﻿---
title: Avg aggregation
description: A single-value metrics aggregation that computes the average of numeric values that are extracted from the aggregated documents. These values can be extracted...
url: https://www.elastic.co/elastic/docs-builder/docs/2654/reference/aggregations/search-aggregations-metrics-avg-aggregation
products:
  - Elasticsearch
---

# 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 or [histogram](https://www.elastic.co/elastic/docs-builder/docs/2654/reference/elasticsearch/mapping-reference/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:
```json

{
  "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:
```json
{
  ...
  "aggregations": {
    "avg_grade": {
      "value": 75.0
    }
  }
}
```

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 field](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/2654/manage-data/data-store/mapping/runtime-fields) to get a corrected average:
```json

{
  "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"
      }
    }
  }
}
```


## 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.
```json

{
  "aggs": {
    "grade_avg": {
      "avg": {
        "field": "grade",
        "missing": 10     <1>
      }
    }
  }
}
```


## Histogram fields

When average is computed on [histogram fields](https://www.elastic.co/elastic/docs-builder/docs/2654/reference/elasticsearch/mapping-reference/histogram), 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:
```json

{
  "network.name" : "net-1",
  "latency_histo" : {
      "values" : [0.1, 0.2, 0.3, 0.4, 0.5],
      "counts" : [3, 7, 23, 12, 6]
   }
}


{
  "network.name" : "net-2",
  "latency_histo" : {
      "values" :  [0.1, 0.2, 0.3, 0.4, 0.5],
      "counts" : [8, 17, 8, 7, 6]
   }
}


{
  "aggs": {
    "avg_latency":
      { "avg": { "field": "latency_histo" }
    }
  }
}
```

For each histogram field the `avg` aggregation adds each number in the `values` array multiplied by its associated count in the `counts` array. Eventually, it will compute the average over those values for all histograms and return the following result:
```json
{
  ...
  "aggregations": {
    "avg_latency": {
      "value": 0.29690721649
    }
  }
}
```