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https://issues.apache.org/jira/browse/PHOENIX-2288?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14970208#comment-14970208
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ASF GitHub Bot commented on PHOENIX-2288:
-----------------------------------------
Github user JamesRTaylor commented on a diff in the pull request:
https://github.com/apache/phoenix/pull/124#discussion_r42823983
--- Diff:
phoenix-core/src/main/java/org/apache/phoenix/util/PhoenixRuntime.java ---
@@ -459,19 +459,30 @@ public static ColumnInfo getColumnInfo(PTable table,
String columnName) throws S
return getColumnInfo(pColumn);
}
- /**
+ /**
* Constructs a column info for the supplied pColumn
* @param pColumn
* @return columnInfo
* @throws SQLException if the parameter is null.
*/
public static ColumnInfo getColumnInfo(PColumn pColumn) throws
SQLException {
- if (pColumn==null) {
+ if (pColumn == null) {
throw new SQLException("pColumn must not be null.");
}
int sqlType = pColumn.getDataType().getSqlType();
- ColumnInfo columnInfo = new ColumnInfo(pColumn.toString(),sqlType);
- return columnInfo;
+ if (pColumn.getMaxLength() == null) {
+ return new ColumnInfo(pColumn.toString(), sqlType);
+ }
+ if (sqlType == Types.CHAR || sqlType == Types.VARCHAR) {
--- End diff --
Rather than check for particular types, it'd be more general to check for
null like this:
Integer maxLength = pColumn.getMaxLength();
Integer scale = pColumn.getScale();
return new ColumnInfo(pColumn.toString(), sqlType, maxLength, scale);
Then make sure that ColumnInfo handles a null maxLength and scale.
> 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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