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https://issues.apache.org/jira/browse/PHOENIX-2288?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14989982#comment-14989982
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James Taylor commented on PHOENIX-2288:
---------------------------------------
I think that's an improvement, but how about the following change to get rid of
the type comparisons? {code}
+ public static ColumnInfo create(String columnName, int sqlType, Integer
maxLength, Integer scale) {
+ if (scale != null) {
+ assert(maxLength != null); // If we have a scale, we should always
have a max length
+ scale = scale == null ? 0 : Math.min(maxLength, scale);
+ return new ColumnInfo(columnName, sqlType, maxLength, scale);
+ }
+ if (maxLength != null) {
+ return new ColumnInfo(columnName, sqlType, maxLength);
+ }
+ return new ColumnInfo(columnName, sqlType);
+ }
+
{code}
Needs tests for var length, fixed length, and arrays, for example:
{code}
col1 CHAR(3) ARRAY, col2 DECIMAL(10,2) ARRAY, col3 VARCHAR(4) ARRAY,
col4 VARCHAR(5), col5 CHAR(3), col6 VARCHAR, col7 DECIMAL(8,2)
{code}
> 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.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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