Path parameters

• name string Required

  the name of the autoscaling policy

Query parameters

• master_timeout string

  Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.

  Values are -1 or 0.

• timeout string

  Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.

  Values are -1 or 0.

Responses

• 200 application/json
  Hide response attribute Show response attribute object

  • acknowledged boolean Required

    For a successful response, this value is always true. On failure, an exception is returned instead.

Path parameters

• name string Required

  The name of the analytics collection to be created or updated.

Responses

• 200 application/json
  Hide response attributes Show response attributes object

  • acknowledged boolean Required

    For a successful response, this value is always true. On failure, an exception is returned instead.

  • name string Required

Query parameters

• h string | array[string]

  List of columns to appear in the response. Supports simple wildcards.

• s string | array[string]

  List of columns that determine how the table should be sorted. Sorting defaults to ascending and can be changed by setting :asc or :desc as a suffix to the column name.

• local boolean

  If true, the request computes the list of selected nodes from the local cluster state. If false the list of selected nodes are computed from the cluster state of the master node. In both cases the coordinating node will send requests for further information to each selected node.

• master_timeout string

  The period to wait for a connection to the master node.

  Values are -1 or 0.

Responses

• 200 application/json
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  • name string Required

  • version string | null Required

    One of:

    string-1 string string-2 string | null
  • alias_count string Required
  • mapping_count string Required
  • settings_count string Required
  • metadata_count string Required
  • included_in string Required

Query parameters

• allow_no_match boolean

  Whether to ignore if a wildcard expression matches no configs. (This includes _all string or when no configs have been specified)

• bytes string

  The unit in which to display byte values

  Values are b, kb, mb, gb, tb, or pb.

• h string | array[string]

  Comma-separated list of column names to display.

  Supported values include:

  • assignment_explanation (or ae): Contains messages relating to the selection of a node.
  • create_time (or ct, createTime): The time when the data frame analytics job was created.
  • description (or d): A description of a job.
  • dest_index (or di, destIndex): Name of the destination index.
  • failure_reason (or fr, failureReason): Contains messages about the reason why a data frame analytics job failed.
  • id: Identifier for the data frame analytics job.
  • model_memory_limit (or mml, modelMemoryLimit): The approximate maximum amount of memory resources that are permitted for the data frame analytics job.
  • node.address (or na, nodeAddress): The network address of the node that the data frame analytics job is assigned to.
  • node.ephemeral_id (or ne, nodeEphemeralId): The ephemeral ID of the node that the data frame analytics job is assigned to.
  • node.id (or ni, nodeId): The unique identifier of the node that the data frame analytics job is assigned to.
  • node.name (or nn, nodeName): The name of the node that the data frame analytics job is assigned to.
  • progress (or p): The progress report of the data frame analytics job by phase.
  • source_index (or si, sourceIndex): Name of the source index.
  • state (or s): Current state of the data frame analytics job.
  • type (or t): The type of analysis that the data frame analytics job performs.
  • version (or v): The Elasticsearch version number in which the data frame analytics job was created.

  Values are assignment_explanation, ae, create_time, ct, createTime, description, d, dest_index, di, destIndex, failure_reason, fr, failureReason, id, model_memory_limit, mml, modelMemoryLimit, node.address, na, nodeAddress, node.ephemeral_id, ne, nodeEphemeralId, node.id, ni, nodeId, node.name, nn, nodeName, progress, p, source_index, si, sourceIndex, state, s, type, t, version, or v.

• s string | array[string]

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

  Supported values include:

  • assignment_explanation (or ae): Contains messages relating to the selection of a node.
  • create_time (or ct, createTime): The time when the data frame analytics job was created.
  • description (or d): A description of a job.
  • dest_index (or di, destIndex): Name of the destination index.
  • failure_reason (or fr, failureReason): Contains messages about the reason why a data frame analytics job failed.
  • id: Identifier for the data frame analytics job.
  • model_memory_limit (or mml, modelMemoryLimit): The approximate maximum amount of memory resources that are permitted for the data frame analytics job.
  • node.address (or na, nodeAddress): The network address of the node that the data frame analytics job is assigned to.
  • node.ephemeral_id (or ne, nodeEphemeralId): The ephemeral ID of the node that the data frame analytics job is assigned to.
  • node.id (or ni, nodeId): The unique identifier of the node that the data frame analytics job is assigned to.
  • node.name (or nn, nodeName): The name of the node that the data frame analytics job is assigned to.
  • progress (or p): The progress report of the data frame analytics job by phase.
  • source_index (or si, sourceIndex): Name of the source index.
  • state (or s): Current state of the data frame analytics job.
  • type (or t): The type of analysis that the data frame analytics job performs.
  • version (or v): The Elasticsearch version number in which the data frame analytics job was created.

  Values are assignment_explanation, ae, create_time, ct, createTime, description, d, dest_index, di, destIndex, failure_reason, fr, failureReason, id, model_memory_limit, mml, modelMemoryLimit, node.address, na, nodeAddress, node.ephemeral_id, ne, nodeEphemeralId, node.id, ni, nodeId, node.name, nn, nodeName, progress, p, source_index, si, sourceIndex, state, s, type, t, version, or v.

• time string

  Unit used to display time values.

  Values are nanos, micros, ms, s, m, h, or d.

Responses

• 200 application/json
  Hide response attributes Show response attributes object

  • id string
  • type string

    The type of analysis that the job performs.

  • create_time string

    The time when the job was created.

  • version string
  • source_index string
  • dest_index string
  • description string

    A description of the job.

  • model_memory_limit string

    The approximate maximum amount of memory resources that are permitted for the job.

  • state string

    The current status of the job.

  • failure_reason string

    Messages about the reason why the job failed.

  • progress string

    The progress report for the job by phase.

  • assignment_explanation string

    Messages related to the selection of a node.

  • node.id string
  • node.name string
  • node.ephemeral_id string
  • node.address string

    The network address of the assigned node.

Path parameters

• job_id string Required

  Identifier for the anomaly detection job.

Query parameters

• allow_no_match 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.

  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.

• bytes string

  The unit used to display byte values.

  Values are b, kb, mb, gb, tb, or pb.

• h string | array[string]

  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 string | array[string]

  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.
• time string

  The unit used to display time values.

  Values are nanos, micros, ms, s, m, h, or d.

Responses

• 200 application/json
  Hide response attributes Show response attributes object

  • id string
  • state string

    Values are closing, closed, opened, failed, or 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

    One of:

    ByteSize number ByteSize string
  • 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

    One of:

    ByteSize number ByteSize string
  • model.memory_status string

    Values are ok, soft_limit, or hard_limit.

  • model.bytes_exceeded number | string

    One of:

    ByteSize number ByteSize string
  • 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 string

    Values are ok or 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
  • node.name string

    The name of the assigned node.

  • node.ephemeral_id string
  • 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.

Query parameters

• bytes string

  The unit used to display byte values.

  Values are b, kb, mb, gb, tb, or pb.

• h string | array[string]

  List of columns to appear in the response. Supports simple wildcards.

• s string | array[string]

  List of columns that determine how the table should be sorted. Sorting defaults to ascending and can be changed by setting :asc or :desc as a suffix to the column name.

• master_timeout string

  Period to wait for a connection to the master node.

  Values are -1 or 0.

• time string

  Unit used to display time values.

  Values are nanos, micros, ms, s, m, h, or d.

Responses

• 200 application/json
  Hide response attributes Show response attributes object

  • index string

    The index name.

  • shard string

    The shard name.

  • prirep string

    The shard type: primary or replica.

  • state string

    The shard state. Returned values include: INITIALIZING: The shard is recovering from a peer shard or gateway. RELOCATING: The shard is relocating. STARTED: The shard has started. UNASSIGNED: The shard is not assigned to any node.

  • docs string | null

    The number of documents in the shard.

    One of:

    string-1 string string-2 string | null

  • store string | null

    The disk space used by the shard.

    One of:

    string-1 string string-2 string | null

  • dataset string | null

    total size of dataset (including the cache for partially mounted indices)

    One of:

    string-1 string string-2 string | null

  • ip string | null

    The IP address of the node.

    One of:

    string-1 string string-2 string | null
  • id string

    The unique identifier for the node.

  • node string | null

    The name of node.

    One of:

    string-1 string string-2 string | null
  • sync_id string

    The sync identifier.

  • unassigned.reason string

    The reason for the last change to the state of an unassigned shard. It does not explain why the shard is currently unassigned; use the cluster allocation explain API for that information. Returned values include: ALLOCATION_FAILED: Unassigned as a result of a failed allocation of the shard. CLUSTER_RECOVERED: Unassigned as a result of a full cluster recovery. DANGLING_INDEX_IMPORTED: Unassigned as a result of importing a dangling index. EXISTING_INDEX_RESTORED: Unassigned as a result of restoring into a closed index. FORCED_EMPTY_PRIMARY: The shard’s allocation was last modified by forcing an empty primary using the cluster reroute API. INDEX_CLOSED: Unassigned because the index was closed. INDEX_CREATED: Unassigned as a result of an API creation of an index. INDEX_REOPENED: Unassigned as a result of opening a closed index. MANUAL_ALLOCATION: The shard’s allocation was last modified by the cluster reroute API. NEW_INDEX_RESTORED: Unassigned as a result of restoring into a new index. NODE_LEFT: Unassigned as a result of the node hosting it leaving the cluster. NODE_RESTARTING: Similar to NODE_LEFT, except that the node was registered as restarting using the node shutdown API. PRIMARY_FAILED: The shard was initializing as a replica, but the primary shard failed before the initialization completed. REALLOCATED_REPLICA: A better replica location is identified and causes the existing replica allocation to be cancelled. REINITIALIZED: When a shard moves from started back to initializing. REPLICA_ADDED: Unassigned as a result of explicit addition of a replica. REROUTE_CANCELLED: Unassigned as a result of explicit cancel reroute command.

  • unassigned.at string

    The time at which the shard became unassigned in Coordinated Universal Time (UTC).

  • unassigned.for string

    The time at which the shard was requested to be unassigned in Coordinated Universal Time (UTC).

  • unassigned.details string

    Additional details as to why the shard became unassigned. It does not explain why the shard is not assigned; use the cluster allocation explain API for that information.

  • recoverysource.type string

    The type of recovery source.

  • completion.size string

    The size of completion.

  • fielddata.memory_size string

    The used fielddata cache memory.

  • fielddata.evictions string

    The fielddata cache evictions.

  • query_cache.memory_size string

    The used query cache memory.

  • query_cache.evictions string

    The query cache evictions.

  • flush.total string

    The number of flushes.

  • flush.total_time string

    The time spent in flush.

  • get.current string

    The number of current get operations.

  • get.time string

    The time spent in get operations.

  • get.total string

    The number of get operations.

  • get.exists_time string

    The time spent in successful get operations.

  • get.exists_total string

    The number of successful get operations.

  • get.missing_time string

    The time spent in failed get operations.

  • get.missing_total string

    The number of failed get operations.

  • indexing.delete_current string

    The number of current deletion operations.

  • indexing.delete_time string

    The time spent in deletion operations.

  • indexing.delete_total string

    The number of delete operations.

  • indexing.index_current string

    The number of current indexing operations.

  • indexing.index_time string

    The time spent in indexing operations.

  • indexing.index_total string

    The number of indexing operations.

  • indexing.index_failed string

    The number of failed indexing operations.

  • merges.current string

    The number of current merge operations.

  • merges.current_docs string

    The number of current merging documents.

  • merges.current_size string

    The size of current merge operations.

  • merges.total string

    The number of completed merge operations.

  • merges.total_docs string

    The nuber of merged documents.

  • merges.total_size string

    The size of current merges.

  • merges.total_time string

    The time spent merging documents.

  • refresh.total string

    The total number of refreshes.

  • refresh.time string

    The time spent in refreshes.

  • refresh.external_total string

    The total nunber of external refreshes.

  • refresh.external_time string

    The time spent in external refreshes.

  • refresh.listeners string

    The number of pending refresh listeners.

  • search.fetch_current string

    The current fetch phase operations.

  • search.fetch_time string

    The time spent in fetch phase.

  • search.fetch_total string

    The total number of fetch operations.

  • search.open_contexts string

    The number of open search contexts.

  • search.query_current string

    The current query phase operations.

  • search.query_time string

    The time spent in query phase.

  • search.query_total string

    The total number of query phase operations.

  • search.scroll_current string

    The open scroll contexts.

  • search.scroll_time string

    The time scroll contexts were held open.

  • search.scroll_total string

    The number of completed scroll contexts.

  • segments.count string

    The number of segments.

  • segments.memory string

    The memory used by segments.

  • segments.index_writer_memory string

    The memory used by the index writer.

  • segments.version_map_memory string

    The memory used by the version map.

  • segments.fixed_bitset_memory string

    The memory used by fixed bit sets for nested object field types and export type filters for types referred in _parent fields.

  • seq_no.max string

    The maximum sequence number.

  • seq_no.local_checkpoint string

    The local checkpoint.

  • seq_no.global_checkpoint string

    The global checkpoint.

  • warmer.current string

    The number of current warmer operations.

  • warmer.total string

    The total number of warmer operations.

  • warmer.total_time string

    The time spent in warmer operations.

  • path.data string

    The shard data path.

  • path.state string

    The shard state path.

  • bulk.total_operations string

    The number of bulk shard operations.

  • bulk.total_time string

    The time spent in shard bulk operations.

  • bulk.total_size_in_bytes string

    The total size in bytes of shard bulk operations.

  • bulk.avg_time string

    The average time spent in shard bulk operations.

  • bulk.avg_size_in_bytes string

    The average size in bytes of shard bulk operations.

Path parameters

• transform_id string Required

  A transform identifier or a wildcard expression. If you do not specify one of these options, the API returns information for all transforms.

Query parameters

• allow_no_match boolean

  Specifies what to do when the request: contains wildcard expressions and there are no transforms 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, it returns an empty transforms array when there are no matches and the subset of results when there are partial matches. If false, the request returns a 404 status code when there are no matches or only partial matches.

• from number

  Skips the specified number of transforms.

• h string | array[string]

  Comma-separated list of column names to display.

  Supported values include:

  • changes_last_detection_time (or cldt): The timestamp when changes were last detected in the source indices.
  • checkpoint (or cp): The sequence number for the checkpoint.
  • checkpoint_duration_time_exp_avg (or cdtea, checkpointTimeExpAvg): Exponential moving average of the duration of the checkpoint, in milliseconds.
  • checkpoint_progress (or c, checkpointProgress): The progress of the next checkpoint that is currently in progress.
  • create_time (or ct, createTime): The time the transform was created.
  • delete_time (or dtime): The amount of time spent deleting, in milliseconds.
  • description (or d): The description of the transform.
  • dest_index (or di, destIndex): The destination index for the transform. The mappings of the destination index are deduced based on the source fields when possible. If alternate mappings are required, use the Create index API prior to starting the transform.
  • documents_deleted (or docd): The number of documents that have been deleted from the destination index due to the retention policy for this transform.
  • documents_indexed (or doci): The number of documents that have been indexed into the destination index for the transform.
  • docs_per_second (or dps): Specifies a limit on the number of input documents per second. This setting throttles the transform by adding a wait time between search requests. The default value is null, which disables throttling.
  • documents_processed (or docp): The number of documents that have been processed from the source index of the transform.
  • frequency (or f): The interval between checks for changes in the source indices when the transform is running continuously. Also determines the retry interval in the event of transient failures while the transform is searching or indexing. The minimum value is 1s and the maximum is 1h. The default value is 1m.
  • id: Identifier for the transform.
  • index_failure (or if): The number of indexing failures.
  • index_time (or itime): The amount of time spent indexing, in milliseconds.
  • index_total (or it): The number of index operations.
  • indexed_documents_exp_avg (or idea): Exponential moving average of the number of new documents that have been indexed.
  • last_search_time (or lst, lastSearchTime): The timestamp of the last search in the source indices. This field is only shown if the transform is running.
  • max_page_search_size (or mpsz): Defines the initial page size to use for the composite aggregation for each checkpoint. If circuit breaker exceptions occur, the page size is dynamically adjusted to a lower value. The minimum value is 10 and the maximum is 65,536. The default value is 500.
  • pages_processed (or pp): The number of search or bulk index operations processed. Documents are processed in batches instead of individually.
  • pipeline (or p): The unique identifier for an ingest pipeline.
  • processed_documents_exp_avg (or pdea): Exponential moving average of the number of documents that have been processed.
  • processing_time (or pt): The amount of time spent processing results, in milliseconds.
  • reason (or r): If a transform has a failed state, this property provides details about the reason for the failure.
  • search_failure (or sf): The number of search failures.
  • search_time (or stime): The amount of time spent searching, in milliseconds.
  • search_total (or st): The number of search operations on the source index for the transform.
  • source_index (or si, sourceIndex): The source indices for the transform. It can be a single index, an index pattern (for example, "my-index-*"), an array of indices (for example, ["my-index-000001", "my-index-000002"]), or an array of index patterns (for example, ["my-index-*", "my-other-index-*"]. For remote indices use the syntax "remote_name:index_name". If any indices are in remote clusters then the master node and at least one transform node must have the remote_cluster_client node role.

  • state (or s): The status of the transform, which can be one of the following values:

    • aborting: The transform is aborting.
    • failed: The transform failed. For more information about the failure, check the reason field.
    • indexing: The transform is actively processing data and creating new documents.
    • started: The transform is running but not actively indexing data.
    • stopped: The transform is stopped.
    • stopping: The transform is stopping.

  • transform_type (or tt): Indicates the type of transform: batch or continuous.

  • trigger_count (or tc): The number of times the transform has been triggered by the scheduler. For example, the scheduler triggers the transform indexer to check for updates or ingest new data at an interval specified in the frequency property.

  • version (or v): The version of Elasticsearch that existed on the node when the transform was created.

  Values are changes_last_detection_time, cldt, checkpoint, cp, checkpoint_duration_time_exp_avg, cdtea, checkpointTimeExpAvg, checkpoint_progress, c, checkpointProgress, create_time, ct, createTime, delete_time, dtime, description, d, dest_index, di, destIndex, documents_deleted, docd, documents_indexed, doci, docs_per_second, dps, documents_processed, docp, frequency, f, id, index_failure, if, index_time, itime, index_total, it, indexed_documents_exp_avg, idea, last_search_time, lst, lastSearchTime, max_page_search_size, mpsz, pages_processed, pp, pipeline, p, processed_documents_exp_avg, pdea, processing_time, pt, reason, r, search_failure, sf, search_time, stime, search_total, st, source_index, si, sourceIndex, state, s, transform_type, tt, trigger_count, tc, version, or v.

• s string | array[string]

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

  Supported values include:

  • changes_last_detection_time (or cldt): The timestamp when changes were last detected in the source indices.
  • checkpoint (or cp): The sequence number for the checkpoint.
  • checkpoint_duration_time_exp_avg (or cdtea, checkpointTimeExpAvg): Exponential moving average of the duration of the checkpoint, in milliseconds.
  • checkpoint_progress (or c, checkpointProgress): The progress of the next checkpoint that is currently in progress.
  • create_time (or ct, createTime): The time the transform was created.
  • delete_time (or dtime): The amount of time spent deleting, in milliseconds.
  • description (or d): The description of the transform.
  • dest_index (or di, destIndex): The destination index for the transform. The mappings of the destination index are deduced based on the source fields when possible. If alternate mappings are required, use the Create index API prior to starting the transform.
  • documents_deleted (or docd): The number of documents that have been deleted from the destination index due to the retention policy for this transform.
  • documents_indexed (or doci): The number of documents that have been indexed into the destination index for the transform.
  • docs_per_second (or dps): Specifies a limit on the number of input documents per second. This setting throttles the transform by adding a wait time between search requests. The default value is null, which disables throttling.
  • documents_processed (or docp): The number of documents that have been processed from the source index of the transform.
  • frequency (or f): The interval between checks for changes in the source indices when the transform is running continuously. Also determines the retry interval in the event of transient failures while the transform is searching or indexing. The minimum value is 1s and the maximum is 1h. The default value is 1m.
  • id: Identifier for the transform.
  • index_failure (or if): The number of indexing failures.
  • index_time (or itime): The amount of time spent indexing, in milliseconds.
  • index_total (or it): The number of index operations.
  • indexed_documents_exp_avg (or idea): Exponential moving average of the number of new documents that have been indexed.
  • last_search_time (or lst, lastSearchTime): The timestamp of the last search in the source indices. This field is only shown if the transform is running.
  • max_page_search_size (or mpsz): Defines the initial page size to use for the composite aggregation for each checkpoint. If circuit breaker exceptions occur, the page size is dynamically adjusted to a lower value. The minimum value is 10 and the maximum is 65,536. The default value is 500.
  • pages_processed (or pp): The number of search or bulk index operations processed. Documents are processed in batches instead of individually.
  • pipeline (or p): The unique identifier for an ingest pipeline.
  • processed_documents_exp_avg (or pdea): Exponential moving average of the number of documents that have been processed.
  • processing_time (or pt): The amount of time spent processing results, in milliseconds.
  • reason (or r): If a transform has a failed state, this property provides details about the reason for the failure.
  • search_failure (or sf): The number of search failures.
  • search_time (or stime): The amount of time spent searching, in milliseconds.
  • search_total (or st): The number of search operations on the source index for the transform.
  • source_index (or si, sourceIndex): The source indices for the transform. It can be a single index, an index pattern (for example, "my-index-*"), an array of indices (for example, ["my-index-000001", "my-index-000002"]), or an array of index patterns (for example, ["my-index-*", "my-other-index-*"]. For remote indices use the syntax "remote_name:index_name". If any indices are in remote clusters then the master node and at least one transform node must have the remote_cluster_client node role.

  • state (or s): The status of the transform, which can be one of the following values:

    • aborting: The transform is aborting.
    • failed: The transform failed. For more information about the failure, check the reason field.
    • indexing: The transform is actively processing data and creating new documents.
    • started: The transform is running but not actively indexing data.
    • stopped: The transform is stopped.
    • stopping: The transform is stopping.

  • transform_type (or tt): Indicates the type of transform: batch or continuous.

  • trigger_count (or tc): The number of times the transform has been triggered by the scheduler. For example, the scheduler triggers the transform indexer to check for updates or ingest new data at an interval specified in the frequency property.

  • version (or v): The version of Elasticsearch that existed on the node when the transform was created.

  Values are changes_last_detection_time, cldt, checkpoint, cp, checkpoint_duration_time_exp_avg, cdtea, checkpointTimeExpAvg, checkpoint_progress, c, checkpointProgress, create_time, ct, createTime, delete_time, dtime, description, d, dest_index, di, destIndex, documents_deleted, docd, documents_indexed, doci, docs_per_second, dps, documents_processed, docp, frequency, f, id, index_failure, if, index_time, itime, index_total, it, indexed_documents_exp_avg, idea, last_search_time, lst, lastSearchTime, max_page_search_size, mpsz, pages_processed, pp, pipeline, p, processed_documents_exp_avg, pdea, processing_time, pt, reason, r, search_failure, sf, search_time, stime, search_total, st, source_index, si, sourceIndex, state, s, transform_type, tt, trigger_count, tc, version, or v.

• time string

  The unit used to display time values.

  Values are nanos, micros, ms, s, m, h, or d.

• size number

  The maximum number of transforms to obtain.

Responses

• 200 application/json
  Hide response attributes Show response attributes object

  • id string
  • state string

    The status of the transform. Returned values include: aborting: The transform is aborting. failed: The transform failed. For more information about the failure, check thereasonfield. indexing: The transform is actively processing data and creating new documents. started: The transform is running but not actively indexing data. stopped: The transform is stopped. stopping`: The transform is stopping.

  • checkpoint string

    The sequence number for the checkpoint.

  • documents_processed string

    The number of documents that have been processed from the source index of the transform.

  • checkpoint_progress string | null

    The progress of the next checkpoint that is currently in progress.

    One of:

    string-1 string string-2 string | null

  • last_search_time string | null

    The timestamp of the last search in the source indices. This field is shown only if the transform is running.

    One of:

    string-1 string string-2 string | null

  • changes_last_detection_time string | null

    The timestamp when changes were last detected in the source indices.

    One of:

    string-1 string string-2 string | null
  • create_time string

    The time the transform was created.

  • version string
  • source_index string

    The source indices for the transform.

  • dest_index string

    The destination index for the transform.

  • pipeline string

    The unique identifier for the ingest pipeline.

  • description string

    The description of the transform.

  • transform_type string

    The type of transform: batch or continuous.

  • frequency string

    The interval between checks for changes in the source indices when the transform is running continuously.

  • max_page_search_size string

    The initial page size that is used for the composite aggregation for each checkpoint.

  • docs_per_second string

    The number of input documents per second.

  • reason string

    If a transform has a failed state, these details describe the reason for failure.

  • search_total string

    The total number of search operations on the source index for the transform.

  • search_failure string

    The total number of search failures.

  • search_time string

    The total amount of search time, in milliseconds.

  • index_total string

    The total number of index operations done by the transform.

  • index_failure string

    The total number of indexing failures.

  • index_time string

    The total time spent indexing documents, in milliseconds.

  • documents_indexed string

    The number of documents that have been indexed into the destination index for the transform.

  • delete_time string

    The total time spent deleting documents, in milliseconds.

  • documents_deleted string

    The number of documents deleted from the destination index due to the retention policy for the transform.

  • trigger_count string

    The number of times the transform has been triggered by the scheduler. For example, the scheduler triggers the transform indexer to check for updates or ingest new data at an interval specified in the frequency property.

  • pages_processed string

    The number of search or bulk index operations processed. Documents are processed in batches instead of individually.

  • processing_time string

    The total time spent processing results, in milliseconds.

  • checkpoint_duration_time_exp_avg string

    The exponential moving average of the duration of the checkpoint, in milliseconds.

  • indexed_documents_exp_avg string

    The exponential moving average of the number of new documents that have been indexed.

  • processed_documents_exp_avg string

    The exponential moving average of the number of documents that have been processed.

Query parameters

• local boolean

  If true, the request retrieves information from the local node only. If false, information is retrieved from the master node.

• master_timeout string

  Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.

  Values are -1 or 0.

Responses

• 200 application/json
  Hide response attribute Show response attribute object

  • tasks array[object] Required
    Hide tasks attributes Show tasks attributes object

    • executing boolean Required

      Indicates whether the pending tasks are currently executing or not.

    • insert_order number Required

      The number that represents when the task has been inserted into the task queue.

    • priority string Required

      The priority of the pending task. The valid priorities in descending priority order are: IMMEDIATE > URGENT > HIGH > NORMAL > LOW > LANGUID.

    • source string Required

      A general description of the cluster task that may include a reason and origin.

    • time_in_queue string

      A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.

    • time_in_queue_millis number

      Time unit for milliseconds

Responses

• 200 application/json

Responses

• 200 application/json

Path parameters

• feature string | array[string] Required

  A feature of the cluster, as returned by the top-level health report API.

Query parameters

• timeout string

  Explicit operation timeout.

  Values are -1 or 0.

• verbose boolean

  Opt-in for more information about the health of the system.

• size number

  Limit the number of affected resources the health report API returns.

Responses

• 200 application/json
  Hide response attributes Show response attributes object

  • cluster_name string Required
  • indicators object Required
    Hide indicators attributes Show indicators attributes object

    • master_is_stable object
      Hide master_is_stable attributes Show master_is_stable attributes object

      • status string Required

        Values are green, yellow, red, or unknown.

      • symptom string Required
      • impacts array[object]
        Hide impacts attributes Show impacts attributes object

        • description string Required
        • id string Required
        • impact_areas array[string] Required

          Values are search, ingest, backup, or deployment_management.

        • severity number Required
      • diagnosis array[object]
        Hide diagnosis attributes Show diagnosis attributes object

        • id string Required
        • action string Required
        • affected_resources object Required
        • cause string Required
        • help_url string Required
      • details object
        Hide details attributes Show details attributes object

        • current_master object Required
          Hide current_master attributes Show current_master attributes object

          • name
          • node_id
        • recent_masters array[object] Required
        • exception_fetching_history object
          Hide exception_fetching_history attributes Show exception_fetching_history attributes object

          • message string Required
          • stack_trace string Required
        • cluster_formation array[object]
    • shards_availability object
      Hide shards_availability attributes Show shards_availability attributes object

      • status string Required

        Values are green, yellow, red, or unknown.

      • symptom string Required
      • impacts array[object]
        Hide impacts attributes Show impacts attributes object

        • description string Required
        • id string Required
        • impact_areas array[string] Required

          Values are search, ingest, backup, or deployment_management.

        • severity number Required
      • diagnosis array[object]
        Hide diagnosis attributes Show diagnosis attributes object

        • id string Required
        • action string Required
        • affected_resources object Required
        • cause string Required
        • help_url string Required
      • details object
        Hide details attributes Show details attributes object

        • creating_primaries number Required
        • creating_replicas number Required
        • initializing_primaries number Required
        • initializing_replicas number Required
        • restarting_primaries number Required
        • restarting_replicas number Required
        • started_primaries number Required
        • started_replicas number Required
        • unassigned_primaries number Required
        • unassigned_replicas number Required
    • disk object
      Hide disk attributes Show disk attributes object

      • status string Required

        Values are green, yellow, red, or unknown.

      • symptom string Required
      • impacts array[object]
        Hide impacts attributes Show impacts attributes object

        • description string Required
        • id string Required
        • impact_areas array[string] Required

          Values are search, ingest, backup, or deployment_management.

        • severity number Required
      • diagnosis array[object]
        Hide diagnosis attributes Show diagnosis attributes object

        • id string Required
        • action string Required
        • affected_resources object Required
        • cause string Required
        • help_url string Required
      • details object
        Hide details attributes Show details attributes object

        • indices_with_readonly_block number Required
        • nodes_with_enough_disk_space number Required
        • nodes_over_high_watermark number Required
        • nodes_over_flood_stage_watermark number Required
        • nodes_with_unknown_disk_status number Required
    • repository_integrity object
      Hide repository_integrity attributes Show repository_integrity attributes object

      • status string Required

        Values are green, yellow, red, or unknown.

      • symptom string Required
      • impacts array[object]
        Hide impacts attributes Show impacts attributes object

        • description string Required
        • id string Required
        • impact_areas array[string] Required

          Values are search, ingest, backup, or deployment_management.

        • severity number Required
      • diagnosis array[object]
        Hide diagnosis attributes Show diagnosis attributes object

        • id string Required
        • action string Required
        • affected_resources object Required
        • cause string Required
        • help_url string Required
      • details object
        Hide details attributes Show details attributes object

        • total_repositories number
        • corrupted_repositories number
        • corrupted array[string]
    • data_stream_lifecycle object
      Hide data_stream_lifecycle attributes Show data_stream_lifecycle attributes object

      • status string Required

        Values are green, yellow, red, or unknown.

      • symptom string Required
      • impacts array[object]
        Hide impacts attributes Show impacts attributes object

        • description string Required
        • id string Required
        • impact_areas array[string] Required

          Values are search, ingest, backup, or deployment_management.

        • severity number Required
      • diagnosis array[object]
        Hide diagnosis attributes Show diagnosis attributes object

        • id string Required
        • action string Required
        • affected_resources object Required
        • cause string Required
        • help_url string Required
      • details object
        Hide details attributes Show details attributes object

        • stagnating_backing_indices_count number Required
        • total_backing_indices_in_error number Required
        • stagnating_backing_indices array[object]
    • ilm object
      Hide ilm attributes Show ilm attributes object

      • status string Required

        Values are green, yellow, red, or unknown.

      • symptom string Required
      • impacts array[object]
        Hide impacts attributes Show impacts attributes object

        • description string Required
        • id string Required
        • impact_areas array[string] Required

          Values are search, ingest, backup, or deployment_management.

        • severity number Required
      • diagnosis array[object]
        Hide diagnosis attributes Show diagnosis attributes object

        • id string Required
        • action string Required
        • affected_resources object Required
        • cause string Required
        • help_url string Required
      • details object
        Hide details attributes Show details attributes object

        • ilm_status string Required

          Values are RUNNING, STOPPING, or STOPPED.

        • policies number Required
        • stagnating_indices number Required
    • slm object
      Hide slm attributes Show slm attributes object

      • status string Required

        Values are green, yellow, red, or unknown.

      • symptom string Required
      • impacts array[object]
        Hide impacts attributes Show impacts attributes object

        • description string Required
        • id string Required
        • impact_areas array[string] Required

          Values are search, ingest, backup, or deployment_management.

        • severity number Required
      • diagnosis array[object]
        Hide diagnosis attributes Show diagnosis attributes object

        • id string Required
        • action string Required
        • affected_resources object Required
        • cause string Required
        • help_url string Required
      • details object
        Hide details attributes Show details attributes object

        • slm_status string Required

          Values are RUNNING, STOPPING, or STOPPED.

        • policies number Required
        • unhealthy_policies object
          Hide unhealthy_policies attributes Show unhealthy_policies attributes object

          • count number Required
          • invocations_since_last_success object
    • shards_capacity object
      Hide shards_capacity attributes Show shards_capacity attributes object

      • status string Required

        Values are green, yellow, red, or unknown.

      • symptom string Required
      • impacts array[object]
        Hide impacts attributes Show impacts attributes object

        • description string Required
        • id string Required
        • impact_areas array[string] Required

          Values are search, ingest, backup, or deployment_management.

        • severity number Required
      • diagnosis array[object]
        Hide diagnosis attributes Show diagnosis attributes object

        • id string Required
        • action string Required
        • affected_resources object Required
        • cause string Required
        • help_url string Required
      • details object
        Hide details attributes Show details attributes object

        • data object Required
          Hide data attributes Show data attributes object

          • max_shards_in_cluster number Required
          • current_used_shards number
        • frozen object Required
          Hide frozen attributes Show frozen attributes object

          • max_shards_in_cluster number Required
          • current_used_shards number
    • file_settings object
      Hide file_settings attributes Show file_settings attributes object

      • status string Required

        Values are green, yellow, red, or unknown.

      • symptom string Required
      • impacts array[object]
        Hide impacts attributes Show impacts attributes object

        • description string Required
        • id string Required
        • impact_areas array[string] Required

          Values are search, ingest, backup, or deployment_management.

        • severity number Required
      • diagnosis array[object]
        Hide diagnosis attributes Show diagnosis attributes object

        • id string Required
        • action string Required
        • affected_resources object Required
        • cause string Required
        • help_url string Required
      • details object
        Hide details attributes Show details attributes object

        • failure_streak number Required
        • most_recent_failure string Required
  • status string

    Values are green, yellow, red, or unknown.