[SPARK-51423][SQL] Add the current_time() function for TIME datatype

### What changes were proposed in this pull request?
This PR adds support for a new function current_time() which returns the current time at the start of query evaluation.

```bash
# happy cases
scala> spark.sql("SELECT current_time(0);").show()
+---------------+
|current_time(0)|
+---------------+
|       17:11:26|
+---------------+

scala> spark.sql("SELECT current_time(3);").show()
+---------------+
|current_time(3)|
+---------------+
|   17:11:50.225|
+---------------+

scala> spark.sql("SELECT current_time(6);").show()
+---------------+
|current_time(6)|
+---------------+
|17:12:00.734735|
+---------------+

# No braces and Empty braces
scala> spark.sql("SELECT current_time;").show()
+---------------+
|current_time(6)|
+---------------+
|17:12:23.132088|
+---------------+

scala> spark.sql("SELECT current_time();").show()
+---------------+
|current_time(6)|
+---------------+
|17:12:26.718602|
+---------------+

# foldability
## Nested Arithmetic
scala> spark.sql("SELECT current_time((4 - 2) * (1 + 1));").show()
+---------------------------------+
|current_time(((4 - 2) * (1 + 1)))|
+---------------------------------+
|                    17:13:04.4647|
+---------------------------------+

## Casting String literals
scala> spark.sql("SELECT current_time(CAST(' 0005 ' AS INT));").show()
+---------------------------------+
|current_time(CAST( 0005  AS INT))|
+---------------------------------+
|                   17:13:26.28039|
+---------------------------------+

scala> spark.sql("SELECT current_time('5');").show()
+---------------+
|current_time(5)|
+---------------+
| 22:34:07.65007|
+---------------+

## Combine Cast and Arithmetic
scala> spark.sql("SELECT current_time(CAST('4' AS INT) * CAST('1' AS INT));").show()
+-----------------------------------------------+
|current_time((CAST(4 AS INT) * CAST(1 AS INT)))|
+-----------------------------------------------+
|                                  17:14:06.7151|
+-----------------------------------------------+

# failure cases
scala> spark.sql("SELECT current_time(-1);").show()
org.apache.spark.sql.catalyst.ExtendedAnalysisException: [DATATYPE_MISMATCH.VALUE_OUT_OF_RANGE] Cannot resolve "current_time(-1)" due to data type mismatch: The `precision` must be between [0, 6] (current value = -1). SQLSTATE: 42K09; line 1 pos 7;
'Project [unresolvedalias(current_time(-1))]
+- OneRowRelation'

scala> spark.sql("SELECT current_time('foo');").show()
org.apache.spark.SparkNumberFormatException: [CAST_INVALID_INPUT] The value 'foo' of the type "STRING" cannot be cast to "INT" because it is malformed. Correct the value as per the syntax, or change its target type. Use `try_cast` to tolerate malformed input and return NULL instead. SQLSTATE: 22018
== SQL (line 1, position 8) ==
SELECT current_time('foo');

scala> spark.sql("SELECT current_time(2,2);").show()
org.apache.spark.sql.AnalysisException: [WRONG_NUM_ARGS.WITHOUT_SUGGESTION] The `current_time` requires [0, 1] parameters but the actual number is 2. Please, refer to 'https://spark.apache.org/docs/latest/sql-ref-functions.html' for a fix. SQLSTATE: 42605; line 1 pos 7

# All calls of current_time within the same query should return the same value.
scala> val df = spark.sql("""
     |   SELECT
     |     current_time AS col1,
     |     current_time() AS col2,
     |  current_time(0) AS col3,
     |  current_time(1) AS col4,
     |  current_time(2) AS col5,
     |  current_time(3) AS col6,
     |  current_time(4) AS col7,
     |  current_time(5) AS col8,
     |  current_time(6) AS col9,
     |     current_time AS col10
     | """)
val df: org.apache.spark.sql.DataFrame = [col1: time(6), col2: time(6) ... 8 more fields]

scala> df.show()
+---------------+---------------+--------+----------+-----------+-----------+-------------+--------------+---------------+---------------+
|           col1|           col2|    col3|      col4|       col5|       col6|         col7|          col8|           col9|          col10|
+---------------+---------------+--------+----------+-----------+-----------+-------------+--------------+---------------+---------------+
|17:15:47.680648|17:15:47.680648|17:15:47|17:15:47.6|17:15:47.68|17:15:47.68|17:15:47.6806|17:15:47.68064|17:15:47.680648|17:15:47.680648|
+---------------+---------------+--------+----------+-----------+-----------+-------------+--------------+---------------+---------------+

```

### Why are the changes needed?
Adds a built-in current_time([n]) function returning just the time portion (in a TIME(n) type). This aligns Spark with other SQL systems offering a native time function, improves convenience for time-only queries, and complements existing functions like current_date and current_timestamp.

### Does this PR introduce _any_ user-facing change?
Yes, adds a new function. Users can now get the current time using this function.

### How was this patch tested?
Manual testing as shown above and running UTs added:

```bash
$ build/sbt "test:testOnly *TimeExpressionsSuite.scala"
$ build/sbt "test:testOnly *ComputeCurrentTimeSuite.scala"
$ build/sbt "test:testOnly *ResolveInlineTablesSuite.scala
$ build/sbt "test:testOnly *AnalysisSuite.scala
```

### Was this patch authored or co-authored using generative AI tooling?
No

Closes #50336 from the-sakthi/SPARK-51162.

Authored-by: Sakthi <[email protected]>
Signed-off-by: Max Gekk <[email protected]>

Author: the-sakthi

sql/api/src/main/antlr4/org/apache/spark/sql/catalyst/parser/SqlBaseParser.g4

@@ -1155,7 +1155,7 @@ datetimeUnit
     ;
 
 primaryExpression
-    : name=(CURRENT_DATE | CURRENT_TIMESTAMP | CURRENT_USER | USER | SESSION_USER)             #currentLike
+    : name=(CURRENT_DATE | CURRENT_TIMESTAMP | CURRENT_USER | USER | SESSION_USER | CURRENT_TIME)             #currentLike
     | name=(TIMESTAMPADD | DATEADD | DATE_ADD) LEFT_PAREN (unit=datetimeUnit | invalidUnit=stringLit) COMMA unitsAmount=valueExpression COMMA timestamp=valueExpression RIGHT_PAREN             #timestampadd
     | name=(TIMESTAMPDIFF | DATEDIFF | DATE_DIFF | TIMEDIFF) LEFT_PAREN (unit=datetimeUnit | invalidUnit=stringLit) COMMA startTimestamp=valueExpression COMMA endTimestamp=valueExpression RIGHT_PAREN    #timestampdiff
     | CASE whenClause+ (ELSE elseExpression=expression)? END                                   #searchedCase

sql/api/src/main/scala/org/apache/spark/sql/catalyst/util/SparkDateTimeUtils.scala

@@ -133,6 +133,39 @@ trait SparkDateTimeUtils {
     }
   }
 
+  /**
+   * Gets the number of microseconds since midnight using the session time zone.
+   */
+  def instantToMicrosOfDay(instant: Instant, timezone: String): Long = {
+    val zoneId = getZoneId(timezone)
+    val localDateTime = LocalDateTime.ofInstant(instant, zoneId)
+    localDateTime.toLocalTime.getLong(MICRO_OF_DAY)
+  }
+
+  /**
+   * Truncates a time value (in microseconds) to the specified fractional precision `p`.
+   *
+   * For example, if `p = 3`, we keep millisecond resolution and discard any digits beyond the
+   * thousand-microsecond place. So a value like `123456` microseconds (12:34:56.123456) becomes
+   * `123000` microseconds (12:34:56.123).
+   *
+   * @param micros
+   *   The original time in microseconds.
+   * @param p
+   *   The fractional second precision (range 0 to 6).
+   * @return
+   *   The truncated microsecond value, preserving only `p` fractional digits.
+   */
+  def truncateTimeMicrosToPrecision(micros: Long, p: Int): Long = {
+    assert(
+      p >= TimeType.MIN_PRECISION && p <= TimeType.MICROS_PRECISION,
+      s"Fractional second precision $p out" +
+        s" of range [${TimeType.MIN_PRECISION}..${TimeType.MICROS_PRECISION}].")
+    val scale = TimeType.MICROS_PRECISION - p
+    val factor = math.pow(10, scale).toLong
+    (micros / factor) * factor
+  }
+
   /**
    * Converts the timestamp `micros` from one timezone to another.
    *

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala

@@ -627,6 +627,7 @@ object FunctionRegistry {
     expression[CurrentDate]("current_date"),
     expressionBuilder("curdate", CurDateExpressionBuilder, setAlias = true),
     expression[CurrentTimestamp]("current_timestamp"),
+    expression[CurrentTime]("current_time"),
     expression[CurrentTimeZone]("current_timezone"),
     expression[LocalTimestamp]("localtimestamp"),
     expression[DateDiff]("datediff"),

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/LiteralFunctionResolution.scala

@@ -17,16 +17,7 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
-import org.apache.spark.sql.catalyst.expressions.{
-  Alias,
-  CurrentDate,
-  CurrentTimestamp,
-  CurrentUser,
-  Expression,
-  GroupingID,
-  NamedExpression,
-  VirtualColumn
-}
+import org.apache.spark.sql.catalyst.expressions.{Alias, CurrentDate, CurrentTime, CurrentTimestamp, CurrentUser, Expression, GroupingID, NamedExpression, VirtualColumn}
 import org.apache.spark.sql.catalyst.util.toPrettySQL
 
 /**
@@ -47,10 +38,12 @@ object LiteralFunctionResolution {
     }
   }
 
-  // support CURRENT_DATE, CURRENT_TIMESTAMP, CURRENT_USER, USER, SESSION_USER and grouping__id
+  // support CURRENT_DATE, CURRENT_TIMESTAMP, CURRENT_TIME,
+  //  CURRENT_USER, USER, SESSION_USER and grouping__id
   private val literalFunctions: Seq[(String, () => Expression, Expression => String)] = Seq(
     (CurrentDate().prettyName, () => CurrentDate(), toPrettySQL(_)),
     (CurrentTimestamp().prettyName, () => CurrentTimestamp(), toPrettySQL(_)),
+    (CurrentTime().prettyName, () => CurrentTime(), toPrettySQL(_)),
     (CurrentUser().prettyName, () => CurrentUser(), toPrettySQL),
     ("user", () => CurrentUser(), toPrettySQL),
     ("session_user", () => CurrentUser(), toPrettySQL),

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

@@ -19,13 +19,17 @@ package org.apache.spark.sql.catalyst.expressions
 
 import java.time.DateTimeException
 
-import org.apache.spark.sql.catalyst.analysis.ExpressionBuilder
+import org.apache.spark.sql.catalyst.analysis.{ExpressionBuilder, TypeCheckResult}
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{DataTypeMismatch, TypeCheckSuccess}
+import org.apache.spark.sql.catalyst.expressions.Cast.{toSQLExpr, toSQLId, toSQLType, toSQLValue}
 import org.apache.spark.sql.catalyst.expressions.objects.{Invoke, StaticInvoke}
+import org.apache.spark.sql.catalyst.trees.TreePattern.{CURRENT_LIKE, TreePattern}
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.catalyst.util.TimeFormatter
+import org.apache.spark.sql.catalyst.util.TypeUtils.{ordinalNumber}
 import org.apache.spark.sql.errors.{QueryCompilationErrors, QueryExecutionErrors}
 import org.apache.spark.sql.internal.types.StringTypeWithCollation
-import org.apache.spark.sql.types.{AbstractDataType, IntegerType, ObjectType, TimeType, TypeCollection}
+import org.apache.spark.sql.types.{AbstractDataType, DataType, IntegerType, ObjectType, TimeType, TypeCollection}
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -349,3 +353,109 @@ object SecondExpressionBuilder extends ExpressionBuilder {
   }
 }
 
+/**
+ * Returns the current time at the start of query evaluation.
+ * There is no code generation since this expression should get constant folded by the optimizer.
+ */
+// scalastyle:off line.size.limit
+@ExpressionDescription(
+  usage = """
+    _FUNC_([precision]) - Returns the current time at the start of query evaluation.
+    All calls of current_time within the same query return the same value.
+
+    _FUNC_ - Returns the current time at the start of query evaluation.
+  """,
+  arguments = """
+    Arguments:
+      * precision - An optional integer literal in the range [0..6], indicating how many
+                    fractional digits of seconds to include. If omitted, the default is 6.
+  """,
+  examples = """
+    Examples:
+      > SELECT _FUNC_();
+       15:49:11.914120
+      > SELECT _FUNC_;
+       15:49:11.914120
+      > SELECT _FUNC_(0);
+       15:49:11
+      > SELECT _FUNC_(3);
+       15:49:11.914
+      > SELECT _FUNC_(1+1);
+       15:49:11.91
+  """,
+  group = "datetime_funcs",
+  since = "4.1.0"
+)
+case class CurrentTime(child: Expression = Literal(TimeType.MICROS_PRECISION))
+  extends UnaryExpression with FoldableUnevaluable with ImplicitCastInputTypes {
+
+  def this() = {
+    this(Literal(TimeType.MICROS_PRECISION))
+  }
+
+  final override val nodePatterns: Seq[TreePattern] = Seq(CURRENT_LIKE)
+
+  override def nullable: Boolean = false
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    // Check foldability
+    if (!child.foldable) {
+      return DataTypeMismatch(
+        errorSubClass = "NON_FOLDABLE_INPUT",
+        messageParameters = Map(
+          "inputName" -> toSQLId("precision"),
+          "inputType" -> toSQLType(child.dataType),
+          "inputExpr" -> toSQLExpr(child)
+        )
+      )
+    }
+
+    // Evaluate
+    val precisionValue = child.eval()
+    if (precisionValue == null) {
+      return DataTypeMismatch(
+        errorSubClass = "UNEXPECTED_NULL",
+        messageParameters = Map("exprName" -> "precision"))
+    }
+
+    // Check numeric range
+    precisionValue match {
+      case n: Number =>
+        val p = n.intValue()
+        if (p < TimeType.MIN_PRECISION || p > TimeType.MICROS_PRECISION) {
+          return DataTypeMismatch(
+            errorSubClass = "VALUE_OUT_OF_RANGE",
+            messageParameters = Map(
+              "exprName" -> toSQLId("precision"),
+              "valueRange" -> s"[${TimeType.MIN_PRECISION}, ${TimeType.MICROS_PRECISION}]",
+              "currentValue" -> toSQLValue(p, IntegerType)
+            )
+          )
+        }
+      case _ =>
+        return DataTypeMismatch(
+          errorSubClass = "UNEXPECTED_INPUT_TYPE",
+          messageParameters = Map(
+            "paramIndex" -> ordinalNumber(0),
+            "requiredType" -> toSQLType(IntegerType),
+            "inputSql" -> toSQLExpr(child),
+            "inputType" -> toSQLType(child.dataType))
+        )
+    }
+    TypeCheckSuccess
+  }
+
+  // Because checkInputDataTypes ensures the argument is foldable & valid,
+  // we can directly evaluate here.
+  lazy val precision: Int = child.eval().asInstanceOf[Number].intValue()
+
+  override def dataType: DataType = TimeType(precision)
+
+  override def prettyName: String = "current_time"
+
+  override protected def withNewChildInternal(newChild: Expression): Expression = {
+    copy(child = newChild)
+  }
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(IntegerType)
+}

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/finishAnalysis.scala

@@ -31,6 +31,7 @@ import org.apache.spark.sql.catalyst.trees.TreePattern._
 import org.apache.spark.sql.catalyst.trees.TreePatternBits
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.catalyst.util.DateTimeUtils.{convertSpecialDate, convertSpecialTimestamp, convertSpecialTimestampNTZ, instantToMicros, localDateTimeToMicros}
+import org.apache.spark.sql.catalyst.util.SparkDateTimeUtils.{instantToMicrosOfDay, truncateTimeMicrosToPrecision}
 import org.apache.spark.sql.catalyst.util.TypeUtils.toSQLExpr
 import org.apache.spark.sql.connector.catalog.CatalogManager
 import org.apache.spark.sql.types._
@@ -113,6 +114,7 @@ object ComputeCurrentTime extends Rule[LogicalPlan] {
     val instant = Instant.now()
     val currentTimestampMicros = instantToMicros(instant)
     val currentTime = Literal.create(currentTimestampMicros, TimestampType)
+    val currentTimeOfDayMicros = instantToMicrosOfDay(instant, conf.sessionLocalTimeZone)
     val timezone = Literal.create(conf.sessionLocalTimeZone, StringType)
     val currentDates = collection.mutable.HashMap.empty[ZoneId, Literal]
     val localTimestamps = collection.mutable.HashMap.empty[ZoneId, Literal]
@@ -129,6 +131,10 @@ object ComputeCurrentTime extends Rule[LogicalPlan] {
               Literal.create(
                 DateTimeUtils.microsToDays(currentTimestampMicros, cd.zoneId), DateType)
             })
+          case currentTimeType : CurrentTime =>
+            val truncatedTime = truncateTimeMicrosToPrecision(currentTimeOfDayMicros,
+              currentTimeType.precision)
+            Literal.create(truncatedTime, TimeType(currentTimeType.precision))
           case CurrentTimestamp() | Now() => currentTime
           case CurrentTimeZone() => timezone
           case localTimestamp: LocalTimestamp =>

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/AstBuilder.scala

@@ -2888,12 +2888,14 @@ class AstBuilder extends DataTypeAstBuilder
           CurrentDate()
         case SqlBaseParser.CURRENT_TIMESTAMP =>
           CurrentTimestamp()
+        case SqlBaseParser.CURRENT_TIME =>
+          CurrentTime()
         case SqlBaseParser.CURRENT_USER | SqlBaseParser.USER | SqlBaseParser.SESSION_USER =>
           CurrentUser()
       }
     } else {
       // If the parser is not in ansi mode, we should return `UnresolvedAttribute`, in case there
-      // are columns named `CURRENT_DATE` or `CURRENT_TIMESTAMP`.
+      // are columns named `CURRENT_DATE` or `CURRENT_TIMESTAMP` or `CURRENT_TIME`
       UnresolvedAttribute.quoted(ctx.name.getText)
     }
   }

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala

@@ -819,6 +819,25 @@ class AnalysisSuite extends AnalysisTest with Matchers {
     }
   }
 
+  test("CURRENT_TIME should be case insensitive") {
+    withSQLConf(SQLConf.CASE_SENSITIVE.key -> "true") {
+      val input = Project(Seq(
+        // The user references "current_time" or "CURRENT_TIME" in the query
+        UnresolvedAttribute("current_time"),
+        UnresolvedAttribute("CURRENT_TIME")
+      ), testRelation)
+
+      // The analyzer should resolve both to the same expression: CurrentTime()
+      val expected = Project(Seq(
+        Alias(CurrentTime(), toPrettySQL(CurrentTime()))(),
+        Alias(CurrentTime(), toPrettySQL(CurrentTime()))()
+      ), testRelation).analyze
+
+      checkAnalysis(input, expected)
+    }
+  }
+
+
   test("CTE with non-existing column alias") {
     assertAnalysisErrorCondition(parsePlan("WITH t(x) AS (SELECT 1) SELECT * FROM t WHERE y = 1"),
       "UNRESOLVED_COLUMN.WITH_SUGGESTION",

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ResolveInlineTablesSuite.scala

@@ -21,11 +21,11 @@ import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.EvaluateUnresolvedInlineTable
-import org.apache.spark.sql.catalyst.expressions.{Alias, Cast, CurrentTimestamp, Literal, Rand}
+import org.apache.spark.sql.catalyst.expressions.{Alias, Cast, CurrentTime, CurrentTimestamp, Literal, Rand}
 import org.apache.spark.sql.catalyst.expressions.aggregate.Count
 import org.apache.spark.sql.catalyst.optimizer.{ComputeCurrentTime, EvalInlineTables}
 import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
-import org.apache.spark.sql.types.{LongType, NullType, TimestampType}
+import org.apache.spark.sql.types.{LongType, NullType, TimestampType, TimeType}
 
 /**
  * Unit tests for [[ResolveInlineTables]]. Note that there are also test cases defined in
@@ -113,6 +113,32 @@ class ResolveInlineTablesSuite extends AnalysisTest with BeforeAndAfter {
     }
   }
 
+  test("cast and execute CURRENT_TIME expressions") {
+    val table = UnresolvedInlineTable(
+      Seq("c1"),
+      Seq(
+        Seq(CurrentTime()),
+        Seq(CurrentTime())
+      )
+    )
+    val resolved = ResolveInlineTables(table)
+    assert(resolved.isInstanceOf[ResolvedInlineTable],
+      "Expected an inline table to be resolved into a ResolvedInlineTable")
+
+    val transformed = ComputeCurrentTime(resolved)
+    EvalInlineTables(transformed) match {
+      case LocalRelation(output, data, _, _) =>
+        // expect default precision = 6
+        assert(output.map(_.dataType) == Seq(TimeType(6)))
+        // Should have 2 rows
+        assert(data.size == 2)
+        // Both rows should have the *same* microsecond value for current_time
+        assert(data(0).getLong(0) == data(1).getLong(0),
+          "Both CURRENT_TIME calls must yield the same value in the same query")
+    }
+  }
+
+
   test("convert TimeZoneAwareExpression") {
     val table = UnresolvedInlineTable(Seq("c1"),
       Seq(Seq(Cast(lit("1991-12-06 00:00:00.0"), TimestampType))))

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

@@ -19,8 +19,10 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.{SPARK_DOC_ROOT, SparkDateTimeException, SparkFunSuite}
 import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{DataTypeMismatch, TypeCheckSuccess}
+import org.apache.spark.sql.catalyst.expressions.Cast.{toSQLId, toSQLValue}
 import org.apache.spark.sql.catalyst.util.DateTimeTestUtils._
-import org.apache.spark.sql.types.{StringType, TimeType}
+import org.apache.spark.sql.types.{IntegerType, StringType, TimeType}
 
 class TimeExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
   test("ParseToTime") {
@@ -226,4 +228,49 @@ class TimeExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkConsistencyBetweenInterpretedAndCodegen(
       (child: Expression) => SecondsOfTime(child).replacement, TimeType())
   }
+
+  test("CurrentTime") {
+    // test valid precision
+    var expr = CurrentTime(Literal(3))
+    assert(expr.dataType == TimeType(3), "Should produce TIME(3) data type")
+    assert(expr.checkInputDataTypes() == TypeCheckSuccess)
+
+    // test default constructor => TIME(6)
+    expr = CurrentTime()
+    assert(expr.precision == 6, "Default precision should be 6")
+    assert(expr.dataType == TimeType(6))
+    assert(expr.checkInputDataTypes() == TypeCheckSuccess)
+
+    // test no value => TIME()
+    expr = CurrentTime()
+    assert(expr.precision == 6, "Default precision should be 6")
+    assert(expr.dataType == TimeType(6))
+    assert(expr.checkInputDataTypes() == TypeCheckSuccess)
+
+    // test foldable value
+    expr = CurrentTime(Literal(1 + 1))
+    assert(expr.precision == 2, "Precision should be 2")
+    assert(expr.dataType == TimeType(2))
+    assert(expr.checkInputDataTypes() == TypeCheckSuccess)
+
+    // test out of range precision => checkInputDataTypes fails
+    expr = CurrentTime(Literal(2 + 8))
+    assert(expr.checkInputDataTypes() ==
+      DataTypeMismatch(
+        errorSubClass = "VALUE_OUT_OF_RANGE",
+        messageParameters = Map(
+          "exprName" -> toSQLId("precision"),
+          "valueRange" -> s"[${TimeType.MIN_PRECISION}, ${TimeType.MICROS_PRECISION}]",
+          "currentValue" -> toSQLValue(10, IntegerType)
+        )
+      )
+    )
+
+    // test non number value should fail since we skip analyzer here
+    expr = CurrentTime(Literal("2"))
+    val failure = intercept[ClassCastException] {
+      expr.precision
+    }
+    assert(failure.getMessage.contains("cannot be cast to class java.lang.Number"))
+  }
 }