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https://issues.apache.org/jira/browse/PHOENIX-2288?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14970217#comment-14970217
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ASF GitHub Bot commented on PHOENIX-2288:
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Github user JamesRTaylor commented on the pull request:
https://github.com/apache/phoenix/pull/124#issuecomment-150397877
Thanks for the pull request, @navis. A couple of minor comments, but
overall it looks great. FYI, our PDataType class is stateless (it was an enum
originally), so we currently access maxLength/precision and scale through the
PDatum interface (from which PColumn and Expression are derived). Now that
PDataType is no longer an enum, it might be nice to allow instantiation with
maxLength and scale provided at construction time. Please file a JIRA.
> 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.
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