OpenSearch/docs/reference/search/aggregations/metrics/avg-aggregation.asciidoc

[[search-aggregations-metrics-avg-aggregation]]
=== Avg Aggregation

A `single-value` metrics aggregation that computes the average of numeric values that are 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.

Assuming the data consists of documents representing exams grades (between 0 and 100) of students

[source,js]
--------------------------------------------------
{
    "aggs" : {
        "avg_grade" : { "avg" : { "field" : "grade" } }
    }
}
--------------------------------------------------

The above aggregation computes the average grade over all documents. The aggregation type is `avg` and the `field` setting defines the numeric field of the documents the average will be computed on. The above will return the following:


[source,js]
--------------------------------------------------
{
    ...

    "aggregations": {
        "avg_grade": {
            "value": 75
        }
    }
}
--------------------------------------------------

The name of the aggregation (`avg_grade` above) also serves as the key by which the aggregation result can be retrieved from the returned response.

==== Script

Computing the average grade based on a script:

[source,js]
--------------------------------------------------
{
    ...,

    "aggs" : {
        "avg_grade" : { "avg" : { "script" : "doc['grade'].value" } }
    }
}
--------------------------------------------------

===== Value Script

It turned out that the exam was way above the level of the students and a grade correction needs to be applied. We can use value script to get the new average:

[source,js]
--------------------------------------------------
{
    "aggs" : {
        ...

        "aggs" : {
            "avg_corrected_grade" : {
                "avg" : {
                    "field" : "grade",
                    "script" : "_value * correction",
                    "params" : {
                        "correction" : 1.2
                    }
                }
            }
        }
    }
}
--------------------------------------------------