The 10 Levels of Building a Data Grid - My 1 Year Journey of Building a Table.

Data Structures

I started with a simple array of row objects, but quickly realized this approach had severe performance limitations. The first major optimization was switching to a column-major data structure where each column stores its own array of values. This allowed for much faster column operations and reduced memory overhead when only specific columns needed to be rendered.

Rendering Optimizations

The initial implementation rendered every cell in the visible viewport, which caused noticeable lag when scrolling through large datasets. I implemented virtual scrolling that only renders rows within the visible window plus a small buffer zone. This reduced DOM nodes from thousands to just a few dozen.

Key rendering techniques used:

• Windowed rendering: Only render rows visible in the viewport
• Row recycling: Reuse DOM elements instead of creating new ones
• Batch DOM updates: Group all DOM mutations into single operations
• RequestAnimationFrame: Sync rendering with browser paint cycles

Column Expansion System

One of the main reasons I avoided AG-Grid was the need for dynamic column expansion. When a field contains an object or array, my table automatically creates nested columns to the right of the parent column. This required:

• Recursive column definition generation
• Dynamic column width calculation
• Proper handling of deeply nested data structures
• Maintaining column state during expansion/collapse

Embedded Text Search

The search functionality needed to find text within non-visible columns (those inside objects or arrays). I implemented a custom search algorithm that:

1. Flattens all nested data into a searchable index
2. Performs case-insensitive substring matching
3. Highlights matching cells in the rendered view
4. Scrolls to and expands the first match automatically

Performance Benchmarks

I used MongoDB Compass (which uses AG-Grid) as my baseline comparison. In my testing with datasets of 100,000 rows and 50 columns, my custom implementation showed:

• Initial render: 40% faster than AG-Grid
• Scroll performance: 60% smoother frame rates
• Memory usage: 30% lower peak memory consumption
• Search response time: Under 100ms for full-text search across all columns

Lessons Learned

Building a performant data grid from scratch taught me several important lessons:

• Never underestimate the complexity of UI components
• Profile early and often - assumptions about bottlenecks are usually wrong
• Virtual scrolling is essential but tricky to implement correctly
• Column-major data layouts can significantly improve cache performance
• Custom solutions are sometimes necessary despite excellent existing libraries

The journey took a full year of incremental improvements, but the result is a table that handles enterprise-scale data with desktop-grade smoothness in a web browser.