Increasing performance on Large Data Tables

The Problem

As data volume increased, table performance degraded noticeably. Once a table reached a few thousand rows, scrolling became laggy, interactions felt delayed, and the slowdown spilled into the rest of the page.

Even when users weren’t actively interacting with the table, the cost of rendering and maintaining a large DOM tree made the entire application feel slower, increasing frustration and abandonment.

Why This Was Hard

Tables are deceptively expensive UI components. Each row brings layout, paint, and event-handling costs, and naïvely rendering all rows upfront scales poorly as data grows.

At the same time, these tables needed to remain flexible: supporting sorting, filtering, dynamic row heights, and interactive cells. Any performance improvement had to preserve usability, not trade it away.

The Decision

To support large datasets without degrading perceived performance, rendering all rows as static HTML was no longer viable.

Virtualizing the table — rendering only the rows visible in the viewport — allowed us to decouple dataset size from render cost, keeping interactions responsive regardless of how much data was loaded.

The Approach

Rather than optimizing individual bottlenecks in the existing table, I focused on reducing work altogether.

Key observations guided the implementation

• Rendering fewer DOM nodes consistently outperformed micro-optimizations
• Simpler row structures improved both performance and readability
• Transforming and reshaping data on every render was significantly more expensive than expected

The table was restructured so that:

• Only visible rows were rendered at any given time
• Data transformation happened once, upstream, instead of during render
• Row components were kept deliberately minimal to reduce layout and paint costs.

This allowed the table to scale to large datasets while preserving a smooth scrolling experience.

The Result

Table interactions remained fast and predictable even with large datasets. Scrolling was smooth, input latency was reduced, and the rest of the application no longer felt sluggish when a large table was present.

By focusing on reducing unnecessary work rather than chasing incremental optimizations, the solution scaled cleanly and remained easy to reason about as new features were added.