Machine learning settings in Elasticsearchedit

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

Machine learning uses SSE4.2 instructions, so it works only on machines whose CPUs support SSE4.2. If you run Elasticsearch on older hardware, you must disable machine learning (by setting xpack.ml.enabled to false).

General machine learning settingsedit

node.roles: [ ml ]

(Static) Set node.roles to contain ml to identify the node as a machine learning node. If you want to run machine learning jobs, there must be at least one machine learning node in your cluster.

If you set node.roles, you must explicitly specify all the required roles for the node. To learn more, refer to Node.

  • On dedicated coordinating nodes or dedicated master nodes, do not set the ml role.
  • It is strongly recommended that dedicated machine learning nodes also have the remote_cluster_client role; otherwise, cross-cluster search fails when used in machine learning jobs or datafeeds. See Remote-eligible node.
xpack.ml.enabled

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

If set to false, 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. If the node is a coordinating node, machine learning requests from clients (including Kibana) also fail. 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, it is recommended that you set xpack.ml.enabled to true on all nodes. This is the default behavior. At a minimum, it must be enabled on all master-eligible nodes. If you want to use machine learning features in clients or Kibana, it must also be enabled on all coordinating nodes.

xpack.ml.inference_model.cache_size
(Static) The maximum inference cache size allowed. The inference cache exists in the JVM heap on each ingest node. The cache affords faster processing times for the inference processor. The value can be a static byte sized value (such as 2gb) or a percentage of total allocated heap. Defaults to 40%. See also Machine learning circuit breaker settings.
xpack.ml.inference_model.time_to_live logo cloud
(Static) The time to live (TTL) for trained models in the inference model cache. The TTL is calculated from last access. Users of the cache (such as the inference processor or inference aggregator) cache a model on its first use and reset the TTL on every use. If a cached model is not accessed for the duration of the TTL, it is flagged for eviction from the cache. If a document is processed later, the model is again loaded into the cache. To update this setting in Elasticsearch Service, see Add Elasticsearch user settings. Defaults to 5m.
xpack.ml.max_inference_processors
(Dynamic) The total number of inference type processors allowed across all ingest pipelines. Once the limit is reached, adding an inference processor to a pipeline is disallowed. Defaults to 50.
xpack.ml.max_machine_memory_percent

(Dynamic) 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. The limit is based on the total memory of the machine, not current free memory. Jobs are not allocated to a node if doing so would cause the estimated memory use of machine learning jobs to exceed the limit. When the operator privileges feature is enabled, this setting can be updated only by operator users. The minimum value is 5; the maximum value is 200. Defaults to 30.

Do not configure this setting to a value higher than the amount of memory left over after running the Elasticsearch JVM unless you have enough swap space to accommodate it and have determined this is an appropriate configuration for a specialist use case. The maximum setting value is for the special case where it has been determined that using swap space for machine learning jobs is acceptable. The general best practice is to not use swap on Elasticsearch nodes.

xpack.ml.max_model_memory_limit
(Dynamic) The maximum model_memory_limit property value that can be set for any machine learning jobs in this cluster. 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. If this setting is 0 or unset, there is no maximum model_memory_limit value. If there are no nodes that meet the memory requirements for a job, this lack of a maximum memory limit means it’s possible to create jobs that cannot be assigned to any available nodes. For more information about the model_memory_limit property, see Create anomaly detection jobs or Create data frame analytics jobs. Defaults to 0.
xpack.ml.max_open_jobs
(Dynamic) The maximum number of jobs that can run simultaneously on a node. In this context, jobs include both anomaly detection jobs and data frame analytics jobs. The maximum number of jobs is also constrained by memory usage. Thus if the estimated memory usage of the jobs would be higher than allowed, fewer jobs will run on a node. Prior to version 7.1, this setting was a per-node non-dynamic setting. It became a cluster-wide dynamic setting in version 7.1. As a result, changes to its value after node startup are used only after every node in the cluster is running version 7.1 or higher. The minimum value is 1; the maximum value is 512. Defaults to 20.
xpack.ml.nightly_maintenance_requests_per_second
(Dynamic) The rate at which the nightly maintenance task deletes expired model snapshots and results. The setting is a proxy to the requests_per_second parameter used in the delete by query requests and controls throttling. When the operator privileges feature is enabled, this setting can be updated only by operator users. Valid values must be greater than 0.0 or equal to -1.0, where -1.0 means a default value is used. Defaults to -1.0
xpack.ml.node_concurrent_job_allocations
(Dynamic) 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. When the operator privileges feature is enabled, this setting can be updated only by operator users. Defaults to 2.

Advanced machine learning settingsedit

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

xpack.ml.enable_config_migration
(Dynamic) Reserved. When the operator privileges feature is enabled, this setting can be updated only by operator users.
xpack.ml.max_anomaly_records
(Dynamic) The maximum number of records that are output per bucket. Defaults to 500.
xpack.ml.max_lazy_ml_nodes

(Dynamic) The number of lazily spun up machine learning nodes. Useful in situations where machine learning nodes are not desired until the first machine learning job opens. If the current number of machine learning nodes is greater than or equal to this setting, it is assumed that there are no more lazy nodes available as the desired number of nodes have already been provisioned. If a job is opened and this setting has a value greater than zero and there are no nodes that can accept the job, the job stays in the OPENING state until a new machine learning node is added to the cluster and the job is assigned to run on that node. When the operator privileges feature is enabled, this setting can be updated only by operator users. Defaults to 0.

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

xpack.ml.max_ml_node_size
(Dynamic) The maximum node size for machine learning nodes in a deployment that supports automatic cluster scaling. If you set it to the maximum possible size of future machine learning nodes, when a machine learning job is assigned to a lazy node it can check (and fail quickly) when scaling cannot support the size of the job. When the operator privileges feature is enabled, this setting can be updated only by operator users. Defaults to 0b, which means it will be assumed that automatic cluster scaling can add arbitrarily large nodes to the cluster.
xpack.ml.persist_results_max_retries
(Dynamic) The maximum number of times to retry bulk indexing requests that fail while processing machine learning results. If the limit is reached, the machine learning job stops processing data and its status is failed. When the operator privileges feature is enabled, this setting can be updated only by operator users. The minimum value is 0; the maximum value is 50. Defaults to 20.
xpack.ml.process_connect_timeout
(Dynamic) The connection timeout for machine learning processes that run separately from the Elasticsearch JVM. When such processes are started they must connect to the Elasticsearch JVM. If the process does not connect within the time period specified by this setting then the process is assumed to have failed. When the operator privileges feature is enabled, this setting can be updated only by operator users. The minimum value is 5s. Defaults to 10s.
xpack.ml.use_auto_machine_memory_percent

(Dynamic) If this setting is true, the xpack.ml.max_machine_memory_percent setting is ignored. Instead, the maximum percentage of the machine’s memory that can be used for running machine learning analytics processes is calculated automatically and takes into account the total node size and the size of the JVM on the node. If this setting differs between nodes, the value on the current master node is heeded. When the operator privileges feature is enabled, this setting can be updated only by operator users. The default value is false.

  • If you do not have dedicated machine learning nodes (that is to say, the node has multiple roles), do not enable this setting. Its calculations assume that machine learning analytics are the main purpose of the node.
  • The calculation assumes that dedicated machine learning nodes have at least 256MB memory reserved outside of the JVM. If you have tiny machine learning nodes in your cluster, you shouldn’t use this setting.

Machine learning circuit breaker settingsedit

breaker.model_inference.limit
(Dynamic) The limit for the trained model circuit breaker. This value is defined as a percentage of the JVM heap. Defaults to 50%. If the parent circuit breaker is set to a value less than 50%, this setting uses that value as its default instead.
breaker.model_inference.overhead
(Dynamic) A constant that all trained model estimations are multiplied by to determine a final estimation. See Circuit breaker settings. Defaults to 1.
breaker.model_inference.type
(Static) The underlying type of the circuit breaker. There are two valid options: noop and memory. noop means the circuit breaker does nothing to prevent too much memory usage. memory means the circuit breaker tracks the memory used by trained models and can potentially break and prevent OutOfMemory errors. The default value is memory.