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A module that is everything required to understands Druid Segments
/*
* 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.druid.segment;
import com.google.common.base.Preconditions;
import org.apache.druid.query.extraction.ExtractionFn;
import org.apache.druid.query.filter.DruidObjectPredicate;
import org.apache.druid.query.filter.DruidPredicateFactory;
import org.apache.druid.query.filter.ValueMatcher;
import org.apache.druid.query.monomorphicprocessing.RuntimeShapeInspector;
import org.apache.druid.segment.data.IndexedInts;
import org.apache.druid.segment.data.SingleIndexedInt;
import javax.annotation.Nullable;
import java.util.ArrayList;
import java.util.List;
import java.util.Objects;
/**
* A special {@link DimensionSelector} for projected time columns
* - it assumes time values are scanned once and values are grouped together
* (i.e. we never revisit a timestamp we have seen before, unless it is the same as the last accessed one)
* - it also applies and caches extraction function values at the {@link DimensionSelector} level to speed things up
*/
public class SingleScanTimeDimensionSelector implements DimensionSelector
{
private final ExtractionFn extractionFn;
private final BaseLongColumnValueSelector selector;
private final boolean descending;
private final List timeValues = new ArrayList<>();
private final SingleIndexedInt row = new SingleIndexedInt();
private long currentTimestamp = Long.MIN_VALUE;
private int index = -1;
@Nullable
private String currentValue = null;
public SingleScanTimeDimensionSelector(
BaseLongColumnValueSelector selector,
@Nullable ExtractionFn extractionFn,
boolean descending
)
{
Preconditions.checkNotNull(extractionFn, "time dimension must provide an extraction function");
this.extractionFn = extractionFn;
this.selector = selector;
this.descending = descending;
}
@Override
public IndexedInts getRow()
{
row.setValue(getDimensionValueIndex());
return row;
}
@Override
public ValueMatcher makeValueMatcher(final @Nullable String value)
{
return new ValueMatcher()
{
@Override
public boolean matches(boolean includeUnknown)
{
final String rowVal = lookupName(getDimensionValueIndex());
return (includeUnknown && rowVal == null) || Objects.equals(rowVal, value);
}
@Override
public void inspectRuntimeShape(RuntimeShapeInspector inspector)
{
inspector.visit("selector", SingleScanTimeDimensionSelector.this);
}
};
}
@Override
public ValueMatcher makeValueMatcher(final DruidPredicateFactory predicateFactory)
{
final DruidObjectPredicate predicate = predicateFactory.makeStringPredicate();
return new ValueMatcher()
{
@Override
public boolean matches(boolean includeUnknown)
{
return predicate.apply(lookupName(getDimensionValueIndex())).matches(includeUnknown);
}
@Override
public void inspectRuntimeShape(RuntimeShapeInspector inspector)
{
inspector.visit("selector", SingleScanTimeDimensionSelector.this);
inspector.visit("predicate", predicateFactory);
}
};
}
private int getDimensionValueIndex()
{
// if this the first timestamp, apply and cache extraction function result
final long timestamp = selector.getLong();
if (index < 0) {
currentTimestamp = timestamp;
currentValue = extractionFn.apply(timestamp);
++index;
timeValues.add(currentValue);
// if this is a new timestamp, apply and cache extraction function result
// since timestamps are assumed grouped and scanned once, we only need to
// check if the current timestamp is different than the current timestamp.
//
// If this new timestamp is mapped to the same value by the extraction function,
// we can also avoid creating a dimension value and corresponding index
// and use the current one
} else if (timestamp != currentTimestamp) {
if (descending ? timestamp > currentTimestamp : timestamp < currentTimestamp) {
// re-using this selector for multiple scans would cause the same rows to return different IDs
// we might want to re-visit if we ever need to do multiple scans with this dimension selector
throw new IllegalStateException("cannot re-use time dimension selector for multiple scans");
}
currentTimestamp = timestamp;
final String value = extractionFn.apply(timestamp);
if (!Objects.equals(value, currentValue)) {
currentValue = value;
++index;
timeValues.add(currentValue);
}
// Note: this could be further optimized by checking if the new value is one we have
// previously seen, but would require keeping track of both the current and the maximum index
}
// otherwise, if the current timestamp is the same as the previous timestamp,
// keep using the same dimension value index
return index;
}
@Override
public int getValueCardinality()
{
return Integer.MAX_VALUE;
}
@Override
public String lookupName(int id)
{
if (id == index) {
return currentValue;
} else {
return timeValues.get(id);
}
}
@Override
public boolean nameLookupPossibleInAdvance()
{
return false;
}
@Nullable
@Override
public IdLookup idLookup()
{
return null;
}
@Nullable
@Override
public String getObject()
{
return currentValue;
}
@Override
public Class classOfObject()
{
return String.class;
}
@Override
public void inspectRuntimeShape(RuntimeShapeInspector inspector)
{
inspector.visit("selector", selector);
inspector.visit("extractionFn", extractionFn);
inspector.visit("descending", descending);
}
}