LuciferYang commented on a change in pull request #25309:
[SPARK-28577][YARN]Resource capability requested for each executor add
offHeapMemorySize
URL: https://github.com/apache/spark/pull/25309#discussion_r318526858
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File path: docs/configuration.md
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@@ -250,17 +250,17 @@ of the most common options to set are:
<td><code>spark.executor.memoryOverhead</code></td>
<td>executorMemory * 0.10, with minimum of 384 </td>
<td>
- Amount of non-heap memory to be allocated per executor process in cluster
mode, in MiB unless
+ Amount of additional memory to be allocated per executor process in
cluster mode, in MiB unless
otherwise specified. This is memory that accounts for things like VM
overheads, interned strings,
other native overheads, etc. This tends to grow with the executor size
(typically 6-10%).
This option is currently supported on YARN and Kubernetes.
<br/>
- <em>Note:</em> Non-heap memory includes off-heap memory
- (when <code>spark.memory.offHeap.enabled=true</code>) and memory used by
other executor processes
- (e.g. python process that goes with a PySpark executor) and memory used by
other non-executor
- processes running in the same container. The maximum memory size of
container to running executor
- is determined by the sum of <code>spark.executor.memoryOverhead</code> and
- <code>spark.executor.memory</code>.
+ <em>Note:</em> Additional memory includes PySpark executor memory
Review comment:
How to explain Non-heap? Now MemoryOverHead not includes offheap, and maybe
PySpark executor memory should separate from MemoryOverHead, should we
redefinition this concept.
Any other suggested names?
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