Job Resources

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A job resource has the following properties:

analysis_config: (object) The analysis configuration, which specifies how to analyze the data. See analysis configuration objects.
analysis_limits: (object) Defines approximate limits on the memory resource requirements for the job. See analysis limits.
background_persist_interval: (time units) Advanced configuration option. The time between each periodic persistence of the model. The default value is a randomized value between 3 to 4 hours, which avoids all jobs persisting at exactly the same time. The smallest allowed value is 1 hour.
For very large models (several GB), persistence could take 10-20 minutes, so do not set the background_persist_interval value too low.
create_time: (string) The time the job was created. For example, 1491007356077.
custom_settings: (object) Advanced configuration option. Contains custom meta data about the job. For example, it can contain custom URL information as shown in Adding Custom URLs to Machine Learning Results.
data_description: (object) Describes the data format and how APIs parse timestamp fields. See data description objects.
description: (string) An optional description of the job.
finished_time: (string) If the job closed or failed, this is the time the job finished, otherwise it is null.
job_id: (string) The unique identifier for the job.
job_type: (string) Reserved for future use, currently set to anomaly_detector.
model_plot_config: (object) Configuration properties for storing additional model information. See model plot configuration.
model_snapshot_id: (string) A numerical character string that uniquely identifies the model snapshot. For example, 1491007364. For more information about model snapshots, see Model Snapshot Resources.
model_snapshot_retention_days: (long) The time in days that model snapshots are retained for the job. Older snapshots are deleted. The default value is 1 day.
renormalization_window_days: (long) Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen. The default value is the longer of 30 days or 100 bucket_spans.
results_index_name: (string) The name of the index in which to store the machine learning results. The default value is shared, which corresponds to the index name .ml-anomalies-shared
results_retention_days: (long) Advanced configuration option. The number of days for which job results are retained. Once per day at 00:30 (server time), results older than this period are deleted from Elasticsearch. The default value is null, which means results are retained.

Analysis Configuration Objects

An analysis configuration object has the following properties:

bucket_span

(time units) The size of the interval that the analysis is aggregated into, typically between 5m and 1h. The default value is 5m.

categorization_field_name

(string) If this property is specified, the values of the specified field will be categorized. The resulting categories must be used in a detector by setting by_field_name, over_field_name, or partition_field_name to the keyword mlcategory. For more information, see Categorizing Log Messages.

categorization_filters

(array of strings) If categorization_field_name is specified, you can also define optional filters. This property expects an array of regular expressions. The expressions are used to filter out matching sequences off the categorization field values. This functionality is useful to fine tune categorization by excluding sequences that should not be taken into consideration for defining categories. For example, you can exclude SQL statements that appear in your log files. For more information, see Categorizing Log Messages.

detectors

(array) An array of detector configuration objects, which describe the anomaly detectors that are used in the job. See detector configuration objects.

If the detectors array does not contain at least one detector, no analysis can occur and an error is returned.

influencers

(array of strings) A comma separated list of influencer field names. Typically these can be the by, over, or partition fields that are used in the detector configuration. You might also want to use a field name that is not specifically named in a detector, but is available as part of the input data. When you use multiple detectors, the use of influencers is recommended as it aggregates results for each influencer entity.

latency

(time units) The size of the window in which to expect data that is out of time order. The default value is 0 (no latency). If you specify a non-zero value, it must be greater than or equal to one second. For more information about time units, see Time Units.

Latency is only applicable when you send data by using the post data API.

multivariate_by_fields

(boolean) This functionality is reserved for internal use. It is not supported for use in customer environments and is not subject to the support SLA of official GA features.

If set to true, the analysis will automatically find correlations between metrics for a given by field value and report anomalies when those correlations cease to hold. For example, suppose CPU and memory usage on host A is usually highly correlated with the same metrics on host B. Perhaps this correlation occurs because they are running a load-balanced application. If you enable this property, then anomalies will be reported when, for example, CPU usage on host A is high and the value of CPU usage on host B is low. That is to say, you’ll see an anomaly when the CPU of host A is unusual given the CPU of host B.

To use the multivariate_by_fields property, you must also specify by_field_name in your detector.

summary_count_field_name

(string) If this property is specified, the data that is fed to the job is expected to be pre-summarized. This property value is the name of the field that contains the count of raw data points that have been summarized. The same summary_count_field_name applies to all detectors in the job.

The summary_count_field_name property cannot be used with the metric function.

Detector Configuration Objects

Detector configuration objects specify which data fields a job analyzes. They also specify which analytical functions are used. You can specify multiple detectors for a job. Each detector has the following properties:

by_field_name: (string) The field used to split the data. In particular, this property is used for analyzing the splits with respect to their own history. It is used for finding unusual values in the context of the split.
detector_description: (string) A description of the detector. For example, Low event rate.
exclude_frequent: (string) Contains one of the following values: all, none, by, or over. If set, frequent entities are excluded from influencing the anomaly results. Entities can be considered frequent over time or frequent in a population. If you are working with both over and by fields, then you can set exclude_frequent to all for both fields, or to by or over for those specific fields.
field_name: (string) The field that the detector uses in the function. If you use an event rate function such as count or rare, do not specify this field.
The field_name cannot contain double quotes or backslashes.
function: (string) The analysis function that is used. For example, count, rare, mean, min, max, and sum. For more information, see Function Reference.
over_field_name: (string) The field used to split the data. In particular, this property is used for analyzing the splits with respect to the history of all splits. It is used for finding unusual values in the population of all splits. For more information, see Performing Population Analysis.
partition_field_name: (string) The field used to segment the analysis. When you use this property, you have completely independent baselines for each value of this field.
use_null: (boolean) Defines whether a new series is used as the null series when there is no value for the by or partition fields. The default value is false.
Field names are case sensitive, for example a field named Bytes is different from one named bytes.

detector_index: (integer) Unique ID for the detector, used when updating it. Based on the order of detectors within the analysis_config, starting at zero.

Data Description Objects

The data description defines the format of the input data when you send data to the job by using the post data API. Note that when configure a datafeed, these properties are automatically set.

When data is received via the post data API, it is not stored in Elasticsearch. Only the results for anomaly detection are retained.

A data description object has the following properties:

format: (string) Only JSON format is supported at this time.
time_field: (string) The name of the field that contains the timestamp. The default value is time.
time_format: (string) The time format, which can be epoch, epoch_ms, or a custom pattern. The default value is epoch, which refers to UNIX or Epoch time (the number of seconds since 1 Jan 1970). The value epoch_ms indicates that time is measured in milliseconds since the epoch. The epoch and epoch_ms time formats accept either integer or real values.
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.

Analysis Limits

Limits can be applied for the resources required to hold the mathematical models in memory. These limits are approximate and can be set per job. They do not control the memory used by other processes, for example the Elasticsearch Java processes. If necessary, you can increase the limits after the job is created.

The analysis_limits object has the following properties:

categorization_examples_limit: (long) The maximum number of examples stored per category in memory and in the results data store. The default value is 4. If you increase this value, more examples are available, however it requires that you have more storage available. If you set this value to 0, no examples are stored.
The categorization_examples_limit only applies to analysis that uses categorization. For more information, see Categorizing Log Messages.
model_memory_limit: (long) The approximate maximum amount of memory resources that are required for analytical processing, in MiB. Once this limit is approached, data pruning becomes more aggressive. Upon exceeding this limit, new entities are not modeled. The default value is 4096.

Model Plot Config

This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection.

If you enable model plot it can add considerable overhead to the performance of the system; it is not feasible for jobs with many entities.

Model plot provides a simplified and indicative view of the model and its bounds. It does not display complex features such as multivariate correlations or multimodal data. As such, anomalies may occasionally be reported which cannot be seen in the model plot.

Model plot config can be configured when the job is created or updated later. It must be disabled if performance issues are experienced.

The model_plot_config object has the following properties:

enabled: (boolean) If true, enables calculation and storage of the model bounds for each entity that is being analyzed. By default, this is not enabled.
terms: (string) Limits data collection to this comma separated list of partition or by field values. If terms are not specified or it is an empty string, no filtering is applied. For example, "CPU,NetworkIn,DiskWrites". This is experimental. Only the specified terms can be viewed when using the Single Metric Viewer.

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