The Bug That Cost Us $10,000: A Postmortem on a Rogue AWS Lambda Function
We've all been there, staring blankly at logs, the clock ticking relentlessly, as a critical issue cripples our systems. But sometimes, those issues come with a sting that hits harder than usual. Today, we're pulling back the curtain on a recent incident that cost our team a painful $10,000, all thanks to a seemingly innocuous rogue AWS Lambda function.
This isn't a tale of flawless engineering; it's a candid postmortem, a deep dive into what went wrong, how it impacted us, and the crucial lessons we learned. We hope that by sharing this experience, you can avoid similar pitfalls in your serverless journey.
The Setup: Serverless Simplicity Gone Sideways
Our architecture heavily leverages the power and scalability of AWS Lambda for various background processing tasks. One particular function was designed to process user-generated data and update our analytics database. It was a seemingly simple piece of the puzzle, triggered by events from an S3 bucket. For months, it hummed along reliably, a testament to the efficiency of serverless.
The Anomaly: Unexpected Spikes and Empty Pockets
Then came the anomaly. Over a weekend, our AWS bill skyrocketed. Initially, we suspected a broad infrastructure issue or perhaps a sudden surge in legitimate user activity. However, digging deeper into our CloudWatch metrics revealed a different story. Our seemingly quiet Lambda function was executing millions upon millions of times, far beyond any reasonable expectation.
The kicker? The function wasn't processing any meaningful data during this runaway execution. It was stuck in a loop, triggered by its output. Due to a subtle misconfiguration in its trigger settings combined with a specific edge case in the data it had processed much earlier, the Lambda function was effectively triggering itself endlessly. Each execution, while individually cheap, compounded into a massive, unexpected cost.
The Investigation: Tracing the Rogue Execution
Our on-call team sprang into action, the urgency palpable. The first step was to disable the problematic Lambda function to stop the bleeding immediately. Then came the painstaking process of tracing the execution flow and identifying the root cause.
We meticulously reviewed:
- CloudWatch Logs: While filled with millions of identical execution logs, they confirmed the runaway nature of the function.
- Lambda Configuration: This is where we found the critical misconfiguration. The function's trigger was set up in a way that, under specific (and rare) circumstances, its output could trigger a new invocation.
- S3 Event Notifications: We examined the S3 bucket events to understand what initially kicked off this chain reaction.
The puzzle pieces slowly came together. A specific type of (now outdated) user data, when initially processed, created an output in S3 that inadvertently matched the trigger configuration of the same Lambda function. This set off a chain reaction:
- Lambda processes the old data.
- The output of this processing triggers the same Lambda function again.
- This new invocation processes nothing meaningful, but its completion triggers yet another invocation.
- This loop continued unabated throughout the weekend.
The $10,000 Lesson: A Hard-Earned Education
The financial impact was significant, a stark reminder that even seemingly small misconfigurations in a highly scalable environment can have severe consequences. Beyond the monetary loss, this incident highlighted several crucial areas for improvement in our development and operations processes:
- Stricter Review of Infrastructure-as-Code (IaC): While we use IaC, this incident underscored the need for more rigorous reviews, specifically focusing on trigger configurations and potential recursive loops.
- Enhanced Monitoring and Alerting: Our existing monitoring alerted us to the increased AWS costs, but it wasn't specific enough. We're now implementing more granular alerts for individual Lambda functions, including execution counts and unusual activity patterns.
- Idempotency as a First Principle: This incident reinforced the importance of designing all our Lambda functions to be idempotent. This ensures that even with multiple invocations, the end state remains consistent and unintended side effects are minimised.
- Thorough Testing of Trigger Configurations: Our testing processes didn't adequately cover the nuances of different trigger scenarios. We are now adding specific test cases to simulate various event patterns and ensure our triggers behave as expected under all conditions.
- Cost Optimisation as an Ongoing Effort: This wasn't just a technical failure; it was also a financial one. We're now integrating cost monitoring and optimisation as a more integral part of our development lifecycle.
Moving Forward: Smarter, Safer Serverless
This $10,000 bug was a painful but ultimately valuable lesson. It forced us to re-evaluate our assumptions and strengthen our processes around serverless deployments. By sharing this case study, we hope to contribute to a more robust and cost-effective cloud ecosystem for everyone.
Have you experienced a similar "oops" moment in your cloud journey? What lessons did you learn? Share your experiences in the comments below. We're all in this together!
Top comments (1)
Thank you. I love postmortems. It should be mandatory to write them.