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Posted to commits@spark.apache.org by do...@apache.org on 2026/10/20 00:32:55 UTC
(spark) branch master updated: [SPARK-60355][SQL] Support TIME precision widening in `UnwrapCastInBinaryComparison`
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dongjoon-hyun pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/spark.git
The following commit(s) were added to refs/heads/master by this push:
new e792c8321f5a [SPARK-60355][SQL] Support TIME precision widening in `UnwrapCastInBinaryComparison`
e792c8321f5a is described below
commit e792c8321f5a3bf9c621e4c12f4f95fdedd2b378
Author: Dongjoon Hyun <do...@apache.org>
AuthorDate: Mon Oct 19 17:32:45 2026 -0700
[SPARK-60355][SQL] Support TIME precision widening in `UnwrapCastInBinaryComparison`
### What changes were proposed in this pull request?
This PR aims to support the cast from `TIME(p)` to `TIME(q)` (`p < q`) in `UnwrapCastInBinaryComparison`. Like a decimal cast to a larger scale, this cast keeps the value, so the existing algorithm for numeric types is reused: the literal is truncated to `TIME(p)`, and the comparison is adjusted if the truncation drops digits. For a `TIME(6)` column `t`:
| Before | After |
|---|---|
| `CAST(t AS TIME(9)) = TIME '12:00:00.100000000'` | `t = TIME '12:00:00.1'` |
| `CAST(t AS TIME(9)) = TIME '12:00:00.100000001'` | `isnull(t) AND null` |
| `CAST(t AS TIME(9)) < TIME '12:00:00.100000001'` | `t <= TIME '12:00:00.1'` |
`In` and `InSet` are supported in the same way, including the DSv2 runtime `IN` filter (SPARK-59301).
### Why are the changes needed?
When a `TIME(p)` column is compared with a value of a larger precision, type coercion casts the column (SPARK-57585), and the cast blocks the filter pushdown. For example, for a `TIME(6)` Parquet column `t`, `WHERE t = TIME '12:00:00.100000000'` pushes down only `IsNotNull(t)`, while `WHERE t = TIME '12:00:00.1'` pushes down `EqualTo(t,12:00:00.100)`. This was raised in the review of #59571.
### Does this PR introduce _any_ user-facing change?
No. More filters on `TIME` columns are pushed down, and the query results are the same.
### How was this patch tested?
Pass the CIs with the newly added test cases in `UnwrapCastInBinaryComparisonSuite`, `ParquetFilterSuite` and `DataSourceV2StrategySuite`.
### Was this patch authored or co-authored using generative AI tooling?
Generated-by: Claude Opus 5.5
Closes #59602 from dongjoon-hyun/SPARK-60355.
Authored-by: Dongjoon Hyun <do...@apache.org>
Signed-off-by: Dongjoon Hyun <do...@apache.org>
---
.../optimizer/UnwrapCastInBinaryComparison.scala | 31 ++++----
.../UnwrapCastInBinaryComparisonSuite.scala | 85 +++++++++++++++++++---
.../datasources/parquet/ParquetFilterSuite.scala | 31 ++++++++
.../datasources/v2/DataSourceV2StrategySuite.scala | 13 +++-
4 files changed, 135 insertions(+), 25 deletions(-)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparison.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparison.scala
index 3d4877526625..9d8d0a270d30 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparison.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparison.scala
@@ -43,7 +43,7 @@ import org.apache.spark.sql.types._
* 1). `fromType` (of `fromExp`) and `toType` are of numeric types (i.e., short, int, float,
* decimal, etc), boolean type or datetime type
* 2). `fromType` can be safely coerced to `toType` without precision loss (e.g., short to int,
- * int to long, but not long to int, nor int to boolean)
+ * int to long, time(3) to time(6), but not long to int, nor int to boolean)
*
* If the above conditions are satisfied, the rule checks to see if the literal `value` is within
* range `(min, max)`, where `min` and `max` are the minimum and maximum value of `fromType`,
@@ -127,9 +127,10 @@ object UnwrapCastInBinaryComparison extends Rule[LogicalPlan] {
unwrapCast(swap(exp)).map(swap)
// In case both sides have numeric type, optimize the comparison by removing casts or
- // moving cast to the literal side.
+ // moving cast to the literal side. The same applies to a time cast to a larger precision,
+ // which keeps the value like a decimal cast to a larger scale.
case be @ BinaryComparison(
- Cast(fromExp, toType: NumericType, _, _), Literal(value, literalType))
+ Cast(fromExp, toType @ (_: NumericType | _: TimeType), _, _), Literal(value, literalType))
if canImplicitlyCast(fromExp, toType, literalType) && value != null =>
Some(simplifyNumericComparison(be, fromExp, toType, value))
@@ -165,9 +166,10 @@ object UnwrapCastInBinaryComparison extends Rule[LogicalPlan] {
// 1. this rule doesn't convert in when `in.list` is empty or `in.list` contains only null
// values.
// 2. this rule only handles the case when both `fromExp` and value in `in.list` are of numeric
- // type.
+ // type, or of time type.
// 3. this rule doesn't optimize In when `in.list` contains an expression that is not literal.
- case in @ In(Cast(fromExp, toType: NumericType, tz, mode), list @ Seq(firstLit, _*))
+ case in @ In(Cast(fromExp, toType @ (_: NumericType | _: TimeType), tz, mode),
+ list @ Seq(firstLit, _*))
if canImplicitlyCast(fromExp, toType, firstLit.dataType) && in.inSetConvertible =>
val (nullList, canCastList) = castLiterals(fromExp.dataType, toType, list)
if (nullList.isEmpty && canCastList.isEmpty) {
@@ -199,7 +201,7 @@ object UnwrapCastInBinaryComparison extends Rule[LogicalPlan] {
private def simplifyNumericComparison(
exp: BinaryComparison,
fromExp: Expression,
- toType: NumericType,
+ toType: DataType,
value: Any): Expression = {
val fromType = fromExp.dataType
@@ -273,7 +275,7 @@ object UnwrapCastInBinaryComparison extends Rule[LogicalPlan] {
// decimal type), or that the literal `value` is within range `(min, max)`. For these, we
// optimize by moving the cast to the literal side.
- val newValue = Cast(Literal(value), fromType, ansiEnabled = false).eval()
+ val newValue = Cast(Literal(value, toType), fromType, ansiEnabled = false).eval()
if (newValue == null) {
// This means the cast failed, for instance, due to the value is not representable in the
// narrower type. In this case we simply return the original expression.
@@ -418,8 +420,8 @@ object UnwrapCastInBinaryComparison extends Rule[LogicalPlan] {
private[sql] def unwrapCastInSet(inSet: InSet): Option[(Expression, Set[Any])] = inSet match {
// The same with `In` expression, the analyzer makes sure that the hset of InSet is already of
// the same data type, so simply check `fromExp.dataType` can implicitly cast to `toType` and
- // both `fromExp.dataType` and `toType` is numeric type or not.
- case InSet(Cast(fromExp, toType: NumericType, _, _), hset)
+ // both `fromExp.dataType` and `toType` is numeric type (or time type) or not.
+ case InSet(Cast(fromExp, toType @ (_: NumericType | _: TimeType), _, _), hset)
if hset.nonEmpty && canImplicitlyCast(fromExp, toType, toType) =>
val (nullList, canCastList) =
castLiterals(fromExp.dataType, toType, hset.map(v => Literal.create(v, toType)).toSeq)
@@ -433,7 +435,7 @@ object UnwrapCastInBinaryComparison extends Rule[LogicalPlan] {
*/
private def castLiterals(
fromType: DataType,
- toType: NumericType,
+ toType: DataType,
list: Seq[Expression]): (ArrayBuffer[Literal], ArrayBuffer[Literal]) = {
// There are 3 kinds of literals in the list:
// 1. null literals
@@ -457,7 +459,7 @@ object UnwrapCastInBinaryComparison extends Rule[LogicalPlan] {
list.foreach {
case lit @ Literal(null, _) => nullList += lit
case NonNullLiteral(value, _) =>
- val newValue = Cast(Literal(value), fromType, ansiEnabled = false).eval()
+ val newValue = Cast(Literal(value, toType), fromType, ansiEnabled = false).eval()
val valueRoundTrip = Cast(Literal(newValue, fromType), toType).eval()
if (newValue != null && ordering.compare(value, valueRoundTrip) == 0) {
canCastList += Literal(newValue, fromType)
@@ -470,7 +472,7 @@ object UnwrapCastInBinaryComparison extends Rule[LogicalPlan] {
/**
* Check if the input `fromExp` can be safely cast to `toType` without any loss of precision,
* i.e., the conversion is injective. Note this only handles the case when both sides are of
- * numeric type.
+ * numeric type, or of time type.
*/
private def canImplicitlyCast(
fromExp: Expression,
@@ -478,7 +480,7 @@ object UnwrapCastInBinaryComparison extends Rule[LogicalPlan] {
literalType: DataType): Boolean = {
DataTypeUtils.sameType(toType, literalType) &&
!fromExp.foldable &&
- toType.isInstanceOf[NumericType] &&
+ (toType.isInstanceOf[NumericType] || toType.isInstanceOf[TimeType]) &&
canUnwrapCast(fromExp.dataType, toType)
}
@@ -490,6 +492,9 @@ object UnwrapCastInBinaryComparison extends Rule[LogicalPlan] {
case (LongType, FloatType) => false
case (LongType, DoubleType) => false
case _ if from.isInstanceOf[NumericType] => Cast.canUpCast(from, to)
+ // A time(p) value has at most p fractional digits, so casting it to a larger precision keeps
+ // the value. Casting a literal back to time(p) truncates it, i.e. rounds it down.
+ case (f: TimeType, t: TimeType) => f.precision < t.precision
case _ => false
}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparisonSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparisonSuite.scala
index 9e8e31c69c3c..7b8ec6170f24 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparisonSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparisonSuite.scala
@@ -17,7 +17,7 @@
package org.apache.spark.sql.catalyst.optimizer
-import java.time.{LocalDate, LocalDateTime, ZoneId}
+import java.time.{LocalDate, LocalDateTime, LocalTime, ZoneId}
import scala.collection.immutable.HashSet
@@ -43,7 +43,8 @@ class UnwrapCastInBinaryComparisonSuite extends PlanTest with ExpressionEvalHelp
}
val testRelation: LocalRelation = LocalRelation($"a".short, $"b".float,
- $"c".decimal(5, 2), $"d".boolean, $"e".timestamp, $"f".timestampNTZ, $"g".date)
+ $"c".decimal(5, 2), $"d".boolean, $"e".timestamp, $"f".timestampNTZ, $"g".date,
+ AttributeReference("h", TimeType(6))())
val f: BoundReference = $"a".short.canBeNull.at(0)
val f2: BoundReference = $"b".float.canBeNull.at(1)
val f3: BoundReference = $"c".decimal(5, 2).canBeNull.at(2)
@@ -51,6 +52,7 @@ class UnwrapCastInBinaryComparisonSuite extends PlanTest with ExpressionEvalHelp
val f5: BoundReference = $"e".timestamp.notNull.at(4)
val f6: BoundReference = $"f".timestampNTZ.canBeNull.at(5)
val f7: BoundReference = $"g".date.canBeNull.at(6)
+ val f8: BoundReference = AttributeReference("h", TimeType(6))().canBeNull.at(7)
test("unwrap casts when literal == max") {
val v = Short.MaxValue
@@ -502,6 +504,56 @@ class UnwrapCastInBinaryComparisonSuite extends PlanTest with ExpressionEvalHelp
doTest(tsNtzLit4)
}
+ test("SPARK-60355: Support unwrap time type to time type with a larger precision") {
+ // The literal is representable as TIME(6), so the cast is moved to the literal side.
+ Seq(time("12:00:00.1", 9), time("00:00:00", 9), time("23:59:59.999999", 7)).foreach { lit =>
+ val v = Literal(lit.value, f8.dataType)
+ assertEquivalent(Cast(f8, lit.dataType) > lit, f8 > v)
+ assertEquivalent(Cast(f8, lit.dataType) >= lit, f8 >= v)
+ assertEquivalent(Cast(f8, lit.dataType) === lit, f8 === v)
+ assertEquivalent(Cast(f8, lit.dataType) <=> lit, f8 <=> v)
+ assertEquivalent(Cast(f8, lit.dataType) <= lit, f8 <= v)
+ assertEquivalent(Cast(f8, lit.dataType) < lit, f8 < v)
+ assertEquivalent(lit < Cast(f8, lit.dataType), v < f8)
+ }
+
+ // The literal has more fractional digits than TIME(6), so it is truncated to TIME(6), which
+ // rounds it down.
+ Seq(
+ time("12:00:00.100000001", 9) -> time("12:00:00.1", 6),
+ time("23:59:59.999999999", 9) -> time("23:59:59.999999", 6),
+ time("12:00:00.1000005", 7) -> time("12:00:00.1", 6)
+ ).foreach { case (lit, v) =>
+ assertEquivalent(Cast(f8, lit.dataType) > lit, f8 > v)
+ assertEquivalent(Cast(f8, lit.dataType) >= lit, f8 > v)
+ assertEquivalent(Cast(f8, lit.dataType) === lit, falseIfNotNull(f8))
+ assertEquivalent(Cast(f8, lit.dataType) <=> lit, FalseLiteral)
+ assertEquivalent(Cast(f8, lit.dataType) <= lit, f8 <= v)
+ assertEquivalent(Cast(f8, lit.dataType) < lit, f8 <= v)
+ assertEquivalent(lit < Cast(f8, lit.dataType), v < f8)
+ }
+
+ // Neither a null literal nor a cast to a smaller precision is unwrapped.
+ Seq(
+ Cast(f8, TimeType(9)) === Literal(null, TimeType(9)),
+ Cast(f8, TimeType(3)) === time("12:00:00.1", 3)
+ ).foreach { e =>
+ assertEquivalent(e, e)
+ }
+
+ checkInAndInSet(
+ In(Cast(f8, TimeType(9)),
+ Seq(time("12:00:00.1", 9), time("12:00:00.100000001", 9), time("00:00:00", 9))),
+ f8.in(time("12:00:00.1", 6), time("00:00:00", 6)))
+ checkInAndInSet(
+ In(Cast(f8, TimeType(9)), Seq(time("12:00:00.100000001", 9))),
+ falseIfNotNull(f8))
+ val nullLit = Literal(null, TimeType(9))
+ checkInAndInSet(
+ In(Cast(f8, TimeType(9)), Seq(nullLit, time("12:00:00.1", 9))),
+ f8.in(nullLit.cast(TimeType(6)), time("12:00:00.1", 6)))
+ }
+
private val ts1 = LocalDateTime.of(2023, 1, 1, 23, 59, 59, 99999000)
private val ts2 = LocalDateTime.of(2023, 1, 1, 23, 59, 59, 999998000)
private val ts3 = LocalDateTime.of(9999, 12, 31, 23, 59, 59, 999999999)
@@ -509,6 +561,15 @@ class UnwrapCastInBinaryComparisonSuite extends PlanTest with ExpressionEvalHelp
private val dt1 = java.sql.Date.valueOf("2023-01-01")
+ private val tm1 = LocalTime.of(12, 0, 0, 100000000)
+ private val tm2 = LocalTime.of(12, 0, 0, 100001000)
+ private val tm3 = LocalTime.of(23, 59, 59, 999999000)
+ private val tm4 = LocalTime.MIDNIGHT
+ private val tm5 = LocalTime.of(12, 0, 0, 99999000)
+
+ private def time(s: String, precision: Int): Literal =
+ Literal(LocalTime.parse(s).toNanoOfDay, TimeType(precision))
+
private def castInt(e: Expression): Expression = Cast(e, IntegerType)
private def castDouble(e: Expression): Expression = Cast(e, DoubleType)
private def castDecimal2(e: Expression): Expression = Cast(e, DecimalType(10, 4))
@@ -530,16 +591,18 @@ class UnwrapCastInBinaryComparisonSuite extends PlanTest with ExpressionEvalHelp
if (evaluate) {
Seq(
- (100.toShort, 3.14.toFloat, decimal2(100), true, ts1, ts1, dt1),
- (-300.toShort, 3.1415927.toFloat, decimal2(-3000.50), false, ts2, ts2, dt1),
- (null, Float.NaN, decimal2(12345.6789), null, null, null, null),
- (null, null, null, null, null, null, null),
- (Short.MaxValue, Float.PositiveInfinity, decimal2(Short.MaxValue), true, ts3, ts3, dt1),
- (Short.MinValue, Float.NegativeInfinity, decimal2(Short.MinValue), false, ts4, ts4, dt1),
- (0.toShort, Float.MaxValue, decimal2(0), null, null, null, null),
- (0.toShort, Float.MinValue, decimal2(0.01), null, null, null, null)
+ (100.toShort, 3.14.toFloat, decimal2(100), true, ts1, ts1, dt1, tm1),
+ (-300.toShort, 3.1415927.toFloat, decimal2(-3000.50), false, ts2, ts2, dt1, tm2),
+ (null, Float.NaN, decimal2(12345.6789), null, null, null, null, null),
+ (null, null, null, null, null, null, null, null),
+ (Short.MaxValue, Float.PositiveInfinity, decimal2(Short.MaxValue), true, ts3, ts3, dt1,
+ tm3),
+ (Short.MinValue, Float.NegativeInfinity, decimal2(Short.MinValue), false, ts4, ts4, dt1,
+ tm4),
+ (0.toShort, Float.MaxValue, decimal2(0), null, null, null, null, tm5),
+ (0.toShort, Float.MinValue, decimal2(0.01), null, null, null, null, null)
).foreach(v => {
- val row = create_row(v._1, v._2, v._3, v._4, v._5, v._6, v._7)
+ val row = create_row(v._1, v._2, v._3, v._4, v._5, v._6, v._7, v._8)
checkEvaluation(e1, e2.eval(row), row)
})
}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
index cbcb1c9843f0..ab5925f34d99 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
@@ -2958,6 +2958,37 @@ abstract class ParquetFilterSuite extends ParquetTest with SharedSparkSession {
| optional int64 t(TIME(MICROS,true));
|}""".stripMargin))
}
+
+ test("SPARK-60355: filter pushdown - time compared with a time of a larger precision") {
+ // Comparing a TIME(6) column with a TIME(9) literal casts the column to TIME(9).
+ // UnwrapCastInBinaryComparison moves the cast to the literal side, so the comparison is pushed
+ // down. A literal with sub-microsecond digits is truncated to TIME(6) and the comparison is
+ // adjusted, e.g. `t < 12:00:00.100000001` is pushed as `t <= 12:00:00.1`.
+ def time(s: String): LocalTime = LocalTime.parse(s)
+ def time9(s: String): Literal = Literal(time(s).toNanoOfDay, TimeType(9))
+ val data = Seq("12:00:00.099999", "12:00:00.1", "12:00:00.100001").map(s => Tuple1(time(s)))
+ withNestedParquetDataFrame(data) { case (inputDF, colName, resultFun) =>
+ implicit val df: DataFrame = inputDF
+
+ val tAttr = df(colName).expr
+ assert(df(colName).expr.dataType === TimeType())
+
+ checkFilterPredicate(tAttr === time9("12:00:00.1"), classOf[Eq[_]],
+ resultFun(time("12:00:00.1")))
+ checkFilterPredicate(tAttr <=> time9("12:00:00.1"), classOf[Eq[_]],
+ resultFun(time("12:00:00.1")))
+ checkFilterPredicate(time9("12:00:00.1") < tAttr, classOf[Gt[_]],
+ resultFun(time("12:00:00.100001")))
+ checkFilterPredicate(tAttr > time9("12:00:00.100000001"), classOf[Gt[_]],
+ resultFun(time("12:00:00.100001")))
+ checkFilterPredicate(tAttr < time9("12:00:00.100000001"), classOf[LtEq[_]],
+ Seq("12:00:00.099999", "12:00:00.1").map(s => Row(resultFun(time(s)))))
+ checkFilterPredicate(
+ In(tAttr, Seq("12:00:00.1", "12:00:00.100000001", "12:00:00.100001").map(time9)),
+ classOf[Operators.Or],
+ Seq("12:00:00.1", "12:00:00.100001").map(s => Row(resultFun(time(s)))))
+ }
+ }
}
@ExtendedSQLTest
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2StrategySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2StrategySuite.scala
index f5380ea085c0..fabebe4f4f69 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2StrategySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2StrategySuite.scala
@@ -17,6 +17,7 @@
package org.apache.spark.sql.execution.datasources.v2
+import java.time.LocalTime
import java.util.{EnumSet, OptionalLong}
import org.apache.spark.SparkConf
@@ -42,7 +43,7 @@ import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.internal.connector.SupportsPushDownCatalystFilters
import org.apache.spark.sql.sources.{BaseRelation, TableScan}
import org.apache.spark.sql.test.SharedSparkSession
-import org.apache.spark.sql.types.{ArrayType, BooleanType, DoubleType, FloatType, IntegerType, LongType, StringType, StructField, StructType, TimestampType, VariantType}
+import org.apache.spark.sql.types.{ArrayType, BooleanType, DoubleType, FloatType, IntegerType, LongType, StringType, StructField, StructType, TimestampType, TimeType, VariantType}
import org.apache.spark.sql.util.CaseInsensitiveStringMap
import org.apache.spark.udf.worker.UDFWorkerSpecification
import org.apache.spark.unsafe.types.UTF8String
@@ -1918,6 +1919,16 @@ class DataSourceV2StrategySuite extends SharedSparkSession {
runtimeFilter(Cast($"cint".int, StringType), Array[Any](UTF8String.fromString("1")))).isEmpty)
}
+ test("SPARK-60355: translate runtime IN filter on a time column cast to a larger precision") {
+ // values with more fractional digits than the column type are dropped and nulls are kept,
+ // the same as `UnwrapCastInBinaryComparison` does for `InSet`
+ val noon = LocalTime.NOON.toNanoOfDay
+ val ctime = AttributeReference("ctime", TimeType(6))()
+ val in = runtimeFilter(Cast(ctime, TimeType(9)), Array[Any](noon, noon + 1, null))
+ checkRuntimeFilter(in, "ctime",
+ Set(LiteralValue(noon, TimeType(6)), LiteralValue(null, TimeType(6))))
+ }
+
private def runtimeFilter(child: Expression, values: Array[Any]): InSubqueryExec = {
val plan = SubqueryExec("dpp", LocalTableScanExec(Nil, Nil, None))
InSubqueryExec(child, plan, ExprId(0), isDynamicPruning = true, resultBroadcast = null,
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