HyukjinKwon commented on a change in pull request #25545: [SPARK-28843][PYTHON]
Set OMP_NUM_THREADS to executor cores for python
URL: https://github.com/apache/spark/pull/25545#discussion_r318678179
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File path: core/src/main/scala/org/apache/spark/api/python/PythonRunner.scala
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@@ -106,6 +106,13 @@ private[spark] abstract class BasePythonRunner[IN, OUT](
val startTime = System.currentTimeMillis
val env = SparkEnv.get
val localdir = env.blockManager.diskBlockManager.localDirs.map(f =>
f.getPath()).mkString(",")
+ // if OMP_NUM_THREADS is not explicitly set, override it with the number
of cores
+ if (conf.getOption("spark.executorEnv.OMP_NUM_THREADS").isEmpty) {
+ // SPARK-28843: limit the OpenMP thread pool to the number of cores
assigned to this executor
+ // this avoids high memory consumption with pandas/numpy because of a
large OpenMP thread pool
+ // see https://github.com/numpy/numpy/issues/10455
Review comment:
Can we clarify here? I think we don't usually open a PR to make such change
in followups.
What does "limit the OpenMP thread pool to the number of cores assigned to
this executor" means in general PySpark jobs?
Also, can you please clarify this in migration guide?
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