elasticsearch-js/docs/reference/api/CatMlJobsJobsRecord.md

CatMlJobsJobsRecord [interface-CatMlJobsJobsRecord]

Name	Type	Description
"buckets.count"	string	The number of bucket results produced by the job.
"buckets.time.exp_avg_hour"	string	The exponential moving average of bucket processing times calculated in a one hour time window, in milliseconds.
"buckets.time.exp_avg"	string	The exponential moving average of all bucket processing times, in milliseconds.
"buckets.time.max"	string	The maximum of all bucket processing times, in milliseconds.
"buckets.time.min"	string	The minimum of all bucket processing times, in milliseconds.
"buckets.time.total"	string	The sum of all bucket processing times, in milliseconds.
"data.buckets"	string	The total number of buckets processed.
"data.earliest_record"	string	The timestamp of the earliest chronologically input document.
"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.input_bytes"	ByteSize	The number of bytes of input data 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.input_records"	string	The number of input documents posted to the anomaly detection 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.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.
"data.last"	string	The timestamp at which data was last analyzed, according to server time.
"data.latest_record"	string	The timestamp of the latest chronologically input document.
"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.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.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.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.
"forecasts.memory.avg"	string	The average 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.min"	string	The minimum 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.avg"	string	The average 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.min"	string	The minimum 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.avg"	string	The average 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.min"	string	The minimum 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.
"forecasts.total"	string	The number of individual forecasts currently available for the job. A value of one or more indicates that forecasts exist.
"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.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.bytes_exceeded"	ByteSize	The number of bytes over the high limit for memory usage at the last allocation failure.
"model.bytes"	ByteSize	The number of bytes of memory used by the models. This is the maximum value since the last time the model was persisted. If the job is closed, this value indicates the latest size.
"model.categorization_status"	MlCategorizationStatus	The status of categorization for the job.
"model.categorized_doc_count"	string	The number of documents that have had a field categorized.
"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.frequent_category_count"	string	The number of categories that match more than 1% of categorized documents.
"model.log_time"	string	The timestamp when the model stats were gathered, according to server time.
"model.memory_limit"	string	The upper limit for model memory usage, checked on increasing values.
"model.memory_status"	MlMemoryStatus	The status of the mathematical models.
"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.rare_category_count"	string	The number of categories that match just one categorized document.
"model.timestamp"	string	The timestamp of the last record when the model stats were gathered.
"model.total_category_count"	string	The number of categories created by categorization.
"node.address"	string	The network address of the assigned node.
"node.ephemeral_id"	NodeId	The ephemeral identifier of the assigned node.
"node.id"	NodeId	The uniqe identifier of the assigned node.
"node.name"	string	The name of the assigned node.
ae	string	For open anomaly detection jobs only, contains messages relating to the selection of a node to run the job. assignment_explanation
assignment_explanation	string	For open anomaly detection jobs only, contains messages relating to the selection of a node to run the job.
bc	string	The number of bucket results produced by the job. 'buckets.count'
btea	string	The exponential moving average of all bucket processing times, in milliseconds. 'buckets.time.exp_avg'
bteah	string	The exponential moving average of bucket processing times calculated in a one hour time window, in milliseconds. 'buckets.time.exp_avg_hour'
btmax	string	The maximum of all bucket processing times, in milliseconds. 'buckets.time.max'
btmin	string	The minimum of all bucket processing times, in milliseconds. 'buckets.time.min'
btt	string	The sum of all bucket processing times, in milliseconds. 'buckets.time.total'
bucketsCount	string	The number of bucket results produced by the job. 'buckets.count'
bucketsTimeExpAvg	string	The exponential moving average of all bucket processing times, in milliseconds. 'buckets.time.exp_avg'
bucketsTimeExpAvgHour	string	The exponential moving average of bucket processing times calculated in a one hour time window, in milliseconds. 'buckets.time.exp_avg_hour'
bucketsTimeMax	string	The maximum of all bucket processing times, in milliseconds. 'buckets.time.max'
bucketsTimeMin	string	The minimum of all bucket processing times, in milliseconds. 'buckets.time.min'
bucketsTimeTotal	string	The sum of all bucket processing times, in milliseconds. 'buckets.time.total'
dataBuckets	string	The total number of buckets processed. 'data.buckets'
dataEarliestRecord	string	The timestamp of the earliest chronologically input document. 'data.earliest_record'
dataEmptyBuckets	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.empty_buckets'
dataInputBytes	ByteSize	The number of bytes of input data posted to the anomaly detection job. 'data.input_bytes'
dataInputFields	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.input_fields'
dataInputRecords	string	The number of input documents posted to the anomaly detection job. 'data.input_records'
dataInvalidDates	string	The number of input documents with either a missing date field or a date that could not be parsed. 'data.invalid_dates'
dataLast	string	The timestamp at which data was last analyzed, according to server time. 'data.last'
dataLastEmptyBucket	string	The timestamp of the last bucket that did not contain any data. 'data.last_empty_bucket'
dataLastSparseBucket	string	The timestamp of the last bucket that was considered sparse. 'data.last_sparse_bucket'
dataLatestRecord	string	The timestamp of the latest chronologically input document. 'data.latest_record'
dataMissingFields	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.missing_fields'
dataOutOfOrderTimestamps	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.out_of_order_timestamps'
dataProcessedFields	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.processed_fields'
dataProcessedRecords	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_records'
dataSparseBuckets	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.sparse_buckets'
db	string	The total number of buckets processed. 'data.buckets'
deb	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.empty_buckets'
der	string	The timestamp of the earliest chronologically input document. 'data.earliest_record'
dib	ByteSize	The number of bytes of input data posted to the anomaly detection job. 'data.input_bytes'
did	string	The number of input documents with either a missing date field or a date that could not be parsed. 'data.invalid_dates'
dif	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.input_fields'
dir	string	The number of input documents posted to the anomaly detection job. 'data.input_records'
dl	string	The timestamp at which data was last analyzed, according to server time. 'data.last'
dleb	string	The timestamp of the last bucket that did not contain any data. 'data.last_empty_bucket'
dlr	string	The timestamp of the latest chronologically input document. 'data.latest_record'
dlsb	string	The timestamp of the last bucket that was considered sparse. 'data.last_sparse_bucket'
dmf	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.missing_fields'
doot	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.out_of_order_timestamps'
dpf	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.processed_fields'
dpr	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_records'
dsb	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.sparse_buckets'
fmavg	string	The average memory usage in bytes for forecasts related to the anomaly detection job. 'forecasts.memory.avg'
fmmax	string	The maximum memory usage in bytes for forecasts related to the anomaly detection job. 'forecasts.memory.max'
fmmin	string	The minimum memory usage in bytes for forecasts related to the anomaly detection job. 'forecasts.memory.min'
fmt	string	The total memory usage in bytes for forecasts related to the anomaly detection job. 'forecasts.memory.total'
forecastsMemoryAvg	string	The average memory usage in bytes for forecasts related to the anomaly detection job. 'forecasts.memory.avg'
forecastsMemoryMax	string	The maximum memory usage in bytes for forecasts related to the anomaly detection job. 'forecasts.memory.max'
forecastsMemoryMin	string	The minimum memory usage in bytes for forecasts related to the anomaly detection job. 'forecasts.memory.min'
forecastsMemoryTotal	string	The total memory usage in bytes for forecasts related to the anomaly detection job. 'forecasts.memory.total'
forecastsRecordsAvg	string	The average number of model_forecast documents written for forecasts related to the anomaly detection job. 'forecasts.records.avg'
forecastsRecordsMax	string	The maximum number of model_forecast documents written for forecasts related to the anomaly detection job. 'forecasts.records.max'
forecastsRecordsMin	string	The minimum number of model_forecast documents written for forecasts related to the anomaly detection job. 'forecasts.records.min'
forecastsRecordsTotal	string	The total number of model_forecast documents written for forecasts related to the anomaly detection job. 'forecasts.records.total'
forecastsTimeAvg	string	The average runtime in milliseconds for forecasts related to the anomaly detection job. 'forecasts.time.avg'
forecastsTimeMax	string	The maximum runtime in milliseconds for forecasts related to the anomaly detection job. 'forecasts.time.max'
forecastsTimeMin	string	The minimum runtime in milliseconds for forecasts related to the anomaly detection job. 'forecasts.time.min'
forecastsTimeTotal	string	The total runtime in milliseconds for forecasts related to the anomaly detection job. 'forecasts.time.total'
forecastsTotal	string	The number of individual forecasts currently available for the job. A value of one or more indicates that forecasts exist. 'forecasts.total'
fravg	string	The average number of model_forecast documents written for forecasts related to the anomaly detection job. 'forecasts.records.avg'
frmax	string	The maximum number of model_forecast documents written for forecasts related to the anomaly detection job. 'forecasts.records.max'
frmin	string	The minimum number of model_forecast documents written for forecasts related to the anomaly detection job. 'forecasts.records.min'
frt	string	The total number of model_forecast documents written for forecasts related to the anomaly detection job. 'forecasts.records.total'
ft	string	The number of individual forecasts currently available for the job. A value of one or more indicates that forecasts exist. 'forecasts.total'
ftavg	string	The average runtime in milliseconds for forecasts related to the anomaly detection job. 'forecasts.time.avg'
ftmax	string	The maximum runtime in milliseconds for forecasts related to the anomaly detection job. 'forecasts.time.max'
ftmin	string	The minimum runtime in milliseconds for forecasts related to the anomaly detection job. 'forecasts.time.min'
ftt	string	The total runtime in milliseconds for forecasts related to the anomaly detection job. 'forecasts.time.total'
id	Id	The anomaly detection job identifier.
mb	ByteSize	The number of bytes of memory used by the models. This is the maximum value since the last time the model was persisted. If the job is closed, this value indicates the latest size. 'model.bytes'
mbaf	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.bucket_allocation_failures'
mbe	ByteSize	The number of bytes over the high limit for memory usage at the last allocation failure. 'model.bytes_exceeded'
mbf	string	The number of by field values that were analyzed by the models. This value is cumulative for all detectors in the job. 'model.by_fields'
mcdc	string	The number of documents that have had a field categorized. 'model.categorized_doc_count'
mcs	MlCategorizationStatus	The status of categorization for the job. 'model.categorization_status'
mdcc	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.dead_category_count'
mfcc	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.failed_category_count'
mlt	string	The timestamp when the model stats were gathered, according to server time. 'model.log_time'
mml	string	The upper limit for model memory usage, checked on increasing values. 'model.memory_limit'
mms	MlMemoryStatus	The status of the mathematical models. 'model.memory_status'
modelBucketAllocationFailures	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.bucket_allocation_failures'
modelByFields	string	The number of by field values that were analyzed by the models. This value is cumulative for all detectors in the job. 'model.by_fields'
modelBytes	ByteSize	The number of bytes of memory used by the models. This is the maximum value since the last time the model was persisted. If the job is closed, this value indicates the latest size. 'model.bytes'
modelBytesExceeded	ByteSize	The number of bytes over the high limit for memory usage at the last allocation failure. 'model.bytes_exceeded'
modelCategorizationStatus	MlCategorizationStatus	The status of categorization for the job. 'model.categorization_status'
modelCategorizedDocCount	string	The number of documents that have had a field categorized. 'model.categorized_doc_count'
modelDeadCategoryCount	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.dead_category_count'
modelFailedCategoryCount	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.failed_category_count'
modelFrequentCategoryCount	string	The number of categories that match more than 1% of categorized documents. 'model.frequent_category_count'
modelLogTime	string	The timestamp when the model stats were gathered, according to server time. 'model.log_time'
modelMemoryLimit	string	The upper limit for model memory usage, checked on increasing values. 'model.memory_limit'
modelMemoryStatus	MlMemoryStatus	The status of the mathematical models. 'model.memory_status'
modelOverFields	string	The number of over field values that were analyzed by the models. This value is cumulative for all detectors in the job. 'model.over_fields'
modelPartitionFields	string	The number of partition field values that were analyzed by the models. This value is cumulative for all detectors in the job. 'model.partition_fields'
modelRareCategoryCount	string	The number of categories that match just one categorized document. 'model.rare_category_count'
modelTimestamp	string	The timestamp of the last record when the model stats were gathered. 'model.timestamp'
modelTotalCategoryCount	string	The number of categories created by categorization. 'model.total_category_count'
mof	string	The number of over field values that were analyzed by the models. This value is cumulative for all detectors in the job. 'model.over_fields'
mpf	string	The number of partition field values that were analyzed by the models. This value is cumulative for all detectors in the job. 'model.partition_fields'
mrcc	string	The number of categories that match just one categorized document. 'model.rare_category_count'
mt	string	The timestamp of the last record when the model stats were gathered. 'model.timestamp'
mtcc	string	The number of categories created by categorization. 'model.total_category_count'
na	string	The network address of the assigned node. 'node.address'
ne	NodeId	The ephemeral identifier of the assigned node. 'node.ephemeral_id'
ni	NodeId	The uniqe identifier of the assigned node. 'node.id'
nn	string	The name of the assigned node. 'node.name'
nodeAddress	string	The network address of the assigned node. 'node.address'
nodeEphemeralId	NodeId	The ephemeral identifier of the assigned node. 'node.ephemeral_id'
nodeId	NodeId	The uniqe identifier of the assigned node. 'node.id'
nodeName	string	The name of the assigned node. 'node.name'
opened_time	string	For open jobs only, the amount of time the job has been opened.
ot	string	For open jobs only, the amount of time the job has been opened. opened_time
s	MlJobState	The status of the anomaly detection job. state
state	MlJobState	The status of the anomaly detection job.