elasticsearch/docs/reference/mapping/params/fielddata.asciidoc

[[fielddata]]
=== `fielddata`

Most fields are <<mapping-index,indexed>> by default, which makes them
searchable. The inverted index allows queries to look up the search term in
unique sorted list of terms, and from that immediately have access to the list
of documents that contain the term.

Sorting, aggregations, and access to field values in scripts requires a
different data access pattern.  Instead of lookup up the term and finding
documents, we need to be able to look up the document and find the terms that
it has in a field.

Most fields can use index-time, on-disk <<doc-values,`doc_values`>> to support
this type of data access pattern, but `text` fields do not support `doc_values`.

Instead, `text` strings use a query-time data structure called
`fielddata`.  This data structure is built on demand the first time that a
field is used for aggregations, sorting, or is accessed 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.

Loading fielddata is an expensive process so it is disabled by default. Also,
when enabled, once it has been loaded, it remains in memory for the lifetime of
the segment.

[WARNING]
.Fielddata can fill up your heap space
==============================================================================
Fielddata can consume a lot of heap space, especially when loading high
cardinality `text` fields.  Most of the time, it doesn't make sense
to sort or aggregate on `text` fields (with the notable exception
of the
<<search-aggregations-bucket-significantterms-aggregation,`significant_terms`>>
aggregation).  Always think about whether a <<keyword,`keyword`>> field (which can
use `doc_values`) would be  a better fit for your use case.
==============================================================================

TIP: The `fielddata.*` settings 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>>.


[[global-ordinals]]
.Global ordinals
*****************************************

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.

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
aggregation relies purely on global ordinals to perform the aggregation at the
shard level, then converts global ordinals to the real term only for the final
reduce phase, which combines results from different shards.

Global ordinals for a specified field are tied to _all the segments of a
shard_, while fielddata and doc values ordinals are tied to a single segment.
which is different than for field data for a specific field which is tied to a
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.

*****************************************

[[field-data-filtering]]
==== `fielddata_frequency_filter`

Fielddata filtering can be used to reduce the number of terms loaded into
memory, and thus reduce memory usage. Terms can be filtered by _frequency_:

The frequency filter allows you to only load terms whose term frequency falls
between a `min` and `max` value, which can be expressed an absolute
number (when the number is bigger than 1.0) or as a percentage
(eg `0.01` is `1%` and `1.0` is `100%`). Frequency is calculated
*per segment*. Percentages are based on the number of docs which have a
value for the field, as opposed to all docs in the segment.

Small segments can be excluded completely by specifying the minimum
number of docs that the segment should contain with `min_segment_size`:

[source,js]
--------------------------------------------------
PUT my_index
{
  "mappings": {
    "my_type": {
      "properties": {
        "tag": {
          "type": "text",
          "fielddata": true,
          "fielddata_frequency_filter": {
            "min": 0.001,
            "max": 0.1,
            "min_segment_size": 500
          }
        }
      }
    }
  }
}
--------------------------------------------------
// AUTOSENSE