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docs/reference/aggregations/search-aggregations-bucket-adjacency-matrix-aggregation.md

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`filters`
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: (Required, object) Filters used to create buckets.
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::::{dropdown} Properties of `filters`
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::::{dropdown} Properties of filters
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`<filter>`
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: (Required, [Query DSL object](/reference/query-languages/querydsl.md)) Query used to filter documents. The key is the filter name.
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docs/reference/aggregations/search-aggregations-bucket-categorize-text-aggregation.md

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The `categorization_analyzer` field can be specified either as a string or as an object. If it is a string it must refer to a [built-in analyzer](/reference/text-analysis/analyzer-reference.md) or one added by another plugin. If it is an object it has the following properties:
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:::::{dropdown} Properties of `categorization_analyzer`
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:::::{dropdown} Properties of categorization_analyzer
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`char_filter`
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: (array of strings or objects) One or more [character filters](/reference/text-analysis/character-filter-reference.md). In addition to the built-in character filters, other plugins can provide more character filters. This property is optional. If it is not specified, no character filters are applied prior to categorization. If you are customizing some other aspect of the analyzer and you need to achieve the equivalent of `categorization_filters` (which are not permitted when some other aspect of the analyzer is customized), add them here as [pattern replace character filters](/reference/text-analysis/analysis-pattern-replace-charfilter.md).
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docs/reference/aggregations/search-aggregations-bucket-correlation-aggregation.md

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## Parameters [bucket-correlation-agg-syntax]
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`buckets_path`
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: (Required, string) Path to the buckets that contain one set of values to correlate. For syntax, see [`buckets_path` Syntax](/reference/aggregations/pipeline.md#buckets-path-syntax).
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### `buckets_path`
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`function`
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: (Required, object) The correlation function to execute.
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(Required, string) Path to the buckets that contain one set of values to correlate. For syntax, see [`buckets_path` Syntax](/reference/aggregations/pipeline.md#buckets-path-syntax).
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::::{dropdown} Properties of `function`
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`count_correlation`
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: (Required*, object) The configuration to calculate a count correlation. This function is designed for determining the correlation of a term value and a given metric. Consequently, it needs to meet the following requirements.
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### `function`
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* The `buckets_path` must point to a `_count` metric.
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* The total count of all the `bucket_path` count values must be less than or equal to `indicator.doc_count`.
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* When utilizing this function, an initial calculation to gather the required `indicator` values is required.
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(Required, object) The correlation function to execute.
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::::{dropdown} Properties of `count_correlation`
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`indicator`
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: (Required, object) The indicator with which to correlate the configured `bucket_path` values.
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#### `count_correlation`
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::::{dropdown} Properties of `indicator`
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`doc_count`
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: (Required, integer) The total number of documents that initially created the `expectations`. It’s required to be greater than or equal to the sum of all values in the `buckets_path` as this is the originating superset of data to which the term values are correlated.
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(Required*, object) The configuration to calculate a count correlation. This function is designed for determining the correlation of a term value and a given metric. Consequently, it needs to meet the following requirements.
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`expectations`
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: (Required, array) An array of numbers with which to correlate the configured `bucket_path` values. The length of this value must always equal the number of buckets returned by the `bucket_path`.
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* The `buckets_path` must point to a `_count` metric.
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* The total count of all the `bucket_path` count values must be less than or equal to `indicator.doc_count`.
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* When utilizing this function, an initial calculation to gather the required `indicator` values is required.
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`fractions`
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: (Optional, array) An array of fractions to use when averaging and calculating variance. This should be used if the pre-calculated data and the `buckets_path` have known gaps. The length of `fractions`, if provided, must equal `expectations`.
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`indicator`
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: (Required, object) The indicator with which to correlate the configured `bucket_path` values.
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::::
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::::
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::::
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`doc_count`
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: (Required, integer) The total number of documents that initially created the `expectations`. It’s required to be greater than or equal to the sum of all values in the `buckets_path` as this is the originating superset of data to which the term values are correlated.
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`expectations`
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: (Required, array) An array of numbers with which to correlate the configured `bucket_path` values. The length of this value must always equal the number of buckets returned by the `bucket_path`.
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`fractions`
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: (Optional, array) An array of fractions to use when averaging and calculating variance. This should be used if the pre-calculated data and the `buckets_path` have known gaps. The length of `fractions`, if provided, must equal `expectations`.
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## Syntax [_syntax_8]
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docs/reference/aggregations/search-aggregations-bucket-significantterms-aggregation.md

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::::
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::::{admonition} Use the *"like this but not this"* pattern
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::::{admonition} Use the "like this but not this" pattern
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You can spot mis-categorized content by first searching a structured field e.g. `category:adultMovie` and use significant_terms on the free-text "movie_description" field. Take the suggested words (I’ll leave them to your imagination) and then search for all movies NOT marked as category:adultMovie but containing these keywords. You now have a ranked list of badly-categorized movies that you should reclassify or at least remove from the "familyFriendly" category.
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The significance score from each term can also provide a useful `boost` setting to sort matches. Using the `minimum_should_match` setting of the `terms` query with the keywords will help control the balance of precision/recall in the result set i.e a high setting would have a small number of relevant results packed full of keywords and a setting of "1" would produce a more exhaustive results set with all documents containing *any* keyword.

docs/reference/aggregations/search-aggregations-bucket-significanttext-aggregation.md

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::::
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::::{admonition} Use the *"like this but not this"* pattern
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::::{admonition} Use the "like this but not this" pattern
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You can spot mis-categorized content by first searching a structured field e.g. `category:adultMovie` and use significant_text on the text "movie_description" field. Take the suggested words (I’ll leave them to your imagination) and then search for all movies NOT marked as category:adultMovie but containing these keywords. You now have a ranked list of badly-categorized movies that you should reclassify or at least remove from the "familyFriendly" category.
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The significance score from each term can also provide a useful `boost` setting to sort matches. Using the `minimum_should_match` setting of the `terms` query with the keywords will help control the balance of precision/recall in the result set i.e a high setting would have a small number of relevant results packed full of keywords and a setting of "1" would produce a more exhaustive results set with all documents containing *any* keyword.

docs/reference/aggregations/search-aggregations-metrics-geocentroid-aggregation.md

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}
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```
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::::{admonition} Using `geo_centroid` as a sub-aggregation of `geohash_grid`
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::::{admonition} Using geo_centroid as a sub-aggregation of geohash_grid
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:class: warning
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The [`geohash_grid`](/reference/aggregations/search-aggregations-bucket-geohashgrid-aggregation.md) aggregation places documents, not individual geopoints, into buckets. If a document’s `geo_point` field contains [multiple values](/reference/elasticsearch/mapping-reference/array.md), the document could be assigned to multiple buckets, even if one or more of its geopoints are outside the bucket boundaries.

docs/reference/elasticsearch-plugins/mapper-size-usage.md

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5. Uses a [script field](/reference/elasticsearch/rest-apis/retrieve-selected-fields.md#script-fields) to return the `_size` field in the search response.
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::::{admonition} Using `_size` in {kib}
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::::{admonition} Using _size in {{kib}}
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To use the `_size` field in {{kib}}, update the `metaFields` setting and add `_size` to the list of meta fields. `metaFields` can be configured in {{kib}} from the Advanced Settings page in Management.

docs/reference/elasticsearch/command-line-tools/service-tokens-command.md

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## Parameters [service-tokens-command-parameters]
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`create`
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: Creates a service account token for the specified service account.
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### `create`
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::::{dropdown} Properties of `create`
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`<service_account_principal>`
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: (Required, string) Service account principal that takes the format of `<namespace>/<service>`, where the `namespace` is a top-level grouping of service accounts, and `service` is the name of the service. For example, `elastic/fleet-server`.
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Creates a service account token for the specified service account.
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The service account principal must match a known service account.
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**Properties of `create`**:
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`<service_account_principal>`
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: (Required, string) Service account principal that takes the format of `<namespace>/<service>`, where the `namespace` is a top-level grouping of service accounts, and `service` is the name of the service. For example, `elastic/fleet-server`.
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The service account principal must match a known service account.
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: (Required, string) An identifier for the token name.
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Token names must be at least 1 and no more than 256 characters. They can contain alphanumeric characters (`a-z`, `A-Z`, `0-9`), dashes (`-`), and underscores (`_`), but cannot begin with an underscore.
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: (Optional, string) Service account principal that takes the format of `<namespace>/<service>`, where the `namespace` is a top-level grouping of service accounts, and `service` is the name of the service. For example, `elastic/fleet-server`.
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## Examples [_examples_21]

docs/reference/elasticsearch/configuration-reference/monitoring-settings.md

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