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Copy file name to clipboardExpand all lines: raw-migrated-files/cloud/cloud-enterprise/ece-autoscaling.md
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* Through ILM policies. For example, if a deployment has only hot nodes and autoscaling is enabled, it automatically creates warm or cold nodes, if an ILM policy is trying to move data from hot to warm or cold nodes.
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On machine learning nodes, scaling is determined by an estimate of the memory and CPU requirements for the currently configured jobs and trained models. When a new machine learning job tries to start, it looks for a node with adequate native memory and CPU capacity. If one cannot be found, it stays in an `opening` state. If this waiting job exceeds the queueing limit set in the machine learning decider, a scale up is requested. Conversely, as machine learning jobs run, their memory and CPU usage might decrease or other running jobs might finish or close. In this case, if the duration of decreased resource usage exceeds the set value for `down_scale_delay`, a scale down is requested. Check [Machine learning decider](../../../deploy-manage/autoscaling/autoscaling-deciders.md) for more detail. To learn more about machine learning jobs in general, check [Create anomaly detection jobs](https://www.elastic.co/guide/en/machine-learning/current/create-jobs.html).
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On machine learning nodes, scaling is determined by an estimate of the memory and CPU requirements for the currently configured jobs and trained models. When a new machine learning job tries to start, it looks for a node with adequate native memory and CPU capacity. If one cannot be found, it stays in an `opening` state. If this waiting job exceeds the queueing limit set in the machine learning decider, a scale up is requested. Conversely, as machine learning jobs run, their memory and CPU usage might decrease or other running jobs might finish or close. In this case, if the duration of decreased resource usage exceeds the set value for `down_scale_delay`, a scale down is requested. Check [Machine learning decider](../../../deploy-manage/autoscaling/autoscaling-deciders.md) for more detail. To learn more about machine learning jobs in general, check [Create anomaly detection jobs](https://www.elastic.co/guide/en/machine-learning/current/ml-ad-run-jobs.html#ml-ad-create-job).
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On a highly available deployment, autoscaling events are always applied to instances in each availability zone simultaneously, to ensure consistency.
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The following are known limitations and restrictions with autoscaling:
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* Autoscaling will not run if the cluster is unhealthy or if the last Elasticsearch plan failed.
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* In the event that an override is set for the instance size or disk quota multiplier for an instance by means of the [Instance Overrides API](https://www.elastic.co/guide/en/cloud-enterprise/current/set-all-instances-settings-overrides.html), autoscaling will be effectively disabled. It’s recommended to avoid adjusting the instance size or disk quota multiplier for an instance that uses autoscaling, since the setting prevents autoscaling.
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* In the event that an override is set for the instance size or disk quota multiplier for an instance by means of the [Instance Overrides API](https://www.elastic.co/docs/api/doc/cloud-enterprise/operation/operation-set-all-instances-settings-overrides), autoscaling will be effectively disabled. It’s recommended to avoid adjusting the instance size or disk quota multiplier for an instance that uses autoscaling, since the setting prevents autoscaling.
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## Enable or disable autoscaling [ece-autoscaling-enable]
Copy file name to clipboardExpand all lines: raw-migrated-files/cloud/cloud-enterprise/ece-secure-clusters-oidc.md
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@@ -15,7 +15,7 @@ To prepare for using OpenID Connect for authentication for deployments:
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* Create or use an existing deployment. Make note of the Kibana endpoint URL, it will be referenced as `<KIBANA_ENDPOINT_URL>` in the following steps.
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* The steps in this section required a moderate understanding of [OpenID Connect](https://openid.net/specs/openid-connect-core-1_0.md#Authentication) in general and the Authorization Code Grant flow specifically. For more information about OpenID Connect and how it works with the Elastic Stack check:
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* Our [configuration guide for Elasticsearch](https://www.elastic.co/guide/en/elasticsearch/reference/current/oidc-guide-authentication.html).
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* Our [configuration guide for Elasticsearch](https://www.elastic.co/guide/en/elasticsearch/reference/current/oidc-guide.html#oidc-elasticsearch-authentication).
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7. The URL forthe Token Endpointin the OpenID Connect Provider. This is the endpoint where Elastic Cloud Enterprise will send a request to exchange the code for an ID Token, as part of the Authorization Code flow. The value for this setting should be provided by your OpenID Connect Provider.
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8. (Optional) The URL forthe UserInfo Endpointin the OpenID Connect Provider. This is the endpoint of the OP that can be queried to get further user information, if required. The value for this setting should be provided by your OpenID Connect Provider.
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9. The path to a file or an HTTPS URL pointing to a JSON Web Key Set with the key material that the OpenID Connect Provider uses for signing tokens and claims responses. Your OpenID Connect Provider should provide you with this file.
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10. Defines the OpenID Connect claim that is going to be mapped to the principal (username) of the authenticated user in Kibana. In this example, we map the value of the `sub` claim, but this is not a requirement, other claims can be used too. Check [the claims mapping documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/oidc-guide-authentication.html#oidc-claims-mapping) for details and available options.
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11. Defines the OpenID Connect claim that is going to be used for role mapping. Note that the value `"http://example.info/claims/groups"` that is used here, is an arbitrary example. Check [the claims mapping documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/oidc-guide-authentication.html#oidc-claims-mapping) for a very detailed description of how the claim mapping works and how can these be used for role mapping. The name of this claim should be determined by the configuration of your OpenID Connect Provider. NOTE: According to the OpenID Connect specification, the OP should also make their configuration available at a well known URL, which is the concatenation of their `Issuer` value with the `.well-known/openid-configuration` string. To configure the OpenID Connect realm, refer to the `https://op.org.com/.well-known/openid-configuration` documentation.
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10. Defines the OpenID Connect claim that is going to be mapped to the principal (username) of the authenticated user in Kibana. In this example, we map the value of the `sub` claim, but this is not a requirement, other claims can be used too. Check [the claims mapping documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/oidc-guide.html#oidc-claims-mapping) for details and available options.
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11. Defines the OpenID Connect claim that is going to be used for role mapping. Note that the value `"http://example.info/claims/groups"` that is used here, is an arbitrary example. Check [the claims mapping documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/oidc-guide.html#oidc-claims-mapping) for a very detailed description of how the claim mapping works and how can these be used for role mapping. The name of this claim should be determined by the configuration of your OpenID Connect Provider. NOTE: According to the OpenID Connect specification, the OP should also make their configuration available at a well known URL, which is the concatenation of their `Issuer` value with the `.well-known/openid-configuration` string. To configure the OpenID Connect realm, refer to the `https://op.org.com/.well-known/openid-configuration` documentation.
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2. By default, users authenticating through OpenID Connect have no roles assigned to them. For example, if you want all your users authenticating with OpenID Connect to get access to Kibana, issue the following request to Elasticsearch:
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* Upgrading to 2.12.x or 2.13.0 is not recommended as it can cause issues and you may lose access to the admin console. You are strongly advised to upgrade to 2.13.1 and later.
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* If you are upgrading to ECE versions 2.10, 2.11, or 2.12, refer to the ECE version 2.12 [upgrade steps](https://www.elastic.co/guide/en/cloud-enterprise/2.12/ece-upgrade.html#ece-upgrade-system-deployments) for guidance about certain default ECE visualizations not working.
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* We strongly recommend that you routinely update your ECE installation to the most current version so that any bugs and security issues are fixed promptly. If you need to upgrade but are currently experiencing any issues with your platform, note that as long as ZooKeeper is running and healthy you should be able to upgrade (you can use the [get runners API](https://www.elastic.co/guide/en/cloud-enterprise/current/get-runners.html) to easily verify the health of the runners on the [ECE allocators](../../../deploy-manage/deploy/cloud-enterprise/ece-architecture.md#ece-architecture-allocators)). That is, healthy system deployments are not required in order to perform an upgrade successfully.
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* We strongly recommend that you routinely update your ECE installation to the most current version so that any bugs and security issues are fixed promptly. If you need to upgrade but are currently experiencing any issues with your platform, note that as long as ZooKeeper is running and healthy you should be able to upgrade (you can use the [get runners API](https://www.elastic.co/docs/api/doc/cloud-enterprise/operation/operation-get-runners) to easily verify the health of the runners on the [ECE allocators](../../../deploy-manage/deploy/cloud-enterprise/ece-architecture.md#ece-architecture-allocators)). That is, healthy system deployments are not required in order to perform an upgrade successfully.
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* Before upgrading to Elastic Cloud Enterprise 3.0, refer to the [lists of removals](https://www.elastic.co/guide/en/cloud-enterprise/current/ece-3-0-removals.html) to find out about features and API endpoints that are no longer supported.
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* We strongly recommend that you do not attempt to perform certain actions during the upgrade process, such as:
For an explanation of the allowed parameters, check [URI Search](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-uri-request.html).
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For an explanation of the allowed parameters, check [URI Search](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-search.html).
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To make {{es}} return a more human readable JSON response, add `?pretty=true` to the request:
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The following general settings are supported:
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$$$http-cors-settings$$$`http.cors.*`
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: Enables cross-origin resource sharing (CORS) settings for the [HTTP module](https://www.elastic.co/guide/en/elasticsearch/reference/current/modules-http.html).
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: Enables cross-origin resource sharing (CORS) settings for the [HTTP module](https://www.elastic.co/guide/en/elasticsearch/reference/current/modules-network.html).
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::::{note}
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If your use case depends on the ability to receive CORS requests and you have a cluster that was provisioned prior to January 25th 2019, you must manually set `http.cors.enabled` to `true` and allow a specific set of hosts with `http.cors.allow-origin`. Applying these changes in your Elasticsearch configuration allows cross-origin resource sharing requests.
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::::
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`http.compression`
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: Support for [HTTP compression](https://www.elastic.co/guide/en/elasticsearch/reference/current/modules-http.html) when possible (with Accept-Encoding). Defaults to `true`.
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: Support for [HTTP compression](https://www.elastic.co/guide/en/elasticsearch/reference/current/modules-network.html) when possible (with Accept-Encoding). Defaults to `true`.
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`transport.compress`
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: Configures [transport compression](https://www.elastic.co/guide/en/elasticsearch/reference/current/modules-transport.html) for node-to-node traffic.
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: Configures [transport compression](https://www.elastic.co/guide/en/elasticsearch/reference/current/modules-network.html) for node-to-node traffic.
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`transport.compression_scheme`
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: Configures [transport compression](https://www.elastic.co/guide/en/elasticsearch/reference/current/modules-transport.html) for node-to-node traffic.
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: Configures [transport compression](https://www.elastic.co/guide/en/elasticsearch/reference/current/modules-network.html) for node-to-node traffic.
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`repositories.url.allowed_urls`
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: Enables explicit allowing of [read-only URL repositories](../../../deploy-manage/tools/snapshot-and-restore/read-only-url-repository.md).
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The following disk-based allocation settings are supported:
: Enable or disable [disk allocation](https://www.elastic.co/guide/en/elasticsearch/reference/current/disk-allocator.html) decider and defaults to `true`.
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: Enable or disable [disk allocation](https://www.elastic.co/guide/en/elasticsearch/reference/current/modules-cluster.html#disk-based-shard-allocation) decider and defaults to `true`.
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* Through ILM policies. For example, if a deployment has only hot nodes and autoscaling is enabled, it automatically creates warm or cold nodes, if an ILM policy is trying to move data from hot to warm or cold nodes.
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On machine learning nodes, scaling is determined by an estimate of the memory and CPU requirements for the currently configured jobs and trained models. When a new machine learning job tries to start, it looks for a node with adequate native memory and CPU capacity. If one cannot be found, it stays in an `opening` state. If this waiting job exceeds the queueing limit set in the machine learning decider, a scale up is requested. Conversely, as machine learning jobs run, their memory and CPU usage might decrease or other running jobs might finish or close. In this case, if the duration of decreased resource usage exceeds the set value for `down_scale_delay`, a scale down is requested. Check [Machine learning decider](../../../deploy-manage/autoscaling/autoscaling-deciders.md) for more detail. To learn more about machine learning jobs in general, check [Create anomaly detection jobs](https://www.elastic.co/guide/en/machine-learning/current/create-jobs.html).
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On machine learning nodes, scaling is determined by an estimate of the memory and CPU requirements for the currently configured jobs and trained models. When a new machine learning job tries to start, it looks for a node with adequate native memory and CPU capacity. If one cannot be found, it stays in an `opening` state. If this waiting job exceeds the queueing limit set in the machine learning decider, a scale up is requested. Conversely, as machine learning jobs run, their memory and CPU usage might decrease or other running jobs might finish or close. In this case, if the duration of decreased resource usage exceeds the set value for `down_scale_delay`, a scale down is requested. Check [Machine learning decider](../../../deploy-manage/autoscaling/autoscaling-deciders.md) for more detail. To learn more about machine learning jobs in general, check [Create anomaly detection jobs](https://www.elastic.co/guide/en/machine-learning/current/ml-ad-run-jobs.html#ml-ad-create-job).
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On a highly available deployment, autoscaling events are always applied to instances in each availability zone simultaneously, to ensure consistency.
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If you are using SAML to secure your clusters, these settings are supported in Elasticsearch Add-On for Heroku.
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To learn more, refer to [configuring Kibana to use SAML](https://www.elastic.co/guide/en/elasticsearch/reference/current/saml-kibana.html).
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To learn more, refer to [configuring Kibana to use SAML](https://www.elastic.co/guide/en/elasticsearch/reference/current/saml-guide-stack.html#saml-configure-kibana).
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#### Version 8.0.0+ [echversion_8_0_0]
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`xpack.security.authc.oidc.realm`
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: Specifies which OpenID Connect realm in Elasticsearch should be used.
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To learn more, check [configuring Kibana to use OpenID Connect](https://www.elastic.co/guide/en/elasticsearch/reference/current/oidc-kibana.html).
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To learn more, check [configuring Kibana to use OpenID Connect](https://www.elastic.co/guide/en/elasticsearch/reference/current/oidc-guide.html).
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**Health check**
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* Use the [Metrics inventory](https://www.elastic.co/guide/en/observability/current/analyze-metrics.html) to identify unavailable or unhealthy nodes. If the number of minimum master nodes is down, {{es}} is not available.
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* Use the [Metrics inventory](https://www.elastic.co/guide/en/observability/current/monitor-infrastructure-and-hosts.html) to identify unavailable or unhealthy nodes. If the number of minimum master nodes is down, {{es}} is not available.
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