wombatu-kun commented on code in PR #18403: URL: https://github.com/apache/hudi/pull/18403#discussion_r3032178659
########## hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/io/storage/LanceBatchIterator.java: ########## @@ -0,0 +1,166 @@ +/* + * 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.hudi.io.storage; + +import org.apache.hudi.exception.HoodieException; +import org.apache.hudi.exception.HoodieIOException; + +import org.apache.arrow.memory.BufferAllocator; +import org.apache.arrow.vector.VectorSchemaRoot; +import org.apache.arrow.vector.ipc.ArrowReader; +import org.apache.spark.sql.vectorized.ColumnVector; +import org.apache.spark.sql.vectorized.ColumnarBatch; +import org.apache.spark.sql.vectorized.LanceArrowColumnVector; +import org.lance.file.LanceFileReader; + +import java.io.Closeable; +import java.io.IOException; +import java.util.Iterator; +import java.util.NoSuchElementException; + +/** + * Iterator that returns {@link ColumnarBatch} directly from Lance files without + * decomposing to individual rows. Used for vectorized/columnar batch reading + * in Spark's COW base-file-only read path. + * + * <p>Unlike {@link LanceRecordIterator} which extracts rows one by one, + * this iterator preserves the columnar format for zero-copy batch processing. + * + * <p>Manages the lifecycle of: + * <ul> + * <li>BufferAllocator - Arrow memory management</li> + * <li>LanceFileReader - Lance file handle</li> + * <li>ArrowReader - Arrow batch reader</li> + * </ul> + */ +public class LanceBatchIterator implements Iterator<ColumnarBatch>, Closeable { + private final BufferAllocator allocator; + private final LanceFileReader lanceReader; + private final ArrowReader arrowReader; + private final String path; + + private ColumnVector[] columnVectors; + private ColumnarBatch currentBatch; + private boolean nextBatchLoaded = false; + private boolean finished = false; + private boolean closed = false; + + /** + * Creates a new Lance batch iterator. + * + * @param allocator Arrow buffer allocator for memory management + * @param lanceReader Lance file reader + * @param arrowReader Arrow reader for batch reading + * @param path File path (for error messages) + */ + public LanceBatchIterator(BufferAllocator allocator, + LanceFileReader lanceReader, + ArrowReader arrowReader, + String path) { + this.allocator = allocator; + this.lanceReader = lanceReader; + this.arrowReader = arrowReader; + this.path = path; + } + + @Override + public boolean hasNext() { + if (finished) { + return false; + } + if (nextBatchLoaded) { + return true; + } + + try { + if (arrowReader.loadNextBatch()) { + VectorSchemaRoot root = arrowReader.getVectorSchemaRoot(); + + // Create column vector wrappers once and reuse across batches + // (ArrowReader reuses the same VectorSchemaRoot) + if (columnVectors == null) { + columnVectors = root.getFieldVectors().stream() + .map(LanceArrowColumnVector::new) + .toArray(ColumnVector[]::new); + } + + currentBatch = new ColumnarBatch(columnVectors, root.getRowCount()); + nextBatchLoaded = true; + return true; + } + } catch (IOException e) { Review Comment: No — the throw on line 108 unwinds the stack, so `finished` is never read after this point. Setting it before the throw would be dead code. If we wanted to swallow the exception and return false instead, then yes, `finished = true` would be required — but silently hiding an I/O error would be worse. ########## hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/spark/sql/execution/datasources/lance/SparkLanceReaderBase.scala: ########## @@ -111,65 +112,285 @@ class SparkLanceReaderBase(enableVectorizedReader: Boolean) extends SparkColumna // Read data with column projection (filters not supported yet) val arrowReader = lanceReader.readAll(columnNames, null, DEFAULT_BATCH_SIZE) - // Create iterator using shared LanceRecordIterator - lanceIterator = new LanceRecordIterator( - allocator, - lanceReader, - arrowReader, - requestSchema, - filePath - ) - - // Register cleanup listener - Option(TaskContext.get()).foreach { ctx => - ctx.addTaskCompletionListener[Unit](_ => lanceIterator.close()) - } - - // Create the following projections for schema evolution: - // 1. Padding projection: add NULL for missing columns - // 2. Casting projection: handle type conversions - val schemaUtils = sparkAdapter.getSchemaUtils - val paddingProj = SparkSchemaTransformUtils.generateNullPaddingProjection(requestSchema, requiredSchema) - val castProj = SparkSchemaTransformUtils.generateUnsafeProjection( - schemaUtils.toAttributes(requiredSchema), - Some(SQLConf.get.sessionLocalTimeZone), - implicitTypeChangeInfo, - requiredSchema, - new StructType(), - schemaUtils - ) - - // Unify projections by applying padding and then casting for each row - val projection: UnsafeProjection = new UnsafeProjection { - def apply(row: InternalRow): UnsafeRow = - castProj(paddingProj(row)) - } - val projectedIter = lanceIterator.asScala.map(projection.apply) - - // Handle partition columns - if (partitionSchema.length == 0) { - // No partition columns - return rows directly - projectedIter + // Decide between batch mode and row mode. + // Fall back to row mode if type casting is needed (batch-level type casting deferred to follow-up). + val hasTypeChanges = !implicitTypeChangeInfo.isEmpty + if (enableVectorizedReader && !hasTypeChanges) { + readBatch(file, allocator, lanceReader, arrowReader, filePath, + requestSchema, requiredSchema, partitionSchema) } else { - // Create UnsafeProjection to convert JoinedRow to UnsafeRow - val fullSchema = (requiredSchema.fields ++ partitionSchema.fields).map(f => - AttributeReference(f.name, f.dataType, f.nullable, f.metadata)()) - val unsafeProjection = GenerateUnsafeProjection.generate(fullSchema, fullSchema) - - // Append partition values to each row using JoinedRow, then convert to UnsafeRow - val joinedRow = new JoinedRow() - projectedIter.map(row => unsafeProjection(joinedRow(row, file.partitionValues))) + readRows(file, allocator, lanceReader, arrowReader, filePath, + requestSchema, requiredSchema, partitionSchema, implicitTypeChangeInfo) } } catch { case e: Exception => - if (lanceIterator != null) { - lanceIterator.close() // Close iterator which handles lifecycle for all objects + allocator.close() + throw new IOException(s"Failed to read Lance file: $filePath", e) + } + } + } + + /** + * Columnar batch reading path. Returns Iterator[ColumnarBatch] type-erased as Iterator[InternalRow]. + * Used when enableVectorizedReader=true and no type casting is needed. + */ + private def readBatch(file: PartitionedFile, + allocator: org.apache.arrow.memory.BufferAllocator, Review Comment: done -- 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]
