anchor.diff:  <2> Each runtime field has its own field type, just like any other field.
   <3> The script defines the evaluation to calculate at search time.
   
  -The `runtime` section supports `boolean`, `date`, `double`, `geo_point`, `ip`,
  -`keyword`, and `long` data types. Runtime fields with a `type` of `date` can
  -accept the <<mapping-date-format,`format`>> parameter exactly as the `date`
  -field type.
  +The `runtime` section can be any of these data types:
  +
  +* `boolean`
  +* `date`
  +* `double`
  +* `geo_point`
  +* `ip`
  +* `keyword`
  +* `long`
  +
  +Runtime fields with a `type` of `date` can accept the
  +<<mapping-date-format,`format`>> parameter exactly as the `date` field type.
   
   [[runtime-updating-scripts]]
   .Updating runtime scripts
  
positive.code: for each scripted field that is required for the query.
  
  NOTE: You can define a runtime field in the mapping definition without a
  script. If you define a runtime field without a script, {es} evaluates the
  field at search time, looks at each document containing that field, retrieves
  the `_source`, and returns a value if one exists.
  
  If <<dynamic-field-mapping,dynamic field mapping>> is enabled where the
  `dynamic` parameter is set to `runtime`, new fields are automatically added to
  
negative.code: is an indexed field. You have the flexibility to choose which fields to index
  and which ones to keep as runtime fields.
  
  At its core, the most important benefit of runtime fields is the ability to
  add fields to documents after you've ingested them. This capability simplifies
  mapping decisions because you don't have to decide how to parse your data up
  front, and can use runtime fields to amend the mapping at any time. Using
  runtime fields allows for a smaller index and faster ingest time, which
  combined use less resources and reduce your operating costs.
  
  [discrete]
  [[runtime-use-cases]]
  === Use cases
  