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[SPARK-35207][SQL] Normalize hash function behavior with negative zero (floating point types) #32496

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[SPARK-35207][SQL] Normalize hash function behavior with negative zero (floating point types) #32496

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- In Spark 3.2, Spark supports `DayTimeIntervalType` and `YearMonthIntervalType` as inputs and outputs of `TRANSFORM` clause in Hive `SERDE` mode, the behavior is different between Hive `SERDE` mode and `ROW FORMAT DELIMITED` mode when these two types are used as inputs. In Hive `SERDE` mode, `DayTimeIntervalType` column is converted to `HiveIntervalDayTime`, its string format is `[-]?d h:m:s.n`, but in `ROW FORMAT DELIMITED` mode the format is `INTERVAL '[-]?d h:m:s.n' DAY TO TIME`. In Hive `SERDE` mode, `YearMonthIntervalType` column is converted to `HiveIntervalYearMonth`, its string format is `[-]?y-m`, but in `ROW FORMAT DELIMITED` mode the format is `INTERVAL '[-]?y-m' YEAR TO MONTH`.

- In Spark 3.2, `hash(0) == hash(-0)` for floating point types. Previously, different values were generated.

## Upgrading from Spark SQL 3.0 to 3.1

- In Spark 3.1, statistical aggregation function includes `std`, `stddev`, `stddev_samp`, `variance`, `var_samp`, `skewness`, `kurtosis`, `covar_samp`, `corr` will return `NULL` instead of `Double.NaN` when `DivideByZero` occurs during expression evaluation, for example, when `stddev_samp` applied on a single element set. In Spark version 3.0 and earlier, it will return `Double.NaN` in such case. To restore the behavior before Spark 3.1, you can set `spark.sql.legacy.statisticalAggregate` to `true`.
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