Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/16536#discussion_r98801781
--- Diff: python/pyspark/sql/functions.py ---
@@ -1826,23 +1826,35 @@ class UserDefinedFunction(object):
def __init__(self, func, returnType, name=None):
self.func = func
self.returnType = returnType
- self._judf = self._create_judf(name)
-
- def _create_judf(self, name):
+ self._judf_placeholder = None
+ self._name = name or (
+ func.__name__ if hasattr(func, '__name__')
+ else func.__class__.__name__)
+
+ @property
+ def _judf(self):
+ # It is possible that concurent access, to newly created UDF,
+ # will initalize multiple UserDefinedPythonFunctions.
+ # This is unlikely, doesn't affect correctness,
+ # and should have a minimal performance impact.
+ if self._judf_placeholder is None:
+ self._judf_placeholder = self._create_judf()
+ return self._judf_placeholder
+
+ def _create_judf(self):
from pyspark.sql import SparkSession
+
sc = SparkContext.getOrCreate()
- wrapped_func = _wrap_function(sc, self.func, self.returnType)
spark = SparkSession.builder.getOrCreate()
+
+ wrapped_func = _wrap_function(sc, self.func, self.returnType)
jdt = spark._jsparkSession.parseDataType(self.returnType.json())
- if name is None:
- f = self.func
- name = f.__name__ if hasattr(f, '__name__') else
f.__class__.__name__
judf =
sc._jvm.org.apache.spark.sql.execution.python.UserDefinedPythonFunction(
- name, wrapped_func, jdt)
+ self._name, wrapped_func, jdt)
return judf
def __call__(self, *cols):
- sc = SparkContext._active_spark_context
+ sc = SparkContext.getOrCreate()
--- End diff --
So by switching this to `getOrCreate` we put a lock acquisition in the path
of `__call__` which is maybe not ideal. We could maybe fix this by getting
`_judf` first (e.g. `judf = self._judf`)? (Although it should be a mostly
uncontended lock so it shouldn't be that bad, but if we ended up having a
multi-threaded PySpark DataFrame UDF application this could maybe degrade a
little bit).
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