MaxGekk commented on code in PR #48004:
URL: https://github.com/apache/spark/pull/48004#discussion_r1752520445


##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/randomExpressions.scala:
##########
@@ -181,3 +189,215 @@ case class Randn(child: Expression, hideSeed: Boolean = 
false) extends RDG {
 object Randn {
   def apply(seed: Long): Randn = Randn(Literal(seed, LongType))
 }
+
+@ExpressionDescription(
+  usage = """
+    _FUNC_(min, max[, seed]) - Returns a random value with independent and 
identically
+      distributed (i.i.d.) values with the specified range of numbers. The 
random seed is optional.
+      The provided numbers specifying the minimum and maximum values of the 
range must be constant.
+      If both of these numbers are integers, then the result will also be an 
integer. Otherwise if
+      one or both of these are floating-point numbers, then the result will 
also be a floating-point
+      number.
+  """,
+  examples = """
+    Examples:
+      > SELECT _FUNC_(10, 20) > 0;
+      true
+  """,
+  since = "4.0.0",
+  group = "math_funcs")
+case class Uniform(min: Expression, max: Expression, seed: Expression)
+  extends RuntimeReplaceable with TernaryLike[Expression] with 
ExpressionWithRandomSeed {
+  def this(min: Expression, max: Expression) =
+    this(min, max, Literal(Uniform.random.nextLong(), LongType))
+
+  final override lazy val deterministic: Boolean = false
+  override val nodePatterns: Seq[TreePattern] =
+    Seq(RUNTIME_REPLACEABLE, EXPRESSION_WITH_RANDOM_SEED)
+
+  override val dataType: DataType = {
+    val first = min.dataType
+    val second = max.dataType
+    (min.dataType, max.dataType) match {
+      case _ if !valid(min) || !valid(max) => NullType
+      case (_, LongType) | (LongType, _) if Seq(first, second).forall(integer) 
=> LongType
+      case (_, IntegerType) | (IntegerType, _) if Seq(first, 
second).forall(integer) => IntegerType
+      case (_, ShortType) | (ShortType, _) if Seq(first, 
second).forall(integer) => ShortType
+      case (_, DoubleType) | (DoubleType, _) => DoubleType
+      case (_, FloatType) | (FloatType, _) => FloatType
+      case _ => NullType
+    }
+  }
+
+  private def valid(e: Expression): Boolean = e.dataType match {
+    case _ if !e.foldable => false
+    case _: ShortType | _: IntegerType | _: LongType | _: FloatType | _: 
DoubleType => true
+    case _ => false
+  }
+
+  private def integer(t: DataType): Boolean = t match {
+    case _: ShortType | _: IntegerType | _: LongType => true
+    case _ => false
+  }
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    var result: TypeCheckResult = TypeCheckResult.TypeCheckSuccess
+    Seq(min, max, seed).zipWithIndex.foreach { case (expr: Expression, index: 
Int) =>
+      if (!valid(expr)) {
+        result = DataTypeMismatch(
+          errorSubClass = "UNEXPECTED_INPUT_TYPE",
+          messageParameters = Map(
+            "paramIndex" -> ordinalNumber(index),
+            "requiredType" -> "constant value of integer or floating-point",

Review Comment:
   Precisely speaking, you require a foldable expr not just a constant.



##########
sql/core/src/test/scala/org/apache/spark/sql/ExpressionsSchemaSuite.scala:
##########
@@ -118,11 +118,11 @@ class ExpressionsSchemaSuite extends QueryTest with 
SharedSparkSession {
         // SET spark.sql.parser.escapedStringLiterals=true
         example.split("  > 
").tail.filterNot(_.trim.startsWith("SET")).take(1).foreach {
           case _ if funcName == "from_avro" || funcName == "to_avro" ||
-            funcName == "from_protobuf" || funcName == "to_protobuf" =>
+            funcName == "from_protobuf" || funcName == "to_protobuf" || 
funcName == "uniform" =>

Review Comment:
   Could you elaborate the changes, please. As I can see, the file 
`sql-expression-schema.md` contains some random like expr/functions, see:
   ```
   | org.apache.spark.sql.catalyst.expressions.Rand | rand | SELECT rand() | 
struct<rand():double> |
   | org.apache.spark.sql.catalyst.expressions.Rand | random | SELECT random() 
| struct<rand():double> |
   ```
   Why do you want to exclude `uniform` from it? 



##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/randomExpressions.scala:
##########
@@ -181,3 +189,215 @@ case class Randn(child: Expression, hideSeed: Boolean = 
false) extends RDG {
 object Randn {
   def apply(seed: Long): Randn = Randn(Literal(seed, LongType))
 }
+
+@ExpressionDescription(
+  usage = """
+    _FUNC_(min, max[, seed]) - Returns a random value with independent and 
identically
+      distributed (i.i.d.) values with the specified range of numbers. The 
random seed is optional.
+      The provided numbers specifying the minimum and maximum values of the 
range must be constant.
+      If both of these numbers are integers, then the result will also be an 
integer. Otherwise if
+      one or both of these are floating-point numbers, then the result will 
also be a floating-point
+      number.
+  """,
+  examples = """
+    Examples:
+      > SELECT _FUNC_(10, 20) > 0;
+      true
+  """,
+  since = "4.0.0",
+  group = "math_funcs")
+case class Uniform(min: Expression, max: Expression, seed: Expression)
+  extends RuntimeReplaceable with TernaryLike[Expression] with 
ExpressionWithRandomSeed {
+  def this(min: Expression, max: Expression) =
+    this(min, max, Literal(Uniform.random.nextLong(), LongType))
+
+  final override lazy val deterministic: Boolean = false
+  override val nodePatterns: Seq[TreePattern] =
+    Seq(RUNTIME_REPLACEABLE, EXPRESSION_WITH_RANDOM_SEED)
+
+  override val dataType: DataType = {
+    val first = min.dataType
+    val second = max.dataType
+    (min.dataType, max.dataType) match {
+      case _ if !valid(min) || !valid(max) => NullType
+      case (_, LongType) | (LongType, _) if Seq(first, second).forall(integer) 
=> LongType
+      case (_, IntegerType) | (IntegerType, _) if Seq(first, 
second).forall(integer) => IntegerType
+      case (_, ShortType) | (ShortType, _) if Seq(first, 
second).forall(integer) => ShortType
+      case (_, DoubleType) | (DoubleType, _) => DoubleType
+      case (_, FloatType) | (FloatType, _) => FloatType
+      case _ => NullType
+    }
+  }
+
+  private def valid(e: Expression): Boolean = e.dataType match {
+    case _ if !e.foldable => false
+    case _: ShortType | _: IntegerType | _: LongType | _: FloatType | _: 
DoubleType => true
+    case _ => false
+  }
+
+  private def integer(t: DataType): Boolean = t match {
+    case _: ShortType | _: IntegerType | _: LongType => true
+    case _ => false
+  }
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    var result: TypeCheckResult = TypeCheckResult.TypeCheckSuccess
+    Seq(min, max, seed).zipWithIndex.foreach { case (expr: Expression, index: 
Int) =>
+      if (!valid(expr)) {
+        result = DataTypeMismatch(
+          errorSubClass = "UNEXPECTED_INPUT_TYPE",
+          messageParameters = Map(
+            "paramIndex" -> ordinalNumber(index),
+            "requiredType" -> "constant value of integer or floating-point",
+            "inputSql" -> toSQLExpr(expr),
+            "inputType" -> toSQLType(expr.dataType)))
+      }
+    }
+    result
+  }
+
+  override def first: Expression = min
+  override def second: Expression = max
+  override def third: Expression = seed
+
+  override def seedExpression: Expression = seed
+  override def withNewSeed(newSeed: Long): Expression =
+    Uniform(min, max, Literal(newSeed, LongType))
+
+  override def withNewChildrenInternal(
+      newFirst: Expression, newSecond: Expression, newThird: Expression): 
Expression =
+    Uniform(newFirst, newSecond, newThird)
+
+  override def toString: String = prettyName + truncatedString(
+    Seq(min, max), "(", ", ", ")", SQLConf.get.maxToStringFields)

Review Comment:
   Do you exclude `seed` intentionally? 



##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/randomExpressions.scala:
##########
@@ -181,3 +189,215 @@ case class Randn(child: Expression, hideSeed: Boolean = 
false) extends RDG {
 object Randn {
   def apply(seed: Long): Randn = Randn(Literal(seed, LongType))
 }
+
+@ExpressionDescription(
+  usage = """
+    _FUNC_(min, max[, seed]) - Returns a random value with independent and 
identically
+      distributed (i.i.d.) values with the specified range of numbers. The 
random seed is optional.
+      The provided numbers specifying the minimum and maximum values of the 
range must be constant.
+      If both of these numbers are integers, then the result will also be an 
integer. Otherwise if
+      one or both of these are floating-point numbers, then the result will 
also be a floating-point
+      number.
+  """,
+  examples = """
+    Examples:
+      > SELECT _FUNC_(10, 20) > 0;
+      true
+  """,
+  since = "4.0.0",
+  group = "math_funcs")
+case class Uniform(min: Expression, max: Expression, seed: Expression)
+  extends RuntimeReplaceable with TernaryLike[Expression] with 
ExpressionWithRandomSeed {
+  def this(min: Expression, max: Expression) =
+    this(min, max, Literal(Uniform.random.nextLong(), LongType))
+
+  final override lazy val deterministic: Boolean = false
+  override val nodePatterns: Seq[TreePattern] =
+    Seq(RUNTIME_REPLACEABLE, EXPRESSION_WITH_RANDOM_SEED)
+
+  override val dataType: DataType = {
+    val first = min.dataType
+    val second = max.dataType
+    (min.dataType, max.dataType) match {
+      case _ if !valid(min) || !valid(max) => NullType
+      case (_, LongType) | (LongType, _) if Seq(first, second).forall(integer) 
=> LongType
+      case (_, IntegerType) | (IntegerType, _) if Seq(first, 
second).forall(integer) => IntegerType
+      case (_, ShortType) | (ShortType, _) if Seq(first, 
second).forall(integer) => ShortType
+      case (_, DoubleType) | (DoubleType, _) => DoubleType
+      case (_, FloatType) | (FloatType, _) => FloatType
+      case _ => NullType
+    }
+  }
+
+  private def valid(e: Expression): Boolean = e.dataType match {
+    case _ if !e.foldable => false
+    case _: ShortType | _: IntegerType | _: LongType | _: FloatType | _: 
DoubleType => true
+    case _ => false
+  }
+
+  private def integer(t: DataType): Boolean = t match {
+    case _: ShortType | _: IntegerType | _: LongType => true
+    case _ => false
+  }
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    var result: TypeCheckResult = TypeCheckResult.TypeCheckSuccess
+    Seq(min, max, seed).zipWithIndex.foreach { case (expr: Expression, index: 
Int) =>
+      if (!valid(expr)) {
+        result = DataTypeMismatch(
+          errorSubClass = "UNEXPECTED_INPUT_TYPE",

Review Comment:
   We special error condition for non-foldable args: `NON_FOLDABLE_INPUT`. Can 
you return it in the case of non-foldable expr, please.



##########
sql/core/src/test/resources/sql-functions/sql-expression-schema.md:
##########
@@ -265,6 +265,7 @@
 | org.apache.spark.sql.catalyst.expressions.RaiseErrorExpressionBuilder | 
raise_error | SELECT raise_error('custom error message') | 
struct<raise_error(USER_RAISED_EXCEPTION, map(errorMessage, custom error 
message)):void> |
 | org.apache.spark.sql.catalyst.expressions.Rand | rand | SELECT rand() | 
struct<rand():double> |
 | org.apache.spark.sql.catalyst.expressions.Rand | random | SELECT random() | 
struct<rand():double> |
+| org.apache.spark.sql.catalyst.expressions.RandStr | randstr | SELECT 
randstr(3, 0) | struct<randstr(3, 0):string> |

Review Comment:
   Why only `RandStr`? Where is `Uniform`?



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