elasticsearch/docs/reference/aggregations/metrics/avg-aggregation.asciidoc

[[search-aggregations-metrics-avg-aggregation]]
=== Avg aggregation
++++
<titleabbrev>Avg</titleabbrev>
++++

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 or <<histogram,histogram>> fields in the documents.

Assuming the data consists of documents representing exams grades (between 0
and 100) of students we can average their scores with:

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

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,console-result]
--------------------------------------------------
{
  ...
  "aggregations": {
    "avg_grade": {
      "value": 75.0
    }
  }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]

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

Let's say the exam was exceedingly difficult, and you need to apply a grade correction. Average a <<runtime,runtime field>> to get a corrected average:

[source,console]
----
POST /exams/_search?size=0
{
  "runtime_mappings": {
    "grade.corrected": {
      "type": "double",
      "script": {
        "source": "emit(Math.min(100, doc['grade'].value * params.correction))",
        "params": {
          "correction": 1.2
        }
      }
    }
  },
  "aggs": {
    "avg_corrected_grade": {
      "avg": {
        "field": "grade.corrected"
      }
    }
  }
}
----
// TEST[setup:exams]
// TEST[s/size=0/size=0&filter_path=aggregations/]

////
[source,console-result]
----
{
  "aggregations": {
    "avg_corrected_grade": {
      "value": 80.0
    }
  }
}
----
////


==== 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": {
    "grade_avg": {
      "avg": {
        "field": "grade",
        "missing": 10     <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 `10`.


[[search-aggregations-metrics-avg-aggregation-histogram-fields]]
==== Histogram fields
When average is computed on <<histogram,histogram fields>>, the result of the aggregation is the weighted average
of all elements in the `values` array taking into consideration the number in the same position in the `counts` array.

For example, for the following index that stores pre-aggregated histograms with latency metrics for different networks:

[source,console]
--------------------------------------------------
PUT metrics_index/_doc/1
{
  "network.name" : "net-1",
  "latency_histo" : {
      "values" : [0.1, 0.2, 0.3, 0.4, 0.5], <1>
      "counts" : [3, 7, 23, 12, 6] <2>
   }
}

PUT metrics_index/_doc/2
{
  "network.name" : "net-2",
  "latency_histo" : {
      "values" :  [0.1, 0.2, 0.3, 0.4, 0.5], <1>
      "counts" : [8, 17, 8, 7, 6] <2>
   }
}

POST /metrics_index/_search?size=0
{
  "aggs": {
    "avg_latency": 
      { "avg": { "field": "latency_histo" } 
    }
  }
}
--------------------------------------------------

For each histogram field the `avg` aggregation adds each number in the `values` array <1> multiplied by its associated count
in the `counts` array <2>. Eventually, it will compute the average over those values for all histograms and return the following result:

[source,console-result]
--------------------------------------------------
{
  ...
  "aggregations": {
    "avg_latency": {
      "value": 0.29690721649
    }
  }
}
--------------------------------------------------
// TESTRESPONSE[skip:test not setup]