[SPARK-36283] Bug when creating dataframe without schema and with Arrow disabled

• Type: Sub-task
• Status: Resolved
• Priority: Major
• Resolution: Duplicate
• Affects Version/s: 3.1.1
• Component/s: PySpark
• Assignee: Unassigned
• Reporter: Darcy Shen
• Created: 26/Jul/21 03:01
• Updated: 27/Jul/21 08:42
• Resolved: 27/Jul/21 08:42

Description

A reproducible small repo can be found here: https://github.com/darcy-shen/spark-36283

Case 1: Create PySpark Dataframe using Pandas DataFrame with Arrow disabled and without schema

spark = SparkSession.builder.getOrCreate()
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "false")
pdf = pd.DataFrame({'point': pd.Series([ExamplePoint(1, 1), ExamplePoint(2, 2)])})
df = spark.createDataFrame(pdf)
df.show()

Incorrect result

+----------+
|     point|
+----------+
|(0.0, 0.0)|
|(0.0, 0.0)|
+----------+

Case 2: Create PySpark Dataframe using Pandas DataFrame with Arrow disabled and with unmatched schema

spark = SparkSession.builder.getOrCreate()
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "false")
pdf = pd.DataFrame({'point': pd.Series([ExamplePoint(1, 1), ExamplePoint(2, 2)])})
schema = StructType([StructField('point', ExamplePointUDT(), False)])
df = spark.createDataFrame(pdf, schema)
df.show()

Error throwed as expected

Traceback (most recent call last):
  File "bug2.py", line 54, in <module>
    df = spark.createDataFrame(pdf, schema)
  File "/Users/da/.pyenv/versions/spark-36283/lib/python3.8/site-packages/pyspark/sql/session.py", line 673, in createDataFrame
    return super(SparkSession, self).createDataFrame(
  File "/Users/da/.pyenv/versions/spark-36283/lib/python3.8/site-packages/pyspark/sql/pandas/conversion.py", line 300, in createDataFrame
    return self._create_dataframe(data, schema, samplingRatio, verifySchema)
  File "/Users/da/.pyenv/versions/spark-36283/lib/python3.8/site-packages/pyspark/sql/session.py", line 700, in _create_dataframe
    rdd, schema = self._createFromLocal(map(prepare, data), schema)
  File "/Users/da/.pyenv/versions/spark-36283/lib/python3.8/site-packages/pyspark/sql/session.py", line 509, in _createFromLocal
    data = list(data)
  File "/Users/da/.pyenv/versions/spark-36283/lib/python3.8/site-packages/pyspark/sql/session.py", line 682, in prepare
    verify_func(obj)
  File "/Users/da/.pyenv/versions/spark-36283/lib/python3.8/site-packages/pyspark/sql/types.py", line 1409, in verify
    verify_value(obj)
  File "/Users/da/.pyenv/versions/spark-36283/lib/python3.8/site-packages/pyspark/sql/types.py", line 1390, in verify_struct
    verifier(v)
  File "/Users/da/.pyenv/versions/spark-36283/lib/python3.8/site-packages/pyspark/sql/types.py", line 1409, in verify
    verify_value(obj)
  File "/Users/da/.pyenv/versions/spark-36283/lib/python3.8/site-packages/pyspark/sql/types.py", line 1304, in verify_udf
    verifier(dataType.toInternal(obj))
  File "/Users/da/.pyenv/versions/spark-36283/lib/python3.8/site-packages/pyspark/sql/types.py", line 1409, in verify
    verify_value(obj)
  File "/Users/da/.pyenv/versions/spark-36283/lib/python3.8/site-packages/pyspark/sql/types.py", line 1354, in verify_array
    element_verifier(i)
  File "/Users/da/.pyenv/versions/spark-36283/lib/python3.8/site-packages/pyspark/sql/types.py", line 1409, in verify
    verify_value(obj)
  File "/Users/da/.pyenv/versions/spark-36283/lib/python3.8/site-packages/pyspark/sql/types.py", line 1403, in verify_default
    verify_acceptable_types(obj)
  File "/Users/da/.pyenv/versions/spark-36283/lib/python3.8/site-packages/pyspark/sql/types.py", line 1291, in verify_acceptable_types
    raise TypeError(new_msg("%s can not accept object %r in type %s"
TypeError: element in array field point: DoubleType can not accept object 1 in type <class 'int'>

Case 3: Create PySpark Dataframe using Pandas DataFrame with Arrow disabled and with matched schema

spark = SparkSession.builder.getOrCreate()
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "false")
pdf = pd.DataFrame({'point': pd.Series([ExamplePoint(1.0, 1.0), ExamplePoint(2.0, 2.0)])})
schema = StructType([StructField('point', ExamplePointUDT(), False)])
df = spark.createDataFrame(pdf, schema)
df.show()

Correct result

+----------+
|     point|
+----------+
|(1.0, 1.0)|
|(2.0, 2.0)|
+----------+