jayzhan211 commented on code in PR #13564:
URL: https://github.com/apache/datafusion/pull/13564#discussion_r1865744969
##########
datafusion/core/tests/fuzz_cases/aggregation_fuzzer/data_generator.rs:
##########
@@ -87,7 +87,12 @@ impl DatasetGeneratorConfig {
.iter()
.filter_map(|d| {
if d.column_type.is_numeric()
- && !matches!(d.column_type, DataType::Float32 |
DataType::Float64)
+ && !matches!(
Review Comment:
The real issue is that we have decimal(38, 10) which is the fixed precision
for `sum` and it mismatches with the fuzz test which has random precision
```rust
fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> {
match &arg_types[0] {
DataType::Int64 => Ok(DataType::Int64),
DataType::UInt64 => Ok(DataType::UInt64),
DataType::Float64 => Ok(DataType::Float64),
DataType::Decimal128(precision, scale) => {
// in the spark, the result type is
DECIMAL(min(38,precision+10), s)
// ref:
https://github.com/apache/spark/blob/fcf636d9eb8d645c24be3db2d599aba2d7e2955a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/Sum.scala#L66
let new_precision = DECIMAL128_MAX_PRECISION.min(*precision
+ 10);
Ok(DataType::Decimal128(new_precision, *scale))
}
DataType::Decimal256(precision, scale) => {
// in the spark, the result type is
DECIMAL(min(38,precision+10), s)
// ref:
https://github.com/apache/spark/blob/fcf636d9eb8d645c24be3db2d599aba2d7e2955a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/Sum.scala#L66
let new_precision = DECIMAL256_MAX_PRECISION.min(*precision
+ 10);
Ok(DataType::Decimal256(new_precision, *scale))
}
other => {
exec_err!("[return_type] SUM not supported for {}", other)
}
}
}
```
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