[[search-aggregations-metrics-stats-aggregation]]
=== Stats Aggregation

A `multi-value` metrics aggregation that computes stats 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.

The stats that are returned consist of: `min`, `max`, `sum`, `count` and `avg`.

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

[source,console]
--------------------------------------------------
POST /exams/_search?size=0
{
    "aggs" : {
        "grades_stats" : { "stats" : { "field" : "grade" } }
    }
}
--------------------------------------------------
// TEST[setup:exams]

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


[source,console-result]
--------------------------------------------------
{
    ...

    "aggregations": {
        "grades_stats": {
            "count": 2,
            "min": 50.0,
            "max": 100.0,
            "avg": 75.0,
            "sum": 150.0
        }
    }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]

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

==== Script

Computing the grades stats based on a script:

[source,console]
--------------------------------------------------
POST /exams/_search?size=0
{
    "aggs" : {
        "grades_stats" : {
             "stats" : {
                 "script" : {
                     "lang": "painless",
                     "source": "doc['grade'].value"
                 }
             }
         }
    }
}
--------------------------------------------------
// TEST[setup:exams]

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,console]
--------------------------------------------------
POST /exams/_search?size=0
{
    "aggs" : {
        "grades_stats" : {
            "stats" : { 
                "script" : {
                    "id": "my_script",
                    "params" : {
                        "field" : "grade"
                    }
                }
            }
        }
    }
}
--------------------------------------------------
// TEST[setup:exams,stored_example_script]

===== 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 a value script to get the new stats:

[source,console]
--------------------------------------------------
POST /exams/_search?size=0
{
    "aggs" : {
        "grades_stats" : {
            "stats" : {
                "field" : "grade",
                "script" : {
                    "lang": "painless",
                    "source": "_value * params.correction",
                    "params" : {
                        "correction" : 1.2
                    }
                }
            }
        }
    }
}
--------------------------------------------------
// TEST[setup:exams]

==== 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,console]
--------------------------------------------------
POST /exams/_search?size=0
{
    "aggs" : {
        "grades_stats" : {
            "stats" : {
                "field" : "grade",
                "missing": 0 <1>
            }
        }
    }
}
--------------------------------------------------
// TEST[setup:exams]

<1> Documents without a value in the `grade` field will fall into the same bucket as documents that have the value `0`.