Marcio Cunha

Heavy bundle in Vite? How to apply code splitting

Learn how to optimize web page loading by splitting your JavaScript project code in Vite into smaller and smarter chunks.

Marcio Cunha•3 min
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Summary
  • Applying code splitting drastically reduces the initial loading time of heavy web applications.
  • The bundler packs all code into giant files if the developer does not configure clear rules.
  • On-demand dynamic loading prevents downloading features that the user might never use.
  • Configuring code chunks manually improves browser cache utilization.
  • Studying alternatives like legacy package optimization helps understand the evolution of modern tools.

Why your website is taking so long to open on mobile

When developing modern web applications using tools like Vite, it is very common to add external libraries for almost everything. The problem is that the bundler, which is the program responsible for gathering all our code files into packages readable by the browser, usually puts everything into a single giant file. In practice, this means the user needs to download the entire application code all at once, even if they are only going to access the home page. This delay frustrates users with slower mobile connections and can make visitors abandon the page before it even opens.

To better understand the severity of this, it is worth remembering classic performance problems discussed in other scenarios, such as dealing with a bloated Webpack bundle. Just like with older tools, Vite needs to be intelligently instructed on what should be sent immediately and what can wait. Code splitting solves this exact bottleneck by slicing the large bundle into smaller pieces called chunks.

How on-demand loading works in your code

The main strategy behind file splitting is dynamic loading. Instead of importing all screens and components at the top of the main file right at startup, we tell JavaScript to fetch a specific code snippet only when the user clicks a button or navigates to a new route. In practice, the browser downloads a minimal fraction of the system to display the initial interface and only fetches the rest when necessary.

In the modern JavaScript ecosystem, we do this by using the dynamic import function, which warns the runtime environment that this specific snippet should become a separate file. This same optimization and selective fetching logic resembles the care we must take when structuring complex systems, where each piece consumes resources only when triggered, avoiding memory and network bandwidth waste. When we apply this technique correctly, the impact on user experience is immediate and noticeable.

Configuring chunk splitting rules in your Vite configuration file

Vite uses Rollup under the hood to bundle production files. This means we have full control over how chunks are generated through the build configuration property. We can create custom rules to separate large third-party libraries, like frameworks and heavy utilities, from the code we write ourselves every day.

Below is a practical example of how to structure this separation within the configuration file:

export default defineConfig({  build: {    rollupOptions: {      output: {        manualChunks(id) {          if (id.includes('node_modules')) {            return 'vendor';          }        }      }    }  }});

In practice, this simple snippet separates all external dependencies residing in the project's module folder into a file called vendor. When the user updates the application, the browser reuses the library file if it hasn't changed, downloading only the part of the code you modified.

Although slicing code sounds like a magical solution for slowness, overdoing it can bring the opposite effect. If we divide the application into dozens of tiny pieces, the browser will need to make hundreds of separate HTTP requests to assemble the page. In practice, the cost of opening so many simultaneous network connections can be higher than downloading a single, slightly bulkier file.

The secret lies in balance and continuous analysis of the size reports generated during project build time. Visual analysis tools help see exactly which libraries are taking up the most space and whether they really need to be in the main bundle. This way, we maintain strict performance control without compromising code maintainability in the long run. To complement this implementation in production environments, see our related technical analyses on AI generating garbage? How to use DSPy to optimize prompts and Microservices freezing? How to use the Orchestrated Saga.

Final thoughts on performance and efficient delivery

Optimizing the loading of a web application requires discipline and constant testing on real devices with limited connections. Applying efficient chunk splitting techniques transforms the browsing experience and ensures the user gets fast responses regardless of where they are accessing the system. Continuing to refine these details ensures your application remains agile and prepared to grow sustainably.