Average bucket aggregation

A sibling pipeline aggregation which calculates the mean value of a specified metric in a sibling aggregation. The specified metric must be numeric and the sibling aggregation must be a multi-bucket aggregation.

Syntax

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"avg_bucket": {
  "buckets_path": "sales_per_month>sales",
  "gap_policy": "skip",
  "format": "#,##0.00;(#,##0.00)"
}

Parameters

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buckets_path: (Required, string) Path to the buckets to average. For syntax, see buckets_path Syntax.
gap_policy: (Optional, string) Policy to apply when gaps are found in the data. For valid values, see Dealing with gaps in the data. Defaults to skip.
format: (Optional, string) DecimalFormat pattern for the output value. If specified, the formatted value is returned in the aggregation’s value_as_string property.

Response body

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value: (float) Mean average value for the metric specified in buckets_path.
value_as_string: (string) Formatted output value for the aggregation. This property is only provided if a format is specified in the request.

Example

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The following avg_monthly_sales aggregation uses avg_bucket to calculate average sales per month:

resp = client.search(
    size=0,
    aggs={
        "sales_per_month": {
            "date_histogram": {
                "field": "date",
                "calendar_interval": "month"
            },
            "aggs": {
                "sales": {
                    "sum": {
                        "field": "price"
                    }
                }
            }
        },
        "avg_monthly_sales": {
            "avg_bucket": {
                "buckets_path": "sales_per_month>sales",
                "gap_policy": "skip",
                "format": "#,##0.00;(#,##0.00)"
            }
        }
    },
)
print(resp)

const response = await client.search({
  size: 0,
  aggs: {
    sales_per_month: {
      date_histogram: {
        field: "date",
        calendar_interval: "month",
      },
      aggs: {
        sales: {
          sum: {
            field: "price",
          },
        },
      },
    },
    avg_monthly_sales: {
      avg_bucket: {
        buckets_path: "sales_per_month>sales",
        gap_policy: "skip",
        format: "#,##0.00;(#,##0.00)",
      },
    },
  },
});
console.log(response);

POST _search
{
  "size": 0,
  "aggs": {
    "sales_per_month": {
      "date_histogram": {
        "field": "date",
        "calendar_interval": "month"
      },
      "aggs": {
        "sales": {
          "sum": {
            "field": "price"
          }
        }
      }
    },
    "avg_monthly_sales": {
// tag::avg-bucket-agg-syntax[]               
      "avg_bucket": {
        "buckets_path": "sales_per_month>sales",
        "gap_policy": "skip",
        "format": "#,##0.00;(#,##0.00)"
      }
// end::avg-bucket-agg-syntax[]               
    }
  }
}

	Start of the `avg_bucket` configuration. Comment is not part of the example.
	End of the `avg_bucket` configuration. Comment is not part of the example.

The request returns the following response:

{
  "took": 11,
  "timed_out": false,
  "_shards": ...,
  "hits": ...,
  "aggregations": {
    "sales_per_month": {
      "buckets": [
        {
          "key_as_string": "2015/01/01 00:00:00",
          "key": 1420070400000,
          "doc_count": 3,
          "sales": {
            "value": 550.0
          }
        },
        {
          "key_as_string": "2015/02/01 00:00:00",
          "key": 1422748800000,
          "doc_count": 2,
          "sales": {
            "value": 60.0
          }
        },
        {
          "key_as_string": "2015/03/01 00:00:00",
          "key": 1425168000000,
          "doc_count": 2,
          "sales": {
            "value": 375.0
          }
        }
      ]
    },
    "avg_monthly_sales": {
      "value": 328.33333333333333,
      "value_as_string": "328.33"
    }
  }
}

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