Title

AWS re:Invent 2023 - Demystifying and mitigating AWS Lambda cold starts (COM305)

Summary

AJ Steifenberg, an AWS serverless hero, discusses strategies to manage AWS Lambda cold starts.
Cold starts occur when a new Lambda instance is initialized, which can introduce latency.
Key strategies include reducing the code you load, accurately observing service performance, not fighting the platform, and considering containers.
The talk covers proactive initialization, where AWS pre-warms instances, potentially reducing perceived cold starts.
AJ emphasizes the importance of understanding your actual cold start impact through proper observation and metrics.
He advises against default memory settings and encourages using tools like Lambda Power Tuning to optimize performance.
Containers on Lambda have improved, with AWS's work on deterministic serialization and multi-tier caching, making them a viable option for reducing cold starts.
AJ busts myths about Lambda cold starts, such as the impact of function memory size and runtime on initialization time.
He concludes by recommending a focus on dependency optimization and establishing a P99 performance budget.

Reducing Code Load: Minimizing the amount of code and dependencies loaded during initialization can significantly reduce cold start times. This includes lazy loading and direct importing of necessary components.
Accurate Observations: Many developers may not accurately understand when a cold start is impacting user experience. Tools like AWS X-Ray and end-to-end tracing can provide a clearer picture of cold start impacts.
Platform Features: Utilizing AWS Lambda's features, such as provisioned concurrency, can mitigate cold starts. However, AJ advises against Lambda warmers as they may not effectively prevent cold starts in overflow situations.
Containers as a Solution: The advancements in AWS Lambda's container image support suggest that containers can now be as performant as traditional Lambda deployments, especially for larger applications.
Performance Budgeting: Establishing a P99 performance budget can help teams prioritize optimization efforts and manage the trade-offs between performance and cost.
Myth Busting: AJ debunks common misconceptions about Lambda cold starts, such as the belief that higher memory allocation always leads to better performance or that longer function runtimes increase cold start times.
Resource Links: AJ provides resources for further learning and tools to assist with Lambda optimization, emphasizing the community's role in developing best practices for serverless architectures.