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https://issues.apache.org/jira/browse/SPARK-27612?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16830945#comment-16830945
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Marco Gaido commented on SPARK-27612:
-------------------------------------
I am not able to reproduce...
{code}
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/__ / .__/\_,_/_/ /_/\_\ version 3.0.0-SNAPSHOT
/_/
Using Python version 2.7.10 (default, Oct 6 2017 22:29:07)
SparkSession available as 'spark'.
>>> from pyspark.sql.types import ArrayType, IntegerType
>>> df = spark.createDataFrame([[1, 2, 3, 4]] * 100, ArrayType(IntegerType(),
>>> True))
>>> df.distinct().collect()
[Row(value=[1, 2, 3, 4])]
>>>
{code}
> 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
> Priority: Major
>
> 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:python}
> 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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