Github user tejasapatil commented on a diff in the pull request:

    https://github.com/apache/spark/pull/13775#discussion_r83759570
  
    --- Diff: 
sql/hive/src/main/java/org/apache/hadoop/hive/ql/io/orc/VectorizedSparkOrcNewRecordReader.java
 ---
    @@ -0,0 +1,318 @@
    +/*
    + * 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.hadoop.hive.ql.io.orc;
    +
    +import java.io.IOException;
    +import java.nio.charset.StandardCharsets;
    +import java.util.ArrayList;
    +import java.util.List;
    +
    +import org.apache.commons.lang.NotImplementedException;
    +
    +import org.apache.hadoop.conf.Configuration;
    +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector;
    +import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector;
    +import org.apache.hadoop.hive.ql.exec.vector.DecimalColumnVector;
    +import org.apache.hadoop.hive.ql.exec.vector.DoubleColumnVector;
    +import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector;
    +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
    +import org.apache.hadoop.io.NullWritable;
    +import org.apache.hadoop.mapred.JobConf;
    +import org.apache.hadoop.mapreduce.InputSplit;
    +import org.apache.hadoop.mapreduce.TaskAttemptContext;
    +import org.apache.hadoop.mapreduce.lib.input.FileSplit;
    +
    +import org.apache.spark.sql.catalyst.InternalRow;
    +import org.apache.spark.sql.catalyst.util.ArrayData;
    +import org.apache.spark.sql.catalyst.util.MapData;
    +import org.apache.spark.sql.types.DataType;
    +import org.apache.spark.sql.types.Decimal;
    +import org.apache.spark.unsafe.types.CalendarInterval;
    +import org.apache.spark.unsafe.types.UTF8String;
    +
    +/**
    + * A RecordReader that returns InternalRow for Spark SQL execution.
    + * This reader uses an internal reader that returns Hive's 
VectorizedRowBatch. An adapter
    + * class is used to return internal row by directly accessing data in 
column vectors.
    + */
    +public class VectorizedSparkOrcNewRecordReader
    +    extends org.apache.hadoop.mapreduce.RecordReader<NullWritable, 
InternalRow> {
    +  private final org.apache.hadoop.mapred.RecordReader<NullWritable, 
VectorizedRowBatch> reader;
    +  private final int numColumns;
    +  private VectorizedRowBatch internalValue;
    +  private float progress = 0.0f;
    +  private List<Integer> columnIDs;
    +
    +  private long numRowsOfBatch = 0;
    +  private int indexOfRow = 0;
    +
    +  private final Row row;
    +
    +  public VectorizedSparkOrcNewRecordReader(
    +      Reader file,
    +      JobConf conf,
    +      FileSplit fileSplit,
    +      List<Integer> columnIDs) throws IOException {
    +    List<OrcProto.Type> types = file.getTypes();
    +    numColumns = (types.size() == 0) ? 0 : types.get(0).getSubtypesCount();
    +    this.reader = new SparkVectorizedOrcRecordReader(file, conf,
    +      new org.apache.hadoop.mapred.FileSplit(fileSplit));
    +
    +    this.columnIDs = new ArrayList<>(columnIDs);
    +    this.internalValue = this.reader.createValue();
    +    this.progress = reader.getProgress();
    +    this.row = new Row(this.internalValue.cols, this.columnIDs);
    +  }
    +
    +  @Override
    +  public void close() throws IOException {
    +    reader.close();
    +  }
    +
    +  @Override
    +  public NullWritable getCurrentKey() throws IOException,
    +      InterruptedException {
    +    return NullWritable.get();
    +  }
    +
    +  @Override
    +  public InternalRow getCurrentValue() throws IOException,
    +      InterruptedException {
    +    if (indexOfRow >= numRowsOfBatch) {
    +      return null;
    +    }
    +    row.rowId = indexOfRow;
    +    indexOfRow++;
    +
    +    return row;
    +  }
    +
    +  @Override
    +  public float getProgress() throws IOException, InterruptedException {
    +    return progress;
    +  }
    +
    +  @Override
    +  public void initialize(InputSplit split, TaskAttemptContext context)
    +      throws IOException, InterruptedException {
    +  }
    +
    +  @Override
    +  public boolean nextKeyValue() throws IOException, InterruptedException {
    +    if (indexOfRow == numRowsOfBatch) {
    +      if (reader.next(NullWritable.get(), internalValue)) {
    +        if (internalValue.endOfFile) {
    +          progress = 1.0f;
    +          numRowsOfBatch = 0;
    +          indexOfRow = 0;
    +          return false;
    +        } else {
    +          assert internalValue.numCols == numColumns : "Incorrect number 
of columns in OrcBatch";
    +          numRowsOfBatch = internalValue.count();
    +          indexOfRow = 0;
    +          progress = reader.getProgress();
    +        }
    +        return true;
    +      } else {
    +        return false;
    +      }
    +    } else {
    +      if (indexOfRow < numRowsOfBatch) {
    +        return true;
    +      } else {
    +        return false;
    +      }
    +    }
    +  }
    +
    +  /**
    +   * Adapter class to return an internal row.
    +   */
    +  public static final class Row extends InternalRow {
    +    protected int rowId;
    +    private List<Integer> columnIDs;
    +    private final ColumnVector[] columns;
    +
    +    private Row(ColumnVector[] columns, List<Integer> columnIDs) {
    +      this.columns = columns;
    +      this.columnIDs = columnIDs;
    +    }
    +
    +    @Override
    +    public int numFields() { return columnIDs.size(); }
    +
    +    @Override
    +    public boolean anyNull() {
    +      for (int i = 0; i < columns.length; i++) {
    +        if (columnIDs.contains(i)) {
    +          if (columns[i].isRepeating && columns[i].isNull[0]) {
    +            return true;
    +          } else if (!columns[i].isRepeating && columns[i].isNull[rowId]) {
    --- End diff --
    
    This if-else is double fetching `columns[i].isRepeating`. You could either 
save to a var OR add one more level of branching.


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