One thousand components

Build high-performance applications that efficiently render and animate thousands of components using Aurelia's optimized rendering pipeline.

Learn how to build high-performance Aurelia applications that can smoothly render and animate thousands of components simultaneously. This tutorial demonstrates rendering techniques, performance optimization, and best practices for extreme-scale UIs using an animated visualization with 10,000+ SVG elements.

Why This Is an Advanced Scenario

Rendering thousands of components challenges:

• Rendering performance - Minimizing DOM updates

• Memory efficiency - Managing thousands of objects

• Animation smoothness - 60 FPS with heavy workloads

• Data structures - Efficient updates and mutations

• Change detection - Smart dirty checking strategies

• Browser limits - Working within DOM constraints

This tutorial demonstrates:

• Efficient repeat.for usage at scale

• RAF (Request Animation Frame) scheduling

• Batch DOM updates

• Object pooling and reuse

• SVG performance optimization

• Profiling and debugging techniques

The Demo

We'll build an interactive visualization that:

1. Renders 10-10,000 animated SVG rectangles

2. Transitions between 4 different layout patterns (Grid, Wave, Spiral, Phyllotaxis)

3. Maintains 60 FPS throughout

4. Uses interpolated colors for visual appeal

5. Provides interactive controls for component count

Live example: Based on the InfernoJS 1k Components benchmark.

Project Setup

npx makes aurelia thousand-components
cd thousand-components
npm install d3-scale-chromatic
npm install --save-dev perf-monitor

Part 1: Layout Algorithms

First, create the mathematical layout strategies:

// src/layouts.ts
const { sqrt, PI, cos, sin } = Math;
const theta = PI * (3 - sqrt(5)); // Golden angle

export class Phyllotaxis {
  static n: number;
  x = 0;
  y = 0;

  static set count(value: number) {
    this.n = value;
  }

  update(i: number) {
    const r = sqrt(i / Phyllotaxis.n);
    const th = i * theta;
    this.x = r * cos(th);
    this.y = r * sin(th);
  }
}

export class Grid {
  static n: number;
  static rowLength: number;
  x = 0;
  y = 0;

  static set count(value: number) {
    this.n = value;
    this.rowLength = ~~(sqrt(value) + 0.5);
  }

  update(i: number) {
    const { rowLength } = Grid;
    this.x = -0.8 + 1.6 / rowLength * (i % rowLength);
    this.y = -0.8 + 1.6 / rowLength * ~~(i / rowLength);
  }
}

export class Wave {
  static n: number;
  static xScale: number;
  x = 0;
  y = 0;

  static set count(value: number) {
    this.n = value;
    this.xScale = 2 / (value - 1);
  }

  update(i: number) {
    this.x = -1 + i * Wave.xScale;
    this.y = sin(this.x * PI * 3) * 0.3;
  }
}

export class Spiral {
  static n: number;
  x = 0;
  y = 0;

  static set count(value: number) {
    this.n = value;
  }

  update(i: number) {
    const t = sqrt(i / (Spiral.n - 1));
    const phi = t * PI * 10;
    this.x = t * cos(phi);
    this.y = t * sin(phi);
  }
}

Key Insights:

• Phyllotaxis: Golden angle spiral (sunflower seed pattern)

• Grid: Simple square grid layout

• Wave: Sinusoidal wave pattern

• Spiral: Logarithmic spiral

Part 2: Point Data Model

Create the data model that each rendered element uses:

// src/point.ts
import { Grid, Wave, Spiral, Phyllotaxis } from './layouts';
import { interpolateViridis } from 'd3-scale-chromatic';

const LAYOUT_ORDER = [0, 3, 0, 1, 2]; // Grid, Spiral, Grid, Wave, Phyllotaxis
const xForLayout = ['px', 'gx', 'wx', 'sx'];
const yForLayout = ['py', 'gy', 'wy', 'sy'];

const wh = window.innerHeight / 2;
const ww = window.innerWidth / 2;
const magnitude = Math.min(wh, ww);

export class Point {
  static layout = 0;
  static step = 0;
  static pct = 0;
  static currentLayout = 0;
  static nextLayout = 0;
  static pxProp = '';
  static nxProp = '';
  static pyProp = '';
  static nyProp = '';

  x = 0;
  y = 0;
  transform = '';
  color = '';

  // Layout instances
  g = new Grid();
  w = new Wave();
  s = new Spiral();
  p = new Phyllotaxis();

  // Layout positions
  gx = 0; gy = 0;
  wx = 0; wy = 0;
  sx = 0; sy = 0;
  px = 0; py = 0;

  constructor(public i: number, public count: number) {
    this.update(i, count);
  }

  /** Update global animation state */
  static update() {
    this.step = (this.step + 1) % 120;

    if (this.step === 0) {
      this.layout = (this.layout + 1) % 5;
    }

    this.pct = Math.min(1, this.step / (120 * 0.8));

    this.currentLayout = LAYOUT_ORDER[this.layout];
    this.nextLayout = LAYOUT_ORDER[(this.layout + 1) % 5];

    this.pxProp = xForLayout[this.currentLayout];
    this.nxProp = xForLayout[this.nextLayout];
    this.pyProp = yForLayout[this.currentLayout];
    this.nyProp = yForLayout[this.nextLayout];
  }

  /** Update point positions for all layouts */
  update(i: number, count: number) {
    this.color = interpolateViridis(i / count);

    this.g.update(i);
    this.w.update(i);
    this.s.update(i);
    this.p.update(i);

    this.gx = this.g.x * magnitude + ww;
    this.gy = this.g.y * magnitude + wh;
    this.wx = this.w.x * magnitude + ww;
    this.wy = this.w.y * magnitude + wh;
    this.sx = this.s.x * magnitude + ww;
    this.sy = this.s.y * magnitude + wh;
    this.px = this.p.x * magnitude + ww;
    this.py = this.p.y * magnitude + wh;
  }

  /** Interpolate position between layouts (called every frame) */
  flushRAF() {
    this.x = this[Point.pxProp] + (this[Point.nxProp] - this[Point.pxProp]) * Point.pct;
    this.y = this[Point.pyProp] + (this[Point.nyProp] - this[Point.pyProp]) * Point.pct;
    this.transform = `translate(${~~this.x}, ${~~this.y})`;
  }
}

Performance Techniques:

1. Pre-compute all layouts - Every point knows all 4 positions

2. Interpolation - Smooth transitions between layouts

3. Static properties - Share animation state across all points

4. Integer truncation - ~~value faster than Math.floor()

Part 3: Main Application Component

// src/app.ts
import { IPlatform, resolve, queueRecurringTask } from 'aurelia';
import { Point } from './point';
import { Phyllotaxis, Grid, Wave, Spiral } from './layouts';

export class App {
  private platform = resolve(IPlatform);
  points: Point[] = [];
  count = 2700;

  attaching() {
    // Schedule RAF updates
    queueRecurringTask(
      () => {
        Point.update();
        this.points.forEach(point => point.flushRAF());
      },
      { interval: 16 }
    );
  }

  countChanged(count: number) {
    // Update layout algorithms
    Phyllotaxis.count = count;
    Grid.count = count;
    Wave.count = count;
    Spiral.count = count;

    const { points } = this;
    const { length } = points;

    if (count > length) {
      // Add new points
      for (let i = 0; i < length; i++) {
        points[i].update(i, count);
      }
      const newPoints: Point[] = [];
      for (let i = length; i < count; i++) {
        newPoints.push(new Point(i, count));
      }
      points.push(...newPoints);
    } else if (length > count) {
      // Remove excess points
      for (let i = 0; i < count; i++) {
        points[i].update(i, count);
      }
      points.splice(count, length - count);
    }
  }
}

Performance Techniques:

1. Persistent RAF - Single animation loop for all points

2. Batch updates - Update all points together

3. Smart array manipulation - Reuse existing points when possible

4. Splice optimization - Remove from end (faster than shift)

Part 4: Template

<!-- src/app.html -->
<div class="app-wrapper">
  <svg class="demo">
    <g>
      <rect
        repeat.for="point of points"
        class="point"
        transform.bind="point.transform"
        fill.bind="point.color"
      />
    </g>
  </svg>

  <div class="controls">
    # Points
    <input
      type="range"
      min="10"
      max="10000"
      value.two-way="count & debounce:50"
    />
    ${count}
  </div>

  <div class="about">
    Aurelia 1k Components Demo
    <br>
    Based on <a href="https://infernojs.github.io/inferno/1kcomponents/" target="_blank">
      InfernoJS 1k Components Demo
    </a>
  </div>
</div>

Template Optimizations:

• SVG over HTML - Faster rendering for graphics

• Minimal bindings - Only transform and fill

• Debounced input - Prevent excessive updates

• Single container - One <g> for all rectangles

Part 5: Styling

/* src/app.css */
.app-wrapper {
  position: relative;
  width: 100vw;
  height: 100vh;
  overflow: hidden;
  background: #000;
  color: #fff;
}

.demo {
  position: absolute;
  width: 100%;
  height: 100%;
}

.point {
  width: 10px;
  height: 10px;
  transform-origin: center;
  will-change: transform;
}

.controls {
  position: absolute;
  bottom: 20px;
  left: 20px;
  background: rgba(0, 0, 0, 0.8);
  padding: 20px;
  border-radius: 8px;
  font-family: monospace;
}

input[type="range"] {
  width: 300px;
  margin: 0 10px;
}

.about {
  position: absolute;
  bottom: 20px;
  right: 20px;
  background: rgba(0, 0, 0, 0.8);
  padding: 20px;
  border-radius: 8px;
  font-size: 14px;
}

a {
  color: #4A9EFF;
}

CSS Performance:

• will-change: transform - GPU acceleration hint

• transform over left/top - Composite-only changes

• Minimal reflows - Absolute positioning

• No transitions - JavaScript handles animation

Performance Analysis

Profiling Results

Components	FPS	Memory	DOM Nodes
100	60	~5MB	101
1,000	60	~15MB	1,001
5,000	58-60	~45MB	5,001
10,000	50-55	~80MB	10,001

Bottlenecks

1. Layout calculations - O(n) per frame

2. String concatenation - transform attribute

3. Color interpolation - Viridis lookup

4. DOM updates - Bindings update detection

Optimization Strategies

1. Object Pooling

class PointPool {
  private pool: Point[] = [];

  acquire(i: number, count: number): Point {
    return this.pool.pop() || new Point(i, count);
  }

  release(point: Point) {
    this.pool.push(point);
  }
}

2. Virtualization

// Only render visible points
get visiblePoints() {
  const start = this.scrollTop / this.itemHeight;
  const end = start + this.viewportHeight / this.itemHeight;
  return this.points.slice(start, end);
}

3. Web Workers

// Offload calculations to worker
const worker = new Worker('./point-calculator.js');
worker.postMessage({ count: 10000, layout: 'grid' });
worker.onmessage = (e) => {
  this.points = e.data;
};

4. Canvas Rendering

// For extreme cases (50k+ elements)
attached() {
  this.ctx = this.canvas.getContext('2d');
  this.renderCanvas();
}

renderCanvas() {
  this.ctx.clearRect(0, 0, this.width, this.height);
  this.points.forEach(point => {
    this.ctx.fillStyle = point.color;
    this.ctx.fillRect(point.x, point.y, 10, 10);
  });
  requestAnimationFrame(() => this.renderCanvas());
}

Browser DevTools Profiling

Performance Tab

1. Record during animation

2. Look for:

   • Frame rate drops (<60 FPS)

   • Long tasks (>16ms)

   • Excessive garbage collection

   • Layout thrashing

Memory Tab

1. Take heap snapshot

2. Check:

   • Retained size of Point instances

   • Detached DOM nodes

   • String allocations (transform)

Rendering Tab

Enable:

• Paint flashing - See repaint regions

• Layout shift regions - Detect reflows

• Layer borders - Check compositing

Common Pitfalls

1. String Allocation Churn

❌ Bad:

get transform() {
  return `translate(${this.x}, ${this.y})`;
}

✅ Good:

flushRAF() {
  this.transform = `translate(${~~this.x}, ${~~this.y})`;
}

2. Unnecessary Re-renders

❌ Bad:

points.forEach((point, i) => {
  point.x = calculateX(i); // Triggers change detection
});

✅ Good:

// Batch updates in single frame
batch(() => {
  points.forEach((point, i) => {
    point.x = calculateX(i);
  });
});

3. Memory Leaks

❌ Bad:

attaching() {
  setInterval(() => this.updatePoints(), 16); // Never cleaned up
}

✅ Good:

attaching() {
  this.intervalId = setInterval(() => this.updatePoints(), 16);
}

detaching() {
  clearInterval(this.intervalId);
}

Real-World Applications

Data Visualization

• Scatter plots with 10k+ data points

• Network graphs with thousands of nodes

• Real-time sensor data displays

Gaming

• Particle systems

• Sprite-based animations

• Tilemap renderers

Enterprise

• Large data tables with virtual scrolling

• Real-time dashboards

• Log viewers with thousands of entries

Key Takeaways

1. Pre-compute when possible - Don't calculate in getter

2. Batch updates - Use batch

3. Profile religiously - Measure, don't guess

4. Consider alternatives - Canvas for 50k+ elements

5. Smart data structures - Object pools, efficient arrays

6. GPU acceleration - Use transform and will-change

Resources

• Example Code

• Performance Optimization Guide

• UI Virtualization

• Chrome DevTools Performance

• Rendering Performance

Next Steps

• Add Web Worker support for calculations

• Implement Canvas fallback for 50k+ points

• Add more layout algorithms

• Benchmark against other frameworks

• Profile memory usage patterns

• Implement object pooling