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new ce32a65db4d7 [SPARK-58761][SQL][TEST] Add dedicated e2e test coverage
for GROUP BY over nanosecond-precision timestamp keys
ce32a65db4d7 is described below
commit ce32a65db4d758b219ecb0ccd24cba75af55838a
Author: Stevo Mitric <[email protected]>
AuthorDate: Sat Aug 15 11:04:23 2026 +0000
[SPARK-58761][SQL][TEST] Add dedicated e2e test coverage for GROUP BY over
nanosecond-precision timestamp keys
### What changes were proposed in this pull request?
Add end-to-end regression-lock coverage for `GROUP BY` over the
nanosecond-precision timestamp types `TIMESTAMP_NTZ(p)` / `TIMESTAMP_LTZ(p)`
(`p` in `[7, 9]`).
New suite `TimestampNanosGroupBySuiteBase` (with
`TimestampNanosGroupByAnsiOnSuite` / `TimestampNanosGroupByAnsiOffSuite`
subclasses), mirroring `TimestampNanosJoinSuiteBase`. For NTZ and LTZ, across
`p` in `[7, 9]` and both whole-stage-codegen modes, it asserts:
- GROUP BY keeps two keys that share `epochMicros` but differ within the
microsecond in separate groups, while collapsing exact duplicates, and computes
per-group `count` / `sum`;
- a `UNION` of two different nanos precisions widens the key to the higher
precision (`findWiderDateTimeType`) and preserves the distinction;
- NULL keys form a single group (unlike an equi-join, where NULL never
matches NULL).
Also extends the golden files `timestamp-ntz-nanos.sql` /
`timestamp-ltz-nanos.sql` with a GROUP-BY-with-aggregates-and-NULL-group case.
### Why are the changes needed?
GROUP BY on a nanosecond-timestamp key already works today -- it rides on
the generic nanos hashing/equality implemented in SPARK-57103 (`Murmur3Hash` /
`XxHash64` / `HiveHash` over the carrier's `epochMicros: Long` and
`nanosWithinMicro: Short in [0, 999]`) -- but lacked dedicated coverage. These
tests lock the regression: if the nanos hash or equality path is later broken,
the sub-microsecond keys collapse and the tests fail loudly. No production
change.
### Does this PR introduce _any_ user-facing change?
No. Test-only.
### How was this patch tested?
`TimestampNanosGroupByAnsiOnSuite` / `TimestampNanosGroupByAnsiOffSuite`
(24 tests, all pass) and the regenerated golden SQL files.
### Was this patch authored or co-authored using generative AI tooling?
Co-Authored-By: Claude Opus 4.8
Closes #57992 from stevomitric/stevomitric/nanos-groupby-tests.
Authored-by: Stevo Mitric <[email protected]>
Signed-off-by: Uros Bojanic <[email protected]>
(cherry picked from commit a2da2926fe69842f1fb158d7cc3fe912b93db303)
Signed-off-by: Uros Bojanic <[email protected]>
---
.../analyzer-results/timestamp-ltz-nanos.sql.out | 15 ++
.../analyzer-results/timestamp-ntz-nanos.sql.out | 15 ++
.../sql-tests/inputs/timestamp-ltz-nanos.sql | 11 ++
.../sql-tests/inputs/timestamp-ntz-nanos.sql | 11 ++
.../sql-tests/results/timestamp-ltz-nanos.sql.out | 16 ++
.../sql-tests/results/timestamp-ntz-nanos.sql.out | 16 ++
.../spark/sql/TimestampNanosGroupBySuiteBase.scala | 190 +++++++++++++++++++++
7 files changed, 274 insertions(+)
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 fd2179dd3cdf..553fe363f815 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
@@ -740,6 +740,21 @@ Sort [c#x ASC NULLS FIRST], true
+- LocalRelation [c#x]
+-- !query
+SELECT k, count(*), sum(v) FROM VALUES
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000001 UTC', 1),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000001 UTC', 2),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000999 UTC', 3),
+ (CAST(NULL AS timestamp_ltz(9)), 4),
+ (CAST(NULL AS timestamp_ltz(9)), 5) AS t(k, v)
+ GROUP BY k ORDER BY k
+-- !query analysis
+Sort [k#x ASC NULLS FIRST], true
++- Aggregate [k#x], [k#x, count(1) AS count(1)#xL, sum(v#x) AS sum(v)#xL]
+ +- SubqueryAlias t
+ +- LocalRelation [k#x, v#x]
+
+
-- !query
SELECT mode(c) FROM VALUES
(TIMESTAMP_LTZ '2020-01-01 00:00:00.000000001 UTC'),
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 5445f457e3ab..6c6bbf4bf6c6 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
@@ -668,6 +668,21 @@ Sort [c#x ASC NULLS FIRST], true
+- LocalRelation [c#x]
+-- !query
+SELECT k, count(*), sum(v) FROM VALUES
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000001', 1),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000001', 2),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000999', 3),
+ (CAST(NULL AS timestamp_ntz(9)), 4),
+ (CAST(NULL AS timestamp_ntz(9)), 5) AS t(k, v)
+ GROUP BY k ORDER BY k
+-- !query analysis
+Sort [k#x ASC NULLS FIRST], true
++- Aggregate [k#x], [k#x, count(1) AS count(1)#xL, sum(v#x) AS sum(v)#xL]
+ +- SubqueryAlias t
+ +- LocalRelation [k#x, v#x]
+
+
-- !query
SELECT mode(c) FROM VALUES
(TIMESTAMP_NTZ '2020-01-01 00:00:00.000000001'),
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 1840ca375ea4..236facfcf88c 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
@@ -219,6 +219,17 @@ SELECT c, count(*) FROM VALUES
(TIMESTAMP_LTZ '2020-01-01 00:00:00.000000999 UTC'),
(TIMESTAMP_LTZ '2020-01-01 00:00:00.000000001 UTC') AS t(c)
GROUP BY c ORDER BY c;
+-- GROUP BY a nanosecond key with aggregates and a NULL group: exact-duplicate
keys collapse, two
+-- keys sharing epochMicros but differing within the microsecond stay in
separate groups, and all
+-- NULL keys group together (unlike an equi-join). Three groups: .000000001
(count 2, sum 3),
+-- .000000999 (count 1, sum 3), NULL (count 2, sum 9). Values render in the
session time zone.
+SELECT k, count(*), sum(v) FROM VALUES
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000001 UTC', 1),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000001 UTC', 2),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000999 UTC', 3),
+ (CAST(NULL AS timestamp_ltz(9)), 4),
+ (CAST(NULL AS timestamp_ltz(9)), 5) AS t(k, v)
+ GROUP BY k ORDER BY k;
-- SPARK-56822: mode over nanosecond-precision TIMESTAMP_LTZ. Frequencies are
counted on the full
-- nanos value, so the most-frequent value is selected down to the
sub-microsecond and the result
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 7fb6fdcbc019..44aca6e23ad6 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
@@ -192,6 +192,17 @@ SELECT c, count(*) FROM VALUES
(TIMESTAMP_NTZ '2020-01-01 00:00:00.000000999'),
(TIMESTAMP_NTZ '2020-01-01 00:00:00.000000001') AS t(c)
GROUP BY c ORDER BY c;
+-- GROUP BY a nanosecond key with aggregates and a NULL group: exact-duplicate
keys collapse, two
+-- keys sharing epochMicros but differing within the microsecond stay in
separate groups, and all
+-- NULL keys group together (unlike an equi-join). Three groups: .000000001
(count 2, sum 3),
+-- .000000999 (count 1, sum 3), NULL (count 2, sum 9).
+SELECT k, count(*), sum(v) FROM VALUES
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000001', 1),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000001', 2),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000999', 3),
+ (CAST(NULL AS timestamp_ntz(9)), 4),
+ (CAST(NULL AS timestamp_ntz(9)), 5) AS t(k, v)
+ GROUP BY k ORDER BY k;
-- SPARK-56822: mode over nanosecond-precision TIMESTAMP_NTZ. Frequencies are
counted on the full
-- nanos value, so the most-frequent value is selected down to the
sub-microsecond and the result
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 a28831c6b7d8..5da97df45d9b 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
@@ -832,6 +832,22 @@ struct<c:timestamp_ltz(9),count(1):bigint>
2019-12-31 16:00:00.000000999 1
+-- !query
+SELECT k, count(*), sum(v) FROM VALUES
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000001 UTC', 1),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000001 UTC', 2),
+ (TIMESTAMP_LTZ '2020-01-01 00:00:00.000000999 UTC', 3),
+ (CAST(NULL AS timestamp_ltz(9)), 4),
+ (CAST(NULL AS timestamp_ltz(9)), 5) AS t(k, v)
+ GROUP BY k ORDER BY k
+-- !query schema
+struct<k:timestamp_ltz(9),count(1):bigint,sum(v):bigint>
+-- !query output
+NULL 2 9
+2019-12-31 16:00:00.000000001 2 3
+2019-12-31 16:00:00.000000999 1 3
+
+
-- !query
SELECT mode(c) FROM VALUES
(TIMESTAMP_LTZ '2020-01-01 00:00:00.000000001 UTC'),
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 bbec01467ac2..ac132ba8d771 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
@@ -750,6 +750,22 @@ struct<c:timestamp_ntz(9),count(1):bigint>
2020-01-01 00:00:00.000000999 1
+-- !query
+SELECT k, count(*), sum(v) FROM VALUES
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000001', 1),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000001', 2),
+ (TIMESTAMP_NTZ '2020-01-01 00:00:00.000000999', 3),
+ (CAST(NULL AS timestamp_ntz(9)), 4),
+ (CAST(NULL AS timestamp_ntz(9)), 5) AS t(k, v)
+ GROUP BY k ORDER BY k
+-- !query schema
+struct<k:timestamp_ntz(9),count(1):bigint,sum(v):bigint>
+-- !query output
+NULL 2 9
+2020-01-01 00:00:00.000000001 2 3
+2020-01-01 00:00:00.000000999 1 3
+
+
-- !query
SELECT mode(c) FROM VALUES
(TIMESTAMP_NTZ '2020-01-01 00:00:00.000000001'),
diff --git
a/sql/core/src/test/scala/org/apache/spark/sql/TimestampNanosGroupBySuiteBase.scala
b/sql/core/src/test/scala/org/apache/spark/sql/TimestampNanosGroupBySuiteBase.scala
new file mode 100644
index 000000000000..731db201836b
--- /dev/null
+++
b/sql/core/src/test/scala/org/apache/spark/sql/TimestampNanosGroupBySuiteBase.scala
@@ -0,0 +1,190 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import java.time.{Instant, LocalDateTime}
+
+import org.apache.spark.SparkConf
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.test.SharedSparkSession
+import org.apache.spark.sql.types._
+
+/**
+ * End-to-end `GROUP BY` correctness tests over the nanosecond-precision
timestamp types
+ * `TIMESTAMP_NTZ(p)` / `TIMESTAMP_LTZ(p)` (`p` in `[7, 9]`), part of the
nanosecond timestamp
+ * preview (SPARK-56822). Grouping over these types executes today with no
production change -- it
+ * rides on the nanos hashing and equality the types already implement
(SPARK-57103 extended
+ * `Murmur3Hash` / `XxHash64` / `HiveHash` to hash the carrier's two fields,
`epochMicros: Long`
+ * and `nanosWithinMicro: Short in [0, 999]`). There is no coverage organized
around grouping by
+ * column type, so this per-type suite adds it and locks the regression.
+ *
+ * Headline risk: SUB-MICROSECOND key correctness, mirroring the join suite.
The carrier is
+ * `TimestampNanosVal = (epochMicros: Long, nanosWithinMicro: Short in [0,
999])`. Every key in
+ * these tables shares the SAME epochMicros (1577836800000000, =
2020-01-01T00:00:00Z) and differs
+ * ONLY in nanosWithinMicro. So the micro-level path alone cannot tell the
keys apart -- correct
+ * grouping MUST be driven by the full nanos value:
+ * - two rows equal in epochMicros but DIFFERENT in nanosWithinMicro must
NOT collapse into one
+ * group,
+ * - exact duplicates (equal incl. the sub-microsecond remainder) MUST
collapse,
+ * - NULL keys, unlike an equi-join, DO group together -- all NULL rows land
in a single group.
+ * If the sub-microsecond remainder were ignored, the two distinct
sub-microsecond keys here would
+ * fold into one, dropping the group count from 3 to 2 and failing these tests
loudly.
+ *
+ * Precision-safety: all sub-microsecond remainders are multiples of 100ns
(100 / 900), which are
+ * exact at every p in [7, 9] (`createDataFrame` floors nanosWithinMicro to
(n/100)*100 at p=7 and
+ * (n/10)*10 at p=8). So the SAME inputs and the SAME expected results are
valid verbatim at all
+ * three precisions, and the two distinct remainders never collide even at the
coarsest p=7.
+ *
+ * Each test runs under whole-stage codegen on and off, so the same
sub-microsecond grouping is
+ * proven on the nanos hash path in both codegen modes, for NTZ and LTZ. The
mixed-precision test
+ * additionally pins that a `UNION` of two different nanos precisions widens
the key to the higher
+ * precision (`findWiderDateTimeType`) and preserves the distinction.
+ *
+ * The nanosecond timestamp types are gated behind a preview flag enabled by
default under tests
+ * (`Utils.isTesting`), so it is not set here. The session time zone is fixed
so the
+ * `TIMESTAMP_LTZ` (`Instant`) values are deterministic. The two subclasses
run every test with
+ * ANSI mode on and off.
+ */
+abstract class TimestampNanosGroupBySuiteBase extends QueryTest with
SharedSparkSession {
+
+ override def sparkConf: SparkConf = super.sparkConf
+ .set(SQLConf.SESSION_LOCAL_TIMEZONE.key, "America/Los_Angeles")
+
+ // Whole-stage codegen on (CODEGEN_ONLY) vs off (NO_CODEGEN). The
hash-aggregate exec is the same
+ // in both modes; only the WholeStageCodegenExec wrapper differs. Mirrors
the join and functions
+ // suites.
+ protected val codegenModes: Seq[Seq[(String, String)]] = 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"))
+
+ private def cgLabel(cgConf: Seq[(String, String)]): String =
+ if (cgConf.contains(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> "true"))
"codegen on"
+ else "codegen off"
+
+ // ---- relation builders: key column "k" of the given nanos type + an Int
value column "v" ----
+ //
+ // Five rows, all sharing epochMicros (2020-01-01T00:00:00Z) and differing
only within the
+ // microsecond:
+ // - two rows with the 100ns key (an exact duplicate): must collapse into
one group,
+ // - one row with the 900ns key: same microsecond, distinct
sub-microsecond remainder,
+ // - two NULL-key rows: group together into a single NULL group.
+ // The value column distinguishes the group signatures: sum(v) is 1+2=3 for
the 100ns group,
+ // 3 for the 900ns group, and 4+5=9 for the NULL group, so the three groups
are all identifiable
+ // by their (count, sum) pair even without inspecting the key.
+
+ private def ntzDF(p: Int): DataFrame =
+ spark.createDataFrame(
+ spark.sparkContext.parallelize(Seq(
+ Row(LocalDateTime.parse("2020-01-01T00:00:00.000000100"), 1),
+ Row(LocalDateTime.parse("2020-01-01T00:00:00.000000100"), 2),
+ Row(LocalDateTime.parse("2020-01-01T00:00:00.000000900"), 3),
+ Row(null, 4),
+ Row(null, 5))),
+ new StructType().add("k", TimestampNTZNanosType(p)).add("v",
IntegerType))
+
+ private def ltzDF(p: Int): DataFrame =
+ spark.createDataFrame(
+ spark.sparkContext.parallelize(Seq(
+ Row(Instant.parse("2020-01-01T00:00:00.000000100Z"), 1),
+ Row(Instant.parse("2020-01-01T00:00:00.000000100Z"), 2),
+ Row(Instant.parse("2020-01-01T00:00:00.000000900Z"), 3),
+ Row(null, 4),
+ Row(null, 5))),
+ new StructType().add("k", TimestampLTZNanosType(p)).add("v",
IntegerType))
+
+ // Each family as (label, builder(p), widerType(p)). GROUP BY is
type-agnostic, so the family only
+ // picks the data builder and (for the mixed-precision widening assertion)
the expected widened
+ // key type.
+ protected val families: Seq[(String, Int => DataFrame, Int => DataType)] =
Seq(
+ ("NTZ", ntzDF, (p: Int) => TimestampNTZNanosType(p)),
+ ("LTZ", ltzDF, (p: Int) => TimestampLTZNanosType(p)))
+
+ // Mixed-precision UNION pairs; the key widens to max(pl, pr).
+ private val mixedPrecisionPairs: Seq[(Int, Int)] = Seq((7, 9), (7, 8), (8,
9))
+
+ for {
+ (family, builder, widerType) <- families
+ cgConf <- codegenModes
+ } {
+ //
========================================================================================
+ // GROUP BY a nanos key: exact duplicates collapse, but two keys sharing
epochMicros and
+ // differing only within the microsecond stay in separate groups.
Aggregates (count, sum) are
+ // computed per group. Expected three groups: 100ns (count 2, sum 3),
900ns (count 1, sum 3),
+ // NULL (count 2, sum 9). If the sub-microsecond remainder were ignored,
100ns and 900ns would
+ // merge into a single (count 3, sum 6) group and this checkAnswer would
fail.
+ //
========================================================================================
+ test(s"$family nanos GROUP BY distinguishes the sub-microsecond remainder
- " +
+ s"${cgLabel(cgConf)}") {
+ withSQLConf(cgConf: _*) {
+ Seq(7, 8, 9).foreach { p =>
+ val grouped = builder(p).groupBy("k")
+ .agg(count(lit(1)).as("c"), sum(col("v")).as("s"))
+ checkAnswer(grouped.select("c", "s"),
+ Seq(Row(2L, 3L), Row(1L, 3L), Row(2L, 9L)))
+ }
+ }
+ }
+
+ //
========================================================================================
+ // GROUP BY over a mixed-precision key: a UNION of the same data at two
different nanos
+ // precisions widens the key to max(pl, pr) (findWiderDateTimeType).
Grouping the widened column
+ // still separates the two sub-microsecond keys (all remainders are
multiples of 100ns, exact at
+ // every p in [7, 9]). The union doubles every row, so counts/sums double:
100ns (4, 6),
+ // 900ns (2, 6), NULL (4, 18).
+ //
========================================================================================
+ test(s"$family nanos GROUP BY across mixed precisions widens key -
${cgLabel(cgConf)}") {
+ withSQLConf(cgConf: _*) {
+ mixedPrecisionPairs.foreach { case (pl, pr) =>
+ val union = builder(pl).union(builder(pr))
+ assert(union.schema("k").dataType == widerType(math.max(pl, pr)),
+ s"expected key widened to ${widerType(math.max(pl, pr))} for ($pl,
$pr)")
+ val grouped = union.groupBy("k")
+ .agg(count(lit(1)).as("c"), sum(col("v")).as("s"))
+ checkAnswer(grouped.select("c", "s"),
+ Seq(Row(4L, 6L), Row(2L, 6L), Row(4L, 18L)))
+ }
+ }
+ }
+
+ //
========================================================================================
+ // NULL grouping: unlike an equi-join (where NULL never matches NULL),
GROUP BY treats NULL as a
+ // single group. Both NULL-key rows land in one group (count 2).
+ //
========================================================================================
+ test(s"$family nanos GROUP BY collapses NULL keys into a single group -
${cgLabel(cgConf)}") {
+ withSQLConf(cgConf: _*) {
+ Seq(7, 8, 9).foreach { p =>
+ val nullGroup =
builder(p).groupBy("k").count().filter(col("k").isNull)
+ checkAnswer(nullGroup.select("count"), Seq(Row(2L)))
+ }
+ }
+ }
+ }
+}
+
+// Runs the nanosecond timestamp GROUP BY tests with ANSI mode enabled
explicitly.
+class TimestampNanosGroupByAnsiOnSuite extends TimestampNanosGroupBySuiteBase {
+ override def sparkConf: SparkConf =
super.sparkConf.set(SQLConf.ANSI_ENABLED.key, "true")
+}
+
+// Runs the nanosecond timestamp GROUP BY tests with ANSI mode disabled
explicitly.
+class TimestampNanosGroupByAnsiOffSuite extends TimestampNanosGroupBySuiteBase
{
+ override def sparkConf: SparkConf =
super.sparkConf.set(SQLConf.ANSI_ENABLED.key, "false")
+}
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