Extended Stats Bucket Aggregationedit

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.

Syntaxedit

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 extended stats for monthly sales bucket:

POST /sales/_search
{
    "size": 0,
    "aggs" : {
        "sales_per_month" : {
            "date_histogram" : {
                "field" : "date",
                "interval" : "month"
            },
            "aggs": {
                "sales": {
                    "sum": {
                        "field": "price"
                    }
                }
            }
        },
        "stats_monthly_sales": {
            "extended_stats_bucket": {
                "buckets_path": "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:

{
   "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,
               "sales": {
                  "value": 550.0
               }
            },
            {
               "key_as_string": "2015/02/01 00:00:00",
               "key": 1422748800000,
               "doc_count": 2,
               "sales": {
                  "value": 60.0
               }
            },
            {
               "key_as_string": "2015/03/01 00:00:00",
               "key": 1425168000000,
               "doc_count": 2,
               "sales": {
                  "value": 375.0
               }
            }
         ]
      },
      "stats_monthly_sales": {
         "count": 3,
         "min": 60.0,
         "max": 550.0,
         "avg": 328.3333333333333,
         "sum": 985.0,
         "sum_of_squares": 446725.0,
         "variance": 41105.55555555556,
         "std_deviation": 202.74505063146563,
         "std_deviation_bounds": {
           "upper": 733.8234345962646,
           "lower": -77.15676792959795
         }
      }
   }
}