beliefer opened a new pull request, #37805:
URL: https://github.com/apache/spark/pull/37805
### What changes were proposed in this pull request?
Extend Catalyst's type system by a new decimal type: Decimal128Type with
Int128.
### Why are the changes needed?
Spark SQL today supports the Decimal data type. The implementation of Spark
Decimal holds a BigDecimal or Long value. Spark Decimal provides some operators
like +, -, *, /, % and so on. These operators rely heavily on the computational
power of BigDecimal or Long itself. For ease of understanding, take the + as an
example. The implementation shows below.
```
def + (that: Decimal): Decimal = {
if (decimalVal.eq(null) && that.decimalVal.eq(null) && scale ==
that.scale) {
Decimal(longVal + that.longVal, Math.max(precision, that.precision) +
1, scale)
} else {
Decimal(toBigDecimal.bigDecimal.add(that.toBigDecimal.bigDecimal))
}
}
```
We can see the + of Long will be called if Spark Decimal holds a Long value.
Otherwise, the add of BigDecimal will be called if Spark Decimal holds a
BigDecimal value. The other operators of Spark Decimal adopt the similar way.
Furthermore, the code shown above calls Decimal.apply to construct a new
instance of Spark Decimal. As we know, the add operator of BigDecimal
constructed a new instance of BigDecimal. So, if we call the + operator of
Spark Decimal who holds a Long value, Spark will construct a new instance of
Decimal. Otherwise, Spark will construct a new instance of BigDecimal and a new
instance of Decimal.
Through rough analysis, we know:
1. The computational power of Spark Decimal may depend on BigDecimal.
2. The calculation operators of Spark Decimal create a lot of new instances
of Decimal and may create a lot of new instances of BigDecimal.
If a large table has a field called 'colA whose type is Spark Decimal, the
execution of SUM('colA) will involve the creation of a large number of Spark
Decimal instances and BigDecimal instances. These Spark Decimal instances and
BigDecimal instances will lead to garbage collection frequently.
Int128 is a high-performance data type about 1X~6X more efficient than
BigDecimal for typical operations. It uses a finite (128 bit) precision and can
handle up to decimal(38, X). The implementation of Int128 just uses two Long
values to represent the high and low bits of 128 bits respectively. Int128 is
lighter than BigDecimal, reduces the cost of new() and garbage collection.
If Spark could support a new type to represent the decimal type with Int128,
it would improve the performance for calculation operators.
Now, let's call the new decimal type Decimal128.
### Does this PR introduce _any_ user-facing change?
No, a new decimal type with Int128. It is still in development.
### How was this patch tested?
New test cases.
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