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Elastic Docs ›Elasticsearch Guide [6.5] ›Set up Elasticsearch ›X-Pack Settings in Elasticsearch

Machine learning settings in Elasticsearch

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Machine learning settings in Elasticsearch

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You do not need to configure any settings to use machine learning. It is enabled by default.

All of these settings can be added to the elasticsearch.yml configuration file. The dynamic settings can also be updated across a cluster with the cluster update settings API.

Dynamic settings take precedence over settings in the elasticsearch.yml file.

General machine learning settings

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node.ml

Set to true (default) to identify the node as a machine learning node.

If set to false in elasticsearch.yml, the node cannot run jobs. If set to true but xpack.ml.enabled is set to false, the node.ml setting is ignored and the node cannot run jobs. If you want to run jobs, there must be at least one machine learning node in your cluster.

On dedicated coordinating nodes or dedicated master nodes, disable the node.ml role.

xpack.ml.enabled

Set to true (default) to enable machine learning on the node.

If set to false in elasticsearch.yml, the machine learning APIs are disabled on the node. Therefore the node cannot open jobs, start datafeeds, or receive transport (internal) communication requests related to machine learning APIs. It also affects all Kibana instances that connect to this Elasticsearch instance; you do not need to disable machine learning in those kibana.yml files. For more information about disabling machine learning in specific Kibana instances, see Kibana Machine Learning Settings.

If you want to use machine learning features in your cluster, you must have xpack.ml.enabled set to true on all master-eligible nodes. This is the default behavior.

xpack.ml.max_machine_memory_percent
The maximum percentage of the machine’s memory that machine learning may use for running analytics processes. (These processes are separate to the Elasticsearch JVM.) Defaults to 30 percent. The limit is based on the total memory of the machine, not current free memory. Jobs will not be allocated to a node if doing so would cause the estimated memory use of machine learning jobs to exceed the limit.
xpack.ml.max_model_memory_limit
The maximum model_memory_limit property value that can be set for any job on this node. If you try to create a job with a model_memory_limit property value that is greater than this setting value, an error occurs. Existing jobs are not affected when you update this setting. For more information about the model_memory_limit property, see Analysis Limits.
xpack.ml.max_open_jobs
The maximum number of jobs that can run on a node. Defaults to 20. The maximum number of jobs is also constrained by memory usage, so fewer jobs than specified by this setting will run on a node if the estimated memory use of the jobs would be higher than allowed.
xpack.ml.node_concurrent_job_allocations
The maximum number of jobs that can concurrently be in the opening state on each node. Typically, jobs spend a small amount of time in this state before they move to open state. Jobs that must restore large models when they are opening spend more time in the opening state. Defaults to 2.

Advanced machine learning settings

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These settings are for advanced use cases; the default values are generally sufficient:

xpack.ml.max_anomaly_records (Dynamic)
The maximum number of records that are output per bucket. The default value is 500.
xpack.ml.max_lazy_ml_nodes (Dynamic)

The number of lazily spun up Machine Learning nodes. Useful in situations where ML nodes are not desired until the first Machine Learning Job is opened. It defaults to 0 and has a maximum acceptable value of 3. If the current number of ML nodes is >= than this setting, then it is assumed that there are no more lazy nodes available as the desired number of nodes have already been provisioned. When a job is opened with this setting set at >0 and there are no nodes that can accept the job, then the job will stay in the OPENING state until a new ML node is added to the cluster and the job is assigned to run on that node.

This setting assumes some external process is capable of adding ML nodes to the cluster. This setting is only useful when used in conjunction with such an external process.

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