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 * the Apache License, Version 2.0 (the "License"); you may
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 * You may obtain a copy of the License at
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 *    http://www.apache.org/licenses/LICENSE-2.0
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package org.elasticsearch.search.aggregations.bucket.significant;

import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.LeafReaderContext;
import org.elasticsearch.common.lease.Releasables;
import org.elasticsearch.search.DocValueFormat;
import org.elasticsearch.search.aggregations.Aggregator;
import org.elasticsearch.search.aggregations.AggregatorFactories;
import org.elasticsearch.search.aggregations.LeafBucketCollector;
import org.elasticsearch.search.aggregations.LeafBucketCollectorBase;
import org.elasticsearch.search.aggregations.bucket.significant.heuristics.SignificanceHeuristic;
import org.elasticsearch.search.aggregations.bucket.terms.IncludeExclude;
import org.elasticsearch.search.aggregations.bucket.terms.LongTermsAggregator;
import org.elasticsearch.search.aggregations.pipeline.PipelineAggregator;
import org.elasticsearch.search.aggregations.support.ValuesSource;
import org.elasticsearch.search.internal.ContextIndexSearcher;
import org.elasticsearch.search.internal.SearchContext;

import java.io.IOException;
import java.util.Arrays;
import java.util.List;
import java.util.Map;

import static java.util.Collections.emptyList;

public class SignificantLongTermsAggregator extends LongTermsAggregator {

    public SignificantLongTermsAggregator(String name, AggregatorFactories factories, ValuesSource.Numeric valuesSource,
            DocValueFormat format, BucketCountThresholds bucketCountThresholds, SearchContext context, Aggregator parent,
            SignificanceHeuristic significanceHeuristic, SignificantTermsAggregatorFactory termsAggFactory,
            IncludeExclude.LongFilter includeExclude,
            List pipelineAggregators, Map metaData) throws IOException {

        super(name, factories, valuesSource, format, null, bucketCountThresholds, context, parent,
                SubAggCollectionMode.BREADTH_FIRST, false, includeExclude, pipelineAggregators, metaData);
        this.significanceHeuristic = significanceHeuristic;
        this.termsAggFactory = termsAggFactory;
    }

    protected long numCollectedDocs;
    private final SignificantTermsAggregatorFactory termsAggFactory;
    private final SignificanceHeuristic significanceHeuristic;

    @Override
    public LeafBucketCollector getLeafCollector(LeafReaderContext ctx,
            final LeafBucketCollector sub) throws IOException {
        return new LeafBucketCollectorBase(super.getLeafCollector(ctx, sub), null) {
            @Override
            public void collect(int doc, long bucket) throws IOException {
                super.collect(doc, bucket);
                numCollectedDocs++;
            }
        };
    }

    @Override
    public SignificantLongTerms buildAggregation(long owningBucketOrdinal) throws IOException {
        assert owningBucketOrdinal == 0;

        final int size = (int) Math.min(bucketOrds.size(), bucketCountThresholds.getShardSize());

        long supersetSize = termsAggFactory.getSupersetNumDocs();
        long subsetSize = numCollectedDocs;

        BucketSignificancePriorityQueue ordered = new BucketSignificancePriorityQueue<>(size);
        SignificantLongTerms.Bucket spare = null;
        for (long i = 0; i < bucketOrds.size(); i++) {
            final int docCount = bucketDocCount(i);
            if (docCount < bucketCountThresholds.getShardMinDocCount()) {
                continue;
            }
            if (spare == null) {
                spare = new SignificantLongTerms.Bucket(0, 0, 0, 0, 0, null, format, 0);
            }
            spare.term = bucketOrds.get(i);
            spare.subsetDf = docCount;
            spare.subsetSize = subsetSize;
            spare.supersetDf = termsAggFactory.getBackgroundFrequency(spare.term);
            spare.supersetSize = supersetSize;
            // During shard-local down-selection we use subset/superset stats that are for this shard only
            // Back at the central reducer these properties will be updated with global stats
            spare.updateScore(significanceHeuristic);

            spare.bucketOrd = i;
            spare = ordered.insertWithOverflow(spare);
            if (spare == null) {
                consumeBucketsAndMaybeBreak(1);
            }
        }

        SignificantLongTerms.Bucket[] list = new SignificantLongTerms.Bucket[ordered.size()];
        final long[] survivingBucketOrds = new long[ordered.size()];
        for (int i = ordered.size() - 1; i >= 0; i--) {
            final SignificantLongTerms.Bucket bucket = ordered.pop();
            survivingBucketOrds[i] = bucket.bucketOrd;
            list[i] = bucket;
        }

        runDeferredCollections(survivingBucketOrds);

        for (SignificantLongTerms.Bucket bucket : list) {
            bucket.aggregations = bucketAggregations(bucket.bucketOrd);
        }

        return new SignificantLongTerms(name, bucketCountThresholds.getRequiredSize(), bucketCountThresholds.getMinDocCount(),
                pipelineAggregators(), metaData(), format, subsetSize, supersetSize, significanceHeuristic, Arrays.asList(list));
    }

    @Override
    public SignificantLongTerms buildEmptyAggregation() {
        // We need to account for the significance of a miss in our global stats - provide corpus size as context
        ContextIndexSearcher searcher = context.searcher();
        IndexReader topReader = searcher.getIndexReader();
        int supersetSize = topReader.numDocs();
        return new SignificantLongTerms(name, bucketCountThresholds.getRequiredSize(), bucketCountThresholds.getMinDocCount(),
                pipelineAggregators(), metaData(), format, 0, supersetSize, significanceHeuristic, emptyList());
    }

    @Override
    public void doClose() {
        Releasables.close(bucketOrds, termsAggFactory);
    }

}




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