Level Up Your Serverless Game

Is your serverless project running into unexpected snags? You’re not alone. From basic misconfigurations to frustrating deployment nightmares, we’ve seen the same pitfalls trap countless teams. This guide is your cheat sheet to avoiding those common mistakes and accelerating your journey to serverless mastery. Think of each level as a lesson learned—a mistake you don’t have to make. From basic misconfigurations to complex deployment nightmares, the patterns are frustratingly consistent. We’ve compiled this blog post to help you avoid these common pitfalls and accelerate your journey to serverless excellence. Think of each level as a lesson learned, a mistake you don’t have to make. To learn more about the details and apply the learnings yourself, follow our open source Serverless Workshop. This blog post as well as the workshop both use AWS Lambda but the learnings can be applied to any function as a service (FaaS) platform. Now, let’s dive in.

Level 0 - This is easy!

⚠️ Mistake: None yet

To get started, we need to create our first function. Many projects jump into complex architectures without understanding the fundamentals, leading to confusion and wasted effort. This level is about demystifying the basic mechanics of serverless: understanding how to create a function, configure its runtime, and pass parameters using environment variables. You should also get acquainted with your cloud provider’s permission model, a cornerstone of secure serverless applications. By adding an HTTP trigger through an API gateway, you can transform our function into a web-accessible service, making it tangible and interactive.

✅ Start as simple and small as possible and take it from there.

Level 1: Loggin’ it!

⚠️ Mistake: Lack of structured logging

Having deployed our first function, we have no idea what it does and if it succeeds or not. When things go wrong, it’s very hard to debug and investigate issues. Many serverless projects suffer from poor observability, making troubleshooting a nightmare. Using structured logging (e.g., formatted as JSON), we get a set of logs that we can query and which give us custom insights into what our function does. We’ll know if it succeeds or fails in doing so and get context about what went wrong if things don’t go as expected. Such a log line could look as follows:

{
  "correlationId": "9ac54d82-75e0-4f0d-ae3c-e84ca400b3bd",
  "requestId": "58d9c96e-ae9f-43db-a353-c48e7a70bfa8",
  "commitHash": "9d9154e",
  "level": "INFO",
  "requestPath": "/users/1",
  "requestMethod": "GET",
  "responseCode": 200,
  "responseBody": "All good"
}

✅ Don’t wait until production issues arise to implement structured logging.

Level 2: Tracin’ it!

⚠️ Mistake: Lack of distributed tracing

As your serverless applications grow, understanding the flow of requests becomes critical. Distributed tracing provides invaluable insights into performance bottlenecks and errors. Most cloud providers provide comprehensive toolsets to easily add tracing IDs and even trace HTTP calls and other function calls by just adding a couple of lines of code. Therefore, it’s usually quite cheap to add tracing and the value it provides is quite large. A low-hanging fruit you shouldn’t miss out on.

✅ Activate distributed tracing for all your functions.

Level 3: Timin’ it!

⚠️ Mistake: Function timeouts not handled gracefully

Functions operate within time constraints, making it essential to handle timeouts gracefully. Unhandled timeouts can lead to unexpected behavior and data inconsistencies. We need to ensure our functions terminate cleanly and predictably, even when approaching their time limits. To achieve that, simply monitor the remaining execution time and implement mechanisms for aborting long-running operations with enough time left. This allows to either perform a proper cleanup or partial results instead, ensuring a smooth and reliable user experience.

✅ Don’t let timeouts cause chaos in your application.

Level 4: Optimized Cold Starts!

⚠️ Mistake: Ignoring cold start performance

Cold starts, the initial latency experienced when a Lambda function is invoked after a period of inactivity, can impact performance. In latency-sensitive applications, even a few extra milliseconds can be noticeable to users. This level explores techniques to minimize cold start times, such as moving initialization code outside the handler. By optimizing our function’s startup process, we can enhance responsiveness and improve the overall user experience.

✅ Optimize for cold starts early to avoid performance bottlenecks later.

Level 5: Decoupled!

⚠️ Mistake: Tight coupling

As your serverless applications grow, the need for asynchronous communication becomes essential. Without it, services are often tightly coupled, meaning a failure in one can cause a domino effect of failures throughout your entire system. This level introduces Amazon SQS, a simple yet powerful message queuing service. Think of it as a waiting room for messages; your function can drop off a message and move on to other tasks, while another function can pick it up later when it’s ready. This decoupling creates more scalable and resilient architectures, making your application much more robust.

✅ Use message queues like SQS to decouple services and build a more resilient system.

Level 6: Infrastructure as Code!

⚠️ Mistake: Manual infrastructure management

Managing cloud resources manually is slow, inconsistent, and highly prone to human error. It’s a recipe for chaos, especially as your application scales. This level is all about adopting Infrastructure as Code (IaC) using a tool like Terraform. By defining your serverless functions and their dependencies in a configuration file, you can treat your infrastructure like any other code. This means repeatable, version-controlled deployments that are consistent across all your environments.

✅ Stop clicking around in the console and start managing your infrastructure with code.

Level 7: Testing it All!

⚠️ Mistake: Lack of testing

Developing without a safety net is risky business. Untested code is a disaster waiting to happen and can lead to bugs, unexpected behavior, and production outages. This level tackles the critical practice of unit testing and local execution for your functions. By writing tests that check your code’s behavior and running them locally, you can catch errors early and ensure your functions are reliable before they ever hit production.

✅ Don’t just hope your code works—test it to be sure.

Level 8: Securin it!

⚠️ Mistake: Overly permissive roles

Giving a function more permissions than it needs is like handing out a master key to your entire house. It creates a massive security vulnerability. This level is about applying the principle of least privilege, which means granting your functions only the absolute minimum permissions required to do their job. By meticulously defining and restricting access, you significantly reduce the potential damage from a security breach and strengthen your application’s overall security posture.

✅ Implement the principle of least privilege to lock down your serverless functions.

Level 9: The Gradual Rollout!

⚠️ Mistake: Risky deployments

Deploying a new version of your application to 100% of your users at once is a high-stakes gamble. If there’s a problem, everyone feels the pain immediately. This level introduces a safer approach: canary deployments. Using a service like AWS CodeDeploy, you can automatically release a new version to a small subset of your users first. If the new version performs well, you can gradually roll it out to the rest. If not, you can roll it back quickly, minimizing the impact of any errors.

✅ Minimize deployment risk by gradually rolling out new changes to your users.