Class JobsRecord
- All Implemented Interfaces:
JsonpSerializable
- See Also:
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Nested Class Summary
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Field Summary
Modifier and TypeFieldDescriptionstatic final JsonpDeserializer<JobsRecord>
Json deserializer forJobsRecord
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Method Summary
Modifier and TypeMethodDescriptionfinal String
For open anomaly detection jobs only, contains messages relating to the selection of a node to run the job.final String
The number of bucket results produced by the job.final String
The exponential moving average of all bucket processing times, in milliseconds.final String
The exponential moving average of bucket processing times calculated in a one hour time window, in milliseconds.final String
The maximum of all bucket processing times, in milliseconds.final String
The minimum of all bucket processing times, in milliseconds.final String
The sum of all bucket processing times, in milliseconds.final String
The total number of buckets processed.final String
The timestamp of the earliest chronologically input document.final String
The number of buckets which did not contain any data.final String
The number of bytes of input data posted to the anomaly detection job.final String
The total number of fields in input documents posted to the anomaly detection job.final String
The number of input documents posted to the anomaly detection job.final String
The number of input documents with either a missing date field or a date that could not be parsed.final String
dataLast()
The timestamp at which data was last analyzed, according to server time.final String
The timestamp of the last bucket that did not contain any data.final String
The timestamp of the last bucket that was considered sparse.final String
The timestamp of the latest chronologically input document.final String
The number of input documents that are missing a field that the anomaly detection job is configured to analyze.final 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.final String
The total number of fields in all the documents that have been processed by the anomaly detection job.final String
The number of input documents that have been processed by the anomaly detection job.final String
The number of buckets that contained few data points compared to the expected number of data points.final String
The average memory usage in bytes for forecasts related to the anomaly detection job.final String
The maximum memory usage in bytes for forecasts related to the anomaly detection job.final String
The minimum memory usage in bytes for forecasts related to the anomaly detection job.final String
The total memory usage in bytes for forecasts related to the anomaly detection job.final String
The average number ofmodel_forecast
documents written for forecasts related to the anomaly detection job.final String
The maximum number ofmodel_forecast
documents written for forecasts related to the anomaly detection job.final String
The minimum number ofmodel_forecast
documents written for forecasts related to the anomaly detection job.final String
The total number ofmodel_forecast
documents written for forecasts related to the anomaly detection job.final String
The average runtime in milliseconds for forecasts related to the anomaly detection job.final String
The maximum runtime in milliseconds for forecasts related to the anomaly detection job.final String
The minimum runtime in milliseconds for forecasts related to the anomaly detection job.final String
The total runtime in milliseconds for forecasts related to the anomaly detection job.final String
The number of individual forecasts currently available for the job.final String
id()
The anomaly detection job identifier.final String
The number of buckets for which new entities in incoming data were not processed due to insufficient model memory.final String
The number ofby
field values that were analyzed by the models.final String
The number of bytes of memory used by the models.final String
The number of bytes over the high limit for memory usage at the last allocation failure.final CategorizationStatus
The status of categorization for the job.final String
The number of documents that have had a field categorized.final 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.final String
The number of times that categorization wanted to create a new category but couldn’t because the job had hit itsmodel_memory_limit
.final String
The number of categories that match more than 1% of categorized documents.final String
The timestamp when the model stats were gathered, according to server time.final String
The upper limit for model memory usage, checked on increasing values.final MemoryStatus
The status of the mathematical models.final String
The number ofover
field values that were analyzed by the models.final String
The number ofpartition
field values that were analyzed by the models.final String
The number of categories that match just one categorized document.final String
The timestamp of the last record when the model stats were gathered.final String
The number of categories created by categorization.final String
The network address of the assigned node.final String
The ephemeral identifier of the assigned node.final String
nodeId()
The uniqe identifier of the assigned node.final String
nodeName()
The name of the assigned node.static JobsRecord
final String
For open jobs only, the amount of time the job has been opened.void
serialize
(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper) Serialize this object to JSON.protected void
serializeInternal
(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper) protected static void
final JobState
state()
The status of the anomaly detection job.toString()
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Field Details
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_DESERIALIZER
Json deserializer forJobsRecord
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Method Details
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of
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id
The anomaly detection job identifier.API name:
id
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state
The status of the anomaly detection job.API name:
state
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openedTime
For open jobs only, the amount of time the job has been opened.API name:
opened_time
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assignmentExplanation
For open anomaly detection jobs only, contains messages relating to the selection of a node to run the job.API name:
assignment_explanation
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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, theprocessed_record_count
is the number of aggregation results processed, not the number of Elasticsearch documents.API name:
data.processed_records
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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.API name:
data.processed_fields
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dataInputBytes
The number of bytes of input data posted to the anomaly detection job.API name:
data.input_bytes
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dataInputRecords
The number of input documents posted to the anomaly detection job.API name:
data.input_records
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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.API name:
data.input_fields
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dataInvalidDates
The number of input documents with either a missing date field or a date that could not be parsed.API name:
data.invalid_dates
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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. If you are using datafeeds or posting data to the job in JSON format, a highmissing_field_count
is often not an indication of data issues. It is not necessarily a cause for concern.API name:
data.missing_fields
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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.API name:
data.out_of_order_timestamps
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dataEmptyBuckets
The number of buckets which did not contain any data. If your data contains many empty buckets, consider increasing yourbucket_span
or using functions that are tolerant to gaps in data such as mean,non_null_sum
ornon_zero_count
.API name:
data.empty_buckets
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dataSparseBuckets
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 longerbucket_span
.API name:
data.sparse_buckets
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dataBuckets
The total number of buckets processed.API name:
data.buckets
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dataEarliestRecord
The timestamp of the earliest chronologically input document.API name:
data.earliest_record
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dataLatestRecord
The timestamp of the latest chronologically input document.API name:
data.latest_record
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dataLast
The timestamp at which data was last analyzed, according to server time.API name:
data.last
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dataLastEmptyBucket
The timestamp of the last bucket that did not contain any data.API name:
data.last_empty_bucket
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dataLastSparseBucket
The timestamp of the last bucket that was considered sparse.API name:
data.last_sparse_bucket
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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.API name:
model.bytes
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modelMemoryStatus
The status of the mathematical models.API name:
model.memory_status
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modelBytesExceeded
The number of bytes over the high limit for memory usage at the last allocation failure.API name:
model.bytes_exceeded
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modelMemoryLimit
The upper limit for model memory usage, checked on increasing values.API name:
model.memory_limit
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modelByFields
The number ofby
field values that were analyzed by the models. This value is cumulative for all detectors in the job.API name:
model.by_fields
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modelOverFields
The number ofover
field values that were analyzed by the models. This value is cumulative for all detectors in the job.API name:
model.over_fields
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modelPartitionFields
The number ofpartition
field values that were analyzed by the models. This value is cumulative for all detectors in the job.API name:
model.partition_fields
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modelBucketAllocationFailures
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 ahard_limit: memory_status
property value.API name:
model.bucket_allocation_failures
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modelCategorizationStatus
The status of categorization for the job.API name:
model.categorization_status
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modelCategorizedDocCount
The number of documents that have had a field categorized.API name:
model.categorized_doc_count
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modelTotalCategoryCount
The number of categories created by categorization.API name:
model.total_category_count
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modelFrequentCategoryCount
The number of categories that match more than 1% of categorized documents.API name:
model.frequent_category_count
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modelRareCategoryCount
The number of categories that match just one categorized document.API name:
model.rare_category_count
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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.API name:
model.dead_category_count
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modelFailedCategoryCount
The number of times that categorization wanted to create a new category but couldn’t because the job had hit itsmodel_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.API name:
model.failed_category_count
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modelLogTime
The timestamp when the model stats were gathered, according to server time.API name:
model.log_time
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modelTimestamp
The timestamp of the last record when the model stats were gathered.API name:
model.timestamp
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forecastsTotal
The number of individual forecasts currently available for the job. A value of one or more indicates that forecasts exist.API name:
forecasts.total
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forecastsMemoryMin
The minimum memory usage in bytes for forecasts related to the anomaly detection job.API name:
forecasts.memory.min
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forecastsMemoryMax
The maximum memory usage in bytes for forecasts related to the anomaly detection job.API name:
forecasts.memory.max
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forecastsMemoryAvg
The average memory usage in bytes for forecasts related to the anomaly detection job.API name:
forecasts.memory.avg
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forecastsMemoryTotal
The total memory usage in bytes for forecasts related to the anomaly detection job.API name:
forecasts.memory.total
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forecastsRecordsMin
The minimum number ofmodel_forecast
documents written for forecasts related to the anomaly detection job.API name:
forecasts.records.min
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forecastsRecordsMax
The maximum number ofmodel_forecast
documents written for forecasts related to the anomaly detection job.API name:
forecasts.records.max
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forecastsRecordsAvg
The average number ofmodel_forecast
documents written for forecasts related to the anomaly detection job.API name:
forecasts.records.avg
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forecastsRecordsTotal
The total number ofmodel_forecast
documents written for forecasts related to the anomaly detection job.API name:
forecasts.records.total
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forecastsTimeMin
The minimum runtime in milliseconds for forecasts related to the anomaly detection job.API name:
forecasts.time.min
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forecastsTimeMax
The maximum runtime in milliseconds for forecasts related to the anomaly detection job.API name:
forecasts.time.max
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forecastsTimeAvg
The average runtime in milliseconds for forecasts related to the anomaly detection job.API name:
forecasts.time.avg
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forecastsTimeTotal
The total runtime in milliseconds for forecasts related to the anomaly detection job.API name:
forecasts.time.total
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nodeId
The uniqe identifier of the assigned node.API name:
node.id
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nodeName
The name of the assigned node.API name:
node.name
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nodeEphemeralId
The ephemeral identifier of the assigned node.API name:
node.ephemeral_id
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nodeAddress
The network address of the assigned node.API name:
node.address
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bucketsCount
The number of bucket results produced by the job.API name:
buckets.count
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bucketsTimeTotal
The sum of all bucket processing times, in milliseconds.API name:
buckets.time.total
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bucketsTimeMin
The minimum of all bucket processing times, in milliseconds.API name:
buckets.time.min
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bucketsTimeMax
The maximum of all bucket processing times, in milliseconds.API name:
buckets.time.max
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bucketsTimeExpAvg
The exponential moving average of all bucket processing times, in milliseconds.API name:
buckets.time.exp_avg
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bucketsTimeExpAvgHour
The exponential moving average of bucket processing times calculated in a one hour time window, in milliseconds.API name:
buckets.time.exp_avg_hour
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serialize
Serialize this object to JSON.- Specified by:
serialize
in interfaceJsonpSerializable
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serializeInternal
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toString
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setupJobsRecordDeserializer
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