yaooqinn opened a new pull request, #45956:
URL: https://github.com/apache/spark/pull/45956
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SPARK-30252 has disabled the definition of the negative scale for decimals.
It has a regression that also disabled reading negative scale decimals from
data sources. **Although there is a legacy config to restore the old
behavior**, it seemed neither designed for such a case, nor convenient in a
data pipeline that extracts negative scale decimals from a database such as
Oracle to Parquet files w/o negative scale decimal support.
In addition, Postgres has the negative scale decimals support since v15,
which was one of the supporters for disabling negative scale decimals on our
side.
In this PR, we change the schema from `decimal(p,s)` to `decimal(p-s,0)` if
s<0.
This PR also follows SPARK-45905 (#43781) to prefer to retain integral
digits rather than decimal digits when reading decimals with exceeded precisions
### Why are the changes needed?
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Negative scale decimals have many supporters for rounding the internal
parts, such as Oracle, Postgres, etc.
- Oracle
> Negative scale is the number of significant digits to the left of the
decimal point, to but not including the least significant digit. For negative
scale the least significant digit is on the left side of the decimal point,
because the actual data is rounded to the specified number of places to the
left of the decimal point. For example, a specification of (10,-2) means to
round to hundreds.
- Postgres
> Beginning in PostgreSQL 15, it is allowed to declare a numeric column with
a negative scale. Then values will be rounded to the left of the decimal point.
The precision still represents the maximum number of non-rounded digits. Thus,
a column declared as
NUMERIC(2, -3)
will round values to the nearest thousand and can store values between
-99000 and 99000, inclusive. It is also allowed to declare a scale larger than
the declared precision. Such a column can only hold fractional values, and it
requires the number of zero digits just to the right of the decimal point to be
at least the declared scale minus the declared precision. For example, a column
declared as
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new tests
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