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Job resources

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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. This property is informational; you cannot change its value.
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.
established_model_memory: (long) The approximate amount of memory resources that have been used for analytical processing. This field is present only when the analytics have used a stable amount of memory for several consecutive buckets.
finished_time: (string) If the job closed or failed, this is the time the job finished, otherwise it is null. This property is informational; you cannot change its value.
groups: (array of strings) A list of job groups. A job can belong to no groups or many. For example, ["group1", "group2"].
job_id: (string) The unique identifier for the job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters. This property is informational; you cannot change the identifier for existing jobs.
job_type: (string) Reserved for future use, currently set to anomaly_detector.
job_version: (string) The version of Elasticsearch that existed on the node when the job was created.
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. This property is informational; you cannot change its value. 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, which means snapshots are retained for one day (twenty-four hours).
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

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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. For more information about time units, see Common options.

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 from the categorization field values. You can use this functionality to fine tune the categorization by excluding sequences from consideration when categories are defined. For example, you can exclude SQL statements that appear in your log files. For more information, see Categorizing log messages. This property cannot be used at the same time as categorization_analyzer. If you only want to define simple regular expression filters that are applied prior to tokenization, setting this property is the easiest method. If you also want to customize the tokenizer or post-tokenization filtering, use the categorization_analyzer property instead and include the filters as pattern_replace character filters. The effect is exactly the same.

categorization_analyzer

(object or string) If categorization_field_name is specified, you can also define the analyzer that is used to interpret the categorization field. This property cannot be used at the same time as categorization_filters. See categorization analyzer.

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 Common options.

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.

After you create a job, you cannot change the analysis configuration object; all the properties are informational.

Detector Configuration Objects

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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.
detector_index: (integer) A unique identifier for the detector. This identifier is based on the order of the detectors in the analysis_config, starting at zero. You can use this identifier when you want to update a specific detector.
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.
custom_rules: (array) An array of custom rule objects, which enable customizing how the detector works. For example, a rule may dictate to the detector conditions under which results should be skipped. For more information see detector custom rule objects.

Field names are case sensitive, for example a field named Bytes is different from one named bytes.

After you create a job, the only properties you can change in the detector configuration object are the detector_description and the custom_rules; all other properties are informational.

Data Description Objects

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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.

Categorization Analyzer

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The categorization analyzer specifies how the categorization_field is interpreted by the categorization process. The syntax is very similar to that used to define the analyzer in the Analyze endpoint.

The categorization_analyzer field can be specified either as a string or as an object.

If it is a string it must refer to a built-in analyzer or one added by another plugin.

If it is an object it has the following properties:

char_filter: (array of strings or objects) One or more character filters. In addition to the built-in character filters, other plugins can provide more character filters. This property is optional. If it is not specified, no character filters are applied prior to categorization. If you are customizing some other aspect of the analyzer and you need to achieve the equivalent of categorization_filters (which are not permitted when some other aspect of the analyzer is customized), add them here as pattern replace character filters.
tokenizer: (string or object) The name or definition of the tokenizer to use after character filters are applied. This property is compulsory if categorization_analyzer is specified as an object. Machine learning provides a tokenizer called ml_classic that tokenizes in the same way as the non-customizable tokenizer in older versions of the product. If you want to use that tokenizer but change the character or token filters, specify "tokenizer": "ml_classic" in your categorization_analyzer.
filter: (array of strings or objects) One or more token filters. In addition to the built-in token filters, other plugins can provide more token filters. This property is optional. If it is not specified, no token filters are applied prior to categorization.

If you omit the categorization_analyzer, the following default values are used:

POST _xpack/ml/anomaly_detectors/_validate
{
  "analysis_config" : {
    "categorization_analyzer" : {
      "tokenizer" : "ml_classic",
      "filter" : [
        { "type" : "stop", "stopwords": [
          "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday",
          "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun",
          "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December",
          "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec",
          "GMT", "UTC"
        ] }
      ]
    },
    "categorization_field_name": "message",
    "detectors" :[{
      "function":"count",
      "by_field_name": "mlcategory"
    }]
  },
  "data_description" : {
  }
}

If you specify any part of the categorization_analyzer, however, any omitted sub-properties are not set to default values.

If you are categorizing non-English messages in a language where words are separated by spaces, you might get better results if you change the day or month words in the stop token filter to the appropriate words in your language. If you are categorizing messages in a language where words are not separated by spaces, you must use a different tokenizer as well in order to get sensible categorization results.

It is important to be aware that analyzing for categorization of machine generated log messages is a little different from tokenizing for search. Features that work well for search, such as stemming, synonym substitution, and lowercasing are likely to make the results of categorization worse. However, in order for drill down from machine learning results to work correctly, the tokens that the categorization analyzer produces must be similar to those produced by the search analyzer. If they are sufficiently similar, when you search for the tokens that the categorization analyzer produces then you find the original document that the categorization field value came from.

For more information, see Categorizing log messages.

Detector Custom Rule

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Custom rules enable you to customize the way detectors operate.

A custom rule has the following properties:

actions

(array) The set of actions to be triggered when the rule applies. If more than one action is specified the effects of all actions are combined. The available actions include:

skip_result: The result will not be created. This is the default value. Unless you also specify skip_model_update, the model will be updated as usual with the corresponding series value.
skip_model_update: The value for that series will not be used to update the model. Unless you also specify skip_result, the results will be created as usual. This action is suitable when certain values are expected to be consistently anomalous and they affect the model in a way that negatively impacts the rest of the results.

scope

(object) An optional scope of series where the rule applies. By default, the scope includes all series. Scoping is allowed for any of the fields that are also specified in by_field_name, over_field_name, or partition_field_name. To add a scope for a field, add the field name as a key in the scope object and set its value to an object with the following properties:

filter_id: (string) The id of the filter to be used.
filter_type: (string) Either include (the rule applies for values in the filter) or exclude (the rule applies for values not in the filter). Defaults to include.

conditions

(array) An optional array of numeric conditions when the rule applies. Multiple conditions are combined together with a logical AND.

If your detector uses lat_long, metric, rare, or freq_rare functions, you can only specify conditions that apply to time.

A condition has the following properties:

applies_to: (string) Specifies the result property to which the condition applies. The available options are actual, typical, diff_from_typical, time.
operator: (string) Specifies the condition operator. The available options are gt (greater than), gte (greater than or equals), lt (less than) and lte (less than or equals).
value: (double) The value that is compared against the applies_to field using the operator.

A rule is required to either have a non-empty scope or at least one condition. For more examples see Configuring Detector Custom Rules.

Analysis Limits

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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 or string) The approximate maximum amount of memory resources that are required for analytical processing. Once this limit is approached, data pruning becomes more aggressive. Upon exceeding this limit, new entities are not modeled. The default value for jobs created in version 6.1 and later is 1024mb. This value will need to be increased for jobs that are expected to analyze high cardinality fields, but the default is set to a relatively small size to ensure that high resource usage is a conscious decision. The default value for jobs created in versions earlier than 6.1 is 4096mb.

If you specify a number instead of a string, the units are assumed to be MiB. Specifying a string is recommended for clarity. If you specify a byte size unit of b or kb and the number does not equate to a discrete number of megabytes, it is rounded down to the closest MiB. The minimum valid value is 1 MiB. If you specify a value less than 1 MiB, an error occurs. For more information about supported byte size units, see Common options.

If your elasticsearch.yml file contains an xpack.ml.max_model_memory_limit setting, an error occurs when you try to create jobs that have model_memory_limit values greater than that setting. For more information, see Machine learning settings.

Model Plot Config

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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: [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. (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". Wildcards are not supported. Only the specified terms can be viewed when using the Single Metric Viewer.

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