Using Tags to Organize AWS Resources

本文介绍了AWS中使用标签来管理资源的基础知识,包括如何定义和应用标签,并列举了常见的标签类别,如环境、应用、集群、角色和所有者等。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

Tagging is an important capability that helps AWS customers streamline the process of managing their environments.

In this first in a two-part series, I provide an overview of tagging, describe how one can apply tags to resources, and highlight common tag classes that mature AWS customers employ. In the next post in the series, I will describe how people use tags to streamline management of their AWS resources.

Tagging Basics

Consider the following before you begin using tagging:

  • Tags are key-value pairs.
  • There is a default tag for most resources (“NAME”).
  • Most AWS services, including EC2, S3, and RDS, support customer-defined tags.
  • Some AWS services, such as ELB, SQS, and DynamoDB, do not support customer-defined tags (other than name).
  • There is a maximum tag key length of 128 characters and a maximum value length of 256 characters.
  • Tags are case sensitive.

 

Tagging Your Resources

You can define tags from the EC2 console by selecting the relevant instance and selecting the “Tags” tag.  Alternatively, you can select the instance, click “Actions” and select “Add/Edit Tags.”

Review EC2 Tags

  • From there, you can add tags until your heart’s content.
  • Or until you hit the maximum of 10 tags per resource.

EC2 Tagging Interface

  • You can add or remove tags in bulk by navigating to the EC2 console and clicking on “Manage Tags”.
  • In S3, you can apply tags at the object or bucket level.  You’ll note that tags are called “Metadata” in S3:

Editing S3 Tags

  • You an also manage tags via the AWS APIs. See Amazon’s documentation for more information and helpful code samples for this.
  • Tags are automatically generated by certain AWS features, including Autoscaling groups, CloudFormation stacks, and OpsWorks stacks.

 

Common Uses for Tags

Most large AWS users have, at a minimum, the following five classes of tags:

  • Environment – Used to distinguish between production, development, and staging infrastructure.
  • Application – Used to describe the set of disparate resources (or clusters) that work together to deliver a product or service to a customer.
  • Cluster – Used to identify the set of instances that share the responsibility for perform a specific function as part of an application.  Clustered instances typically share the same configuration and exist behind a load balancer.
  • Role – Used to describe the function of a particular node (web server, database server, load balancer, etc.).  Instances within a cluster generally play the same role, but machines with the same role are not necessarily part of the same cluster or application (i.e. web server for app 1, web server for app 2).
  • Owner – Used to identify the individual who is responsible for the instance.

Here is an illustration of how many customers use tags to represent their application taxonomies:
TagTopology
Other common tag classes include:

  • Customer – Used to identify the particular client that a particular resource serves.
  • Launch time – Used to help determine when an instance was created.
  • Cost center – Used for cost accounting purposes.

 

The good news is that you can add and update tags on the fly–so you can go back and change things whenever you need to. Nonetheless, I’d recommend putting some thought into how you want to organize things up front. Also, keep in mind that several key services do not support custom tags. Most customers work around this by embedding key information in resource names for those services (i.e. “LB-Cluster1-App1-Prod”).

So why are we adding all of these tags anyway? Check out our next post to learn how you can use tags to streamline management for your AWS environment.

内容概要:《中文大模型基准测评2025年上半年报告》由SuperCLUE团队发布,详细评估了2025年上半年中文大模型的发展状况。报告涵盖了大模型的关键进展、国内外大模型全景图及差距、专项测评基准介绍等。通过SuperCLUE基准,对45个国内外代表性大模型进行了六大任务(数学推理、科学推理、代码生成、智能体Agent、精确指令遵循、幻觉控制)的综合测评。结果显示,海外模型如o3、o4-mini(high)在推理任务上表现突出,而国内模型如Doubao-Seed-1.6-thinking-250715在智能体Agent和幻觉控制任务上表现出色。此外,报告还分析了模型性价比、效能区间分布,并对代表性模型如Doubao-Seed-1.6-thinking-250715、DeepSeek-R1-0528、GLM-4.5等进行了详细介绍。整体来看,国内大模型在特定任务上已接近国际顶尖水平,但在综合推理能力上仍有提升空间。 适用人群:对大模型技术感兴趣的科研人员、工程师、产品经理及投资者。 使用场景及目标:①了解2025年上半年中文大模型的发展现状与趋势;②评估国内外大模型在不同任务上的表现差异;③为技术选型和性能优化提供参考依据。 其他说明:报告提供了详细的测评方法、评分标准及结果分析,确保评估的科学性和公正性。此外,SuperCLUE团队还发布了多个专项测评基准,涵盖多模态、文本、推理等多个领域,为业界提供全面的测评服务。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值