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From "Josh Mahonin (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (PHOENIX-2288) Phoenix-Spark: PDecimal precision and scale aren't carried through to Spark DataFrame
Date Fri, 13 Nov 2015 17:48:11 GMT

     [ https://issues.apache.org/jira/browse/PHOENIX-2288?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Josh Mahonin resolved PHOENIX-2288.
-----------------------------------
       Resolution: Fixed
    Fix Version/s: 4.7.0

> Phoenix-Spark: PDecimal precision and scale aren't carried through to Spark DataFrame
> -------------------------------------------------------------------------------------
>
>                 Key: PHOENIX-2288
>                 URL: https://issues.apache.org/jira/browse/PHOENIX-2288
>             Project: Phoenix
>          Issue Type: Bug
>    Affects Versions: 4.5.2
>            Reporter: Josh Mahonin
>            Assignee: Josh Mahonin
>             Fix For: 4.7.0
>
>         Attachments: PHOENIX-2288-v2.patch, PHOENIX-2288-v3.patch, PHOENIX-2288.patch
>
>
> When loading a Spark dataframe from a Phoenix table with a 'DECIMAL' type, the underlying
precision and scale aren't carried forward to Spark.
> The Spark catalyst schema converter should load these from the underlying column. These
appear to be exposed in the ResultSetMetaData, but if there was a way to expose these somehow
through ColumnInfo, it would be cleaner.
> I'm not sure if Pig has the same issues or not, but I suspect it may.



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