Hey everyone, welcome back to another edition of Coding with Alex at sysseder.com. If you’ve been hanging around the JavaScript ecosystem for the last couple of years, you’ve undoubtedly heard the hype surrounding Bun. Jarred Sumner’s baby promised us a blisteringly fast runtime, package manager, test runner, and bundler all rolled into one cohesive tool written in Zig. And for the most part, it has delivered. We’ve all seen the benchmarks of Bun installing packages at warp speed or booting up HTTP servers in milliseconds.
But here’s the cold, hard truth about modern web development: as your project grows, your build times inevitably creep up. We import a massive library here, a utility package there, and suddenly our lightweight app is dragging a 5MB bundle behind it. When your Bun build takes three seconds instead of 100 milliseconds, "fast" starts to feel subjective. How do you find the culprit? How do you know which dependency is bloating your output or stalling your compilation?
This week, a fascinating project popped up on Hacker News: a custom-built visualizer designed specifically to dissect and understand Bun's compile times and bundle outputs. It reminded me that while raw speed is great, observability is what actually keeps our systems maintainable. Today, we are going to dive deep into Bun's bundler, look at how we can analyze and visualize our builds, and walk through practical strategies to optimize your Bun compilation pipeline.
The Black Box of High-Speed Bundlers
Traditional JavaScript bundlers like Webpack and Rollup are slow, but they have one massive advantage: maturity. The ecosystem is packed with diagnostic tools. If your Webpack build is slow, you drop in the webpack-bundle-analyzer, spin up an interactive treemap, and instantly see that some junior dev accidentally imported all of Lodash instead of tree-shaking a single function.
With next-generation, native-speed bundlers like Esbuild and Bun, we trade some of that mature tooling ecosystem for raw, unadulterated performance. Because Bun compile runs in native code, it feels like a black box. You run bun build ./index.ts --outdir ./dist, the terminal flashes, and it’s done. But when things go wrong—or when you need to optimize for edge deployments where every kilobyte of cold-start overhead matters—you need more than a terminal summary.
To solve this, Bun exposes metadata about the build process in the form of a JSON build manifest. Let's look at how we can extract this data and how the community is building visualizers to make sense of it.
Step 1: Generating Build Metadata in Bun
To inspect what is actually happening under the hood during a Bun compilation, we can leverage Bun's JavaScript API. Instead of running a simple CLI command, we can write a build script that runs the bundler and outputs a detailed compilation manifest.
Create a file named build.ts in your project root:
import Bun from "bun";
import { writeFile } from "node:fs/promises";
const buildResult = await Bun.build({
entrypoints: ["./src/index.tsx"],
outdir: "./dist",
minify: true,
sourcemap: "external",
naming: "[name]-[hash].[ext]",
});
if (!buildResult.success) {
console.error("Build failed");
for (const message of buildResult.logs) {
console.error(message);
}
process.exit(1);
}
// Extract compilation metadata
const buildStats = {
outputs: buildResult.outputs.map((output) => ({
path: output.path,
type: output.type,
size: output.size, // in bytes
inputs: Object.keys(output.sourcemap?.mappings || {}).length > 0 ? "Has mappings" : "No mappings",
})),
};
await writeFile("bun-manifest.json", JSON.stringify(buildStats, null, 2));
console.log("Build complete! Metadata saved to bun-manifest.json");
When you run this script using bun run build.ts, Bun compiles your application and writes a bun-manifest.json file. This file contains the raw sizes and paths of your entry points and outputs. However, if we want to build a truly interactive treemap or compile-time visualizer, we need to dig deeper into the actual dependency graph.
Step 2: Parsing Bun's Import Graph
To understand compile times, we need to know not just how big the final file is, but how long the bundler spent resolving and parsing each module in the dependency graph. The build visualizer concept uses Bun's AST (Abstract Syntax Tree) parsing capabilities and import reflection to construct an interactive tree map of your bundle.
Let's write a custom script that traverses our imports and calculates the impact of each module on our bundle size. This mimics the core logic of a visualizer engine:
import { Transpiler } from "bun";
import { readFileSync, readdirSync, statSync } from "node:fs";
import { join } from "node:path";
const transpiler = new Transpiler({ loader: "tsx" });
interface ModuleNode {
name: string;
size: number;
imports: string[];
}
const dependencyGraph: Record<string, ModuleNode> = {};
function analyzeFile(filePath: string) {
if (dependencyGraph[filePath]) return;
const content = readFileSync(filePath, "utf8");
const stats = statSync(filePath);
// Use Bun's native Transpiler to scan for imports without executing
const imports = transpiler.scanImports(content);
dependencyGraph[filePath] = {
name: filePath,
size: stats.size,
imports: imports.map(imp => imp.path)
};
// Recursively analyze local imports
for (const imp of imports) {
if (imp.path.startsWith(".") || imp.path.startsWith("/")) {
const resolvedPath = resolveLocalImport(filePath, imp.path);
if (resolvedPath) {
analyzeFile(resolvedPath);
}
}
}
}
function resolveLocalImport(fromFile: string, importPath: string): string | null {
const directory = fromFile.substring(0, fromFile.lastIndexOf("/"));
const extensions = [".ts", ".tsx", ".js", ".jsx"];
for (const ext of extensions) {
const fullPath = join(directory, importPath + ext);
try {
if (statSync(fullPath).isFile()) {
return fullPath;
}
} catch {
continue;
}
}
return null;
}
// Run the analyzer on your main entry point
analyzeFile("./src/index.tsx");
console.log(JSON.stringify(dependencyGraph, null, 2));
This script showcases the absolute power of Bun's native tooling. We are using Bun's built-in Transpiler class to scan for imports statically. It runs at native C++/Zig speeds, allowing us to generate an incredibly fast snapshot of our dependency graph before we even trigger a full compilation step.
Step 3: Visualizing the Data (Under the Hood)
Once you have generated this JSON dependency map, how does a tool like the Bun Build Visualizer render it? It typically relies on a hierarchical visualization layout, most commonly a Treemap or a Flamegraph.
In a D3.js or Canvas-based visualizer, the data is fed into a partition layout. Each file is represented as a node with a weight determined by its size (kilobytes) or its compileTime (milliseconds). If you are building your own visualizer dashboard, the UI architecture looks something like this:
+-------------------------------------------------------------+ | Bun Build Visualizer | +-------------------------------------------------------------+ | [ src/index.tsx (120kb) ] | | +--------------------------------+ +---------------------+ | | | node_modules/lodash (85kb) | | src/components (35kb| | | | +------------------+ +-------+ | | +--------+ +-------+| | | | | debounce.js (50k)| | etc. | | | |Button | |Header || | | | | | | (35k) | | | |(20kb) | |(15kb) || | | | +------------------+ +-------+ | | +--------+ +-------+| | | +--------------------------------+ +---------------------+ | +-------------------------------------------------------------+
By mapping out the bundle this way, you instantly see that even though your build finished in a respectable 80ms, 70% of that time and bundle space was spent processing a single, heavy third-party library that could easily be replaced with a lightweight alternative.
Practical Strategies to Cut Bun's Compile Times Even Further
If your visualization reveals that your builds are getting bloated or slow, what can you do about it? Here are three battle-tested strategies to optimize your Bun compilation pipeline:
1. Leverage Bun's "External" Flag for Heavy Dependencies
If you have massive dependencies (such as large React icon libraries or heavy utility frameworks like Three.js) that rarely change, exclude them from the compilation process using the external configuration. This prevents Bun from parsing and minifying them on every single build iteration.
await Bun.build({
entrypoints: ["./src/index.tsx"],
outdir: "./dist",
external: ["three", "react-icons"], // Don't compile these!
});
2. Optimize Module Resolution and Aliasing
Bun's resolver is highly optimized, but it can still get bogged down searching through complex, nested node_modules paths if you have deep relative imports (e.g., ../../../../components/Button). Use path aliasing in your tsconfig.json or jsconfig.json to give the compiler direct paths to your directories:
{
"compilerOptions": {
"baseUrl": "./",
"paths": {
"@components/*": ["src/components/*"],
"@utils/*": ["src/utils/*"]
}
}
}
Bun natively understands these paths and will resolve them significantly faster during the compilation phase.
3. Turn Off Source Maps in Development Builds
Generating source maps is incredibly useful for production debugging, but it requires Bun to calculate and write mapping arrays for every single line of code. If you are experiencing a slow development loop, ensure source maps are set to "none" or only generated during production releases.
The Verdict: Speed is Good, Insight is Better
The developer community's push toward building custom visualization tools for new runtimes like Bun is a sign of a maturing ecosystem. We are moving past the initial phase of "Look how fast this CLI tool runs" and moving into the "How do I scale this in production?" phase.
By learning how to tap into Bun’s metadata, programmatically parse imports, and analyze build footprints, you aren't just relying on magic to keep your applications fast. You're engineering them to stay that way.
What does your build pipeline look like? Have you fully migrated to Bun for your bundler needs, or are you still sticking with Vite and Esbuild? Let me know in the comments below, or hit me up on Twitter/X at @sysseder. If you found this post helpful, don't forget to subscribe to the newsletter for more deep dives into DevOps, system design, and modern web development.
Until next time, keep your builds fast and your bundles light. Happy coding!