anchor.diff:  Global ordinals is a data-structure on top of fielddata and doc values, that
   maintains an incremental numbering for each unique term in a lexicographic
   order. Each term has a unique number and the number of term 'A' is lower than
  -the number of term 'B'. Global ordinals are only supported on string fields.
  +the number of term 'B'. Global ordinals are only supported on <<text,`text`>>
  +and <<keyword,`keyword`>> fields.
   
  -Fielddata and doc values also have ordinals, which is a unique numbering for all terms
  -in a particular segment and field. Global ordinals just build on top of this,
  -by providing a mapping between the segment ordinals and the global ordinals,
  -the latter being unique across the entire shard.
  +Fielddata and doc values also have ordinals, which is a unique numbering for
  +all terms in a particular segment and field. Global ordinals just build on top
  +of this, by providing a mapping between the segment ordinals and the global
  +ordinals, the latter being unique across the entire shard.
   
   Global ordinals are used for features that use segment ordinals, such as
   sorting and the terms aggregation, to improve the execution time. A terms
  
positive.code: single segment. For this reason global ordinals need to be entirely rebuilt
  whenever a once new segment becomes visible.
  
  The loading time of global ordinals depends on the number of terms in a field, but in general
  it is low, since it source field data has already been loaded. The memory overhead of global
  ordinals is a small because it is very efficiently compressed. Eager loading of global ordinals
  can move the loading time from the first search request, to the refresh itself.
  
  *****************************************
  
  
negative.code: === `fielddata`
  
  Most fields are <<mapping-index,indexed>> by default, which makes them
  searchable. Sorting, aggregations, and accessing field values in scripts,
  however, requires a different access pattern from search.
  
  Search needs to answer the question _"Which documents contain this term?"_,
  while sorting and aggregations need to answer a different question: _"What is
  the value of this field for **this** document?"_.
  
  Most fields can use index-time, on-disk <<doc-values,`doc_values`>> for this
  data access pattern, but <<text,`text`>> fields do not support `doc_values`.
  
  Instead, `text` fields use a query-time *in-memory* data structure called
  `fielddata`.  This data structure is built on demand the first time that a
  field is used for aggregations, sorting, or in a script.  It is built by
  reading the entire inverted index for each segment from disk, inverting the
  term ↔︎ document relationship, and storing the result in memory, in the JVM
  heap.
  
  ==== Fielddata is disabled on `text` fields by default
  
  Fielddata can consume a *lot* of heap space, especially when loading high
  cardinality `text` fields.  Once fielddata has been loaded into the heap, it
  remains there for the lifetime of the segment. Also, loading fielddata is an
  expensive process which can cause users to experience latency hits.  This is
  why fielddata is disabled by default.
  
  If you try to sort, aggregate, or access values from a script on a `text`
  field, you will see this exception:
  
  [quote]
  --
  Fielddata is disabled on text fields by default.  Set `fielddata=true` on
  [`your_field_name`] in order to load  fielddata in memory by uninverting the
  inverted index. Note that this can however use significant memory.
  --
  
  [[before-enabling-fielddata]]
  ==== Before enabling fielddata
  
  Before you enable fielddata, consider why you are using a `text` field for
  aggregations, sorting, or in a script.  It usually doesn't make sense to do
  so.
  
  A text field is analyzed before indexing so that a value like
  `New York` can be found by searching for `new` or for `york`.  A `terms`
  aggregation on this field will return a `new` bucket and a `york` bucket, when
  you probably want a single bucket called `New York`.
  
  Instead, you should have a `text` field for full text searches, and an
  unanalyzed <<keyword,`keyword`>> field with <<doc-values,`doc_values`>>
  enabled for aggregations, as follows:
  
  [source,js]
  ---------------------------------
  PUT my_index
  {
    "mappings": {
      "my_type": {
        "properties": {
          "my_field": { <1>
            "type": "text",
            "fields": {
              "keyword": { <2>
                "type": "keyword"
              }
            }
          }
        }
      }
    }
  }
  ---------------------------------
  // CONSOLE
  <1> Use the `my_field` field for searches.
  <2> Use the `my_field.keyword` field for aggregations, sorting, or in scripts.
  
  ==== Enabling fielddata on `text` fields
  
  You can enable fielddata on an existing `text` field using the
  <<indices-put-mapping,PUT mapping API>> as follows:
  
  [source,js]
  -----------------------------------
  PUT my_index/_mapping/my_type
  {
    "properties": {
      "my_field": { <1>
        "type":     "text",
        "fielddata": true
      }
    }
  }
  -----------------------------------
  // CONSOLE
  // TEST[continued]
  
  <1> The mapping that you specify for `my_field` should consist of the existing
      mapping for that field, plus the `fielddata` parameter.
  
  TIP: The `fielddata.*` parameter must have the same settings for fields of the
  same name in the same index.  Its value can be updated on existing fields
  using the <<indices-put-mapping,PUT mapping API>>.
  
  