Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/6796#discussion_r33132365
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala ---
@@ -229,11 +231,15 @@ private[parquet] object ParquetTypesConverter extends
Logging {
case LongType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT64))
case TimestampType =>
Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT96))
case DecimalType.Fixed(precision, scale) if precision <= 18 =>
- // TODO: for now, our writer only supports decimals that fit in a
Long
Some(ParquetTypeInfo(ParquetPrimitiveTypeName.FIXED_LEN_BYTE_ARRAY,
Some(ParquetOriginalType.DECIMAL),
Some(new DecimalMetadata(precision, scale)),
Some(BYTES_FOR_PRECISION(precision))))
+ case DecimalType.Fixed(precision, scale) =>
+ Some(ParquetTypeInfo(ParquetPrimitiveTypeName.BINARY,
--- End diff --
Using `BINARY` here conforms to Parquet format spec. But according to the
spec, `FIXED_LENGTH_BYTE_ARRAY` with different length can also be used to store
decimals with different precisions. From the perspective of storage efficiency,
`FIXED_LENGTH_BYTE_ARRAY` is probably more preferable, since `BINARY` has
variable length and needs 4 extra bytes to encode the length (before being
encoded and compressed).
Another benefit here is that we can just unify cases for precision <= 18
and precision > 18.
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