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Posted to reviews@spark.apache.org by "ueshin (via GitHub)" <gi...@apache.org> on 2023/08/24 19:08:52 UTC
[GitHub] [spark] ueshin opened a new pull request, #42663: [SPARK-44952][SQL][PYTHON] Support named arguments in aggregate Pandas UDFs
ueshin opened a new pull request, #42663:
URL: https://github.com/apache/spark/pull/42663
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[GitHub] [spark] dtenedor commented on a diff in pull request #42663: [SPARK-44952][SQL][PYTHON] Support named arguments in aggregate Pandas UDFs
Posted by "dtenedor (via GitHub)" <gi...@apache.org>.
dtenedor commented on code in PR #42663:
URL: https://github.com/apache/spark/pull/42663#discussion_r1310564760
##########
python/pyspark/sql/tests/pandas/test_pandas_udf_grouped_agg.py:
##########
@@ -575,6 +575,128 @@ def mean(x):
assert filtered.collect()[0]["mean"] == 42.0
+ def test_named_arguments(self):
+ df = self.data
+ weighted_mean = self.pandas_agg_weighted_mean_udf
+
+ with self.tempView("v"):
+ df.createOrReplaceTempView("v")
+ self.spark.udf.register("weighted_mean", weighted_mean)
+
+ for i, aggregated in enumerate(
+ [
+ df.groupby("id").agg(weighted_mean(df.v, w=df.w).alias("wm")),
+ df.groupby("id").agg(weighted_mean(v=df.v, w=df.w).alias("wm")),
+ df.groupby("id").agg(weighted_mean(w=df.w, v=df.v).alias("wm")),
+ self.spark.sql("SELECT id, weighted_mean(v, w => w) as wm FROM v GROUP BY id"),
+ self.spark.sql(
+ "SELECT id, weighted_mean(v => v, w => w) as wm FROM v GROUP BY id"
+ ),
+ self.spark.sql(
+ "SELECT id, weighted_mean(w => w, v => v) as wm FROM v GROUP BY id"
+ ),
+ ]
+ ):
+ with self.subTest(query_no=i):
+ assertDataFrameEqual(aggregated, df.groupby("id").agg(mean(df.v).alias("wm")))
+
+ def test_named_arguments_negative(self):
Review Comment:
can you please also port these negative tests to the 'kwargs' Pandas UDF case as well? It would be good to make sure we do the same checks there. Same for the window functions testing.
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[GitHub] [spark] ueshin commented on a diff in pull request #42663: [SPARK-44952][SQL][PYTHON] Support named arguments in aggregate Pandas UDFs
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on code in PR #42663:
URL: https://github.com/apache/spark/pull/42663#discussion_r1304764142
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala:
##########
@@ -3021,6 +3021,13 @@ class Analyzer(override val catalogManager: CatalogManager) extends RuleExecutor
ne
case e: Expression if e.foldable =>
e // No need to create an attribute reference if it will be evaluated as a Literal.
+ case e: NamedArgumentExpression =>
+ // For NamedArgumentExpression, we extract the value and replace it with
+ // an AttributeReference (with an internal column name, e.g. "_w0").
+ NamedArgumentExpression(
+ e.key,
+ extractedExprMap.getOrElseUpdate(e.canonicalized,
+ Alias(e.value, s"_w${extractedExprMap.size}")()).toAttribute)
Review Comment:
@dtenedor Does this change make sense when the named arguments are specified to window functions?
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[GitHub] [spark] ueshin commented on a diff in pull request #42663: [SPARK-44952][SQL][PYTHON] Support named arguments in aggregate Pandas UDFs
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on code in PR #42663:
URL: https://github.com/apache/spark/pull/42663#discussion_r1304764142
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala:
##########
@@ -3021,6 +3021,13 @@ class Analyzer(override val catalogManager: CatalogManager) extends RuleExecutor
ne
case e: Expression if e.foldable =>
e // No need to create an attribute reference if it will be evaluated as a Literal.
+ case e: NamedArgumentExpression =>
+ // For NamedArgumentExpression, we extract the value and replace it with
+ // an AttributeReference (with an internal column name, e.g. "_w0").
+ NamedArgumentExpression(
+ e.key,
+ extractedExprMap.getOrElseUpdate(e.canonicalized,
+ Alias(e.value, s"_w${extractedExprMap.size}")()).toAttribute)
Review Comment:
@dtenedor Does this change make sense when the named arguments are specified to window functions?
This is trying to keep the named arguments in the window functions side; otherwise, the named arguments will go to `Project` that will be generated here and it can't be processed because `NamedArgumentExpression` is marked as `Unevaluable`.
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[GitHub] [spark] dtenedor commented on a diff in pull request #42663: [SPARK-44952][SQL][PYTHON] Support named arguments in aggregate Pandas UDFs
Posted by "dtenedor (via GitHub)" <gi...@apache.org>.
dtenedor commented on code in PR #42663:
URL: https://github.com/apache/spark/pull/42663#discussion_r1304938615
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala:
##########
@@ -3021,6 +3021,13 @@ class Analyzer(override val catalogManager: CatalogManager) extends RuleExecutor
ne
case e: Expression if e.foldable =>
e // No need to create an attribute reference if it will be evaluated as a Literal.
+ case e: NamedArgumentExpression =>
+ // For NamedArgumentExpression, we extract the value and replace it with
+ // an AttributeReference (with an internal column name, e.g. "_w0").
+ NamedArgumentExpression(
+ e.key,
+ extractedExprMap.getOrElseUpdate(e.canonicalized,
+ Alias(e.value, s"_w${extractedExprMap.size}")()).toAttribute)
Review Comment:
Good question, the reason the `NamedArgumentExpression` is marked as `Unevaluable` is because the intention is for the analyzer to match the provided argument (name, value) pairs for a function call and compare them against the expected ordered parameter list (including parameter names and types) of the function signature.
By the end of analysis, these expressions should be gone as a result of rearranging the provided function arguments to match the expected order, if necessary.
We might just want to deduplicate the logic from L3034-3035 into one place that we can reuse here, but the general idea looks right.
[1] https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/FunctionBuilderBase.scala#L74-L179
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[GitHub] [spark] ueshin closed pull request #42663: [SPARK-44952][SQL][PYTHON] Support named arguments in aggregate Pandas UDFs
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin closed pull request #42663: [SPARK-44952][SQL][PYTHON] Support named arguments in aggregate Pandas UDFs
URL: https://github.com/apache/spark/pull/42663
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[GitHub] [spark] dtenedor commented on a diff in pull request #42663: [SPARK-44952][SQL][PYTHON] Support named arguments in aggregate Pandas UDFs
Posted by "dtenedor (via GitHub)" <gi...@apache.org>.
dtenedor commented on code in PR #42663:
URL: https://github.com/apache/spark/pull/42663#discussion_r1310566256
##########
python/pyspark/sql/tests/pandas/test_pandas_udf_grouped_agg.py:
##########
@@ -575,6 +575,128 @@ def mean(x):
assert filtered.collect()[0]["mean"] == 42.0
+ def test_named_arguments(self):
+ df = self.data
+ weighted_mean = self.pandas_agg_weighted_mean_udf
+
+ with self.tempView("v"):
+ df.createOrReplaceTempView("v")
+ self.spark.udf.register("weighted_mean", weighted_mean)
+
+ for i, aggregated in enumerate(
+ [
+ df.groupby("id").agg(weighted_mean(df.v, w=df.w).alias("wm")),
+ df.groupby("id").agg(weighted_mean(v=df.v, w=df.w).alias("wm")),
+ df.groupby("id").agg(weighted_mean(w=df.w, v=df.v).alias("wm")),
+ self.spark.sql("SELECT id, weighted_mean(v, w => w) as wm FROM v GROUP BY id"),
+ self.spark.sql(
+ "SELECT id, weighted_mean(v => v, w => w) as wm FROM v GROUP BY id"
+ ),
+ self.spark.sql(
+ "SELECT id, weighted_mean(w => w, v => v) as wm FROM v GROUP BY id"
+ ),
+ ]
+ ):
+ with self.subTest(query_no=i):
+ assertDataFrameEqual(aggregated, df.groupby("id").agg(mean(df.v).alias("wm")))
+
+ def test_named_arguments_negative(self):
+ df = self.data
+ weighted_mean = self.pandas_agg_weighted_mean_udf
+
+ with self.tempView("v"):
+ df.createOrReplaceTempView("v")
+ self.spark.udf.register("weighted_mean", weighted_mean)
+
+ with self.assertRaisesRegex(
+ AnalysisException,
+ "DUPLICATE_ROUTINE_PARAMETER_ASSIGNMENT.DOUBLE_NAMED_ARGUMENT_REFERENCE",
+ ):
+ self.spark.sql(
+ "SELECT id, weighted_mean(v => v, v => w) as wm FROM v GROUP BY id"
Review Comment:
thanks for this test! In addition could we add a negative test case where we provide a positional argument matching the first parameter of the function, then a named argument with the same name as that first parameter? It should return an error in that case. Same for the window functions testing.
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[GitHub] [spark] ueshin commented on pull request #42663: [SPARK-44952][SQL][PYTHON] Support named arguments in aggregate Pandas UDFs
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on PR #42663:
URL: https://github.com/apache/spark/pull/42663#issuecomment-1703108548
Thanks! merging to master.
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[GitHub] [spark] ueshin commented on pull request #42663: [SPARK-44952][SQL][PYTHON] Support named arguments in aggregate Pandas UDFs
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on PR #42663:
URL: https://github.com/apache/spark/pull/42663#issuecomment-1692276364
cc @dtenedor @HyukjinKwon @xinrong-meng
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