anchor.diff:  				documents that "belong" to it.
   
   _Metric_::
  -				Aggregations that keep track and compute metrics over a set of documents  
  +				Aggregations that keep track and compute metrics over a set of documents.
   
  -The interesting part comes next, since each bucket effectively defines a document set (all documents belonging to
  -the bucket), one can potentially associated aggregations on the bucket level, and those will execute within the context
  +The interesting part comes next. Since each bucket effectively defines a document set (all documents belonging to
  +the bucket), one can potentially associate aggregations on the bucket level, and those will execute within the context
   of that bucket. This is where the real power of aggregations kicks in: *aggregations can be nested!*
   
  -NOTE:	Bucketing aggregations can have sub-aggregations (bucketing or metric). The sub aggregations will be computed for 
  -		the buckets their parent aggregation generates. There is not hard limit on the level/depth of nested
  -		aggregations (one can nest an aggregation under a "parent" aggregation which is itself a sub-aggregation of
  -		another higher aggregations)
  +NOTE:	Bucketing aggregations can have sub-aggregations (bucketing or metric). The sub-aggregations will be computed for
  +		the buckets which their parent aggregation generates. There is no hard limit on the level/depth of nested
  +		aggregations (one can nest an aggregation under a "parent" aggregation, which is itself a sub-aggregation of
  +		another higher-level aggregation).
   
   [float]
   === Structuring Aggregations
  
positive.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`>> that 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 that 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, and by that keep the script 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 documents. When generating multiple values, once 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
  
negative.code: <<search-facets, Facets>> provide a great way to aggregate data within a document set context.
  This context is defined by the executed query in combination with the different levels of filters that can be defined
  (filtered queries, top-level filters, and facet level filters). While powerful, their implementation is not designed
  from the ground up to support complex aggregations and is thus limited.
  
  .Are facets deprecated?
  **********************************
  As the functionality facets offer is a subset of the one offered by aggregations, over time, we would like to
  see users move to aggregations for all realtime data analytics. That said, we are well aware that such
  transitions/migrations take time, and for this reason we are keeping facets around for the time being.
  Facets are not officially deprecated yet but are likely to be in the future.
  **********************************
  
  