Get anomaly detection jobs

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.

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.

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.

Specifies the format to return the columnar data in, can be set to text, json, cbor, yaml, or smile.

When set to true will output available columns. This option can't be combined with any other query string option.

When set to true will enable verbose output.

Sets the units for columns that contain a byte-size value. Note that byte-size value units work in terms of powers of 1024. For instance 1kb means 1024 bytes, not 1000 bytes. If omitted, byte-size values are rendered with a suffix such as kb, mb, or gb, chosen such that the numeric value of the column is as small as possible whilst still being at least 1.0. If given, byte-size values are rendered as an integer with no suffix, representing the value of the column in the chosen unit. Values that are not an exact multiple of the chosen unit are rounded down.