[GitHub] [spark] sunchao commented on a change in pull request #31776: [SPARK-34661][SQL] Clean up `OriginalType` and `DecimalMetadata ` usage in Parquet related code

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

sunchao commented on a change in pull request #31776:
URL: https://github.com/apache/spark/pull/31776#discussion_r604249720



##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
##########
@@ -105,23 +109,29 @@ class ParquetFilters(
       fieldType: ParquetSchemaType)
 
   private case class ParquetSchemaType(
-      originalType: OriginalType,
+      logicalTypeAnnotation: LogicalTypeAnnotation,
       primitiveTypeName: PrimitiveTypeName,
       length: Int,
-      decimalMetadata: DecimalMetadata)
+      decimalLogicalType: DecimalLogicalTypeAnnotation)

Review comment:
       this may no longer needed since we can just use `logicalTypeAnnotation`?

##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
##########
@@ -105,23 +109,29 @@ class ParquetFilters(
       fieldType: ParquetSchemaType)
 
   private case class ParquetSchemaType(
-      originalType: OriginalType,
+      logicalTypeAnnotation: LogicalTypeAnnotation,
       primitiveTypeName: PrimitiveTypeName,
       length: Int,
-      decimalMetadata: DecimalMetadata)
+      decimalLogicalType: DecimalLogicalTypeAnnotation)
 
   private val ParquetBooleanType = ParquetSchemaType(null, BOOLEAN, 0, null)
-  private val ParquetByteType = ParquetSchemaType(INT_8, INT32, 0, null)
-  private val ParquetShortType = ParquetSchemaType(INT_16, INT32, 0, null)
+  private val ParquetByteType =
+    ParquetSchemaType(LogicalTypeAnnotation.intType(8, true), INT32, 0, null)
+  private val ParquetShortType =
+    ParquetSchemaType(LogicalTypeAnnotation.intType(16, true), INT32, 0, null)
   private val ParquetIntegerType = ParquetSchemaType(null, INT32, 0, null)
   private val ParquetLongType = ParquetSchemaType(null, INT64, 0, null)
   private val ParquetFloatType = ParquetSchemaType(null, FLOAT, 0, null)
   private val ParquetDoubleType = ParquetSchemaType(null, DOUBLE, 0, null)
-  private val ParquetStringType = ParquetSchemaType(UTF8, BINARY, 0, null)
+  private val ParquetStringType =
+    ParquetSchemaType(LogicalTypeAnnotation.stringType(), BINARY, 0, null)
   private val ParquetBinaryType = ParquetSchemaType(null, BINARY, 0, null)
-  private val ParquetDateType = ParquetSchemaType(DATE, INT32, 0, null)
-  private val ParquetTimestampMicrosType = ParquetSchemaType(TIMESTAMP_MICROS, INT64, 0, null)
-  private val ParquetTimestampMillisType = ParquetSchemaType(TIMESTAMP_MILLIS, INT64, 0, null)
+  private val ParquetDateType =
+    ParquetSchemaType(LogicalTypeAnnotation.dateType(), INT32, 0, null)
+  private val ParquetTimestampMicrosType =
+    ParquetSchemaType(LogicalTypeAnnotation.timestampType(true, TimeUnit.MICROS), INT64, 0, null)
+  private val ParquetTimestampMillisType =
+    ParquetSchemaType(LogicalTypeAnnotation.timestampType(true, TimeUnit.MILLIS), INT64, 0, null)

Review comment:
       I think there is also `TimeUnit.NANOS` but seems Spark doesn't support it yet.

##########
File path: sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java
##########
@@ -894,4 +904,15 @@ private void readPageV2(DataPageV2 page) throws IOException {
       throw new IOException("could not read page " + page + " in col " + descriptor, e);
     }
   }
+
+  private boolean isTimestampTypeMatched(TimeUnit unit) {

Review comment:
       nit: these can be static methods




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