[role="xpack"]
[testenv="basic"]
[[search-aggregations-pipeline-moving-percentiles-aggregation]]
=== Moving Percentiles Aggregation

Given an ordered series of <<search-aggregations-metrics-percentile-aggregation, percentiles>>, the Moving Percentile aggregation
will slide a window across those percentiles and allow the user to compute the cumulative percentile.

This is conceptually very similar to the <<search-aggregations-pipeline-movfn-aggregation, Moving Function>> pipeline aggregation,
except it works on the percentiles sketches instead of the actual buckets values.

==== Syntax

A `moving_percentiles` aggregation looks like this in isolation:

[source,js]
--------------------------------------------------
{
    "moving_percentiles": {
        "buckets_path": "the_percentile",
        "window": 10
    }
}
--------------------------------------------------
// NOTCONSOLE

[[moving-percentiles-params]]
.`moving_percentiles` Parameters
[options="header"]
|===
|Parameter Name |Description |Required |Default Value
|`buckets_path` |Path to the percentile of interest (see <<buckets-path-syntax, `buckets_path` Syntax>> for more details |Required |
|`window` |The size of window to "slide" across the histogram. |Required |
|`shift` |<<shift-parameter, Shift>> of window position. |Optional | 0
|===

`moving_percentiles` aggregations must be embedded inside of a `histogram` or `date_histogram` aggregation.  They can be
embedded like any other metric aggregation:

[source,console]
--------------------------------------------------
POST /_search
{
    "size": 0,
    "aggs": {
        "my_date_histo":{                <1>
            "date_histogram":{
                "field":"date",
                "calendar_interval":"1M"
            },
            "aggs":{
                "the_percentile":{        <2>
                    "percentiles":{
                      "field": "price",
                      "percents": [ 1.0, 99.0 ]
                     }
                },
                "the_movperc": {
                    "moving_percentiles": {
                        "buckets_path": "the_percentile", <3>
                        "window": 10
                    }
                }
            }
        }
    }
}
--------------------------------------------------
// TEST[setup:sales]

<1> A `date_histogram` named "my_date_histo" is constructed on the "timestamp" field, with one-day intervals
<2> A `percentile` metric is used to calculate the percentiles of a field.
<3> Finally, we specify a `moving_percentiles` aggregation which uses "the_percentile" sketch as its input.

Moving percentiles are built by first specifying a `histogram` or `date_histogram` over a field.  You  then add
a percentile metric inside of that histogram.  Finally, the `moving_percentiles` is embedded inside the histogram.
The `buckets_path` parameter is then used to "point" at the percentiles aggregation inside of the histogram (see
<<buckets-path-syntax>> for a description of the syntax for `buckets_path`).

And the following may be the response:

[source,console-result]
--------------------------------------------------
{
   "took": 11,
   "timed_out": false,
   "_shards": ...,
   "hits": ...,
   "aggregations": {
      "my_date_histo": {
         "buckets": [
             {
                 "key_as_string": "2015/01/01 00:00:00",
                 "key": 1420070400000,
                 "doc_count": 3,
                 "the_percentile": {
                     "values": {
                       "1.0": 150.0,
                       "99.0": 200.0
                     }
                 }
             },
             {
                 "key_as_string": "2015/02/01 00:00:00",
                 "key": 1422748800000,
                 "doc_count": 2,
                 "the_percentile": {
                     "values": {
                       "1.0": 10.0,
                       "99.0": 50.0
                     }
                 },
                 "the_movperc": {
                   "values": {
                     "1.0": 150.0,
                     "99.0": 200.0
                   }
                 }
             },
             {
                 "key_as_string": "2015/03/01 00:00:00",
                 "key": 1425168000000,
                 "doc_count": 2,
                 "the_percentile": {
                    "values": {
                      "1.0": 175.0,
                      "99.0": 200.0
                    }
                 },
                 "the_movperc": {
                    "values": {
                      "1.0": 10.0,
                      "99.0": 200.0
                    }
                 }
             }
         ]
      }
   }
}
--------------------------------------------------
// TESTRESPONSE[s/"took": 11/"took": $body.took/]
// TESTRESPONSE[s/"_shards": \.\.\./"_shards": $body._shards/]
// TESTRESPONSE[s/"hits": \.\.\./"hits": $body.hits/]

The output format of the `moving_percentiles` aggregation is inherited from the format of the referenced
<<search-aggregations-metrics-percentile-aggregation,`percentiles`>> aggregation.

Moving percentiles pipeline aggregations always run with `skip` gap policy.


[[moving-percentiles-shift-parameter]]
==== shift parameter

By default (with `shift = 0`), the window that is offered for calculation is the last `n` values excluding the current bucket.
Increasing `shift` by 1 moves starting window position by `1` to the right.

- To include current bucket to the window, use `shift = 1`.
- For center alignment (`n / 2` values before and after the current bucket), use `shift = window / 2`.
- For right alignment (`n` values after the current bucket), use `shift = window`.

If either of window edges moves outside the borders of data series, the window shrinks to include available values only.