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Merged
merged 8 commits into from
Jan 22, 2018
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Calculate sum in Kahan summation algorithm in aggregations (#27807) #27848

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30f7228
Calculate sum in Kahan summation algorithm in aggregations (#27807)
liketic Dec 16, 2017
6c45e08
Compute summation with Kahan summation algorithm for internal aggrega…
liketic Dec 19, 2017
95aff79
Merge remote-tracking branch 'upstream/master' into feature/issues/27807
liketic Dec 31, 2017
15cdec2
Properly calculate sum of NaN and infinities
liketic Jan 1, 2018
1f07cca
Simplify summing up algorithm for aggregations
liketic Jan 3, 2018
594f750
Merge remote-tracking branch 'upstream/master' into feature/issues/27807
liketic Jan 15, 2018
b2ce7cb
Merge branch 'master' of https://github.com/elastic/elasticsearch int…
liketic Jan 16, 2018
176be1f
Add test case for summing up NaN and infinities
liketic Jan 16, 2018
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26 changes: 22 additions & 4 deletions core/src/main/java/org/elasticsearch/search/aggregations/metrics/avg/AvgAggregator.java
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,7 @@ public class AvgAggregator extends NumericMetricsAggregator.SingleValue {

LongArray counts;
DoubleArray sums;
DoubleArray compensations;
DocValueFormat format;

public AvgAggregator(String name, ValuesSource.Numeric valuesSource, DocValueFormat formatter, SearchContext context,
Expand All @@ -55,6 +56,7 @@ public AvgAggregator(String name, ValuesSource.Numeric valuesSource, DocValueFor
final BigArrays bigArrays = context.bigArrays();
counts = bigArrays.newLongArray(1, true);
sums = bigArrays.newDoubleArray(1, true);
compensations = bigArrays.newDoubleArray(1, true);
}
}

Expand All @@ -76,15 +78,31 @@ public LeafBucketCollector getLeafCollector(LeafReaderContext ctx,
public void collect(int doc, long bucket) throws IOException {
counts = bigArrays.grow(counts, bucket + 1);
sums = bigArrays.grow(sums, bucket + 1);
compensations = bigArrays.grow(compensations, bucket + 1);

if (values.advanceExact(doc)) {
final int valueCount = values.docValueCount();
counts.increment(bucket, valueCount);
double sum = 0;
// Compute the sum of double values with Kahan summation algorithm which is more
// accurate than naive summation.
double sum = sums.get(bucket);
double compensation = compensations.get(bucket);

for (int i = 0; i < valueCount; i++) {
sum += values.nextValue();
double value = values.nextValue();
if (Double.isNaN(value) || Double.isInfinite(value)) {
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@jpountz jpountz Jan 2, 2018

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We could test both at once by doing Double.isFinite(value) == false?

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@liketic liketic Jan 3, 2018

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Yes, it's much better.

sum += value;
if (Double.isNaN(sum))
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@jpountz jpountz Jan 2, 2018

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I would not try to break once the sum is NaN, this doesn't bring much imo

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@liketic liketic Jan 3, 2018

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Agreed.

break;
} else if (Double.isFinite(sum)) {
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@jpountz jpountz Jan 2, 2018

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this is always true I think? So we could make it an else block?

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@liketic liketic Jan 3, 2018

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The check statement is needed. For example when summing up [Double.POSITIVE_INFINITY, 1, 2, 3], if no this check, we'll got NaN, and we expect Double.POSITIVE_INFINITY here.

double corrected = value - compensation;
double newSum = sum + corrected;
compensation = (newSum - sum) - corrected;
sum = newSum;
}
}
sums.increment(bucket, sum);
sums.set(bucket, sum);
compensations.set(bucket, compensation);
}
}
};
Expand Down Expand Up @@ -113,7 +131,7 @@ public InternalAggregation buildEmptyAggregation() {

@Override
public void doClose() {
Releasables.close(counts, sums);
Releasables.close(counts, sums, compensations);
}

}
17 changes: 15 additions & 2 deletions core/src/main/java/org/elasticsearch/search/aggregations/metrics/avg/InternalAvg.java
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Original file line number Diff line number Diff line change
Expand Up @@ -91,9 +91,22 @@ public String getWriteableName() {
public InternalAvg doReduce(List<InternalAggregation> aggregations, ReduceContext reduceContext) {
long count = 0;
double sum = 0;
double compensation = 0;
// Compute the sum of double values with Kahan summation algorithm which is more
// accurate than naive summation.
for (InternalAggregation aggregation : aggregations) {
count += ((InternalAvg) aggregation).count;
sum += ((InternalAvg) aggregation).sum;
InternalAvg avg = (InternalAvg) aggregation;
count += avg.count;
if (Double.isNaN(sum) == false) {
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@jpountz jpountz Jan 2, 2018

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I would remove this if statement to keep things simple?

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@liketic liketic Jan 3, 2018

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Make sense.

if (Double.isNaN(avg.sum) || Double.isInfinite(avg.sum)) {
sum += avg.sum;
} else if (Double.isFinite(sum)) {
double corrected = avg.sum - compensation;
double newSum = sum + corrected;
compensation = (newSum - sum) - corrected;
sum = newSum;
}
}
}
return new InternalAvg(getName(), sum, count, format, pipelineAggregators(), getMetaData());
}
Expand Down
15 changes: 14 additions & 1 deletion core/src/main/java/org/elasticsearch/search/aggregations/metrics/stats/InternalStats.java
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Original file line number Diff line number Diff line change
Expand Up @@ -152,12 +152,25 @@ public InternalStats doReduce(List<InternalAggregation> aggregations, ReduceCont
double min = Double.POSITIVE_INFINITY;
double max = Double.NEGATIVE_INFINITY;
double sum = 0;
double compensation = 0;
for (InternalAggregation aggregation : aggregations) {
InternalStats stats = (InternalStats) aggregation;
count += stats.getCount();
min = Math.min(min, stats.getMin());
max = Math.max(max, stats.getMax());
sum += stats.getSum();
// Compute the sum of double values with Kahan summation algorithm which is more
// accurate than naive summation.
if (Double.isNaN(sum) == false) {
double value = stats.getSum();
if (Double.isNaN(value) || Double.isInfinite(value)) {
sum += value;
} else if (Double.isFinite(sum)) {
double corrected = value - compensation;
double newSum = sum + corrected;
compensation = (newSum - sum) - corrected;
sum = newSum;
}
}
}
return new InternalStats(name, count, sum, min, max, format, pipelineAggregators(), getMetaData());
}
Expand Down
26 changes: 22 additions & 4 deletions core/src/main/java/org/elasticsearch/search/aggregations/metrics/stats/StatsAggregator.java
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Original file line number Diff line number Diff line change
Expand Up @@ -48,6 +48,8 @@ public class StatsAggregator extends NumericMetricsAggregator.MultiValue {
DoubleArray mins;
DoubleArray maxes;

private DoubleArray compensations;


public StatsAggregator(String name, ValuesSource.Numeric valuesSource, DocValueFormat format,
SearchContext context,
Expand All @@ -59,6 +61,7 @@ public StatsAggregator(String name, ValuesSource.Numeric valuesSource, DocValueF
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);
Expand Down Expand Up @@ -88,6 +91,7 @@ public void collect(int doc, long bucket) throws IOException {
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);
mins.fill(from, overSize, Double.POSITIVE_INFINITY);
Expand All @@ -97,16 +101,30 @@ public void collect(int doc, long bucket) throws IOException {
if (values.advanceExact(doc)) {
final int valuesCount = values.docValueCount();
counts.increment(bucket, valuesCount);
double sum = 0;
double min = mins.get(bucket);
double max = maxes.get(bucket);
// Compute the sum of double values with Kahan summation algorithm which is more
// accurate than naive summation.
double sum = sums.get(bucket);
double compensation = compensations.get(bucket);

for (int i = 0; i < valuesCount; i++) {
double value = values.nextValue();
sum += value;
if (Double.isNaN(sum) == false) {
if (Double.isNaN(value) || Double.isInfinite(value)) {
sum += value;
} else if (Double.isFinite(sum)) {
double corrected = value - compensation;
double newSum = sum + corrected;
compensation = (newSum - sum) - corrected;
sum = newSum;
}
}
min = Math.min(min, value);
max = Math.max(max, value);
}
sums.increment(bucket, sum);
sums.set(bucket, sum);
compensations.set(bucket, compensation);
mins.set(bucket, min);
maxes.set(bucket, max);
}
Expand Down Expand Up @@ -164,6 +182,6 @@ public InternalAggregation buildEmptyAggregation() {

@Override
public void doClose() {
Releasables.close(counts, maxes, mins, sums);
Releasables.close(counts, maxes, mins, sums, compensations);
}
}
41 changes: 34 additions & 7 deletions ...org/elasticsearch/search/aggregations/metrics/stats/extended/ExtendedStatsAggregator.java
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Original file line number Diff line number Diff line change
Expand Up @@ -49,9 +49,11 @@ public class ExtendedStatsAggregator extends NumericMetricsAggregator.MultiValue

LongArray counts;
DoubleArray sums;
DoubleArray compensations;
DoubleArray mins;
DoubleArray maxes;
DoubleArray sumOfSqrs;
DoubleArray compensationOfSqrs;

public ExtendedStatsAggregator(String name, ValuesSource.Numeric valuesSource, DocValueFormat formatter,
SearchContext context, Aggregator parent, double sigma, List<PipelineAggregator> pipelineAggregators,
Expand All @@ -65,11 +67,13 @@ public ExtendedStatsAggregator(String name, ValuesSource.Numeric valuesSource, D
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);
}
}

Expand All @@ -95,29 +99,52 @@ public void collect(int doc, long bucket) throws IOException {
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 sum = 0;
double sumOfSqr = 0;
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);
double sumOfSqr = sumOfSqrs.get(bucket);
double compensationOfSqr = compensationOfSqrs.get(bucket);
for (int i = 0; i < valuesCount; i++) {
double value = values.nextValue();
sum += value;
sumOfSqr += value * value;
if (Double.isNaN(value) || Double.isInfinite(value)) {
sum += value;
sumOfSqr += value * value;
} else {
if (Double.isFinite(sum)) {
double corrected = value - compensation;
double newSum = sum + corrected;
compensation = (newSum - sum) - corrected;
sum = newSum;
}
if (Double.isFinite(sumOfSqr)) {
double correctedOfSqr = value * value - compensationOfSqr;
double newSumOfSqr = sumOfSqr + correctedOfSqr;
compensationOfSqr = (newSumOfSqr - sumOfSqr) - correctedOfSqr;
sumOfSqr = newSumOfSqr;
}
}
min = Math.min(min, value);
max = Math.max(max, value);
}
sums.increment(bucket, sum);
sumOfSqrs.increment(bucket, sumOfSqr);
sums.set(bucket, sum);
compensations.set(bucket, compensation);
sumOfSqrs.set(bucket, sumOfSqr);
compensationOfSqrs.set(bucket, compensationOfSqr);
mins.set(bucket, min);
maxes.set(bucket, max);
}
Expand Down Expand Up @@ -196,6 +223,6 @@ public InternalAggregation buildEmptyAggregation() {

@Override
public void doClose() {
Releasables.close(counts, maxes, mins, sumOfSqrs, sums);
Releasables.close(counts, maxes, mins, sumOfSqrs, compensationOfSqrs, sums, compensations);
}
}
13 changes: 12 additions & 1 deletion ...a/org/elasticsearch/search/aggregations/metrics/stats/extended/InternalExtendedStats.java
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Original file line number Diff line number Diff line change
Expand Up @@ -142,12 +142,23 @@ public String getStdDeviationBoundAsString(Bounds bound) {
@Override
public InternalExtendedStats doReduce(List<InternalAggregation> aggregations, ReduceContext reduceContext) {
double sumOfSqrs = 0;
double compensationOfSqrs = 0;
for (InternalAggregation aggregation : aggregations) {
InternalExtendedStats stats = (InternalExtendedStats) aggregation;
if (stats.sigma != sigma) {
throw new IllegalStateException("Cannot reduce other stats aggregations that have a different sigma");
}
sumOfSqrs += stats.getSumOfSquares();
if (Double.isNaN(sumOfSqrs) == false) {
double value = stats.getSumOfSquares();
if (Double.isNaN(value) || Double.isInfinite(value)) {
sumOfSqrs += value;
} else if (Double.isFinite(sumOfSqrs)) {
double correctedOfSqrs = value - compensationOfSqrs;
double newSumOfSqrs = sumOfSqrs + correctedOfSqrs;
compensationOfSqrs = (newSumOfSqrs - sumOfSqrs) - correctedOfSqrs;
sumOfSqrs = newSumOfSqrs;
}
}
}
final InternalStats stats = super.doReduce(aggregations, reduceContext);
return new InternalExtendedStats(name, stats.getCount(), stats.getSum(), stats.getMin(), stats.getMax(), sumOfSqrs, sigma,
Expand Down
17 changes: 15 additions & 2 deletions core/src/main/java/org/elasticsearch/search/aggregations/metrics/sum/InternalSum.java
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Expand Up @@ -35,7 +35,7 @@ public class InternalSum extends InternalNumericMetricsAggregation.SingleValue i
private final double sum;

public InternalSum(String name, double sum, DocValueFormat formatter, List<PipelineAggregator> pipelineAggregators,
Map<String, Object> metaData) {
Map<String, Object> metaData) {
super(name, pipelineAggregators, metaData);
this.sum = sum;
this.format = formatter;
Expand Down Expand Up @@ -73,9 +73,22 @@ public double getValue() {

@Override
public InternalSum doReduce(List<InternalAggregation> aggregations, ReduceContext reduceContext) {
// Compute the sum of double values with Kahan summation algorithm which is more
// accurate than naive summation.
double sum = 0;
double compensation = 0;
for (InternalAggregation aggregation : aggregations) {
sum += ((InternalSum) aggregation).sum;
double value = ((InternalSum) aggregation).sum;
if (Double.isNaN(value) || Double.isInfinite(value)) {
sum += value;
if (Double.isNaN(sum))
break;
} else if (Double.isFinite(sum)) {
double corrected = value - compensation;
double newSum = sum + corrected;
compensation = (newSum - sum) - corrected;
sum = newSum;
}
}
return new InternalSum(name, sum, format, pipelineAggregators(), getMetaData());
}
Expand Down
26 changes: 22 additions & 4 deletions core/src/main/java/org/elasticsearch/search/aggregations/metrics/sum/SumAggregator.java
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,7 @@ public class SumAggregator extends NumericMetricsAggregator.SingleValue {
private final DocValueFormat format;

private DoubleArray sums;
private DoubleArray compensations;

SumAggregator(String name, ValuesSource.Numeric valuesSource, DocValueFormat formatter, SearchContext context,
Aggregator parent, List<PipelineAggregator> pipelineAggregators, Map<String, Object> metaData) throws IOException {
Expand All @@ -51,6 +52,7 @@ public class SumAggregator extends NumericMetricsAggregator.SingleValue {
this.format = formatter;
if (valuesSource != null) {
sums = context.bigArrays().newDoubleArray(1, true);
compensations = context.bigArrays().newDoubleArray(1, true);
}
}

Expand All @@ -71,13 +73,29 @@ public LeafBucketCollector getLeafCollector(LeafReaderContext ctx,
@Override
public void collect(int doc, long bucket) throws IOException {
sums = bigArrays.grow(sums, bucket + 1);
compensations = bigArrays.grow(compensations, bucket + 1);

if (values.advanceExact(doc)) {
final int valuesCount = values.docValueCount();
double sum = 0;
// Compute the sum of double values with Kahan summation algorithm which is more
// accurate than naive summation.
double sum = sums.get(bucket);
double compensation = compensations.get(bucket);
for (int i = 0; i < valuesCount; i++) {
sum += values.nextValue();
double value = values.nextValue();
if (Double.isNaN(value) || Double.isInfinite(value)) {
sum += value;
if (Double.isNaN(sum))
break;
} else if (Double.isFinite(sum)) {
double corrected = value - compensation;
double newSum = sum + corrected;
compensation = (newSum - sum) - corrected;
sum = newSum;
}
}
sums.increment(bucket, sum);
compensations.set(bucket, compensation);
sums.set(bucket, sum);
}
}
};
Expand Down Expand Up @@ -106,6 +124,6 @@ public InternalAggregation buildEmptyAggregation() {

@Override
public void doClose() {
Releasables.close(sums);
Releasables.close(sums, compensations);
}
}
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