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Posted to issues@spark.apache.org by "Bryan Cutler (JIRA)" <ji...@apache.org> on 2019/04/30 23:27:00 UTC
[jira] [Updated] (SPARK-27612) Creating a DataFrame in PySpark with
ArrayType produces some Rows with Arrays of None
[ https://issues.apache.org/jira/browse/SPARK-27612?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Bryan Cutler updated SPARK-27612:
---------------------------------
Description:
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))}}
was:
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))}}
> 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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