This documentation contains work-in-progress information for future Elastic Stack and Cloud releases. Use the version selector to view supported release docs. It also contains some Elastic Cloud serverless information. Check out our serverless docs for more details.
« Average bucket aggregation Bucket count K-S test correlation aggregation »
Elastic Docs ›Elasticsearch Guide [8.16] ›Aggregations ›Pipeline aggregations

Bucket script aggregation

edit

Bucket script aggregation

edit

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

edit

A bucket_script aggregation looks like this in isolation:

{
  "bucket_script": {
    "buckets_path": {
      "my_var1": "the_sum",                     
      "my_var2": "the_value_count"
    },
    "script": "params.my_var1 / params.my_var2"
  }
}

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.

Table 56. 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 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 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 ratio percentage of t-shirt sales compared to total sales each month:

resp = client.search(
    index="sales",
    size=0,
    aggs={
        "sales_per_month": {
            "date_histogram": {
                "field": "date",
                "calendar_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": "params.tShirtSales / params.totalSales * 100"
                    }
                }
            }
        }
    },
)
print(resp)
response = client.search(
  index: 'sales',
  body: {
    size: 0,
    aggregations: {
      sales_per_month: {
        date_histogram: {
          field: 'date',
          calendar_interval: 'month'
        },
        aggregations: {
          total_sales: {
            sum: {
              field: 'price'
            }
          },
          "t-shirts": {
            filter: {
              term: {
                type: 't-shirt'
              }
            },
            aggregations: {
              sales: {
                sum: {
                  field: 'price'
                }
              }
            }
          },
          "t-shirt-percentage": {
            bucket_script: {
              buckets_path: {
                "tShirtSales": 't-shirts>sales',
                "totalSales": 'total_sales'
              },
              script: 'params.tShirtSales / params.totalSales * 100'
            }
          }
        }
      }
    }
  }
)
puts response
const response = await client.search({
  index: "sales",
  size: 0,
  aggs: {
    sales_per_month: {
      date_histogram: {
        field: "date",
        calendar_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: "params.tShirtSales / params.totalSales * 100",
          },
        },
      },
    },
  },
});
console.log(response);
POST /sales/_search
{
  "size": 0,
  "aggs": {
    "sales_per_month": {
      "date_histogram": {
        "field": "date",
        "calendar_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": "params.tShirtSales / params.totalSales * 100"
          }
        }
      }
    }
  }
}

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,
               "total_sales": {
                   "value": 550.0
               },
               "t-shirts": {
                   "doc_count": 1,
                   "sales": {
                       "value": 200.0
                   }
               },
               "t-shirt-percentage": {
                   "value": 36.36363636363637
               }
            },
            {
               "key_as_string": "2015/02/01 00:00:00",
               "key": 1422748800000,
               "doc_count": 2,
               "total_sales": {
                   "value": 60.0
               },
               "t-shirts": {
                   "doc_count": 1,
                   "sales": {
                       "value": 10.0
                   }
               },
               "t-shirt-percentage": {
                   "value": 16.666666666666664
               }
            },
            {
               "key_as_string": "2015/03/01 00:00:00",
               "key": 1425168000000,
               "doc_count": 2,
               "total_sales": {
                   "value": 375.0
               },
               "t-shirts": {
                   "doc_count": 1,
                   "sales": {
                       "value": 175.0
                   }
               },
               "t-shirt-percentage": {
                   "value": 46.666666666666664
               }
            }
         ]
      }
   }
}
« Average bucket aggregation Bucket count K-S test correlation aggregation »