Amazon Managed Grafana 现在支持 Grafana 版本 10.4

Amazon Managed Grafana 版本 10.4 引入了一系列新功能，包括关联、子文件夹、改进的导航、用于自动化的服务帐户和 API 以及 Grafana 警报预览。这些增强功能使用户能够更有效、更精细地控制其监控和可观察性设置。添加关联可以实现指标和日志之间的无缝链接，而子文件夹可以更好地组织仪表板。新的可视化面板，包括趋势、数据网格和 XY 图表，为数据表示提供了多种选项。这些进步增强了 Amazon Managed Grafana 的整体可用性和功能，使其成为监控和了解应用程序和基础设施的性能和运行状况的更强大的工具。

We are excited to announce support for Grafana version 10.4 in Amazon Managed Grafana. You can now run Grafana version 10.4 on a secure, scalable, highly available, and fully managed service. This update gives you access to key features introduced in the open-source Grafana versions 9.5 to 10.4, including Correlations, Subfolders, Transformations redesign, and new visualization panels. It also introduces new APIs for Grafana Service Accounts and Service Account Tokens, which replace the traditional Grafana API keys. When compared to the legacy Grafana API keys, the Service Accounts Tokens approach offers better security and improved automation abilities by integrating with native AWS APIs.

我们很高兴地宣布 Amazon Managed Grafana 支持 Grafana 版本 10.4。您现在可以在安全、可扩展、高度可用且完全托管的服务上运行 Grafana 版本 10.4。通过此更新，您可以访问开源 Grafana 版本 9.5 至 10.4 中引入的关键功能，包括关联、子文件夹、重新设计的转换和新的可视化面板。它还引入了适用于 Grafana 服务帐户和服务帐户令牌的新 API，取代了传统的 Grafana API 密钥。与传统的 Grafana API 密钥相比，服务帐户令牌方法通过与本机 AWS API 集成提供了更好的安全性和改进的自动化能力。

In this blog post, we will take a closer look at the new Amazon Managed Grafana v10.4 features to understand the changes.

在这篇博文中，我们将仔细研究新的 Amazon Managed Grafana v10.4 功能以了解这些变化。

Walkthrough

Follow us as we introduce the new capabilities in Amazon Managed Grafana version 10.4, including Correlations for seamlessly linking metrics and logs, Subfolders for better organization of dashboards, and new visualization panels like Trend, Datagrid, and XY Chart. We’ll also explore enhancements to navigation, Service Accounts and APIs for automation, and the Grafana Alerting preview.

演练 请关注我们，了解 Amazon Managed Grafana 版本 10.4 中的新功能，包括用于无缝链接指标和日志的关联、用于更好地组织仪表板的子文件夹，以及趋势、数据网格和 XY 图表等新的可视化面板。我们还将探索导航、服务帐户和自动化 API 以及 Grafana 警报预览的增强功能。

We will start with creating a new workspace with version 10.4, deploy an example to get metrics into Amazon Managed Service for Prometheus and logs into Amazon CloudWatch. This will allow us to explore key new features.

我们将首先使用版本 10.4 创建一个新工作区，部署一个示例以将指标获取到 Amazon Managed Service for Prometheus 中并登录到 Amazon CloudWatch。这将使我们能够探索关键的新功能。

We will leverage Amazon Elastic Kubernetes Service (Amazon EKS) cluster metrics from the AWS Observability Accelerator and highlight relevant portions of it. The AWS Observability Accelerator provides opinionated infrastructure as code to set up observability for AWS environments using managed services like Amazon Managed Service for Prometheus, Amazon Managed Grafana, and AWS Distro for OpenTelemetry, with curated metrics, logs, traces collection, alerting rules, and Amazon Managed Grafana dashboards.

我们将利用 AWS Observability Accelerator 中的 Amazon Elastic Kubernetes Service (Amazon EKS) 集群指标，并突出显示其中的相关部分。 AWS Observability Accelerator 提供固定的基础设施即代码，以便使用 Amazon Managed Service for Prometheus、Amazon Managed Grafana 和 AWS Distro for OpenTelemetry 等托管服务设置 AWS 环境的可观察性，并提供精选指标、日志、跟踪收集、警报规则和 Amazon托管 Grafana 仪表板。

Creating a new workspace

When creating a workspace in Amazon Managed Grafana, you now have the possibility to select between multiple versions including 8.4, 9.4 and now 10.4. The entire process of creating a new workspace can be found in the Amazon Managed Grafana documentation.

创建新工作区 在 Amazon Managed Grafana 中创建工作区时，您现在可以在多个版本之间进行选择，包括 8.4、9.4 和现在的 10.4。创建新工作区的整个过程可以在 Amazon Managed Grafana 文档中找到。

Figure 1 – Selecting a Grafana version for a new workspace

图 1 – 为新工作区选择 Grafana 版本

Alternatively, you can create a new workspace with your favorite infrastructure as code tools like AWS CloudFormation, AWS Cloud Development Kit (AWS CDK), Terraform or the AWS Command Line Interface (AWS CLI).

或者，您可以使用您喜欢的基础设施作为代码工具（例如 AWS CloudFormation、AWS 云开发套件 (AWS CDK)、Terraform 或 AWS 命令​​行界面 (AWS CLI)）创建新工作区。

Below are examples for creating a workspace with the AWS CLI and Terraform.

以下是使用 AWS CLI 和 Terraform 创建工作区的示例。

Using the AWS CLI

1. Creating an AWS Identity and Access Management (IAM) role for the workspace

Here, we create an IAM role that will be assumed by the Amazon Managed Grafana workspace, enabling it to access AWS data sources like Amazon Managed Prometheus, CloudWatch, and AWS X-Ray. In addition to these data sources, Amazon Managed Grafana supports several other default data sources, as listed on built-in data sources.

使用 AWS CLI 1. 为工作区创建 AWS Identity and Access Management (IAM) 角色 在此，我们创建一个将由 Amazon Managed Grafana 工作区承担的 IAM 角色，使其能够访问 AWS 数据源，例如 Amazon Managed Prometheus、 CloudWatch 和 AWS X-Ray。除了这些数据源之外，Amazon Managed Grafana 还支持其他几个默认数据源（如内置数据源中所列）。

cat < assume-role.json {     "Version": "2012-10-17",     "Statement": [         {             "Sid": "GrafanaAssume",             "Effect": "Allow",             "Principal": {                 "Service": "grafana.amazonaws.com"             },             "Action": "sts:AssumeRole"         }     ] } EOF  GRAFANA_WORKSPACE_ROLE_ARN=$(aws iam create-role --role-name grafana-v10-demo  --assume-role-policy-document file://assume-role.json --query 'Role.Arn' --output text)  # Amazon CloudWatch aws iam attach-role-policy                                                         --role-name $GRAFANA_WORKSPACE_ROLE_ARN  --policy-arn arn:aws:iam::aws:policy/service-role/AmazonGrafanaCloudWatchAccess  # AWS X-Ray aws iam attach-role-policy                                                         --role-name grafana-v10-demo  --policy-arn arn:aws:iam::aws:policy/AWSXrayReadOnlyAccess  # Amazon Managed Service for Prometheus cat < aps-policy.json {     "Version": "2012-10-17",     "Statement": [         {             "Effect": "Allow",             "Action": [                 "aps:ListWorkspaces",                 "aps:DescribeWorkspace",                 "aps:QueryMetrics",                 "aps:GetLabels",                 "aps:GetSeries",                 "aps:GetMetricMetadata"             ],             "Resource": "*"         }     ] } EOF  APS_POLICY_ARN=$(aws iam create-policy  --policy-name grafana-v10-demo-aps-policy  --policy-document file://aps-policy.json  --query 'Policy.Arn')  aws iam attach-role-policy      --role-name $GRAFANA_WORKSPACE_ROLE_ARN      --policy-arn $APS_POLICY_ARN

2. Creating the workspace

The following code snippet configures the workspace to access resources within the current AWS account. Alternatively, you can configure it at the AWS Organization level. The code reuses the role created in the first step, granting permissions to access CloudWatch, Amazon Managed Service for Prometheus, and X-Ray. For logging into the workspace, we use AWS IAM Identity Center (successor to AWS SSO) as the authentication method for the workspace. Finally we enable the plugin management feature to allow workspace admins to install or uninstall plugins from the plugin catalog.

猫

Check Managing Workspaces, Users, and Policies for more information on how to setup workspaces.

有关如何设置工作区的更多信息，请查看管理工作区、用户和策略。

aws grafana create-workspace  --account-access-type CURRENT_ACCOUNT  --workspace-role-arn $GRAFANA_WORKSPACE_ROLE_ARN  --authentication-providers AWS_SSO  --permission-type SERVICE_MANAGED  --grafana-version 10.4  --configuration '{"plugins": {"pluginAdminEnabled": true}}'

Using Terraform

An alternative approach to deploying Amazon Managed Grafana is through Terraform. We will utilize an example from the AWS Observability Accelerator for Terraform to streamline the deployment of a workspace configured with CloudWatch, X-Ray, and Amazon Managed Service for Prometheus as data sources. Additionally, this workspace will be set up to leverage AWS IAM Identity Center for user authentication. Visit Creating a new Amazon Managed Grafana Workspace for more information on the example.

aws grafana create-workspace --account-access-type CURRENT_ACCOUNT --workspace-role-arn $GRAFANA_WORKSPACE_ROLE_ARN --authentication-providers AWS_SSO --permission-type SERVICE_MANAGED --grafana-version 10.4 --configuration '{"plugins": { "pluginAdminEnabled": true}}' 使用 Terraform 部署 Amazon Managed Grafana 的另一种方法是通过 Terraform。我们将利用 AWS Observability Accelerator for Terraform 中的示例来简化配置了 CloudWatch、X-Ray 和 Amazon Managed Service for Prometheus 作为数据源的工作区的部署。此外，该工作区将设置为利用 AWS IAM Identity Center 进行用户身份验证。有关该示例的更多信息，请访问创建新的 Amazon 托管 Grafana 工作区。

Replace with your current AWS Region.

替换为您当前的 AWS 区域。

git clone https://github.com/aws-observability/terraform-aws-observability-accelerator.git cd terraform-aws-observability-accelerator/examples/managed-grafana-workspace terraform init export TF_VAR_aws_region= terraform apply  

After a few minutes your workspace should be ready to use.

git clone https://github.com/aws-observability/terraform-aws-observability-accelerator.git cd terraform-aws-observability-accelerator/examples/management-grafana-workspace terraform init export TF_VAR_aws_region= terraform apply 几分钟后您的工作区应该可以使用了。

Updating workspace version to v10.4

If you are an existing user of Amazon Managed Grafana, you can directly migrate your current workspace from 9.4 to 10.4 in the AWS console. This can be also done with the AWS CLI as shown in the example below. However, it’s important to note that the migration process is not reversible. We recommend testing the upgrade in a non-prod environment, before upgrading your production workspace.

将工作区版本更新到 v10.4 如果您是 Amazon Managed Grafana 的现有用户，您可以在 AWS 控制台中直接将当前工作区从 9.4 迁移到 10.4。这也可以使用 AWS CLI 来完成，如下例所示。但是，需要注意的是，迁移过程是不可逆的。我们建议在升级生产工作区之前在非生产环境中测试升级。

If you are still running 8.4, the upgrade will be done in two steps, first to 9.4 and then 10.4.

如果您仍在运行 8.4，升级将分两步完成，首先升级到 9.4，然后升级到 10.4。

aws grafana update-workspace-configuration  --workspace-id   --configuration  --grafana-version 9.4  aws grafana update-workspace-configuration  --workspace-id   --configuration  --grafana-version 10.4

Note: We recommend that you review the breaking changes before upgrading the workspace version. Some plugins may need an update, to work properly with Grafana version 10.

aws grafana update-workspace-configuration --workspace-id --configuration --grafana-version 9.4 aws grafana update-workspace-configuration --workspace-id --configuration --grafana-version 10.4 注意：我们建议您查看在升级工作区版本之前进行重大更改。某些插件可能需要更新才能与 Grafana 版本 10 正常工作。

Migrating Grafana content to version 10.4

In addition to manually migrating the content such as data sources, folders, and dashboards from your self hosted Grafana yourself, you can easily do this with the amazon-managed-grafana-migrator. This solution can be useful to migrate from self-managed Grafana to Amazon Managed Grafana or to test in a lower environment easily, before upgrading a production Amazon Managed Grafana workspace.

将 Grafana 内容迁移到版本 10.4 除了自行从自托管 Grafana 手动迁移数据源、文件夹和仪表板等内容之外，您还可以使用 amazon-management-grafana-migrator 轻松完成此操作。此解决方案对于从自我管理的 Grafana 迁移到 Amazon Managed Grafana 或在升级生产 Amazon Managed Grafana 工作区之前轻松在较低环境中进行测试非常有用。

Service Accounts and new APIs

In Grafana v10.4, Service Accounts replace API Keys, as the primary method for authenticating applications that interact with Grafana using Service Account Tokens. To support this change, we have introduced new control plane APIs that allow you to create, read, and delete service accounts and service account tokens. This enhancement enables you to fully automate the provisioning of Amazon Managed Grafana resources such as dashboards, folders, and data sources.

服务帐户和新 API 在 Grafana v10.4 中，服务帐户取代了 API 密钥，作为使用服务帐户令牌对与 Grafana 交互的应用程序进行身份验证的主要方法。为了支持这一更改，我们引入了新的控制平面 API，允许您创建、读取和删除服务帐户和服务帐户令牌。此增强功能使您能够完全自动预置 Amazon Managed Grafana 资源，例如仪表板、文件夹和数据源。

In the below snippet, using the AWS CLI, we will create a service account and token. This will allow us to provision dashboards automatically by a Grafana Operator running in an Amazon EKS cluster in this example.

在下面的代码片段中，我们将使用 AWS CLI 创建一个服务账户和令牌。在此示例中，这将使我们能够通过在 Amazon EKS 集群中运行的 Grafana Operator 自动配置仪表板。

SERVICE_ACCOUNT_TOKEN_ID=$(aws grafana create-workspace-service-account  --workspace-id   --grafana-role ADMIN  --name terraform-accelerator-eks  --query 'id' --output text)  # Creating a 3600s lived token as we deploy manually. # In an automated pipeline, security token should be shorter in duration. export TF_VAR_grafana_api_key=$(aws grafana create-workspace-service-account-token  --workspace-id   --name grafana-operator  --seconds-to-live 3600  --service-account-id $SERVICE_ACCOUNT_TOKEN_ID --query 'serviceAccountToken.key' --output text) 

Demo environment

We will be utilizing the Amazon EKS cluster metrics example to collect metrics into a new Amazon Managed Service for Prometheus workspace and collect logs into CloudWatch.

SERVICE_ACCOUNT_TOKEN_ID=$(aws grafana create-workspace-service-account --workspace-id --grafana-role ADMIN --name terraform-accelerator-eks --query 'id' --output text) # 创建一个 3600 秒的生存令牌我们手动部署。 # 在自动化管道中，安全令牌的持续时间应该较短。导出 TF_VAR_grafana_api_key=$(aws grafana create-workspace-service-account-token --workspace-id --name grafana-operator --seconds-to-live 360​​0 --service-account-id $SERVICE_ACCOUNT_TOKEN_ID --query 'serviceAccountToken. key' --output text) 演示环境 我们将利用 Amazon EKS 集群指标示例将指标收集到新的 Amazon Managed Service for Prometheus 工作区中，并将日志收集到 CloudWatch 中。

cd terraform-aws-observability-accelerator/examples/existing-cluster-with-base-and-infra terraform init terraform apply -var eks_cluster_id=  -var managed_grafana_workspace_id=

After a few minutes, we should see new dashboards created in the Amazon Managed Grafana workspace.

cd terraform-aws-observability-accelerator/examples/existing-cluster-with-base-and-infra terraform init terraform apply -var eks_cluster_id= -var Managed_grafana_workspace_id= 几分钟后，我们应该会看到在 Amazon Managed Grafana 中创建的新仪表板工作区。

Exploring the new features

Navigation

Amazon Managed Grafana 10.4 includes enhancements to the navigation interface, streamlining user workflows and simplifying interactions across the platform. A notable enhancement is the detachable breadcrumb navigation, now universally accessible. This feature allows users to easily backtrack through their activity in complex monitoring setups.

探索新功能 导航 Amazon Managed Grafana 10.4 包括导航界面的增强功能、简化用户工作流程并简化跨平台的交互。一个显着的增强是可拆卸的面包屑导航，现在可供所有人使用。此功能允许用户轻松回溯复杂监控设置中的活动。

Figure 2 – Configuring AWS Data Sources in the navigation menu

图 2 – 在导航菜单中配置 AWS 数据源

The simplified experience of setting up AWS Data Sources can be found under the Apps section. If you ran the Terraform commands above, you will notice that some data sources have already been provisioned for you.

设置 AWS 数据源的简化体验可以在“应用程序”部分找到。如果您运行上面的 Terraform 命令，您会注意到已经为您配置了一些数据源。

You can quickly navigate to the explore view with a single keyboard shortcut (g+e) , where you can query your data sources. Let’s now select the Amazon Managed Service for Prometheus metrics and issue a query. We are now presented with a new outline view on the left side which helps navigating between the query, the resulting graphs and raw data.

您可以使用单个键盘快捷键 (g+e) 快速导航到探索视图，您可以在其中查询数据源。现在，我们选择 Amazon Managed Service for Prometheus 指标并发出查询。现在，我们在左侧看到了一个新的大纲视图，它有助于在查询、结果图表和原始数据之间进行导航。

Figure 3 – Using the outline menu in the Explore section

图 3 – 使用“探索”部分中的大纲菜单

Correlations

A correlation defines how data in one data source is used to query data in another data source. Correlations are supported when using the explore view with Logs or Table visualizations. The explore view provides you with an editor that allows you to build those correlations for any result field. To better understand correlations, let’s consider the following scenario as an example.

相关性 相关性定义如何使用一个数据源中的数据来查询另一数据源中的数据。将探索视图与日志或表格可视化结合使用时支持相关性。探索视图为您提供了一个编辑器，允许您为任何结果字段构建这些关联。为了更好地理解相关性，我们以以下场景为例。

Metrics to logs correlation

In our example, we have Amazon Managed Service for Prometheus metrics for containers running in an Amazon EKS cluster. With the following query, we have an average CPU utilization for containers running in the default namespace, we do an aggregation while preserving only a few labels.

日志关联指标 在我们的示例中，我们为 Amazon EKS 集群中运行的容器提供了 Amazon Managed Service for Prometheus 指标。通过以下查询，我们获得了在默认命名空间中运行的容器的平均 CPU 利用率，我们进行聚合，同时仅保留一些标签。

sum(rate(container_cpu_usage_seconds_total{namespace="default", container!=""}[5m])) by (pod, cluster, container, k8s_node_name)

sum(rate(container_cpu_usage_seconds_total{namespace="default",container!=""}[5m])) by (pod, cluster, container, k8s_node_name)

Let’s assume we have CPU utilization spikes for a few containers and we want to understand the root cause.

假设一些容器的 CPU 利用率出现峰值，并且我们想要了解根本原因。

One natural way to solve this would be be to identify the logs location for this particular container, and then run a query. This process is simplified with the new correlations feature. By clicking on Add and Add correlation button on the top action bar, we can instruct Amazon Managed Grafana do this process for us and save time in further investigations.

解决此问题的一种自然方法是识别此特定容器的日志位置，然后运行查询。新的关联功能简化了此过程。通过单击顶部操作栏上的添加和添加关联按钮，我们可以指示 Amazon Managed Grafana 为我们执行此过程，并节省进一步调查的时间。

Figure 4 – Starting the correlation editor in Explore

图 4 – 在 Explore 中启动关联编辑器

This opens the correlation editor, where any table column can be a hyperlink. When selecting the pod, we get presented with a split view where we can select the CloudWatch datasource, and use all the variables in the query as dynamic arguments for querying. In our case, where we have a CloudWatch log group per namespace, and store each pod log in a separate CloudWatch log stream, this allows us to precisely find which log stream to query to get the pod logs.

这将打开关联编辑器，其中任何表列都可以是超链接。选择 Pod 时，我们会看到一个拆分视图，可以在其中选择 CloudWatch 数据源，并使用查询中的所有变量作为查询的动态参数。在我们的例子中，每个命名空间都有一个 CloudWatch 日志组，并将每个 pod 日志存储在单独的 CloudWatch 日志流中，这使我们能够精确地找到要查询哪个日志流来获取 pod 日志。

Figure 5 – Creating a correlation with the explorer editor

图 5 – 使用资源管理器编辑器创建关联

With the correlation saved, we can select any pod using the hyperlink in the result table to view the logs with all the correct values provided in the CloudWatch Logs Insights query.

保存关联后，我们可以使用结果表中的超链接选择任何 Pod，以查看包含 CloudWatch Logs Insights 查询中提供的所有正确值的日志。

Figure 6 – Clicking on a particular pod column displays associated logs

图 6 – 单击特定的 pod 列会显示关联的日志

We can also perform transformations to extract values that exist in a field with other data. For example, using a transformation, you can extract one portion of a log line to use in a correlation. Correlations can also be created, edited and deleted in the dedicated Correlation page in Home > Administration > Plugins and data > Correlations.

我们还可以执行转换以提取存在于其他数据的字段中的值。例如，使用转换，您可以提取日志行的一部分以用于关联。还可以在主页 > 管理 > 插件和数据 > 关联的专用关联页面中创建、编辑和删除关联。

Correlations can be done on the same data source, for example we can make a correlation with the k8s_node_name column to find the associated node metrics. This can be useful for a scenario where we want to understand if the node hosting the pod is degraded and affects the pod’s performance.

可以在同一数据源上完成关联，例如我们可以与 k8s_node_name 列进行关联以查找关联的节点指标。这对于我们想要了解托管 Pod 的节点是否降级并影响 Pod 性能的场景非常有用。

Logs to trace correlation

It’s worth noting that some plugins have a form of correlation in their settings, introduced before Amazon Managed Grafana v10. For example, the CloudWatch logs plugins allows you to select a log field that contains an X-Ray trace ID value which can be used to query and display the associated transaction trace map. This scenario is explained in the One Observablity Workshop.

用于跟踪相关性的日志 值得注意的是，某些插件在其设置中具有某种形式的相关性，这是在 Amazon Managed Grafana v10 之前引入的。例如，CloudWatch 日志插件允许您选择包含 X-Ray 跟踪 ID 值的日志字段，该值可用于查询和显示关联的事务跟踪图。 One Observability Workshop 中解释了这一场景。

Visualization improvements

Trend Visual

The Trend visual in Amazon Managed Grafana is an experimental panel that allows you to visualize data trends where the x-axis represents numeric values instead of time. This panel fills a gap not addressed by traditional Time Series or XY Chart panels, making it ideal for datasets with a sequential, numeric x-axis, such as function graphs, RPM/torque curves, supply/demand relationships, or elevation/heart rate plots along a racecourse.

可视化改进 趋势视觉效果 Amazon Managed Grafana 中的趋势视觉效果是一个实验性面板，可让您可视化数据趋势，其中 x 轴代表数值而不是时间。该面板填补了传统时间序列或 XY 图表面板无法解决的空白，使其非常适合具有顺序数字 x 轴的数据集，例如函数图、RPM/扭矩曲线、供给/需求关系或海拔/心率沿着赛马场的地块。

The Trend Panel supports various visual styles and options similar to the time series visualization, including rendering series as lines, points, or bars. However, it lacks certain features like annotations, time regions, shared cursor/crosshair, multi-timezone x-axis, and drag-selection to change the dashboard time range.

趋势面板支持与时间序列可视化类似的各种视觉样式和选项，包括将序列渲染为线、点或条形。但是，它缺乏某些功能，例如注释、时间区域、共享光标/十字准线、多时区 x 轴以及用于更改仪表板时间范围的拖动选择。

You can leverage the Trend Panel to showcase visually engaging examples, such as plotting function graphs to illustrate mathematical relationships or simulations, visualizing RPM/torque curves of an engine to understand its performance characteristics, analyzing supply/demand relationships in economics or business scenarios, or mapping elevation or heart rate changes along a racecourse, providing valuable insights for athletes and trainers.

您可以利用趋势面板来展示视觉上引人入胜的示例，例如绘制函数图来说明数学关系或模拟、可视化发动机的 RPM/扭矩曲线以了解其性能特征、分析经济或业务场景中的供需关系，或者绘制赛马场沿线的海拔或心率变化，为运动员和教练员提供宝贵的见解。

Figure 7 – Trend visual

图 7 – 趋势视觉

Datagrid Visual

The Datagrid visual allows users to create, edit, and adjust data directly within the platform. It can be used as both a standalone data source and a tabular visualization tool within a dashboard. Users have the flexibility to manipulate existing data, start with a blank slate, or import data via drag-and-drop. This feature enables significant customization and editing, including the deletion of data to reset the state.

Datagrid Visual Datagrid Visual 允许用户直接在平台内创建、编辑和调整数据。它既可以用作独立数据源，也可以用作仪表板中的表格可视化工具。用户可以灵活地操作现有数据、从空白开始或通过拖放导入数据。此功能可以实现重要的自定义和编辑，包括删除数据以重置状态。

Figure 8 – Datagrid visual

图 8 – Datagrid 视觉效果

XY Chart

The XY Chart plots data points on numerical X and Y axes. Ideal for scientific, engineering, financial, and statistical applications, this chart excels at illustrating the relationships between two variables. Users can effectively analyze correlations, trends, and outliers within their datasets, enabling clear and precise visualizations of complex information.

XY 图表 XY 图表在数字 X 和 Y 轴上绘制数据点。该图表非常适合科学、工程、金融和统计应用，擅长说明两个变量之间的关系。用户可以有效地分析数据集中的相关性、趋势和异常值，从而实现复杂信息的清晰、精确的可视化。

Figure 9 – XY Chart

图 9 – XY 图表

Take a look at the Panels and Visualizations documentation to learn more.

查看面板和可视化文档以了解更多信息。

Subfolders

The subfolders feature in Amazon Managed Grafana 10.4 improves dashboard management by allowing you to organize dashboards into a hierarchical structure, similar to a file system. This organization aids in clearer, more manageable setups, especially in environments with many dashboards or multiple teams. It also simplifies access control, as permissions can be set at the folder level, streamlining management and enhancing security for different user groups. This feature is particularly useful for larger organizations or those with complex monitoring setups, ensuring that dashboards are easier to find, manage, and secure.

子文件夹 Amazon Managed Grafana 10.4 中的子文件夹功能允许您将仪表板组织成类似于文件系统的分层结构，从而改进了仪表板管理。该组织有助于实现更清晰、更易于管理的设置，尤其是在具有许多仪表板或多个团队的环境中。它还简化了访问控制，因为可以在文件夹级别设置权限，从而简化管理并增强不同用户组的安全性。此功能对于大型组织或具有复杂监控设置的组织特别有用，可确保仪表板更易于查找、管理和保护。

Figure 10 – Organizing folders and subfolders

图 10 – 组织文件夹和子文件夹

Grafana alerting preview

With version 10.4, Grafana introduced Grafana alerting upgrade with rule preview, a feature that allows users to view, edit, and delete migrated rules prior to cutting over, with the option to roll back to classic alerting if needed. Users looking to migrate from classic alerting to Grafana Alerting can do so with confidence with the migration preview. Classic alerting will be completely removed from Grafana in version 11.

Grafana 警报预览 在 10.4 版本中，Grafana 引入了带有规则预览的 Grafana 警报升级，该功能允许用户在切换之前查看、编辑和删除迁移的规则，并且可以选择在需要时回滚到经典警报。希望从经典警报迁移到 Grafana Alerting 的用户可以通过迁移预览充满信心地做到这一点。经典警报将在版本 11 中从 Grafana 中完全删除。

Figure 11 – Previewing alerting upgrade

图 11 – 预览警报升级

Cleanup

If you ran the instructions, make sure to delete resources to stop incurring any charges.

清理 如果您运行了说明，请确保删除资源以避免产生任何费用。

If you deployed Amazon Managed Grafana using the AWS Console you will need to navigate to the Amazon Managed Grafana service section and deleted the workspace you created.

如果您使用 AWS 控制台部署 Amazon Managed Grafana，则需要导航到 Amazon Managed Grafana 服务部分并删除您创建的工作区。

If you deployed using the AWS CLI you will need to execute the below commands. Replace with the actual ID of your workspace.

如果您使用 AWS CLI 进行部署，则需要执行以下命令。替换为您工作区的实际 ID。

aws grafana delete-workspace --workspace-id 

If you deployed using the Terraform module you will need to execute the below commands.

aws grafana delete-workspace --workspace-id 如果您使用 Terraform 模块进行部署，则需要执行以下命令。

cd terraform-aws-observability-accelerator/examples/existing-cluster-with-base-and-infra terraform destroy cd terraform-aws-observability-accelerator/examples/managed-grafana-workspace terraform destroy

Finally, if you used the AWS CLI to create your Amazon Managed Grafana workspace, run:

cd terraform-aws-observability-accelerator/examples/existing-cluster-with-base-and-infra terraform destroy cd terraform-aws-observability-accelerator/examples/driven-grafana-workspace terraform destroy 最后，如果您使用 AWS CLI要创建 Amazon Managed Grafana 工作区，请运行：

aws iam detach-role-policy      --role-name grafana-v10-demo      --policy-arn $APS_POLICY_ARN  aws iam delete-policy  --policy-arn $APS_POLICY_ARN  aws iam detach-role-policy                                                         --role-name grafana-v10-demo  --policy-arn arn:aws:iam::aws:policy/AWSXrayReadOnlyAccess  aws iam detach-role-policy                                                         --role-name grafana-v10-demo  --policy-arn arn:aws:iam::aws:policy/service-role/AmazonGrafanaCloudWatchAccess  aws iam delete-role --role-name grafana-v10-demo  aws grafana delete-workspace --workspace-id  

Conclusion

In this blog, we’ve highlighted key new features of Grafana version 10.4 in Amazon Managed Grafana including Correlations, new navigation experience, and panels updates. We’ve seen how to setup a new workspace in 10.4, and how to migrate to the Amazon Managed Grafana v10.4 workspaces. With the replacement of Grafana API keys by Grafana Service Accounts, we’ve also covered our related new control plane APIs to help you perform operations in your workspace.

aws iam detach-role-policy --role-name grafana-v10-demo --policy-arn $APS_POLICY_ARN aws iam delete-policy --policy-arn $APS_POLICY_ARN aws iam detach-role-policy --role-name grafana- v10-demo --policy-arn arn:aws:iam::aws:policy/AWSXrayReadOnlyAccess aws iam detach-role-policy --role-name grafana-v10-demo --policy-arn arn:aws:iam::aws :policy/service-role/AmazonGrafanaCloudWatchAccess aws iam delete-role --role-name grafana-v10-demo aws grafana delete-workspace --workspace-id 结论 在本博客中，我们重点介绍了 Grafana 版本 10.4 的主要新功能： Amazon Managed Grafana 包括关联、新的导航体验和面板更新。我们已经了解了如何在 10.4 中设置新工作区，以及如何迁移到 Amazon Managed Grafana v10.4 工作区。随着 Grafana 服务帐户替换 Grafana API 密钥，我们还介绍了相关的新控制平面 API，以帮助您在工作区中执行操作。

To explore the complete list of new features in version 10.4, refer to the user documentation. To learn more about Amazon Managed Grafana features and its pricing, visit the product page and pricing page.

要了解版本 10.4 中新功能的完整列表，请参阅用户文档。要了解有关 Amazon Managed Grafana 功能及其定价的更多信息，请访问产品页面和定价页面。

• Check out One Observability Workshop aimed at providing a hands-on experience for you on the wide variety of toolsets AWS offers to setup monitoring and observability on your applications.
• Refer to AWS Observability best practices to learn more about prescriptive guidance and recommendations with implementation examples.
• If you are interested in Observability with infrastructure-as-code check out the AWS Observability accelerator which is a set of Terraform modules or CDK patterns to help you configure Observability for your workloads with AWS Observability services.

查看一个可观察性研讨会，旨在为您提供 AWS 提供的各种工具集的实践经验，以便在您的应用程序上设置监控和可观察性。请参阅 AWS Observability 最佳实践，了解有关规范性指南和建议以及实施示例的更多信息。如果您对基础设施即代码的可观察性感兴趣，请查看 AWS 可观察性加速器，它是一组 Terraform 模块或 CDK 模式，可帮助您使用 AWS 可观察性服务为工作负载配置可观察性。