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From "Takuya Ueshin (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-16542) bugs about types that result an array of null when creating dataframe using python
Date Thu, 20 Jul 2017 03:46:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-16542?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Takuya Ueshin reassigned SPARK-16542:
-------------------------------------

    Assignee: Xiang Gao

> 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
>            Assignee: 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}
> 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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