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

### What changes were proposed in this pull request?

Generally, we would expect that x = y => hash( x ) = hash( y ). However +-0 hash to different values for floating point types.
```
scala> spark.sql("select hash(cast('0.0' as double)), hash(cast('-0.0' as double))").show
+-------------------------+--------------------------+
|hash(CAST(0.0 AS DOUBLE))|hash(CAST(-0.0 AS DOUBLE))|
+-------------------------+--------------------------+
|              -1670924195|                -853646085|
+-------------------------+--------------------------+
scala> spark.sql("select cast('0.0' as double) == cast('-0.0' as double)").show
+--------------------------------------------+
|(CAST(0.0 AS DOUBLE) = CAST(-0.0 AS DOUBLE))|
+--------------------------------------------+
|                                        true|
+--------------------------------------------+
```
Here is an extract from IEEE 754:

> The two zeros are distinguishable arithmetically only by either division-byzero ( producing appropriately signed infinities ) or else by the CopySign function recommended by IEEE 754 /854. Infinities, SNaNs, NaNs and Subnormal numbers necessitate four more special cases

From this, I deduce that the hash function must produce the same result for 0 and -0.

### Why are the changes needed?

It is a correctness issue

### Does this PR introduce _any_ user-facing change?

This changes only affect to the hash function applied to -0 value in float and double types

### How was this patch tested?

Unit testing and manual testing

Closes #32496 from planga82/feature/spark35207_hashnegativezero.

Authored-by: Pablo Langa <soypab@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

Author: planga82

docs/sql-migration-guide.md

@@ -87,6 +87,8 @@ license: |
 
   - 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`.

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/hash.scala

@@ -369,11 +369,25 @@ abstract class HashExpression[E] extends Expression {
   protected def genHashBoolean(input: String, result: String): String =
     genHashInt(s"$input ? 1 : 0", result)
 
-  protected def genHashFloat(input: String, result: String): String =
-    genHashInt(s"Float.floatToIntBits($input)", result)
+  protected def genHashFloat(input: String, result: String): String = {
+    s"""
+       |if($input == -0.0f) {
+       |  ${genHashInt("0", result)}
+       |} else {
+       |  ${genHashInt(s"Float.floatToIntBits($input)", result)}
+       |}
+     """.stripMargin
+  }
 
-  protected def genHashDouble(input: String, result: String): String =
-    genHashLong(s"Double.doubleToLongBits($input)", result)
+  protected def genHashDouble(input: String, result: String): String = {
+    s"""
+      |if($input == -0.0d) {
+      |  ${genHashLong("0L", result)}
+      |} else {
+      |  ${genHashLong(s"Double.doubleToLongBits($input)", result)}
+      |}
+     """.stripMargin
+  }
 
   protected def genHashDecimal(
       ctx: CodegenContext,
@@ -523,7 +537,9 @@ abstract class InterpretedHashFunction {
       case s: Short => hashInt(s, seed)
       case i: Int => hashInt(i, seed)
       case l: Long => hashLong(l, seed)
+      case f: Float if (f == -0.0f) => hashInt(0, seed)
       case f: Float => hashInt(java.lang.Float.floatToIntBits(f), seed)
+      case d: Double if (d == -0.0d) => hashLong(0L, seed)
       case d: Double => hashLong(java.lang.Double.doubleToLongBits(d), seed)
       case d: Decimal =>
         val precision = dataType.asInstanceOf[DecimalType].precision

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/HashExpressionsSuite.scala

@@ -708,6 +708,17 @@ class HashExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(HiveHash(Seq(yearMonth)), 1234)
   }
 
+  test("SPARK-35207: Compute hash consistent between -0.0 and 0.0") {
+    def checkResult(exprs1: Expression, exprs2: Expression): Unit = {
+      checkEvaluation(Murmur3Hash(Seq(exprs1), 42), Murmur3Hash(Seq(exprs2), 42).eval())
+      checkEvaluation(XxHash64(Seq(exprs1), 42), XxHash64(Seq(exprs2), 42).eval())
+      checkEvaluation(HiveHash(Seq(exprs1)), HiveHash(Seq(exprs2)).eval())
+    }
+
+    checkResult(Literal.create(-0D, DoubleType), Literal.create(0D, DoubleType))
+    checkResult(Literal.create(-0F, FloatType), Literal.create(0F, FloatType))
+  }
+
   private def testHash(inputSchema: StructType): Unit = {
     val inputGenerator = RandomDataGenerator.forType(inputSchema, nullable = false).get
     val toRow = RowEncoder(inputSchema).createSerializer()