zero323 commented on a change in pull request #27406:
[SPARK-30681][PYSPARK][SQL] Add higher order functions API to PySpark
URL: https://github.com/apache/spark/pull/27406#discussion_r373494544
##########
File path: python/pyspark/sql/column.py
##########
@@ -129,6 +129,103 @@ def _(self, other):
return _
+def _unresolved_named_lambda_variable(*name_parts):
+ """
+ Create o.a.s.sql.expressions.UnresolvedNamedLambdaVariable and
+ convert it to o.s.sql.Column
+
+ :param name_parts: str
+ """
+ sc = SparkContext._active_spark_context
+ name_parts_seq = _to_seq(sc, name_parts)
+ expressions = sc._jvm.org.apache.spark.sql.catalyst.expressions
+ return Column(
+ sc._jvm.Column(
+ expressions.UnresolvedNamedLambdaVariable(name_parts_seq)
+ )
+ )
+
+
+def _get_lambda_parameters(f):
+ import inspect
+
+ signature = inspect.signature(f)
+ parameters = signature.parameters.values()
+
+ # We should exclude functions that use
+ # variable args and keyword argnames
+ # as well as keyword only args
+ supported_parmeter_types = {
+ inspect.Parameter.POSITIONAL_OR_KEYWORD,
+ inspect.Parameter.POSITIONAL_ONLY,
+ }
+
+ # Validate that
+ # function arity is between 1 and 3
+ if not (1 <= len(parameters) <= 3):
Review comment:
If you don't have strong opinion about it @HyukjinKwon, I'd keep it as is.
While `AnalysisException` is good enough for seasoned Spark users, I see that
many beginners are intimidated by JVM stack trace that comes with it, and often
don't even try to figure out what's going on.
Also Python side checks, can prevent some naive mistakes early in cases when
expressions are defined upfront. This is good, because it doesn't wait for the
middle of the job to fail (obviously I'll add type hints on my side, to address
that 😁), and there is really not much add code or complexity here ‒ we have to
check arguments and arity anyway.
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