[GitHub] [spark] sadikovi commented on a change in pull request #34495: [SPARK-36182][SQL] Support TimestampNTZ type in Parquet data source

[GitBox <gi...@apache.org>]

sadikovi commented on a change in pull request #34495:
URL: https://github.com/apache/spark/pull/34495#discussion_r744323273



##########
File path: sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
##########
@@ -158,6 +158,48 @@ abstract class ParquetQuerySuite extends QueryTest with ParquetTest with SharedS
     }
   }
 
+  test("SPARK-36182: writing and reading TimestampNTZType column") {
+    withTable("ts") {
+      sql("create table ts (c1 timestamp_ntz) using parquet")
+      sql("insert into ts values (timestamp_ntz'2016-01-01 10:11:12.123456')")
+      sql("insert into ts values (null)")
+      sql("insert into ts values (timestamp_ntz'1965-01-01 10:11:12.123456')")
+      val expectedSchema = new StructType().add(StructField("c1", TimestampNTZType))
+      assert(spark.table("ts").schema == expectedSchema)
+      val expected = Seq(
+        ("2016-01-01 10:11:12.123456"),
+        (null),
+        ("1965-01-01 10:11:12.123456"))
+        .toDS().select($"value".cast("timestamp_ntz"))
+      checkAnswer(sql("select * from ts"), expected)
+    }
+  }
+
+  test("SPARK-36182: can't read TimestampLTZ as TimestampNTZ") {

Review comment:
       Can we add a test for reading TimestampNTZ as TimestampLTZ?

##########
File path: sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/ParquetVectorUpdaterFactory.java
##########
@@ -179,6 +183,21 @@ boolean isTimestampTypeMatched(LogicalTypeAnnotation.TimeUnit unit) {
       ((TimestampLogicalTypeAnnotation) logicalTypeAnnotation).getUnit() == unit;
   }
 
+  void validateTimestampType(DataType sparkType) {
+    assert(logicalTypeAnnotation instanceof TimestampLogicalTypeAnnotation);
+    // Throw an exception if the Parquet type is TimestampLTZ and the Catalyst type is TimestampNTZ.
+    // This is to avoid mistakes in reading the timestamp values.
+    if (((TimestampLogicalTypeAnnotation) logicalTypeAnnotation).isAdjustedToUTC() &&
+      sparkType == DataTypes.TimestampNTZType) {
+      converterErrorForTimestampNTZ("int64 time(" + logicalTypeAnnotation + ")");
+    }
+  }
+
+  void converterErrorForTimestampNTZ(String parquetType) {

Review comment:
       nit: `convertErrorForTimestampNTZ`?

##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala
##########
@@ -370,6 +370,31 @@ private[parquet] class ParquetRowConverter(
           }
         }
 
+      // The converter doesn't support the TimestampLTZ Parquet type and TimestampNTZ Catalyst type.
+      // This is to avoid mistakes in reading the timestamp values.
+      case TimestampNTZType
+        if parquetType.asPrimitiveType().getPrimitiveTypeName == INT64 &&
+          parquetType.getLogicalTypeAnnotation.isInstanceOf[TimestampLogicalTypeAnnotation] &&
+          !parquetType.getLogicalTypeAnnotation
+            .asInstanceOf[TimestampLogicalTypeAnnotation].isAdjustedToUTC &&
+          parquetType.getLogicalTypeAnnotation
+            .asInstanceOf[TimestampLogicalTypeAnnotation].getUnit == TimeUnit.MICROS =>
+        new ParquetPrimitiveConverter(updater)
+
+      case TimestampNTZType
+        if parquetType.asPrimitiveType().getPrimitiveTypeName == INT64 &&
+          parquetType.getLogicalTypeAnnotation.isInstanceOf[TimestampLogicalTypeAnnotation] &&
+          !parquetType.getLogicalTypeAnnotation
+            .asInstanceOf[TimestampLogicalTypeAnnotation].isAdjustedToUTC &&
+          parquetType.getLogicalTypeAnnotation
+            .asInstanceOf[TimestampLogicalTypeAnnotation].getUnit == TimeUnit.MILLIS =>

Review comment:
       I think this one is for millisecond conversion, the one above is for microsecond conversion.




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