Normalize aggregation

A parent pipeline aggregation which calculates the specific normalized/rescaled value for a specific bucket value. Values that cannot be normalized, will be skipped using the skip gap policy.

A normalize aggregation looks like this in isolation:

{
  "normalize": {
    "buckets_path": "normalized",
    "method": "percent_of_sum"
  }
}

The Normalize Aggregation supports multiple methods to transform the bucket values. Each method definition will use the following original set of bucket values as examples: [5, 5, 10, 50, 10, 20].

rescale_0_1

This method rescales the data such that the minimum number is zero, and the maximum number is 1, with the rest normalized linearly in-between.

x' = (x - min_x) / (max_x - min_x)

[0, 0, .1111, 1, .1111, .3333]

rescale_0_100

This method rescales the data such that the minimum number is zero, and the maximum number is 100, with the rest normalized linearly in-between.

x' = 100 * (x - min_x) / (max_x - min_x)

[0, 0, 11.11, 100, 11.11, 33.33]

percent_of_sum

This method normalizes each value so that it represents a percentage of the total sum it attributes to.

x' = x / sum_x

[5%, 5%, 10%, 50%, 10%, 20%]

mean

This method normalizes such that each value is normalized by how much it differs from the average.

x' = (x - mean_x) / (max_x - min_x)

[4.63, 4.63, 9.63, 49.63, 9.63, 9.63, 19.63]

z-score

This method normalizes such that each value represents how far it is from the mean relative to the standard deviation

x' = (x - mean_x) / stdev_x

[-0.68, -0.68, -0.39, 1.94, -0.39, 0.19]

softmax

This method normalizes such that each value is exponentiated and relative to the sum of the exponents of the original values.

x' = e^x / sum_e_x

[2.862E-20, 2.862E-20, 4.248E-18, 0.999, 9.357E-14, 4.248E-18]

The following snippet calculates the percent of total sales for each month:

 POST /sales/_search {
  "size": 0,
  "aggs": {
    "sales_per_month": {
      "date_histogram": {
        "field": "date",
        "calendar_interval": "month"
      },
      "aggs": {
        "sales": {
          "sum": {
            "field": "price"
          }
        },
        "percent_of_total_sales": {
          "normalize": {
            "buckets_path": "sales",          1
            "method": "percent_of_sum",       2
            "format": "00.00%"                3
          }
        }
      }
    }
  }
}

1. buckets_path instructs this normalize aggregation to use the output of the sales aggregation for rescaling
2. method sets which rescaling to apply. In this case, percent_of_sum will calculate the sales value as a percent of all sales in the parent bucket
3. format influences how to format the metric as a string using Java’s DecimalFormat pattern. In this case, multiplying by 100 and adding a %

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
               },
               "percent_of_total_sales": {
                  "value": 0.5583756345177665,
                  "value_as_string": "55.84%"
               }
            },
            {
               "key_as_string": "2015/02/01 00:00:00",
               "key": 1422748800000,
               "doc_count": 2,
               "sales": {
                  "value": 60.0
               },
               "percent_of_total_sales": {
                  "value": 0.06091370558375635,
                  "value_as_string": "06.09%"
               }
            },
            {
               "key_as_string": "2015/03/01 00:00:00",
               "key": 1425168000000,
               "doc_count": 2,
               "sales": {
                  "value": 375.0
               },
               "percent_of_total_sales": {
                  "value": 0.38071065989847713,
                  "value_as_string": "38.07%"
               }
            }
         ]
      }
   }
}