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Extended Stats Bucket Aggregation

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Extended Stats Bucket Aggregation

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This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

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.

This aggregation provides a few more statistics (sum of squares, standard deviation, etc) compared to the stats_bucket aggregation.

Syntax

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A extended_stats_bucket aggregation looks like this in isolation:

{
    "extended_stats_bucket": {
        "buckets_path": "the_sum"
    }
}

Table 7. extended_stats_bucket Parameters

Parameter Name	Description	Required	Default Value
`buckets_path`	The path to the buckets we wish to calculate stats 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`	format to apply to the output value of this aggregation	Optional	`null`
`sigma`	The number of standard deviations above/below the mean to display	Optional	2

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

{
    "aggs" : {
        "sales_per_month" : {
            "date_histogram" : {
                "field" : "date",
                "interval" : "month"
            },
            "aggs": {
                "sales": {
                    "sum": {
                        "field": "price"
                    }
                }
            }
        },
        "stats_monthly_sales": {
            "extended_stats_bucket": {
                "buckets_paths": "sales_per_month>sales" 
            }
        }
    }
}

bucket_paths instructs this extended_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:

{
   "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,
         "sum_of_squares": 446725,
         "variance": 41105.5555556,
         "std_deviation": 117.054909559,
         "std_deviation_bounds": {
           "upper": 562.443152451,
           "lower": 94.2235142151
         }
      }
   }
}

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