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[MINOR][DOCS] Add a note that 'spark.executor.pyspark.memory' is dependent on 'resource' #23664

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[MINOR][DOCS] Add a note that 'spark.executor.pyspark.memory' is dependent on 'resource' #23664

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Add a note that 'spark.executor.pyspark.memory' is dependent on 'reso…
HyukjinKwon Jan 27, 2019
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Make the note style consistent
HyukjinKwon Jan 27, 2019
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HyukjinKwon Jan 28, 2019
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HyukjinKwon Jan 31, 2019
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HyukjinKwon committed Jan 28, 2019
commit b24a4ac40d5ebfe0d6f1ac9a2e2880a7b45b6e06
6 changes: 2 additions & 4 deletions docs/configuration.md
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Expand Up @@ -191,9 +191,7 @@ of the most common options to set are:
shared with other non-JVM processes. When PySpark is run in YARN or Kubernetes, this memory
is added to executor resource requests.
<br/>
<em>Note:</em> Python memory usage may not be limited on platforms that do not support resource limiting, such as Windows.
<br/>
<em>Note:</em> Python memory usage is dependent on Python's 'resource' module; therefore, the behaviors and limitations are inherited.
<em>Note:</em> This feature is dependent on Python's `resource` module; therefore, the behaviors and limitations are inherited.
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Expand Down Expand Up @@ -226,7 +224,7 @@ of the most common options to set are:
comma-separated list of multiple directories on different disks.

<br/>
<em>Note:</em> In Spark 1.0 and later this will be overridden by SPARK_LOCAL_DIRS (Standalone), MESOS_SANDBOX (Mesos) or
<em>Note:</em> This will be overridden by SPARK_LOCAL_DIRS (Standalone), MESOS_SANDBOX (Mesos) or
LOCAL_DIRS (YARN) environment variables set by the cluster manager.
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