Get anomaly detection jobs

GET /_cat/ml/anomaly_detectors/{job_id}
GET /_cat/ml/anomaly_detectors

Get configuration and usage information for anomaly detection jobs. This API returns a maximum of 10,000 jobs. If the Elasticsearch security features are enabled, you must have monitor_ml, monitor, manage_ml, or manage cluster privileges to use this API.

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

Required authorization

Cluster privileges: monitor_ml

Path Parameters

job_id string Id optional

Identifier for the anomaly detection job.

Leave it out to use /_cat/ml/anomaly_detectors.

Query Parameters

allow_no_match boolean · default: true

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.

If true, the API returns an empty jobs array when there are no matches and the subset of results when there are partial matches. If false, the API returns a 404 status code when there are no matches or only partial matches.

h

CatAnomalyDetectorColumns · default: buckets.count,data.processed_records,forecasts.total,id,model.bytes,model.memory_status,state

Comma-separated list of column names to display.

Supported values include:

assignment_explanation (or ae): For open anomaly detection jobs only, contains messages relating to the selection of a node to run the job.
buckets.count (or bc, bucketsCount): The number of bucket results produced by the job.
buckets.time.exp_avg (or btea, bucketsTimeExpAvg): Exponential moving average of all bucket processing times, in milliseconds.
buckets.time.exp_avg_hour (or bteah, bucketsTimeExpAvgHour): Exponentially-weighted moving average of bucket processing times calculated in a 1 hour time window, in milliseconds.
buckets.time.max (or btmax, bucketsTimeMax): Maximum among all bucket processing times, in milliseconds.
buckets.time.min (or btmin, bucketsTimeMin): Minimum among all bucket processing times, in milliseconds.
buckets.time.total (or btt, bucketsTimeTotal): Sum of all bucket processing times, in milliseconds.
data.buckets (or db, dataBuckets): The number of buckets processed.
data.earliest_record (or der, dataEarliestRecord): The timestamp of the earliest chronologically input document.
data.empty_buckets (or deb, dataEmptyBuckets): The number of buckets which did not contain any data.
data.input_bytes (or dib, dataInputBytes): The number of bytes of input data posted to the anomaly detection job.
data.input_fields (or 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 (or dir, dataInputRecords): The number of input documents posted to the anomaly detection job.
data.invalid_dates (or did, dataInvalidDates): The number of input documents with either a missing date field or a date that could not be parsed.
data.last (or dl, dataLast): The timestamp at which data was last analyzed, according to server time.
data.last_empty_bucket (or dleb, dataLastEmptyBucket): The timestamp of the last bucket that did not contain any data.
data.last_sparse_bucket (or dlsb, dataLastSparseBucket): The timestamp of the last bucket that was considered sparse.
data.latest_record (or dlr, dataLatestRecord): The timestamp of the latest chronologically input document.
data.missing_fields (or 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.
data.out_of_order_timestamps (or 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 (or 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 (or dpr, dataProcessedRecords): 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 (or dsb, dataSparseBuckets): The number of buckets that contained few data points compared to the expected number of data points.
forecasts.memory.avg (or fmavg, forecastsMemoryAvg): The average memory usage in bytes for forecasts related to the anomaly detection job.
forecasts.memory.max (or fmmax, forecastsMemoryMax): The maximum memory usage in bytes for forecasts related to the anomaly detection job.
forecasts.memory.min (or fmmin, forecastsMemoryMin): The minimum memory usage in bytes for forecasts related to the anomaly detection job.
forecasts.memory.total (or fmt, forecastsMemoryTotal): The total memory usage in bytes for forecasts related to the anomaly detection job.
forecasts.records.avg (or fravg, forecastsRecordsAvg): The average number of model_forecast` documents written for forecasts related to the anomaly detection job.
forecasts.records.max (or frmax, forecastsRecordsMax): The maximum number of model_forecast documents written for forecasts related to the anomaly detection job.
forecasts.records.min (or frmin, forecastsRecordsMin): The minimum number of model_forecast documents written for forecasts related to the anomaly detection job.
forecasts.records.total (or frt, forecastsRecordsTotal): The total number of model_forecast documents written for forecasts related to the anomaly detection job.
forecasts.time.avg (or ftavg, forecastsTimeAvg): The average runtime in milliseconds for forecasts related to the anomaly detection job.
forecasts.time.max (or ftmax, forecastsTimeMax): The maximum runtime in milliseconds for forecasts related to the anomaly detection job.
forecasts.time.min (or ftmin, forecastsTimeMin): The minimum runtime in milliseconds for forecasts related to the anomaly detection job.
forecasts.time.total (or ftt, forecastsTimeTotal): The total runtime in milliseconds for forecasts related to the anomaly detection job.
forecasts.total (or ft, forecastsTotal): The number of individual forecasts currently available for the job.
id: Identifier for the anomaly detection job.
model.bucket_allocation_failures (or mbaf, modelBucketAllocationFailures): The number of buckets for which new entities in incoming data were not processed due to insufficient model memory.
model.by_fields (or 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 (or mb, modelBytes): 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 (or mbe, modelBytesExceeded): The number of bytes over the high limit for memory usage at the last allocation failure.
model.categorization_status (or mcs, modelCategorizationStatus): The status of categorization for the job: ok or warn. If ok, categorization is performing acceptably well (or not being used at all). If 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 (or mcdc, modelCategorizedDocCount): The number of documents that have had a field categorized.
model.dead_category_count (or 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 (or 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 (or mfcc, modelFrequentCategoryCount): The number of categories that match more than 1% of categorized documents.
model.log_time (or mlt, modelLogTime): The timestamp when the model stats were gathered, according to server time.
model.memory_limit (or mml, modelMemoryLimit): The timestamp when the model stats were gathered, according to server time.
model.memory_status (or mms, modelMemoryStatus): The status of the mathematical models: ok, soft_limit, or hard_limit. If ok, the models stayed below the configured value. If 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. If hard_limit, the models used more space than the configured memory limit. As a result, not all incoming data was processed.
model.over_fields (or 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 (or 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 (or mrcc, modelRareCategoryCount): The number of categories that match just one categorized document.
model.timestamp (or mt, modelTimestamp): The timestamp of the last record when the model stats were gathered.
model.total_category_count (or mtcc, modelTotalCategoryCount): The number of categories created by categorization.
node.address (or na, nodeAddress): The network address of the node that runs the job. This information is available only for open jobs.
node.ephemeral_id (or ne, nodeEphemeralId): The ephemeral ID of the node that runs the job. This information is available only for open jobs.
node.id (or ni, nodeId): The unique identifier of the node that runs the job. This information is available only for open jobs.
node.name (or nn, nodeName): The name of the node that runs the job. This information is available only for open jobs.
opened_time (or ot): For open jobs only, the elapsed time for which the job has been open.
state (or s): The status of the anomaly detection job: closed, closing, failed, opened, or opening. If closed, the job finished successfully with its model state persisted. The job must be opened before it can accept further data. If closing, the job close action is in progress and has not yet completed. A closing job cannot accept further data. If 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. If opened, the job is available to receive and process data. If opening, the job open action is in progress and has not yet completed.

s union CatAnomalyDetectorColumns

Comma-separated list of column names or column aliases used to sort the response.

Supported values include:

assignment_explanation (or ae): For open anomaly detection jobs only, contains messages relating to the selection of a node to run the job.
buckets.count (or bc, bucketsCount): The number of bucket results produced by the job.
buckets.time.exp_avg (or btea, bucketsTimeExpAvg): Exponential moving average of all bucket processing times, in milliseconds.
buckets.time.exp_avg_hour (or bteah, bucketsTimeExpAvgHour): Exponentially-weighted moving average of bucket processing times calculated in a 1 hour time window, in milliseconds.
buckets.time.max (or btmax, bucketsTimeMax): Maximum among all bucket processing times, in milliseconds.
buckets.time.min (or btmin, bucketsTimeMin): Minimum among all bucket processing times, in milliseconds.
buckets.time.total (or btt, bucketsTimeTotal): Sum of all bucket processing times, in milliseconds.
data.buckets (or db, dataBuckets): The number of buckets processed.
data.earliest_record (or der, dataEarliestRecord): The timestamp of the earliest chronologically input document.
data.empty_buckets (or deb, dataEmptyBuckets): The number of buckets which did not contain any data.
data.input_bytes (or dib, dataInputBytes): The number of bytes of input data posted to the anomaly detection job.
data.input_fields (or 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 (or dir, dataInputRecords): The number of input documents posted to the anomaly detection job.
data.invalid_dates (or did, dataInvalidDates): The number of input documents with either a missing date field or a date that could not be parsed.
data.last (or dl, dataLast): The timestamp at which data was last analyzed, according to server time.
data.last_empty_bucket (or dleb, dataLastEmptyBucket): The timestamp of the last bucket that did not contain any data.
data.last_sparse_bucket (or dlsb, dataLastSparseBucket): The timestamp of the last bucket that was considered sparse.
data.latest_record (or dlr, dataLatestRecord): The timestamp of the latest chronologically input document.
data.missing_fields (or 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.
data.out_of_order_timestamps (or 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 (or 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 (or dpr, dataProcessedRecords): 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 (or dsb, dataSparseBuckets): The number of buckets that contained few data points compared to the expected number of data points.
forecasts.memory.avg (or fmavg, forecastsMemoryAvg): The average memory usage in bytes for forecasts related to the anomaly detection job.
forecasts.memory.max (or fmmax, forecastsMemoryMax): The maximum memory usage in bytes for forecasts related to the anomaly detection job.
forecasts.memory.min (or fmmin, forecastsMemoryMin): The minimum memory usage in bytes for forecasts related to the anomaly detection job.
forecasts.memory.total (or fmt, forecastsMemoryTotal): The total memory usage in bytes for forecasts related to the anomaly detection job.
forecasts.records.avg (or fravg, forecastsRecordsAvg): The average number of model_forecast` documents written for forecasts related to the anomaly detection job.
forecasts.records.max (or frmax, forecastsRecordsMax): The maximum number of model_forecast documents written for forecasts related to the anomaly detection job.
forecasts.records.min (or frmin, forecastsRecordsMin): The minimum number of model_forecast documents written for forecasts related to the anomaly detection job.
forecasts.records.total (or frt, forecastsRecordsTotal): The total number of model_forecast documents written for forecasts related to the anomaly detection job.
forecasts.time.avg (or ftavg, forecastsTimeAvg): The average runtime in milliseconds for forecasts related to the anomaly detection job.
forecasts.time.max (or ftmax, forecastsTimeMax): The maximum runtime in milliseconds for forecasts related to the anomaly detection job.
forecasts.time.min (or ftmin, forecastsTimeMin): The minimum runtime in milliseconds for forecasts related to the anomaly detection job.
forecasts.time.total (or ftt, forecastsTimeTotal): The total runtime in milliseconds for forecasts related to the anomaly detection job.
forecasts.total (or ft, forecastsTotal): The number of individual forecasts currently available for the job.
id: Identifier for the anomaly detection job.
model.bucket_allocation_failures (or mbaf, modelBucketAllocationFailures): The number of buckets for which new entities in incoming data were not processed due to insufficient model memory.
model.by_fields (or 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 (or mb, modelBytes): 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 (or mbe, modelBytesExceeded): The number of bytes over the high limit for memory usage at the last allocation failure.
model.categorization_status (or mcs, modelCategorizationStatus): The status of categorization for the job: ok or warn. If ok, categorization is performing acceptably well (or not being used at all). If 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 (or mcdc, modelCategorizedDocCount): The number of documents that have had a field categorized.
model.dead_category_count (or 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 (or 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 (or mfcc, modelFrequentCategoryCount): The number of categories that match more than 1% of categorized documents.
model.log_time (or mlt, modelLogTime): The timestamp when the model stats were gathered, according to server time.
model.memory_limit (or mml, modelMemoryLimit): The timestamp when the model stats were gathered, according to server time.
model.memory_status (or mms, modelMemoryStatus): The status of the mathematical models: ok, soft_limit, or hard_limit. If ok, the models stayed below the configured value. If 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. If hard_limit, the models used more space than the configured memory limit. As a result, not all incoming data was processed.
model.over_fields (or 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 (or 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 (or mrcc, modelRareCategoryCount): The number of categories that match just one categorized document.
model.timestamp (or mt, modelTimestamp): The timestamp of the last record when the model stats were gathered.
model.total_category_count (or mtcc, modelTotalCategoryCount): The number of categories created by categorization.
node.address (or na, nodeAddress): The network address of the node that runs the job. This information is available only for open jobs.
node.ephemeral_id (or ne, nodeEphemeralId): The ephemeral ID of the node that runs the job. This information is available only for open jobs.
node.id (or ni, nodeId): The unique identifier of the node that runs the job. This information is available only for open jobs.
node.name (or nn, nodeName): The name of the node that runs the job. This information is available only for open jobs.
opened_time (or ot): For open jobs only, the elapsed time for which the job has been open.
state (or s): The status of the anomaly detection job: closed, closing, failed, opened, or opening. If closed, the job finished successfully with its model state persisted. The job must be opened before it can accept further data. If closing, the job close action is in progress and has not yet completed. A closing job cannot accept further data. If 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. If opened, the job is available to receive and process data. If opening, the job open action is in progress and has not yet completed.

One of: CatAnomalyDetectorColumn or CatAnomalyDetectorColumn[]

Response

application/json

Response Type: [] JobsRecord

id string Id

The anomaly detection job identifier.

state enum JobState

The status of the anomaly detection job.

Supported values include:

closing: The job close action is in progress and has not yet completed. A closing job cannot accept further data.
closed: The job finished successfully with its model state persisted. The job must be opened before it can accept further data.
opened: The job is available to receive and process 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.
opening: The job open action is in progress and has not yet completed.

Values: closing closed opened failed opening

opened_time string

For open jobs only, the amount of time the job has been opened.

assignment_explanation string

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

data.processed_records string

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.processed_fields string

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.input_bytes number | string ByteSize

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

data.input_records string

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

data.input_fields string

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.invalid_dates string

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

data.missing_fields string

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 string

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.empty_buckets string

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.sparse_buckets string

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.

data.buckets string

The total number of buckets processed.

data.earliest_record string

The timestamp of the earliest chronologically input document.

data.latest_record string

The timestamp of the latest chronologically input document.

data.last string

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

data.last_empty_bucket string

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

data.last_sparse_bucket string

The timestamp of the last bucket that was considered sparse.

model.bytes number | string ByteSize

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.memory_status enum MemoryStatus

The status of the mathematical models.

Values: ok soft_limit hard_limit

model.bytes_exceeded number | string ByteSize

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

model.memory_limit string

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

model.by_fields string

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

model.over_fields string

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 string

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

model.bucket_allocation_failures string

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.categorization_status enum CategorizationStatus

The status of categorization for the job.

Values: ok warn

model.categorized_doc_count string

The number of documents that have had a field categorized.

model.total_category_count string

The number of categories created by categorization.

model.frequent_category_count string

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

model.rare_category_count string

The number of categories that match just one categorized document.

model.dead_category_count string

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 string

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.log_time string

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

model.timestamp string

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

forecasts.total string

The number of individual forecasts currently available for the job. A value of one or more indicates that forecasts exist.

forecasts.memory.min string

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

forecasts.memory.max string

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

forecasts.memory.avg string

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

forecasts.memory.total string

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

forecasts.records.min string

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

forecasts.records.max string

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

forecasts.records.avg string

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

forecasts.records.total string

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

forecasts.time.min string

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

forecasts.time.max string

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

forecasts.time.avg string

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

forecasts.time.total string

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

node.id string

The uniqe identifier of the assigned node.

node.name string

The name of the assigned node.

node.ephemeral_id string

The ephemeral identifier of the assigned node.

node.address string

The network address of the assigned node.

buckets.count string

The number of bucket results produced by the job.

buckets.time.total string

The sum of all bucket processing times, in milliseconds.

buckets.time.min string

The minimum of all bucket processing times, in milliseconds.

buckets.time.max string

The maximum of all bucket processing times, in milliseconds.

buckets.time.exp_avg string

The exponential moving average of all bucket processing times, in milliseconds.

buckets.time.exp_avg_hour string

The exponential moving average of bucket processing times calculated in a one hour time window, in milliseconds.

Examples

A successful response from GET _cat/ml/anomaly_detectors?h=id,s,dpr,mb&v=true&format=json.

Request Console 1 / 7 languages

Console Kibana Dev Tools

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

JavaScript Elasticsearch JavaScript Client

const response = await client.cat.mlJobs({
  h: "id,s,dpr,mb",
  v: "true",
  format: "json",
});

Python Elasticsearch Python Client

resp = client.cat.ml_jobs(
    h="id,s,dpr,mb",
    v=True,
    format="json",
)

curl Shell

curl -X GET "$ELASTICSEARCH_URL/_cat/ml/anomaly_detectors?h=id,s,dpr,mb&v=true&format=json" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY"

Java Elasticsearch Java Client

client.cat().mlJobs(m -> m
    .h(List.of("id","s","dpr","mb"))
    .s(List.of())
);

PHP Elasticsearch PHP Client

$resp = $client->cat()->mlJobs([
    "h" => "id,s,dpr,mb",
    "v" => "true",
    "format" => "json",
]);

Ruby Elasticsearch Ruby Client

response = client.cat.ml_jobs(
  h: "id,s,dpr,mb",
  v: "true",
  format: "json"
)

Response

[
  {
    "id": "high_sum_total_sales",
    "s": "closed",
    "dpr": "14022",
    "mb": "1.5mb"
  },
  {
    "id": "low_request_rate",
    "s": "closed",
    "dpr": "1216",
    "mb": "40.5kb"
  },
  {
    "id": "response_code_rates",
    "s": "closed",
    "dpr": "28146",
    "mb": "132.7kb"
  },
  {
    "id": "url_scanning",
    "s": "closed",
    "dpr": "28146",
    "mb": "501.6kb"
  }
]

Required authorization

Path Parameters

Query Parameters { allow_no_match , h , s , }

Response

Query Parameters