<h2>Building a High-Performance, Fully Typed React Data Grid — Architecture, Tradeoffs, and Lessons</h2>
<p><a href="https://react-table-craft.vercel.app/docs" target="_blank" rel="noopener nofollow">Documentation</a>
<a href="https://www.npmjs.com/package/react-table-craft/access" target="_blank" rel="noopener nofollow">NPM</a>
<a href="https://github.com/Ahmed-Elkhdrawy/table-craft" target="_blank" rel="noopener nofollow">GitHub</a></p>
<p>Data tables look simple — until they aren’t.</p>
<p>In real production dashboards, tables quickly become the most complex UI surface in the entire system. Once you combine virtualization, server-side data, filtering, sorting, URL state sync, accessibility, i18n, and RTL layouts — you’re no longer building a component. You’re building infrastructure.</p>
<p>This post walks through how I designed a modern React data grid focusing on performance, type safety, extensibility, and global-ready UI.</p>
<h2>The Real Problem with Most Table Libraries</h2>
<p>Most libraries optimize for one of three things:</p>
<h3>1️⃣ Simplicity</h3>
<p>Great DX, but breaks at scale.</p>
<h3>2️⃣ Feature Power</h3>
<p>Massive feature set, but:</p>
<ul>
<li>Hard to customize</li>
<li>Heavy bundle size</li>
<li>Steep learning curve</li>
</ul>
<h3>3️⃣ UI-first Design</h3>
<p>Looks good, but logic is tightly coupled to rendering.</p>
<p>I wanted something different:</p>
<ul>
<li>Headless-first architecture</li>
<li>Fully typed API surface</li>
<li>Config-driven feature system</li>
<li>Built-in global readiness (i18n + RTL)</li>
<li>Performance predictable at scale</li>
</ul>
<h2>Core Architectural Principles</h2>
<h3>Headless Core + Styled Shell</h3>
<p>Instead of shipping a monolithic component:</p>
<pre><code>core/
  <span class="hljs-keyword">state</span> engine
  feature engine
  data pipeline

react/
  hooks
  adapters

ui/
  styled components (shadcn + tailwind)
</code></pre>
<p>This separation allowed:</p>
<ul>
<li>UI swapping</li>
<li>Custom renderers</li>
<li>Framework portability</li>
<li>Easier testing</li>
</ul>
<h3>Config Layering System</h3>
<p>One of the hardest problems in reusable UI infrastructure is override strategy.</p>
<p>I implemented multi-layer config merging:</p>
<pre><code>Global <span class="hljs-built_in">Config</span>
   ↓
Provider <span class="hljs-built_in">Config</span>
   ↓
<span class="hljs-keyword">Instance </span><span class="hljs-built_in">Config</span>
   ↓
Feature <span class="hljs-built_in">Config</span>
</code></pre>
<p>This enables:</p>
<ul>
<li>Enterprise default behavior</li>
<li>Per-product overrides</li>
<li>Per-table customization</li>
<li>Feature-level fine control</li>
</ul>
<p>Without prop explosion.</p>
<h2>Performance Strategy</h2>
<h3>Virtualization First — Not Optional</h3>
<p>Instead of making virtualization a plugin, it’s core behavior.</p>
<p>Goals:</p>
<ul>
<li>Constant DOM node count</li>
<li>Predictable memory usage</li>
<li>Stable FPS during scroll</li>
</ul>
<h3>Render Minimization Strategy</h3>
<p>Key decisions:</p>
<ul>
<li>Column definitions are memoized</li>
<li>Feature state isolated by module</li>
<li>Derived state cached via selectors</li>
<li>Stable callback references</li>
</ul>
<p>Target:</p>
<p>Zero unnecessary re-renders.</p>
<h3>Data Pipeline Design</h3>
<pre><code>Raw Data
 → <span class="hljs-keyword">Server</span> Adapter
 → Feature Transforms
 → <span class="hljs-keyword">Row</span> Model
 → Virtual <span class="hljs-keyword">Window</span>
 → Renderer
</code></pre>
<p>Each step is isolated and testable.</p>
<h2>Type System Design</h2>
<p>Strict generics across:</p>
<ul>
<li>Row data model</li>
<li>Column definitions</li>
<li>Filter models</li>
<li>Sorting model</li>
<li>API adapters</li>
</ul>
<p>Example design philosophy:</p>
<p>If it can be typed — it must be typed.</p>
<p>Result:</p>
<p>Autocomplete becomes documentation.</p>
<h2>i18n and RTL — Built Into State, Not UI</h2>
<p>Common mistake:</p>
<p>Translation handled only in UI layer.</p>
<p>Instead:</p>
<ul>
<li>Locale affects formatting logic</li>
<li>Direction affects layout calculations</li>
<li>State is locale-stable</li>
<li>Language switch doesn’t remount table</li>
</ul>
<h2>Feature Flag Architecture</h2>
<p>Features are modular:</p>
<ul>
<li>Sorting</li>
<li>Filtering</li>
<li>Selection</li>
<li>Export</li>
<li>Column visibility</li>
<li>Search</li>
<li>Floating actions</li>
</ul>
<p>Each feature registers:</p>
<ul>
<li>State slice</li>
<li>Reducers</li>
<li>Selectors</li>
<li>UI bindings</li>
</ul>
<h2>Performance Targets</h2>
<ul>
<li>Initial render (1k rows): &lt; 16ms</li>
<li>Virtualized datasets: 100k+ rows</li>
<li>Bundle goal: &lt; 8KB gzipped core</li>
<li>DOM nodes: visible rows only</li>
</ul>
<h2>Lessons Learned</h2>
<h3>Tables Are Systems, Not Components</h3>
<p>If you don’t design them like systems, they collapse under product growth.</p>
<h3>Type Safety Multiplies Value Over Time</h3>
<p>The bigger the codebase, the more generics pay off.</p>
<h3>Config &gt; Props</h3>
<p>Props scale poorly in infrastructure components.</p>
<h2>What I’d Do Differently</h2>
<p>If starting today:</p>
<ul>
<li>Build plugin API earlier</li>
<li>Add DevTools from day one</li>
<li>Ship performance profiling hooks built-in</li>
</ul>
<h2>Final Thought</h2>
<p>The moment your product depends on data — your table stops being UI.</p>
<p>It becomes architecture.</p>
<p>If you’re building something similar, I’d love to hear:</p>
<p>What’s been the hardest part of scaling tables in production?</p>


Ahmed Elkhdrawy

A deep dive into the architecture of react-table-craft, a custom React data grid library built for production scale. Covers the headless-first design separating core state engine from UI, a multi-layer config merging system to avoid prop explosion, virtualization as a first-class feature, strict TypeScript generics throughout, and built-in i18n/RTL support at the state level. Also discusses a modular feature flag system for sorting, filtering, selection, and export, along with performance targets like sub-16ms initial render for 1k rows and support for 100k+ row virtualized datasets.

Build Powerful Tables. Ship Faster.