elasticsearch/docs/reference/index-modules/fielddata.asciidoc

[[index-modules-fielddata]]
== Field data

The field data cache is used mainly when sorting on or faceting on a
field. It loads all the field values to memory in order to provide fast
document based access to those values. The field data cache can be
expensive to build for a field, so its recommended to have enough memory
to allocate it, and to keep it loaded.

The amount of memory used for the field
data cache can be controlled using `indices.fielddata.cache.size`. Note:
reloading  the field data which does not fit into your cache will be expensive
and  perform poorly.

[cols="<,<",options="header",]
|=======================================================================
|Setting |Description
|`indices.fielddata.cache.size` |The max size of the field data cache,
eg `30%` of node heap space, or an absolute value, eg `12GB`. Defaults
to unbounded.

|`indices.fielddata.cache.expire` |A time based setting that expires
field data after a certain time of inactivity. Defaults to `-1`. For
example, can be set to `5m` for a 5 minute expiry.
|=======================================================================

[float]
[[fielddata-circuit-breaker]]
=== Field data circuit breaker
The field data circuit breaker allows Elasticsearch to estimate the amount of
memory a field will required to be loaded into memory. It can then prevent the
field data loading by raising an exception. By default the limit is configured
to 60% of the maximum JVM heap. It can be configured with the following
parameters:

[cols="<,<",options="header",]
|=======================================================================
|Setting |Description
|`indices.fielddata.breaker.limit` |Maximum size of estimated field data
to allow loading. Defaults to 60% of the maximum JVM heap.
|`indices.fielddata.breaker.overhead` |A constant that all field data
estimations are multiplied with to determine a final estimation. Defaults to
1.03
|=======================================================================

Both the `indices.fielddata.breaker.limit` and
`indices.fielddata.breaker.overhead` can be changed dynamically using the
cluster update settings API.

[float]
[[fielddata-monitoring]]
=== Monitoring field data

You can monitor memory usage for field data as well as the field data circuit
breaker using
<<cluster-nodes-stats,Nodes Stats API>>

[[fielddata-formats]]
== Field data formats

The field data format controls how field data should be stored.

Depending on the field type, there might be several field data types
available. In particular, string and numeric types support the `doc_values`
format which allows for computing the field data data-structures at indexing
time and storing them on disk. Although it will make the index larger and may
be slightly slower, this implementation will be more near-realtime-friendly
and will require much less memory from the JVM than other implementations.

Here is an example of how to configure the `tag` field to use the `fst` field
data format.

[source,js]
--------------------------------------------------
{
    "tag": {
        "type":      "string",
        "fielddata": {
            "format": "fst"
        }
    }
}
--------------------------------------------------

It is possible to change the field data format (and the field data settings
in general) on a live index by using the update mapping API. When doing so,
field data which had already been loaded for existing segments will remain
alive while new segments will use the new field data configuration. Thanks to
the background merging process, all segments will eventually use the new
field data format.

[float]
==== String field data types

`paged_bytes` (default)::
    Stores unique terms sequentially in a large buffer and maps documents to
    the indices of the terms they contain in this large buffer.

`fst`::
    Stores terms in a FST. Slower to build than `paged_bytes` but can help lower
    memory usage if many terms share common prefixes and/or suffixes.

`doc_values`::
    Computes and stores field data data-structures on disk at indexing time.
    Lowers memory usage but only works on non-analyzed strings (`index`: `no` or
    `not_analyzed`) and doesn't support filtering.

[float]
==== Numeric field data types

`array` (default)::
    Stores field values in memory using arrays.

`doc_values`::
    Computes and stores field data data-structures on disk at indexing time.
    Doesn't support filtering.

[float]
==== Geo point field data types

`array` (default)::
    Stores latitudes and longitudes in arrays.

`doc_values`::
    Computes and stores field data data-structures on disk at indexing time.

[float]
==== Global ordinals

added[1.2.0]

Global ordinals is a data-structure on top of field data, that maintains an
incremental numbering for all the terms in field data 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.

Field data on string also has 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 can be beneficial in search features that use segment ordinals already
such as the terms aggregator to improve the execution time. Often these search features
need to merge the segment ordinal results to a cross segment terms result. With
global ordinals this mapping happens during field data load time instead of during each
query execution. With global ordinals search features only need to resolve the actual
term when building the (shard) response, but during the execution there is no need
at all to use the actual terms and the unique numbering global ordinals provided is
sufficient and improves the execution time.

Global ordinals for a specified field are tied to all the segments of a shard (Lucene index),
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 rebuilt in its entirety once new segments
become visible. This one time cost would happen anyway without global ordinals, but
then it would happen for each search execution instead!

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.

[float]
=== Fielddata loading

By default, field data is loaded lazily, ie. the first time that a query that
requires them is executed. However, this can make the first requests that
follow a merge operation quite slow since fielddata loading is a heavy
operation.

It is possible to force field data to be loaded and cached eagerly through the
`loading` setting of fielddata:

[source,js]
--------------------------------------------------
{
    "category": {
        "type":      "string",
        "fielddata": {
            "loading": "eager"
        }
    }
}
--------------------------------------------------

Global ordinals can also be eagerly loaded:

[source,js]
--------------------------------------------------
{
    "category": {
        "type":      "string",
        "fielddata": {
            "loading": "eager_global_ordinals"
        }
    }
}
--------------------------------------------------

With the above setting both field data and global ordinals for a specific field
are eagerly loaded.

[float]
==== Disabling field data loading

Field data can take a lot of RAM so it makes sense to disable field data
loading on the fields that don't need field data, for example those that are
used for full-text search only. In order to disable field data loading, just
change the field data format to `disabled`. When disabled, all requests that
will try to load field data, e.g. when they include aggregations and/or sorting,
will return an error.

[source,js]
--------------------------------------------------
{
    "text": {
        "type":      "string",
        "fielddata": {
            "format": "disabled"
        }
    }
}
--------------------------------------------------

The `disabled` format is supported by all field types.

[float]
[[field-data-filtering]]
=== Filtering fielddata

It is possible to control which field values are loaded into memory,
which is particularly useful for string fields. When specifying the
<<mapping-core-types,mapping>> for a field, you
can also specify a fielddata filter.

Fielddata filters can be changed using the
<<indices-put-mapping,PUT mapping>>
API. After changing the filters, use the
<<indices-clearcache,Clear Cache>> API
to reload the fielddata using the new filters.

[float]
==== Filtering by frequency:

The frequency filter allows you to only load terms whose frequency falls
between a `min` and `max` value, which can be expressed an absolute
number or as a percentage (eg `0.01` is `1%`). 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]
--------------------------------------------------
{
    "tag": {
        "type":      "string",
        "fielddata": {
            "filter": {
                "frequency": {
                    "min":              0.001,
                    "max":              0.1,
                    "min_segment_size": 500
                }
            }
        }
    }
}
--------------------------------------------------

[float]
==== Filtering by regex

Terms can also be filtered by regular expression - only values which
match the regular expression are loaded. Note: the regular expression is
applied to each term in the field, not to the whole field value. For
instance, to only load hashtags from a tweet, we can use a regular
expression which matches terms beginning with `#`:

[source,js]
--------------------------------------------------
{
    "tweet": {
        "type":      "string",
        "analyzer":  "whitespace"
        "fielddata": {
            "filter": {
                "regex": {
                    "pattern": "^#.*"
                }
            }
        }
    }
}
--------------------------------------------------

[float]
==== Combining filters

The `frequency` and `regex` filters can be combined:

[source,js]
--------------------------------------------------
{
    "tweet": {
        "type":      "string",
        "analyzer":  "whitespace"
        "fielddata": {
            "filter": {
                "regex": {
                    "pattern":          "^#.*",
                },
                "frequency": {
                    "min":              0.001,
                    "max":              0.1,
                    "min_segment_size": 500
                }
            }
        }
    }
}
--------------------------------------------------