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Percentiles bucket aggregation

A sibling pipeline aggregation which calculates percentiles 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.

Syntax

A percentiles_bucket aggregation looks like this in isolation:

{
  "percentiles_bucket": {
    "buckets_path": "the_sum"
  }
}

Table 78. percentiles_bucket Parameters

Parameter Name	Description	Required	Default Value
`buckets_path`	The path to the buckets we wish to find the percentiles 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`
`percents`	The list of percentiles to calculate	Optional	`[ 1, 5, 25, 50, 75, 95, 99 ]`
`keyed`	Flag which returns the range as an hash instead of an array of key-value pairs	Optional	`true`

The following snippet calculates the percentiles for the total monthly sales buckets:

resp = client.search(
    index="sales",
    size=0,
    aggs={
        "sales_per_month": {
            "date_histogram": {
                "field": "date",
                "calendar_interval": "month"
            },
            "aggs": {
                "sales": {
                    "sum": {
                        "field": "price"
                    }
                }
            }
        },
        "percentiles_monthly_sales": {
            "percentiles_bucket": {
                "buckets_path": "sales_per_month>sales",
                "percents": [
                    25,
                    50,
                    75
                ]
            }
        }
    },
)
print(resp)

response = client.search(
  index: 'sales',
  body: {
    size: 0,
    aggregations: {
      sales_per_month: {
        date_histogram: {
          field: 'date',
          calendar_interval: 'month'
        },
        aggregations: {
          sales: {
            sum: {
              field: 'price'
            }
          }
        }
      },
      percentiles_monthly_sales: {
        percentiles_bucket: {
          buckets_path: 'sales_per_month>sales',
          percents: [
            25,
            50,
            75
          ]
        }
      }
    }
  }
)
puts response

const response = await client.search({
  index: "sales",
  size: 0,
  aggs: {
    sales_per_month: {
      date_histogram: {
        field: "date",
        calendar_interval: "month",
      },
      aggs: {
        sales: {
          sum: {
            field: "price",
          },
        },
      },
    },
    percentiles_monthly_sales: {
      percentiles_bucket: {
        buckets_path: "sales_per_month>sales",
        percents: [25, 50, 75],
      },
    },
  },
});
console.log(response);

POST /sales/_search
{
  "size": 0,
  "aggs": {
    "sales_per_month": {
      "date_histogram": {
        "field": "date",
        "calendar_interval": "month"
      },
      "aggs": {
        "sales": {
          "sum": {
            "field": "price"
          }
        }
      }
    },
    "percentiles_monthly_sales": {
      "percentiles_bucket": {
        "buckets_path": "sales_per_month>sales", 
        "percents": [ 25.0, 50.0, 75.0 ]         
      }
    }
  }
}

	`buckets_path` instructs this percentiles_bucket aggregation that we want to calculate percentiles for the `sales` aggregation in the `sales_per_month` date histogram.
	`percents` specifies which percentiles we wish to calculate, in this case, the 25th, 50th and 75th percentiles.

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
               }
            }
         ]
      },
      "percentiles_monthly_sales": {
        "values" : {
            "25.0": 375.0,
            "50.0": 375.0,
            "75.0": 550.0
         }
      }
   }
}

Percentiles_bucket implementation

The percentiles are calculated exactly and is not an approximation (unlike the Percentiles Metric). This means the implementation maintains an in-memory, sorted list of your data to compute the percentiles, before discarding the data. You may run into memory pressure issues if you attempt to calculate percentiles over many millions of data-points in a single percentiles_bucket.

The Percentile Bucket returns the nearest input data point to the requested percentile, rounding indices toward positive infinity; it does not interpolate between data points. For example, if there are eight data points and you request the 50%th percentile, it will return the 4th item because ROUND_UP(.50 * (8-1)) is 4.

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https://www.elastic.co/guide/en/elasticsearch/reference/8.17/search-aggregations-pipeline-percentiles-bucket-aggregation.html