SPRKPY1063

pyspark.sql.pandas.functions.pandas_udf

Message: pyspark.sql.pandas.functions.pandas_udf has workaround.

Category: Warning

Description

This issue appears when the tool detects the usage of pyspark.sql.pandas.functions.pandas_udf which has a workaround.

Scenario

Input

The pandas_udf function is used to create a user defined functions that works with large amounts of data.

@pandas_udf(schema, PandasUDFType.GROUPED_MAP)
def modify_df(pdf):
    return pd.DataFrame({'result': pdf['col1'] + pdf['col2'] + 1})
df = spark.createDataFrame([(1, 2), (3, 4), (1, 1)], ["col1", "col2"])
new_df = df.groupby().apply(modify_df)

Output

The SMA adds an EWI message indicating that the pandas_udf has a workaround.

#EWI: SPRKPY1062 => pyspark.sql.pandas.functions.pandas_udf has a workaround, see documentation for more info
@pandas_udf(schema, PandasUDFType.GROUPED_MAP)

def modify_df(pdf):
    return pd.DataFrame({'result': pdf['col1'] + pdf['col2'] + 1})

df = spark.createDataFrame([(1, 2), (3, 4), (1, 1)], ["col1", "col2"])

new_df = df.groupby().apply(modify_df)

Recommended fix

Specify explicitly the parameters types as a new parameter input_types, and remove functionType parameter if applies. Created function must be called inside a select statement.

@pandas_udf(
    return_type = schema,
    input_types = [PandasDataFrameType([IntegerType(), IntegerType()])]
)

def modify_df(pdf):
    return pd.DataFrame({'result': pdf['col1'] + pdf['col2'] + 1})

df = spark.createDataFrame([(1, 2), (3, 4), (1, 1)], ["col1", "col2"])

new_df = df.groupby().apply(modify_df) # You must modify function call to be a select and not an apply

Additional recommendations

• For more support, you can email us at [e-mail] or post an issue in the SMA.