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cat anomaly detectors API

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cat anomaly detectors API

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cat APIs are only intended for human consumption using the command line or Kibana console. They are not intended for use by applications. For application consumption, use the get anomaly detection job statistics API.

Returns configuration and usage information about anomaly detection jobs.

Request

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GET /_cat/ml/anomaly_detectors/<job_id>

GET /_cat/ml/anomaly_detectors

Prerequisites

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If the Elasticsearch security features are enabled, you must have monitor_ml, monitor, manage_ml, or manage cluster privileges to use this API. See Security privileges and Machine learning security privileges.

Description

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This API returns a maximum of 10,000 jobs.

For more information about anomaly detection, see Finding anomalies.

Path parameters

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<job_id>: (Optional, string) Identifier for the anomaly detection job.

Query parameters

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allow_no_match

(Optional, Boolean) Specifies what to do when the request:

Contains wildcard expressions and there are no jobs that match.
Contains the _all string or no identifiers and there are no matches.
Contains wildcard expressions and there are only partial matches.

The default value is true, which returns an empty jobs array when there are no matches and the subset of results when there are partial matches. If this parameter is false, the request returns a 404 status code when there are no matches or only partial matches.

bytes

(Optional, byte size units) Unit used to display byte values.

format

(Optional, string) Short version of the HTTP accept header. Valid values include JSON, YAML, etc.

h

(Optional, string) Comma-separated list of column names to display.

If you do not specify which columns to include, the API returns the default columns. If you explicitly specify one or more columns, it returns only the specified columns.

Valid columns are:

assignment_explanation, ae

For open anomaly detection jobs only, contains messages relating to the selection of a node to run the job.

buckets.count, bc, bucketsCount

(Default) The number of bucket results produced by the job.

buckets.time.exp_avg, btea, bucketsTimeExpAvg

Exponential moving average of all bucket processing times, in milliseconds.

buckets.time.exp_avg_hour, bteah, bucketsTimeExpAvgHour

Exponentially-weighted moving average of bucket processing times calculated in a 1 hour time window, in milliseconds.

buckets.time.max, btmax, bucketsTimeMax

Maximum among all bucket processing times, in milliseconds.

buckets.time.min, btmin, bucketsTimeMin

Minimum among all bucket processing times, in milliseconds.

buckets.time.total, btt, bucketsTimeTotal

Sum of all bucket processing times, in milliseconds.

data.buckets, db, dataBuckets

The number of buckets processed.

data.earliest_record, der, dataEarliestRecord

The timestamp of the earliest chronologically input document.

data.empty_buckets, deb, dataEmptyBuckets

The number of buckets which did not contain any data. If your data contains many empty buckets, consider increasing your bucket_span or using functions that are tolerant to gaps in data such as mean, non_null_sum or non_zero_count.

data.input_bytes, dib, dataInputBytes

The number of bytes of input data posted to the anomaly detection job.

data.input_fields, dif, dataInputFields

The total number of fields in input documents posted to the anomaly detection job. This count includes fields that are not used in the analysis. However, be aware that if you are using a datafeed, it extracts only the required fields from the documents it retrieves before posting them to the job.

data.input_records, dir, dataInputRecords

The number of input documents posted to the anomaly detection job.

data.invalid_dates, did, dataInvalidDates

The number of input documents with either a missing date field or a date that could not be parsed.

data.last, dl, dataLast

The timestamp at which data was last analyzed, according to server time.

data.last_empty_bucket, dleb, dataLastEmptyBucket

The timestamp of the last bucket that did not contain any data.

data.last_sparse_bucket, dlsb, dataLastSparseBucket

The timestamp of the last bucket that was considered sparse.

data.latest_record, dlr, dataLatestRecord

The timestamp of the latest chronologically input document.

data.missing_fields, dmf, dataMissingFields

The number of input documents that are missing a field that the anomaly detection job is configured to analyze. Input documents with missing fields are still processed because it is possible that not all fields are missing.

If you are using datafeeds or posting data to the job in JSON format, a high missing_field_count is often not an indication of data issues. It is not necessarily a cause for concern.

data.out_of_order_timestamps, doot, dataOutOfOrderTimestamps

The number of input documents that have a timestamp chronologically preceding the start of the current anomaly detection bucket offset by the latency window. This information is applicable only when you provide data to the anomaly detection job by using the post data API. These out of order documents are discarded, since jobs require time series data to be in ascending chronological order.

data.processed_fields, dpf, dataProcessedFields

The total number of fields in all the documents that have been processed by the anomaly detection job. Only fields that are specified in the detector configuration object contribute to this count. The timestamp is not included in this count.

data.processed_records, dpr, dataProcessedRecords

(Default) The number of input documents that have been processed by the anomaly detection job. This value includes documents with missing fields, since they are nonetheless analyzed. If you use datafeeds and have aggregations in your search query, the processed_record_count is the number of aggregation results processed, not the number of Elasticsearch documents.

data.sparse_buckets, dsb, dataSparseBuckets

The number of buckets that contained few data points compared to the expected number of data points. If your data contains many sparse buckets, consider using a longer bucket_span.

forecasts.memory.avg, fmavg, forecastsMemoryAvg

The average memory usage in bytes for forecasts related to the anomaly detection job.

forecasts.memory.max, fmmax, forecastsMemoryMax

The maximum memory usage in bytes for forecasts related to the anomaly detection job.

forecasts.memory.min, fmmin, forecastsMemoryMin

The minimum memory usage in bytes for forecasts related to the anomaly detection job.

forecasts.memory.total, fmt, forecastsMemoryTotal

The total memory usage in bytes for forecasts related to the anomaly detection job.

forecasts.records.avg, fravg, forecastsRecordsAvg

The average number of model_forecast documents written for forecasts related to the anomaly detection job.

forecasts.records.max, frmax, forecastsRecordsMax

The maximum number of model_forecast documents written for forecasts related to the anomaly detection job.

forecasts.records.min, frmin, forecastsRecordsMin

The minimum number of model_forecast documents written for forecasts related to the anomaly detection job.

forecasts.records.total, frt, forecastsRecordsTotal

The total number of model_forecast documents written for forecasts related to the anomaly detection job.

forecasts.time.avg, ftavg, forecastsTimeAvg

The average runtime in milliseconds for forecasts related to the anomaly detection job.

forecasts.time.max, ftmax, forecastsTimeMax

The maximum runtime in milliseconds for forecasts related to the anomaly detection job.

forecasts.time.min, ftmin, forecastsTimeMin

The minimum runtime in milliseconds for forecasts related to the anomaly detection job.

forecasts.time.total, ftt, forecastsTimeTotal

The total runtime in milliseconds for forecasts related to the anomaly detection job.

forecasts.total, ft, forecastsTotal

(Default) The number of individual forecasts currently available for the job. A value of 1 or more indicates that forecasts exist.

id

(Default) Identifier for the anomaly detection job.

model.bucket_allocation_failures, mbaf, modelBucketAllocationFailures

The number of buckets for which new entities in incoming data were not processed due to insufficient model memory. This situation is also signified by a hard_limit: memory_status property value.

model.by_fields, mbf, modelByFields

The number of by field values that were analyzed by the models. This value is cumulative for all detectors in the job.

model.bytes, mb, modelBytes

(Default) The number of bytes of memory used by the models. This is the maximum value since the last time the model was persisted. If the job is closed, this value indicates the latest size.

model.bytes_exceeded, mbe, modelBytesExceeded

The number of bytes over the high limit for memory usage at the last allocation failure.

model.categorization_status, mcs, modelCategorizationStatus

The status of categorization for the job. Contains one of the following values:

ok: Categorization is performing acceptably well (or not being used at all).
warn: Categorization is detecting a distribution of categories that suggests the input data is inappropriate for categorization. Problems could be that there is only one category, more than 90% of categories are rare, the number of categories is greater than 50% of the number of categorized documents, there are no frequently matched categories, or more than 50% of categories are dead.

model.categorized_doc_count, mcdc, modelCategorizedDocCount

The number of documents that have had a field categorized.

model.dead_category_count, mdcc, modelDeadCategoryCount

The number of categories created by categorization that will never be assigned again because another category’s definition makes it a superset of the dead category. (Dead categories are a side effect of the way categorization has no prior training.)

model.failed_category_count, mdcc, modelFailedCategoryCount

The number of times that categorization wanted to create a new category but couldn’t because the job had hit its model_memory_limit. This count does not track which specific categories failed to be created. Therefore you cannot use this value to determine the number of unique categories that were missed.

model.frequent_category_count, mfcc, modelFrequentCategoryCount

The number of categories that match more than 1% of categorized documents.

model.log_time, mlt, modelLogTime

The timestamp when the model stats were gathered, according to server time.

model.memory_limit, mml, modelMemoryLimit

The upper limit for model memory usage, checked on increasing values.

model.memory_status, mms, modelMemoryStatus

(Default) The status of the mathematical models, which can have one of the following values:

ok: The models stayed below the configured value.
soft_limit: The models used more than 60% of the configured memory limit and older unused models will be pruned to free up space. Additionally, in categorization jobs no further category examples will be stored.
hard_limit: The models used more space than the configured memory limit. As a result, not all incoming data was processed.

model.over_fields, mof, modelOverFields

The number of over field values that were analyzed by the models. This value is cumulative for all detectors in the job.

model.partition_fields, mpf, modelPartitionFields

The number of partition field values that were analyzed by the models. This value is cumulative for all detectors in the job.

model.rare_category_count, mrcc, modelRareCategoryCount

The number of categories that match just one categorized document.

model.timestamp, mt, modelTimestamp

The timestamp of the last record when the model stats were gathered.

model.total_category_count, mtcc, modelTotalCategoryCount

The number of categories created by categorization.

node.address, na, nodeAddress

The network address of the node.

Contains properties for the node that runs the job. This information is available only for open jobs.

node.ephemeral_id, ne, nodeEphemeralId

The ephemeral ID of the node.

Contains properties for the node that runs the job. This information is available only for open jobs.

node.id, ni, nodeId

The unique identifier of the node.

Contains properties for the node that runs the job. This information is available only for open jobs.

node.name, nn, nodeName

The node name.

Contains properties for the node that runs the job. This information is available only for open jobs.

opened_time, ot

For open jobs only, the elapsed time for which the job has been open.

state, s

(Default) The status of the anomaly detection job, which can be one of the following values:

closed: The job finished successfully with its model state persisted. The job must be opened before it can accept further data.
closing: The job close action is in progress and has not yet completed. A closing job cannot accept further data.
failed: The job did not finish successfully due to an error. This situation can occur due to invalid input data, a fatal error occurring during the analysis, or an external interaction such as the process being killed by the Linux out of memory (OOM) killer. If the job had irrevocably failed, it must be force closed and then deleted. If the datafeed can be corrected, the job can be closed and then re-opened.
opened: The job is available to receive and process data.
opening: The job open action is in progress and has not yet completed.

help

(Optional, Boolean) If true, the response includes help information. Defaults to false.

s

(Optional, string) Comma-separated list of column names or column aliases used to sort the response.

time

(Optional, time units) Unit used to display time values.

v

(Optional, Boolean) If true, the response includes column headings. Defaults to false.

Examples

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response = client.cat.ml_jobs(
  h: 'id,s,dpr,mb',
  v: true
)
puts response

GET _cat/ml/anomaly_detectors?h=id,s,dpr,mb&v=true

id                        s dpr   mb
high_sum_total_sales closed 14022 1.5mb
low_request_rate     closed 1216  40.5kb
response_code_rates  closed 28146 132.7kb
url_scanning         closed 28146 501.6kb

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