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https://issues.apache.org/jira/browse/SPARK-16542?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiang Gao updated SPARK-16542:
------------------------------
Description:
This is a bugs about types that result an array of null when creating dataframe
using python.
Python's array.array have richer type than python itself, e.g. we can have
array('f',[1,2,3]) and array('d',[1,2,3]). Codes in spark-sql didn't take this
into consideration which might cause a problem that you get an array of null
values when you have array('f') in your rows.
A simple code to reproduce this is:
{{from pyspark import SparkContext}}
{{from pyspark.sql import SQLContext,Row,DataFrame}}
{{from array import array}}
{{sc = SparkContext()}}
{{sqlContext = SQLContext(sc)}}
{{row1 = Row(floatarray=array('f',[1,2,3]), doublearray=array('d',[1,2,3]))}}
{{rows = sc.parallelize([ row1 ])}}
{{df = sqlContext.createDataFrame(rows)}}
{{df.show()}}
which have output
{{+---------------+------------------+}}
{{| doublearray| floatarray|}}
{{+---------------+------------------+}}
{{|[1.0, 2.0, 3.0]|[null, null, null]|}}
+---------------+------------------+}}
was:
This is a bugs about types that result an array of null when creating dataframe
using python.
Python's array.array have richer type than python itself, e.g. we can have
array('f',[1,2,3]) and array('d',[1,2,3]). Codes in spark-sql didn't take this
into consideration which might cause a problem that you get an array of null
values when you have array('f') in your rows.
A simple code to reproduce this is:
{{from pyspark import SparkContext}}
{{from pyspark.sql import SQLContext,Row,DataFrame}}
{{from array import array}}
{{sc = SparkContext()}}
{{sqlContext = SQLContext(sc)}}
{{row1 = Row(floatarray=array('f',[1,2,3]), doublearray=array('d',[1,2,3]))}}
{{rows = sc.parallelize([ row1 ])}}
{{df = sqlContext.createDataFrame(rows)}}
{{df.show()}}
which have output
+---------------+------------------+
| doublearray| floatarray|
+---------------+------------------+
|[1.0, 2.0, 3.0]|[null, null, null]|
+---------------+------------------+
> bugs about types that result an array of null when creating dataframe using
> python
> ----------------------------------------------------------------------------------
>
> Key: SPARK-16542
> URL: https://issues.apache.org/jira/browse/SPARK-16542
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Reporter: Xiang Gao
>
> This is a bugs about types that result an array of null when creating
> dataframe using python.
> Python's array.array have richer type than python itself, e.g. we can have
> array('f',[1,2,3]) and array('d',[1,2,3]). Codes in spark-sql didn't take
> this into consideration which might cause a problem that you get an array of
> null values when you have array('f') in your rows.
> A simple code to reproduce this is:
> {{from pyspark import SparkContext}}
> {{from pyspark.sql import SQLContext,Row,DataFrame}}
> {{from array import array}}
> {{sc = SparkContext()}}
> {{sqlContext = SQLContext(sc)}}
> {{row1 = Row(floatarray=array('f',[1,2,3]), doublearray=array('d',[1,2,3]))}}
> {{rows = sc.parallelize([ row1 ])}}
> {{df = sqlContext.createDataFrame(rows)}}
> {{df.show()}}
> which have output
> {{+---------------+------------------+}}
> {{| doublearray| floatarray|}}
> {{+---------------+------------------+}}
> {{|[1.0, 2.0, 3.0]|[null, null, null]|}}
> +---------------+------------------+}}
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