org.opensearch.search.aggregations.metrics.ExtendedStatsAggregator Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of opensearch Show documentation
Show all versions of opensearch Show documentation
OpenSearch subproject :server
/*
* SPDX-License-Identifier: Apache-2.0
*
* The OpenSearch Contributors require contributions made to
* this file be licensed under the Apache-2.0 license or a
* compatible open source license.
*/
/*
* 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.
*/
/*
* Modifications Copyright OpenSearch Contributors. See
* GitHub history for details.
*/
package org.opensearch.search.aggregations.metrics;
import org.apache.lucene.index.LeafReaderContext;
import org.apache.lucene.search.ScoreMode;
import org.opensearch.common.lease.Releasables;
import org.opensearch.common.util.BigArrays;
import org.opensearch.common.util.DoubleArray;
import org.opensearch.common.util.LongArray;
import org.opensearch.core.ParseField;
import org.opensearch.index.fielddata.SortedNumericDoubleValues;
import org.opensearch.search.DocValueFormat;
import org.opensearch.search.aggregations.Aggregator;
import org.opensearch.search.aggregations.InternalAggregation;
import org.opensearch.search.aggregations.LeafBucketCollector;
import org.opensearch.search.aggregations.LeafBucketCollectorBase;
import org.opensearch.search.aggregations.support.ValuesSource;
import org.opensearch.search.aggregations.support.ValuesSourceConfig;
import org.opensearch.search.internal.SearchContext;
import java.io.IOException;
import java.util.Map;
/**
* Aggregate all docs into their extended statistics
*
* @opensearch.internal
*/
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,
ValuesSourceConfig valuesSourceConfig,
SearchContext context,
Aggregator parent,
double sigma,
Map metadata
) throws IOException {
super(name, context, parent, metadata);
// TODO: stop depending on nulls here
this.valuesSource = valuesSourceConfig.hasValues() ? (ValuesSource.Numeric) valuesSourceConfig.getValuesSource() : null;
this.format = valuesSourceConfig.format();
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 variance_population:
return Double.NaN;
case variance_sampling:
return Double.NaN;
case std_deviation:
return Double.NaN;
case std_deviation_population:
return Double.NaN;
case std_deviation_sampling:
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 variance_population:
return variancePopulation(owningBucketOrd);
case variance_sampling:
return varianceSampling(owningBucketOrd);
case std_deviation:
return Math.sqrt(variance(owningBucketOrd));
case std_deviation_population:
return Math.sqrt(variance(owningBucketOrd));
case std_deviation_sampling:
return Math.sqrt(varianceSampling(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) {
return variancePopulation(owningBucketOrd);
}
private double variancePopulation(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;
}
private double varianceSampling(long owningBucketOrd) {
double sum = sums.get(owningBucketOrd);
long count = counts.get(owningBucketOrd);
double variance = (sumOfSqrs.get(owningBucketOrd) - ((sum * sum) / count)) / (count - 1);
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,
metadata()
);
}
@Override
public InternalAggregation buildEmptyAggregation() {
return new InternalExtendedStats(name, 0, 0d, Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, 0d, sigma, format, metadata());
}
@Override
public void doClose() {
Releasables.close(counts, maxes, mins, sumOfSqrs, compensationOfSqrs, sums, compensations);
}
}