Optimizing diff rendering is critical for developer productivity at scale. This engineering deep dive shows how reducing per-unit overhead in React components can prevent browser crashes and high input lag when handling massive datasets in the DOM.
Pull requests are the beating heart of GitHub. As engineers, this is where we spend a good portion of our time. And at GitHub’s scale—where pull requests can range from tiny one-line fixes to changes spanning thousands of files and millions of lines—the pull request review experience has to stay fast and responsive.
We recently shipped the new React-based experience for the Files changed tab (now the default experience for all users). One of our main goals was to ensure a more performant experience across the board, especially for large pull requests. That meant investing in, and consistently prioritizing, the hard problems like optimized rendering, interaction latency, and memory consumption.
For most users before optimization, the experience was fast and responsive. But when viewing large pull requests, performance would noticeably decline. For example, we observed that in extreme cases, the JavaScript heap could exceed 1 GB, DOM node counts surpassed 400,000, and page interactions became extremely sluggish or even unusable. Interaction to Next Paint (INP) scores (a key metric in determining responsiveness) were above acceptable levels, resulting in an experience where users could quantifiably feel the input lag.
Our recent improvements to the Files changed tab have meaningfully improved some of these core performance metrics. While we covered several of these changes briefly in a recent changelog, we’re going to cover them in more detail here. Read on for why they mattered, what we measured, and how those updates improved responsiveness and memory pressure across the board and especially in large pull requests.
As we started to investigate and plan our next steps for improving these performance issues, it became clear early on that there wouldn’t be one silver bullet. Techniques that preserve every feature and browser-native behavior can still hit a ceiling at the extreme end. Meanwhile, mitigations designed to keep the worst-case from tipping over can be the wrong tradeoff for everyday reviews.
Instead of looking for a single solution, we began developing a set of strategies. We selected multiple targeted approaches, each designed to address a specific pull request size and complexity.
Those strategies focused on the following themes:
With these strategies in mind, let’s explore the specific steps we took to address these challenges and how our initial iterations set the stage for the improvements that followed.
With our team’s goal of improving pull request performance, we had three main objectives:
To hit these goals, we focused on simplification: less state, fewer elements, less JavaScript, and fewer React components. Before we look at the results and new architecture, let’s take a step back and look at where we started.
In v1, each diff line was expensive to render. In unified view, a single line required roughly 10 DOM elements; in split view, closer to 15. That’s before syntax highlighting, which adds many more <span> tags and drives the DOM count even higher.
The following is a simplified visual of the React Component structure mixed with the DOM tree elements for v1 diffs.
At the React layer, unified diffs typically contain at least eight components per line, while the split view contain a minimum of 13. And these numbers represent baseline counts; extra UI states like comments, hover, and focus could add more components on top.
This approach made sense to us in v1, when we first ported the diff lines to React from our classic Rails view. Our original plan centered around lots of small reusable React components and maintaining DOM tree structure.
But we also ended up attaching a lot of React event handlers in our small components, often five to six per component. On a small scale, that was fine, but on a large scale that compounded quickly. A single diff line could carry 20+ event handlers multiplied across thousands of lines.
Beyond performance impact, it also increased complexity for developers. This is a familiar scenario where you implement an initial design, only to discover later its limitations when faced with the demands of unbounded data.
To summarize, for every v1 diff line there would be:
This v1 strategy proved unsustainable for our largest pull requests, as we consistently observed that larger pull request sizes directly led to slower INP and increased JavaScript heap usage. We needed to determine the best path for improving this setup.
No change is too small when it comes to performance, especially at scale. For example, we removed unnecessary <code> tags from our line number cells. While dropping two DOM nodes per diff line might appear minor, across 10,000 lines, that’s 20,000 fewer nodes in the DOM. These kinds of targeted, incremental optimizations, no matter how small, compound to create a much faster and more efficient experience. By not overlooking these details, we ensured that every opportunity for improvement was captured, amplifying the overall impact on our largest pull requests.
Refer to the images below to see how v1 looks compared to v2.
This becomes clearer if we look at the component structure behind this HTML:
We went from eight components per diff line to two. Most of the v1 components were thin wrappers that let us share code between Split and Unified views. But that abstraction had a cost: each wrapper carried logic for both views, even though only one rendered at a time. In v2, we gave each view its own dedicated component. Some code is duplicated, but the result is simpler and faster.
For v2, we removed deeply nested component trees, opting for dedicated components for each split and unified diff line. While this led to some code duplication, it simplified data access and reduced complexity.
Event handling is now managed by a single top-level handler using data-attribute values. So, for instance, when you click and drag to select multiple diff lines, the handler checks each event’s data-attribute to determine which lines to highlight, instead of each line having its own mouse enter function. This approach streamlines both code and improves performance.
The most impactful change from v1 to v2 was moving app state for commenting and context menus into their respective components. Given GitHub’s scale, where some pull requests exceed thousands of lines of code, it isn’t practical for every line to carry complex commenting state when only a small subset of lines will ever have comments or menus open. By moving the commenting state into the nested components for each diff line, we ensured that the diff-line component’s main responsibility is just rendering code—aligning more closely with the Single Responsibility Principle.
In v1, we gradually accumulated a lot of O(n) lookups across shared data stores and component state. We also introduced extra re-rendering through useEffect hooks scattered throughout the diff-line component tree.
To address this in v2, we adopted a two-part strategy. First, we restricted useEffect usage strictly to the top level of diff files. We also established linting rules to prevent the introduction of useEffect hooks in line-wrapping React components. This approach enables accurate memoization of diff line components and ensures reliable, predictable behavior.
Next, we redesigned our global and diff state machines to utilize O(1) constant time lookups by employing JavaScript Map. This let us build fast, consistent selectors for common operations throughout our codebase, such as line selection and comment management. These changes have enhanced code quality, improved performance, and reduced complexity by maintaining flattened, mapped data structures.
Now, any given diff line simply checks a map by passing the file path and the line number to determine whether or not there are comments on that line. An access might look like: commentsMap[‘path/to/file.tsx’][‘L8’]
Definitely. The page runs faster than it ever did, and JavaScript heap and INP numbers are massively reduced. For a numeric look, check out the results below. These metrics were evaluated on a pull request using a split diff setting with 10,000 line changes in the diff comparison.
| Metric | v1 | v2 | Improvement |
|---|---|---|---|
| Total lines of code | 2,800 | 2,000 | 27% less |
| Total unique component types | 19 | 10 | 47% fewer |
| Total components rendered | ~183,504 | ~50,004 | 74% fewer |
| Total DOM nodes | ~200,000 | ~180,000 | 10% fewer |
| Total memory usage | ~150-250 MB | ~80-120 MB | ~50% less |
| INP on a large pull request using m1 MacBook pro with 4x slowdown: | ~450 ms | ~100 ms | ~78% faster |
As you can see, this effort had a massive impact, but the improvements didn’t end there.
When you’re working with massive pull requests—p95+ (those with over 10,000 diff lines and surrounding context lines)—the usual performance tricks just don’t cut it. Even the most efficient components will struggle if we try to render tens of thousands of them at once. That’s where window virtualization steps in.
In front-end development, window virtualization is a technique that keeps only the visible portion of a large list or dataset in the DOM at any given time. Instead of loading everything (which would crush memory and slow things to a crawl), it dynamically renders just what you see on screen, and swaps in new elements as you scroll. This approach is like having a moving “window” over your data, so your browser isn’t bogged down by off-screen content.
To make this happen, we integrated TanStack Virtual into our diff view, ensuring that only the visible portion of the diff list is present in the DOM at any time. The impact was huge: we saw a 10X reduction in JavaScript heap usage and DOM nodes for p95+ pull requests. INP fell from 275–700+ milliseconds (ms) to just 40–80 ms for those big pull requests. By only showing what’s needed, the experience is much faster.
To push performance even further, we tackled several major areas across our stack, each delivering meaningful wins for speed and responsiveness. By focusing on trimming unnecessary React re-renders and honing our state management, we cut down wasted computation, making UI updates noticeably faster and interactions smoother.
On the styling front, we swapped out heavy CSS selectors (e.g. :has(...)) and re-engineered drag and resize handling with GPU transforms, eliminating forced layouts and sluggishness and giving users a crisp, efficient interface for complex actions.
We also stepped up our monitoring game with interaction-level INP tracking, diff-size segmentation, and memory tagging, all surfaced in a Datadog dashboard. This continues to give our developers real-time, actionable metrics to spot and squash bottlenecks before they become issues.
On the server side, we optimized rendering to hydrate only visible diff lines. This slashed our time-to-interactive and keeps memory usage in check, ensuring that even huge pull requests feel fast and responsive on load.
Finally, with progressive diff loading and smart background fetches, users are now able to see and interact with content sooner. No more waiting for a massive number of diffs to finish loading.
All together, these targeted optimizations made our UI feel lighter, faster, and ready for anything our users throw at it.
This exciting journey to streamline the diff line architecture yielded substantial improvements in performance, efficiency and maintainability. By reducing unnecessary DOM nodes, simplifying our React component tree, and relocating complex state to conditionally rendered child components, we achieved faster rendering times and lower memory consumption. The adoption of more O(1) data access patterns and stricter rules for state management further optimized performance. This made our UI more responsive (faster INP!) and easier to reason with.
These measurable gains demonstrate that targeted refactoring, even within our large and mature codebase, can deliver meaningful benefits to all users—and that sometimes focusing on small, simple improvements can have the largest impact. To see the performance gains in action, go check out your open pull requests.
The post The uphill climb of making diff lines performant appeared first on The GitHub Blog.
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