org.elasticsearch.search.aggregations.metrics.ExtendedStatsAggregator Maven / Gradle / Ivy
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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.metrics;
import org.apache.lucene.index.LeafReaderContext;
import org.apache.lucene.search.ScoreMode;
import org.elasticsearch.common.ParseField;
import org.elasticsearch.common.lease.Releasables;
import org.elasticsearch.common.util.BigArrays;
import org.elasticsearch.common.util.DoubleArray;
import org.elasticsearch.common.util.LongArray;
import org.elasticsearch.index.fielddata.SortedNumericDoubleValues;
import org.elasticsearch.search.DocValueFormat;
import org.elasticsearch.search.aggregations.Aggregator;
import org.elasticsearch.search.aggregations.InternalAggregation;
import org.elasticsearch.search.aggregations.LeafBucketCollector;
import org.elasticsearch.search.aggregations.LeafBucketCollectorBase;
import org.elasticsearch.search.aggregations.pipeline.PipelineAggregator;
import org.elasticsearch.search.aggregations.support.ValuesSource;
import org.elasticsearch.search.internal.SearchContext;
import java.io.IOException;
import java.util.List;
import java.util.Map;
class ExtendedStatsAggregator extends NumericMetricsAggregator.MultiValue {
static final ParseField SIGMA_FIELD = new ParseField("sigma");
final ValuesSource.Numeric valuesSource;
final DocValueFormat format;
final double sigma;
LongArray counts;
DoubleArray sums;
DoubleArray compensations;
DoubleArray mins;
DoubleArray maxes;
DoubleArray sumOfSqrs;
DoubleArray compensationOfSqrs;
ExtendedStatsAggregator(String name, ValuesSource.Numeric valuesSource, DocValueFormat formatter,
SearchContext context, Aggregator parent, double sigma, List pipelineAggregators,
Map metaData)
throws IOException {
super(name, context, parent, pipelineAggregators, metaData);
this.valuesSource = valuesSource;
this.format = formatter;
this.sigma = sigma;
if (valuesSource != null) {
final BigArrays bigArrays = context.bigArrays();
counts = bigArrays.newLongArray(1, true);
sums = bigArrays.newDoubleArray(1, true);
compensations = bigArrays.newDoubleArray(1, true);
mins = bigArrays.newDoubleArray(1, false);
mins.fill(0, mins.size(), Double.POSITIVE_INFINITY);
maxes = bigArrays.newDoubleArray(1, false);
maxes.fill(0, maxes.size(), Double.NEGATIVE_INFINITY);
sumOfSqrs = bigArrays.newDoubleArray(1, true);
compensationOfSqrs = bigArrays.newDoubleArray(1, true);
}
}
@Override
public ScoreMode scoreMode() {
return valuesSource != null && valuesSource.needsScores() ? ScoreMode.COMPLETE : ScoreMode.COMPLETE_NO_SCORES;
}
@Override
public LeafBucketCollector getLeafCollector(LeafReaderContext ctx,
final LeafBucketCollector sub) throws IOException {
if (valuesSource == null) {
return LeafBucketCollector.NO_OP_COLLECTOR;
}
final BigArrays bigArrays = context.bigArrays();
final SortedNumericDoubleValues values = valuesSource.doubleValues(ctx);
final CompensatedSum compensatedSum = new CompensatedSum(0, 0);
final CompensatedSum compensatedSumOfSqr = new CompensatedSum(0, 0);
return new LeafBucketCollectorBase(sub, values) {
@Override
public void collect(int doc, long bucket) throws IOException {
if (bucket >= counts.size()) {
final long from = counts.size();
final long overSize = BigArrays.overSize(bucket + 1);
counts = bigArrays.resize(counts, overSize);
sums = bigArrays.resize(sums, overSize);
compensations = bigArrays.resize(compensations, overSize);
mins = bigArrays.resize(mins, overSize);
maxes = bigArrays.resize(maxes, overSize);
sumOfSqrs = bigArrays.resize(sumOfSqrs, overSize);
compensationOfSqrs = bigArrays.resize(compensationOfSqrs, overSize);
mins.fill(from, overSize, Double.POSITIVE_INFINITY);
maxes.fill(from, overSize, Double.NEGATIVE_INFINITY);
}
if (values.advanceExact(doc)) {
final int valuesCount = values.docValueCount();
counts.increment(bucket, valuesCount);
double min = mins.get(bucket);
double max = maxes.get(bucket);
// Compute the sum and sum of squires for double values with Kahan summation algorithm
// which is more accurate than naive summation.
double sum = sums.get(bucket);
double compensation = compensations.get(bucket);
compensatedSum.reset(sum, compensation);
double sumOfSqr = sumOfSqrs.get(bucket);
double compensationOfSqr = compensationOfSqrs.get(bucket);
compensatedSumOfSqr.reset(sumOfSqr, compensationOfSqr);
for (int i = 0; i < valuesCount; i++) {
double value = values.nextValue();
compensatedSum.add(value);
compensatedSumOfSqr.add(value * value);
min = Math.min(min, value);
max = Math.max(max, value);
}
sums.set(bucket, compensatedSum.value());
compensations.set(bucket, compensatedSum.delta());
sumOfSqrs.set(bucket, compensatedSumOfSqr.value());
compensationOfSqrs.set(bucket, compensatedSumOfSqr.delta());
mins.set(bucket, min);
maxes.set(bucket, max);
}
}
};
}
@Override
public boolean hasMetric(String name) {
try {
InternalExtendedStats.Metrics.resolve(name);
return true;
} catch (IllegalArgumentException iae) {
return false;
}
}
@Override
public double metric(String name, long owningBucketOrd) {
if (valuesSource == null || owningBucketOrd >= counts.size()) {
switch(InternalExtendedStats.Metrics.resolve(name)) {
case count: return 0;
case sum: return 0;
case min: return Double.POSITIVE_INFINITY;
case max: return Double.NEGATIVE_INFINITY;
case avg: return Double.NaN;
case sum_of_squares: return 0;
case variance: return Double.NaN;
case std_deviation: return Double.NaN;
case std_upper: return Double.NaN;
case std_lower: return Double.NaN;
default:
throw new IllegalArgumentException("Unknown value [" + name + "] in common stats aggregation");
}
}
switch(InternalExtendedStats.Metrics.resolve(name)) {
case count: return counts.get(owningBucketOrd);
case sum: return sums.get(owningBucketOrd);
case min: return mins.get(owningBucketOrd);
case max: return maxes.get(owningBucketOrd);
case avg: return sums.get(owningBucketOrd) / counts.get(owningBucketOrd);
case sum_of_squares: return sumOfSqrs.get(owningBucketOrd);
case variance: return variance(owningBucketOrd);
case std_deviation: return Math.sqrt(variance(owningBucketOrd));
case std_upper:
return (sums.get(owningBucketOrd) / counts.get(owningBucketOrd)) + (Math.sqrt(variance(owningBucketOrd)) * this.sigma);
case std_lower:
return (sums.get(owningBucketOrd) / counts.get(owningBucketOrd)) - (Math.sqrt(variance(owningBucketOrd)) * this.sigma);
default:
throw new IllegalArgumentException("Unknown value [" + name + "] in common stats aggregation");
}
}
private double variance(long owningBucketOrd) {
double sum = sums.get(owningBucketOrd);
long count = counts.get(owningBucketOrd);
double variance = (sumOfSqrs.get(owningBucketOrd) - ((sum * sum) / count)) / count;
return variance < 0 ? 0 : variance;
}
@Override
public InternalAggregation buildAggregation(long bucket) {
if (valuesSource == null || bucket >= counts.size()) {
return buildEmptyAggregation();
}
return new InternalExtendedStats(name, counts.get(bucket), sums.get(bucket),
mins.get(bucket), maxes.get(bucket), sumOfSqrs.get(bucket), sigma, format,
pipelineAggregators(), metaData());
}
@Override
public InternalAggregation buildEmptyAggregation() {
return new InternalExtendedStats(name, 0, 0d, Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, 0d,
sigma, format, pipelineAggregators(), metaData());
}
@Override
public void doClose() {
Releasables.close(counts, maxes, mins, sumOfSqrs, compensationOfSqrs, sums, compensations);
}
}