dtenedor commented on code in PR #44678:
URL: https://github.com/apache/spark/pull/44678#discussion_r1456357135
##########
python/pyspark/sql/udtf.py:
##########
@@ -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 MB 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 MB.
+ acquireExecutionMemoryMbActual: long
+ If there is a task context available, Spark will assign this field to
the number of MB
+ 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.
"""
schema: StructType
withSinglePartition: bool = False
partitionBy: Sequence[PartitioningColumn] = field(default_factory=tuple)
orderBy: Sequence[OrderingColumn] = field(default_factory=tuple)
+ acquireExecutionMemoryMbRequested: Optional[int] = 100
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