[[search-aggregations-pipeline-stats-bucket-aggregation]] === Stats Bucket Aggregation experimental[] A sibling pipeline aggregation which calculates a variety of stats 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 `stats_bucket` aggregation looks like this in isolation: [source,js] -------------------------------------------------- { "stats_bucket": { "buckets_path": "the_sum" } } -------------------------------------------------- .`stats_bucket` Parameters |=== |Parameter Name |Description |Required |Default Value |`buckets_path` |The path to the buckets we wish to calculate stats for (see <> for more details) |Required | |`gap_policy` |The policy to apply when gaps are found in the data (see <> for more details)|Optional | `skip` |`format` |format to apply to the output value of this aggregation |Optional | `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" } } } }, "stats_monthly_sales": { "stats_bucket": { "buckets_paths": "sales_per_month>sales" <1> } } } } -------------------------------------------------- <1> `bucket_paths` instructs this `stats_bucket` aggregation that we want the calculate stats for 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 } } ] }, "stats_monthly_sales": { "count": 3, "min": 60, "max": 550, "avg": 328.333333333, "sum": 985 } } } --------------------------------------------------