237 lines
7.6 KiB
Plaintext
237 lines
7.6 KiB
Plaintext
[[search-aggregations-metrics-percentile-rank-aggregation]]
|
|
=== Percentile Ranks Aggregation
|
|
|
|
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 either from specific numeric fields in the documents, or
|
|
be generated by a provided script.
|
|
|
|
[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 completely within 15ms and 99% of page loads complete within 30ms.
|
|
|
|
Let's look at a range of percentiles representing load time:
|
|
|
|
[source,js]
|
|
--------------------------------------------------
|
|
GET latency/_search
|
|
{
|
|
"size": 0,
|
|
"aggs" : {
|
|
"load_time_ranks" : {
|
|
"percentile_ranks" : {
|
|
"field" : "load_time", <1>
|
|
"values" : [500, 600]
|
|
}
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// CONSOLE
|
|
// TEST[setup:latency]
|
|
<1> The field `load_time` must be a numeric field
|
|
|
|
The response will look like this:
|
|
|
|
[source,js]
|
|
--------------------------------------------------
|
|
{
|
|
...
|
|
|
|
"aggregations": {
|
|
"load_time_ranks": {
|
|
"values" : {
|
|
"500.0": 55.00000000000001,
|
|
"600.0": 64.0
|
|
}
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]
|
|
|
|
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,js]
|
|
--------------------------------------------------
|
|
GET latency/_search
|
|
{
|
|
"size": 0,
|
|
"aggs": {
|
|
"load_time_ranks": {
|
|
"percentile_ranks": {
|
|
"field": "load_time",
|
|
"values": [500, 600],
|
|
"keyed": false
|
|
}
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// CONSOLE
|
|
// TEST[setup:latency]
|
|
|
|
Response:
|
|
|
|
[source,js]
|
|
--------------------------------------------------
|
|
{
|
|
...
|
|
|
|
"aggregations": {
|
|
"load_time_ranks": {
|
|
"values": [
|
|
{
|
|
"key": 500.0,
|
|
"value": 55.00000000000001
|
|
},
|
|
{
|
|
"key": 600.0,
|
|
"value": 64.0
|
|
}
|
|
]
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]
|
|
|
|
|
|
==== Script
|
|
|
|
The percentile rank metric supports scripting. For example, if our load times
|
|
are in milliseconds but we want to specify values in seconds, we could use
|
|
a script to convert them on-the-fly:
|
|
|
|
[source,js]
|
|
--------------------------------------------------
|
|
GET latency/_search
|
|
{
|
|
"size": 0,
|
|
"aggs" : {
|
|
"load_time_ranks" : {
|
|
"percentile_ranks" : {
|
|
"values" : [500, 600],
|
|
"script" : {
|
|
"lang": "painless",
|
|
"source": "doc['load_time'].value / params.timeUnit", <1>
|
|
"params" : {
|
|
"timeUnit" : 1000 <2>
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// CONSOLE
|
|
// TEST[setup:latency]
|
|
<1> The `field` parameter is replaced with a `script` parameter, which uses the
|
|
script to generate values which percentile ranks are calculated on
|
|
<2> Scripting supports parameterized input just like any other script
|
|
|
|
This will interpret the `script` parameter as an `inline` script with the `painless` script language and no script parameters. To use a stored script use the following syntax:
|
|
|
|
[source,js]
|
|
--------------------------------------------------
|
|
GET latency/_search
|
|
{
|
|
"size": 0,
|
|
"aggs" : {
|
|
"load_time_ranks" : {
|
|
"percentile_ranks" : {
|
|
"values" : [500, 600],
|
|
"script" : {
|
|
"id": "my_script",
|
|
"params": {
|
|
"field": "load_time"
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// CONSOLE
|
|
// TEST[setup:latency,stored_example_script]
|
|
|
|
==== 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 `method` parameter in the request:
|
|
|
|
[source,js]
|
|
--------------------------------------------------
|
|
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>
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// CONSOLE
|
|
// 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,js]
|
|
--------------------------------------------------
|
|
GET latency/data/_search
|
|
{
|
|
"size": 0,
|
|
"aggs" : {
|
|
"load_time_ranks" : {
|
|
"percentile_ranks" : {
|
|
"field" : "load_time",
|
|
"values" : [500, 600],
|
|
"missing": 10 <1>
|
|
}
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// CONSOLE
|
|
// 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`.
|