Tackling the WebAssembly Server Conundrum

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February 5, 2024. Another day in techlandia, where the lines between what’s possible and what’s practical are constantly being redrawn. Today, I spent some quality time wrestling with a classic ops problem: how to make WebAssembly (Wasm) work on the server side.

The Backstory

Last year, with the AI/LLM infrastructure explosion post-ChatGPT, everyone was talking about running Wasm on servers as part of this exciting new era. But like most things in tech, it’s not just about the shiny new toys; there are real-world challenges that need to be addressed.

The Problem

At work, we’re always pushing the boundaries of what our applications can do. A few months ago, a colleague threw out an idea: “Why don’t we use Wasm for some parts of our application? It would make sense for some compute-heavy tasks, right?” Easy enough to say, but how does that actually translate into something useful?

The Setup

Our app is built on Node.js, and the backend services are a mix of JavaScript and Python. The idea was to create a Wasm module that could handle some complex number-crunching tasks. Initially, it seemed straightforward: compile some C code into Wasm, import/export functions between JS/Wasm, and off we go.

The Challenges

However, as I started digging into the implementation, reality set in. Turns out, there are a few gotchas:

1. Memory Management: Wasm doesn’t handle memory allocation as well as native languages. I had to spend hours figuring out how to manage memory between JS and Wasm without causing a segmentation fault.

2. Environment Issues: Wasm modules need an environment to run in, which often means dealing with std::env and other low-level stuff that can be finicky.

3. Tooling: The tools for building and deploying Wasm are still maturing. We ended up having to use a combination of Emscripten and WebAssembly Studio, but even then, it was a bit of a clunky setup.

4. Performance Overhead: While the theoretical performance benefits were tempting, I found that there was still some overhead in setting up and tearing down Wasm modules compared to native code execution.

The Debugging

Debugging Wasm can be a nightmare. You’re basically debugging through an ABI (Application Binary Interface), which means you need to map out all the function calls and memory locations carefully. Add in the fact that errors often don’t give much context, and it’s like trying to solve a puzzle without all the pieces.

One particularly frustrating moment was when I hit a segfault but couldn’t find the source. After hours of tracing back through my code, I finally found out that I had forgotten to initialize one of the global variables in Wasm properly. A simple mistake, but one that cost me dearly.

The Decision

In the end, we decided to hold off on using Wasm for now. While it’s exciting and promising, the current state of tooling and support just isn’t quite there yet. Plus, with everything else going on—like AI/LLM infrastructure, FinOps, and DORA metrics—we can’t afford to spend too much time chasing bleeding-edge tech that might not pan out.

Moving Forward

Instead, we’ll focus on optimizing our existing codebase and exploring other areas where Wasm could be useful. Maybe in the future, when tooling has matured a bit more, we can come back to this idea with fresh eyes.

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Debugging Wasm is like peeling an onion: you think it’s all done, but there’s always another layer. But that’s what keeps things interesting. Until next time, happy coding!