RussellSpitzer commented on code in PR #14948:
URL: https://github.com/apache/iceberg/pull/14948#discussion_r3320782031


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spark/v4.1/spark/src/main/java/org/apache/iceberg/spark/source/MergingSortedRowDataReader.java:
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@@ -0,0 +1,298 @@
+/*
+ * 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.iceberg.spark.source;
+
+import java.io.IOException;
+import java.io.UncheckedIOException;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.Comparator;
+import java.util.List;
+import org.apache.iceberg.BaseScanTaskGroup;
+import org.apache.iceberg.FileScanTask;
+import org.apache.iceberg.ScanTaskGroup;
+import org.apache.iceberg.Schema;
+import org.apache.iceberg.SortField;
+import org.apache.iceberg.SortOrder;
+import org.apache.iceberg.SortOrderComparators;
+import org.apache.iceberg.StructLike;
+import org.apache.iceberg.Table;
+import org.apache.iceberg.io.CloseableGroup;
+import org.apache.iceberg.io.CloseableIterable;
+import org.apache.iceberg.io.CloseableIterator;
+import org.apache.iceberg.relocated.com.google.common.base.Preconditions;
+import org.apache.iceberg.relocated.com.google.common.collect.Lists;
+import org.apache.iceberg.spark.SparkSchemaUtil;
+import org.apache.iceberg.spark.source.metrics.TaskNumDeletes;
+import org.apache.iceberg.spark.source.metrics.TaskNumSplits;
+import org.apache.iceberg.types.TypeUtil;
+import org.apache.iceberg.types.Types;
+import org.apache.iceberg.util.SortedMerge;
+import org.apache.spark.sql.catalyst.InternalRow;
+import org.apache.spark.sql.catalyst.expressions.GenericInternalRow;
+import org.apache.spark.sql.connector.metric.CustomTaskMetric;
+import org.apache.spark.sql.connector.read.PartitionReader;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.StructType;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/**
+ * A {@link PartitionReader} that reads multiple sorted files and merges them 
into a single sorted
+ * stream using a k-way heap merge ({@link SortedMerge}).
+ *
+ * <p>This reader is used when {@code preserve-data-ordering} is enabled and 
the task group contains
+ * multiple files that all have the same sort order.
+ *
+ * <p>Sort key columns absent from the requested projection are temporarily 
added to the read schema
+ * so that {@link SortOrderComparators} can access them during the merge. The 
extra columns are
+ * stripped from each row before it is returned to Spark.
+ */
+class MergingSortedRowDataReader implements PartitionReader<InternalRow> {
+  private static final Logger LOG = 
LoggerFactory.getLogger(MergingSortedRowDataReader.class);
+
+  private final CloseableGroup resources;
+  private final CloseableIterator<InternalRow> mergedIterator;
+  private final List<RowDataReader> fileReaders;
+  // non-null only when sort key columns were added to the read schema beyond 
what Spark projected
+  private final int[] outputPositions;
+  private final DataType[] outputDataTypes;
+  private final Object[] outputValues; // reused per row to avoid per-row 
allocation
+  private InternalRow current;
+
+  MergingSortedRowDataReader(SparkInputPartition partition) {
+    Table table = partition.table();
+    ScanTaskGroup<FileScanTask> taskGroup = partition.taskGroup();
+    Schema projection = partition.projection();
+    SortOrder sortOrder = table.sortOrder();
+
+    int numFiles = taskGroup.tasks().size();
+
+    Preconditions.checkState(
+        sortOrder.isSorted(), "Cannot create merging reader for unsorted table 
%s", table.name());
+    Preconditions.checkState(
+        numFiles > 1, "Merging reader requires multiple files, got %s", 
numFiles);
+
+    LOG.info(
+        "Creating merging reader for {} files with sort order {} in table {}",
+        numFiles,
+        sortOrder.orderId(),
+        table.name());
+
+    // Augment the projected schema with any sort key columns Spark did not 
request so that
+    // SortOrderComparators can access every sort key field during the merge.
+    Schema mergeReadSchema = mergeReadSchema(projection, sortOrder, table);
+    this.outputPositions = buildOutputPositions(projection, mergeReadSchema);
+    this.outputDataTypes = buildOutputDataTypes(projection, outputPositions);
+    this.outputValues = outputPositions != null ? new 
Object[outputPositions.length] : null;
+
+    this.resources = new CloseableGroup();
+    this.fileReaders =
+        taskGroup.tasks().stream()
+            .map(
+                task ->
+                    new RowDataReader(
+                        table,
+                        partition.io(),
+                        new 
BaseScanTaskGroup<>(Collections.singletonList(task)),
+                        mergeReadSchema,
+                        partition.isCaseSensitive(),
+                        partition.cacheDeleteFilesOnExecutors()))
+            .toList();
+    // Wrap each reader as a CloseableIterable and feed into SortedMerge.
+    List<CloseableIterable<InternalRow>> fileIterables =
+        fileReaders.stream().map(this::readerToIterable).toList();
+    SortedMerge<InternalRow> sortedMerge =
+        new SortedMerge<>(buildComparator(mergeReadSchema, sortOrder), 
fileIterables);
+    resources.addCloseable(sortedMerge);
+    this.mergedIterator = sortedMerge.iterator();
+  }
+
+  /**
+   * Adapts a {@link RowDataReader} to a {@link CloseableIterable} for use 
with {@link SortedMerge}.
+   * Each row is copied before it enters the priority queue because Spark's 
Parquet/ORC readers
+   * reuse {@link InternalRow} instances for performance.
+   */
+  private CloseableIterable<InternalRow> readerToIterable(RowDataReader 
reader) {
+    return CloseableIterable.withNoopClose(
+        () ->
+            new CloseableIterator<>() {
+              private boolean advanced = false;
+              private boolean hasNext = false;
+
+              @Override
+              public boolean hasNext() {
+                if (!advanced) {
+                  try {
+                    hasNext = reader.next();
+                    advanced = true;
+                  } catch (IOException e) {
+                    throw new UncheckedIOException("Failed to advance reader", 
e);
+                  }
+                }
+                return hasNext;
+              }
+
+              @Override
+              public InternalRow next() {
+                if (!advanced) {
+                  hasNext();
+                }
+                advanced = false;
+                return reader.get().copy();
+              }
+
+              @Override
+              public void close() throws IOException {
+                reader.close();
+              }
+            });
+  }
+
+  @Override
+  public boolean next() throws IOException {
+    if (!mergedIterator.hasNext()) {
+      return false;
+    }
+
+    InternalRow merged = mergedIterator.next();
+    if (outputPositions == null) {
+      this.current = merged;
+    } else {
+      // Strip the extra sort key columns that were added for comparison 
purposes.
+      for (int i = 0; i < outputPositions.length; i++) {
+        outputValues[i] = merged.get(outputPositions[i], outputDataTypes[i]);
+      }
+      this.current = new GenericInternalRow(outputValues);

Review Comment:
   This is probably an uncessary allocation. The Spark contract allows us to 
re-use the container so rather than making an ew row each time we can just use 
a single wrapper and put new values in for each returned row.
   
   So do something like
   
   In initalizer make a MergedRow
   For each output, set the values in the merged row
   Return that over and over on each "get()"
   
   There are a few options for doing the wrapping without a copy (of even the 
field values) 
   
   I don't think we have exactly the right thing already in Iceberg but 
essentially you want something like
   
   InternalRowWrapper(schema, mapping: Int -> Int) which does get(i) { 
internal.get(mapping[i])}
   
   There may be a better solution so think on it a bit
   
   Anyway, think about it. We definitely don't want to create a bunch of 
objects here when we are re-using containers in all of our readers



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