK_MEM`) to cache RDDs. This means that - 33% of memory is available for any objects created during task execution. +One important configuration parameter for GC is the amount of memory that should be used for caching RDDs. +By default, Spark uses 66% of the configured executor memory (`spark.executor.memory` or `SPARK_MEM`) to +cache RDDs. This means that 33% of memory is available for any objects created during task execution. In case your tasks slow down and you find that your JVM is garbage-collecting frequently or running out of memory, lowering this value will help reduce the memory consumption. To change this to say 50%, you can call |