peter-toth commented on code in PR #56059:
URL: https://github.com/apache/spark/pull/56059#discussion_r3303444730


##########
common/unsafe/src/main/java/org/apache/spark/unsafe/types/TimestampNanosVal.java:
##########
@@ -0,0 +1,103 @@
+/*
+ * 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.unsafe.types;
+
+import org.apache.spark.SparkIllegalArgumentException;
+import org.apache.spark.annotation.Unstable;
+
+import java.io.Serializable;
+import java.util.Map;
+import java.util.Objects;
+
+/**
+ * Physical representation for nanosecond-capable timestamp types ({@code 
TIMESTAMP_NTZ(p)} and
+ * {@code TIMESTAMP_LTZ(p)} with {@code p} in [7, 9]). Analogous to {@link 
GeometryVal} for
+ * GEOMETRY: this class is only a container for the composite value; NTZ vs 
LTZ semantics live in
+ * {@link org.apache.spark.sql.catalyst.util.TimestampNTZNanos} and
+ * {@link org.apache.spark.sql.catalyst.util.TimestampLTZNanos}.
+ *
+ * <p>Values are stored as two components:
+ * <ul>
+ *   <li>{@link #epochMicros} - microseconds since the Unix epoch (same unit 
as microsecond
+ *   timestamp types),</li>
+ *   <li>{@link #nanosWithinMicro} - additional nanoseconds within that 
microsecond, in [0, 999].
+ *   </li>
+ * </ul>
+ *
+ * <p>Logical row-size estimation uses 10 bytes (8 + 2). In {@code UnsafeRow}, 
values are stored in
+ * the variable-length region using a 16-byte payload (see
+ * {@link org.apache.spark.sql.catalyst.expressions.TimestampNanosRowValues}), 
the same pattern as
+ * {@link CalendarInterval}.
+ *
+ * @since 4.3.0
+ */
+@Unstable
+public final class TimestampNanosVal implements Serializable {
+  /** Size of the {@code UnsafeRow} variable-length payload for this type (two 
8-byte words). */
+  public static final int SIZE_IN_BYTES = 16;
+
+  /** Maximum valid value for {@link #nanosWithinMicro} (three sub-micro 
decimal digits). */
+  public static final int MAX_NANOS_WITHIN_MICRO = 999;
+
+  /** Microseconds since the Unix epoch. */
+  public final long epochMicros;
+  /** Nanoseconds within {@link #epochMicros}, in [0, 999]. */
+  public final short nanosWithinMicro;
+
+  /**
+   * @param epochMicros microseconds since the Unix epoch
+   * @param nanosWithinMicro nanoseconds within {@code epochMicros}, must be 
in [0, 999]
+   */
+  public TimestampNanosVal(long epochMicros, short nanosWithinMicro) {

Review Comment:
   This constructor (and the `fromParts` factory at `:82` that wraps it) is 
also the read-path constructor: `TimestampNanosRowValues.readVal` 
(`TimestampNanosRowValues.java:76`) builds a fresh value here on every 
UnsafeRow / UnsafeArrayData get. So the `nanosWithinMicro` range check runs on 
every cell read, even though every `TimestampNanosVal` that ever reaches a row 
was already validated at its origin (the only path to one is this constructor). 
Sibling types in this package — `CalendarInterval`, `VariantVal`, 
`GeographyVal`, `GeometryVal` — all leave the constructor unchecked for the 
same reason.
   
   Consider exposing a package-private trusted factory and routing the row 
reader through it:
   
   ```java
   // in TimestampNanosVal.java
   static TimestampNanosVal fromTrustedRowBytes(long epochMicros, short 
nanosWithinMicro) {
     return new TimestampNanosVal(epochMicros, nanosWithinMicro, /*trusted*/ 
true);
   }
   private TimestampNanosVal(long epochMicros, short nanosWithinMicro, boolean 
trusted) {
     this.epochMicros = epochMicros;
     this.nanosWithinMicro = nanosWithinMicro;
   }
   ```
   
   and have `TimestampNanosRowValues.readVal` call `fromTrustedRowBytes`. The 
validating public constructor and `fromParts` stay for SQL-layer / user-facing 
callers where the value can come from anywhere.



##########
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/TimestampNanosRowSuite.scala:
##########
@@ -0,0 +1,132 @@
+/*
+ * 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.catalyst.expressions
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.InternalRow
+import 
org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.TimestampNanosVal
+import org.apache.spark.util.ArrayImplicits._
+
+class TimestampNanosRowSuite extends SparkFunSuite with ExpressionEvalHelper {
+
+  private val ntzValue = TimestampNanosVal.fromParts(1234567890123L, 
42.toShort)
+  private val ltzValue = TimestampNanosVal.fromParts(9876543210987L, 
999.toShort)
+
+  test("GenerateUnsafeProjection.canSupport for nanos timestamp types") {
+    assert(GenerateUnsafeProjection.canSupport(TimestampNTZNanosType(9)))
+    assert(GenerateUnsafeProjection.canSupport(TimestampLTZNanosType(7)))
+  }
+
+  test("GenericInternalRow roundtrip for TIMESTAMP_NTZ nanos") {
+    val row = new GenericInternalRow(Array[Any](ntzValue, null))
+    val accessor = InternalRow.getAccessor(TimestampNTZNanosType(9))
+    val writer = InternalRow.getWriter(0, TimestampNTZNanosType(9))
+    assert(accessor(row, 0) === ntzValue)
+    assert(accessor(row, 1) === null)
+
+    val row2 = new GenericInternalRow(Array[Any](null, null))
+    writer(row2, ntzValue)
+    assert(accessor(row2, 0) === ntzValue)
+  }
+
+  test("GenericInternalRow roundtrip for TIMESTAMP_LTZ nanos") {
+    val row = new GenericInternalRow(Array[Any](ltzValue, null))
+    val accessor = InternalRow.getAccessor(TimestampLTZNanosType(8))
+    val writer = InternalRow.getWriter(0, TimestampLTZNanosType(8))
+    assert(accessor(row, 0) === ltzValue)
+    assert(accessor(row, 1) === null)
+
+    val row2 = new GenericInternalRow(Array[Any](null, null))
+    writer(row2, ltzValue)
+    assert(accessor(row2, 0) === ltzValue)
+  }
+
+  testBothCodegenAndInterpreted("UnsafeRow roundtrip for nanos timestamp 
columns") {
+    val schema = StructType(Seq(

Review Comment:
   The schema only includes top-level nanos columns, so 
`UnsafeArrayWriter.write(int, TimestampNanosVal)` 
(`UnsafeArrayWriter.java:212`) is unexercised — the codegen path through 
`GenerateUnsafeProjection.writeArrayToBuffer` for 
`ArrayType(TimestampNTZNanosType, ...)` has no test coverage. A small 
additional case would close the gap and follow the same shape as the 
`CalendarInterval`-array tests in `UnsafeRowConverterSuite`:
   
   ```scala
   testBothCodegenAndInterpreted("UnsafeArrayWriter for nanos timestamp 
arrays") {
     val arrType = ArrayType(TimestampNTZNanosType(9), containsNull = true)
     val converter = UnsafeProjection.create(Array[DataType](arrType))
     val input = new GenericInternalRow(Array[Any](
       new GenericArrayData(Array[Any](ntzValue, null, ntzValue))))
     val output = converter.apply(input)
     val arr = output.getArray(0)
     assert(arr.numElements() == 3)
     assert(arr.getTimestampNTZNanos(0) === ntzValue)
     assert(arr.isNullAt(1))
     assert(arr.getTimestampNTZNanos(2) === ntzValue)
   }
   ```



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