[[search-aggregations-pipeline-bucket-script-aggregation]] === Bucket Script Aggregation experimental[] A parent pipeline aggregation which executes a script which can perform per bucket computations on specified metrics in the parent multi-bucket aggregation. The specified metric must be numeric and the script must return a numeric value. ==== Syntax A `bucket_script` aggregation looks like this in isolation: [source,js] -------------------------------------------------- { "bucket_script": { "buckets_path": { "my_var1": "the_sum", <1> "my_var2": "the_value_count" }, "script": "my_var1 / my_var2" } } -------------------------------------------------- <1> Here, `my_var1` is the name of the variable for this buckets path to use in the script, `the_sum` is the path to the metrics to use for that variable. .`bucket_script` Parameters |=== |Parameter Name |Description |Required |Default Value |`script` |The script to run for this aggregation. The script can be inline, file or indexed. (see <<modules-scripting>> for more details) |Required | |`buckets_path` |A map of script variables and their associated path to the buckets we wish to use for the variable (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 ratio percentage of t-shirt sales compared to total sales each month: [source,js] -------------------------------------------------- { "aggs" : { "sales_per_month" : { "date_histogram" : { "field" : "date", "interval" : "month" }, "aggs": { "total_sales": { "sum": { "field": "price" } }, "t-shirts": { "filter": { "term": { "type": "t-shirt" } }, "aggs": { "sales": { "sum": { "field": "price" } } } }, "t-shirt-percentage": { "bucket_script": { "buckets_path": { "tShirtSales": "t-shirts>sales", "totalSales": "total_sales" }, "script": "tShirtSales / totalSales * 100" } } } } } } -------------------------------------------------- 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, "total_sales": { "value": 50 }, "t-shirts": { "doc_count": 2, "sales": { "value": 10 } }, "t-shirt-percentage": { "value": 20 } }, { "key_as_string": "2015/02/01 00:00:00", "key": 1422748800000, "doc_count": 2 "total_sales": { "value": 60 }, "t-shirts": { "doc_count": 1, "sales": { "value": 15 } }, "t-shirt-percentage": { "value": 25 } }, { "key_as_string": "2015/03/01 00:00:00", "key": 1425168000000, "doc_count": 2, "total_sales": { "value": 40 }, "t-shirts": { "doc_count": 1, "sales": { "value": 20 } }, "t-shirt-percentage": { "value": 50 } } ] } } } --------------------------------------------------