AWS for Backend Developers (EC2, S3, RDS, Lambda) · 课时

自动扩展与负载均衡

将 Auto Scaling 组与 Application Load Balancer 结合起来,应对不断变化的流量,让基于 EC2 的后端具备弹性和高可用性。

第 4 / 4 课13 个步骤

自动扩展与负载均衡 是 CoddyKit 上的免费 AWS for Backend Developers (EC2, S3, RDS, Lambda) 课时。 这是第 4 节课,共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 AWS for Backend Developers (EC2, S3, RDS, Lambda) 学习路径的一部分,你的进度在网页和 CoddyKit 应用中同步。 AWS for Backend Developers (EC2, S3, RDS, Lambda) 课程共包含 4 节课。

本课时的部分内容尚未翻译,以英文显示。

From One Server to Many

One EC2 instance is a single point of failure. Backends run an Auto Scaling Group of instances behind a load balancer that spreads requests across them.

The Launch Template

An ASG needs a recipe for the instances it spins up. A launch template defines the AMI, instance type, and startup configuration.

Anatomy of an Auto Scaling Group

An Auto Scaling Group holds a minimum, desired, and maximum count. It keeps the desired number running and replaces unhealthy instances on its own.

Health Checks

The ASG runs health checks: when an instance fails, it terminates that one and launches a replacement, holding the fleet at the desired size.

Why a Load Balancer?

A load balancer gives clients one stable endpoint while spreading requests across healthy instances. The ALB works at HTTP and routes by path or host.

Listeners and Target Groups

Two ALB pieces: a listener watches a port (like 80) for connections, and a target group is the pool of instances it forwards to.

Connecting the ASG to the ALB

The ASG auto-registers its instances with the target group, so new instances start getting traffic and terminated ones are removed — no manual wiring.

Scaling Policies

A scaling policy adjusts the desired count by metric. Target tracking is simplest: hold average CPU near, say, 50% and AWS adds or removes instances to keep it.

A Python Scaling Simulation

This snippet models target tracking — comparing current CPU to the target to compute the new instance count.

current_cpu = 80
target_cpu = 50
current_count = 4

desired = round(current_count * current_cpu / target_cpu)
print('Scale to', desired, 'instances')

Multi-AZ for High Availability

Spread the ASG across multiple Availability Zones. If one AZ goes down, instances in the others keep serving and the ALB stops routing to the failed zone.

Cost and Cooldowns

Scaling out costs money, and rapid up-down thrashing wastes it. A cooldown pauses further scaling so the fleet stabilizes before reacting again.

Quick Check

A traffic spike hits your fleet — what does target tracking do, and how does the ALB keep responses flowing?

Recap: Elastic, Resilient Backends

Recap: launch templates define instances, Auto Scaling Groups self-heal around min/desired/max, and an ALB with target-tracking and multi-AZ adds elasticity and resilience.

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常见问题解答

「自动扩展与负载均衡」课时是免费的吗?

是的 — 「自动扩展与负载均衡」的完整文本可在网页上免费阅读。要进行交互式练习(内置代码编辑器和全天候 AI 导师)并解锁 AWS for Backend Developers (EC2, S3, RDS, Lambda) 课程的其余内容,请升级到 CoddyKit PRO。 AWS for Backend Developers (EC2, S3, RDS, Lambda) 课程共包含 4 节课。

「自动扩展与负载均衡」这节课中我会学到什么?

将 Auto Scaling 组与 Application Load Balancer 结合起来,应对不断变化的流量,让基于 EC2 的后端具备弹性和高可用性。 你通过在浏览器中直接运行的动手代码来练习 AWS for Backend Developers (EC2, S3, RDS, Lambda),全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 AWS for Backend Developers (EC2, S3, RDS, Lambda) 需要有经验吗?

无需任何先前经验。CoddyKit 上的 AWS for Backend Developers (EC2, S3, RDS, Lambda) 课程适合初学者到高级学习者,你可以从这里开始或从头开始,按照自己的节奏学习。 这是第 4 节课,共 4 节。

「自动扩展与负载均衡」课时需要多长时间?

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动,所以你能稳步进步,并在网页和应用中从离开的地方继续。

我能在这节 AWS for Backend Developers (EC2, S3, RDS, Lambda) 课中编写并运行代码吗?

能。每节 AWS for Backend Developers (EC2, S3, RDS, Lambda) 课都包含内置代码编辑器,你可以在浏览器中直接编写并运行真实代码,并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

  1. AWS 后端开发入门
  2. 启动您的第一个 EC2 实例
  3. 连接与 EC2 基础管理
  4. 自动扩展与负载均衡
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