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From "Josh Mahonin (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (PHOENIX-2288) Phoenix-Spark: PDecimal precision and scale aren't carried through to Spark DataFrame
Date Fri, 06 Nov 2015 18:49:11 GMT

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

Josh Mahonin updated PHOENIX-2288:
----------------------------------
    Attachment: PHOENIX-2288-v2.patch

Fixed type comparisons in ColumnInfo.create()

Fleshed out ColumnInfo.toTypeString() to include all data types with precision / scale.

Added recommended unit tests for arrays. Previous unit tests covered fixed and var length
chars.

> 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
>         Attachments: PHOENIX-2288-v2.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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