--- type: api title: Create an anomaly detection job description: If you include a `datafeed_config`, you must have read index privileges on the source index. url: https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-put-job resource: https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-put-job products: - Elasticsearch --- # Create an anomaly detection job Authentication: required Availability: Generally available since 5.4 ## Paths - `PUT` `/_ml/anomaly_detectors/{job_id}` #### Path parameters - `job_id` (string) — required The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters. ## Description If you include a `datafeed_config`, you must have read index privileges on the source index. If you include a `datafeed_config` but do not provide a query, the datafeed uses `{"match_all": {"boost": 1}}`. ## Required privileges - Index privileges: `read` - Cluster privileges: `manage_ml` ## Query string parameters - `allow_no_indices` (boolean · default: true) — optional A setting that does two separate checks on the index expression. If `false`, the request returns an error (1) if any wildcard expression (including `_all` and `*`) resolves to zero matching indices or (2) if the complete set of resolved indices, aliases or data streams is empty after all expressions are evaluated. If `true`, index expressions that resolve to no indices are allowed and the request returns an empty result. - `expand_wildcards` (union oneOf · default: open) — optional Type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. Supports comma-separated values. Supported values include: - `all`: Match any data stream or index, including hidden ones. - `open`: Match open, non-hidden indices. Also matches any non-hidden data stream. - `closed`: Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed. - `hidden`: Match hidden data streams and hidden indices. Must be combined with `open`, `closed`, or `both`. - `none`: Wildcard expressions are not accepted. Values: `all`, `open`, `closed`, `hidden`, `none` - `ignore_throttled` (boolean · default: true) — optional — deprecated If `true`, concrete, expanded or aliased indices are ignored when frozen. - `ignore_unavailable` (boolean · default: false) — optional If `false`, the request returns an error if it targets a concrete (non-wildcarded) index, alias, or data stream that is missing, closed, or otherwise unavailable. If `true`, unavailable concrete targets are silently ignored. ## Request body `application/json` - `allow_lazy_open` (boolean · default: false) — optional Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node. By default, if a machine learning node with capacity to run the job cannot immediately be found, the open anomaly detection jobs API returns an error. However, this is also subject to the cluster-wide `xpack.ml.max_lazy_ml_nodes` setting. If this option is set to true, the open anomaly detection jobs API does not return an error and the job waits in the opening state until sufficient machine learning node capacity is available. - `analysis_config` (object) — required Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational. - `bucket_span` (union oneOf · default: 5m) — optional The size of the interval that the analysis is aggregated into, typically between `5m` and `1h`. This value should be either a whole number of days or equate to a whole number of buckets in one day. If the anomaly detection job uses a datafeed with aggregations, this value must also be divisible by the interval of the date histogram aggregation. Values: `-1`, `0` - `categorization_analyzer` (union oneOf) — optional If `categorization_field_name` is specified, you can also define the analyzer that is used to interpret the categorization field. This property cannot be used at the same time as `categorization_filters`. The categorization analyzer specifies how the `categorization_field` is interpreted by the categorization process. 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 or one added by another plugin. - **string** - **CategorizationAnalyzerDefinition** - `char_filter` (union oneOf[]) — optional One or more character filters. In addition to the built-in character filters, other plugins can provide more character filters. If this property 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. - **string** - **CharFilterDefinition** One of: - **type: html_strip** (`type: html_strip`) - `type` (enum) — required Values: `html_strip` - `version` (string) — optional - `escaped_tags` (string[]) — optional - **type: mapping** (`type: mapping`) - `type` (enum) — required Values: `mapping` - `version` (string) — optional - `mappings` (string[]) — optional - `mappings_path` (string) — optional - **type: pattern_replace** (`type: pattern_replace`) - `type` (enum) — required Values: `pattern_replace` - `version` (string) — optional - `flags` (string) — optional - `pattern` (string) — required - `replacement` (string) — optional - **type: icu_normalizer** (`type: icu_normalizer`) - `type` (enum) — required Values: `icu_normalizer` - `version` (string) — optional - `mode` (enum) — optional Values: `decompose`, `compose` - `name` (enum) — optional Values: `nfc`, `nfkc`, `nfkc_cf` - `unicode_set_filter` (string) — optional - **type: kuromoji_iteration_mark** (`type: kuromoji_iteration_mark`) - `type` (enum) — required Values: `kuromoji_iteration_mark` - `version` (string) — optional - `normalize_kana` (boolean) — required - `normalize_kanji` (boolean) — required - `filter` (union oneOf[]) — optional One or more token filters. In addition to the built-in token filters, other plugins can provide more token filters. If this property is not specified, no token filters are applied prior to categorization. - **string** - **TokenFilterDefinition** One of: - **type: apostrophe** (`type: apostrophe`) - `type` (enum) — required Values: `apostrophe` - `version` (string) — optional - **type: arabic_stem** (`type: arabic_stem`) - `type` (enum) — required Values: `arabic_stem` - `version` (string) — optional - **type: arabic_normalization** (`type: arabic_normalization`) - `type` (enum) — required Values: `arabic_normalization` - `version` (string) — optional - **type: asciifolding** (`type: asciifolding`) - `type` (enum) — required Values: `asciifolding` - `version` (string) — optional - `preserve_original` (boolean | string) — optional If `true`, emit both original tokens and folded tokens. Defaults to `false`. - **type: bengali_normalization** (`type: bengali_normalization`) - `type` (enum) — required Values: `bengali_normalization` - `version` (string) — optional - **type: brazilian_stem** (`type: brazilian_stem`) - `type` (enum) — required Values: `brazilian_stem` - `version` (string) — optional - **type: cjk_bigram** (`type: cjk_bigram`) - `type` (enum) — required Values: `cjk_bigram` - `version` (string) — optional - `ignored_scripts` (enum[]) — optional Array of character scripts for which to disable bigrams. Values: `han`, `hangul`, `hiragana`, `katakana` - `output_unigrams` (boolean) — optional If `true`, emit tokens in both bigram and unigram form. If `false`, a CJK character is output in unigram form when it has no adjacent characters. Defaults to `false`. - **type: cjk_width** (`type: cjk_width`) - `type` (enum) — required Values: `cjk_width` - `version` (string) — optional - **type: classic** (`type: classic`) - `type` (enum) — required Values: `classic` - `version` (string) — optional - **type: common_grams** (`type: common_grams`) - `type` (enum) — required Values: `common_grams` - `version` (string) — optional - `common_words` (string[]) — optional A list of tokens. The filter generates bigrams for these tokens. Either this or the `common_words_path` parameter is required. - `common_words_path` (string) — optional Path to a file containing a list of tokens. The filter generates bigrams for these tokens. This path must be absolute or relative to the `config` location. The file must be UTF-8 encoded. Each token in the file must be separated by a line break. Either this or the `common_words` parameter is required. - `ignore_case` (boolean) — optional If `true`, matches for common words matching are case-insensitive. Defaults to `false`. - `query_mode` (boolean) — optional If `true`, the filter excludes the following tokens from the output: - Unigrams for common words - Unigrams for terms followed by common words Defaults to `false`. We recommend enabling this parameter for search analyzers. - **type: condition** (`type: condition`) - `type` (enum) — required Values: `condition` - `version` (string) — optional - `filter` (string[]) — required Array of token filters. If a token matches the predicate script in the `script` parameter, these filters are applied to the token in the order provided. - `script` (string) — required Predicate script used to apply token filters. If a token matches this script, the filters in the `filter` parameter are applied to the token. - **type: czech_stem** (`type: czech_stem`) - `type` (enum) — required Values: `czech_stem` - `version` (string) — optional - **type: decimal_digit** (`type: decimal_digit`) - `type` (enum) — required Values: `decimal_digit` - `version` (string) — optional - **type: delimited_payload** (`type: delimited_payload`) - `type` (enum) — required Values: `delimited_payload` - `version` (string) — optional - `delimiter` (string) — optional Character used to separate tokens from payloads. Defaults to `|`. - `encoding` (enum) — optional Data type for the stored payload. Values: `int`, `float`, `identity` - **type: dutch_stem** (`type: dutch_stem`) - `type` (enum) — required Values: `dutch_stem` - `version` (string) — optional - **type: edge_ngram** (`type: edge_ngram`) - `type` (enum) — required Values: `edge_ngram` - `version` (string) — optional - `max_gram` (number) — optional Maximum character length of a gram. For custom token filters, defaults to `2`. For the built-in edge_ngram filter, defaults to `1`. - `min_gram` (number) — optional Minimum character length of a gram. Defaults to `1`. - `side` (enum) — optional Indicates whether to truncate tokens from the `front` or `back`. Defaults to `front`. Values: `front`, `back` - `preserve_original` (boolean | string) — optional Emits original token when set to `true`. Defaults to `false`. - **type: elision** (`type: elision`) - `type` (enum) — required Values: `elision` - `version` (string) — optional - `articles` (string[]) — optional List of elisions to remove. To be removed, the elision must be at the beginning of a token and be immediately followed by an apostrophe. Both the elision and apostrophe are removed. For custom `elision` filters, either this parameter or `articles_path` must be specified. - `articles_path` (string) — optional Path to a file that contains a list of elisions to remove. This path must be absolute or relative to the `config` location, and the file must be UTF-8 encoded. Each elision in the file must be separated by a line break. To be removed, the elision must be at the beginning of a token and be immediately followed by an apostrophe. Both the elision and apostrophe are removed. For custom `elision` filters, either this parameter or `articles` must be specified. - `articles_case` (boolean | string) — optional If `true`, elision matching is case insensitive. If `false`, elision matching is case sensitive. Defaults to `false`. - **type: fingerprint** (`type: fingerprint`) - `type` (enum) — required Values: `fingerprint` - `version` (string) — optional - `max_output_size` (number) — optional Maximum character length, including whitespace, of the output token. Defaults to `255`. Concatenated tokens longer than this will result in no token output. - `separator` (string) — optional Character to use to concatenate the token stream input. Defaults to a space. - **type: flatten_graph** (`type: flatten_graph`) - `type` (enum) — required Values: `flatten_graph` - `version` (string) — optional - **type: french_stem** (`type: french_stem`) - `type` (enum) — required Values: `french_stem` - `version` (string) — optional - **type: german_normalization** (`type: german_normalization`) - `type` (enum) — required Values: `german_normalization` - `version` (string) — optional - **type: german_stem** (`type: german_stem`) - `type` (enum) — required Values: `german_stem` - `version` (string) — optional - **type: hindi_normalization** (`type: hindi_normalization`) - `type` (enum) — required Values: `hindi_normalization` - `version` (string) — optional - **type: hunspell** (`type: hunspell`) - `type` (enum) — required Values: `hunspell` - `version` (string) — optional - `dedup` (boolean) — optional If `true`, duplicate tokens are removed from the filter’s output. Defaults to `true`. - `dictionary` (string) — optional One or more `.dic` files (e.g, `en_US.dic`, my_custom.dic) to use for the Hunspell dictionary. By default, the `hunspell` filter uses all `.dic` files in the `<$ES_PATH_CONF>/hunspell/` directory specified using the `lang`, `language`, or `locale` parameter. - `locale` (string) — required Locale directory used to specify the `.aff` and `.dic` files for a Hunspell dictionary. - `longest_only` (boolean) — optional If `true`, only the longest stemmed version of each token is included in the output. If `false`, all stemmed versions of the token are included. Defaults to `false`. - **type: hyphenation_decompounder** (`type: hyphenation_decompounder`) - `type` (enum) — required Values: `hyphenation_decompounder` - `version` (string) — optional - `max_subword_size` (number) — optional Maximum subword character length. Longer subword tokens are excluded from the output. Defaults to `15`. - `min_subword_size` (number) — optional Minimum subword character length. Shorter subword tokens are excluded from the output. Defaults to `2`. - `min_word_size` (number) — optional Minimum word character length. Shorter word tokens are excluded from the output. Defaults to `5`. - `only_longest_match` (boolean) — optional If `true`, only include the longest matching subword. Defaults to `false`. - `word_list` (string[]) — optional A list of subwords to look for in the token stream. If found, the subword is included in the token output. Either this parameter or `word_list_path` must be specified. - `word_list_path` (string) — optional Path to a file that contains a list of subwords to find in the token stream. If found, the subword is included in the token output. This path must be absolute or relative to the config location, and the file must be UTF-8 encoded. Each token in the file must be separated by a line break. Either this parameter or `word_list` must be specified. - `hyphenation_patterns_path` (string) — required Path to an Apache FOP (Formatting Objects Processor) XML hyphenation pattern file. This path must be absolute or relative to the `config` location. Only FOP v1.2 compatible files are supported. - `no_sub_matches` (boolean) — optional If `true`, do not match sub tokens in tokens that are in the word list. Defaults to `false`. - `no_overlapping_matches` (boolean) — optional If `true`, do not allow overlapping tokens. Defaults to `false`. - **type: indic_normalization** (`type: indic_normalization`) - `type` (enum) — required Values: `indic_normalization` - `version` (string) — optional - **type: keep_types** (`type: keep_types`) - `type` (enum) — required Values: `keep_types` - `version` (string) — optional - `mode` (enum) — optional Indicates whether to keep or remove the specified token types. Values: `include`, `exclude` - `types` (string[]) — required List of token types to keep or remove. - **type: keep** (`type: keep`) - `type` (enum) — required Values: `keep` - `version` (string) — optional - `keep_words` (string[]) — optional List of words to keep. Only tokens that match words in this list are included in the output. Either this parameter or `keep_words_path` must be specified. - `keep_words_case` (boolean) — optional If `true`, lowercase all keep words. Defaults to `false`. - `keep_words_path` (string) — optional Path to a file that contains a list of words to keep. Only tokens that match words in this list are included in the output. This path must be absolute or relative to the `config` location, and the file must be UTF-8 encoded. Each word in the file must be separated by a line break. Either this parameter or `keep_words` must be specified. - **type: keyword_marker** (`type: keyword_marker`) - `type` (enum) — required Values: `keyword_marker` - `version` (string) — optional - `ignore_case` (boolean) — optional If `true`, matching for the `keywords` and `keywords_path` parameters ignores letter case. Defaults to `false`. - `keywords` (union string | string[]) — optional Array of keywords. Tokens that match these keywords are not stemmed. This parameter, `keywords_path`, or `keywords_pattern` must be specified. You cannot specify this parameter and `keywords_pattern`. - `keywords_path` (string) — optional Path to a file that contains a list of keywords. Tokens that match these keywords are not stemmed. This path must be absolute or relative to the `config` location, and the file must be UTF-8 encoded. Each word in the file must be separated by a line break. This parameter, `keywords`, or `keywords_pattern` must be specified. You cannot specify this parameter and `keywords_pattern`. - `keywords_pattern` (string) — optional Java regular expression used to match tokens. Tokens that match this expression are marked as keywords and not stemmed. This parameter, `keywords`, or `keywords_path` must be specified. You cannot specify this parameter and `keywords` or `keywords_pattern`. - **type: keyword_repeat** (`type: keyword_repeat`) - `type` (enum) — required Values: `keyword_repeat` - `version` (string) — optional - **type: kstem** (`type: kstem`) - `type` (enum) — required Values: `kstem` - `version` (string) — optional - **type: length** (`type: length`) - `type` (enum) — required Values: `length` - `version` (string) — optional - `max` (number) — optional Maximum character length of a token. Longer tokens are excluded from the output. Defaults to `Integer.MAX_VALUE`, which is `2^31-1` or `2147483647`. - `min` (number) — optional Minimum character length of a token. Shorter tokens are excluded from the output. Defaults to `0`. - **type: limit** (`type: limit`) - `type` (enum) — required Values: `limit` - `version` (string) — optional - `consume_all_tokens` (boolean) — optional If `true`, the limit filter exhausts the token stream, even if the `max_token_count` has already been reached. Defaults to `false`. - `max_token_count` (number | string) — optional Maximum number of tokens to keep. Once this limit is reached, any remaining tokens are excluded from the output. Defaults to `1`. - **type: lowercase** (`type: lowercase`) - `type` (enum) — required Values: `lowercase` - `version` (string) — optional - `language` (enum) — optional Language-specific lowercase token filter to use. Values: `greek`, `irish`, `turkish` - **type: min_hash** (`type: min_hash`) - `type` (enum) — required Values: `min_hash` - `version` (string) — optional - `bucket_count` (number) — optional Number of buckets to which hashes are assigned. Defaults to `512`. - `hash_count` (number) — optional Number of ways to hash each token in the stream. Defaults to `1`. - `hash_set_size` (number) — optional Number of hashes to keep from each bucket. Defaults to `1`. Hashes are retained by ascending size, starting with the bucket’s smallest hash first. - `with_rotation` (boolean) — optional If `true`, the filter fills empty buckets with the value of the first non-empty bucket to its circular right if the `hash_set_size` is `1`. If the `bucket_count` argument is greater than 1, this parameter defaults to `true`. Otherwise, this parameter defaults to `false`. - **type: multiplexer** (`type: multiplexer`) - `type` (enum) — required Values: `multiplexer` - `version` (string) — optional - `filters` (string[]) — required A list of token filters to apply to incoming tokens. - `preserve_original` (boolean | string) — optional If `true` (the default) then emit the original token in addition to the filtered tokens. - **type: ngram** (`type: ngram`) - `type` (enum) — required Values: `ngram` - `version` (string) — optional - `max_gram` (number) — optional Maximum length of characters in a gram. Defaults to `2`. - `min_gram` (number) — optional Minimum length of characters in a gram. Defaults to `1`. - `preserve_original` (boolean | string) — optional Emits original token when set to `true`. Defaults to `false`. - **type: nori_part_of_speech** (`type: nori_part_of_speech`) - `type` (enum) — required Values: `nori_part_of_speech` - `version` (string) — optional - `stoptags` (string[]) — optional An array of part-of-speech tags that should be removed. - **type: pattern_capture** (`type: pattern_capture`) - `type` (enum) — required Values: `pattern_capture` - `version` (string) — optional - `patterns` (string[]) — required A list of regular expressions to match. - `preserve_original` (boolean | string) — optional If set to `true` (the default) it will emit the original token. - **type: pattern_replace** (`type: pattern_replace`) - `type` (enum) — required Values: `pattern_replace` - `version` (string) — optional - `all` (boolean) — optional If `true`, all substrings matching the pattern parameter’s regular expression are replaced. If `false`, the filter replaces only the first matching substring in each token. Defaults to `true`. - `flags` (string) — optional - `pattern` (string) — required Regular expression, written in Java’s regular expression syntax. The filter replaces token substrings matching this pattern with the substring in the `replacement` parameter. - `replacement` (string) — optional Replacement substring. Defaults to an empty substring (`""`). - **type: persian_normalization** (`type: persian_normalization`) - `type` (enum) — required Values: `persian_normalization` - `version` (string) — optional - **type: persian_stem** (`type: persian_stem`) - `type` (enum) — required Values: `persian_stem` - `version` (string) — optional - **type: porter_stem** (`type: porter_stem`) - `type` (enum) — required Values: `porter_stem` - `version` (string) — optional - **type: predicate_token_filter** (`type: predicate_token_filter`) - `type` (enum) — required Values: `predicate_token_filter` - `version` (string) — optional - `script` (string) — required Script containing a condition used to filter incoming tokens. Only tokens that match this script are included in the output. - **type: remove_duplicates** (`type: remove_duplicates`) - `type` (enum) — required Values: `remove_duplicates` - `version` (string) — optional - **type: reverse** (`type: reverse`) - `type` (enum) — required Values: `reverse` - `version` (string) — optional - **type: russian_stem** (`type: russian_stem`) - `type` (enum) — required Values: `russian_stem` - `version` (string) — optional - **type: scandinavian_folding** (`type: scandinavian_folding`) - `type` (enum) — required Values: `scandinavian_folding` - `version` (string) — optional - **type: scandinavian_normalization** (`type: scandinavian_normalization`) - `type` (enum) — required Values: `scandinavian_normalization` - `version` (string) — optional - **type: serbian_normalization** (`type: serbian_normalization`) - `type` (enum) — required Values: `serbian_normalization` - `version` (string) — optional - **type: shingle** (`type: shingle`) - `type` (enum) — required Values: `shingle` - `version` (string) — optional - `filler_token` (string) — optional String used in shingles as a replacement for empty positions that do not contain a token. This filler token is only used in shingles, not original unigrams. Defaults to an underscore (`_`). - `max_shingle_size` (number | string) — optional Maximum number of tokens to concatenate when creating shingles. Defaults to `2`. - `min_shingle_size` (number | string) — optional Minimum number of tokens to concatenate when creating shingles. Defaults to `2`. - `output_unigrams` (boolean) — optional If `true`, the output includes the original input tokens. If `false`, the output only includes shingles; the original input tokens are removed. Defaults to `true`. - `output_unigrams_if_no_shingles` (boolean) — optional If `true`, the output includes the original input tokens only if no shingles are produced; if shingles are produced, the output only includes shingles. Defaults to `false`. - `token_separator` (string) — optional Separator used to concatenate adjacent tokens to form a shingle. Defaults to a space (`" "`). - **type: snowball** (`type: snowball`) - `type` (enum) — required Values: `snowball` - `version` (string) — optional - `language` (enum) — optional Controls the language used by the stemmer. Values: `Arabic`, `Armenian`, `Basque`, `Catalan`, `Danish`, `Dutch`, `English`, `Estonian`, `Finnish`, `French`, `German`, `German2`, `Hungarian`, `Italian`, `Irish`, `Kp`, `Lithuanian`, `Lovins`, `Norwegian`, `Porter`, `Portuguese`, `Romanian`, `Russian`, `Serbian`, `Spanish`, `Swedish`, `Turkish` - **type: sorani_normalization** (`type: sorani_normalization`) - `type` (enum) — required Values: `sorani_normalization` - `version` (string) — optional - **type: stemmer_override** (`type: stemmer_override`) - `type` (enum) — required Values: `stemmer_override` - `version` (string) — optional - `rules` (string[]) — optional A list of mapping rules to use. - `rules_path` (string) — optional A path (either relative to `config` location, or absolute) to a list of mappings. - **type: stemmer** (`type: stemmer`) - `type` (enum) — required Values: `stemmer` - `version` (string) — optional - `language` (string) — optional - **type: stop** (`type: stop`) - `type` (enum) — required Values: `stop` - `version` (string) — optional - `ignore_case` (boolean) — optional If `true`, stop word matching is case insensitive. For example, if `true`, a stop word of the matches and removes `The`, `THE`, or `the`. Defaults to `false`. - `remove_trailing` (boolean) — optional If `true`, the last token of a stream is removed if it’s a stop word. Defaults to `true`. - `stopwords` (union oneOf) — optional Language value, such as `_arabic_` or `_thai_`. Defaults to `_english_`. Values: `_arabic_`, `_armenian_`, `_basque_`, `_bengali_`, `_brazilian_`, `_bulgarian_`, `_catalan_`, `_cjk_`, `_czech_`, `_danish_`, `_dutch_`, `_english_`, `_estonian_`, `_finnish_`, `_french_`, `_galician_`, `_german_`, `_greek_`, `_hindi_`, `_hungarian_`, `_indonesian_`, `_irish_`, `_italian_`, `_latvian_`, `_lithuanian_`, `_norwegian_`, `_persian_`, `_portuguese_`, `_romanian_`, `_russian_`, `_serbian_`, `_sorani_`, `_spanish_`, `_swedish_`, `_thai_`, `_turkish_`, `_none_` - `stopwords_path` (string) — optional Path to a file that contains a list of stop words to remove. This path must be absolute or relative to the `config` location, and the file must be UTF-8 encoded. Each stop word in the file must be separated by a line break. - **type: synonym_graph** (`type: synonym_graph`) - `type` (enum) — required Values: `synonym_graph` - `version` (string) — optional - `expand` (boolean) — optional Expands definitions for equivalent synonym rules. Defaults to `true`. - `format` (enum) — optional Sets the synonym rules format. Values: `solr`, `wordnet` - `lenient` (boolean) — optional If `true` ignores errors while parsing the synonym rules. It is important to note that only those synonym rules which cannot get parsed are ignored. Defaults to the value of the `updateable` setting. - `synonyms` (string[]) — optional Used to define inline synonyms. - `synonyms_path` (string) — optional Used to provide a synonym file. This path must be absolute or relative to the `config` location. - `synonyms_set` (union string | string[]) — optional Provide one or more synonym sets created through the Synonyms Management APIs. Maximum 100 sets per filter. - `tokenizer` (string) — optional — deprecated Controls the tokenizers that will be used to tokenize the synonym, this parameter is for backwards compatibility for indices that created before 6.0. - `updateable` (boolean) — optional If `true` allows reloading search analyzers to pick up changes to synonym files. Only to be used for search analyzers. Defaults to `false`. - **type: synonym** (`type: synonym`) - `type` (enum) — required Values: `synonym` - `version` (string) — optional - `expand` (boolean) — optional Expands definitions for equivalent synonym rules. Defaults to `true`. - `format` (enum) — optional Sets the synonym rules format. Values: `solr`, `wordnet` - `lenient` (boolean) — optional If `true` ignores errors while parsing the synonym rules. It is important to note that only those synonym rules which cannot get parsed are ignored. Defaults to the value of the `updateable` setting. - `synonyms` (string[]) — optional Used to define inline synonyms. - `synonyms_path` (string) — optional Used to provide a synonym file. This path must be absolute or relative to the `config` location. - `synonyms_set` (union string | string[]) — optional Provide one or more synonym sets created through the Synonyms Management APIs. Maximum 100 sets per filter. - `tokenizer` (string) — optional — deprecated Controls the tokenizers that will be used to tokenize the synonym, this parameter is for backwards compatibility for indices that created before 6.0. - `updateable` (boolean) — optional If `true` allows reloading search analyzers to pick up changes to synonym files. Only to be used for search analyzers. Defaults to `false`. - **type: trim** (`type: trim`) - `type` (enum) — required Values: `trim` - `version` (string) — optional - **type: truncate** (`type: truncate`) - `type` (enum) — required Values: `truncate` - `version` (string) — optional - `length` (number) — optional Character limit for each token. Tokens exceeding this limit are truncated. Defaults to `10`. - **type: unique** (`type: unique`) - `type` (enum) — required Values: `unique` - `version` (string) — optional - `only_on_same_position` (boolean) — optional If `true`, only remove duplicate tokens in the same position. Defaults to `false`. - **type: uppercase** (`type: uppercase`) - `type` (enum) — required Values: `uppercase` - `version` (string) — optional - **type: word_delimiter_graph** (`type: word_delimiter_graph`) - `type` (enum) — required Values: `word_delimiter_graph` - `version` (string) — optional - `catenate_all` (boolean) — optional If `true`, the filter produces catenated tokens for chains of alphanumeric characters separated by non-alphabetic delimiters. Defaults to `false`. - `catenate_numbers` (boolean) — optional If `true`, the filter produces catenated tokens for chains of numeric characters separated by non-alphabetic delimiters. Defaults to `false`. - `catenate_words` (boolean) — optional If `true`, the filter produces catenated tokens for chains of alphabetical characters separated by non-alphabetic delimiters. Defaults to `false`. - `generate_number_parts` (boolean) — optional If `true`, the filter includes tokens consisting of only numeric characters in the output. If `false`, the filter excludes these tokens from the output. Defaults to `true`. - `generate_word_parts` (boolean) — optional If `true`, the filter includes tokens consisting of only alphabetical characters in the output. If `false`, the filter excludes these tokens from the output. Defaults to `true`. - `preserve_original` (boolean | string) — optional If `true`, the filter includes the original version of any split tokens in the output. This original version includes non-alphanumeric delimiters. Defaults to `false`. - `protected_words` (string[]) — optional Array of tokens the filter won’t split. - `protected_words_path` (string) — optional Path to a file that contains a list of tokens the filter won’t split. This path must be absolute or relative to the `config` location, and the file must be UTF-8 encoded. Each token in the file must be separated by a line break. - `split_on_case_change` (boolean) — optional If `true`, the filter splits tokens at letter case transitions. For example: camelCase -> [ camel, Case ]. Defaults to `true`. - `split_on_numerics` (boolean) — optional If `true`, the filter splits tokens at letter-number transitions. For example: j2se -> [ j, 2, se ]. Defaults to `true`. - `stem_english_possessive` (boolean) — optional If `true`, the filter removes the English possessive (`'s`) from the end of each token. For example: O'Neil's -> [ O, Neil ]. Defaults to `true`. - `type_table` (string[]) — optional Array of custom type mappings for characters. This allows you to map non-alphanumeric characters as numeric or alphanumeric to avoid splitting on those characters. - `type_table_path` (string) — optional Path to a file that contains custom type mappings for characters. This allows you to map non-alphanumeric characters as numeric or alphanumeric to avoid splitting on those characters. - `adjust_offsets` (boolean) — optional If `true`, the filter adjusts the offsets of split or catenated tokens to better reflect their actual position in the token stream. Defaults to `true`. - `ignore_keywords` (boolean) — optional If `true`, the filter skips tokens with a keyword attribute of true. Defaults to `false`. - **type: word_delimiter** (`type: word_delimiter`) - `type` (enum) — required Values: `word_delimiter` - `version` (string) — optional - `catenate_all` (boolean) — optional If `true`, the filter produces catenated tokens for chains of alphanumeric characters separated by non-alphabetic delimiters. Defaults to `false`. - `catenate_numbers` (boolean) — optional If `true`, the filter produces catenated tokens for chains of numeric characters separated by non-alphabetic delimiters. Defaults to `false`. - `catenate_words` (boolean) — optional If `true`, the filter produces catenated tokens for chains of alphabetical characters separated by non-alphabetic delimiters. Defaults to `false`. - `generate_number_parts` (boolean) — optional If `true`, the filter includes tokens consisting of only numeric characters in the output. If `false`, the filter excludes these tokens from the output. Defaults to `true`. - `generate_word_parts` (boolean) — optional If `true`, the filter includes tokens consisting of only alphabetical characters in the output. If `false`, the filter excludes these tokens from the output. Defaults to `true`. - `preserve_original` (boolean | string) — optional If `true`, the filter includes the original version of any split tokens in the output. This original version includes non-alphanumeric delimiters. Defaults to `false`. - `protected_words` (string[]) — optional Array of tokens the filter won’t split. - `protected_words_path` (string) — optional Path to a file that contains a list of tokens the filter won’t split. This path must be absolute or relative to the `config` location, and the file must be UTF-8 encoded. Each token in the file must be separated by a line break. - `split_on_case_change` (boolean) — optional If `true`, the filter splits tokens at letter case transitions. For example: camelCase -> [ camel, Case ]. Defaults to `true`. - `split_on_numerics` (boolean) — optional If `true`, the filter splits tokens at letter-number transitions. For example: j2se -> [ j, 2, se ]. Defaults to `true`. - `stem_english_possessive` (boolean) — optional If `true`, the filter removes the English possessive (`'s`) from the end of each token. For example: O'Neil's -> [ O, Neil ]. Defaults to `true`. - `type_table` (string[]) — optional Array of custom type mappings for characters. This allows you to map non-alphanumeric characters as numeric or alphanumeric to avoid splitting on those characters. - `type_table_path` (string) — optional Path to a file that contains custom type mappings for characters. This allows you to map non-alphanumeric characters as numeric or alphanumeric to avoid splitting on those characters. - **type: ja_stop** (`type: ja_stop`) - `type` (enum) — required Values: `ja_stop` - `version` (string) — optional - `stopwords` (union oneOf) — optional Values: `_arabic_`, `_armenian_`, `_basque_`, `_bengali_`, `_brazilian_`, `_bulgarian_`, `_catalan_`, `_cjk_`, `_czech_`, `_danish_`, `_dutch_`, `_english_`, `_estonian_`, `_finnish_`, `_french_`, `_galician_`, `_german_`, `_greek_`, `_hindi_`, `_hungarian_`, `_indonesian_`, `_irish_`, `_italian_`, `_latvian_`, `_lithuanian_`, `_norwegian_`, `_persian_`, `_portuguese_`, `_romanian_`, `_russian_`, `_serbian_`, `_sorani_`, `_spanish_`, `_swedish_`, `_thai_`, `_turkish_`, `_none_` - **type: kuromoji_stemmer** (`type: kuromoji_stemmer`) - `type` (enum) — required Values: `kuromoji_stemmer` - `version` (string) — optional - `minimum_length` (number) — required - **type: kuromoji_readingform** (`type: kuromoji_readingform`) - `type` (enum) — required Values: `kuromoji_readingform` - `version` (string) — optional - `use_romaji` (boolean) — required - **type: kuromoji_part_of_speech** (`type: kuromoji_part_of_speech`) - `type` (enum) — required Values: `kuromoji_part_of_speech` - `version` (string) — optional - `stoptags` (string[]) — required - **type: icu_collation** (`type: icu_collation`) - `type` (enum) — required Values: `icu_collation` - `version` (string) — optional - `alternate` (enum) — optional Values: `shifted`, `non-ignorable` - `caseFirst` (enum) — optional Values: `lower`, `upper` - `caseLevel` (boolean) — optional - `country` (string) — optional - `decomposition` (enum) — optional Values: `no`, `identical` - `hiraganaQuaternaryMode` (boolean) — optional - `language` (string) — optional - `numeric` (boolean) — optional - `rules` (string) — optional - `strength` (enum) — optional Values: `primary`, `secondary`, `tertiary`, `quaternary`, `identical` - `variableTop` (string) — optional - `variant` (string) — optional - **type: icu_folding** (`type: icu_folding`) - `type` (enum) — required Values: `icu_folding` - `version` (string) — optional - `unicode_set_filter` (string) — required - **type: icu_normalizer** (`type: icu_normalizer`) - `type` (enum) — required Values: `icu_normalizer` - `version` (string) — optional - `name` (enum) — required Values: `nfc`, `nfkc`, `nfkc_cf` - **type: icu_transform** (`type: icu_transform`) - `type` (enum) — required Values: `icu_transform` - `version` (string) — optional - `dir` (enum) — optional Values: `forward`, `reverse` - `id` (string) — required - **type: phonetic** (`type: phonetic`) - `type` (enum) — required Values: `phonetic` - `version` (string) — optional - `encoder` (enum) — required Values: `metaphone`, `double_metaphone`, `soundex`, `refined_soundex`, `caverphone1`, `caverphone2`, `cologne`, `nysiis`, `koelnerphonetik`, `haasephonetik`, `beider_morse`, `daitch_mokotoff` - `languageset` (union enum | enum[]) — optional Supported values include: `any`, `common`, `cyrillic`, `english`, `french`, `german`, `hebrew`, `hungarian`, `polish`, `romanian`, `russian`, `spanish` Values: `any`, `common`, `cyrillic`, `english`, `french`, `german`, `hebrew`, `hungarian`, `polish`, `romanian`, `russian`, `spanish` - `max_code_len` (number) — optional - `name_type` (enum) — optional Values: `generic`, `ashkenazi`, `sephardic` - `replace` (boolean) — optional - `rule_type` (enum) — optional Values: `approx`, `exact` - **type: dictionary_decompounder** (`type: dictionary_decompounder`) - `type` (enum) — required Values: `dictionary_decompounder` - `version` (string) — optional - `max_subword_size` (number) — optional Maximum subword character length. Longer subword tokens are excluded from the output. Defaults to `15`. - `min_subword_size` (number) — optional Minimum subword character length. Shorter subword tokens are excluded from the output. Defaults to `2`. - `min_word_size` (number) — optional Minimum word character length. Shorter word tokens are excluded from the output. Defaults to `5`. - `only_longest_match` (boolean) — optional If `true`, only include the longest matching subword. Defaults to `false`. - `word_list` (string[]) — optional A list of subwords to look for in the token stream. If found, the subword is included in the token output. Either this parameter or `word_list_path` must be specified. - `word_list_path` (string) — optional Path to a file that contains a list of subwords to find in the token stream. If found, the subword is included in the token output. This path must be absolute or relative to the config location, and the file must be UTF-8 encoded. Each token in the file must be separated by a line break. Either this parameter or `word_list` must be specified. - `tokenizer` (union oneOf) — optional The name or definition of the tokenizer to use after character filters are applied. This property is compulsory if `categorization_analyzer` is specified as an object. Machine learning provides a tokenizer called `ml_standard` that tokenizes in a way that has been determined to produce good categorization results on a variety of log file formats for logs in English. If you want to use that tokenizer but change the character or token filters, specify "tokenizer": "ml_standard" in your `categorization_analyzer`. Additionally, the `ml_classic` tokenizer is available, which tokenizes in the same way as the non-customizable tokenizer in old versions of the product (before 6.2). `ml_classic` was the default categorization tokenizer in versions 6.2 to 7.13, so if you need categorization identical to the default for jobs created in these versions, specify "tokenizer": "ml_classic" in your `categorization_analyzer`. - **string** - **TokenizerDefinition** One of: - **type: char_group** (`type: char_group`) - `type` (enum) — required Values: `char_group` - `version` (string) — optional - `tokenize_on_chars` (string[]) — required - `max_token_length` (number) — optional - **type: classic** (`type: classic`) - `type` (enum) — required Values: `classic` - `version` (string) — optional - `max_token_length` (number) — optional - **type: edge_ngram** (`type: edge_ngram`) - `type` (enum) — required Values: `edge_ngram` - `version` (string) — optional - `custom_token_chars` (string) — optional - `max_gram` (number) — optional - `min_gram` (number) — optional - `token_chars` (enum[] · default: []) — optional Values: `letter`, `digit`, `whitespace`, `punctuation`, `symbol`, `custom` - **type: keyword** (`type: keyword`) - `type` (enum) — required Values: `keyword` - `version` (string) — optional - `buffer_size` (number · default: 256) — optional - **type: letter** (`type: letter`) - `type` (enum) — required Values: `letter` - `version` (string) — optional - **type: lowercase** (`type: lowercase`) - `type` (enum) — required Values: `lowercase` - `version` (string) — optional - **type: ngram** (`type: ngram`) - `type` (enum) — required Values: `ngram` - `version` (string) — optional - `custom_token_chars` (string) — optional - `max_gram` (number) — optional - `min_gram` (number) — optional - `token_chars` (enum[] · default: []) — optional Values: `letter`, `digit`, `whitespace`, `punctuation`, `symbol`, `custom` - **type: path_hierarchy** (`type: path_hierarchy`) - `type` (enum) — required Values: `path_hierarchy` - `version` (string) — optional - `buffer_size` (number | string) — optional - `delimiter` (string) — optional - `replacement` (string) — optional - `reverse` (boolean | string) — optional - `skip` (number | string) — optional - **type: pattern** (`type: pattern`) - `type` (enum) — required Values: `pattern` - `version` (string) — optional - `flags` (string) — optional - `group` (number) — optional - `pattern` (string) — optional - **type: simple_pattern** (`type: simple_pattern`) - `type` (enum) — required Values: `simple_pattern` - `version` (string) — optional - `pattern` (string) — optional - **type: simple_pattern_split** (`type: simple_pattern_split`) - `type` (enum) — required Values: `simple_pattern_split` - `version` (string) — optional - `pattern` (string) — optional - **type: standard** (`type: standard`) - `type` (enum) — required Values: `standard` - `version` (string) — optional - `max_token_length` (number) — optional - **type: thai** (`type: thai`) - `type` (enum) — required Values: `thai` - `version` (string) — optional - **type: uax_url_email** (`type: uax_url_email`) - `type` (enum) — required Values: `uax_url_email` - `version` (string) — optional - `max_token_length` (number) — optional - **type: whitespace** (`type: whitespace`) - `type` (enum) — required Values: `whitespace` - `version` (string) — optional - `max_token_length` (number) — optional - **type: icu_tokenizer** (`type: icu_tokenizer`) - `type` (enum) — required Values: `icu_tokenizer` - `version` (string) — optional - `rule_files` (string) — required - **type: kuromoji_tokenizer** (`type: kuromoji_tokenizer`) - `type` (enum) — required Values: `kuromoji_tokenizer` - `version` (string) — optional - `discard_punctuation` (boolean) — optional - `mode` (enum) — required Values: `normal`, `search`, `extended` - `nbest_cost` (number) — optional - `nbest_examples` (string) — optional - `user_dictionary` (string) — optional - `user_dictionary_rules` (string[]) — optional - `discard_compound_token` (boolean) — optional - **type: nori_tokenizer** (`type: nori_tokenizer`) - `type` (enum) — required Values: `nori_tokenizer` - `version` (string) — optional - `decompound_mode` (enum) — optional Values: `discard`, `none`, `mixed` - `discard_punctuation` (boolean) — optional - `user_dictionary` (string) — optional - `user_dictionary_rules` (string[]) — optional - `categorization_field_name` (string) — optional If this property is specified, the values of the specified field will be categorized. The resulting categories must be used in a detector by setting `by_field_name`, `over_field_name`, or `partition_field_name` to the keyword `mlcategory`. - `categorization_filters` (string[]) — optional If `categorization_field_name` is specified, you can also define optional filters. This property expects an array of regular expressions. The expressions are used to filter out matching sequences from the categorization field values. You can use this functionality to fine tune the categorization by excluding sequences from consideration when categories are defined. For example, you can exclude SQL statements that appear in your log files. This property cannot be used at the same time as `categorization_analyzer`. If you only want to define simple regular expression filters that are applied prior to tokenization, setting this property is the easiest method. If you also want to customize the tokenizer or post-tokenization filtering, use the `categorization_analyzer` property instead and include the filters as pattern_replace character filters. The effect is exactly the same. - `detectors` (object[]) — required Detector configuration objects specify which data fields a job analyzes. They also specify which analytical functions are used. You can specify multiple detectors for a job. If the detectors array does not contain at least one detector, no analysis can occur and an error is returned. - `by_field_name` (string) — optional The field used to split the data. In particular, this property is used for analyzing the splits with respect to their own history. It is used for finding unusual values in the context of the split. - `custom_rules` (object[]) — optional Custom rules enable you to customize the way detectors operate. For example, a rule may dictate conditions under which results should be skipped. Kibana refers to custom rules as job rules. - `actions` (enum[] · default: ["skip_result"]) — optional The set of actions to be triggered when the rule applies. If more than one action is specified the effects of all actions are combined. Supported values include: - `skip_result`: The result will not be created. Unless you also specify `skip_model_update`, the model will be updated as usual with the corresponding series value. - `skip_model_update`: The value for that series will not be used to update the model. Unless you also specify `skip_result`, the results will be created as usual. This action is suitable when certain values are expected to be consistently anomalous and they affect the model in a way that negatively impacts the rest of the results. Values: `skip_result`, `skip_model_update` - `conditions` (object[]) — optional An array of numeric conditions when the rule applies. A rule must either have a non-empty scope or at least one condition. Multiple conditions are combined together with a logical AND. - `applies_to` (enum) — required Specifies the result property to which the condition applies. If your detector uses `lat_long`, `metric`, `rare`, or `freq_rare` functions, you can only specify conditions that apply to time. Values: `actual`, `typical`, `diff_from_typical`, `time` - `operator` (enum) — required Specifies the condition operator. The available options are greater than, greater than or equals, less than, and less than or equals. Values: `gt`, `gte`, `lt`, `lte` - `value` (number) — required The value that is compared against the `applies_to` field using the operator. - `scope` (map string to object) — optional A scope of series where the rule applies. A rule must either have a non-empty scope or at least one condition. By default, the scope includes all series. Scoping is allowed for any of the fields that are also specified in `by_field_name`, `over_field_name`, or `partition_field_name`. Map values (object) - `filter_id` (string) — required The identifier for the filter. - `filter_type` (enum · default: include) — optional If set to `include`, the rule applies for values in the filter. If set to `exclude`, the rule applies for values not in the filter. Values: `include`, `exclude` - `detector_description` (string) — optional A description of the detector. - `detector_index` (number) — optional A unique identifier for the detector. This identifier is based on the order of the detectors in the `analysis_config`, starting at zero. If you specify a value for this property, it is ignored. - `exclude_frequent` (enum) — optional If set, frequent entities are excluded from influencing the anomaly results. Entities can be considered frequent over time or frequent in a population. If you are working with both over and by fields, you can set `exclude_frequent` to `all` for both fields, or to `by` or `over` for those specific fields. Values: `all`, `none`, `by`, `over` - `field_name` (string) — optional The field that the detector uses in the function. If you use an event rate function such as count or rare, do not specify this field. The `field_name` cannot contain double quotes or backslashes. - `function` (string) — optional The analysis function that is used. For example, `count`, `rare`, `mean`, `min`, `max`, or `sum`. - `over_field_name` (string) — optional The field used to split the data. In particular, this property is used for analyzing the splits with respect to the history of all splits. It is used for finding unusual values in the population of all splits. - `partition_field_name` (string) — optional The field used to segment the analysis. When you use this property, you have completely independent baselines for each value of this field. - `use_null` (boolean · default: false) — optional Defines whether a new series is used as the null series when there is no value for the by or partition fields. - `influencers` (string[]) — optional A comma separated list of influencer field names. Typically these can be the by, over, or partition fields that are used in the detector configuration. You might also want to use a field name that is not specifically named in a detector, but is available as part of the input data. When you use multiple detectors, the use of influencers is recommended as it aggregates results for each influencer entity. - `latency` (union oneOf · default: 0) — optional The size of the window in which to expect data that is out of time order. If you specify a non-zero value, it must be greater than or equal to one second. NOTE: Latency is applicable only when you send data by using the post data API. Values: `-1`, `0` - `model_prune_window` (union oneOf) — optional Advanced configuration option. Affects the pruning of models that have not been updated for the given time duration. The value must be set to a multiple of the `bucket_span`. If set too low, important information may be removed from the model. For jobs created in 8.1 and later, the default value is the greater of `30d` or 20 times `bucket_span`. Values: `-1`, `0` - `multivariate_by_fields` (boolean) — optional This functionality is reserved for internal use. It is not supported for use in customer environments and is not subject to the support SLA of official GA features. If set to `true`, the analysis will automatically find correlations between metrics for a given by field value and report anomalies when those correlations cease to hold. For example, suppose CPU and memory usage on host A is usually highly correlated with the same metrics on host B. Perhaps this correlation occurs because they are running a load-balanced application. If you enable this property, anomalies will be reported when, for example, CPU usage on host A is high and the value of CPU usage on host B is low. That is to say, you’ll see an anomaly when the CPU of host A is unusual given the CPU of host B. To use the `multivariate_by_fields` property, you must also specify `by_field_name` in your detector. - `per_partition_categorization` (object) — optional Settings related to how categorization interacts with partition fields. - `enabled` (boolean) — optional To enable this setting, you must also set the `partition_field_name` property to the same value in every detector that uses the keyword `mlcategory`. Otherwise, job creation fails. - `stop_on_warn` (boolean) — optional This setting can be set to true only if per-partition categorization is enabled. If true, both categorization and subsequent anomaly detection stops for partitions where the categorization status changes to warn. This setting makes it viable to have a job where it is expected that categorization works well for some partitions but not others; you do not pay the cost of bad categorization forever in the partitions where it works badly. - `summary_count_field_name` (string) — optional If this property is specified, the data that is fed to the job is expected to be pre-summarized. This property value is the name of the field that contains the count of raw data points that have been summarized. The same `summary_count_field_name` applies to all detectors in the job. NOTE: The `summary_count_field_name` property cannot be used with the `metric` function. - `analysis_limits` (object) — optional Limits can be applied for the resources required to hold the mathematical models in memory. These limits are approximate and can be set per job. They do not control the memory used by other processes, for example the Elasticsearch Java processes. - `categorization_examples_limit` (number · default: 4) — optional The maximum number of examples stored per category in memory and in the results data store. If you increase this value, more examples are available, however it requires that you have more storage available. If you set this value to 0, no examples are stored. NOTE: The `categorization_examples_limit` applies only to analysis that uses categorization. - `model_memory_limit` (number | string · default: 1024mb) — optional The approximate maximum amount of memory resources that are required for analytical processing. Once this limit is approached, data pruning becomes more aggressive. Upon exceeding this limit, new entities are not modeled. If the `xpack.ml.max_model_memory_limit` setting has a value greater than 0 and less than 1024mb, that value is used instead of the default. The default value is relatively small to ensure that high resource usage is a conscious decision. If you have jobs that are expected to analyze high cardinality fields, you will likely need to use a higher value. If you specify a number instead of a string, the units are assumed to be MiB. Specifying a string is recommended for clarity. If you specify a byte size unit of `b` or `kb` and the number does not equate to a discrete number of megabytes, it is rounded down to the closest MiB. The minimum valid value is 1 MiB. If you specify a value less than 1 MiB, an error occurs. If you specify a value for the `xpack.ml.max_model_memory_limit` setting, an error occurs when you try to create jobs that have `model_memory_limit` values greater than that setting value. - `background_persist_interval` (union oneOf) — optional Advanced configuration option. The time between each periodic persistence of the model. The default value is a randomized value between 3 to 4 hours, which avoids all jobs persisting at exactly the same time. The smallest allowed value is 1 hour. For very large models (several GB), persistence could take 10-20 minutes, so do not set the `background_persist_interval` value too low. Values: `-1`, `0` - `custom_settings` (object) — optional Advanced configuration option. Contains custom meta data about the job. - `daily_model_snapshot_retention_after_days` (number · default: 1) — optional Advanced configuration option, which affects the automatic removal of old model snapshots for this job. It specifies a period of time (in days) after which only the first snapshot per day is retained. This period is relative to the timestamp of the most recent snapshot for this job. Valid values range from 0 to `model_snapshot_retention_days`. - `data_description` (object) — required Defines the format of the input data when you send data to the job by using the post data API. Note that when configure a datafeed, these properties are automatically set. When data is received via the post data API, it is not stored in Elasticsearch. Only the results for anomaly detection are retained. - `format` (string) — optional Only JSON format is supported at this time. - `time_field` (string · default: time) — optional The name of the field that contains the timestamp. - `time_format` (string · default: epoch) — optional The time format, which can be `epoch`, `epoch_ms`, or a custom pattern. The value `epoch` refers to UNIX or Epoch time (the number of seconds since 1 Jan 1970). The value `epoch_ms` indicates that time is measured in milliseconds since the epoch. The `epoch` and `epoch_ms` time formats accept either integer or real values. Custom patterns must conform to the Java DateTimeFormatter class. When you use date-time formatting patterns, it is recommended that you provide the full date, time and time zone. For example: `yyyy-MM-dd'T'HH:mm:ssX`. If the pattern that you specify is not sufficient to produce a complete timestamp, job creation fails. - `field_delimiter` (string) — optional - `datafeed_config` (object) — optional Defines a datafeed for the anomaly detection job. If Elasticsearch security features are enabled, your datafeed remembers which roles the user who created it had at the time of creation and runs the query using those same roles. If you provide secondary authorization headers, those credentials are used instead. - `aggregations` (map string to {} Aggregation) — optional If set, the datafeed performs aggregation searches. Support for aggregations is limited and should be used only with low cardinality data. See [Aggregation](https://www.elastic.co/docs/api/doc/elasticsearch/types/_types-aggregations-aggregationcontainer) - `chunking_config` (object) — optional Datafeeds might be required to search over long time periods, for several months or years. This search is split into time chunks in order to ensure the load on Elasticsearch is managed. Chunking configuration controls how the size of these time chunks are calculated and is an advanced configuration option. - `mode` (enum) — required If the mode is `auto`, the chunk size is dynamically calculated; this is the recommended value when the datafeed does not use aggregations. If the mode is `manual`, chunking is applied according to the specified `time_span`; use this mode when the datafeed uses aggregations. If the mode is `off`, no chunking is applied. Values: `auto`, `manual`, `off` - `time_span` (union oneOf · default: 3h) — optional The time span that each search will be querying. This setting is applicable only when the `mode` is set to `manual`. Values: `-1`, `0` - `datafeed_id` (string) — optional A numerical character string that uniquely identifies the datafeed. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters. The default value is the job identifier. - `delayed_data_check_config` (object) — optional Specifies whether the datafeed checks for missing data and the size of the window. The datafeed can optionally search over indices that have already been read in an effort to determine whether any data has subsequently been added to the index. If missing data is found, it is a good indication that the `query_delay` option is set too low and the data is being indexed after the datafeed has passed that moment in time. This check runs only on real-time datafeeds. - `check_window` (union oneOf) — optional The window of time that is searched for late data. This window of time ends with the latest finalized bucket. It defaults to null, which causes an appropriate `check_window` to be calculated when the real-time datafeed runs. In particular, the default `check_window` span calculation is based on the maximum of `2h` or `8 * bucket_span`. Values: `-1`, `0` - `enabled` (boolean) — required Specifies whether the datafeed periodically checks for delayed data. - `frequency` (union oneOf) — optional The interval at which scheduled queries are made while the datafeed runs in real time. The default value is either the bucket span for short bucket spans, or, for longer bucket spans, a sensible fraction of the bucket span. For example: `150s`. When `frequency` is shorter than the bucket span, interim results for the last (partial) bucket are written then eventually overwritten by the full bucket results. If the datafeed uses aggregations, this value must be divisible by the interval of the date histogram aggregation. Values: `-1`, `0` - `indices` (union oneOf) — optional An array of index names. Wildcards are supported. If any indices are in remote clusters, the machine learning nodes must have the `remote_cluster_client` role. One of: `string[]` or `string` - `indices_options` (object) — optional Specifies index expansion options that are used during search. - `allow_no_indices` (boolean) — optional A setting that does two separate checks on the index expression. If `false`, the request returns an error (1) if any wildcard expression (including `_all` and `*`) resolves to zero matching indices or (2) if the complete set of resolved indices, aliases or data streams is empty after all expressions are evaluated. If `true`, index expressions that resolve to no indices are allowed and the request returns an empty result. - `expand_wildcards` (union oneOf) — optional Type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. Supports comma-separated values, such as `open,hidden`. Supported values include: - `all`: Match any data stream or index, including hidden ones. - `open`: Match open, non-hidden indices. Also matches any non-hidden data stream. - `closed`: Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed. - `hidden`: Match hidden data streams and hidden indices. Must be combined with `open`, `closed`, or `both`. - `none`: Wildcard expressions are not accepted. Values: `all`, `open`, `closed`, `hidden`, `none` - `ignore_unavailable` (boolean · default: false) — optional If `false`, the request returns an error if it targets a concrete (non-wildcarded) index, alias, or data stream that is missing, closed, or otherwise unavailable. If `true`, unavailable concrete targets are silently ignored. - `ignore_throttled` (boolean · default: true) — optional If true, concrete, expanded or aliased indices are ignored when frozen. - `job_id` (string) — optional - `max_empty_searches` (number) — optional If a real-time datafeed has never seen any data (including during any initial training period) then it will automatically stop itself and close its associated job after this many real-time searches that return no documents. In other words, it will stop after `frequency` times `max_empty_searches` of real-time operation. If not set then a datafeed with no end time that sees no data will remain started until it is explicitly stopped. - `max_consecutive_extraction_failures` (number) — optional The maximum number of consecutive real-time data extraction failures the datafeed tolerates before it automatically stops itself, leaving the associated job open. The consecutive-failure counter resets on any cycle that extracts successfully, including empty-data cycles. If not set, the threshold defaults to roughly one day's worth of searches based on the datafeed `frequency` (floored at 1). Set to `-1` to disable auto-stop and retry indefinitely. Values of `0` or less than `-1` are rejected. - `query` ({} Query) — optional The Elasticsearch query domain-specific language (DSL). This value corresponds to the query object in an Elasticsearch search POST body. All the options that are supported by Elasticsearch can be used, as this object is passed verbatim to Elasticsearch. See [Query](https://www.elastic.co/docs/api/doc/elasticsearch/types/_types-query_dsl-querycontainer) - `query_delay` (union oneOf) — optional The number of seconds behind real time that data is queried. For example, if data from 10:04 a.m. might not be searchable in Elasticsearch until 10:06 a.m., set this property to 120 seconds. The default value is randomly selected between `60s` and `120s`. This randomness improves the query performance when there are multiple jobs running on the same node. Values: `-1`, `0` - `runtime_mappings` (object) — optional Specifies runtime fields for the datafeed search. - `script_fields` (map string to object) — optional Specifies scripts that evaluate custom expressions and returns script fields to the datafeed. The detector configuration objects in a job can contain functions that use these script fields. Map values (object) - `script` (string) — required - `ignore_failure` (boolean) — optional - `scroll_size` (number · default: 1000) — optional The size parameter that is used in Elasticsearch searches when the datafeed does not use aggregations. The maximum value is the value of `index.max_result_window`, which is 10,000 by default. - `description` (string) — optional A description of the job. - `job_id` (string) — optional The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters. - `groups` (string[]) — optional A list of job groups. A job can belong to no groups or many. - `model_plot_config` (object) — optional This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection. If you enable model plot it can add considerable overhead to the performance of the system; it is not feasible for jobs with many entities. Model plot provides a simplified and indicative view of the model and its bounds. It does not display complex features such as multivariate correlations or multimodal data. As such, anomalies may occasionally be reported which cannot be seen in the model plot. Model plot config can be configured when the job is created or updated later. It must be disabled if performance issues are experienced. - `annotations_enabled` (boolean · default: true) — optional If true, enables calculation and storage of the model change annotations for each entity that is being analyzed. - `enabled` (boolean · default: false) — optional If true, enables calculation and storage of the model bounds for each entity that is being analyzed. - `terms` (string) — optional Limits data collection to this comma separated list of partition or by field values. If terms are not specified or it is an empty string, no filtering is applied. Wildcards are not supported. Only the specified terms can be viewed when using the Single Metric Viewer. - `model_snapshot_retention_days` (number · default: 10) — optional Advanced configuration option, which affects the automatic removal of old model snapshots for this job. It specifies the maximum period of time (in days) that snapshots are retained. This period is relative to the timestamp of the most recent snapshot for this job. By default, snapshots ten days older than the newest snapshot are deleted. - `renormalization_window_days` (number) — optional Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen. The default value is the longer of 30 days or 100 bucket spans. - `results_index_name` (string · default: shared) — optional A text string that affects the name of the machine learning results index. By default, the job generates an index named `.ml-anomalies-shared`. - `results_retention_days` (number) — optional Advanced configuration option. The period of time (in days) that results are retained. Age is calculated relative to the timestamp of the latest bucket result. If this property has a non-null value, once per day at 00:30 (server time), results that are the specified number of days older than the latest bucket result are deleted from Elasticsearch. The default value is null, which means all results are retained. Annotations generated by the system also count as results for retention purposes; they are deleted after the same number of days as results. Annotations added by users are retained forever. ## Response `application/json` - `allow_lazy_open` (boolean) - `analysis_config` (object) - `bucket_span` (union oneOf · default: 5m) The size of the interval that the analysis is aggregated into, typically between `5m` and `1h`. This value should be either a whole number of days or equate to a whole number of buckets in one day. If the anomaly detection job uses a datafeed with aggregations, this value must also be divisible by the interval of the date histogram aggregation. Values: `-1`, `0` - `detectors` (object[]) Detector configuration objects specify which data fields a job analyzes. They also specify which analytical functions are used. You can specify multiple detectors for a job. If the detectors array does not contain at least one detector, no analysis can occur and an error is returned. - `function` (string) The analysis function that is used. For example, `count`, `rare`, `mean`, `min`, `max`, or `sum`. - `by_field_name` (string) The field used to split the data. In particular, this property is used for analyzing the splits with respect to their own history. It is used for finding unusual values in the context of the split. - `custom_rules` (object[]) Custom rules enable you to customize the way detectors operate. For example, a rule may dictate conditions under which results should be skipped. Kibana refers to custom rules as job rules. - `actions` (enum[] · default: ["skip_result"]) The set of actions to be triggered when the rule applies. If more than one action is specified the effects of all actions are combined. Supported values include: - `skip_result`: The result will not be created. Unless you also specify `skip_model_update`, the model will be updated as usual with the corresponding series value. - `skip_model_update`: The value for that series will not be used to update the model. Unless you also specify `skip_result`, the results will be created as usual. This action is suitable when certain values are expected to be consistently anomalous and they affect the model in a way that negatively impacts the rest of the results. Values: `skip_result`, `skip_model_update` - `conditions` (object[]) An array of numeric conditions when the rule applies. A rule must either have a non-empty scope or at least one condition. Multiple conditions are combined together with a logical AND. - `applies_to` (enum) Specifies the result property to which the condition applies. If your detector uses `lat_long`, `metric`, `rare`, or `freq_rare` functions, you can only specify conditions that apply to time. Values: `actual`, `typical`, `diff_from_typical`, `time` - `operator` (enum) Specifies the condition operator. The available options are greater than, greater than or equals, less than, and less than or equals. Values: `gt`, `gte`, `lt`, `lte` - `value` (number) The value that is compared against the `applies_to` field using the operator. - `scope` (map string to object) A scope of series where the rule applies. A rule must either have a non-empty scope or at least one condition. By default, the scope includes all series. Scoping is allowed for any of the fields that are also specified in `by_field_name`, `over_field_name`, or `partition_field_name`. Map values (object) - `filter_id` (string) The identifier for the filter. - `filter_type` (enum · default: include) If set to `include`, the rule applies for values in the filter. If set to `exclude`, the rule applies for values not in the filter. Values: `include`, `exclude` - `detector_description` (string) A description of the detector. - `detector_index` (number) A unique identifier for the detector. This identifier is based on the order of the detectors in the `analysis_config`, starting at zero. If you specify a value for this property, it is ignored. - `exclude_frequent` (enum) If set, frequent entities are excluded from influencing the anomaly results. Entities can be considered frequent over time or frequent in a population. If you are working with both over and by fields, you can set `exclude_frequent` to `all` for both fields, or to `by` or `over` for those specific fields. Values: `all`, `none`, `by`, `over` - `field_name` (string) The field that the detector uses in the function. If you use an event rate function such as count or rare, do not specify this field. The `field_name` cannot contain double quotes or backslashes. - `over_field_name` (string) The field used to split the data. In particular, this property is used for analyzing the splits with respect to the history of all splits. It is used for finding unusual values in the population of all splits. - `partition_field_name` (string) The field used to segment the analysis. When you use this property, you have completely independent baselines for each value of this field. - `use_null` (boolean · default: false) Defines whether a new series is used as the null series when there is no value for the by or partition fields. - `influencers` (string[]) A comma separated list of influencer field names. Typically these can be the by, over, or partition fields that are used in the detector configuration. You might also want to use a field name that is not specifically named in a detector, but is available as part of the input data. When you use multiple detectors, the use of influencers is recommended as it aggregates results for each influencer entity. - `categorization_analyzer` (union oneOf) If `categorization_field_name` is specified, you can also define the analyzer that is used to interpret the categorization field. This property cannot be used at the same time as `categorization_filters`. The categorization analyzer specifies how the `categorization_field` is interpreted by the categorization process. 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 or one added by another plugin. Same options as `categorization_analyzer` in `analysis_config` - `categorization_field_name` (string) If this property is specified, the values of the specified field will be categorized. The resulting categories must be used in a detector by setting `by_field_name`, `over_field_name`, or `partition_field_name` to the keyword `mlcategory`. - `categorization_filters` (string[]) If `categorization_field_name` is specified, you can also define optional filters. This property expects an array of regular expressions. The expressions are used to filter out matching sequences from the categorization field values. You can use this functionality to fine tune the categorization by excluding sequences from consideration when categories are defined. For example, you can exclude SQL statements that appear in your log files. This property cannot be used at the same time as `categorization_analyzer`. If you only want to define simple regular expression filters that are applied prior to tokenization, setting this property is the easiest method. If you also want to customize the tokenizer or post-tokenization filtering, use the `categorization_analyzer` property instead and include the filters as pattern_replace character filters. The effect is exactly the same. - `latency` (union oneOf · default: 0) The size of the window in which to expect data that is out of time order. If you specify a non-zero value, it must be greater than or equal to one second. NOTE: Latency is applicable only when you send data by using the post data API. Values: `-1`, `0` - `model_prune_window` (union oneOf) Advanced configuration option. Affects the pruning of models that have not been updated for the given time duration. The value must be set to a multiple of the `bucket_span`. If set too low, important information may be removed from the model. For jobs created in 8.1 and later, the default value is the greater of `30d` or 20 times `bucket_span`. Values: `-1`, `0` - `multivariate_by_fields` (boolean) This functionality is reserved for internal use. It is not supported for use in customer environments and is not subject to the support SLA of official GA features. If set to `true`, the analysis will automatically find correlations between metrics for a given by field value and report anomalies when those correlations cease to hold. For example, suppose CPU and memory usage on host A is usually highly correlated with the same metrics on host B. Perhaps this correlation occurs because they are running a load-balanced application. If you enable this property, anomalies will be reported when, for example, CPU usage on host A is high and the value of CPU usage on host B is low. That is to say, you’ll see an anomaly when the CPU of host A is unusual given the CPU of host B. To use the `multivariate_by_fields` property, you must also specify `by_field_name` in your detector. - `per_partition_categorization` (object) Settings related to how categorization interacts with partition fields. - `enabled` (boolean) To enable this setting, you must also set the `partition_field_name` property to the same value in every detector that uses the keyword `mlcategory`. Otherwise, job creation fails. - `stop_on_warn` (boolean) This setting can be set to true only if per-partition categorization is enabled. If true, both categorization and subsequent anomaly detection stops for partitions where the categorization status changes to warn. This setting makes it viable to have a job where it is expected that categorization works well for some partitions but not others; you do not pay the cost of bad categorization forever in the partitions where it works badly. - `summary_count_field_name` (string) If this property is specified, the data that is fed to the job is expected to be pre-summarized. This property value is the name of the field that contains the count of raw data points that have been summarized. The same `summary_count_field_name` applies to all detectors in the job. NOTE: The `summary_count_field_name` property cannot be used with the `metric` function. - `analysis_limits` (object) - `categorization_examples_limit` (number · default: 4) The maximum number of examples stored per category in memory and in the results data store. If you increase this value, more examples are available, however it requires that you have more storage available. If you set this value to 0, no examples are stored. NOTE: The `categorization_examples_limit` applies only to analysis that uses categorization. - `model_memory_limit` (number | string · default: 1024mb) The approximate maximum amount of memory resources that are required for analytical processing. Once this limit is approached, data pruning becomes more aggressive. Upon exceeding this limit, new entities are not modeled. If the `xpack.ml.max_model_memory_limit` setting has a value greater than 0 and less than 1024mb, that value is used instead of the default. The default value is relatively small to ensure that high resource usage is a conscious decision. If you have jobs that are expected to analyze high cardinality fields, you will likely need to use a higher value. If you specify a number instead of a string, the units are assumed to be MiB. Specifying a string is recommended for clarity. If you specify a byte size unit of `b` or `kb` and the number does not equate to a discrete number of megabytes, it is rounded down to the closest MiB. The minimum valid value is 1 MiB. If you specify a value less than 1 MiB, an error occurs. If you specify a value for the `xpack.ml.max_model_memory_limit` setting, an error occurs when you try to create jobs that have `model_memory_limit` values greater than that setting value. - `background_persist_interval` (union oneOf) Values: `-1`, `0` - `create_time` (union oneOf) One of: `string` or `number` - `custom_settings` (object) - `daily_model_snapshot_retention_after_days` (number) - `data_description` (object) - `format` (string) Only JSON format is supported at this time. - `time_field` (string · default: time) The name of the field that contains the timestamp. - `time_format` (string · default: epoch) The time format, which can be `epoch`, `epoch_ms`, or a custom pattern. The value `epoch` refers to UNIX or Epoch time (the number of seconds since 1 Jan 1970). The value `epoch_ms` indicates that time is measured in milliseconds since the epoch. The `epoch` and `epoch_ms` time formats accept either integer or real values. Custom patterns must conform to the Java DateTimeFormatter class. When you use date-time formatting patterns, it is recommended that you provide the full date, time and time zone. For example: `yyyy-MM-dd'T'HH:mm:ssX`. If the pattern that you specify is not sufficient to produce a complete timestamp, job creation fails. - `field_delimiter` (string) - `datafeed_config` (object) - `aggregations` (map string to {} Aggregation) See [Aggregation](https://www.elastic.co/docs/api/doc/elasticsearch/types/_types-aggregations-aggregationcontainer) - `authorization` (object) The security privileges that the datafeed uses to run its queries. If Elastic Stack security features were disabled at the time of the most recent update to the datafeed, this property is omitted. - `api_key` (object) If an API key was used for the most recent update to the datafeed, its name and identifier are listed in the response. - `id` (string) The identifier for the API key. - `name` (string) The name of the API key. - `roles` (string[]) If a user ID was used for the most recent update to the datafeed, its roles at the time of the update are listed in the response. - `service_account` (string) If a service account was used for the most recent update to the datafeed, the account name is listed in the response. - `chunking_config` (object) - `mode` (enum) If the mode is `auto`, the chunk size is dynamically calculated; this is the recommended value when the datafeed does not use aggregations. If the mode is `manual`, chunking is applied according to the specified `time_span`; use this mode when the datafeed uses aggregations. If the mode is `off`, no chunking is applied. Values: `auto`, `manual`, `off` - `time_span` (union oneOf · default: 3h) The time span that each search will be querying. This setting is applicable only when the `mode` is set to `manual`. Values: `-1`, `0` - `datafeed_id` (string) - `frequency` (union oneOf) The interval at which scheduled queries are made while the datafeed runs in real time. The default value is either the bucket span for short bucket spans, or, for longer bucket spans, a sensible fraction of the bucket span. For example: `150s`. When `frequency` is shorter than the bucket span, interim results for the last (partial) bucket are written then eventually overwritten by the full bucket results. If the datafeed uses aggregations, this value must be divisible by the interval of the date histogram aggregation. Values: `-1`, `0` - `indices` (string[]) - `indexes` (string[]) - `job_id` (string) - `max_empty_searches` (number) - `query` ({} Query) See [Query](https://www.elastic.co/docs/api/doc/elasticsearch/types/_types-query_dsl-querycontainer) - `query_delay` (union oneOf) Values: `-1`, `0` - `script_fields` (map string to object) Map values (object) - `script` (string) - `ignore_failure` (boolean) - `scroll_size` (number) - `delayed_data_check_config` (object) - `check_window` (union oneOf) The window of time that is searched for late data. This window of time ends with the latest finalized bucket. It defaults to null, which causes an appropriate `check_window` to be calculated when the real-time datafeed runs. In particular, the default `check_window` span calculation is based on the maximum of `2h` or `8 * bucket_span`. Values: `-1`, `0` - `enabled` (boolean) Specifies whether the datafeed periodically checks for delayed data. - `runtime_mappings` (object) - `indices_options` (object) - `allow_no_indices` (boolean) A setting that does two separate checks on the index expression. If `false`, the request returns an error (1) if any wildcard expression (including `_all` and `*`) resolves to zero matching indices or (2) if the complete set of resolved indices, aliases or data streams is empty after all expressions are evaluated. If `true`, index expressions that resolve to no indices are allowed and the request returns an empty result. - `expand_wildcards` (union oneOf) Type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. Supports comma-separated values, such as `open,hidden`. Supported values include: - `all`: Match any data stream or index, including hidden ones. - `open`: Match open, non-hidden indices. Also matches any non-hidden data stream. - `closed`: Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed. - `hidden`: Match hidden data streams and hidden indices. Must be combined with `open`, `closed`, or `both`. - `none`: Wildcard expressions are not accepted. Values: `all`, `open`, `closed`, `hidden`, `none` - `ignore_unavailable` (boolean · default: false) If `false`, the request returns an error if it targets a concrete (non-wildcarded) index, alias, or data stream that is missing, closed, or otherwise unavailable. If `true`, unavailable concrete targets are silently ignored. - `ignore_throttled` (boolean · default: true) If true, concrete, expanded or aliased indices are ignored when frozen. - `description` (string) - `groups` (string[]) - `job_id` (string) - `job_type` (string) - `job_version` (string) - `model_plot_config` (object) - `annotations_enabled` (boolean · default: true) If true, enables calculation and storage of the model change annotations for each entity that is being analyzed. - `enabled` (boolean · default: false) If true, enables calculation and storage of the model bounds for each entity that is being analyzed. - `terms` (string) Limits data collection to this comma separated list of partition or by field values. If terms are not specified or it is an empty string, no filtering is applied. Wildcards are not supported. Only the specified terms can be viewed when using the Single Metric Viewer. - `model_snapshot_id` (string) - `model_snapshot_retention_days` (number) - `renormalization_window_days` (number) - `results_index_name` (string) - `results_retention_days` (number) ## Code examples #### Console ```console PUT /_ml/anomaly_detectors/job-01 { "analysis_config": { "bucket_span": "15m", "detectors": [ { "detector_description": "Sum of bytes", "function": "sum", "field_name": "bytes" } ] }, "data_description": { "time_field": "timestamp", "time_format": "epoch_ms" }, "analysis_limits": { "model_memory_limit": "11MB" }, "model_plot_config": { "enabled": true, "annotations_enabled": true }, "results_index_name": "test-job1", "datafeed_config": { "indices": [ "kibana_sample_data_logs" ], "query": { "bool": { "must": [ { "match_all": {} } ] } }, "runtime_mappings": { "hour_of_day": { "type": "long", "script": { "source": "emit(doc['timestamp'].value.getHour());" } } }, "datafeed_id": "datafeed-test-job1" } } ``` #### JavaScript ```javascript const response = await client.ml.putJob({ job_id: "job-01", analysis_config: { bucket_span: "15m", detectors: [ { detector_description: "Sum of bytes", function: "sum", field_name: "bytes", }, ], }, data_description: { time_field: "timestamp", time_format: "epoch_ms", }, analysis_limits: { model_memory_limit: "11MB", }, model_plot_config: { enabled: true, annotations_enabled: true, }, results_index_name: "test-job1", datafeed_config: { indices: ["kibana_sample_data_logs"], query: { bool: { must: [ { match_all: {}, }, ], }, }, runtime_mappings: { hour_of_day: { type: "long", script: { source: "emit(doc['timestamp'].value.getHour());", }, }, }, datafeed_id: "datafeed-test-job1", }, }); ``` #### Python ```python resp = client.ml.put_job( job_id="job-01", analysis_config={ "bucket_span": "15m", "detectors": [ { "detector_description": "Sum of bytes", "function": "sum", "field_name": "bytes" } ] }, data_description={ "time_field": "timestamp", "time_format": "epoch_ms" }, analysis_limits={ "model_memory_limit": "11MB" }, model_plot_config={ "enabled": True, "annotations_enabled": True }, results_index_name="test-job1", datafeed_config={ "indices": [ "kibana_sample_data_logs" ], "query": { "bool": { "must": [ { "match_all": {} } ] } }, "runtime_mappings": { "hour_of_day": { "type": "long", "script": { "source": "emit(doc['timestamp'].value.getHour());" } } }, "datafeed_id": "datafeed-test-job1" }, ) ``` #### curl ```curl curl -X PUT "$ELASTICSEARCH_URL/_ml/anomaly_detectors/job-01" \ -H "Authorization: ApiKey $ELASTIC_API_KEY" \ -H "Content-Type: application/json" \ -d '{"analysis_config":{"bucket_span":"15m","detectors":[{"detector_description":"Sum of bytes","function":"sum","field_name":"bytes"}]},"data_description":{"time_field":"timestamp","time_format":"epoch_ms"},"analysis_limits":{"model_memory_limit":"11MB"},"model_plot_config":{"enabled":true,"annotations_enabled":true},"results_index_name":"test-job1","datafeed_config":{"indices":["kibana_sample_data_logs"],"query":{"bool":{"must":[{"match_all":{}}]}},"runtime_mappings":{"hour_of_day":{"type":"long","script":{"source":"emit(doc[' \ "'" \ 'timestamp' \ "'" \ '].value.getHour());"}}},"datafeed_id":"datafeed-test-job1"}}' ``` #### Java ```java client.ml().putJob(p -> p .analysisConfig(a -> a .bucketSpan(b -> b .time("15m") ) .detectors(d -> d .detectorDescription("Sum of bytes") .fieldName("bytes") .function("sum") ) ) .analysisLimits(an -> an .modelMemoryLimit("11MB") ) .dataDescription(d -> d .timeField("timestamp") .timeFormat("epoch_ms") ) .datafeedConfig(d -> d .datafeedId("datafeed-test-job1") .indices("kibana_sample_data_logs") .query(q -> q .bool(b -> b .must(m -> m .matchAll(ma -> ma) ) ) ) .runtimeMappings("hour_of_day", r -> r .script(s -> s .source(so -> so .scriptString("emit(doc['timestamp'].value.getHour());") ) ) .type(RuntimeFieldType.Long) ) ) .jobId("job-01") .modelPlotConfig(m -> m .annotationsEnabled(true) .enabled(true) ) .resultsIndexName("test-job1") ); ``` #### C# ```c# var response = await client.MachineLearning .PutJobAsync(jobId: "job-01", d1 => d1 .AnalysisConfig(d2 => d2 .BucketSpan("15m") .Detectors(d3 => d3 .DetectorDescription("Sum of bytes") .FieldName(x => x.Bytes) .Function("sum") ) ) .AnalysisLimits(d2 => d2 .ModelMemoryLimit(new ByteSize("11MB")) ) .DataDescription(d2 => d2 .TimeField(x => x.Timestamp) .TimeFormat("epoch_ms") ) .DatafeedConfig(d2 => d2 .DatafeedId("datafeed-test-job1") .Indices(new[] { "kibana_sample_data_logs" }) .Query(d3 => d3 .Bool(d4 => d4 .Must(d5 => d5 .MatchAll() ) ) ) .RuntimeMappings(d3 => d3 .Add(global::Elastic.Clients.Elasticsearch.Infer.Field(x => x.HourOfDay), d4 => d4 .Script(d5 => d5 .Source("emit(doc['timestamp'].value.getHour());") ) .Type(RuntimeFieldType.Long) ) ) ) .ModelPlotConfig(d2 => d2 .AnnotationsEnabled(true) .Enabled(true) ) .ResultsIndexName("test-job1") ); ``` #### PHP ```php $resp = $client->ml()->putJob([ "job_id" => "job-01", "body" => [ "analysis_config" => [ "bucket_span" => "15m", "detectors" => array( [ "detector_description" => "Sum of bytes", "function" => "sum", "field_name" => "bytes", ], ), ], "data_description" => [ "time_field" => "timestamp", "time_format" => "epoch_ms", ], "analysis_limits" => [ "model_memory_limit" => "11MB", ], "model_plot_config" => [ "enabled" => true, "annotations_enabled" => true, ], "results_index_name" => "test-job1", "datafeed_config" => [ "indices" => array( "kibana_sample_data_logs", ), "query" => [ "bool" => [ "must" => array( [ "match_all" => new ArrayObject([]), ], ), ], ], "runtime_mappings" => [ "hour_of_day" => [ "type" => "long", "script" => [ "source" => "emit(doc['timestamp'].value.getHour());", ], ], ], "datafeed_id" => "datafeed-test-job1", ], ], ]); ``` #### Ruby ```ruby response = client.ml.put_job( job_id: "job-01", body: { "analysis_config": { "bucket_span": "15m", "detectors": [ { "detector_description": "Sum of bytes", "function": "sum", "field_name": "bytes" } ] }, "data_description": { "time_field": "timestamp", "time_format": "epoch_ms" }, "analysis_limits": { "model_memory_limit": "11MB" }, "model_plot_config": { "enabled": true, "annotations_enabled": true }, "results_index_name": "test-job1", "datafeed_config": { "indices": [ "kibana_sample_data_logs" ], "query": { "bool": { "must": [ { "match_all": {} } ] } }, "runtime_mappings": { "hour_of_day": { "type": "long", "script": { "source": "emit(doc['timestamp'].value.getHour());" } } }, "datafeed_id": "datafeed-test-job1" } } ) ``` ## Response examples #### Ml put job response example1 `application/json` A successful response when creating an anomaly detection job and datafeed. ```json { "job_id": "test-job1", "job_type": "anomaly_detector", "job_version": "8.4.0", "create_time": 1656087283340, "datafeed_config": { "datafeed_id": "datafeed-test-job1", "job_id": "test-job1", "authorization": { "roles": [ "superuser" ] }, "query_delay": "61499ms", "chunking_config": { "mode": "auto" }, "indices_options": { "expand_wildcards": [ "open" ], "ignore_unavailable": false, "allow_no_indices": true, "ignore_throttled": true }, "query": { "bool": { "must": [ { "match_all": {} } ] } }, "indices": [ "kibana_sample_data_logs" ], "scroll_size": 1000, "delayed_data_check_config": { "enabled": true }, "runtime_mappings": { "hour_of_day": { "type": "long", "script": { "source": "emit(doc['timestamp'].value.getHour());" } } } }, "analysis_config": { "bucket_span": "15m", "detectors": [ { "detector_description": "Sum of bytes", "function": "sum", "field_name": "bytes", "detector_index": 0 } ], "influencers": [], "model_prune_window": "30d" }, "analysis_limits": { "model_memory_limit": "11mb", "categorization_examples_limit": 4 }, "data_description": { "time_field": "timestamp", "time_format": "epoch_ms" }, "model_plot_config": { "enabled": true, "annotations_enabled": true }, "model_snapshot_retention_days": 10, "daily_model_snapshot_retention_after_days": 1, "results_index_name": "custom-test-job1", "allow_lazy_open": false } ```