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Stats aggregations

The stats aggregation is a multi-value metric aggregation that computes a summary of numeric data. This aggregation is useful for quickly understanding the distribution of numeric fields. It can operate directly on a field, apply a script to derive the values, or handle documents with missing fields. The stats aggregation returns five values:

  • count: The number of values collected
  • min: The lowest value
  • max: The highest value
  • sum: The total of all values
  • avg: The average of the values (sum divided by count)

Parameters

The stats aggregation takes the following optional parameters.

Parameter Data type Description
field String The field to aggregate on. Must be a numeric field.
script Object The script used to calculate custom values for aggregation. Can be used instead of or with field.
missing Number The default value used for documents missing the target field.

Example

The following example computes a stats aggregation for electricity usage.

Create an index named power_usage and add documents containing the number of kilowatt-hours (kWh) consumed during a given hour:

PUT /power_usage/_bulk?refresh=true
{"index": {}}
{"device_id": "A1", "kwh": 1.2}
{"index": {}}
{"device_id": "A2", "kwh": 0.7}
{"index": {}}
{"device_id": "A3", "kwh": 1.5}

To compute statistics on the kwh field across all documents, use a stats aggregation named consumption_stats over the kwh field. Setting size to 0 specifies that document hits should not be returned:

GET /power_usage/_search
{
  "size": 0,
  "aggs": {
    "consumption_stats": {
      "stats": {
        "field": "kwh"
      }
    }
  }
}

The response includes count, min, max, avg, and sum values for the three documents in the index:

{
  ...
  "hits": {
    "total": {
      "value": 3,
      "relation": "eq"
    },
    "max_score": null,
    "hits": []
  },
  "aggregations": {
    "consumption_stats": {
      "count": 3,
      "min": 0.699999988079071,
      "max": 1.5,
      "avg": 1.1333333452542622,
      "sum": 3.400000035762787
    }
  }
}

Running a stats aggregation per bucket

You can compute separate statistics for each device by nesting a stats aggregation inside a terms aggregation in the device_id field. The terms aggregation groups documents into buckets based on unique device_id values, and the stats aggregation computes summary statistics within each bucket:

GET /power_usage/_search
{
  "size": 0,
  "aggs": {
    "per_device": {
      "terms": {
        "field": "device_id.keyword"
      },
      "aggs": {
        "device_usage_stats": {
          "stats": {
            "field": "kwh"
          }
        }
      }
    }
  }
}

The response returns one bucket per device_id, with computed count, min, max, avg, and sum fields within each bucket:

{
  ...
  "hits": {
    "total": {
      "value": 3,
      "relation": "eq"
    },
    "max_score": null,
    "hits": []
  },
  "aggregations": {
    "per_device": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "A1",
          "doc_count": 1,
          "device_usage_stats": {
            "count": 1,
            "min": 1.2000000476837158,
            "max": 1.2000000476837158,
            "avg": 1.2000000476837158,
            "sum": 1.2000000476837158
          }
        },
        {
          "key": "A2",
          "doc_count": 1,
          "device_usage_stats": {
            "count": 1,
            "min": 0.699999988079071,
            "max": 0.699999988079071,
            "avg": 0.699999988079071,
            "sum": 0.699999988079071
          }
        },
        {
          "key": "A3",
          "doc_count": 1,
          "device_usage_stats": {
            "count": 1,
            "min": 1.5,
            "max": 1.5,
            "avg": 1.5,
            "sum": 1.5
          }
        }
      ]
    }
  }
}

This allows you to compare usage statistics across devices with a single query.

Using a script to compute derived values

You can also use a script to compute the values used in the stats aggregation. This is useful when the metric is derived from document fields or requires transformation.

For example, to convert kilowatt-hours (kWh) to watt-hours (Wh) before running the stats aggregation, because 1 kWh equals 1,000 Wh, you can use a script that multiplies each value by 1,000. The following script doc['kwh'].value * 1000 is used to derive the input value for each document:

GET /power_usage/_search
{
  "size": 0,
  "aggs": {
    "usage_wh_stats": {
      "stats": {
        "script": {
          "source": "doc['kwh'].value * 1000"
        }
      }
    }
  }
}

The stats aggregation returned in the response reflects values of 1200, 700, and 1500 Wh:

{
  ...
  "hits": {
    "total": {
      "value": 3,
      "relation": "eq"
    },
    "max_score": null,
    "hits": []
  },
  "aggregations": {
    "usage_wh_stats": {
      "count": 3,
      "min": 699.999988079071,
      "max": 1500,
      "avg": 1133.3333452542622,
      "sum": 3400.000035762787
    }
  }
}

Using a value script with a field

When combining a field with a transformation, you can specify both field and script. This allows using the _value variable to reference the field’s value within the script.

The following example increases each energy reading by 5% before computing the stats aggregation:

GET /power_usage/_search
{
  "size": 0,
  "aggs": {
    "adjusted_usage": {
      "stats": {
        "field": "kwh",
        "script": {
          "source": "_value * 1.05"
        }
      }
    }
  }
}

Missing values

If some documents do not contain the target field, they are excluded by default from the aggregation. To include them using a default value, you can specify the missing parameter.

The following request treats missing kwh values as 0.0:

GET /power_usage/_search
{
  "size": 0,
  "aggs": {
    "consumption_with_default": {
      "stats": {
        "field": "kwh",
        "missing": 0.0
      }
    }
  }
}

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