Spark 3.5, 4.0, 4.1: Backport format model TCK coverage for vectorized reads

spark/v3.5/spark/src/test/java/org/apache/iceberg/spark/data/TestSparkVectorizedFormatModel.java

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+/*
+ * 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.data;
+
+import static org.assertj.core.api.Assertions.assertThat;
+import static org.assertj.core.api.Assumptions.assumeThat;
+
+import java.io.IOException;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.List;
+import java.util.Set;
+import org.apache.arrow.vector.ValueVector;
+import org.apache.iceberg.FileFormat;
+import org.apache.iceberg.Schema;
+import org.apache.iceberg.data.DataGenerator;
+import org.apache.iceberg.data.DataGenerators;
+import org.apache.iceberg.data.ReadFormatModelTests;
+import org.apache.iceberg.data.Record;
+import org.apache.iceberg.io.CloseableIterable;
+import org.apache.iceberg.relocated.com.google.common.collect.ImmutableSet;
+import org.apache.iceberg.spark.SparkSchemaUtil;
+import org.apache.iceberg.spark.SparkUtil;
+import org.apache.iceberg.spark.data.vectorized.IcebergArrowColumnVector;
+import org.apache.iceberg.types.Type;
+import org.apache.spark.sql.vectorized.ColumnarBatch;
+import org.junit.jupiter.params.ParameterizedTest;
+import org.junit.jupiter.params.provider.Arguments;
+import org.junit.jupiter.params.provider.FieldSource;
+
+class TestSparkVectorizedFormatModel extends ReadFormatModelTests<ColumnarBatch> {
+
+  private static final FileFormat[] FILE_FORMATS = {FileFormat.ORC, FileFormat.PARQUET};
+
+  // Spark 3.5 has no TIME, TIMESTAMP_NANO or VARIANT type
+  private static final Set<Type.TypeID> SPARK_UNSUPPORTED_TYPE_IDS =
+      Set.of(Type.TypeID.TIME, Type.TypeID.TIMESTAMP_NANO, Type.TypeID.VARIANT);
+
+  // The Arrow-based Parquet reader cannot read nested or geospatial columns;
+  // the vectorized ORC reader supports nested types.
+  private static final Set<Type.TypeID> ARROW_UNSUPPORTED_TYPE_IDS =
+      Set.of(
+          Type.TypeID.STRUCT,
+          Type.TypeID.LIST,
+          Type.TypeID.MAP,
+          Type.TypeID.GEOMETRY,
+          Type.TypeID.GEOGRAPHY);
+
+  private static final Set<Type.TypeID> PARQUET_UNSUPPORTED_TYPE_IDS =
+      ImmutableSet.<Type.TypeID>builder()
+          .addAll(SPARK_UNSUPPORTED_TYPE_IDS)
+          .addAll(ARROW_UNSUPPORTED_TYPE_IDS)
+          .build();
+
+  private static final List<Arguments> FORMAT_AND_GENERATOR =
+      Arrays.stream(FILE_FORMATS)
+          .flatMap(
+              format ->
+                  Arrays.stream(DataGenerators.ALL)
+                      .filter(generator -> supportsVectorized(format, generator))
+                      .map(generator -> Arguments.of(format, generator)))
+          .toList();
+
+  private static boolean supportsVectorized(FileFormat format, DataGenerator generator) {
+    return supportsGenerator(format, generator)
+        && (format != FileFormat.PARQUET
+            || !containsType(generator.schema(), ARROW_UNSUPPORTED_TYPE_IDS));
+  }
+
+  @Override
+  protected Collection<Type.TypeID> unsupportedTypeIds(FileFormat fileFormat) {
+    return fileFormat == FileFormat.PARQUET
+        ? PARQUET_UNSUPPORTED_TYPE_IDS
+        : SPARK_UNSUPPORTED_TYPE_IDS;
+  }
+
+  @Override
+  protected boolean supportsBatchReads() {
+    return true;
+  }
+
+  @Override
+  protected int countRows(List<ColumnarBatch> batches) {
+    return batches.stream().mapToInt(ColumnarBatch::numRows).sum();
+  }
+
+  @Override
+  protected void assertContainersReused(ColumnarBatch first, ColumnarBatch second) {
+    assertThat(second.numCols()).isEqualTo(first.numCols());
+
+    for (int i = 0; i < first.numCols(); i++) {
+      assertThat(arrowVector(second, i)).isSameAs(arrowVector(first, i));
+    }
+  }
+
+  @Override
+  @ParameterizedTest
+  @FieldSource("FILE_FORMATS")
+  protected void testReadMetadataColumnIsDeleted(FileFormat fileFormat) throws IOException {
+    assumeThat(fileFormat)
+        .as("Vectorized Parquet reader only populates _deleted when a delete filter is set")
+        .isNotEqualTo(FileFormat.PARQUET);
+    super.testReadMetadataColumnIsDeleted(fileFormat);
+  }
+
+  @Override
+  @ParameterizedTest
+  @FieldSource("FILE_FORMATS")
+  protected void testReadMetadataColumnRowLineage(FileFormat fileFormat) throws IOException {
+    assumeRowLineageSupported(fileFormat);
+    super.testReadMetadataColumnRowLineage(fileFormat);
+  }
+
+  @Override
+  @ParameterizedTest
+  @FieldSource("FILE_FORMATS")
+  protected void testReadMetadataColumnRowLineageExistingValues(FileFormat fileFormat)
+      throws IOException {
+    assumeRowLineageSupported(fileFormat);
+    super.testReadMetadataColumnRowLineageExistingValues(fileFormat);
+  }
+
+  // TODO: remove when https://github.com/apache/iceberg/issues/18543 is fixed
+  private static void assumeRowLineageSupported(FileFormat fileFormat) {
+    assumeThat(fileFormat)
+        .as("Vectorized ORC reader does not apply row lineage")
+        .isNotEqualTo(FileFormat.ORC);
+  }
+
+  private ValueVector arrowVector(ColumnarBatch batch, int col) {
+    assertThat(batch.column(col)).isInstanceOf(IcebergArrowColumnVector.class);
+
+    IcebergArrowColumnVector vector = (IcebergArrowColumnVector) batch.column(col);
+    return vector.vectorAccessor().getVector();
+  }
+
+  @Override
+  protected void assertContainersNotReused(ColumnarBatch first, ColumnarBatch second) {
+    assertThat(second.numCols()).isEqualTo(first.numCols());
+
+    for (int i = 0; i < first.numCols(); i++) {
+      assertThat(arrowVector(second, i)).isNotSameAs(arrowVector(first, i));
+    }
+  }
+
+  @Override
+  protected Object engineSchema(Schema schema) {
+    return SparkSchemaUtil.convert(schema);
+  }
+
+  @Override
+  protected Object convertConstantToEngine(Type type, Object value) {
+    return SparkUtil.internalToSpark(type, value);
+  }
+
+  @Override
+  protected Class<ColumnarBatch> engineType() {
+    return ColumnarBatch.class;
+  }
+
+  @Override
+  protected ColumnarBatch convertToEngine(Record record, Schema schema) {
+    throw new UnsupportedOperationException("Vectorized reads are read-only");
+  }
+
+  @Override
+  protected void assertEquals(
+      Schema schema, List<ColumnarBatch> expected, List<ColumnarBatch> actual) {
+    throw new UnsupportedOperationException("Use assertRecordsEqual for batch reads");
+  }
+
+  @Override
+  protected void assertRecordsEqual(
+      Schema schema, List<Record> expected, CloseableIterable<ColumnarBatch> actual) {
+    int rowOffset = 0;
+
+    for (ColumnarBatch batch : actual) {
+      assertThat(rowOffset + batch.numRows()).isLessThanOrEqualTo(expected.size());
+
+      for (int i = 0; i < batch.numRows(); i++) {
+        TestHelpers.assertEquals(
+            schema,
+            InternalRowConverter.convert(schema, expected.get(rowOffset + i)),
+            batch.getRow(i));
+      }
+
+      rowOffset += batch.numRows();
+    }
+
+    assertThat(rowOffset).isEqualTo(expected.size());
+  }
+}