Performance optimization techniques

This guide covers advanced performance optimization techniques for Aurelia applications, including framework-specific optimizations, build configuration, and best practices for high-performance applications.

Framework-Specific Optimizations

Task Queue Performance

The Aurelia task queue provides several performance optimization features:

Task Batching

Batch DOM updates to improve rendering performance:

import { batch } from 'aurelia';

// Batch multiple DOM updates in a single frame
batch(() => {
  // both assignment will not immediately trigger rerendering
  component.prop = someValue;
  component2.prop = someOtherValue;
});

// With mordern browser implementation, normally all DOM changes execute in the same task
// and without triggering layout-ing or reflow unless there's a DOM property read in between
// that triggers those.
element1.style.left = '100px';
element2.style.top = '200px';
element3.textContent = 'Updated';

State Management Performance

Memoization System

Use the built-in memoization system for expensive computations:

Shared Memoization

Share memoized selectors across components for better performance:

Computed Observer Performance

Sync vs Async Flush Modes

Choose the appropriate flush mode for computed properties:

Computed Property Optimization

Optimize computed properties for better performance:

Manual computed dependencies declaration

Read more on @computed decorator here.

Watch Performance Optimization

Efficient Watch Expressions

Use efficient watch expressions to minimize performance impact:

Watch Flush Timing

Control when watch callbacks execute:

Binding Behaviors for Performance

Throttle Binding Behavior

Limit how often a binding updates, useful for expensive operations triggered by user input:

Debounce Binding Behavior

Delay binding updates until user stops typing, ideal for search-as-you-type:

Performance Tips:

  • Use throttle for continuous events (scroll, mousemove, resize)

  • Use debounce for discrete user input (typing, form fields)

  • Throttle allows periodic updates; debounce waits for quiet period

  • Default delay is 200ms if not specified

Virtual Repeat Performance

Optimize Virtual Repeat for Large Collections

Configure virtual-repeat inline for optimal performance with large datasets:

Virtual Repeat with Variable Heights

For items with varying heights, enable the variable-height option:

Performance Note: Variable sizing has more overhead than fixed sizing. Use fixed heights when possible for best performance.

Build Optimization

Bundle Size Optimization

Tree Shaking Configuration

Configure your bundler for optimal tree shaking:

Selective Imports

Import only what you need from Aurelia packages:

Code Splitting

Route-Based Code Splitting

Split your application by routes for better loading performance:

Component-Based Code Splitting

Split large components into separate chunks:

Production Optimization

Minification and Compression

Configure production builds for optimal performance:

Service Worker Integration

Implement caching strategies for better performance:

Memory Management

Component Cleanup

Proper Event Listener Cleanup

Subscription Management

Memory Leak Prevention

Avoid Circular References

WeakMap for Metadata

Use WeakMap for storing metadata that should be garbage collected:

Observable Batching

Batch Multiple State Changes

When making multiple property changes, use batch() to combine them into a single change notification:

Batch Array Mutations

Batch multiple array operations to prevent repeated re-renders:

Performance Benefits:

  • Reduces the number of change notifications

  • Prevents unnecessary intermediate UI updates

  • Particularly effective when updating multiple related properties

  • Essential for bulk data operations

Large Data Handling

Pagination Strategies

Virtual Pagination

Infinite Scroll

Data Streaming

Streaming Large Datasets

Performance Monitoring

Runtime Performance Profiling

Performance Metrics Collection

Real-World Performance Scenarios

Scenario 1: Optimized Data Grid

Build a high-performance data grid with 10,000+ rows:

Performance Features Used:

  • debounce prevents filtering on every keystroke

  • virtual-repeat renders only visible rows

  • @computed with explicit deps caches filter results

  • Fixed item-height enables optimal scrolling

Scenario 2: Real-Time Dashboard Updates

Handle high-frequency updates efficiently:

Performance Features Used:

  • batch() combines multiple updates into one notification

  • Persistent task with delay for regular updates

  • Task cancellation on component detach prevents leaks

Optimize large image galleries:

Performance Features Used:

  • variable-height handles different aspect ratios

  • Native lazy loading with loading="lazy"

  • Task queue prevents UI blocking during data loading

  • Chunked loading for progressive rendering

Scenario 4: Complex Form with Validation

Optimize forms with many fields:

Performance Features Used:

  • debounce on inputs reduces validation frequency

  • batch() when loading initial form data

  • Async flush for validation computation

  • Object.assign for efficient property updates

Best Practices Summary

1. Framework Usage

  • Use batch for array mutation operations

  • Implement memoization with createStateMemoizer for expensive state computations

  • Choose appropriate flush modes (sync or async) for computed properties

  • Optimize watch expressions and prefer computed properties

  • Use throttle for continuous events, debounce for discrete user input

  • Enable deep observation only when needed for nested objects

2. Memory Management

  • Always clean up event listeners and subscriptions in detaching()

  • Use WeakMap for metadata storage that should be garbage collected

  • Avoid circular references or use explicit cleanup

  • Cancel persistent tasks when components detach

  • Monitor memory usage during development

3. Data Handling

  • Implement virtual-repeat for lists with 100+ items

  • Use fixed item-height for best virtual-repeat performance

  • Enable variable-height only when necessary

  • Use pagination or infinite scroll for large datasets

  • Process data in batches with batch() to avoid blocking the UI

  • Stream large datasets when possible using task queue

4. Binding Optimization

  • Use debounce on form inputs (300-500ms for text, 200ms for other fields)

  • Use throttle on scroll/resize/mousemove handlers (100-200ms)

  • Batch multiple observable changes with batch()

  • Prefer @computed with explicit deps over complex expressions in templates

  • Use flush: 'async' (default) unless immediate updates are critical

5. Build Optimization

  • Configure tree shaking properly in your bundler

  • Use code splitting for routes and large components

  • Optimize bundle size with selective imports

  • Implement service worker caching for production apps

  • Minify and compress assets

6. Performance Monitoring

  • Monitor component lifecycle performance

  • Track memory usage patterns with browser DevTools

  • Use performance metrics to identify bottlenecks

  • Test with realistic data volumes

These optimization techniques will help you build high-performance Aurelia applications that scale well and provide excellent user experiences.

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