elasticsearch/docs/reference/mapping/dynamic/templates.asciidoc

[[dynamic-templates]]
=== Dynamic templates

Dynamic templates allow you to define custom mappings that can be applied to
dynamically added fields based on:

* the <<dynamic-mapping,datatype>> detected by Elasticsearch, with <<match-mapping-type,`match_mapping_type`>>.
* the name of the field, with <<match-unmatch,`match` and `unmatch`>> or <<match-pattern,`match_pattern`>>.
* the full dotted path to the field, with <<path-match-unmatch,`path_match` and `path_unmatch`>>.

The original field name `{name}` and the detected datatype
`{dynamic_type`} <<template-variables,template variables>> can be used in
the mapping specification as placeholders.

IMPORTANT: Dynamic field mappings are only added when a field contains a
concrete value -- not `null` or an empty array. This means that if the
`null_value` option  is used in a `dynamic_template`, it will only be applied
after the first document  with a concrete value for the field has been
indexed.

Dynamic templates are specified as an array of named objects:

[source,js]
--------------------------------------------------
  "dynamic_templates": [
    {
      "my_template_name": { <1>
        ...  match conditions ... <2>
        "mapping": { ... } <3>
      }
    },
    ...
  ]
--------------------------------------------------
// NOTCONSOLE
<1> The template name can be any string value.
<2> The match conditions can include any of : `match_mapping_type`, `match`, `match_pattern`, `unmatch`, `path_match`, `path_unmatch`.
<3> The mapping that the matched field should use.


Templates are processed in order -- the first matching template wins. When
putting new dynamic templates through the <<indices-put-mapping, put mapping>> API,
all existing templates are overwritten. This allows for dynamic templates to be
reordered or deleted after they were initially added.

[[match-mapping-type]]
==== `match_mapping_type`

The `match_mapping_type` is the datatype detected by the json parser. Since
JSON doesn't allow to distinguish a `long` from an `integer` or a `double` from
a `float`, it will always choose the wider datatype, ie. `long` for integers
and `double` for floating-point numbers.

The following datatypes may be automatically detected:

 - `boolean` when `true` or `false` are encountered.
 - `date` when <<date-detection,date detection>> is enabled and a string is
   found that matches any of the configured date formats.
 - `double` for numbers with a decimal part.
 - `long` for numbers without a decimal part.
 - `object` for objects, also called hashes.
 - `string` for character strings.

`*` may also be used in order to match all datatypes.

For example, if we wanted to map all integer fields as `integer` instead of
`long`, and all `string` fields as both `text` and `keyword`, we
could use the following template:

[source,js]
--------------------------------------------------
PUT my_index
{
  "mappings": {
    "_doc": {
      "dynamic_templates": [
        {
          "integers": {
            "match_mapping_type": "long",
            "mapping": {
              "type": "integer"
            }
          }
        },
        {
          "strings": {
            "match_mapping_type": "string",
            "mapping": {
              "type": "text",
              "fields": {
                "raw": {
                  "type":  "keyword",
                  "ignore_above": 256
                }
              }
            }
          }
        }
      ]
    }
  }
}

PUT my_index/_doc/1
{
  "my_integer": 5, <1>
  "my_string": "Some string" <2>
}
--------------------------------------------------
// CONSOLE
<1> The `my_integer` field is mapped as an `integer`.
<2> The `my_string` field is mapped as a `text`, with a `keyword` <<multi-fields,multi field>>.


[[match-unmatch]]
==== `match` and `unmatch`

The `match` parameter uses a pattern to match on the field name, while
`unmatch` uses a pattern to exclude fields matched by `match`.

The following example matches all `string` fields whose name starts with
`long_` (except for those which end with `_text`) and maps them as `long`
fields:


[source,js]
--------------------------------------------------
PUT my_index
{
  "mappings": {
    "_doc": {
      "dynamic_templates": [
        {
          "longs_as_strings": {
            "match_mapping_type": "string",
            "match":   "long_*",
            "unmatch": "*_text",
            "mapping": {
              "type": "long"
            }
          }
        }
      ]
    }
  }
}

PUT my_index/_doc/1
{
  "long_num": "5", <1>
  "long_text": "foo" <2>
}
--------------------------------------------------
// CONSOLE
<1> The `long_num` field is mapped as a `long`.
<2> The `long_text` field uses the default `string` mapping.

[[match-pattern]]
==== `match_pattern`

The `match_pattern` parameter adjusts the behavior of the `match` parameter
such that it supports full Java regular expression matching on the field name
instead of simple wildcards, for instance:

[source,js]
--------------------------------------------------
  "match_pattern": "regex",
  "match": "^profit_\d+$"
--------------------------------------------------
// NOTCONSOLE

[[path-match-unmatch]]
==== `path_match` and `path_unmatch`

The `path_match` and `path_unmatch` parameters work in the same way as `match`
and `unmatch`, but operate on the full dotted path to the field, not just the
final name, e.g. `some_object.*.some_field`.

This example copies the values of any fields in the `name` object to the
top-level `full_name` field, except for the `middle` field:

[source,js]
--------------------------------------------------
PUT my_index
{
  "mappings": {
    "_doc": {
      "dynamic_templates": [
        {
          "full_name": {
            "path_match":   "name.*",
            "path_unmatch": "*.middle",
            "mapping": {
              "type":       "text",
              "copy_to":    "full_name"
            }
          }
        }
      ]
    }
  }
}

PUT my_index/_doc/1
{
  "name": {
    "first":  "Alice",
    "middle": "Mary",
    "last":   "White"
  }
}
--------------------------------------------------
// CONSOLE

[[template-variables]]
==== `{name}` and `{dynamic_type}`

The `{name}` and `{dynamic_type}` placeholders are replaced in the `mapping`
with the field name and detected dynamic type.  The following example sets all
string fields to use an <<analyzer,`analyzer`>> with the same name as the
field, and disables <<doc-values,`doc_values`>> for all non-string fields:

[source,js]
--------------------------------------------------
PUT my_index
{
  "mappings": {
    "_doc": {
      "dynamic_templates": [
        {
          "named_analyzers": {
            "match_mapping_type": "string",
            "match": "*",
            "mapping": {
              "type": "text",
              "analyzer": "{name}"
            }
          }
        },
        {
          "no_doc_values": {
            "match_mapping_type":"*",
            "mapping": {
              "type": "{dynamic_type}",
              "doc_values": false
            }
          }
        }
      ]
    }
  }
}

PUT my_index/_doc/1
{
  "english": "Some English text", <1>
  "count":   5 <2>
}
--------------------------------------------------
// CONSOLE
<1> The `english` field is mapped as a `string` field with the `english` analyzer.
<2> The `count` field is mapped as a `long` field with `doc_values` disabled.

[[template-examples]]
==== Template examples

Here are some examples of potentially useful dynamic templates:

===== Structured search

By default Elasticsearch will map string fields as a `text` field with a sub
`keyword` field. However if you are only indexing structured content and not
interested in full text search, you can make Elasticsearch map your fields
only as `keyword`s. Note that this means that in order to search those fields,
you will have to search on the exact same value that was indexed.

[source,js]
--------------------------------------------------
PUT my_index
{
  "mappings": {
    "_doc": {
      "dynamic_templates": [
        {
          "strings_as_keywords": {
            "match_mapping_type": "string",
            "mapping": {
              "type": "keyword"
            }
          }
        }
      ]
    }
  }
}
--------------------------------------------------
// CONSOLE

===== `text`-only mappings for strings

On the contrary to the previous example, if the only thing that you care about
on your string fields is full-text search, and if you don't plan on running
aggregations, sorting or exact search on your string fields, you could tell
Elasticsearch to map it only as a text field (which was the default behaviour
before 5.0):

[source,js]
--------------------------------------------------
PUT my_index
{
  "mappings": {
    "_doc": {
      "dynamic_templates": [
        {
          "strings_as_text": {
            "match_mapping_type": "string",
            "mapping": {
              "type": "text"
            }
          }
        }
      ]
    }
  }
}
--------------------------------------------------
// CONSOLE

===== Disabled norms

Norms are index-time scoring factors. If you do not care about scoring, which
would be the case for instance if you never sort documents by score, you could
disable the storage of these scoring factors in the index and save some space.

[source,js]
--------------------------------------------------
PUT my_index
{
  "mappings": {
    "_doc": {
      "dynamic_templates": [
        {
          "strings_as_keywords": {
            "match_mapping_type": "string",
            "mapping": {
              "type": "text",
              "norms": false,
              "fields": {
                "keyword": {
                  "type": "keyword",
                  "ignore_above": 256
                }
              }
            }
          }
        }
      ]
    }
  }
}
--------------------------------------------------
// CONSOLE

The sub `keyword` field appears in this template to be consistent with the
default rules of dynamic mappings. Of course if you do not need them because
you don't need to perform exact search or aggregate on this field, you could
remove it as described in the previous section.

===== Time-series

When doing time series analysis with Elasticsearch, it is common to have many
numeric fields that you will often aggregate on but never filter on. In such a
case, you could disable indexing on those fields to save disk space and also
maybe gain some indexing speed:

[source,js]
--------------------------------------------------
PUT my_index
{
  "mappings": {
    "_doc": {
      "dynamic_templates": [
        {
          "unindexed_longs": {
            "match_mapping_type": "long",
            "mapping": {
              "type": "long",
              "index": false
            }
          }
        },
        {
          "unindexed_doubles": {
            "match_mapping_type": "double",
            "mapping": {
              "type": "float", <1>
              "index": false
            }
          }
        }
      ]
    }
  }
}
--------------------------------------------------
// CONSOLE
<1> Like the default dynamic mapping rules, doubles are mapped as floats, which
    are usually accurate enough, yet require half the disk space.