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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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planga82 May 11, 2021
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planga82 May 12, 2021
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Merge remote-tracking branch 'upstream/master' into feature/spark3520…
planga82 May 13, 2021
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planga82 May 14, 2021
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24 changes: 20 additions & 4 deletions sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/hash.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -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(s"Float.floatToIntBits(0.0f)", result)}
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Although this has the semantic, shall we use simply "0" instead of s"Float.floatToIntBits(0.0f)"? We may add some comment for the semantic instead.

jshell> Float.floatToIntBits(0.0f)
$1 ==> 0

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You are right, I have tested it to be sure.
Murmur3HashFunction.hashInt(java.lang.Float.floatToIntBits(0.0f), 42) == Murmur3HashFunction.hashInt(0, 42)
It is simpler.

|} 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(s"Double.doubleToLongBits(0.0d)", result)}
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ditto. We had better use the simplest constant here instead of s"Double.doubleToLongBits(0.0d)".

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In this case, 0L?

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The same as the previous point, thanks

|} else {
| ${genHashLong(s"Double.doubleToLongBits($input)", result)}
|}
""".stripMargin
}

protected def genHashDecimal(
ctx: CodegenContext,
Expand Down Expand Up @@ -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(java.lang.Float.floatToIntBits(0.0f), seed)
case f: Float => hashInt(java.lang.Float.floatToIntBits(f), seed)
case d: Double if (d == -0.0d) => hashLong(java.lang.Double.doubleToLongBits(0.0d), seed)
case d: Double => hashLong(java.lang.Double.doubleToLongBits(d), seed)
case d: Decimal =>
val precision = dataType.asInstanceOf[DecimalType].precision
Expand Down
11 changes: 11 additions & 0 deletions ...alyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/HashExpressionsSuite.scala
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Expand Up @@ -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()
Expand Down