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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:
{code:title=test.py|borderStyle=solid}
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()
{code}
which have output
{code}
+---------------+------------------+
| doublearray| floatarray|
+---------------+------------------+
|[1.0, 2.0, 3.0]|[null, null, null]|
+---------------+------------------+
{code}
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:
{code:title=test.py|borderStyle=solid}
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()
{code}
which have output
{code}
+---------------+------------------+
| doublearray| floatarray|
+---------------+------------------+
|[1.0, 2.0, 3.0]|[null, null, null]|
+---------------+------------------+
{code}
> 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:
> {code:title=test.py|borderStyle=solid}
> 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()
> {code}
> which have output
> {code}
> +---------------+------------------+
> | doublearray| floatarray|
> +---------------+------------------+
> |[1.0, 2.0, 3.0]|[null, null, null]|
> +---------------+------------------+
> {code}
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