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Dynamic templates

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Dynamic templates

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Dynamic templates allow you greater control of how Elasticsearch maps your data beyond the default dynamic field mapping rules. You enable dynamic mapping by setting the dynamic parameter to true or runtime. You can then use dynamic templates to define custom mappings that can be applied to dynamically added fields based on the matching condition:

match_mapping_type operates on the data type that Elasticsearch detects
match and unmatch use a pattern to match on the field name
path_match and path_unmatch operate on the full dotted path to the field
If a dynamic template doesn’t define match_mapping_type, match, or path_match, it won’t match any field. You can still refer to the template by name in dynamic_templates section of a bulk request.

Use the {name} and {dynamic_type} template variables in the mapping specification as placeholders.

Dynamic field mappings are only added when a field contains a concrete value. Elasticsearch doesn’t add a dynamic field mapping when the field contains null or an empty array. 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:

  "dynamic_templates": [
    {
      "my_template_name": { 
        ... match conditions ... 
        "mapping": { ... } 
      }
    },
    ...
  ]

	The template name can be any string value.
	The match conditions can include any of : `match_mapping_type`, `match`, `match_pattern`, `unmatch`, `path_match`, `path_unmatch`.
	The mapping that the matched field should use.

Validating dynamic templates

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If a provided mapping contains an invalid mapping snippet, a validation error is returned. Validation occurs when applying the dynamic template at index time, and, in most cases, when the dynamic template is updated. Providing an invalid mapping snippet may cause the update or validation of a dynamic template to fail under certain conditions:

If no match_mapping_type has been specified but the template is valid for at least one predefined mapping type, the mapping snippet is considered valid. However, a validation error is returned at index time if a field matching the template is indexed as a different type. For example, configuring a dynamic template with no match_mapping_type is considered valid as string type, but if a field matching the dynamic template is indexed as a long, a validation error is returned at index time. It is recommended to configure the match_mapping_type to the expected JSON type or configure the desired type in the mapping snippet.
If the {name} placeholder is used in the mapping snippet, validation is skipped when updating the dynamic template. This is because the field name is unknown at that time. Instead, validation occurs when the template is applied at index time.

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

Mapping runtime fields in a dynamic template

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If you want Elasticsearch to dynamically map new fields of a certain type as runtime fields, set "dynamic":"runtime" in the index mappings. These fields are not indexed, and are loaded from _source at query time.

Alternatively, you can use the default dynamic mapping rules and then create dynamic templates to map specific fields as runtime fields. You set "dynamic":"true" in your index mapping, and then create a dynamic template to map new fields of a certain type as runtime fields.

Let’s say you have data where each of the fields start with ip_. Based on the dynamic mapping rules, Elasticsearch maps any string that passes numeric detection as a float or long. However, you can create a dynamic template that maps new strings as runtime fields of type ip.

The following request defines a dynamic template named strings_as_ip. When Elasticsearch detects new string fields matching the ip* pattern, it maps those fields as runtime fields of type ip. Because ip fields aren’t mapped dynamically, you can use this template with either "dynamic":"true" or "dynamic":"runtime".

PUT my-index-000001/
{
  "mappings": {
    "dynamic_templates": [
      {
        "strings_as_ip": {
          "match_mapping_type": "string",
          "match": "ip*",
          "runtime": {
            "type": "ip"
          }
        }
      }
    ]
  }
}

See this example for how to use dynamic templates to map string fields as either indexed fields or runtime fields.

`match_mapping_type`

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The match_mapping_type is the data type detected by the JSON parser. Because JSON doesn’t distinguish a long from an integer or a double from a float, any parsed floating point number is considered a double JSON data type, while any parsed integer number is considered a long.

With dynamic mappings, Elasticsearch will always choose the wider data type. The one exception is float, which requires less storage space than double and is precise enough for most applications. Runtime fields do not support float, which is why "dynamic":"runtime" uses double.

Elasticsearch automatically detects the following data types:

	Elasticsearch data type
JSON data type	`"dynamic":"true"`	`"dynamic":"runtime"`
`null`	No field added	No field added
`true` or `false`	`boolean`	`boolean`
`double`	`float`	`double`
`long`	`long`	`long`
`object`	`object`	No field added
`array`	Depends on the first non-`null` value in the array	Depends on the first non-`null` value in the array
`string` that passes date detection	`date`	`date`
`string` that passes numeric detection	`float` or `long`	`double` or `long`
`string` that doesn’t pass `date` detection or `numeric` detection	`text` with a `.keyword` sub-field	`keyword`

Use a wildcard (*) to match all data types.

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:

PUT my-index-000001
{
  "mappings": {
    "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-000001/_doc/1
{
  "my_integer": 5, 
  "my_string": "Some string" 
}

	The `my_integer` field is mapped as an `integer`.
	The `my_string` field is mapped as a `text`, with a `keyword` multi-field.

`match` and `unmatch`

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

The match_pattern parameter adjusts the behavior of the match parameter to support full Java regular expressions matching on the field name instead of simple wildcards. For example:

  "match_pattern": "regex",
  "match": "^profit_\d+$"

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:

PUT my-index-000001
{
  "mappings": {
    "dynamic_templates": [
      {
        "longs_as_strings": {
          "match_mapping_type": "string",
          "match":   "long_*",
          "unmatch": "*_text",
          "mapping": {
            "type": "long"
          }
        }
      }
    ]
  }
}

PUT my-index-000001/_doc/1
{
  "long_num": "5", 
  "long_text": "foo" 
}

	The `long_num` field is mapped as a `long`.
	The `long_text` field uses the default `string` mapping.

`path_match` and `path_unmatch`

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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:

PUT my-index-000001
{
  "mappings": {
    "dynamic_templates": [
      {
        "full_name": {
          "path_match":   "name.*",
          "path_unmatch": "*.middle",
          "mapping": {
            "type":       "text",
            "copy_to":    "full_name"
          }
        }
      }
    ]
  }
}

PUT my-index-000001/_doc/1
{
  "name": {
    "first":  "John",
    "middle": "Winston",
    "last":   "Lennon"
  }
}

Note that the path_match and path_unmatch parameters match on object paths in addition to leaf fields. As an example, indexing the following document will result in an error because the path_match setting also matches the object field name.title, which can’t be mapped as text:

response = client.index(
  index: 'my-index-000001',
  id: 2,
  body: {
    name: {
      first: 'Paul',
      last: 'McCartney',
      title: {
        value: 'Sir',
        category: 'order of chivalry'
      }
    }
  }
)
puts response

PUT my-index-000001/_doc/2
{
  "name": {
    "first":  "Paul",
    "last":   "McCartney",
    "title": {
      "value": "Sir",
      "category": "order of chivalry"
    }
  }
}

Template variables

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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 with the same name as the field, and disables doc_values for all non-string fields:

PUT my-index-000001
{
  "mappings": {
    "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-000001/_doc/1
{
  "english": "Some English text", 
  "count":   5 
}

	The `english` field is mapped as a `string` field with the `english` analyzer.
	The `count` field is mapped as a `long` field with `doc_values` disabled.

Dynamic template examples

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Here are some examples of potentially useful dynamic templates:

Structured search

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When you set "dynamic":"true", Elasticsearch will map string fields as a text field with a keyword subfield. If you are only indexing structured content and not interested in full text search, you can make Elasticsearch map your fields only as keyword fields. However, you must search on the exact same value that was indexed to search those fields.

PUT my-index-000001
{
  "mappings": {
    "dynamic_templates": [
      {
        "strings_as_keywords": {
          "match_mapping_type": "string",
          "mapping": {
            "type": "keyword"
          }
        }
      }
    ]
  }
}

`text`-only mappings for strings

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Contrary to the previous example, if you only care about full-text search on string fields and don’t plan on running aggregations, sorting, or exact searches, you could tell instruct Elasticsearch to map strings as text:

PUT my-index-000001
{
  "mappings": {
    "dynamic_templates": [
      {
        "strings_as_text": {
          "match_mapping_type": "string",
          "mapping": {
            "type": "text"
          }
        }
      }
    ]
  }
}

Alternatively, you can create a dynamic template to map your string fields as keyword fields in the runtime section of the mapping. When Elasticsearch detects new fields of type string, those fields will be created as runtime fields of type keyword.

Although your string fields won’t be indexed, their values are stored in _source and can be used in search requests, aggregations, filtering, and sorting.

For example, the following request creates a dynamic template to map string fields as runtime fields of type keyword. Although the runtime definition is blank, new string fields will be mapped as keyword runtime fields based on the dynamic mapping rules that Elasticsearch uses for adding field types to the mapping. Any string that doesn’t pass date detection or numeric detection is automatically mapped as a keyword:

PUT my-index-000001
{
  "mappings": {
    "dynamic_templates": [
      {
        "strings_as_keywords": {
          "match_mapping_type": "string",
          "runtime": {}
        }
      }
    ]
  }
}

You index a simple document:

response = client.index(
  index: 'my-index-000001',
  id: 1,
  body: {
    english: 'Some English text',
    count: 5
  }
)
puts response

PUT my-index-000001/_doc/1
{
  "english": "Some English text",
  "count":   5
}

When you view the mapping, you’ll see that the english field is a runtime field of type keyword:

response = client.indices.get_mapping(
  index: 'my-index-000001'
)
puts response

GET my-index-000001/_mapping

{
  "my-index-000001" : {
    "mappings" : {
      "dynamic_templates" : [
        {
          "strings_as_keywords" : {
            "match_mapping_type" : "string",
            "runtime" : { }
          }
        }
      ],
      "runtime" : {
        "english" : {
          "type" : "keyword"
        }
      },
      "properties" : {
        "count" : {
          "type" : "long"
        }
      }
    }
  }
}

Disabled norms

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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.

PUT my-index-000001
{
  "mappings": {
    "dynamic_templates": [
      {
        "strings_as_keywords": {
          "match_mapping_type": "string",
          "mapping": {
            "type": "text",
            "norms": false,
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          }
        }
      }
    ]
  }
}

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

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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:

PUT my-index-000001
{
  "mappings": {
    "dynamic_templates": [
      {
        "unindexed_longs": {
          "match_mapping_type": "long",
          "mapping": {
            "type": "long",
            "index": false
          }
        }
      },
      {
        "unindexed_doubles": {
          "match_mapping_type": "double",
          "mapping": {
            "type": "float", 
            "index": false
          }
        }
      }
    ]
  }
}

Like the default dynamic mapping rules, doubles are mapped as floats, which are usually accurate enough, yet require half the disk space.

« Dynamic field mapping Explicit mapping »

Dynamic templates

Dynamic templates

Validating dynamic templates

Mapping runtime fields in a dynamic template

match_mapping_type

match and unmatch

path_match and path_unmatch

Template variables

Dynamic template examples

Structured search

text-only mappings for strings

Disabled norms

Time series

`match_mapping_type`

`match` and `unmatch`

`path_match` and `path_unmatch`

`text`-only mappings for strings