elasticsearch/docs/reference/mapping.asciidoc

[[mapping]]
= Mapping

[partintro]
--

Mapping is the process of defining how a document, and the fields it contains,
are stored and indexed.  For instance, use mappings to define:

* which string fields should be treated as full text fields.
* which fields contain numbers, dates, or geolocations.
* whether the values of all fields in the document should be
  indexed into the catch-all <<mapping-all-field,`_all`>> field.
* the <<mapping-date-format,format>> of date values.
* custom rules to control the mapping for
  <<dynamic-mapping,dynamically added fields>>.

[float]
[[mapping-type]]
== Mapping Type

Each index has one _mapping type_ which determines how the document will be
indexed.

deprecated[6.0.0,See <<removal-of-types>>].

A mapping type has:

<<mapping-fields,Meta-fields>>::

Meta-fields are used to customize how a document's metadata associated is
treated. Examples of meta-fields include the document's
<<mapping-index-field,`_index`>>, <<mapping-type-field,`_type`>>,
<<mapping-id-field,`_id`>>,  and <<mapping-source-field,`_source`>> fields.

<<mapping-types,Fields>> or _properties_::

A mapping type contains a list of fields or `properties` pertinent to the
document.


[float]
== Field datatypes

Each field has a data `type` which can be:

* a simple type like <<text,`text`>>, <<keyword,`keyword`>>, <<date,`date`>>, <<number,`long`>>,
  <<number,`double`>>, <<boolean,`boolean`>> or <<ip,`ip`>>.
* a type which supports the hierarchical nature of JSON such as
  <<object,`object`>> or <<nested,`nested`>>.
* or a specialised type like <<geo-point,`geo_point`>>,
  <<geo-shape,`geo_shape`>>, or <<search-suggesters-completion,`completion`>>.

It is often useful to index the same field in different ways for different
purposes. For instance, a `string` field could be <<mapping-index,indexed>> as
a `text` field for full-text search, and as a `keyword` field for
sorting or aggregations.  Alternatively, you could index a string field with
the <<analysis-standard-analyzer,`standard` analyzer>>, the
<<english-analyzer,`english`>> analyzer, and the
<<french-analyzer,`french` analyzer>>.

This is the purpose of _multi-fields_.  Most datatypes support multi-fields
via the <<multi-fields>> parameter.

[[mapping-limit-settings]]
[float]
=== Settings to prevent mappings explosion

Defining too many fields in an index is a condition that can lead to a
mapping explosion, which can cause out of memory errors and difficult
situations to recover from. This problem may be more common than expected.
As an example, consider a situation in which every new document inserted
introduces new fields. This is quite common with dynamic mappings.
Every time a document contains new fields, those will end up in the index's
mappings. This isn't worrying for a small amount of data, but it can become a
problem as the mapping grows.
The following settings allow you to limit the number of field mappings that
can be created manually or dynamically, in order to prevent bad documents from
causing a mapping explosion:

`index.mapping.total_fields.limit`::
    The maximum number of fields in an index. The default value is `1000`.

`index.mapping.depth.limit`::
    The maximum depth for a field, which is measured as the number of inner
    objects. For instance, if all fields are defined at the root object level,
    then the depth is `1`. If there is one object mapping, then the depth is
    `2`, etc. The default is `20`.

`index.mapping.nested_fields.limit`::
    The maximum number of `nested` fields in an index, defaults to `50`.
    Indexing 1 document with 100 nested fields actually indexes 101 documents
    as each nested document is indexed as a separate hidden document.


[float]
== Dynamic mapping

Fields and mapping types do not need to be defined before being used. Thanks
to _dynamic mapping_, new field names will be added automatically, just by
indexing a document. New fields can be added both to the top-level mapping
type, and to inner <<object,`object`>>  and <<nested,`nested`>> fields.

The <<dynamic-mapping,dynamic mapping>> rules can be configured to customise
the mapping that is used for new fields.

[float]
== Explicit mappings

You know more about your data than Elasticsearch can guess, so while dynamic
mapping can be useful to get started, at some point you will want to specify
your own explicit mappings.

You can create field mappings when you
<<indices-create-index,create an index>>, and you can add
fields to an existing index with the <<indices-put-mapping,PUT mapping API>>.

[float]
== Updating existing field mappings

Other than where documented, *existing field mappings cannot be
updated*. Changing the mapping would mean invalidating already indexed
documents.  Instead, you should create a new index with the correct mappings
and <<docs-reindex,reindex>> your data into that index.

[float]
== Example mapping

A mapping could be specified when creating an index, as follows:

[source,js]
---------------------------------------
PUT my_index <1>
{
  "mappings": {
    "doc": { <2>
      "properties": { <3>
        "title":    { "type": "text"  }, <4>
        "name":     { "type": "text"  }, <4>
        "age":      { "type": "integer" },  <4>
        "created":  {
          "type":   "date", <4>
          "format": "strict_date_optional_time||epoch_millis"
        }
      }
    }
  }
}
---------------------------------------
// CONSOLE
<1> Create an index called `my_index`.
<2> Add a mapping type called `doc`.
<3> Specify fields or _properties_.
<4> Specify the data `type` and mapping for each field.


--

include::mapping/removal_of_types.asciidoc[]

include::mapping/types.asciidoc[]

include::mapping/fields.asciidoc[]

include::mapping/params.asciidoc[]

include::mapping/dynamic-mapping.asciidoc[]