HyukjinKwon commented on a change in pull request #28089:
[SPARK-30921][PySpark] Predicates on python udf should not be pushdown through
Aggregate
URL: https://github.com/apache/spark/pull/28089#discussion_r402846556
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File path: python/pyspark/sql/tests/test_pandas_udf_grouped_agg.py
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@@ -491,6 +491,28 @@ def max_udf(v):
agg2 = self.spark.sql("select max_udf(id) from table")
assert_frame_equal(agg1.toPandas(), agg2.toPandas())
+ def test_no_predicate_pushdown_through(self):
+ from pyspark.sql.functions import monotonically_increasing_id,
explode_outer
+ import numpy as np
+
+ @pandas_udf('float', PandasUDFType.GROUPED_AGG)
+ def mean(x):
+ return np.mean(x)
+
+ df = self.spark.createDataFrame([
+ Row(foo=[Row(bar=42), Row(bar=43), Row(bar=44)]),
+ ])
+
+ df_with_id = df.withColumn('id', monotonically_increasing_id())
+ exploded = df_with_id.select('id', explode_outer('foo').alias('foos'))
Review comment:
@viirya, is it necessary to use `monotonically_increasing_id` and
`explode_outer`? I know it's extracted from the reported case but I think we
should just make it minimised for the fix.
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