Running Node.js Functions on Lambda and Dealing with Cold Starts

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This chapter delves into running Node.js functions on Lambda, addressing challenges like cold starts leading to delays from resource conservation. It explores the correlation between memory configuration, CPU usage, and cost while recommending optimization strategies to minimize cold start impacts on user experience.

Scott and Wes chat with Richard Davison from AWS about LLRT, a new runtime tailored specifically for Lambda. They dive into the benefits of using LLRT, challenges with JavaScript in serverless, and why Rust was chosen for its development.

Show Notes

00:00 Welcome to Syntax!
01:07 Who is Richard Davison?
05:11 What is LLRT and what’s the motivation for building it?
08:25 AWS Lambda example.
11:20 What makes LLRT specifically tailored to Lambda?
14:55 Brought to you by Sentry.io.
15:22 Node.js in Lambda.
16:00 What are some challenges that people have with JavaScript in serverless?
17:20 Lambda memory configuration.
19:23 Managing cost of compute.
21:29 Simpler and faster than Node, Bun, Dino, but not a replacement.
22:31 The benchmarks.
27:00 Quick.js, the main reason for the performance gains.
- Fabrice Bellard QuickJS.
28:03 The Quick.js engine.
30:35 What was the reason behind creating Quick.js?
33:46 What made you pick Rust for LLRT?
36:34 Abstractions and the value of speed.
39:08 The JIT Compiler.
42:38 Compile cache.
43:27 De-optimizations.
44:59 Node.js Compat, what to use and avoid with LLRT.
- GitHub AWS Labs Compatibility Chart.
47:52 Will you target with WinterCG spec?
50:22 Streams API.
52:06 What about WebSockets?
53:10 Is this going to be promoted from a labs project?
54:49 Sick Picks + Shameless Plugs.