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Keyword type family

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Keyword type family

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The keyword family includes the following field types:

keyword, which is used for structured content such as IDs, email addresses, hostnames, status codes, zip codes, or tags.
constant_keyword for keyword fields that always contain the same value.
wildcard for unstructured machine-generated content. The wildcard type is optimized for fields with large values or high cardinality.

Keyword fields are often used in sorting, aggregations, and term-level queries, such as term.

Avoid using keyword fields for full-text search. Use the text field type instead.

Keyword field type

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Below is an example of a mapping for a basic keyword field:

resp = client.indices.create(
    index="my-index-000001",
    body={"mappings": {"properties": {"tags": {"type": "keyword"}}}},
)
print(resp)

response = client.indices.create(
  index: 'my-index-000001',
  body: {
    mappings: {
      properties: {
        tags: {
          type: 'keyword'
        }
      }
    }
  }
)
puts response

res, err := es.Indices.Create(
	"my-index-000001",
	es.Indices.Create.WithBody(strings.NewReader(`{
	  "mappings": {
	    "properties": {
	      "tags": {
	        "type": "keyword"
	      }
	    }
	  }
	}`)),
)
fmt.Println(res, err)

PUT my-index-000001
{
  "mappings": {
    "properties": {
      "tags": {
        "type":  "keyword"
      }
    }
  }
}

Mapping numeric identifiers

Not all numeric data should be mapped as a numeric field data type. Elasticsearch optimizes numeric fields, such as integer or long, for range queries. However, keyword fields are better for term and other term-level queries.

Identifiers, such as an ISBN or a product ID, are rarely used in range queries. However, they are often retrieved using term-level queries.

Consider mapping a numeric identifier as a keyword if:

You don’t plan to search for the identifier data using range queries.
Fast retrieval is important. term query searches on keyword fields are often faster than term searches on numeric fields.

If you’re unsure which to use, you can use a multi-field to map the data as both a keyword and a numeric data type.

Parameters for basic keyword fields

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The following parameters are accepted by keyword fields:

doc_values

Should the field be stored on disk in a column-stride fashion, so that it can later be used for sorting, aggregations, or scripting? Accepts true (default) or false.

eager_global_ordinals

Should global ordinals be loaded eagerly on refresh? Accepts true or false (default). Enabling this is a good idea on fields that are frequently used for terms aggregations.

fields

Multi-fields allow the same string value to be indexed in multiple ways for different purposes, such as one field for search and a multi-field for sorting and aggregations.

ignore_above

Do not index any string longer than this value. Defaults to 2147483647 so that all values would be accepted. Please however note that default dynamic mapping rules create a sub keyword field that overrides this default by setting ignore_above: 256.

index

Should the field be quickly searchable? Accepts true (default) and false. keyword fields that only have doc_values enabled can still be queried, albeit slower.

index_options

What information should be stored in the index, for scoring purposes. Defaults to docs but can also be set to freqs to take term frequency into account when computing scores.

meta

Metadata about the field.

norms

Whether field-length should be taken into account when scoring queries. Accepts true or false (default).

null_value

Accepts a string value which is substituted for any explicit null values. Defaults to null, which means the field is treated as missing. Note that this cannot be set if the script value is used.

on_script_error

Defines what to do if the script defined by the script parameter throws an error at indexing time. Accepts fail (default), which will cause the entire document to be rejected, and continue, which will register the field in the document’s _ignored metadata field and continue indexing. This parameter can only be set if the script field is also set.

script

If this parameter is set, then the field will index values generated by this script, rather than reading the values directly from the source. If a value is set for this field on the input document, then the document will be rejected with an error. Scripts are in the same format as their runtime equivalent. Values emitted by the script are normalized as usual, and will be ignored if they are longer that the value set on ignore_above.

store

Whether the field value should be stored and retrievable separately from the _source field. Accepts true or false (default).

similarity

Which scoring algorithm or similarity should be used. Defaults to BM25.

normalizer

How to pre-process the keyword prior to indexing. Defaults to null, meaning the keyword is kept as-is.

split_queries_on_whitespace

Whether full text queries should split the input on whitespace when building a query for this field. Accepts true or false (default).

time_series_dimension

(Optional, Boolean)

Marks the field as a time series dimension. Defaults to false.

The index.mapping.dimension_fields.limit index setting limits the number of dimensions in an index.

Dimension fields have the following constraints:

The doc_values and index mapping parameters must be true.
Field values cannot be an array or multi-value.
Field values cannot be larger than 1024 bytes.
The field cannot use a normalizer.

Synthetic `_source`

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Synthetic _source is Generally Available only for TSDB indices (indices that have index.mode set to time_series). For other indices synthetic _source is in technical preview. Features in technical preview may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

keyword fields support synthetic _source in their default configuration. Synthetic _source cannot be used together with a normalizer or copy_to.

By default, synthetic source sorts keyword fields and removes duplicates. For example:

resp = client.indices.create(
    index="idx",
    body={
        "mappings": {
            "_source": {"mode": "synthetic"},
            "properties": {"kwd": {"type": "keyword"}},
        }
    },
)
print(resp)

resp = client.index(
    index="idx",
    id="1",
    body={"kwd": ["foo", "foo", "bar", "baz"]},
)
print(resp)

response = client.indices.create(
  index: 'idx',
  body: {
    mappings: {
      _source: {
        mode: 'synthetic'
      },
      properties: {
        kwd: {
          type: 'keyword'
        }
      }
    }
  }
)
puts response

response = client.index(
  index: 'idx',
  id: 1,
  body: {
    kwd: [
      'foo',
      'foo',
      'bar',
      'baz'
    ]
  }
)
puts response

PUT idx
{
  "mappings": {
    "_source": { "mode": "synthetic" },
    "properties": {
      "kwd": { "type": "keyword" }
    }
  }
}
PUT idx/_doc/1
{
  "kwd": ["foo", "foo", "bar", "baz"]
}

Will become:

{
  "kwd": ["bar", "baz", "foo"]
}

If a keyword field sets store to true then order and duplicates are preserved. For example:

resp = client.indices.create(
    index="idx",
    body={
        "mappings": {
            "_source": {"mode": "synthetic"},
            "properties": {"kwd": {"type": "keyword", "store": True}},
        }
    },
)
print(resp)

resp = client.index(
    index="idx",
    id="1",
    body={"kwd": ["foo", "foo", "bar", "baz"]},
)
print(resp)

response = client.indices.create(
  index: 'idx',
  body: {
    mappings: {
      _source: {
        mode: 'synthetic'
      },
      properties: {
        kwd: {
          type: 'keyword',
          store: true
        }
      }
    }
  }
)
puts response

response = client.index(
  index: 'idx',
  id: 1,
  body: {
    kwd: [
      'foo',
      'foo',
      'bar',
      'baz'
    ]
  }
)
puts response

PUT idx
{
  "mappings": {
    "_source": { "mode": "synthetic" },
    "properties": {
      "kwd": { "type": "keyword", "store": true }
    }
  }
}
PUT idx/_doc/1
{
  "kwd": ["foo", "foo", "bar", "baz"]
}

Will become:

{
  "kwd": ["foo", "foo", "bar", "baz"]
}

Values longer than ignore_above are preserved but sorted to the end. For example:

resp = client.indices.create(
    index="idx",
    body={
        "mappings": {
            "_source": {"mode": "synthetic"},
            "properties": {"kwd": {"type": "keyword", "ignore_above": 3}},
        }
    },
)
print(resp)

resp = client.index(
    index="idx",
    id="1",
    body={"kwd": ["foo", "foo", "bang", "bar", "baz"]},
)
print(resp)

response = client.indices.create(
  index: 'idx',
  body: {
    mappings: {
      _source: {
        mode: 'synthetic'
      },
      properties: {
        kwd: {
          type: 'keyword',
          ignore_above: 3
        }
      }
    }
  }
)
puts response

response = client.index(
  index: 'idx',
  id: 1,
  body: {
    kwd: [
      'foo',
      'foo',
      'bang',
      'bar',
      'baz'
    ]
  }
)
puts response

PUT idx
{
  "mappings": {
    "_source": { "mode": "synthetic" },
    "properties": {
      "kwd": { "type": "keyword", "ignore_above": 3 }
    }
  }
}
PUT idx/_doc/1
{
  "kwd": ["foo", "foo", "bang", "bar", "baz"]
}

Will become:

{
  "kwd": ["bar", "baz", "foo", "bang"]
}

Constant keyword field type

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Constant keyword is a specialization of the keyword field for the case that all documents in the index have the same value.

response = client.indices.create(
  index: 'logs-debug',
  body: {
    mappings: {
      properties: {
        "@timestamp": {
          type: 'date'
        },
        message: {
          type: 'text'
        },
        level: {
          type: 'constant_keyword',
          value: 'debug'
        }
      }
    }
  }
)
puts response

PUT logs-debug
{
  "mappings": {
    "properties": {
      "@timestamp": {
        "type": "date"
      },
      "message": {
        "type": "text"
      },
      "level": {
        "type": "constant_keyword",
        "value": "debug"
      }
    }
  }
}

constant_keyword supports the same queries and aggregations as keyword fields do, but takes advantage of the fact that all documents have the same value per index to execute queries more efficiently.

It is both allowed to submit documents that don’t have a value for the field or that have a value equal to the value configured in mappings. The two below indexing requests are equivalent:

response = client.index(
  index: 'logs-debug',
  body: {
    date: '2019-12-12',
    message: 'Starting up Elasticsearch',
    level: 'debug'
  }
)
puts response

response = client.index(
  index: 'logs-debug',
  body: {
    date: '2019-12-12',
    message: 'Starting up Elasticsearch'
  }
)
puts response

POST logs-debug/_doc
{
  "date": "2019-12-12",
  "message": "Starting up Elasticsearch",
  "level": "debug"
}

POST logs-debug/_doc
{
  "date": "2019-12-12",
  "message": "Starting up Elasticsearch"
}

However providing a value that is different from the one configured in the mapping is disallowed.

In case no value is provided in the mappings, the field will automatically configure itself based on the value contained in the first indexed document. While this behavior can be convenient, note that it means that a single poisonous document can cause all other documents to be rejected if it had a wrong value.

Before a value has been provided (either through the mappings or from a document), queries on the field will not match any documents. This includes exists queries.

The value of the field cannot be changed after it has been set.

Parameters for constant keyword fields

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The following mapping parameters are accepted:

`meta`	Metadata about the field.
`value`	The value to associate with all documents in the index. If this parameter is not provided, it is set based on the first document that gets indexed.

Wildcard field type

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The wildcard field type is a specialized keyword field for unstructured machine-generated content you plan to search using grep-like wildcard and regexp queries. The wildcard type is optimized for fields with large values or high cardinality.

Mapping unstructured content

You can map a field containing unstructured content to either a text or keyword family field. The best field type depends on the nature of the content and how you plan to search the field.

Use the text field type if:

The content is human-readable, such as an email body or product description.
You plan to search the field for individual words or phrases, such as the brown fox jumped, using full text queries. Elasticsearch analyzes text fields to return the most relevant results for these queries.

Use a keyword family field type if:

The content is machine-generated, such as a log message or HTTP request information.
You plan to search the field for exact full values, such as org.foo.bar, or partial character sequences, such as org.foo.*, using term-level queries.

Choosing a keyword family field type

If you choose a keyword family field type, you can map the field as a keyword or wildcard field depending on the cardinality and size of the field’s values. Use the wildcard type if you plan to regularly search the field using a wildcard or regexp query and meet one of the following criteria:

The field contains more than a million unique values.
AND
You plan to regularly search the field using a pattern with leading wildcards, such as *foo or *baz.
The field contains values larger than 32KB.
AND
You plan to regularly search the field using any wildcard pattern.

Otherwise, use the keyword field type for faster searches, faster indexing, and lower storage costs. For an in-depth comparison and decision flowchart, see our related blog post.

Switching from a text field to a keyword field

If you previously used a text field to index unstructured machine-generated content, you can reindex to update the mapping to a keyword or wildcard field. We also recommend you update your application or workflow to replace any word-based full text queries on the field to equivalent term-level queries.

Internally the wildcard field indexes the whole field value using ngrams and stores the full string. The index is used as a rough filter to cut down the number of values that are then checked by retrieving and checking the full values. This field is especially well suited to run grep-like queries on log lines. Storage costs are typically lower than those of keyword fields but search speeds for exact matches on full terms are slower. If the field values share many prefixes, such as URLs for the same website, storage costs for a wildcard field may be higher than an equivalent keyword field.

You index and search a wildcard field as follows

response = client.indices.create(
  index: 'my-index-000001',
  body: {
    mappings: {
      properties: {
        my_wildcard: {
          type: 'wildcard'
        }
      }
    }
  }
)
puts response

response = client.index(
  index: 'my-index-000001',
  id: 1,
  body: {
    my_wildcard: 'This string can be quite lengthy'
  }
)
puts response

response = client.search(
  index: 'my-index-000001',
  body: {
    query: {
      wildcard: {
        my_wildcard: {
          value: '*quite*lengthy'
        }
      }
    }
  }
)
puts response

PUT my-index-000001
{
  "mappings": {
    "properties": {
      "my_wildcard": {
        "type": "wildcard"
      }
    }
  }
}

PUT my-index-000001/_doc/1
{
  "my_wildcard" : "This string can be quite lengthy"
}

GET my-index-000001/_search
{
  "query": {
    "wildcard": {
      "my_wildcard": {
        "value": "*quite*lengthy"
      }
    }
  }
}

Parameters for wildcard fields

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The following parameters are accepted by wildcard fields:

`null_value`	Accepts a string value which is substituted for any explicit `null` values. Defaults to `null`, which means the field is treated as missing.
`ignore_above`	Do not index any string longer than this value. Defaults to `2147483647` so that all values would be accepted.

Limitations

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wildcard fields are untokenized like keyword fields, so do not support queries that rely on word positions such as phrase queries.
When running wildcard queries any rewrite parameter is ignored. The scoring is always a constant score.

Synthetic `_source`

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wildcard fields support synthetic _source so long as they don’t declare copy_to.

Synthetic source always sorts wildcard fields. For example:

response = client.indices.create(
  index: 'idx',
  body: {
    mappings: {
      _source: {
        mode: 'synthetic'
      },
      properties: {
        card: {
          type: 'wildcard'
        }
      }
    }
  }
)
puts response

response = client.index(
  index: 'idx',
  id: 1,
  body: {
    card: [
      'king',
      'ace',
      'ace',
      'jack'
    ]
  }
)
puts response

PUT idx
{
  "mappings": {
    "_source": { "mode": "synthetic" },
    "properties": {
      "card": { "type": "wildcard" }
    }
  }
}
PUT idx/_doc/1
{
  "card": ["king", "ace", "ace", "jack"]
}

Will become:

{
  "card": ["ace", "jack", "king"]
}

« Join field type Nested field type »

Keyword type family

Keyword type family

Keyword field type

Parameters for basic keyword fields

Synthetic _source

Constant keyword field type

Parameters for constant keyword fields

Wildcard field type

Parameters for wildcard fields

Limitations

Synthetic _source

Synthetic `_source`

Synthetic `_source`