Machine learning job tipsedit

When you are creating a job in Kibana, the job creation wizards can provide advice based on the characteristics of your data. By heeding these suggestions, you can create jobs that are more likely to produce insightful machine learning results.

Bucket spanedit

The bucket span is the time interval that machine learning analytics use to summarize and model data for your job. When you create a job in Kibana, you can choose to estimate a bucket span value based on your data characteristics.


The bucket span must contain a valid time interval. For more information, see Analysis configuration objects.

If you choose a value that is larger than one day or is significantly different than the estimated value, you receive an informational message. For more information about choosing an appropriate bucket span, see Buckets.


If there are logical groupings of related entities in your data, machine learning analytics can make data models and generate results that take these groupings into consideration. For example, you might choose to split your data by user ID and detect when users are accessing resources differently than they usually do.

If the field that you use to split your data has many different values, the job uses more memory resources. In particular, if the cardinality of the by_field_name, over_field_name, or partition_field_name is greater than 1000, you are advised that there might be high memory usage.

Likewise if you are performing population analysis and the cardinality of the over_field_name is below 10, you are advised that this might not be a suitable field to use. For more information, see Performing Population Analysis.


Each job must have one or more detectors. A detector applies an analytical function to specific fields in your data. If your job does not contain a detector or the detector does not contain a valid function, you receive an error.

If a job contains duplicate detectors, you also receive an error. Detectors are duplicates if they have the same function, field_name, by_field_name, over_field_name and partition_field_name.


When you create a job, you can specify influencers, which are also sometimes referred to as key fields. Picking an influencer is strongly recommended for the following reasons:

  • It allows you to more easily assign blame for the anomaly
  • It simplifies and aggregates the results

The best influencer is the person or thing that you want to blame for the anomaly. In many cases, users or client IP addresses make excellent influencers. Influencers can be any field in your data; they do not need to be fields that are specified in your detectors, though they often are.

As a best practice, do not pick too many influencers. For example, you generally do not need more than three. If you pick many influencers, the results can be overwhelming and there is a small overhead to the analysis.

The job creation wizards in Kibana can suggest which fields to use as influencers.

Model memory limitsedit

For each job, you can optionally specify a model_memory_limit, which is the approximate maximum amount of memory resources that are required for analytical processing. The default value is 1 GB. Once this limit is approached, data pruning becomes more aggressive. Upon exceeding this limit, new entities are not modeled.

You can also optionally specify the setting. By default, it’s not set, which means there is no upper bound on the acceptable model_memory_limit values in your jobs.


If you set the model_memory_limit too high, it will be impossible to open the job; jobs cannot be allocated to nodes that have insufficient memory to run them.

If the estimated model memory limit for a job is greater than the model memory limit for the job or the maximum model memory limit for the cluster, the job creation wizards in Kibana generate a warning. If the estimated memory requirement is only a little higher than the model_memory_limit, the job will probably produce useful results. Otherwise, the actions you take to address these warnings vary depending on the resources available in your cluster:

  • If you are using the default value for the model_memory_limit and the machine learning nodes in the cluster have lots of memory, the best course of action might be to simply increase the job’s model_memory_limit. Before doing this, however, double-check that the chosen analysis makes sense. The default model_memory_limit is relatively low to avoid accidentally creating a job that uses a huge amount of memory.
  • If the machine learning nodes in the cluster do not have sufficient memory to accommodate a job of the estimated size, the only options are:

    • Add bigger machine learning nodes to the cluster, or
    • Accept that the job will hit its memory limit and will not necessarily find all the anomalies it could otherwise find.

If you are using Elastic Cloud Enterprise or the hosted Elasticsearch Service on Elastic Cloud, is set to prevent you from creating jobs that cannot be allocated to any machine learning nodes in the cluster. If you find that you cannot increase model_memory_limit for your machine learning jobs, the solution is to increase the size of the machine learning nodes in your cluster.

For more information about the model_memory_limit property and the setting, see Analysis limits and Machine learning settings.