anchor.diff:  --------------------------------------------------
   
   The `aggregations` object (the key `aggs` can also be used) in the JSON holds the aggregations to be computed. Each aggregation
  -is associated with a logical name that the user defines (e.g. if the aggregation computes the average price, then it'll
  +is associated with a logical name that the user defines (e.g. if the aggregation computes the average price, then it would
   make sense to name it `avg_price`). These logical names will also be used to uniquely identify the aggregations in the
   response. Each aggregation has a specific type (`<aggregation_type>` in the above snippet) and is typically the first
  -key within the named aggregation body. Each type of aggregation define its own body, depending on the nature of the
  +key within the named aggregation body. Each type of aggregation defines its own body, depending on the nature of the
   aggregation (e.g. an `avg` aggregation on a specific field will define the field on which the average will be calculated).
   At the same level of the aggregation type definition, one can optionally define a set of additional aggregations,
   though this only makes sense if the aggregation you defined is of a bucketing nature. In this scenario, the
   sub-aggregations you define on the bucketing aggregation level will be computed for all the buckets built by the
  -bucketing aggregation. For example, if the you define a set of aggregations under the `range` aggregation, the
  +bucketing aggregation. For example, if you define a set of aggregations under the `range` aggregation, the
   sub-aggregations will be computed for the range buckets that are defined.
   
   [float]
  
positive.code: === Bucket Aggregations
  
  Bucket aggregations don't calculate metrics over fields like the metrics aggregations do, but instead, they create
  buckets of documents. Each bucket is associated with a criteria (depends on the aggregation type) that determines
  whether or not a document in the current context "falls" in it. In other words, the buckets effectively define document
  sets. In addition to the buckets themselves, the `bucket` aggregations also compute and return the number of documents
  that "fell in" each bucket.
  
  Bucket aggregations, as opposed to `metrics` aggregations, can hold sub-aggregations. These sub aggregations will be
  aggregated for the buckets created by their "parent" bucket aggregation.
  
  There are different bucket aggregators, each with a different "bucketing" strategy. Some define a single bucket, some
  
negative.code: 
  Some aggregations work on values extracted from the aggregated documents. Typically, the values will be extracted from
  a specific document field which is set using the `field` key for the aggregations. It is also possible to define a
  <<modules-scripting,`script`>> which will generate the values (per document).
  
  When both `field` and `script` settings are configured for the aggregation, the script will be treated as a
  `value script`.  While normal scripts are evaluated on a document level (i.e. the script has access to all the data
  associated with the document), value scripts are evaluated on the *value* level. In this mode, the values are extracted
  from the configured `field` and the `script` is used to apply a "transformation" over these value/s.
  
  ["NOTE",id="aggs-script-note"]
  ===============================
  When working with scripts, the `lang` and `params` settings can also be defined. The former defines the scripting
  language which is used (assuming the proper language is available in Elasticsearch, either by default or as a plugin). The latter
  enables defining all the "dynamic" expressions in the script as parameters, which enables the script to keep itself static
  between calls (this will ensure the use of the cached compiled scripts in Elasticsearch).
  ===============================
  
  Scripts can generate a single value or multiple values per document. When generating multiple values, one can use the
  `script_values_sorted` settings to indicate whether these values are sorted or not. Internally, Elasticsearch can
  perform optimizations when dealing with sorted values (for example, with the `min` aggregations, knowing the values are
  sorted, Elasticsearch will skip the iterations over all the values and rely on the first value in the list to be the
  