cat anomaly detectors API

Returns configuration and usage information about anomaly detection jobs.

Request

GET /_cat/ml/anomaly_detectors/<job_id>

GET /_cat/ml/anomaly_detectors

Prerequisites

• 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

See Anomaly detection jobs.

This API returns a maximum of 10,000 jobs.

Path parameters

<job_id>

(Optional, string) Identifier for the anomaly detection job.

Query parameters

allow_no_jobs

(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 are out of time sequence and outside of 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.
  • 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

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

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