[
https://issues.apache.org/jira/browse/PHOENIX-2288?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14987440#comment-14987440
]
Josh Mahonin commented on PHOENIX-2288:
---------------------------------------
Any issues with this PR [~jamestaylor] [[email protected]] ?
I've updated the ignored unit test and verifies the precision and scale are
carried forward from schema creation to Spark.
> 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
>
> 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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
