[SPARK-27691] Issue when running queries using filter predicates on pandas GROUPED_AGG udafs

• Type: Bug
• Status: Open
• Priority: Major
• Resolution: Unresolved
• Affects Version/s: 2.4.2
• Component/s: Input/Output
• Assignee: Unassigned
• Reporter: Michael Tong
• Created: 13/May/19 17:32
• Updated: 13/May/19 17:34

Description

Am currently running pyspark 2.4.2 and I am currently unable to run the following code.

 

from pyspark.sql import functions, types
import pandas as pd
import random

# initialize test data
test_data = [[False, int(random.random() * 2)] for i in range(10000)]
test_data = pd.DataFrame(test_data, columns=['bool_value', 'int_value'])

# pandas udaf
pandas_any_udf = functions.pandas_udf(lambda x: x.any(), types.BooleanType(), functions.PandasUDFType.GROUPED_AGG)

# create spark DataFrame and build the query
test_df = spark.createDataFrame(test_data)
test_df = test_df.groupby('int_value').agg(pandas_any_udf('bool_value').alias('bool_any_result'))
test_df = test_df.filter(functions.col('bool_any_result') == True)

# write to output
test_df.write.parquet('/tmp/mtong/write_test', mode='overwrite')

 

Below is a truncated error message.

 

Py4JJavaError: An error occurred while calling o1125.parquet. : org.apache.spark.SparkException: Job aborted.

...

Exchange hashpartitioning(int_value#123L, 2000)
+- *(1) Filter (<lambda>(bool_value#122) = true)
   +- Scan ExistingRDD arrow[bool_value#122,int_value#123L]

...

Caused by: java.lang.UnsupportedOperationException: Cannot evaluate expression: <lambda>(input[0, boolean, true])

 

 

What appears to be happening is that the query optimizer incorrectly pushes up the filter predicate on bool_any_result before the group by operation. This causes the query to error out before spark attempts to execute the query. I have also tried running a variant of this query with functions.count() as the aggregation function and the query ran fine, so I believe that this is an error that only affects pandas udafs.

 

Variant of query with standard aggregation function

test_df = spark.createDataFrame(test_data)
test_df = test_df.groupby('int_value').agg(functions.count('bool_value').alias('bool_counts'))
test_df = test_df.filter(functions.col('bool_counts') > 0)