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From bitblender <...@git.apache.org>
Subject [GitHub] drill pull request #866: DRILL-5657: Implement size-aware result set loader
Date Wed, 09 Aug 2017 00:03:18 GMT
Github user bitblender commented on a diff in the pull request:

    https://github.com/apache/drill/pull/866#discussion_r131284158
  
    --- Diff: exec/java-exec/src/main/java/org/apache/drill/exec/physical/rowSet/impl/LogicalTupleLoader.java
---
    @@ -0,0 +1,204 @@
    +/*
    + * 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.drill.exec.physical.rowSet.impl;
    +
    +import java.util.ArrayList;
    +import java.util.Collection;
    +import java.util.HashSet;
    +import java.util.List;
    +import java.util.Set;
    +
    +import org.apache.drill.exec.physical.rowSet.ColumnLoader;
    +import org.apache.drill.exec.physical.rowSet.TupleLoader;
    +import org.apache.drill.exec.physical.rowSet.TupleSchema;
    +import org.apache.drill.exec.physical.rowSet.TupleSchema.TupleColumnSchema;
    +import org.apache.drill.exec.record.BatchSchema;
    +import org.apache.drill.exec.record.BatchSchema.SelectionVectorMode;
    +import org.apache.drill.exec.record.MaterializedField;
    +
    +/**
    + * Shim inserted between an actual tuple loader and the client to remove columns
    + * that are not projected from input to output. The underlying loader handles only
    + * the projected columns in order to improve efficiency. This class presents the
    + * full table schema, but returns null for the non-projected columns. This allows
    + * the reader to work with the table schema as defined by the data source, but
    + * skip those columns which are not projected. Skipping non-projected columns avoids
    + * creating value vectors which are immediately discarded. It may also save the reader
    + * from reading unwanted data.
    + */
    +public class LogicalTupleLoader implements TupleLoader {
    +
    +  public static final int UNMAPPED = -1;
    +
    +  private static class MappedColumn implements TupleColumnSchema {
    +
    +    private final MaterializedField schema;
    +    private final int mapping;
    +
    +    public MappedColumn(MaterializedField schema, int mapping) {
    +      this.schema = schema;
    +      this.mapping = mapping;
    +    }
    +
    +    @Override
    +    public MaterializedField schema() { return schema; }
    +
    +    @Override
    +    public boolean isSelected() { return mapping != UNMAPPED; }
    +
    +    @Override
    +    public int vectorIndex() { return mapping; }
    +  }
    +
    +  /**
    +   * Implementation of the tuple schema that describes the full data source
    +   * schema. The underlying loader schema is a subset of these columns. Note
    +   * that the columns appear in the same order in both schemas, but the loader
    +   * schema is a subset of the table schema.
    +   */
    +
    +  private class LogicalTupleSchema implements TupleSchema {
    +
    +    private final Set<String> selection = new HashSet<>();
    +    private final TupleSchema physicalSchema;
    +
    +    private LogicalTupleSchema(TupleSchema physicalSchema, Collection<String> selection)
{
    +      this.physicalSchema = physicalSchema;
    +      this.selection.addAll(selection);
    +    }
    +
    +    @Override
    +    public int columnCount() { return logicalSchema.count(); }
    +
    +    @Override
    +    public int columnIndex(String colName) {
    +      return logicalSchema.indexOf(rsLoader.toKey(colName));
    +    }
    +
    +    @Override
    +    public TupleColumnSchema metadata(int colIndex) { return logicalSchema.get(colIndex);
}
    +
    +    @Override
    +    public MaterializedField column(int colIndex) { return logicalSchema.get(colIndex).schema();
}
    +
    +    @Override
    +    public TupleColumnSchema metadata(String colName) { return logicalSchema.get(colName);
}
    +
    +    @Override
    +    public MaterializedField column(String colName) { return logicalSchema.get(colName).schema();
}
    +
    +    @Override
    +    public int addColumn(MaterializedField columnSchema) {
    +      String key = rsLoader.toKey(columnSchema.getName());
    +      int pIndex;
    +      if (selection.contains(key)) {
    --- End diff --
    
    selection is already normalized if caseSensitive is false ?


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