在单个Serverless项目中部署多个独立Lambda函数是否存在影响？

Great question! Let’s break down the similarities and key differences between bundling 10 independent Lambda functions in one Serverless Framework project versus splitting them into 10 separate projects—this goes beyond just deployment package size.

What’s Equivalent? #

These core aspects work the same regardless of how you structure your projects:

• Isolated Execution: Each Lambda runs in its own isolated environment, with its own resource limits (memory, timeout, etc.) and triggers. One Lambda’s execution won’t interfere with another’s, no matter which project it’s in.
• Billing Logic: Cloud providers bill based on individual Lambda invocations and execution time. Your total costs will be identical whether the functions are in one project or 10 separate ones.
• Permission Isolation: As long as you configure unique IAM roles for each Lambda in your serverless.yml, they’ll have independent permissions. There’s no automatic permission sharing just because they’re in the same project (unless you explicitly set up shared roles).

Key Differences to Consider #

1. Deployment Efficiency & Risk #

• Single Project: A single serverless deploy updates all 10 Lambdas at once. This is fast for bulk updates, but if one Lambda has a configuration error, the entire deployment might fail—potentially blocking updates for the other working functions. Also, combining all code into one package can increase deployment time if some functions have large dependencies.
• Separate Projects: Each Lambda deploys independently. If one deployment fails, the others remain unaffected. Smaller individual packages also mean faster deployments per function.

2. Configuration Reusability & Maintenance #

• Single Project: You can reuse common configurations (like shared IAM policies, environment variables, or plugin settings for tools like serverless-webpack or serverless-offline) across all Lambdas. Updating a shared setting only requires one change, making long-term maintenance easier.
• Separate Projects: Each project has its own isolated configuration. Updating a shared setting means editing 10 different serverless.yml files, which is tedious—but it also keeps each project’s configuration clean and free of unrelated settings.

3. Resource Sharing & Lifecycle Management #

• Single Project: If your Lambdas might eventually share resources (like an S3 bucket, DynamoDB table, or API Gateway stage), keeping them in one project simplifies managing those shared resources’ lifecycles. For example, deleting the project will remove all associated Lambdas and shared resources in one go.
• Separate Projects: Shared resources need to be referenced via ARNs in each project’s config. Managing their lifecycle is more complex, and you have to be careful not to accidentally delete a shared resource when removing a single project.

4. Development & Collaboration #

• Single Project: Multiple developers working on different Lambdas may run into code conflicts (e.g., when editing the shared serverless.yml). You’ll need stricter version control workflows (like feature branches) to avoid this.
• Separate Projects: Each developer can work on their own project with minimal risk of conflicting with others. This is more flexible for distributed teams or functions maintained by different people.

5. Cold Starts & Performance #

• No Difference Here: Cold start times and concurrency limits are per-Lambda, regardless of project structure. One Lambda’s cold start won’t impact another’s, and each function gets its own concurrency allocation from the cloud provider.

Final Recommendation #

If your 10 Lambdas are truly independent with no future plans to share resources or configurations, and you want to isolate deployment risk and simplify collaboration, splitting them into 10 separate projects is the way to go.

If there’s overlap in configurations, or you anticipate needing to share resources down the line, keeping them in a single project will make maintenance more efficient.

内容的提问来源于stack exchange，提问作者WeCanBeFriends