[jira] [Created] (SPARK-27612) Creating a DataFrame in PySpark with ArrayType produces some Rows with Arrays of None

["Bryan Cutler (JIRA)" <ji...@apache.org>]

Bryan Cutler created SPARK-27612:
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             Summary: Creating a DataFrame in PySpark with ArrayType produces some Rows with Arrays of None
                 Key: SPARK-27612
                 URL: https://issues.apache.org/jira/browse/SPARK-27612
             Project: Spark
          Issue Type: Bug
          Components: PySpark, SQL
    Affects Versions: 3.0.0
            Reporter: Bryan Cutler


When creating a DataFrame with type {{ArrayType(IntegerType(), True)}} there ends up being rows that are filled with None.

 
{code:java}
In [1]: from pyspark.sql.types import ArrayType, IntegerType                                                                 

In [2]: df = spark.createDataFrame([[1, 2, 3, 4]] * 100, ArrayType(IntegerType(), True))                                     

In [3]: df.distinct().collect()                                                                                              
Out[3]: [Row(value=[None, None, None, None]), Row(value=[1, 2, 3, 4])]
{code}
 

From this example, it is consistently at elements 97, 98:
{code}
In [5]: df.collect()[-5:]                                                                                                    
Out[5]: 
[Row(value=[1, 2, 3, 4]),
 Row(value=[1, 2, 3, 4]),
 Row(value=[None, None, None, None]),
 Row(value=[None, None, None, None]),
 Row(value=[1, 2, 3, 4])]
{code}
This also happens with a type of {{ArrayType(ArrayType(IntegerType(), True))}}



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