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From "James Taylor (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (PHOENIX-2288) Phoenix-Spark: PDecimal precision and scale aren't carried through to Spark DataFrame
Date Thu, 12 Nov 2015 20:35:10 GMT

    [ https://issues.apache.org/jira/browse/PHOENIX-2288?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15002876#comment-15002876
] 

James Taylor edited comment on PHOENIX-2288 at 11/12/15 8:34 PM:
-----------------------------------------------------------------

Thanks, [~jmahonin]. There's some very similar code in PhoenixRuntime (that doesn't match
particular PDataType instances) for the toTypeString() implementation:
{code}
    public static String getSqlTypeName(PColumn pCol) {
        PDataType dataType = pCol.getDataType();
        Integer maxLength = pCol.getMaxLength();
        Integer scale = pCol.getScale();
        return dataType.isArrayType() ? getArraySqlTypeName(maxLength, scale, dataType) :
appendMaxLengthAndScale(maxLength, scale, dataType.getSqlTypeName());
    }
    
    public static String getArraySqlTypeName(@Nullable Integer maxLength, @Nullable Integer
scale, PDataType arrayType) {
        String baseTypeSqlName = PDataType.arrayBaseType(arrayType).getSqlTypeName();
        return appendMaxLengthAndScale(maxLength, scale, baseTypeSqlName) + " " + ARRAY_TYPE_SUFFIX;
// for ex - decimal(10,2) ARRAY
    }

    private static String appendMaxLengthAndScale(@Nullable Integer maxLength, @Nullable Integer
scale, String sqlTypeName) {
        if (maxLength != null) {
             sqlTypeName = sqlTypeName + "(" + maxLength;
             if (scale != null) {
               sqlTypeName = sqlTypeName + "," + scale; // has both max length and scale.
For ex- decimal(10,2)
             }       
             sqlTypeName = sqlTypeName + ")";
        }
        return sqlTypeName;
    }
{code}
Would you mind refactoring that as necessary and perhaps putting it in SchemaUtil?

Everything else looks great!


was (Author: jamestaylor):
Thanks, [~jmahonin]. There's some very similar code in PhoenixRuntime (that doesn't match
particular PDataType instances) for the toTypeString() implementation:
{code}
    public static String getSqlTypeName(PColumn pCol) {
        PDataType dataType = pCol.getDataType();
        Integer maxLength = pCol.getMaxLength();
        Integer scale = pCol.getScale();
        return dataType.isArrayType() ? getArraySqlTypeName(maxLength, scale, dataType) :
appendMaxLengthAndScale(maxLength, scale, dataType.getSqlTypeName());
    }
    
    public static String getArraySqlTypeName(@Nullable Integer maxLength, @Nullable Integer
scale, PDataType arrayType) {
        String baseTypeSqlName = PDataType.arrayBaseType(arrayType).getSqlTypeName();
        return appendMaxLengthAndScale(maxLength, scale, baseTypeSqlName) + " " + ARRAY_TYPE_SUFFIX;
// for ex - decimal(10,2) ARRAY
    }

    private static String appendMaxLengthAndScale(@Nullable Integer maxLength, @Nullable Integer
scale, String sqlTypeName) {
        if (maxLength != null) {
             sqlTypeName = sqlTypeName + "(" + maxLength;
             if (scale != null) {
               sqlTypeName = sqlTypeName + "," + scale; // has both max length and scale.
For ex- decimal(10,2)
             }       
             sqlTypeName = sqlTypeName + ")";
        }
        return sqlTypeName;
    }
{code}
Would you mind refactoring that as necessary and perhaps putting it in SchemaUtil?

> 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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