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Elastic Docs ›Elasticsearch Guide [8.x] ›Aggregations ›Pipeline aggregations

Sum bucket aggregation

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Sum bucket aggregation

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A sibling pipeline aggregation which calculates the sum across all buckets 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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A sum_bucket aggregation looks like this in isolation:

{
  "sum_bucket": {
    "buckets_path": "the_sum"
  }
}

Table 81. sum_bucket Parameters

Parameter Name Description Required Default Value

buckets_path

The path to the buckets we wish to find the sum for (see buckets_path Syntax for more details)

Required

gap_policy

The policy to apply when gaps are found in the data (see Dealing with gaps in the data for more details)

Optional

skip

format

DecimalFormat pattern for the output value. If specified, the formatted value is returned in the aggregation’s value_as_string property.

Optional

null

The following snippet calculates the sum of all the total monthly sales buckets:

resp = client.search(
    index="sales",
    size=0,
    aggs={
        "sales_per_month": {
            "date_histogram": {
                "field": "date",
                "calendar_interval": "month"
            },
            "aggs": {
                "sales": {
                    "sum": {
                        "field": "price"
                    }
                }
            }
        },
        "sum_monthly_sales": {
            "sum_bucket": {
                "buckets_path": "sales_per_month>sales"
            }
        }
    },
)
print(resp)
response = client.search(
  index: 'sales',
  body: {
    size: 0,
    aggregations: {
      sales_per_month: {
        date_histogram: {
          field: 'date',
          calendar_interval: 'month'
        },
        aggregations: {
          sales: {
            sum: {
              field: 'price'
            }
          }
        }
      },
      sum_monthly_sales: {
        sum_bucket: {
          buckets_path: 'sales_per_month>sales'
        }
      }
    }
  }
)
puts response
const response = await client.search({
  index: "sales",
  size: 0,
  aggs: {
    sales_per_month: {
      date_histogram: {
        field: "date",
        calendar_interval: "month",
      },
      aggs: {
        sales: {
          sum: {
            field: "price",
          },
        },
      },
    },
    sum_monthly_sales: {
      sum_bucket: {
        buckets_path: "sales_per_month>sales",
      },
    },
  },
});
console.log(response);
POST /sales/_search
{
  "size": 0,
  "aggs": {
    "sales_per_month": {
      "date_histogram": {
        "field": "date",
        "calendar_interval": "month"
      },
      "aggs": {
        "sales": {
          "sum": {
            "field": "price"
          }
        }
      }
    },
    "sum_monthly_sales": {
      "sum_bucket": {
        "buckets_path": "sales_per_month>sales" 
      }
    }
  }
}

buckets_path instructs this sum_bucket aggregation that we want the sum of the sales aggregation in the sales_per_month date histogram.

And the following may be the 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
               }
            }
         ]
      },
      "sum_monthly_sales": {
          "value": 985.0
      }
   }
}
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