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Copy file name to clipboardExpand all lines: articles/aks/best-practices-performance-scale-large.md
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@@ -52,7 +52,7 @@ You can leverage API Priority and Fairness (APF) to throttle specific clients an
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Kubernetes clients are the applications clients, such as operators or monitoring agents, deployed in the Kubernetes cluster that need to communicate with the kube-api server to perform read or mutate operations. It's important to optimize the behavior of these clients to minimize the load they add to the kube-api server and Kubernetes control plane.
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AKS doesn't expose control plane and API server metrics via Prometheus or through platform metrics. However, you can analyze API server traffic and client behavior through Kube Audit logs. For more information, see [Troubleshoot the Kubernetes control plane](/troubleshoot/azure/azure-kubernetes/troubleshoot-apiserver-etcd).
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You can analyze API server traffic and client behavior through Kube Audit logs. For more information, see [Troubleshoot the Kubernetes control plane](/troubleshoot/azure/azure-kubernetes/troubleshoot-apiserver-etcd).
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LIST requests can be expensive. When working with lists that might have more than a few thousand small objects or more than a few hundred large objects, you should consider the following guidelines:
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As you scale your AKS clusters to larger scale points, keep the following feature limitations in mind:
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* AKS supports scaling up to 5,000 nodes by default for all Standard Tier / LTS clusters. AKS scales your cluster's control plane at runtime based on cluster size and API server resource utilization. If you cannot scale up to the supported limit, enable [control plane metrics (Preview)](./monitor-control-plane-metrics.md) with the [Azure Monitor managed service for Prometheus](../azure-monitor/essentials/prometheus-metrics-overview.md) to monitor the control plane. To help troubleshoot scaling performance or reliability issues, see the following resources:
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* AKS supports scaling up to 5,000 nodes by default for all Standard Tier / LTS clusters. AKS scales your cluster's control plane at runtime based on cluster size and API server resource utilization. If you can't scale up to the supported limit, enable [control plane metrics (Preview)](./monitor-control-plane-metrics.md) with the [Azure Monitor managed service for Prometheus](../azure-monitor/essentials/prometheus-metrics-overview.md) to monitor the control plane. To help troubleshoot scaling performance or reliability issues, see the following resources:
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*[AKS at scale troubleshooting guide](/troubleshoot/azure/azure-kubernetes/aks-at-scale-troubleshoot-guide)
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*[Troubleshoot the Kubernetes control plane](/troubleshoot/azure/azure-kubernetes/troubleshoot-apiserver-etcd)
Copy file name to clipboardExpand all lines: articles/azure-monitor/logs/restore.md
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---
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# Restore logs in Azure Monitor
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The restore operation makes a specific time range of data in a table available in the hot cache for high-performance queries. This article describes how to restore data, query that data, and then dismiss the data when you're done.
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The restore operation makes a specific time range of data in a table available in the hot cache for high-performance queries. This article describes how to restore data, query that data, and then dismiss the data when you're done.
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## Permissions
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## What does restore do?
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When you restore data, you specify the source table that contains the data you want to query and the name of the new destination table to be created.
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The restore operation creates the restore table and allocates additional compute resources for querying the restored data using high-performance queries that support full KQL.
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The restore operation creates the restore table and allocates extra compute resources for querying the restored data using high-performance queries that support full KQL.
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The destination table provides a view of the underlying source data, but doesn't affect it in any way. The table has no retention setting, and you must explicitly [dismiss the restored data](#dismiss-restored-data) when you no longer need it.
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- Restore up to 60 TB.
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- Run up to two restore processes in a workspace concurrently.
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- Run only one active restore on a specific table at a given time. Executing a second restore on a table that already has an active restore will fail.
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- Run only one active restore on a specific table at a given time. Executing a second restore on a table that already has an active restore fails.
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- Perform up to four restores per table per week.
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## Pricing model
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The charge for restored logs is based on the volume of data you restore, and the duration for which the restore is active. Data restores are billed on each UTC-day that the restore is active.
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The charge for restored logs is based on the volume of data you restore, and the duration for which the restore is active. Thus, the units of price are *per GB per day*. Data restores are billed on each UTC-day that the restore is active.
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- Charges are subject to a minimum restored data volume of 2 TB per restore. If you restore less data, you will be charged for the 2 TB minimum each day until the [restore is dismissed](#dismiss-restored-data).
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- On the first and last days that the restore is active, charges are prorated based on the period that the restore is active.
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- On the first and last days that the restore is active, you're only billed for the part of the day the restore was active.
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- The minimum charge will be for a 12-hour restore duration, even if the restore is active for less than 12-hours.
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- The minimum charge is for a 12-hour restore duration, even if the restore is active for less than 12-hours.
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- For more information, see [Azure Monitor pricing](https://azure.microsoft.com/pricing/details/monitor/).
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- For more information on your data restore price, see [Azure Monitor pricing](https://azure.microsoft.com/pricing/details/monitor/) on the Logs tab.
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For example, if your table holds 500 GB a day and you restore 10 days data from that table, you'll be charged for 5 TB of restored data each day until you [dismiss the restored data](#dismiss-restored-data).
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Here are some examples to illustrate data restore cost calculations:
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1. If your table holds 500 GB a day and you restore 10 days data from that table, your total restore size is 5 TB. You are charged for this 5 TB of restored data each day until you [dismiss the restored data](#dismiss-restored-data). Your daily cost is 5,000 GB multiplied by your data restore price (see [Azure Monitor pricing](https://azure.microsoft.com/pricing/details/monitor/).)
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1. If instead, only 700 GB of data is restored, each day that the restore is active is billed for the 2 TB minimum restore level. Your daily cost is 2,000 GB multiplied by your data restore price.
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1. If a 5 TB data restore is only kept active for 1 hour, it is billed for 12-hour minimum. The cost for this data restore is 5,000 GB multiplied by your data restore price multiplied by 0.5 days (the 12-hour minimum).
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1. If a 700 GB data restore is only kept active for 1 hour, it is billed for 12-hour minimum. The cost for this data restore is 2,000 GB (the minimum billed restore size) multiplied by your data restore price multiplied by 0.5 days (the 12-hour minimum).
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> [!NOTE]
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> There is no charge for querying restored logs since they are Analytics logs.
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## Next steps
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- [Learn more about data retention and archiving data.](data-retention-archive.md)
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- [Learn about Search jobs, which is another method for retrieving archived data.](search-jobs.md)
Copy file name to clipboardExpand all lines: articles/kubernetes-fleet/concepts-fleet.md
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title: "Azure Kubernetes Fleet Manager and member clusters"
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description: This article provides a conceptual overview of Azure Kubernetes Fleet Manager and member clusters.
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ms.date: 03/04/2024
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ms.date: 04/01/2024
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author: shashankbarsin
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ms.author: shasb
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ms.service: kubernetes-fleet
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# Azure Kubernetes Fleet Manager and member clusters
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Azure Kubernetes Fleet Manager (Fleet) solves at-scale and multi-cluster problems for Kubernetes clusters. This document provides a conceptual overview of fleet and its relationship with its member Kubernetes clusters. Right now Fleet supports joining AKS clusters as member clusters.
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This article provides a conceptual overview of fleets, member clusters, and hub clusters in Azure Kubernetes Fleet Manager (Fleet).
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[](./media/conceptual-fleet-aks-relationship.png#lightbox)
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## What are fleets?
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## Fleet scenarios
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A fleet resource acts as a grouping entity for multiple AKS clusters. You can use them to manage multiple AKS clusters as a single entity, orchestrate updates across multiple clusters, propagate Kubernetes resources across multiple clusters, and provide a single pane of glass for managing multiple clusters. You can create a fleet with or without a [hub cluster](#what-is-a-hub-cluster-preview).
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A fleet is an Azure resource you can use to group and manage multiple Kubernetes clusters. Currently fleet supports the following scenarios:
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* Create a Fleet resource and group AKS clusters as member clusters.
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* Orchestrate latest or consistent Kubernetes version and node image upgrades across multiple clusters by using update runs, stages, and groups
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* Create Kubernetes resource objects on the Fleet resource's hub cluster and control their propagation to member clusters (preview).
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* Export and import services between member clusters, and load balance incoming L4 traffic across service endpoints on multiple clusters (preview).
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A fleet consists of the following components:
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:::image type="content" source="./media/concepts-fleet/fleet-architecture.png" alt-text="This screenshot shows a diagram of the fleet resource, including the hub cluster agent and the member cluster agent.":::
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***fleet-hub-agent**: A Kubernetes controller that creates and reconciles all the fleet-related custom resources (CRs) in the hub cluster.
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***fleet-member-agent**: A Kubernetes controller that creates and reconciles all the fleet-related CRs in the member clusters. This controller pulls the latest CRs from the hub cluster and consistently reconciles the member clusters to match the desired state.
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## What are member clusters?
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You can join Azure Kubernetes Service (AKS) clusters to a fleet as member clusters. Member clusters must reside in the same Microsoft Entra tenant as the fleet. But they can be in different regions, different resource groups, and/or different subscriptions.
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The `MemberCluster` represents a cluster-scoped API established within the hub cluster, serving as a representation of a cluster within the fleet. This API offers a dependable, uniform, and automated approach for multi-cluster applications to identify registered clusters within a fleet. It also facilitates applications in querying a list of clusters managed by the fleet or in observing cluster statuses for subsequent actions. For more information, see [the upstream Fleet documentation](https://github.com/Azure/fleet/blob/main/docs/concepts/MemberCluster/README.md).
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You can join Azure Kubernetes Service (AKS) clusters to a fleet as member clusters. Member clusters must reside in the same Microsoft Entra tenant as the fleet, but they can be in different regions, different resource groups, and/or different subscriptions.
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## What is a hub cluster (preview)?
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The following table lists the differences between a fleet without hub cluster and a fleet with hub cluster:
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| Feature Dimension| Without hub cluster | With hub cluster (preview) |
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| Feature dimension| Without hub cluster | With hub cluster (preview) |
title: "Azure Kubernetes Fleet Manager scheduler and scheduling framework"
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description: This article provides a conceptual overview of the Azure Kubernetes Fleet Manager scheduler and scheduling framework.
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ms.date: 04/01/2024
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author: schaffererin
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ms.author: schaffererin
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ms.service: kubernetes-fleet
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ms.topic: conceptual
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---
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# Azure Kubernetes Fleet Manager scheduler and scheduling framework
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This article provides a conceptual overview of the scheduler and scheduling framework in Azure Kubernetes Fleet Manager (Fleet).
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## What is the scheduler?
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The scheduler is a core component in the fleet workload with the primary responsibility of determining scheduling decisions for a bundle of resources based on the latest `ClusterSchedulingPolicySnapshot` generated by the [`ClusterResourcePlacement`](./concepts-resource-propagation.md).
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By default, the scheduler operates in *batch mode*, which enhances performance. In this mode, it binds a `ClusterResourceBinding` from a `ClusterResourcePlacement` to multiple clusters whenever possible.
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### Batch mode
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Scheduling resources within a `ClusterResourcePlacement` involves more dependencies compared to scheduling pods within a Kubernetes Deployment. There are two notable distinctions:
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* In a `ClusterResourcePlacement`, multiple replicas of resources can't be scheduled on the same cluster.
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* The `ClusterResourcePlacement` supports different placement types within a single object.
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For more information, see [the upstream Fleet Scheduler documentation](https://github.com/Azure/fleet/blob/main/docs/concepts/Scheduler/README.md).
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## What is the scheduling framework?
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The fleet scheduling framework closely aligns with the native [Kubernetes scheduling framework](https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/), incorporating several modifications and tailored functionalities to support the fleet workload.
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:::image type="content" source="./media/concepts-scheduler-scheduling-framework/scheduling-framework.png" alt-text="This screenshot shows an overview diagram of the fleet scheduling framework.":::
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The primary advantage of this framework is its capability to compile plugins directly into the scheduler. Its API facilitates the implementation of diverse scheduling features as plugins, ensuring a lightweight and maintainable core.
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The fleet scheduler integrates the following fundamental built-in plugins:
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***Topology spread plugin**: Supports the `TopologySpreadConstraints` in the placement policy.
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***Cluster affinity plugin**: Facilitates the affinity clause in the placement policy.
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***Same placement affinity plugin**: Designed specifically for fleet and prevents multiple replicas from being placed within the same cluster.
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***Cluster eligibility plugin**: Enables cluster selection based on specific status criteria.
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For more information, see the [upstream Fleet Scheduling Framework documentation](https://github.com/Azure/fleet/blob/main/docs/concepts/Scheduling-Framework/README.md).
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## Next steps
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*[Create a fleet and join member clusters](./quickstart-create-fleet-and-members.md).
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