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A new metric aggregation that can compute approximate values of arbitrary percentiles. Close #5323
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docs/reference/search/aggregations/metrics/percentile-aggregation.asciidoc
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[[search-aggregations-metrics-percentile-aggregation]] | ||
=== Percentiles | ||
A `multi-value` metrics aggregation that calculates one or more percentiles | ||
over numeric values extracted from the aggregated documents. These values | ||
can be extracted either from specific numeric fields in the documents, or | ||
be generated by a provided script. | ||
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.Experimental! | ||
[IMPORTANT] | ||
===== | ||
This feature is marked as experimental, and may be subject to change in the | ||
future. If you use this feature, please let us know your experience with it! | ||
===== | ||
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Percentiles show the point at which a certain percentage of observed values | ||
occur. For example, the 95th percentile is the value which is greater than 95% | ||
of the observed values. | ||
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Percentiles are often used to find outliers. In normal distributions, the | ||
0.13th and 99.87th percentiles represents three standard deviations from the | ||
mean. Any data which falls outside three standard deviations is often considered | ||
an anomaly. | ||
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When a range of percentiles are retrieved, they can be used to estimate the | ||
data distribution and determine if the data is skewed, bimodal, etc. | ||
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Assume your data consists of website load times. The average and median | ||
load times are not overly useful to an administrator. The max may be interesting, | ||
but it can be easily skewed by a single slow response. | ||
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Let's look at a range of percentiles representing load time: | ||
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[source,js] | ||
-------------------------------------------------- | ||
{ | ||
"aggs" : { | ||
"load_time_outlier" : { | ||
"percentiles" : { | ||
"field" : "load_time" <1> | ||
} | ||
} | ||
} | ||
} | ||
-------------------------------------------------- | ||
<1> The field `load_time` must be a numeric field | ||
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By default, the `percentile` metric will generate a range of | ||
percentiles: `[ 1, 5, 25, 50, 75, 95, 99 ]`. The response will look like this: | ||
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[source,js] | ||
-------------------------------------------------- | ||
{ | ||
... | ||
"aggregations": { | ||
"load_time_outlier": { | ||
"1.0": 15, | ||
"5.0": 20, | ||
"25.0": 23, | ||
"50.0": 25, | ||
"75.0": 29, | ||
"95.0": 60, | ||
"99.0": 150 | ||
} | ||
} | ||
} | ||
-------------------------------------------------- | ||
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As you can see, the aggregation will return a calculated value for each percentile | ||
in the default range. If we assume response times are in milliseconds, it is | ||
immediately obvious that the webpage normally loads in 15-30ms, but occasionally | ||
spikes to 60-150ms. | ||
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Often, administrators are only interested in outliers -- the extreme percentiles. | ||
We can specify just the percents we are interested in (requested percentiles | ||
must be a value between 0-100 inclusive): | ||
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[source,js] | ||
-------------------------------------------------- | ||
{ | ||
"aggs" : { | ||
"load_time_outlier" : { | ||
"percentiles" : { | ||
"field" : "load_time", | ||
"percents" : [95, 99, 99.9] <1> | ||
} | ||
} | ||
} | ||
} | ||
-------------------------------------------------- | ||
<1> Use the `percents` parameter to specify particular percentiles to calculate | ||
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==== Script | ||
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The percentile metric supports scripting. For example, if our load times | ||
are in milliseconds but we want percentiles calculated in seconds, we could use | ||
a script to convert them on-the-fly: | ||
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[source,js] | ||
-------------------------------------------------- | ||
{ | ||
"aggs" : { | ||
"load_time_outlier" : { | ||
"percentiles" : { | ||
"script" : "doc['load_time'].value / timeUnit", <1> | ||
"params" : { | ||
"timeUnit" : 1000 <2> | ||
} | ||
} | ||
} | ||
} | ||
} | ||
-------------------------------------------------- | ||
<1> The `field` parameter is replaced with a `script` parameter, which uses the | ||
script to generate values which percentiles are calculated on | ||
<2> Scripting supports parameterized input just like any other script | ||
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==== Percentiles are (usually) approximate | ||
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There are many different algorithms to calculate percentiles. The naive | ||
implementation simply stores all the values in a sorted array. To find the 50th | ||
percentile, you simple find the value that is at `my_array[count(my_array) * 0.5]`. | ||
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Clearly, the naive implementation does not scale -- the sorted array grows | ||
linearly with the number of values in your dataset. To calculate percentiles | ||
across potentially billions of values in an Elasticsearch cluster, _approximate_ | ||
percentiles are calculated. | ||
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The algorithm used by the `percentile` metric is called TDigest (introduced by | ||
Ted Dunning in | ||
https://github.com/tdunning/t-digest/blob/master/docs/t-digest-paper/histo.pdf[Computing Accurate Quantiles using T-Digests]). | ||
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When using this metric, there are a few guidelines to keep in mind: | ||
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- Accuracy is proportional to `q(1-q)`. This means that extreme percentiles (e.g. 99%) | ||
are more accurate than less extreme percentiles, such as the median | ||
- For small sets of values, percentiles are highly accurate (and potentially | ||
100% accurate if the data is small enough). | ||
- As the quantity of values in a bucket grows, the algorithm begins to approximate | ||
the percentiles. It is effectively trading accuracy for memory savings. The | ||
exact level of inaccuracy is difficult to generalize, since it depends on your | ||
data distribution and volume of data being aggregated | ||
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==== Compression | ||
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Approximate algorithms must balance memory utilization with estimation accuracy. | ||
This balance can be controlled using a `compression` parameter: | ||
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[source,js] | ||
-------------------------------------------------- | ||
{ | ||
"aggs" : { | ||
"load_time_outlier" : { | ||
"percentiles" : { | ||
"field" : "load_time", | ||
"compression" : 200 <1> | ||
} | ||
} | ||
} | ||
} | ||
-------------------------------------------------- | ||
<1> Compression controls memory usage and approximation error | ||
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The TDigest algorithm uses a number of "nodes" to approximate percentiles -- the | ||
more nodes available, the higher the accuracy (and large memory footprint) proportional | ||
to the volume of data. The `compression` parameter limits the maximum number of | ||
nodes to `100 * compression`. | ||
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Therefore, by increasing the compression value, you can increase the accuracy of | ||
your percentiles at the cost of more memory. Larger compression values also | ||
make the algorithm slower since the underlying tree data structure grows in size, | ||
resulting in more expensive operations. The default compression value is | ||
`100`. | ||
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A "node" uses roughly 48 bytes of memory, so under worst-case scenarios (large amount | ||
of data which arrives sorted and in-order) the default settings will produce a | ||
TDigest roughly 480KB in size. In practice data tends to be more random and | ||
the TDigest will use less memory. |
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src/main/java/org/elasticsearch/common/util/ArrayUtils.java
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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. | ||
*/ | ||
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package org.elasticsearch.common.util; | ||
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import java.util.Arrays; | ||
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/** | ||
* | ||
*/ | ||
public class ArrayUtils { | ||
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private ArrayUtils() {} | ||
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/** | ||
* Return the index of <code>value</code> in <code>array</code>, or <tt>-1</tt> if there is no such index. | ||
* If there are several values that are within <code>tolerance</code> or less of <code>value</code>, this method will return the | ||
* index of the closest value. In case of several values being as close ot <code>value</code>, there is no guarantee which index | ||
* will be returned. | ||
* Results are undefined if the array is not sorted. | ||
*/ | ||
public static int binarySearch(double[] array, double value, double tolerance) { | ||
if (array.length == 0) { | ||
return -1; | ||
} | ||
return binarySearch(array, 0, array.length, value, tolerance); | ||
} | ||
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private static int binarySearch(double[] array, int fromIndex, int toIndex, double value, double tolerance) { | ||
int index = Arrays.binarySearch(array, fromIndex, toIndex, value); | ||
if (index < 0) { | ||
final int highIndex = -1 - index; // first index of a value that is > value | ||
final int lowIndex = highIndex - 1; // last index of a value that is < value | ||
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double lowError = Double.POSITIVE_INFINITY; | ||
double highError = Double.POSITIVE_INFINITY; | ||
if (lowIndex >= 0) { | ||
lowError = value - array[lowIndex]; | ||
} | ||
if (highIndex < array.length) { | ||
highError = array[highIndex] - value; | ||
} | ||
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if (highError < lowError) { | ||
if (highError < tolerance) { | ||
index = highIndex; | ||
} | ||
} else if (lowError < tolerance) { | ||
index = lowIndex; | ||
} else { | ||
index = -1; | ||
} | ||
} | ||
return index; | ||
} | ||
} |
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this.size = newSize; | ||
} | ||
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} | ||
} |
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