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

2020-01-25 Thread Dongjoon Hyun (Jira) 


 [ 
https://issues.apache.org/jira/browse/SPARK-27612?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Dongjoon Hyun updated SPARK-27612:
--
Target Version/s: 3.0.0

> 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
>Assignee: Hyukjin Kwon
>Priority: Blocker
>  Labels: correctness
> Fix For: 3.0.0
>
>
> This seems to only affect Python 3.
> 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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[jira] [Updated] (SPARK-27612) Creating a DataFrame in PySpark with ArrayType produces some Rows with Arrays of None

2019-05-02 Thread Hyukjin Kwon (JIRA) 


 [ 
https://issues.apache.org/jira/browse/SPARK-27612?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Hyukjin Kwon updated SPARK-27612:
-
Priority: Blocker  (was: Critical)

> 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: Blocker
>  Labels: correctness
>
> This seems to only affect Python 3.
> 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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[jira] [Updated] (SPARK-27612) Creating a DataFrame in PySpark with ArrayType produces some Rows with Arrays of None

2019-05-02 Thread Hyukjin Kwon (JIRA) 


 [ 
https://issues.apache.org/jira/browse/SPARK-27612?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Hyukjin Kwon updated SPARK-27612:
-
Labels: correctness  (was: )

> 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: Critical
>  Labels: correctness
>
> This seems to only affect Python 3.
> 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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[jira] [Updated] (SPARK-27612) Creating a DataFrame in PySpark with ArrayType produces some Rows with Arrays of None

2019-05-02 Thread Hyukjin Kwon (JIRA) 


 [ 
https://issues.apache.org/jira/browse/SPARK-27612?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Hyukjin Kwon updated SPARK-27612:
-
Priority: Critical  (was: Major)

> 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: Critical
>
> This seems to only affect Python 3.
> 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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[jira] [Updated] (SPARK-27612) Creating a DataFrame in PySpark with ArrayType produces some Rows with Arrays of None

2019-05-01 Thread Bryan Cutler (JIRA) 


 [ 
https://issues.apache.org/jira/browse/SPARK-27612?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Bryan Cutler updated SPARK-27612:
-
Description: 
This seems to only affect Python 3.

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))}}

  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: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))}}


> 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
>
> This seems to only affect Python 3.
> 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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[jira] [Updated] (SPARK-27612) Creating a DataFrame in PySpark with ArrayType produces some Rows with Arrays of None

2019-04-30 Thread Bryan Cutler (JIRA) 


 [ 
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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