viirya commented on code in PR #56334: URL: https://github.com/apache/spark/pull/56334#discussion_r3522716426
########## sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/ArrowCachedBatchSerializer.scala: ########## @@ -0,0 +1,1601 @@ +/* + * 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.columnar + +import java.io.{ByteArrayInputStream, ByteArrayOutputStream} +import java.nio.channels.Channels + +import scala.jdk.CollectionConverters._ + +import org.apache.arrow.compression.{Lz4CompressionCodec, ZstdCompressionCodec} +import org.apache.arrow.vector.{VectorLoader, VectorSchemaRoot, VectorUnloader} +import org.apache.arrow.vector.compression.{CompressionCodec, NoCompressionCodec} +import org.apache.arrow.vector.ipc.{ReadChannel, WriteChannel} +import org.apache.arrow.vector.ipc.message.{ArrowRecordBatch, MessageSerializer} + +import org.apache.spark.{SparkException, TaskContext} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.Attribute +import org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter +import org.apache.spark.sql.catalyst.types.DataTypeUtils +import org.apache.spark.sql.catalyst.util.DateTimeUtils +import org.apache.spark.sql.columnar.{CachedBatch, SimpleMetricsCachedBatchSerializer} +import org.apache.spark.sql.errors.ExecutionErrors +import org.apache.spark.sql.execution.arrow.ArrowWriter +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types._ +import org.apache.spark.sql.util.ArrowUtils +import org.apache.spark.sql.vectorized.{ArrowColumnVector, ColumnarBatch, ColumnVector} +import org.apache.spark.storage.StorageLevel +import org.apache.spark.unsafe.types.UTF8String +import org.apache.spark.util.Utils + +/** + * A [[CachedBatchSerializer]] that uses Apache Arrow as the cache format. + * + * This serializer: + * - Supports both row-based (InternalRow) and columnar (ColumnarBatch) input + * - Stores each batch as an internal, schema-less encapsulated Arrow RecordBatch message with + * optional compression (zstd/lz4); the schema is reconstructed from the relation's attributes + * on read (see [[ArrowCachedBatch]]) + * - Enables zero-copy columnar reads when output is ColumnarBatch + * - Uses off-heap memory via Arrow allocators during encode/decode + * - Collects per-column statistics for partition pruning + * + * Configuration options: + * - spark.sql.cache.serializer: Set to this class name to enable + * - spark.sql.execution.arrow.maxRecordsPerBatch: Max rows per cached batch + * - spark.sql.execution.arrow.compression.codec: Compression (none/zstd/lz4) + * - spark.sql.inMemoryColumnarStorage.enableVectorizedReader: Enable columnar output + */ +class ArrowCachedBatchSerializer extends SimpleMetricsCachedBatchSerializer { + + // supportsColumnarInput selects the columnar-vs-row input path; it does not gate which schemas + // this serializer accepts. The cache framework has no per-type fallback to another serializer + // (whatever spark.sql.cache.serializer selects handles every cached relation), so returning + // false here only routes input through convertInternalRowToCachedBatch, which is still this + // serializer. Type support is enforced once per partition by checkSupportedSchema below; the + // only real precondition for columnar input is that the plan can produce columnar output, which + // InMemoryRelation already checks via cachedPlan.supportsColumnar before calling this. + override def supportsColumnarInput(schema: Seq[Attribute]): Boolean = true + + override def convertInternalRowToCachedBatch( + input: RDD[InternalRow], + schema: Seq[Attribute], + storageLevel: StorageLevel, + conf: SQLConf): RDD[CachedBatch] = { + ArrowCachedBatchSerializer.checkSupportedSchema(schema) + // Capture config values on driver before RDD transformation + val sparkSchema = DataTypeUtils.fromAttributes(schema) + val maxRecordsPerBatch = conf.arrowMaxRecordsPerBatch + val maxBytesPerBatch = conf.arrowMaxBytesPerBatch + val timeZoneId = conf.sessionLocalTimeZone + val compressionCodecName = conf.arrowCompressionCodec + val compressionLevel = conf.arrowZstdCompressionLevel + + input.mapPartitionsInternal { rowIterator => + new InternalRowToArrowCachedBatchIterator( + rowIterator, + schema, + sparkSchema, + maxRecordsPerBatch, + maxBytesPerBatch, + timeZoneId, + compressionCodecName, + compressionLevel) + } + } + + override def convertColumnarBatchToCachedBatch( + input: RDD[ColumnarBatch], + schema: Seq[Attribute], + storageLevel: StorageLevel, + conf: SQLConf): RDD[CachedBatch] = { + ArrowCachedBatchSerializer.checkSupportedSchema(schema) + // Capture config values on driver before RDD transformation + val sparkSchema = DataTypeUtils.fromAttributes(schema) + val timeZoneId = conf.sessionLocalTimeZone + val compressionCodecName = conf.arrowCompressionCodec + val compressionLevel = conf.arrowZstdCompressionLevel + + input.mapPartitionsInternal { batchIterator => + new ColumnarBatchToArrowCachedBatchIterator( + batchIterator, + schema, + sparkSchema, + timeZoneId, + compressionCodecName, + compressionLevel) + } + } + + override def supportsColumnarOutput(schema: StructType): Boolean = { + // Always support columnar output with Arrow + true + } + + override def vectorTypes(attributes: Seq[Attribute], conf: SQLConf): Option[Seq[String]] = { + Option(Seq.fill(attributes.length)(classOf[ArrowColumnVector].getName)) + } + + override def convertCachedBatchToColumnarBatch( + input: RDD[CachedBatch], + cacheAttributes: Seq[Attribute], + selectedAttributes: Seq[Attribute], + conf: SQLConf): RDD[ColumnarBatch] = { + val cacheSchema = DataTypeUtils.fromAttributes(cacheAttributes) + val selectedSchema = DataTypeUtils.fromAttributes(selectedAttributes) + val columnIndices = + selectedAttributes.map(a => cacheAttributes.map(o => o.exprId).indexOf(a.exprId)).toArray + // Capture config on driver + val timeZoneId = conf.sessionLocalTimeZone + val prefetchEnabled = conf.arrowCachePrefetchEnabled + + input.mapPartitionsInternal { batchIterator => + new ArrowCachedBatchToColumnarBatchIterator( + batchIterator, + cacheSchema, + selectedSchema, + columnIndices, + timeZoneId, + prefetchEnabled) + } + } + + override def convertCachedBatchToInternalRow( + input: RDD[CachedBatch], + cacheAttributes: Seq[Attribute], + selectedAttributes: Seq[Attribute], + conf: SQLConf): RDD[InternalRow] = { + val cacheSchema = DataTypeUtils.fromAttributes(cacheAttributes) + val selectedSchema = DataTypeUtils.fromAttributes(selectedAttributes) + val timeZoneId = conf.sessionLocalTimeZone + + // Calculate column indices for projection + val selectedIndices = selectedAttributes.map { attr => + cacheAttributes.indexWhere(_.exprId == attr.exprId) + }.toArray + + // Check if all selected types can use the fast path. + // Types not handled by ArrowColumnReader must use the fallback path. + val needsFallback = selectedSchema.fields.exists { f => + f.dataType match { + case _: ArrayType | _: StructType | _: MapType => true + case CalendarIntervalType | VariantType | NullType => true + case _: UserDefinedType[_] => true + // Geometry/Geography are represented as an Arrow struct (srid + wkb); the fast-path + // ArrowColumnReader does not handle them, so route them through the fallback. + case _: GeometryType | _: GeographyType => true + // Nanosecond timestamps write a 16-byte TimestampNanosVal payload into the UnsafeRow; + // the fast-path typed readers only write fixed primitives, so use the fallback, which + // reads through ArrowColumnVector.getTimestampNTZNanos/getTimestampLTZNanos. + case _: TimestampNTZNanosType | _: TimestampLTZNanosType => true + case _ => false + } + } + + if (needsFallback) { + // Fall back to columnar-to-row conversion via ColumnarBatch for complex types. + // Use UnsafeProjection to convert ColumnarBatchRow to UnsafeRow. + convertCachedBatchToColumnarBatch(input, cacheAttributes, selectedAttributes, conf) + .mapPartitionsInternal { batchIter => + val toUnsafe = org.apache.spark.sql.catalyst.expressions.UnsafeProjection.create( + selectedSchema) + batchIter.flatMap { batch => + val numRows = batch.numRows() + new Iterator[InternalRow] { + private var rowIdx = 0 + override def hasNext: Boolean = rowIdx < numRows + override def next(): InternalRow = { + val row = batch.getRow(rowIdx) + rowIdx += 1 + toUnsafe(row) + } + } + } + } + } else { + val prefetchEnabled = conf.arrowCachePrefetchEnabled + input.mapPartitionsInternal { batchIterator => + new ArrowCachedBatchToInternalRowIterator( + batchIterator, + cacheSchema, + selectedSchema, + selectedIndices, + timeZoneId, + prefetchEnabled) + } + } + } +} + +/** + * Companion object with shared utility methods for Arrow cache serialization. + */ +private object ArrowCachedBatchSerializer { + + /** + * Run an Arrow write block, translating a CalendarInterval microsecond overflow into a clear + * error. Arrow's IntervalMonthDayNano representation is nanosecond-based, so writing a + * CalendarInterval multiplies its microseconds by 1000 with Math.multiplyExact; Spark allows the + * full Long microsecond domain, so values beyond Long.MaxValue/1000 overflow and otherwise abort + * with an opaque "long overflow" ArithmeticException. The catch is only installed when the schema + * actually contains a CalendarInterval column (hasInterval), so there is no per-row cost and no + * effect on schemas without intervals; the try is entered once per batch, not per row. + */ + def withIntervalOverflowTranslation[T](hasInterval: Boolean)(block: => T): T = { + if (!hasInterval) { + block + } else { + try { + block + } catch { + case e: ArithmeticException => + throw SparkException.internalError( + "Arrow cache cannot represent a CalendarInterval whose microseconds exceed " + Review Comment: Fixed. `withIntervalOverflowTranslation` now throws the structured `DATETIME_OVERFLOW` condition (added `ExecutionErrors.datetimeOverflowError`, mirroring `QueryExecutionErrors.timestampNanosEpochNanosOverflowError`'s pattern) instead of `SparkException.internalError`. Caching a CalendarInterval that can't be losslessly converted is a user-facing limitation, same anti-pattern as the `checkSupportedSchema` fix. Test now asserts `getCondition == "DATETIME_OVERFLOW"`. -- 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. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
