MaxGekk commented on code in PR #55326: URL: https://github.com/apache/spark/pull/55326#discussion_r3434520759
########## sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/types/ops/TimeTypeParquetOps.scala: ########## @@ -0,0 +1,117 @@ +/* + * 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.execution.datasources.parquet.types.ops + +import org.apache.parquet.io.api.{Converter, RecordConsumer} +import org.apache.parquet.schema.{LogicalTypeAnnotation, Type, Types} +import org.apache.parquet.schema.LogicalTypeAnnotation.TimeUnit +import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName.INT64 +import org.apache.parquet.schema.Type.Repetition + +import org.apache.spark.sql.catalyst.expressions.SpecializedGetters +import org.apache.spark.sql.catalyst.util.DateTimeUtils +import org.apache.spark.sql.errors.QueryExecutionErrors +import org.apache.spark.sql.execution.datasources.parquet.{HasParentContainerUpdater, ParentContainerUpdater, ParquetPrimitiveConverter} +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types.{DataType, TimeType} + +/** + * Parquet operations for TimeType. + * + * TimeType is a primitive Long-backed type stored in Parquet as INT64 with the + * TIME(isAdjustedToUTC=false, unit=MICROS) logical type annotation. + * + * IMPORTANT - internal vs Parquet representation: + * - Spark internal: nanoseconds since midnight (Long) + * - Parquet storage: microseconds since midnight (INT64) + * - Write path: nanos -> micros (DateTimeUtils.nanosToMicros) + * - Read path: micros -> nanos (DateTimeUtils.microsToNanos) + * + * @param t the TimeType with precision information + * @since 4.3.0 + */ +case class TimeTypeParquetOps(t: TimeType) extends ParquetTypeOps { + + // ==================== Schema Conversion ==================== + + override def convertToParquetType( + fieldName: String, repetition: Repetition, inShredded: Boolean): Type = + Types.primitive(INT64, repetition) + .as(LogicalTypeAnnotation.timeType(false, TimeUnit.MICROS)) + .named(fieldName) + + // ==================== Value Write ==================== + + override def makeWriter( + recordConsumer: () => RecordConsumer, + makeFieldWriter: DataType => (SpecializedGetters, Int) => Unit + ): (SpecializedGetters, Int) => Unit = + // Evaluate the supplier at write time (not creation time) because recordConsumer + // is null during init() and set later in prepareForWrite(). + (row: SpecializedGetters, ordinal: Int) => + recordConsumer().addLong(DateTimeUtils.nanosToMicros(row.getLong(ordinal))) + + // ==================== Row-Based Read ==================== + + override def newConverter( + parquetType: Type, + updater: ParentContainerUpdater): Converter with HasParentContainerUpdater = { + // Framework-first dispatch in ParquetRowConverter routes here whenever the + // requested Spark type is TimeType, regardless of the actual Parquet encoding. + // Without this guard, files whose column is raw INT64, INT64 TIME(NANOS), + // INT64 TIMESTAMP(MICROS), INT32 TIME(MILLIS), etc. would silently decode as + // microsToNanos(value) and produce wrong results. Mirrors the inline guard + // that existed in ParquetRowConverter before the framework dispatch. + TimeTypeParquetOps.requireCompatibleParquetType(t, parquetType) + new ParquetPrimitiveConverter(updater) { + override def addLong(value: Long): Unit = { + this.updater.setLong(DateTimeUtils.microsToNanos(value)) + } + } + } + + // ==================== Vectorized Read ==================== + + override def isBatchReadSupported(sqlConf: SQLConf): Boolean = true Review Comment: Good catch, thanks — you're right that the rejection isn't unconditional. I've updated the PR description accordingly: - The "user-facing change" section now states the stricter guard fires **only on the row-based path**. Since `isBatchReadSupported = true`, a top-level `TimeType` reads via the vectorized `LongAsNanosUpdater` (which never consults `requireCompatibleParquetType`), so `INT64 TIME(MICROS, isAdjustedToUTC=true)` succeeds vectorized (the default) and fails row-based — i.e. conditional on `enableVectorizedReader=false` or the column being nested in a struct/array/map. - The SPARK-57416 follow-up now calls out the **two** levers (`requireCompatibleParquetType` and `isBatchReadSupported`) and frames the reconciliation as aligning the vectorized and row-based paths so the behavior no longer depends on `enableVectorizedReader`/nesting. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. 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