elasticsearch/docs/reference/aggregations/metrics/percentile-rank-aggregation.asciidoc

[[search-aggregations-metrics-percentile-rank-aggregation]]
=== Percentile ranks aggregation
++++
<titleabbrev>Percentile ranks</titleabbrev>
++++

A `multi-value` metrics aggregation that calculates one or more percentile ranks
over numeric values extracted from the aggregated documents. These values can be
extracted from specific numeric or <<histogram,histogram fields>> in the documents.

[NOTE]
==================================================
Please see <<search-aggregations-metrics-percentile-aggregation-approximation>>
and <<search-aggregations-metrics-percentile-aggregation-compression>> for advice
regarding approximation and memory use of the percentile ranks aggregation
==================================================

Percentile rank show the percentage of observed values which are below certain
value. For example, if a value is greater than or equal to 95% of the observed values
it is said to be at the 95th percentile rank.

Assume your data consists of website load times. You may have a service agreement that
95% of page loads complete within 500ms and 99% of page loads complete within 600ms.

Let's look at a range of percentiles representing load time:

[source,console]
--------------------------------------------------
GET latency/_search
{
  "size": 0,
  "aggs": {
    "load_time_ranks": {
      "percentile_ranks": {
        "field": "load_time",   <1>
        "values": [ 500, 600 ]
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:latency]

<1> The field `load_time` must be a numeric field

The response will look like this:

[source,console-result]
--------------------------------------------------
{
  ...

 "aggregations": {
    "load_time_ranks": {
      "values": {
        "500.0": 90.01,
        "600.0": 100.0
      }
    }
  }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]
// TESTRESPONSE[s/"500.0": 90.01/"500.0": 55.00000000000001/]
// TESTRESPONSE[s/"600.0": 100.0/"600.0": 64.0/]

From this information you can determine you are hitting the 99% load time target but not quite
hitting the 95% load time target

==== Keyed Response

By default the `keyed` flag is set to `true` associates a unique string key with each bucket and returns the ranges as a hash rather than an array. Setting the `keyed` flag to `false` will disable this behavior:

[source,console]
--------------------------------------------------
GET latency/_search
{
  "size": 0,
  "aggs": {
    "load_time_ranks": {
      "percentile_ranks": {
        "field": "load_time",
        "values": [ 500, 600 ],
        "keyed": false
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:latency]

Response:

[source,console-result]
--------------------------------------------------
{
  ...

  "aggregations": {
    "load_time_ranks": {
      "values": [
        {
          "key": 500.0,
          "value": 90.01
        },
        {
          "key": 600.0,
          "value": 100.0
        }
      ]
    }
  }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]
// TESTRESPONSE[s/"value": 90.01/"value": 55.00000000000001/]
// TESTRESPONSE[s/"value": 100.0/"value": 64.0/]


==== Script

If you need to run the aggregation against values that aren't indexed, use
a <<runtime,runtime field>>. For example, if our load times
are in milliseconds but we want percentiles calculated in seconds:

[source,console]
----
GET latency/_search
{
  "size": 0,
  "runtime_mappings": {
    "load_time.seconds": {
      "type": "long",
      "script": {
        "source": "emit(doc['load_time'].value / params.timeUnit)",
        "params": {
          "timeUnit": 1000
        }
      }
    }
  },
  "aggs": {
    "load_time_ranks": {
      "percentile_ranks": {
        "values": [ 500, 600 ],
        "field": "load_time.seconds"
      }
    }
  }
}
----
// TEST[setup:latency]
// TEST[s/_search/_search?filter_path=aggregations/]

////
[source,console-result]
--------------------------------------------------
{
  "aggregations": {
    "load_time_ranks": {
      "values": {
        "500.0": 100.0,
        "600.0": 100.0
      }
    }
  }
}
--------------------------------------------------
////

==== HDR Histogram

NOTE: This setting exposes the internal implementation of HDR Histogram and the syntax may change in the future.

https://github.com/HdrHistogram/HdrHistogram[HDR Histogram] (High Dynamic Range Histogram) is an alternative implementation
that can be useful when calculating percentile ranks for latency measurements as it can be faster than the t-digest implementation
with the trade-off of a larger memory footprint. This implementation maintains a fixed worse-case percentage error (specified as a
number of significant digits). This means that if data is recorded with values from 1 microsecond up to 1 hour (3,600,000,000
microseconds) in a histogram set to 3 significant digits, it will maintain a value resolution of 1 microsecond for values up to
1 millisecond and 3.6 seconds (or better) for the maximum tracked value (1 hour).

The HDR Histogram can be used by specifying the `hdr` object in the request:

[source,console]
--------------------------------------------------
GET latency/_search
{
  "size": 0,
  "aggs": {
    "load_time_ranks": {
      "percentile_ranks": {
        "field": "load_time",
        "values": [ 500, 600 ],
        "hdr": {                                  <1>
          "number_of_significant_value_digits": 3 <2>
        }
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:latency]

<1> `hdr` object indicates that HDR Histogram should be used to calculate the percentiles and specific settings for this algorithm can be specified inside the object
<2> `number_of_significant_value_digits` specifies the resolution of values for the histogram in number of significant digits

The HDRHistogram only supports positive values and will error if it is passed a negative value. It is also not a good idea to use
the HDRHistogram if the range of values is unknown as this could lead to high memory usage.

==== Missing value

The `missing` parameter defines how documents that are missing a value should be treated.
By default they will be ignored but it is also possible to treat them as if they
had a value.

[source,console]
--------------------------------------------------
GET latency/_search
{
  "size": 0,
  "aggs": {
    "load_time_ranks": {
      "percentile_ranks": {
        "field": "load_time",
        "values": [ 500, 600 ],
        "missing": 10           <1>
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:latency]

<1> Documents without a value in the `load_time` field will fall into the same bucket as documents that have the value `10`.