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Elastic Docs ›Elasticsearch Guide [6.5] ›Mapping ›Dynamic Mapping

Dynamic templates

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Dynamic templates

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

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

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:

  "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.

Templates are processed in order — the first matching template wins. When putting new dynamic templates through the 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

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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 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:

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, 
  "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 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
{
  "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", 
  "long_text": "foo" 
}

The long_num field is mapped as a long.

The long_text field uses the default string mapping.

match_pattern

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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:

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

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
{
  "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"
  }
}

{name} and {dynamic_type}

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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
{
  "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", 
  "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.

Template examples

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Here are some examples of potentially useful dynamic templates:

Structured search

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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.

PUT my_index
{
  "mappings": {
    "_doc": {
      "dynamic_templates": [
        {
          "strings_as_keywords": {
            "match_mapping_type": "string",
            "mapping": {
              "type": "keyword"
            }
          }
        }
      ]
    }
  }
}

text-only mappings for strings

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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):

PUT my_index
{
  "mappings": {
    "_doc": {
      "dynamic_templates": [
        {
          "strings_as_text": {
            "match_mapping_type": "string",
            "mapping": {
              "type": "text"
            }
          }
        }
      ]
    }
  }
}

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
{
  "mappings": {
    "_doc": {
      "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
{
  "mappings": {
    "_doc": {
      "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.
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