anchor.diff:                 "doc_count": 2,
                  "sales": {
                     "value": 375
  -               },
  -               "sales_deriv": {
  -                  "value": 315
                  }
               }
            ]
  +      },
  +      "max_monthly_sales": {
  +          "keys": ["2015/01/01 00:00:00"], <1>
  +          "value": 550
         }
      }
   }
   --------------------------------------------------
   
  -<1> No derivative for the first bucket since we need at least 2 data points to calculate the derivative
  -<2> Derivative value units are implicitly defined by the `sales` aggregation and the parent histogram so in this case the units 
  -would be $/month assuming the `price` field has units of $.
  +<1> `keys` is an array of strings since the maximum value may be present in multiple buckets
   
  -==== Second Order Derivative
  -
  -A second order derivative can be calculated by chaining the derivative reducer aggregation onto the result of another derivative 
  -reducer aggregation as in the following example which will calculate both the first and the second order derivative of the total 
  -monthly sales:
  -
  -[source,js]
  ---------------------------------------------------
  -{
  -    "aggs" : {
  -        "sales" : {
  -            "date_histogram" : {
  -                "field" : "date",
  -                "interval" : "month"
  -            },
  -            "aggs": {
  -                "sales": {
  -                    "sum": {
  -                        "field": "price"
  -                    }
  -                },
  -                "sales_deriv": {
  -                    "derivative": {
  -                        "buckets_paths": "sales"
  -                    }
  -                },
  -                "sales_2nd_deriv": {
  -                    "derivative": {
  -                        "buckets_paths": "sales_deriv" <1>
  -                    }
  -                }
  -            }
  -        }
  -    }
  -}
  ---------------------------------------------------
  -
  -<1> `bucket_paths` for the second derivative points to the name of the first derivative
  -
  -And the following may be the response:
  -
  -[source,js]
  ---------------------------------------------------
  -{
  -   "aggregations": {
  -      "sales": {
  -         "buckets": [
  -            {
  -               "key_as_string": "2015/01/01 00:00:00",
  -               "key": 1420070400000,
  -               "doc_count": 3,
  -               "sales": {
  -                  "value": 550
  -               } <1>
  -            },
  -            {
  -               "key_as_string": "2015/02/01 00:00:00",
  -               "key": 1422748800000,
  -               "doc_count": 2,
  -               "sales": {
  -                  "value": 60
  -               },
  -               "sales_deriv": {
  -                  "value": -490
  -               } <1>
  -            },
  -            {
  -               "key_as_string": "2015/03/01 00:00:00",
  -               "key": 1425168000000,
  -               "doc_count": 2,
  -               "sales": {
  -                  "value": 375
  -               },
  -               "sales_deriv": {
  -                  "value": 315
  -               },
  -               "sales_2nd_deriv": {
  -                  "value": 805
  -               }
  -            }
  -         ]
  -      }
  -   }
  -}
  ---------------------------------------------------
  -<1> No second derivative for the first two buckets since we need at least 2 data points from the first derivative to calculate the 
  -second derivative
  -
  -==== Dealing with gaps in the data
  -
  -There are a couple of reasons why the data output by the enclosing histogram may have gaps:
  -
  -* There are no documents matching the query for some buckets
  -* The data for a metric is missing in all of the documents falling into a bucket (this is most likely with either a small interval 
  -on the enclosing histogram or with a query matching only a small number of documents)
  -
  -Where there is no data available in a bucket for a given metric it presents a problem for calculating the derivative value for both 
  -the current bucket and the next bucket. In the derivative reducer aggregation has a `gap policy` parameter to define what the behavior 
  -should be when a gap in the data is found. There are currently two options for controlling the gap policy:
  -
  -_ignore_::
  -				This option will not produce a derivative value for any buckets where the value in the current or previous bucket is 
  -				missing
  -
  -_insert_zeros_::
  -				This option will assume the missing value is `0` and calculate the derivative with the value `0`.
  -
  -
  
positive.code: [[search-aggregations-reducer-max-bucket-aggregation]]
  === Max Bucket Aggregation
  
  A parent reducer aggregation which calculates the derivative of a specified metric in a parent histogram (or date_histogram) 
  aggregation. The specified metric must be numeric and the enclosing histogram must have `min_doc_count` set to `0`.
  
  The following snippet calculates the derivative of the total monthly `sales`:
  
  [source,js]
  --------------------------------------------------
  {
      "aggs" : {
          "sales" : {
              "date_histogram" : {
                  "field" : "date",
                  "interval" : "month"
  
negative.code:                "doc_count": 2,
                 "sales": {
                    "value": 60
                 }
              },
              {
  