239 lines
7.8 KiB
Plaintext
239 lines
7.8 KiB
Plaintext
[[search-aggregations-metrics-percentile-rank-aggregation]]
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=== Percentile Ranks Aggregation
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A `multi-value` metrics aggregation that calculates one or more percentile ranks
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over numeric values extracted from the aggregated documents. These values can be
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generated by a provided script or extracted from specific numeric or
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<<histogram,histogram fields>> in the documents.
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[NOTE]
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==================================================
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Please see <<search-aggregations-metrics-percentile-aggregation-approximation>>
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and <<search-aggregations-metrics-percentile-aggregation-compression>> for advice
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regarding approximation and memory use of the percentile ranks aggregation
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==================================================
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Percentile rank show the percentage of observed values which are below certain
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value. For example, if a value is greater than or equal to 95% of the observed values
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it is said to be at the 95th percentile rank.
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Assume your data consists of website load times. You may have a service agreement that
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95% of page loads complete within 500ms and 99% of page loads complete within 600ms.
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Let's look at a range of percentiles representing load time:
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[source,console]
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--------------------------------------------------
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GET latency/_search
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{
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"size": 0,
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"aggs" : {
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"load_time_ranks" : {
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"percentile_ranks" : {
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"field" : "load_time", <1>
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"values" : [500, 600]
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}
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}
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}
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}
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--------------------------------------------------
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// TEST[setup:latency]
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<1> The field `load_time` must be a numeric field
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The response will look like this:
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[source,console-result]
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--------------------------------------------------
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{
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...
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"aggregations": {
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"load_time_ranks": {
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"values" : {
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"500.0": 90.01,
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"600.0": 100.0
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}
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}
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}
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}
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--------------------------------------------------
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// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]
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// TESTRESPONSE[s/"500.0": 90.01/"500.0": 55.00000000000001/]
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// TESTRESPONSE[s/"600.0": 100.0/"600.0": 64.0/]
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From this information you can determine you are hitting the 99% load time target but not quite
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hitting the 95% load time target
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==== Keyed Response
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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:
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[source,console]
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--------------------------------------------------
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GET latency/_search
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{
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"size": 0,
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"aggs": {
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"load_time_ranks": {
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"percentile_ranks": {
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"field": "load_time",
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"values": [500, 600],
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"keyed": false
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}
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}
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}
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}
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--------------------------------------------------
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// TEST[setup:latency]
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Response:
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[source,console-result]
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--------------------------------------------------
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{
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...
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"aggregations": {
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"load_time_ranks": {
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"values": [
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{
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"key": 500.0,
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"value": 90.01
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},
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{
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"key": 600.0,
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"value": 100.0
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}
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]
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}
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}
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}
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--------------------------------------------------
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// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]
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// TESTRESPONSE[s/"value": 90.01/"value": 55.00000000000001/]
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// TESTRESPONSE[s/"value": 100.0/"value": 64.0/]
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==== Script
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The percentile rank metric supports scripting. For example, if our load times
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are in milliseconds but we want to specify values in seconds, we could use
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a script to convert them on-the-fly:
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[source,console]
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--------------------------------------------------
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GET latency/_search
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{
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"size": 0,
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"aggs" : {
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"load_time_ranks" : {
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"percentile_ranks" : {
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"values" : [500, 600],
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"script" : {
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"lang": "painless",
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"source": "doc['load_time'].value / params.timeUnit", <1>
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"params" : {
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"timeUnit" : 1000 <2>
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}
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}
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}
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}
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}
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}
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--------------------------------------------------
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// TEST[setup:latency]
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<1> The `field` parameter is replaced with a `script` parameter, which uses the
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script to generate values which percentile ranks are calculated on
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<2> Scripting supports parameterized input just like any other script
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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:
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[source,console]
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--------------------------------------------------
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GET latency/_search
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{
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"size": 0,
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"aggs" : {
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"load_time_ranks" : {
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"percentile_ranks" : {
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"values" : [500, 600],
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"script" : {
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"id": "my_script",
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"params": {
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"field": "load_time"
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}
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}
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}
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}
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}
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}
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--------------------------------------------------
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// TEST[setup:latency,stored_example_script]
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==== HDR Histogram
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NOTE: This setting exposes the internal implementation of HDR Histogram and the syntax may change in the future.
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https://github.com/HdrHistogram/HdrHistogram[HDR Histogram] (High Dynamic Range Histogram) is an alternative implementation
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that can be useful when calculating percentile ranks for latency measurements as it can be faster than the t-digest implementation
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with the trade-off of a larger memory footprint. This implementation maintains a fixed worse-case percentage error (specified as a
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number of significant digits). This means that if data is recorded with values from 1 microsecond up to 1 hour (3,600,000,000
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microseconds) in a histogram set to 3 significant digits, it will maintain a value resolution of 1 microsecond for values up to
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1 millisecond and 3.6 seconds (or better) for the maximum tracked value (1 hour).
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The HDR Histogram can be used by specifying the `hdr` object in the request:
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[source,console]
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--------------------------------------------------
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GET latency/_search
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{
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"size": 0,
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"aggs" : {
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"load_time_ranks" : {
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"percentile_ranks" : {
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"field" : "load_time",
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"values" : [500, 600],
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"hdr": { <1>
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"number_of_significant_value_digits" : 3 <2>
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}
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}
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}
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}
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}
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--------------------------------------------------
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// TEST[setup:latency]
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<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
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<2> `number_of_significant_value_digits` specifies the resolution of values for the histogram in number of significant digits
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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
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the HDRHistogram if the range of values is unknown as this could lead to high memory usage.
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==== Missing value
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The `missing` parameter defines how documents that are missing a value should be treated.
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By default they will be ignored but it is also possible to treat them as if they
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had a value.
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[source,console]
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--------------------------------------------------
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GET latency/_search
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{
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"size": 0,
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"aggs" : {
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"load_time_ranks" : {
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"percentile_ranks" : {
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"field" : "load_time",
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"values" : [500, 600],
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"missing": 10 <1>
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}
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}
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}
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}
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--------------------------------------------------
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// TEST[setup:latency]
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<1> Documents without a value in the `load_time` field will fall into the same bucket as documents that have the value `10`.
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