232 lines
10 KiB
Plaintext
232 lines
10 KiB
Plaintext
[[ml-job-resource]]
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==== Job Resources
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A job resource has the following properties:
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`analysis_config`::
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(+object+) The analysis configuration, which specifies how to analyze the data. See <<ml-analysisconfig, analysis configuration objects>>.
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`analysis_limits`::
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(+object+) Defines limits on the number of field values and time buckets to be analyzed.
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See <<ml-apilimits,analysis limits>>.
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`create_time`::
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(+string+) The time the job was created, in ISO 8601 format. For example, `1491007356077`.
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`data_description`::
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(+object+) Describes the data format and how APIs parse timestamp fields. See <<ml-datadescription,data description objects>>.
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`description`::
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(+string+) An optional description of the job.
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`finished_time`::
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(+string+) If the job closed of failed, this is the time the job finished, in ISO 8601 format.
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Otherwise, it is `null`. For example, `1491007365347`.
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`job_id`::
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(+string+) A numerical character string that uniquely identifies the job.
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`model_plot_config`:: TBD
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`enabled`:: TBD. For example, `true`.
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`model_snapshot_id`::
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TBD. For example, `1491007364`.
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`model_snapshot_retention_days`::
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(+long+) The time in days that model snapshots are retained for the job. Older snapshots are deleted.
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The default value is 1 day.
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`results_index_name`::
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TBD. For example, `shared`.
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[[ml-analysisconfig]]
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===== Analysis Configuration Objects
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An analysis configuration object has the following properties:
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`bucket_span` (required)::
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(+unsigned integer+) The size of the interval that the analysis is aggregated into, measured in seconds.
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The default value is 300 seconds (5 minutes).
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`categorization_field_name`::
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(+string+) If not null, the values of the specified field will be categorized.
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The resulting categories can be used in a detector by setting `by_field_name`,
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`over_field_name`, or `partition_field_name` to the keyword `prelertcategory`.
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`categorization_filters`::
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(+array of strings+) If `categorization_field_name` is specified, you can also define optional filters.
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This property expects an array of regular expressions.
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The expressions are used to filter out matching sequences off the categorization field values.
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This functionality is useful to fine tune categorization by excluding sequences
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that should not be taken into consideration for defining categories.
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For example, you can exclude SQL statements that appear in your log files.
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`detectors` (required)::
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(+array+) An array of detector configuration objects,
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which describe the anomaly detectors that are used in the job.
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See <<ml-detectorconfig,detector configuration objects>>.
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NOTE: If the `detectors` array does not contain at least one detector, no analysis can occur
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and an error is returned.
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`influencers`::
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(+array of strings+) A comma separated list of influencer field names.
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Typically these can be the by, over, or partition fields that are used in the detector configuration.
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You might also want to use a field name that is not specifically named in a detector,
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but is available as part of the input data. When you use multiple detectors,
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the use of influencers is recommended as it aggregates results for each influencer entity.
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`latency`::
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(+unsigned integer+) The size of the window, in seconds, in which to expect data that is out of time order.
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The default value is 0 seconds (no latency).
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NOTE: Latency is only applicable when you send data by using the <<ml-post-data, Post Data to Jobs>> API.
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`multivariate_by_fields`::
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(+boolean+) If set to `true`, the analysis will automatically find correlations
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between metrics for a given `by` field value and report anomalies when those
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correlations cease to hold. For example, suppose CPU and memory usage on host A
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is usually highly correlated with the same metrics on host B. Perhaps this
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correlation occurs because they are running a load-balanced application.
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If you enable this property, then anomalies will be reported when, for example,
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CPU usage on host A is high and the value of CPU usage on host B is low.
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That is to say, you'll see an anomaly when the CPU of host A is unusual given the CPU of host B.
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NOTE: To use the `multivariate_by_fields` property, you must also specify `by_field_name` in your detector.
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`overlapping_buckets`::
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(+boolean+) If set to `true`, an additional analysis occurs that runs out of phase by half a bucket length.
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This requires more system resources and enhances detection of anomalies that span bucket boundaries.
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`summary_count_field_name`::
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(+string+) If not null, the data fed to the job is expected to be pre-summarized.
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This property value is the name of the field that contains the count of raw data points that have been summarized.
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The same `summary_count_field_name` applies to all detectors in the job.
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NOTE: The `summary_count_field_name` property cannot be used with the `metric` function.
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`use_per_partition_normalization`::
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TBD
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[[ml-detectorconfig]]
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===== Detector Configuration Objects
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Detector configuration objects specify which data fields a job analyzes.
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They also specify which analytical functions are used.
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You can specify multiple detectors for a job.
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Each detector has the following properties:
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`by_field_name`::
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(+string+) The field used to split the data.
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In particular, this property is used for analyzing the splits with respect to their own history.
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It is used for finding unusual values in the context of the split.
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`detector_description`::
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(+string+) A description of the detector. For example, `low_sum(events_per_min)`.
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`detector_rules`::
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TBD
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`exclude_frequent`::
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(+string+) Contains one of the following values: `all`, `none`, `by`, or `over`.
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If set, frequent entities are excluded from influencing the anomaly results.
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Entities can be considered frequent over time or frequent in a population.
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If you are working with both over and by fields, then you can set `exclude_frequent`
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to `all` for both fields, or to `by` or `over` for those specific fields.
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`field_name`::
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(+string+) The field that the detector uses in the function. If you use an event rate
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function such as `count` or `rare`, do not specify this field.
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NOTE: The `field_name` cannot contain double quotes or backslashes.
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`function` (required)::
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(+string+) The analysis function that is used.
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For example, `count`, `rare`, `mean`, `min`, `max`, and `sum`.
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The default function is `metric`, which looks for anomalies in all of `min`, `max`,
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and `mean`.
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NOTE: You cannot use the `metric` function with pre-summarized input. If `summary_count_field_name`
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is not null, you must specify a function other than `metric`.
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`over_field_name`::
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(+string+) The field used to split the data.
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In particular, this property is used for analyzing the splits with respect to the history of all splits.
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It is used for finding unusual values in the population of all splits.
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`partition_field_name`::
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(+string+) The field used to segment the analysis.
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When you use this property, you have completely independent baselines for each value of this field.
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`use_null`::
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(+boolean+) Defines whether a new series is used as the null series
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when there is no value for the by or partition fields. The default value is `false`
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IMPORTANT: Field names are case sensitive, for example a field named 'Bytes' is different to one named 'bytes'.
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[[ml-datadescription]]
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===== Data Description Objects
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The data description settings define the format of the input data.
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When data is read from Elasticsearch, the datafeed must be configured.
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This defines which index data will be taken from, and over what time period.
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When data is received via the <<ml-post-data, Post Data to Jobs>> API,
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you must specify the data format (for example, JSON or CSV). In this scenario,
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the data posted is not stored in Elasticsearch. Only the results for anomaly detection are retained.
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When you create a job, by default it accepts data in tab-separated-values format and expects
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an Epoch time value in a field named `time`. The `time` field must be measured in seconds from the Epoch.
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If, however, your data is not in this format, you can provide a data description object that specifies the
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format of your data.
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A data description object has the following properties:
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`fieldDelimiter`::
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TBD
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`format`::
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TBD
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`time_field`::
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(+string+) The name of the field that contains the timestamp.
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The default value is `time`.
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`time_format`::
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(+string+) The time format, which can be `epoch`, `epoch_ms`, or a custom pattern.
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The default value is `epoch`, which refers to UNIX or Epoch time (the number of seconds
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since 1 Jan 1970) and corresponds to the time_t type in C and C++.
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The value `epoch_ms` indicates that time is measured in milliseconds since the epoch.
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The `epoch` and `epoch_ms` time formats accept either integer or real values. +
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NOTE: Custom patterns must conform to the Java `DateTimeFormatter` class. When you use date-time formatting patterns, it is recommended that you provide the full date, time and time zone. For example: `yyyy-MM-dd'T'HH:mm:ssX`. If the pattern that you specify is not sufficient to produce a complete timestamp, job creation fails.
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`quotecharacter`::
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TBD
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[[ml-apilimits]]
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===== Analysis Limits
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Limits can be applied for the size of the mathematical models that are held in memory.
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These limits can be set per job and do not control the memory used by other processes.
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If necessary, the limits can also be updated after the job is created.
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The `analysis_limits` object has the following properties:
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`categorization_examples_limit`::
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(+long+) The maximum number of examples stored per category in memory and
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in the results data store. The default value is 4. If you increase this value,
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more examples are available, however it requires that you have more storage available.
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If you set this value to `0`, no examples are stored.
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////
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NOTE: The `categorization_examples_limit` only applies to analysis that uses categorization.
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////
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`model_memory_limit`::
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(+long+) The maximum amount of memory, in MiB, that the mathematical models can use.
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Once this limit is approached, data pruning becomes more aggressive.
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Upon exceeding this limit, new entities are not modeled. The default value is 4096.
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