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/*
* Licensed to Elasticsearch under one or more contributor
* license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright
* ownership. Elasticsearch 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.elasticsearch.search.aggregations.bucket.significant;
import org.elasticsearch.common.io.stream.StreamInput;
import org.elasticsearch.common.io.stream.StreamOutput;
import org.elasticsearch.common.xcontent.ToXContent;
import org.elasticsearch.search.DocValueFormat;
import org.elasticsearch.search.aggregations.Aggregations;
import org.elasticsearch.search.aggregations.InternalAggregation;
import org.elasticsearch.search.aggregations.InternalAggregations;
import org.elasticsearch.search.aggregations.InternalMultiBucketAggregation;
import org.elasticsearch.search.aggregations.bucket.significant.heuristics.SignificanceHeuristic;
import org.elasticsearch.search.aggregations.pipeline.PipelineAggregator;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import static java.util.Collections.unmodifiableList;
/**
* Result of the significant terms aggregation.
*/
public abstract class InternalSignificantTerms, B extends InternalSignificantTerms.Bucket>
extends InternalMultiBucketAggregation implements SignificantTerms, ToXContent {
@SuppressWarnings("PMD.ConstructorCallsOverridableMethod")
public abstract static class Bucket> extends SignificantTerms.Bucket {
/**
* Reads a bucket. Should be a constructor reference.
*/
@FunctionalInterface
public interface Reader> {
B read(StreamInput in, long subsetSize, long supersetSize, DocValueFormat format) throws IOException;
}
long bucketOrd;
protected InternalAggregations aggregations;
double score;
final transient DocValueFormat format;
protected Bucket(long subsetDf, long subsetSize, long supersetDf, long supersetSize,
InternalAggregations aggregations, DocValueFormat format) {
super(subsetDf, subsetSize, supersetDf, supersetSize);
this.aggregations = aggregations;
this.format = format;
}
/**
* Read from a stream.
*/
protected Bucket(StreamInput in, long subsetSize, long supersetSize, DocValueFormat format) {
super(in, subsetSize, supersetSize);
this.format = format;
}
@Override
public long getSubsetDf() {
return subsetDf;
}
@Override
public long getSupersetDf() {
return supersetDf;
}
@Override
public long getSupersetSize() {
return supersetSize;
}
@Override
public long getSubsetSize() {
return subsetSize;
}
public void updateScore(SignificanceHeuristic significanceHeuristic) {
score = significanceHeuristic.getScore(subsetDf, subsetSize, supersetDf, supersetSize);
}
@Override
public long getDocCount() {
return subsetDf;
}
@Override
public Aggregations getAggregations() {
return aggregations;
}
public B reduce(List buckets, ReduceContext context) {
long subsetDf = 0;
long supersetDf = 0;
List aggregationsList = new ArrayList<>(buckets.size());
for (B bucket : buckets) {
subsetDf += bucket.subsetDf;
supersetDf += bucket.supersetDf;
aggregationsList.add(bucket.aggregations);
}
InternalAggregations aggs = InternalAggregations.reduce(aggregationsList, context);
return newBucket(subsetDf, subsetSize, supersetDf, supersetSize, aggs);
}
abstract B newBucket(long subsetDf, long subsetSize, long supersetDf, long supersetSize, InternalAggregations aggregations);
@Override
public double getSignificanceScore() {
return score;
}
}
protected final int requiredSize;
protected final long minDocCount;
protected InternalSignificantTerms(String name, int requiredSize, long minDocCount, List pipelineAggregators,
Map metaData) {
super(name, pipelineAggregators, metaData);
this.requiredSize = requiredSize;
this.minDocCount = minDocCount;
}
/**
* Read from a stream.
*/
protected InternalSignificantTerms(StreamInput in) throws IOException {
super(in);
requiredSize = readSize(in);
minDocCount = in.readVLong();
}
protected final void doWriteTo(StreamOutput out) throws IOException {
writeSize(requiredSize, out);
out.writeVLong(minDocCount);
writeTermTypeInfoTo(out);
}
protected abstract void writeTermTypeInfoTo(StreamOutput out) throws IOException;
@Override
public Iterator iterator() {
return getBuckets().iterator();
}
@Override
public List getBuckets() {
return unmodifiableList(getBucketsInternal());
}
protected abstract List getBucketsInternal();
@Override
public InternalAggregation doReduce(List aggregations, ReduceContext reduceContext) {
long globalSubsetSize = 0;
long globalSupersetSize = 0;
// Compute the overall result set size and the corpus size using the
// top-level Aggregations from each shard
for (InternalAggregation aggregation : aggregations) {
@SuppressWarnings("unchecked")
InternalSignificantTerms terms = (InternalSignificantTerms) aggregation;
globalSubsetSize += terms.getSubsetSize();
globalSupersetSize += terms.getSupersetSize();
}
Map> buckets = new HashMap<>();
for (InternalAggregation aggregation : aggregations) {
@SuppressWarnings("unchecked")
InternalSignificantTerms terms = (InternalSignificantTerms) aggregation;
for (B bucket : terms.getBucketsInternal()) {
List existingBuckets = buckets.get(bucket.getKeyAsString());
if (existingBuckets == null) {
existingBuckets = new ArrayList<>(aggregations.size());
buckets.put(bucket.getKeyAsString(), existingBuckets);
}
// Adjust the buckets with the global stats representing the
// total size of the pots from which the stats are drawn
existingBuckets.add(bucket.newBucket(bucket.getSubsetDf(), globalSubsetSize, bucket.getSupersetDf(), globalSupersetSize,
bucket.aggregations));
}
}
SignificanceHeuristic heuristic = getSignificanceHeuristic().rewrite(reduceContext);
final int size = Math.min(requiredSize, buckets.size());
BucketSignificancePriorityQueue ordered = new BucketSignificancePriorityQueue<>(size);
for (Map.Entry> entry : buckets.entrySet()) {
List sameTermBuckets = entry.getValue();
final B b = sameTermBuckets.get(0).reduce(sameTermBuckets, reduceContext);
b.updateScore(heuristic);
if ((b.score > 0) && (b.subsetDf >= minDocCount)) {
ordered.insertWithOverflow(b);
}
}
B[] list = createBucketsArray(ordered.size());
for (int i = ordered.size() - 1; i >= 0; i--) {
list[i] = ordered.pop();
}
return create(globalSubsetSize, globalSupersetSize, Arrays.asList(list));
}
protected abstract A create(long subsetSize, long supersetSize, List buckets);
/**
* Create an array to hold some buckets. Used in collecting the results.
*/
protected abstract B[] createBucketsArray(int size);
protected abstract long getSubsetSize();
protected abstract long getSupersetSize();
protected abstract SignificanceHeuristic getSignificanceHeuristic();
}