Path parameters

• collection_name string Required

  The name of the behavioral analytics collection.

• event_type string Required

  The analytics event type.

  Values are page_view, search, or search_click.

Query parameters

• debug boolean

  Whether the response type has to include more details

Responses

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

  • accepted boolean Required
  • event object

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.

• 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

  Period to wait for a connection to the master node.

Responses

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

  • shards string

    Number of primary and replica shards assigned to the node.

  • shards.undesired string | null

    Amount of shards that are scheduled to be moved elsewhere in the cluster or -1 other than desired balance allocator is used

    One of:

    string-1 string string-2 string | null

  • write_load.forecast number | string | null

    Sum of index write load forecasts

    One of:

    Stringifieddouble number Stringifieddouble string string-2 string | null

  • disk.indices.forecast number | string | null

    Sum of shard size forecasts

    One of:

    ByteSize number ByteSize string string-2 string | null

  • disk.indices number | string | null

    Disk space used by the node’s shards. Does not include disk space for the translog or unassigned shards. IMPORTANT: This metric double-counts disk space for hard-linked files, such as those created when shrinking, splitting, or cloning an index.

    One of:

    ByteSize number ByteSize string string-2 string | null

  • disk.used number | string | null

    Total disk space in use. Elasticsearch retrieves this metric from the node’s operating system (OS). The metric includes disk space for: Elasticsearch, including the translog and unassigned shards; the node’s operating system; any other applications or files on the node. Unlike disk.indices, this metric does not double-count disk space for hard-linked files.

    One of:

    ByteSize number ByteSize string string-2 string | null

  • disk.avail number | string | null

    Free disk space available to Elasticsearch. Elasticsearch retrieves this metric from the node’s operating system. Disk-based shard allocation uses this metric to assign shards to nodes based on available disk space.

    One of:

    ByteSize number ByteSize string string-2 string | null

  • disk.total number | string | null

    Total disk space for the node, including in-use and available space.

    One of:

    ByteSize number ByteSize string string-2 string | null

  • disk.percent string | number | null

    Total percentage of disk space in use. Calculated as disk.used / disk.total.

    One of:

    Percentage string Percentage number string-2 string | null

  • host string | null

    Network host for the node. Set using the network.host setting.

    One of:

    Host string string-2 string | null

  • ip string | null

    IP address and port for the node.

    One of:

    Ip string string-2 string | null
  • node string

    Name for the node. Set using the node.name setting.

  • node.role string | null

    Node roles

    One of:

    string-1 string string-2 string | null

Responses

• 200 application/json

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

• allow_no_match boolean

  Specifies what to do when the request: contains wildcard expressions and there are no models 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 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]

  A comma-separated list of column names to display.

  Supported values include:

  • create_time (or ct): The time when the trained model was created.
  • created_by (or c, createdBy): Information on the creator of the trained model.
  • data_frame_analytics_id (or df, dataFrameAnalytics, dfid): Identifier for the data frame analytics job that created the model. Only displayed if it is still available.
  • description (or d): The description of the trained model.
  • heap_size (or hs, modelHeapSize): The estimated heap size to keep the trained model in memory.
  • id: Identifier for the trained model.
  • ingest.count (or ic, ingestCount): The total number of documents that are processed by the model.
  • ingest.current (or icurr, ingestCurrent): The total number of document that are currently being handled by the trained model.
  • ingest.failed (or if, ingestFailed): The total number of failed ingest attempts with the trained model.
  • ingest.pipelines (or ip, ingestPipelines): The total number of ingest pipelines that are referencing the trained model.
  • ingest.time (or it, ingestTime): The total time that is spent processing documents with the trained model.
  • license (or l): The license level of the trained model.
  • operations (or o, modelOperations): The estimated number of operations to use the trained model. This number helps measuring the computational complexity of the model.
  • version (or v): The Elasticsearch version number in which the trained model was created.
• s string | array[string]

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

  Supported values include:

  • create_time (or ct): The time when the trained model was created.
  • created_by (or c, createdBy): Information on the creator of the trained model.
  • data_frame_analytics_id (or df, dataFrameAnalytics, dfid): Identifier for the data frame analytics job that created the model. Only displayed if it is still available.
  • description (or d): The description of the trained model.
  • heap_size (or hs, modelHeapSize): The estimated heap size to keep the trained model in memory.
  • id: Identifier for the trained model.
  • ingest.count (or ic, ingestCount): The total number of documents that are processed by the model.
  • ingest.current (or icurr, ingestCurrent): The total number of document that are currently being handled by the trained model.
  • ingest.failed (or if, ingestFailed): The total number of failed ingest attempts with the trained model.
  • ingest.pipelines (or ip, ingestPipelines): The total number of ingest pipelines that are referencing the trained model.
  • ingest.time (or it, ingestTime): The total time that is spent processing documents with the trained model.
  • license (or l): The license level of the trained model.
  • operations (or o, modelOperations): The estimated number of operations to use the trained model. This number helps measuring the computational complexity of the model.
  • version (or v): The Elasticsearch version number in which the trained model was created.
• from number

  Skips the specified number of transforms.

• size number

  The maximum number of transforms to display.

• 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
  • created_by string

    Information about the creator of the model.

  • heap_size number | string

    One of:

    ByteSize number ByteSize string
  • operations string

    The estimated number of operations to use the model. This number helps to measure the computational complexity of the model.

  • license string

    The license level of the model.

  • create_time string | number

    A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.

    One of:

    DateTime string UnitMillis number

    Time unit for milliseconds

  • version string
  • description string

    A description of the model.

  • ingest.pipelines string

    The number of pipelines that are referencing the model.

  • ingest.count string

    The total number of documents that are processed by the model.

  • ingest.time string

    The total time spent processing documents with thie model.

  • ingest.current string

    The total number of documents that are currently being handled by the model.

  • ingest.failed string

    The total number of failed ingest attempts with the model.

  • data_frame.id string

    The identifier for the data frame analytics job that created the model. Only displayed if the job is still available.

  • data_frame.create_time string

    The time the data frame analytics job was created.

  • data_frame.source_index string

    The source index used to train in the data frame analysis.

  • data_frame.analysis string

    The analysis used by the data frame to build the model.

  • type string

Path parameters

• model_id string Required

  A unique identifier for the trained model.

Query parameters

• allow_no_match boolean

  Specifies what to do when the request: contains wildcard expressions and there are no models 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 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]

  A comma-separated list of column names to display.

  Supported values include:

  • create_time (or ct): The time when the trained model was created.
  • created_by (or c, createdBy): Information on the creator of the trained model.
  • data_frame_analytics_id (or df, dataFrameAnalytics, dfid): Identifier for the data frame analytics job that created the model. Only displayed if it is still available.
  • description (or d): The description of the trained model.
  • heap_size (or hs, modelHeapSize): The estimated heap size to keep the trained model in memory.
  • id: Identifier for the trained model.
  • ingest.count (or ic, ingestCount): The total number of documents that are processed by the model.
  • ingest.current (or icurr, ingestCurrent): The total number of document that are currently being handled by the trained model.
  • ingest.failed (or if, ingestFailed): The total number of failed ingest attempts with the trained model.
  • ingest.pipelines (or ip, ingestPipelines): The total number of ingest pipelines that are referencing the trained model.
  • ingest.time (or it, ingestTime): The total time that is spent processing documents with the trained model.
  • license (or l): The license level of the trained model.
  • operations (or o, modelOperations): The estimated number of operations to use the trained model. This number helps measuring the computational complexity of the model.
  • version (or v): The Elasticsearch version number in which the trained model was created.
• s string | array[string]

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

  Supported values include:

  • create_time (or ct): The time when the trained model was created.
  • created_by (or c, createdBy): Information on the creator of the trained model.
  • data_frame_analytics_id (or df, dataFrameAnalytics, dfid): Identifier for the data frame analytics job that created the model. Only displayed if it is still available.
  • description (or d): The description of the trained model.
  • heap_size (or hs, modelHeapSize): The estimated heap size to keep the trained model in memory.
  • id: Identifier for the trained model.
  • ingest.count (or ic, ingestCount): The total number of documents that are processed by the model.
  • ingest.current (or icurr, ingestCurrent): The total number of document that are currently being handled by the trained model.
  • ingest.failed (or if, ingestFailed): The total number of failed ingest attempts with the trained model.
  • ingest.pipelines (or ip, ingestPipelines): The total number of ingest pipelines that are referencing the trained model.
  • ingest.time (or it, ingestTime): The total time that is spent processing documents with the trained model.
  • license (or l): The license level of the trained model.
  • operations (or o, modelOperations): The estimated number of operations to use the trained model. This number helps measuring the computational complexity of the model.
  • version (or v): The Elasticsearch version number in which the trained model was created.
• from number

  Skips the specified number of transforms.

• size number

  The maximum number of transforms to display.

• 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
  • created_by string

    Information about the creator of the model.

  • heap_size number | string

    One of:

    ByteSize number ByteSize string
  • operations string

    The estimated number of operations to use the model. This number helps to measure the computational complexity of the model.

  • license string

    The license level of the model.

  • create_time string | number

    A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.

    One of:

    DateTime string UnitMillis number

    Time unit for milliseconds

  • version string
  • description string

    A description of the model.

  • ingest.pipelines string

    The number of pipelines that are referencing the model.

  • ingest.count string

    The total number of documents that are processed by the model.

  • ingest.time string

    The total time spent processing documents with thie model.

  • ingest.current string

    The total number of documents that are currently being handled by the model.

  • ingest.failed string

    The total number of failed ingest attempts with the model.

  • data_frame.id string

    The identifier for the data frame analytics job that created the model. Only displayed if the job is still available.

  • data_frame.create_time string

    The time the data frame analytics job was created.

  • data_frame.source_index string

    The source index used to train in the data frame analysis.

  • data_frame.analysis string

    The analysis used by the data frame to build the model.

  • type string

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.

• time string

  The unit used to display time values.

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

• 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

  Period to wait for a connection to the master node.

Responses

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

  • node_name string

    The node name.

  • node_id string
  • ephemeral_node_id string

    The ephemeral node identifier.

  • pid string

    The process identifier.

  • host string

    The host name for the current node.

  • ip string

    The IP address for the current node.

  • port string

    The bound transport port for the current node.

  • name string

    The thread pool name.

  • type string

    The thread pool type. Returned values include fixed, fixed_auto_queue_size, direct, and scaling.

  • active string

    The number of active threads in the current thread pool.

  • pool_size string

    The number of threads in the current thread pool.

  • queue string

    The number of tasks currently in queue.

  • queue_size string

    The maximum number of tasks permitted in the queue.

  • rejected string

    The number of rejected tasks.

  • largest string

    The highest number of active threads in the current thread pool.

  • completed string

    The number of completed tasks.

  • core string | null

    The core number of active threads allowed in a scaling thread pool.

    One of:

    string-1 string string-2 string | null

  • max string | null

    The maximum number of active threads allowed in a scaling thread pool.

    One of:

    string-1 string string-2 string | null

  • size string | null

    The number of active threads allowed in a fixed thread pool.

    One of:

    string-1 string string-2 string | null

  • keep_alive string | null

    The thread keep alive time.

    One of:

    string-1 string string-2 string | null

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.

• 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.

• 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.