kibana/docs/ml/job-tips.asciidoc

[role="xpack"]
[[job-tips]]
=== Machine learning job tips
++++
<titleabbrev>Job tips</titleabbrev>
++++

When you are creating a job in {kib}, 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 {ml} results.

[[bucket-span]]
==== Bucket span

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

NOTE: The bucket span must contain a valid time interval. For more information, 
see {ref}/ml-job-resource.html#ml-analysisconfig[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 
{stack-ov}/ml-buckets.html[Buckets].

[[cardinality]]
==== Cardinality

If there are logical groupings of related entities in your data, {ml} 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
{stack-ov}/ml-configuring-pop.html[Performing population analysis].

[[detectors]]
==== Detectors

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 
{stack-ov}/ml-functions.html[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`. 

[[influencers]]
==== Influencers

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 {kib} can suggest which fields to use as influencers.

[[model-memory-limits]]
==== Model memory limits

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 `xpack.ml.max_model_memory_limit` setting. 
By default, it's not set, which means there is no upper bound on the acceptable 
`model_memory_limit` values in your jobs. 

TIP: 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 {kib} 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 {ml} 
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 {ml} nodes in the cluster do not have sufficient memory to accommodate 
a job of the estimated size, the only options are:
** Add bigger {ml} 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 {ece} or the hosted Elasticsearch Service on Elastic Cloud, 
`xpack.ml.max_model_memory_limit` is set to prevent you from creating jobs 
that cannot be allocated to any {ml} nodes in the cluster. If you find that you 
cannot increase `model_memory_limit` for your {ml} jobs, the solution is to 
increase the size of the {ml} nodes in your cluster.

For more information about the `model_memory_limit` property and the 
`xpack.ml.max_model_memory_limit` setting, see 
{ref}/ml-job-resource.html#ml-analysisconfig[Analysis limits] and 
{ref}/ml-settings.html[Machine learning settings].