[[search-aggregations-pipeline-sum-bucket-aggregation]] === Sum Bucket Aggregation coming[2.0.0-beta1] experimental[] A sibling pipeline aggregation which calculates the sum across all bucket 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 A `sum_bucket` aggregation looks like this in isolation: [source,js] -------------------------------------------------- { "sum_bucket": { "buckets_path": "the_sum" } } -------------------------------------------------- .`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 <<gap-policy>> for more details)|Optional, defaults to `skip` || |`format` |format to apply to the output value of this aggregation |Optional, defaults to `null` | |=== The following snippet calculates the sum of all the total monthly `sales` buckets: [source,js] -------------------------------------------------- { "aggs" : { "sales_per_month" : { "date_histogram" : { "field" : "date", "interval" : "month" }, "aggs": { "sales": { "sum": { "field": "price" } } } }, "sum_monthly_sales": { "sum_bucket": { "buckets_path": "sales_per_month>sales" <1> } } } } -------------------------------------------------- <1> `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: [source,js] -------------------------------------------------- { "aggregations": { "sales_per_month": { "buckets": [ { "key_as_string": "2015/01/01 00:00:00", "key": 1420070400000, "doc_count": 3, "sales": { "value": 550 } }, { "key_as_string": "2015/02/01 00:00:00", "key": 1422748800000, "doc_count": 2, "sales": { "value": 60 } }, { "key_as_string": "2015/03/01 00:00:00", "key": 1425168000000, "doc_count": 2, "sales": { "value": 375 } } ] }, "sum_monthly_sales": { "value": 985 } } } --------------------------------------------------