/* XXL: The eXtensible and fleXible Library for data processing
Copyright (C) 2000-2011 Prof. Dr. Bernhard Seeger
Head of the Database Research Group
Department of Mathematics and Computer Science
University of Marburg
Germany
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 3 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; If not, see <http://www.gnu.org/licenses/>.
http://code.google.com/p/xxl/
*/
package xxl.core.xxql;
import java.sql.ResultSetMetaData;
import java.sql.SQLException;
import java.util.ArrayList;
import xxl.core.cursors.Cursor;
import xxl.core.cursors.MetaDataCursor;
import xxl.core.functions.Function;
import xxl.core.functions.MetaDataFunction;
import xxl.core.math.functions.AggregationFunction;
import xxl.core.math.functions.MetaDataAggregationFunction;
import xxl.core.relational.cursors.Aggregator;
import xxl.core.relational.metaData.ColumnMetaData;
import xxl.core.relational.metaData.ColumnMetaDatas;
import xxl.core.relational.metaData.ResultSetMetaDatas;
import xxl.core.relational.tuples.Tuple;
import xxl.core.util.WrappingRuntimeException;
import xxl.core.util.metaData.CompositeMetaData;
import xxl.core.util.metaData.MetaDataException;
import xxl.core.xxql.columns.Column;
/**
* The aggregator computes aggregates like SUM, COUNT and VARIANCE for a
* grouped input metadata cursor, i.e., an input cursor delivering groups
* (cursors) of input elements. This aggregator is based on the implementation
* of {@link xxl.core.cursors.mappers.Aggregator}.
*
* <p>Some standard aggregates are defined in the class {@link Aggregator}, but
* new functionality can simply be included by using your own
* {@link MetaDataFunction}.</p>
*
* <p>Important: a group-aggregator executes no group- or sort-operations!
* Normally, a {@link SortBasedGrouper} or
* {@link xxl.core.cursors.groupers.SortBasedGrouper} is applied before the
* aggregator is used.</p>
*/
public class GroupAggregator extends xxl.core.cursors.mappers.Aggregator<Cursor<? extends Tuple>, Tuple> implements MetaDataCursor<Tuple, CompositeMetaData<Object, Object>> {
/**
* The metadata provided by the aggregator.
*/
protected CompositeMetaData<Object, Object> globalMetaData;
/**
* Creates a new aggregator.
*
* @param cursor the metadata cursor delivering the groups (cursors) of
* input elements.
* @param aggregates an array of metadata aggregates that each computes an
* aggregate on the input data.
* @param aggregateColumnNames the column names of the aggregate columns.
* This array must have the same length as the number of given
* aggregates. If an entry equals <code>null</code>, the name
* determined by the aggregate function is taken.
* @param projectedColumns the columns to which the input metadata cursor
* is projected.
* @param createOutputTuple a function that maps a list of objects (column
* values) and a to a new result Tuple.
* {@link xxl.core.relational.tuples.ArrayTuple#FACTORY_METHOD} can be used
* as a default factory method.
*/
public GroupAggregator(MetaDataCursor<? extends Cursor<? extends Tuple>,
CompositeMetaData<Object, Object>> cursor,
final MetaDataAggregationFunction<Tuple, Object, CompositeMetaData<Object, Object>>[] aggregates,
String[] aggregateColumnNames, final Column[] projectedColumns,
final Function<Object, ? extends Tuple> createOutputTuple)
{
super(
cursor,
new AggregationFunction<Cursor<? extends Tuple>, Tuple>() {
protected boolean[] initialized;
@Override
public Tuple invoke(Tuple aggregate, Cursor<? extends Tuple> cursor) {
initialized = new boolean[aggregates.length];
while (cursor.hasNext()) {
Tuple tuple = cursor.next();
ArrayList<Object> result = new ArrayList<Object>(projectedColumns.length + aggregates.length);
for (Column projectedColumn : projectedColumns)
result.add(projectedColumn.invoke(tuple));
for (int i = 0; i < aggregates.length; i++) {
Object agg = initialized[i] ? aggregate.getObject(projectedColumns.length + i + 1) : null;
result.add(
aggregates[i].invoke(
agg,
tuple
)
);
if (!initialized[i] && result.get(projectedColumns.length + i) != null)
initialized[i] = true;
}
aggregate = createOutputTuple.invoke(result);
}
return aggregate;
}
}
);
try {
if (aggregates.length != aggregateColumnNames.length)
throw new MetaDataException("the number of specified aggregate functions and the new column names does not match");
ResultSetMetaData resultSetMetaData = ResultSetMetaDatas.getResultSetMetaData(cursor);
ColumnMetaData[] columnMetaDatas = new ColumnMetaData[aggregates.length];
for (int i = 0; i < aggregates.length; i++) {
columnMetaDatas[i] = ColumnMetaDatas.getColumnMetaData(aggregates[i]);
if (aggregateColumnNames[i] == null)
aggregateColumnNames[i] = columnMetaDatas[i].getColumnName();
}
// globalMetaData = new CompositeMetaData<Object, Object>();
// globalMetaData.add(
// ResultSetMetaDatas.RESULTSET_METADATA_TYPE,
// new AppendedResultSetMetaData(
// new ProjectedResultSetMetaData(resultSetMetaData, projectedColumns),
// new RenamedResultSetMetaData(
// new ColumnMetaDataResultSetMetaData(
// columnMetaDatas
// ),
// aggregateColumnNames
// )
// )
// );
}
catch (SQLException e) {
throw new WrappingRuntimeException(e);
}
}
/**
* Creates a new aggregator. The column names of the aggregates are
* determined by the aggregate functions.
*
* @param cursor the metadata cursor delivering the groups (cursors) of
* input elements.
* @param aggregates an array of metadata aggregates that each computes an
* aggregate on the input data.
* @param projectedColumns the columns to which the input metadata cursor
* is projected.
* @param createOutputTuple a function that maps a list of objects (column
* values) and a to a new result Tuple.
* {@link xxl.core.relational.tuples.ArrayTuple#FACTORY_METHOD} can be used
* as a default factory method.
*/
public GroupAggregator(
MetaDataCursor<? extends Cursor<? extends Tuple>, CompositeMetaData<Object, Object>> cursor,
final MetaDataAggregationFunction<Tuple, Object, CompositeMetaData<Object, Object>>[] aggregates,
final Column[] projectedColumns,
final Function<Object, ? extends Tuple> createOutputTuple
) {
this(cursor, aggregates, new String[aggregates.length], projectedColumns, createOutputTuple);
}
/**
* Returns the metadata information for this metadata-cursor as a composite
* metadata ({@link CompositeMetaData}).
*
* @return the metadata information for this metadata-cursor as a composite
* metadata ({@link CompositeMetaData}).
*/
public CompositeMetaData<Object, Object> getMetaData() {
return globalMetaData;
}
}