Jack Leitch created SPARK-60443:
-----------------------------------

             Summary: VariantUtil.checkDecimal rejects spec-conformant decimal 
values when scale > type precision
                 Key: SPARK-60443
                 URL: https://issues.apache.org/jira/browse/SPARK-60443
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 4.0.1, 4.0.0, 4.1.0
            Reporter: Jack Leitch


VariantUtil.checkDecimal rejects spec-conformant Variant decimal values when 
the scale is greater than the precision of the chosen decimal type.

Repro:

// write v into a parquet file as a VARIANT column, then read:
spark.read.parquet(path).selectExpr("cast(payload as string)").collect();
// throws MALFORMED_VARIANT

 

The value 0.00042626262 is encoded by parquet-variant as decimal4 with scale 
11. The raw value bytes are: 20 0b d6 6c 8a 02 - header 0x20 (decimal4), scale 
0x0b = 11, little-endian int32 42626262.

Why this is a Spark bug and not a parquet-variant bug:

The Variant encoding spec 
([https://github.com/apache/parquet-format/blob/master/VariantEncoding.md]) 
defines scale as [0, 38] across decimal4, decimal8, and decimal16:

decimal4: 1-byte scale in range [0, 38], followed by little-endian unscaled 
value
decimal8: 1-byte scale in range [0, 38], followed by little-endian unscaled 
value
decimal16: 1-byte scale in range [0, 38], followed by little-endian unscaled 
value

The per-type restriction is on precision, not scale. parquet-java PR #3335 
([https://github.com/apache/parquet-java/pull/3335]) explicitly removed scale 
from decimal width selection on that basis.

VariantUtil.checkDecimal conflates the two:

private static void checkDecimal(BigDecimal d, int maxPrecision) {
if (d.precision() > maxPrecision || d.scale() > maxPrecision)

{ throw malformedVariant(); }

}

For decimal4, maxPrecision = 9, so scale = 11 throws. DuckDB, Snowflake, and 
parquet-variant's own Variant.getDecimal() all read these bytes without 
complaint. Spark is the strictest reader, which is why it alone surfaces the 
issue.

Fix:

Bound d.scale() against MAX_DECIMAL16_PRECISION (the universal 38 ceiling) 
rather than the per-type maxPrecision. Precision continues to be bounded by the 
type width.

PR to follow.



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