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new f1ea829482d9 [SPARK-57103][SQL][TEST][FOLLOWUP] Add test coverage for
max_by/min_by over nanosecond-precision timestamp types
f1ea829482d9 is described below
commit f1ea829482d9af285af46a4bd0c9e7397b4115b5
Author: Stevo Mitric <[email protected]>
AuthorDate: Sun Jun 21 22:28:19 2026 +0200
[SPARK-57103][SQL][TEST][FOLLOWUP] Add test coverage for max_by/min_by over
nanosecond-precision timestamp types
### What changes were proposed in this pull request?
Tests-only follow-up to SPARK-57103, extending nanos coverage to
`max_by`/`min_by` over `TIMESTAMP_NTZ(p)`/`TIMESTAMP_LTZ(p)` (`p` in `[7,9]`).
No production change: `MaxMinby` gates only on the ordering's orderability
(which nanos pass) and returns `valueExpr.dataType`, so a nanosecond value
keeps its precision. Adds `ExpressionTypeCheckingSuite` contract asserts,
`TimestampNanosFunctionsSuiteBase` E2E tests (value-is-nanos precision;
sub-microsecond ordering on codegen + interpreted [...]
### Why are the changes needed?
`max_by`/`min_by` over nanos had no coverage, especially the returned-value
precision case. Locks in correct behavior and guards against regressions.
### Does this PR introduce any user-facing change?
No. Tests only; types stay behind `spark.sql.timestampNanosTypes.enabled`.
### How was this patch tested?
New tests on JDK 17: `ExpressionTypeCheckingSuite`,
`TimestampNanosFunctionsAnsiOn/OffSuite`, and `SQLQueryTestSuite -z nanos`
(goldens regenerated, then verified clean).
### Was this patch authored or co-authored using generative AI tooling?
Generated-by: Claude Code (Claude Opus 4.8)
Closes #56612 from stevomitric/stevomitric/min-by-max-by.
Authored-by: Stevo Mitric <[email protected]>
Signed-off-by: Max Gekk <[email protected]>
(cherry picked from commit 78185390c5fcf7c7784636f9051eb922f186c4ff)
Signed-off-by: Max Gekk <[email protected]>
---
.../analysis/ExpressionTypeCheckingSuite.scala | 14 ++++
.../analyzer-results/timestamp-ltz-nanos.sql.out | 12 +++
.../analyzer-results/timestamp-ntz-nanos.sql.out | 12 +++
.../sql-tests/inputs/timestamp-ltz-nanos.sql | 9 +++
.../sql-tests/inputs/timestamp-ntz-nanos.sql | 9 +++
.../sql-tests/results/timestamp-ltz-nanos.sql.out | 12 +++
.../sql-tests/results/timestamp-ntz-nanos.sql.out | 12 +++
.../sql/TimestampNanosFunctionsSuiteBase.scala | 91 ++++++++++++++++++++++
8 files changed, 171 insertions(+)
diff --git
a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
index 9b5db986153e..b7f360424e14 100644
---
a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
+++
b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala
@@ -408,6 +408,20 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite
with SQLHelper with Quer
}
}
+ test("SPARK-57103: max_by/min_by accept a nanosecond ordering and preserve
the value type") {
+ // MaxBy/MinBy gate only on the ordering expression's orderability; a
nanosecond ordering is an
+ // orderable AtomicType (SPARK-57103). The value expression is
unrestricted, and the result type
+ // is the value's type, so a nanosecond value is returned with its
precision preserved.
+ Seq(TimestampNTZNanosType(9), TimestampLTZNanosType(7)).foreach { dt =>
+ val v = AttributeReference("v", dt)()
+ val ord = AttributeReference("ord", dt)()
+ Seq(MaxBy(v, ord), MinBy(v, ord)).foreach { agg =>
+ assert(agg.checkInputDataTypes() == TypeCheckResult.TypeCheckSuccess)
+ assert(agg.dataType == dt)
+ }
+ }
+ }
+
test("check types for aggregates") {
// We use AggregateFunction directly at here because the error will be
thrown from it
// instead of from AggregateExpression, which is the wrapper of an
AggregateFunction.
diff --git
a/sql/core/src/test/resources/sql-tests/analyzer-results/timestamp-ltz-nanos.sql.out
b/sql/core/src/test/resources/sql-tests/analyzer-results/timestamp-ltz-nanos.sql.out
index a4dadf760088..55fe3d6065ec 100644
---
a/sql/core/src/test/resources/sql-tests/analyzer-results/timestamp-ltz-nanos.sql.out
+++
b/sql/core/src/test/resources/sql-tests/analyzer-results/timestamp-ltz-nanos.sql.out
@@ -722,6 +722,18 @@ Project [unix_timestamp(cast(null as timestamp_ltz(9)),
yyyy-MM-dd HH:mm:ss, Som
+- OneRowRelation
+-- !query
+SELECT max_by(v, k), min_by(v, k) FROM VALUES
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000001 UTC', 1),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000999 UTC', 3),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000500 UTC', 2),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000007 UTC', CAST(NULL AS INT)) AS
t(v, k)
+-- !query analysis
+Aggregate [max_by(v#x, k#x) AS max_by(v, k)#x, min_by(v#x, k#x) AS min_by(v,
k)#x]
++- SubqueryAlias t
+ +- LocalRelation [v#x, k#x]
+
+
-- !query
SELECT unix_nanos(TIMESTAMP_LTZ '2020-01-01 13:24:35.123456789 UTC')
-- !query analysis
diff --git
a/sql/core/src/test/resources/sql-tests/analyzer-results/timestamp-ntz-nanos.sql.out
b/sql/core/src/test/resources/sql-tests/analyzer-results/timestamp-ntz-nanos.sql.out
index de0922d74c79..9eda12a1e23c 100644
---
a/sql/core/src/test/resources/sql-tests/analyzer-results/timestamp-ntz-nanos.sql.out
+++
b/sql/core/src/test/resources/sql-tests/analyzer-results/timestamp-ntz-nanos.sql.out
@@ -642,6 +642,18 @@ Project [unix_timestamp(cast(null as timestamp_ntz(9)),
yyyy-MM-dd HH:mm:ss, Som
+- OneRowRelation
+-- !query
+SELECT max_by(v, k), min_by(v, k) FROM VALUES
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000001', 1),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000999', 3),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000500', 2),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000007', CAST(NULL AS INT)) AS t(v, k)
+-- !query analysis
+Aggregate [max_by(v#x, k#x) AS max_by(v, k)#x, min_by(v#x, k#x) AS min_by(v,
k)#x]
++- SubqueryAlias t
+ +- LocalRelation [v#x, k#x]
+
+
-- !query
SELECT unix_nanos(TIMESTAMP_NTZ '2020-01-01 13:24:35.123456789')
-- !query analysis
diff --git
a/sql/core/src/test/resources/sql-tests/inputs/timestamp-ltz-nanos.sql
b/sql/core/src/test/resources/sql-tests/inputs/timestamp-ltz-nanos.sql
index e208704196ba..3b43b0f756de 100644
--- a/sql/core/src/test/resources/sql-tests/inputs/timestamp-ltz-nanos.sql
+++ b/sql/core/src/test/resources/sql-tests/inputs/timestamp-ltz-nanos.sql
@@ -203,6 +203,15 @@ SELECT unix_timestamp(TIMESTAMP_LTZ '1969-12-31
23:59:59.500000000 UTC');
-- NULL nanosecond timestamp.
SELECT unix_timestamp(NULL :: timestamp_ltz(9)), to_unix_timestamp(NULL ::
timestamp_ltz(9));
+-- SPARK-57103: max_by / min_by return the nanosecond-precision TIMESTAMP_LTZ
value at the extreme
+-- ordering key, preserving the nanosecond type. The ordering keys are
distinct so the result is
+-- deterministic; a NULL-ordering row is ignored.
+SELECT max_by(v, k), min_by(v, k) FROM VALUES
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000001 UTC', 1),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000999 UTC', 3),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000500 UTC', 2),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000007 UTC', CAST(NULL AS INT)) AS
t(v, k);
+
-- SPARK-57527: unix_nanos over nanosecond-precision values returns
DECIMAL(21, 0) nanoseconds since
-- the epoch. The explicit-zone literals below fix the instant directly,
independent of the session
-- time zone. The sub-microsecond digits are kept, truncated to the type's
precision.
diff --git
a/sql/core/src/test/resources/sql-tests/inputs/timestamp-ntz-nanos.sql
b/sql/core/src/test/resources/sql-tests/inputs/timestamp-ntz-nanos.sql
index 33b60cf39d9d..ac2443cfc611 100644
--- a/sql/core/src/test/resources/sql-tests/inputs/timestamp-ntz-nanos.sql
+++ b/sql/core/src/test/resources/sql-tests/inputs/timestamp-ntz-nanos.sql
@@ -177,6 +177,15 @@ SELECT unix_timestamp(TIMESTAMP_NTZ '1969-12-31
23:59:59.500000000');
-- NULL nanosecond timestamp.
SELECT unix_timestamp(NULL :: timestamp_ntz(9)), to_unix_timestamp(NULL ::
timestamp_ntz(9));
+-- SPARK-57103: max_by / min_by return the nanosecond-precision TIMESTAMP_NTZ
value at the extreme
+-- ordering key, preserving the nanosecond type. The ordering keys are
distinct so the result is
+-- deterministic; a NULL-ordering row is ignored.
+SELECT max_by(v, k), min_by(v, k) FROM VALUES
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000001', 1),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000999', 3),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000500', 2),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000007', CAST(NULL AS INT)) AS t(v,
k);
+
-- SPARK-57527: unix_nanos over nanosecond-precision values returns
DECIMAL(21, 0) nanoseconds since
-- the epoch; NTZ applies no zone shift, so the wall-clock value is read as
the epoch instant. The
-- sub-microsecond digits are kept, truncated to the type's precision.
diff --git
a/sql/core/src/test/resources/sql-tests/results/timestamp-ltz-nanos.sql.out
b/sql/core/src/test/resources/sql-tests/results/timestamp-ltz-nanos.sql.out
index 1f75f01da848..50b22a2d4f44 100644
--- a/sql/core/src/test/resources/sql-tests/results/timestamp-ltz-nanos.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/timestamp-ltz-nanos.sql.out
@@ -808,6 +808,18 @@ struct<unix_timestamp(CAST(NULL AS TIMESTAMP_LTZ(9)),
yyyy-MM-dd HH:mm:ss):bigin
NULL NULL
+-- !query
+SELECT max_by(v, k), min_by(v, k) FROM VALUES
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000001 UTC', 1),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000999 UTC', 3),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000500 UTC', 2),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000007 UTC', CAST(NULL AS INT)) AS
t(v, k)
+-- !query schema
+struct<max_by(v, k):timestamp_ltz(9),min_by(v, k):timestamp_ltz(9)>
+-- !query output
+2019-12-31 16:00:00.000000999 2019-12-31 16:00:00.000000001
+
+
-- !query
SELECT unix_nanos(TIMESTAMP_LTZ '2020-01-01 13:24:35.123456789 UTC')
-- !query schema
diff --git
a/sql/core/src/test/resources/sql-tests/results/timestamp-ntz-nanos.sql.out
b/sql/core/src/test/resources/sql-tests/results/timestamp-ntz-nanos.sql.out
index 324ea639ccc3..d5ea92ddcc37 100644
--- a/sql/core/src/test/resources/sql-tests/results/timestamp-ntz-nanos.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/timestamp-ntz-nanos.sql.out
@@ -718,6 +718,18 @@ struct<unix_timestamp(CAST(NULL AS TIMESTAMP_NTZ(9)),
yyyy-MM-dd HH:mm:ss):bigin
NULL NULL
+-- !query
+SELECT max_by(v, k), min_by(v, k) FROM VALUES
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000001', 1),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000999', 3),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000500', 2),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000007', CAST(NULL AS INT)) AS t(v, k)
+-- !query schema
+struct<max_by(v, k):timestamp_ntz(9),min_by(v, k):timestamp_ntz(9)>
+-- !query output
+2020-01-01 00:00:00.000000999 2020-01-01 00:00:00.000000001
+
+
-- !query
SELECT unix_nanos(TIMESTAMP_NTZ '2020-01-01 13:24:35.123456789')
-- !query schema
diff --git
a/sql/core/src/test/scala/org/apache/spark/sql/TimestampNanosFunctionsSuiteBase.scala
b/sql/core/src/test/scala/org/apache/spark/sql/TimestampNanosFunctionsSuiteBase.scala
index da2e9d3a8d88..f42920a8a0ee 100644
---
a/sql/core/src/test/scala/org/apache/spark/sql/TimestampNanosFunctionsSuiteBase.scala
+++
b/sql/core/src/test/scala/org/apache/spark/sql/TimestampNanosFunctionsSuiteBase.scala
@@ -450,6 +450,97 @@ abstract class TimestampNanosFunctionsSuiteBase extends
SharedSparkSession {
}
}
+ // ===== max_by / min_by over nanosecond-precision timestamps (SPARK-56822)
=====
+ // `MaxBy`/`MinBy` gate only on the ordering expression's orderability
+ // (`MaxMinBy.checkInputDataTypes` -> `TypeUtils.checkForOrderingExpr`),
which the nanosecond
+ // types pass (SPARK-57103); the value expression is unrestricted and
`dataType = valueExpr
+ // .dataType`, so a nanosecond *value* is returned with its precision
preserved. No change to the
+ // aggregates is needed -- these tests lock in both the nanos-as-value and
nanos-as-ordering
+ // paths.
+
+ test("SPARK-57103: max_by/min_by return a nanosecond value and preserve its
precision") {
+ Seq(7, 8, 9).foreach { p =>
+ // Value columns are nanos; the ordering column is a plain int key (max
at k=3, min at k=1).
+ // The sub-microsecond parts are multiples of 100ns, so they are exact
at every p in [7, 9]
+ // (no flooring) yet still non-zero -- proving the nanos value survives,
not truncated to
+ // micros.
+ val schema = new StructType()
+ .add("ntz", TimestampNTZNanosType(p))
+ .add("ltz", TimestampLTZNanosType(p))
+ .add("k", IntegerType)
+ val data = Seq(
+ Row(LocalDateTime.parse("2020-01-01T00:00:00.000000100"),
+ Instant.parse("2020-01-01T00:00:00.000000100Z"), 1),
+ Row(LocalDateTime.parse("2020-01-01T00:00:00.000000900"),
+ Instant.parse("2020-01-01T00:00:00.000000900Z"), 3),
+ Row(LocalDateTime.parse("2020-01-01T00:00:00.000000500"),
+ Instant.parse("2020-01-01T00:00:00.000000500Z"), 2))
+ val df = spark.createDataFrame(spark.sparkContext.parallelize(data),
schema)
+ val res = df.select(
+ max_by(col("ntz"), col("k")), min_by(col("ntz"), col("k")),
+ max_by(col("ltz"), col("k")), min_by(col("ltz"), col("k")))
+ checkAnswer(res, Row(
+ LocalDateTime.parse("2020-01-01T00:00:00.000000900"),
+ LocalDateTime.parse("2020-01-01T00:00:00.000000100"),
+ Instant.parse("2020-01-01T00:00:00.000000900Z"),
+ Instant.parse("2020-01-01T00:00:00.000000100Z")))
+ // The returned value keeps the family (NTZ/LTZ) and precision of the
value column.
+ assert(res.schema.map(_.dataType) === Seq(
+ TimestampNTZNanosType(p), TimestampNTZNanosType(p),
+ TimestampLTZNanosType(p), TimestampLTZNanosType(p)))
+ }
+ }
+
+ test("SPARK-57103: max_by/min_by order by a nanosecond key down to the
sub-microsecond") {
+ // The ordering values share epochMicros and differ only within the
microsecond, so picking the
+ // extreme requires the full TimestampNanosVal comparison; a NULL-ordering
row is ignored.
+ // Run on both the codegen and the interpreted paths.
+ Seq(
+ Seq(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> "true",
+ SQLConf.CODEGEN_FACTORY_MODE.key -> "CODEGEN_ONLY"),
+ Seq(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> "false",
+ SQLConf.CODEGEN_FACTORY_MODE.key -> "NO_CODEGEN")
+ ).foreach { conf =>
+ withSQLConf(conf: _*) {
+ val df = spark.createDataFrame(
+ spark.sparkContext.parallelize(Seq(
+ Row("lo", Instant.parse("2020-01-01T00:00:00.000000001Z")),
+ Row("hi", Instant.parse("2020-01-01T00:00:00.000000999Z")),
+ Row("skip", null))),
+ new StructType().add("label", StringType).add("ts",
TimestampLTZNanosType(9)))
+ checkAnswer(
+ df.select(max_by(col("label"), col("ts")), min_by(col("label"),
col("ts"))),
+ Row("hi", "lo"))
+ }
+ }
+ }
+
+ test("SPARK-57103: max_by/min_by over nanos handle all-NULL ordering and
GROUP BY") {
+ Seq(7, 8, 9).foreach { p =>
+ // All ordering values NULL -> result NULL.
+ val allNull = spark.createDataFrame(
+ spark.sparkContext.parallelize(Seq(Row("a", null), Row("b", null))),
+ new StructType().add("label", StringType).add("ts",
TimestampNTZNanosType(p)))
+ checkAnswer(
+ allNull.select(max_by(col("label"), col("ts")), min_by(col("label"),
col("ts"))),
+ Row(null, null))
+
+ // GROUP BY: per group, pick the label at the extreme nanosecond
ordering key.
+ val grouped = spark.createDataFrame(
+ spark.sparkContext.parallelize(Seq(
+ Row("g1", "g1-lo",
LocalDateTime.parse("2020-01-01T00:00:00.000000001")),
+ Row("g1", "g1-hi",
LocalDateTime.parse("2020-01-01T00:00:00.000000999")),
+ Row("g2", "g2-only",
LocalDateTime.parse("2020-01-01T00:00:00.000000005")))),
+ new StructType().add("g", StringType).add("label", StringType)
+ .add("ts", TimestampNTZNanosType(p)))
+ checkAnswer(
+ grouped.groupBy("g").agg(
+ max_by(col("label"), col("ts")).as("mx"),
+ min_by(col("label"), col("ts")).as("mn")).orderBy("g"),
+ Seq(Row("g1", "g1-hi", "g1-lo"), Row("g2", "g2-only", "g2-only")))
+ }
+ }
+
test("SPARK-57527: unix_nanos over nanosecond-precision timestamps") {
// unix_nanos returns DECIMAL(21, 0) nanoseconds since the epoch and
applies no zone shift to a
// timestamp argument. The chosen fractions have zeros beyond the 7th
digit, so truncating to
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