elasticsearch/docs/reference/aggregations/pipeline/bucket-correlation-aggregation.asciidoc

[role="xpack"]
[testenv="basic"]
[[search-aggregations-bucket-correlation-aggregation]]
=== Bucket correlation aggregation
++++
<titleabbrev>Bucket correlation aggregation</titleabbrev>
++++

experimental::[]

A sibling pipeline aggregation which executes a correlation function on the
configured sibling multi-bucket aggregation.


[[bucket-correlation-agg-syntax]]
==== Parameters

`buckets_path`::
(Required, string)
Path to the buckets that contain one set of values to correlate.
For syntax, see <<buckets-path-syntax>>.

`function`::
(Required, object)
The correlation function to execute.
+
.Properties of `function`
[%collapsible%open]
====
`count_correlation`:::
(Required^*^, object)
The configuration to calculate a count correlation. This function is designed for
determining the correlation of a term value and a given metric. Consequently, it
needs to meet the following requirements.

* The `buckets_path` must point to a `_count` metric.
* The total count of all the `bucket_path` count values must be less than or equal to `indicator.doc_count`.
* When utilizing this function, an initial calculation to gather the required `indicator` values is required.

.Properties of `count_correlation`
[%collapsible%open]
=====
`indicator`:::
(Required, object)
The indicator with which to correlate the configured `bucket_path` values.

.Properties of `indicator`
[%collapsible%open]
=====
`expectations`:::
(Required, array)
An array of numbers with which to correlate the configured `bucket_path` values. The length of this value must always equal
the number of buckets returned by the `bucket_path`.

`fractions`:::
(Optional, array)
An array of fractions to use when averaging and calculating variance. This should be used if the pre-calculated data and the
`buckets_path` have known gaps. The length of `fractions`, if provided, must equal `expectations`.

`doc_count`:::
(Required, integer)
The total number of documents that initially created the `expectations`. It's required to be greater than or equal to the sum
of all values in the `buckets_path` as this is the originating superset of data to which the term values are correlated.
=====
=====
====

==== Syntax

A `bucket_correlation` aggregation looks like this in isolation:

[source,js]
--------------------------------------------------
{
  "bucket_correlation": {
    "buckets_path": "range_values>_count", <1>
    "function": {
      "count_correlation": { <2>
        "expectations": [...],
        "doc_count": 10000
      }
    }
  }
}
--------------------------------------------------
// NOTCONSOLE
<1> The buckets containing the values to correlate against.
<2> The correlation function definition.


[[bucket-correlation-agg-example]]
==== Example

The following snippet correlates the individual terms in the field `version` with the `latency` metric. Not shown
is the pre-calculation of the `latency` indicator values, which was done utilizing the
<<search-aggregations-metrics-percentile-aggregation,percentiles>> aggregation.

This example is only using the 10s percentiles.

[source,console]
-------------------------------------------------
POST correlate_latency/_search?size=0&filter_path=aggregations
{
  "aggs": {
    "buckets": {
      "terms": {
        "field": "version",
        "size": 2
      },
      "aggs": {
        "latency_ranges": {
          "range": {
            "field": "latency",
            "ranges": [
              { "to": 0.0 },
              { "from": 0, "to": 105 },
              { "from": 105, "to": 225 },
              { "from": 225, "to": 445 },
              { "from": 445, "to": 665 },
              { "from": 665, "to": 885 },
              { "from": 885, "to": 1115 },
              { "from": 1115, "to": 1335 },
              { "from": 1335, "to": 1555 },
              { "from": 1555, "to": 1775 },
              { "from": 1775 }
            ]
          }
        },
        "bucket_correlation": {
          "bucket_correlation": {
            "buckets_path": "latency_ranges>_count",
            "function": {
              "count_correlation": {
                "indicator": {
                   "expectations": [0, 52.5, 165, 335, 555, 775, 1000, 1225, 1445, 1665, 1775],
                   "doc_count": 200
                }
              }
            }
          }
        }
      }
    }
  }
}
-------------------------------------------------
// TEST[setup:correlate_latency]

<1> The term buckets containing a range aggregation and the bucket correlation aggregation. Both are utilized to calculate
    the correlation of the term values with the latency.
<2> The range aggregation on the latency field. The ranges were created referencing the percentiles of the latency field.
<3> The bucket correlation aggregation that calculates the correlation of the number of term values within each range
    and the previously calculated indicator values.

And the following may be the response:

[source,console-result]
----
{
  "aggregations" : {
    "buckets" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "1.0",
          "doc_count" : 100,
          "latency_ranges" : {
            "buckets" : [
              {
                "key" : "*-0.0",
                "to" : 0.0,
                "doc_count" : 0
              },
              {
                "key" : "0.0-105.0",
                "from" : 0.0,
                "to" : 105.0,
                "doc_count" : 1
              },
              {
                "key" : "105.0-225.0",
                "from" : 105.0,
                "to" : 225.0,
                "doc_count" : 9
              },
              {
                "key" : "225.0-445.0",
                "from" : 225.0,
                "to" : 445.0,
                "doc_count" : 0
              },
              {
                "key" : "445.0-665.0",
                "from" : 445.0,
                "to" : 665.0,
                "doc_count" : 0
              },
              {
                "key" : "665.0-885.0",
                "from" : 665.0,
                "to" : 885.0,
                "doc_count" : 0
              },
              {
                "key" : "885.0-1115.0",
                "from" : 885.0,
                "to" : 1115.0,
                "doc_count" : 10
              },
              {
                "key" : "1115.0-1335.0",
                "from" : 1115.0,
                "to" : 1335.0,
                "doc_count" : 20
              },
              {
                "key" : "1335.0-1555.0",
                "from" : 1335.0,
                "to" : 1555.0,
                "doc_count" : 20
              },
              {
                "key" : "1555.0-1775.0",
                "from" : 1555.0,
                "to" : 1775.0,
                "doc_count" : 20
              },
              {
                "key" : "1775.0-*",
                "from" : 1775.0,
                "doc_count" : 20
              }
            ]
          },
          "bucket_correlation" : {
            "value" : 0.8402398981360937
          }
        },
        {
          "key" : "2.0",
          "doc_count" : 100,
          "latency_ranges" : {
            "buckets" : [
              {
                "key" : "*-0.0",
                "to" : 0.0,
                "doc_count" : 0
              },
              {
                "key" : "0.0-105.0",
                "from" : 0.0,
                "to" : 105.0,
                "doc_count" : 19
              },
              {
                "key" : "105.0-225.0",
                "from" : 105.0,
                "to" : 225.0,
                "doc_count" : 11
              },
              {
                "key" : "225.0-445.0",
                "from" : 225.0,
                "to" : 445.0,
                "doc_count" : 20
              },
              {
                "key" : "445.0-665.0",
                "from" : 445.0,
                "to" : 665.0,
                "doc_count" : 20
              },
              {
                "key" : "665.0-885.0",
                "from" : 665.0,
                "to" : 885.0,
                "doc_count" : 20
              },
              {
                "key" : "885.0-1115.0",
                "from" : 885.0,
                "to" : 1115.0,
                "doc_count" : 10
              },
              {
                "key" : "1115.0-1335.0",
                "from" : 1115.0,
                "to" : 1335.0,
                "doc_count" : 0
              },
              {
                "key" : "1335.0-1555.0",
                "from" : 1335.0,
                "to" : 1555.0,
                "doc_count" : 0
              },
              {
                "key" : "1555.0-1775.0",
                "from" : 1555.0,
                "to" : 1775.0,
                "doc_count" : 0
              },
              {
                "key" : "1775.0-*",
                "from" : 1775.0,
                "doc_count" : 0
              }
            ]
          },
          "bucket_correlation" : {
            "value" : -0.5759855613334943
          }
        }
      ]
    }
  }
}
----