Extended stats bucket aggregation

Extended stats bucket aggregation

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

A extended_stats_bucket aggregation looks like this in isolation:

  1. {
  2.   "extended_stats_bucket": {
  3.     "buckets_path": "the_sum"
  4.   }
  5. }

Table 55. extended_stats_bucket Parameters

Parameter NameDescriptionRequiredDefault 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:

  1. POST /sales/_search
  2. {
  3.   "size": 0,
  4.   "aggs": {
  5.     "sales_per_month": {
  6.       "date_histogram": {
  7.         "field": "date",
  8.         "calendar_interval": "month"
  9.       },
  10.       "aggs": {
  11.         "sales": {
  12.           "sum": {
  13.             "field": "price"
  14.           }
  15.         }
  16.       }
  17.     },
  18.     "stats_monthly_sales": {
  19.       "extended_stats_bucket": {
  20.         "buckets_path": "sales_per_month>sales" 
  21.       }
  22.     }
  23.   }
  24. }

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:

  1. {
  2.    "took": 11,
  3.    "timed_out": false,
  4.    "_shards": ...,
  5.    "hits": ...,
  6.    "aggregations": {
  7.       "sales_per_month": {
  8.          "buckets": [
  9.             {
  10.                "key_as_string": "2015/01/01 00:00:00",
  11.                "key": 1420070400000,
  12.                "doc_count": 3,
  13.                "sales": {
  14.                   "value": 550.0
  15.                }
  16.             },
  17.             {
  18.                "key_as_string": "2015/02/01 00:00:00",
  19.                "key": 1422748800000,
  20.                "doc_count": 2,
  21.                "sales": {
  22.                   "value": 60.0
  23.                }
  24.             },
  25.             {
  26.                "key_as_string": "2015/03/01 00:00:00",
  27.                "key": 1425168000000,
  28.                "doc_count": 2,
  29.                "sales": {
  30.                   "value": 375.0
  31.                }
  32.             }
  33.          ]
  34.       },
  35.       "stats_monthly_sales": {
  36.          "count": 3,
  37.          "min": 60.0,
  38.          "max": 550.0,
  39.          "avg": 328.3333333333333,
  40.          "sum": 985.0,
  41.          "sum_of_squares": 446725.0,
  42.          "variance": 41105.55555555556,
  43.          "variance_population": 41105.55555555556,
  44.          "variance_sampling": 61658.33333333334,
  45.          "std_deviation": 202.74505063146563,
  46.          "std_deviation_population": 202.74505063146563,
  47.          "std_deviation_sampling": 248.3109609609156,
  48.          "std_deviation_bounds": {
  49.            "upper": 733.8234345962646,
  50.            "lower": -77.15676792959795,
  51.            "upper_population" : 733.8234345962646,
  52.            "lower_population" : -77.15676792959795,
  53.            "upper_sampling" : 824.9552552551645,
  54.            "lower_sampling" : -168.28858858849787
  55.          }
  56.       }
  57.    }
  58. }