IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.

« Derivative aggregation Inference bucket aggregation »

› › ›

Extended stats bucket aggregation

edit

Extended stats bucket aggregation

edit

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

edit

A extended_stats_bucket aggregation looks like this in isolation:

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

Table 60. 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`	DecimalFormat pattern for the output value. If specified, the formatted value is returned in the aggregation’s `value_as_string` property	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:

response = client.search(
  index: 'sales',
  body: {
    size: 0,
    aggregations: {
      sales_per_month: {
        date_histogram: {
          field: 'date',
          calendar_interval: 'month'
        },
        aggregations: {
          sales: {
            sum: {
              field: 'price'
            }
          }
        }
      },
      stats_monthly_sales: {
        extended_stats_bucket: {
          buckets_path: 'sales_per_month>sales'
        }
      }
    }
  }
)
puts response

POST /sales/_search
{
  "size": 0,
  "aggs": {
    "sales_per_month": {
      "date_histogram": {
        "field": "date",
        "calendar_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,
         "variance_population": 41105.55555555556,
         "variance_sampling": 61658.33333333334,
         "std_deviation": 202.74505063146563,
         "std_deviation_population": 202.74505063146563,
         "std_deviation_sampling": 248.3109609609156,
         "std_deviation_bounds": {
           "upper": 733.8234345962646,
           "lower": -77.15676792959795,
           "upper_population" : 733.8234345962646,
           "lower_population" : -77.15676792959795,
           "upper_sampling" : 824.9552552551645,
           "lower_sampling" : -168.28858858849787
         }
      }
   }
}

« Derivative aggregation Inference bucket aggregation »