Enum Class CatAnomalyDetectorColumn
java.lang.Object
java.lang.Enum<CatAnomalyDetectorColumn>
co.elastic.clients.elasticsearch.cat.CatAnomalyDetectorColumn
- All Implemented Interfaces:
JsonEnum,JsonpSerializable,Serializable,Comparable<CatAnomalyDetectorColumn>,Constable
@JsonpDeserializable
public enum CatAnomalyDetectorColumn
extends Enum<CatAnomalyDetectorColumn>
implements JsonEnum
- See Also:
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Nested Class Summary
Nested classes/interfaces inherited from class java.lang.Enum
Enum.EnumDesc<E extends Enum<E>>Nested classes/interfaces inherited from interface co.elastic.clients.json.JsonEnum
JsonEnum.Deserializer<T extends JsonEnum> -
Enum Constant Summary
Enum ConstantsEnum ConstantDescriptionFor open anomaly detection jobs only, contains messages relating to the selection of a node to run the job.The number of bucket results produced by the job.Exponential moving average of all bucket processing times, in milliseconds.Exponentially-weighted moving average of bucket processing times calculated in a 1 hour time window, in milliseconds.Maximum among all bucket processing times, in milliseconds.Minimum among all bucket processing times, in milliseconds.Sum of all bucket processing times, in milliseconds.The number of buckets processed.The timestamp of the earliest chronologically input document.The number of buckets which did not contain any data.The number of bytes of input data posted to the anomaly detection job.The total number of fields in input documents posted to the anomaly detection job.The number of input documents posted to the anomaly detection job.The number of input documents with either a missing date field or a date that could not be parsed.The timestamp at which data was last analyzed, according to server time.The timestamp of the last bucket that did not contain any data.The timestamp of the last bucket that was considered sparse.The timestamp of the latest chronologically input document.The number of input documents that are missing a field that the anomaly detection job is configured to analyze.The number of input documents that have a timestamp chronologically preceding the start of the current anomaly detection bucket offset by the latency window.The total number of fields in all the documents that have been processed by the anomaly detection job.The number of input documents that have been processed by the anomaly detection job.The number of buckets that contained few data points compared to the expected number of data points.The average memory usage in bytes for forecasts related to the anomaly detection job.The maximum memory usage in bytes for forecasts related to the anomaly detection job.The minimum memory usage in bytes for forecasts related to the anomaly detection job.The total memory usage in bytes for forecasts related to the anomaly detection job.The average number ofmodel_forecast` documents written for forecasts related to the anomaly detection job.The maximum number ofmodel_forecastdocuments written for forecasts related to the anomaly detection job.The minimum number ofmodel_forecastdocuments written for forecasts related to the anomaly detection job.The total number ofmodel_forecastdocuments written for forecasts related to the anomaly detection job.The average runtime in milliseconds for forecasts related to the anomaly detection job.The maximum runtime in milliseconds for forecasts related to the anomaly detection job.The minimum runtime in milliseconds for forecasts related to the anomaly detection job.The total runtime in milliseconds for forecasts related to the anomaly detection job.The number of individual forecasts currently available for the job.Identifier for the anomaly detection job.The number of buckets for which new entities in incoming data were not processed due to insufficient model memory.The number of by field values that were analyzed by the models.The number of bytes of memory used by the models.The number of bytes over the high limit for memory usage at the last allocation failure.The status of categorization for the job:okorwarn.The number of documents that have had a field categorized.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.The number of times that categorization wanted to create a new category but couldn’t because the job had hit its model memory limit.The number of categories that match more than 1% of categorized documents.The timestamp when the model stats were gathered, according to server time.The timestamp when the model stats were gathered, according to server time.The status of the mathematical models:ok,soft_limit, orhard_limit.The number of over field values that were analyzed by the models.The number of partition field values that were analyzed by the models.The number of categories that match just one categorized document.The timestamp of the last record when the model stats were gathered.The number of categories created by categorization.The network address of the node that runs the job.The ephemeral ID of the node that runs the job.The unique identifier of the node that runs the job.The name of the node that runs the job.For open jobs only, the elapsed time for which the job has been open.The status of the anomaly detection job:closed,closing,failed,opened, oropening. -
Field Summary
Fields -
Method Summary
Modifier and TypeMethodDescriptionString[]aliases()static CatAnomalyDetectorColumnReturns the enum constant of this class with the specified name.static CatAnomalyDetectorColumn[]values()Returns an array containing the constants of this enum class, in the order they are declared.
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Enum Constant Details
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AssignmentExplanation
For open anomaly detection jobs only, contains messages relating to the selection of a node to run the job. -
BucketsCount
The number of bucket results produced by the job. -
BucketsTimeExpAvg
Exponential moving average of all bucket processing times, in milliseconds. -
BucketsTimeExpAvgHour
Exponentially-weighted moving average of bucket processing times calculated in a 1 hour time window, in milliseconds. -
BucketsTimeMax
Maximum among all bucket processing times, in milliseconds. -
BucketsTimeMin
Minimum among all bucket processing times, in milliseconds. -
BucketsTimeTotal
Sum of all bucket processing times, in milliseconds. -
DataBuckets
The number of buckets processed. -
DataEarliestRecord
The timestamp of the earliest chronologically input document. -
DataEmptyBuckets
The number of buckets which did not contain any data. -
DataInputBytes
The number of bytes of input data posted to the anomaly detection job. -
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. -
DataInputRecords
The number of input documents posted to the anomaly detection job. -
DataInvalidDates
The number of input documents with either a missing date field or a date that could not be parsed. -
DataLast
The timestamp at which data was last analyzed, according to server time. -
DataLastEmptyBucket
The timestamp of the last bucket that did not contain any data. -
DataLastSparseBucket
The timestamp of the last bucket that was considered sparse. -
DataLatestRecord
The timestamp of the latest chronologically input document. -
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. -
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. -
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. -
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. -
DataSparseBuckets
The number of buckets that contained few data points compared to the expected number of data points. -
ForecastsMemoryAvg
The average memory usage in bytes for forecasts related to the anomaly detection job. -
ForecastsMemoryMax
The maximum memory usage in bytes for forecasts related to the anomaly detection job. -
ForecastsMemoryMin
The minimum memory usage in bytes for forecasts related to the anomaly detection job. -
ForecastsMemoryTotal
The total memory usage in bytes for forecasts related to the anomaly detection job. -
ForecastsRecordsAvg
The average number ofmodel_forecast` documents written for forecasts related to the anomaly detection job. -
ForecastsRecordsMax
The maximum number ofmodel_forecastdocuments written for forecasts related to the anomaly detection job. -
ForecastsRecordsMin
The minimum number ofmodel_forecastdocuments written for forecasts related to the anomaly detection job. -
ForecastsRecordsTotal
The total number ofmodel_forecastdocuments written for forecasts related to the anomaly detection job. -
ForecastsTimeAvg
The average runtime in milliseconds for forecasts related to the anomaly detection job. -
ForecastsTimeMax
The maximum runtime in milliseconds for forecasts related to the anomaly detection job. -
ForecastsTimeMin
The minimum runtime in milliseconds for forecasts related to the anomaly detection job. -
ForecastsTimeTotal
The total runtime in milliseconds for forecasts related to the anomaly detection job. -
ForecastsTotal
The number of individual forecasts currently available for the job. -
Id
Identifier for the anomaly detection job. -
ModelBucketAllocationFailures
The number of buckets for which new entities in incoming data were not processed due to insufficient model memory. -
ModelByFields
The number of by field values that were analyzed by the models. This value is cumulative for all detectors in the job. -
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. -
ModelBytesExceeded
The number of bytes over the high limit for memory usage at the last allocation failure. -
ModelCategorizationStatus
The status of categorization for the job:okorwarn. Ifok, categorization is performing acceptably well (or not being used at all). Ifwarn, 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. -
ModelCategorizedDocCount
The number of documents that have had a field categorized. -
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. -
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. -
ModelFrequentCategoryCount
The number of categories that match more than 1% of categorized documents. -
ModelLogTime
The timestamp when the model stats were gathered, according to server time. -
ModelMemoryLimit
The timestamp when the model stats were gathered, according to server time. -
ModelMemoryStatus
The status of the mathematical models:ok,soft_limit, orhard_limit. Ifok, the models stayed below the configured value. Ifsoft_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. Ifhard_limit, the models used more space than the configured memory limit. As a result, not all incoming data was processed. -
ModelOverFields
The number of over field values that were analyzed by the models. This value is cumulative for all detectors in the job. -
ModelPartitionFields
The number of partition field values that were analyzed by the models. This value is cumulative for all detectors in the job. -
ModelRareCategoryCount
The number of categories that match just one categorized document. -
ModelTimestamp
The timestamp of the last record when the model stats were gathered. -
ModelTotalCategoryCount
The number of categories created by categorization. -
NodeAddress
The network address of the node that runs the job. This information is available only for open jobs. -
NodeEphemeralId
The ephemeral ID of the node that runs the job. This information is available only for open jobs. -
NodeId
The unique identifier of the node that runs the job. This information is available only for open jobs. -
NodeName
The name of the node that runs the job. This information is available only for open jobs. -
OpenedTime
For open jobs only, the elapsed time for which the job has been open. -
State
The status of the anomaly detection job:closed,closing,failed,opened, oropening. Ifclosed, the job finished successfully with its model state persisted. The job must be opened before it can accept further data. Ifclosing, the job close action is in progress and has not yet completed. A closing job cannot accept further data. Iffailed, 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. Ifopened, the job is available to receive and process data. Ifopening, the job open action is in progress and has not yet completed.
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Field Details
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_DESERIALIZER
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Method Details
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values
Returns an array containing the constants of this enum class, in the order they are declared.- Returns:
- an array containing the constants of this enum class, in the order they are declared
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valueOf
Returns the enum constant of this class with the specified name. The string must match exactly an identifier used to declare an enum constant in this class. (Extraneous whitespace characters are not permitted.)- Parameters:
name- the name of the enum constant to be returned.- Returns:
- the enum constant with the specified name
- Throws:
IllegalArgumentException- if this enum class has no constant with the specified nameNullPointerException- if the argument is null
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jsonValue
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aliases
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