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* 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
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package org.elasticsearch.search.aggregations.metrics;
import org.apache.lucene.document.Document;
import org.apache.lucene.document.SortedNumericDocValuesField;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.RandomIndexWriter;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.MatchAllDocsQuery;
import org.apache.lucene.store.Directory;
import org.apache.lucene.util.NumericUtils;
import org.elasticsearch.common.CheckedConsumer;
import org.elasticsearch.index.mapper.MappedFieldType;
import org.elasticsearch.index.mapper.NumberFieldMapper;
import org.elasticsearch.search.aggregations.AggregatorTestCase;
import org.elasticsearch.search.aggregations.metrics.stats.extended.ExtendedStats;
import org.elasticsearch.search.aggregations.metrics.stats.extended.ExtendedStatsAggregationBuilder;
import org.elasticsearch.search.aggregations.metrics.stats.extended.InternalExtendedStats;
import java.io.IOException;
import java.util.function.Consumer;
public class ExtendedStatsAggregatorTests extends AggregatorTestCase {
private static final double TOLERANCE = 1e-5;
public void testEmpty() throws IOException {
MappedFieldType ft =
new NumberFieldMapper.NumberFieldType(NumberFieldMapper.NumberType.LONG);
ft.setName("field");
testCase(ft, iw -> {},
stats -> {
assertEquals(0d, stats.getCount(), 0);
assertEquals(0d, stats.getSum(), 0);
assertEquals(Float.NaN, stats.getAvg(), 0);
assertEquals(Double.POSITIVE_INFINITY, stats.getMin(), 0);
assertEquals(Double.NEGATIVE_INFINITY, stats.getMax(), 0);
assertEquals(Double.NaN, stats.getVariance(), 0);
assertEquals(Double.NaN, stats.getStdDeviation(), 0);
assertEquals(0d, stats.getSumOfSquares(), 0);
}
);
}
public void testRandomDoubles() throws IOException {
MappedFieldType ft =
new NumberFieldMapper.NumberFieldType(NumberFieldMapper.NumberType.DOUBLE);
ft.setName("field");
final ExtendedSimpleStatsAggregator expected = new ExtendedSimpleStatsAggregator();
testCase(ft,
iw -> {
int numDocs = randomIntBetween(10, 50);
for (int i = 0; i < numDocs; i++) {
Document doc = new Document();
int numValues = randomIntBetween(1, 5);
for (int j = 0; j < numValues; j++) {
double value = randomDoubleBetween(-100d, 100d, true);
long valueAsLong = NumericUtils.doubleToSortableLong(value);
doc.add(new SortedNumericDocValuesField("field", valueAsLong));
expected.add(value);
}
iw.addDocument(doc);
}
},
stats -> {
assertEquals(expected.count, stats.getCount(), 0);
assertEquals(expected.sum, stats.getSum(), TOLERANCE);
assertEquals(expected.min, stats.getMin(), 0);
assertEquals(expected.max, stats.getMax(), 0);
assertEquals(expected.sum / expected.count, stats.getAvg(), TOLERANCE);
assertEquals(expected.sumOfSqrs, stats.getSumOfSquares(), TOLERANCE);
assertEquals(expected.stdDev(), stats.getStdDeviation(), TOLERANCE);
assertEquals(expected.variance(), stats.getVariance(), TOLERANCE);
assertEquals(expected.stdDevBound(ExtendedStats.Bounds.LOWER, stats.getSigma()),
stats.getStdDeviationBound(ExtendedStats.Bounds.LOWER), TOLERANCE);
assertEquals(expected.stdDevBound(ExtendedStats.Bounds.UPPER, stats.getSigma()),
stats.getStdDeviationBound(ExtendedStats.Bounds.UPPER), TOLERANCE);
}
);
}
public void testRandomLongs() throws IOException {
MappedFieldType ft =
new NumberFieldMapper.NumberFieldType(NumberFieldMapper.NumberType.LONG);
ft.setName("field");
final ExtendedSimpleStatsAggregator expected = new ExtendedSimpleStatsAggregator();
testCase(ft,
iw -> {
int numDocs = randomIntBetween(10, 50);
for (int i = 0; i < numDocs; i++) {
Document doc = new Document();
int numValues = randomIntBetween(1, 5);
for (int j = 0; j < numValues; j++) {
long value = randomIntBetween(-100, 100);
doc.add(new SortedNumericDocValuesField("field", value));
expected.add(value);
}
iw.addDocument(doc);
}
},
stats -> {
assertEquals(expected.count, stats.getCount(), 0);
assertEquals(expected.sum, stats.getSum(), TOLERANCE);
assertEquals(expected.min, stats.getMin(), 0);
assertEquals(expected.max, stats.getMax(), 0);
assertEquals(expected.sum / expected.count, stats.getAvg(), TOLERANCE);
assertEquals(expected.sumOfSqrs, stats.getSumOfSquares(), TOLERANCE);
assertEquals(expected.stdDev(), stats.getStdDeviation(), TOLERANCE);
assertEquals(expected.variance(), stats.getVariance(), TOLERANCE);
assertEquals(expected.stdDevBound(ExtendedStats.Bounds.LOWER, stats.getSigma()),
stats.getStdDeviationBound(ExtendedStats.Bounds.LOWER), TOLERANCE);
assertEquals(expected.stdDevBound(ExtendedStats.Bounds.UPPER, stats.getSigma()),
stats.getStdDeviationBound(ExtendedStats.Bounds.UPPER), TOLERANCE);
}
);
}
public void testCase(MappedFieldType ft,
CheckedConsumer<RandomIndexWriter, IOException> buildIndex,
Consumer<InternalExtendedStats> verify) throws IOException {
try (Directory directory = newDirectory();
RandomIndexWriter indexWriter = new RandomIndexWriter(random(), directory)) {
buildIndex.accept(indexWriter);
try (IndexReader reader = indexWriter.getReader()) {
IndexSearcher searcher = new IndexSearcher(reader);
ExtendedStatsAggregationBuilder aggBuilder = new ExtendedStatsAggregationBuilder("my_agg")
.field("field")
.sigma(randomDoubleBetween(0, 10, true));
InternalExtendedStats stats = search(searcher, new MatchAllDocsQuery(), aggBuilder, ft);
verify.accept(stats);
}
}
}
static class ExtendedSimpleStatsAggregator extends StatsAggregatorTests.SimpleStatsAggregator {
double sumOfSqrs = 0;
void add(double value) {
super.add(value);
sumOfSqrs += (value * value);
}
double stdDev() {
return Math.sqrt(variance());
}
double stdDevBound(ExtendedStats.Bounds bounds, double sigma) {
if (bounds == ExtendedStats.Bounds.UPPER) {
return (sum / count) + (Math.sqrt(variance()) * sigma);
} else {
return (sum / count) - (Math.sqrt(variance()) * sigma);
}
}
double variance() {
return (sumOfSqrs - ((sum * sum) / count)) / count;
}
}
}