nickstanishadb commented on code in PR #44678:
URL: https://github.com/apache/spark/pull/44678#discussion_r1453648020
##########
python/pyspark/sql/udtf.py:
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@@ -133,12 +133,28 @@ class AnalyzeResult:
If non-empty, this is a sequence of expressions that the UDTF is
specifying for Catalyst to
sort the input TABLE argument by. Note that the 'partitionBy' list
must also be non-empty
in this case.
+ acquireExecutionMemoryMbRequested: long
+ If this is not None, this represents the amount of memory in megabytes
that the UDTF should
+ request from each Spark executor that it runs on. Then the UDTF takes
responsibility to use
+ at most this much memory, including all allocated objects. The purpose
of this functionality
+ is to prevent executors from crashing by running out of memory due to
the extra memory
+ consumption invoked by the UDTF's 'eval' and 'terminate' and 'cleanup'
methods. Spark will
+ then call 'TaskMemoryManager.acquireExecutionMemory' with the
requested number of megabytes.
+ acquireExecutionMemoryMbActual: long
+ If there is a task context available, Spark will assign this field to
the number of
+ megabytes returned from the call to the
TaskMemoryManager.acquireExecutionMemory' method, as
+ consumed by the UDTF's'__init__' method. Therefore, its 'eval' and
'terminate' and 'cleanup'
+ methods will know it thereafter and can ensure to bound memory usage
to at most this number.
+ Note that there is no effect if the UDTF's 'analyze' method assigns a
value to this; it will
+ be overwritten.
"""
Review Comment:
Makes sense! What do you think about this being difficult to set, especially
for UDTF developers? If you think the test I did with `pyUdtfMemProfile` is a
reasonable estimate, what do we think of setting a global `minMemoryMb` to
something like 100MB? I think that could make the manual memory assignment less
prone to user error. It's probably also good to have a floor on some level that
way the number of UDTFs simultaneously running on an executor has a hard ceiling
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