Performance & Optimization Glossary

Category: Programming Tags: glossary, performance, optimization, caching, scalability

Cache

What it is: Temporary storage that saves frequently accessed data for faster retrieval.

Why it matters: Caching is one of the most effective performance optimizations. It reduces database queries, API calls, and computation time, dramatically improving user experience.

Types of caching: - Browser cache - Stores files locally on user's device - CDN cache - Content cached at edge servers globally - Application cache - In-memory storage (Redis, Memcached) - Database cache - Query results stored in memory - CPU cache - Hardware-level caching in processors

Cache strategies: - Cache-aside (Lazy loading) - Application manages cache explicitly - Write-through - Write to cache and database simultaneously - Write-behind (Write-back) - Write to cache immediately, database later - Refresh-ahead - Proactively refresh cache before expiration

Cache invalidation: - TTL (Time To Live) - Automatic expiration after set time - Manual invalidation - Explicitly remove outdated data - Event-based - Invalidate when underlying data changes - LRU (Least Recently Used) - Remove oldest unused items when full

Cache busting techniques: - Query parameters - Add ?v=123 to URLs - Filename versioning - Include version in filename - Cache headers - Control browser caching behavior - CDN purging - Manually clear CDN caches

When you'll use it: Almost every application benefits from caching. It's fundamental to performance optimization.

Load Balancing

What it is: Distribution of incoming requests across multiple servers to ensure no single server becomes overwhelmed.

Why it matters: Load balancing improves application availability, performance, and scalability by distributing traffic and providing redundancy.

Load balancing algorithms: - Round Robin - Requests distributed sequentially to each server - Least Connections - Routes to server with fewest active connections - Weighted Round Robin - Servers get different amounts of traffic - IP Hash - Routes based on client IP address - Least Response Time - Routes to fastest responding server - Random - Distributes requests randomly

Types of load balancers: - Layer 4 (Transport) - Routes based on IP and port information - Layer 7 (Application) - Routes based on content (HTTP headers, URLs) - Hardware load balancers - Dedicated physical devices - Software load balancers - Applications running on standard servers - Cloud load balancers - Managed services (AWS ELB, Azure Load Balancer)

Health checks: - Active monitoring - Load balancer actively checks server health - Passive monitoring - Monitor server responses to real requests - Custom health endpoints - Application-specific health checks - Graceful degradation - Remove unhealthy servers from rotation

When you'll use it: Any application expecting significant traffic or requiring high availability.

Horizontal vs Vertical Scaling

What they are: Two approaches to increasing system capacity to handle more load.

Vertical Scaling (Scale Up): - Definition - Add more power to existing servers (CPU, RAM, storage) - Pros - Simple to implement, no application changes needed - Cons - Limited by hardware constraints, single point of failure - Example - Upgrade server from 8GB to 32GB RAM

Horizontal Scaling (Scale Out): - Definition - Add more servers to handle increased load - Pros - Virtually unlimited scaling, better fault tolerance - Cons - Requires application design for distribution - Example - Add 3 more web servers behind load balancer

Why it matters: Understanding scaling options helps architects design systems that can grow with demand and remain available during failures.

Design considerations: - Stateless applications - Easier to scale horizontally - Database scaling - Often the bottleneck in horizontal scaling - Session management - Sticky sessions vs distributed sessions - Data consistency - Challenges with distributed data

When you'll use it: - Vertical scaling - Quick fixes, legacy applications, database servers - Horizontal scaling - Web applications, microservices, cloud-native apps

Database Optimization

What it is: Techniques to improve database performance, reduce query times, and handle larger datasets efficiently.

Why it matters: Databases are often the bottleneck in application performance. Optimizing database operations can dramatically improve overall system performance.

Query optimization: - Indexing - Create indexes on frequently queried columns - Query analysis - Use EXPLAIN to understand query execution - Query rewriting - Optimize SQL for better performance - Avoiding N+1 queries - Fetch related data in single query

Database design optimization: - Normalization - Reduce data redundancy - Denormalization - Strategic redundancy for performance - Partitioning - Split large tables across multiple storage units - Sharding - Distribute data across multiple database servers

Indexing strategies: - Primary indexes - Unique identifiers (usually automatic) - Secondary indexes - Additional indexes on frequently queried columns - Composite indexes - Indexes on multiple columns - Covering indexes - Include all needed columns in index

Connection management: - Connection pooling - Reuse database connections - Connection limits - Prevent database overload - Read replicas - Distribute read operations across multiple databases - Write/read separation - Route queries to appropriate servers

When you'll use it: Any application with significant database usage needs optimization for good performance.

Lazy Loading

What it is: Design pattern that defers loading of non-critical resources until they're actually needed.

Why it matters: Lazy loading improves initial page load times, reduces bandwidth usage, and provides better user experience by loading content progressively.

Types of lazy loading: - Image lazy loading - Load images as they enter viewport - Code splitting - Load JavaScript modules on demand - Data lazy loading - Fetch additional data when needed - Route-based loading - Load page components when navigating

Implementation techniques: - Intersection Observer API - Modern browser API for viewport detection - Scroll event listeners - Traditional approach (less efficient) - Libraries - LazyLoad, Lozad.js, react-lazyload - Native loading="lazy" - Browser support for images and iframes

Benefits: - Faster initial load - Reduced initial payload size - Bandwidth savings - Don't load unused content - Better perceived performance - Content appears to load faster - Improved mobile experience - Especially important on slower connections

Best practices: - Placeholder content - Show skeleton or blur while loading - Progressive enhancement - Ensure functionality without JavaScript - Preload critical content - Don't lazy load above-the-fold content - Error handling - Graceful degradation when loading fails

When you'll use it: Any application with images, large datasets, or code that isn't immediately needed.

Content Delivery Network (CDN)

What it is: Network of geographically distributed servers that deliver web content to users from the nearest location.

Why it matters: CDNs dramatically improve website performance by reducing latency, decrease server load, and provide better user experience globally.

How CDNs work: 1. User requests content - Browser requests image, CSS, JavaScript 2. CDN edge server responds - Nearest server provides cached content 3. Cache miss handling - If not cached, fetch from origin server 4. Content caching - Store content at edge for future requests 5. Cache expiration - Content refreshed based on TTL settings

Types of CDN content: - Static assets - Images, CSS, JavaScript files - Dynamic content - API responses, personalized content - Video streaming - Optimized for media delivery - Software downloads - Large files distributed globally

Popular CDN providers: - Cloudflare - Free tier, DDoS protection, security features - AWS CloudFront - Integrated with AWS ecosystem - Fastly - Real-time analytics, edge computing capabilities - KeyCDN - Simple pricing, good performance - Azure CDN - Microsoft's global network

CDN optimization techniques: - Cache headers - Control how long content is cached - Compression - Gzip/Brotli compression for text files - Image optimization - WebP format, responsive images - HTTP/2 support - Multiplexing and server push

When you'll use it: Any website with global users, media content, or performance requirements.

Minification

What it is: Process of removing unnecessary characters from code (whitespace, comments, long variable names) without changing functionality.

Why it matters: Minification reduces file sizes, leading to faster downloads, less bandwidth usage, and improved page load times.

What gets minified: - JavaScript - Remove whitespace, shorten variable names, remove comments - CSS - Remove whitespace, combine selectors, optimize properties - HTML - Remove whitespace, comments, optional tags - Images - Compress without quality loss (lossless compression)

Minification techniques: - Whitespace removal - Eliminate unnecessary spaces, tabs, newlines - Comment removal - Strip out developer comments - Variable name shortening - Use shorter variable names (a, b, c) - Dead code elimination - Remove unused functions and variables - Property optimization - Shorthand CSS properties

Popular minification tools: - JavaScript - UglifyJS, Terser, esbuild - CSS - cssnano, clean-css, PurgeCSS - HTML - HTMLMinifier, html-minifier-terser - Build tools - Webpack, Rollup, Parcel (built-in minification)

Best practices: - Source maps - Maintain debugging capability in production - Automated process - Integrate into build pipeline - Test minified code - Ensure functionality isn't broken - Separate concerns - Different strategies for different file types

When you'll use it: Every production web application should minify assets for optimal performance.

Bundling

What it is: Process of combining multiple files into single files to reduce the number of HTTP requests.

Why it matters: Bundling reduces network overhead, improves caching efficiency, and can significantly improve page load times, especially for applications with many small files.

Types of bundling: - JavaScript bundling - Combine multiple JS files into one - CSS bundling - Merge stylesheets into single file - Asset bundling - Include images, fonts as data URLs - Code splitting - Strategic bundling with multiple output files

Bundling strategies: - Single bundle - Everything in one file (simple but not optimal) - Vendor bundling - Separate bundle for third-party libraries - Route-based bundling - Different bundles for different pages - Dynamic imports - Load bundles on demand

Popular bundling tools: - Webpack - Most popular, highly configurable - Rollup - Tree-shaking focus, good for libraries - Parcel - Zero-configuration bundler - esbuild - Extremely fast Go-based bundler - Vite - Fast development with optimized production builds

Bundle optimization: - Tree shaking - Remove unused code from bundles - Code splitting - Split bundles for better caching - Chunk optimization - Balance bundle sizes - Compression - Gzip/Brotli compression

When you'll use it: Any modern web application with multiple JavaScript or CSS files.

Debouncing and Throttling

What they are: Techniques to control the frequency of function execution, particularly useful for performance optimization of event handlers.

Debouncing: - Definition - Delay function execution until after a specified time has passed since the last call - Use case - Search suggestions, form validation, resize events - Example - Only send search request 300ms after user stops typing

Throttling: - Definition - Limit function execution to at most once per specified time interval - Use case - Scroll events, mousemove events, API rate limiting - Example - Update scroll position indicator at most once every 100ms

Why they matter: These techniques prevent performance issues from high-frequency events, reduce server load, and improve user experience.

Implementation examples:

Debounce:

function debounce(func, delay) {
  let timeoutId;
  return function(...args) {
    clearTimeout(timeoutId);
    timeoutId = setTimeout(() => func.apply(this, args), delay);
  };
}

// Usage
const debouncedSearch = debounce(searchFunction, 300);
searchInput.addEventListener('input', debouncedSearch);

Throttle:

function throttle(func, delay) {
  let timeoutId;
  let lastExecTime = 0;
  return function(...args) {
    const currentTime = Date.now();
    if (currentTime - lastExecTime > delay) {
      func.apply(this, args);
      lastExecTime = currentTime;
    }
  };
}

// Usage
const throttledScroll = throttle(handleScroll, 100);
window.addEventListener('scroll', throttledScroll);

When you'll use them: - Debouncing - User input, API calls, expensive calculations - Throttling - Animation, scroll events, mouse tracking

Memory Management

What it is: Process of allocating, using, and freeing memory efficiently in applications to prevent memory leaks and optimize performance.

Why it matters: Poor memory management leads to memory leaks, performance degradation, and application crashes. Good memory management ensures stable, performant applications.

Memory concepts: - Stack memory - Fast, automatic, limited size (local variables) - Heap memory - Slower, manual management, larger (objects, arrays) - Garbage collection - Automatic memory cleanup in managed languages - Memory leaks - Memory that's allocated but never freed

Common memory issues: - Memory leaks - Objects not properly disposed - Circular references - Objects referencing each other prevent cleanup - Event listener leaks - Forgotten event handlers - Closure leaks - Functions holding onto unnecessary scope - DOM leaks - Detached DOM nodes still referenced

Memory optimization techniques: - Object pooling - Reuse objects instead of creating new ones - Weak references - References that don't prevent garbage collection - Manual cleanup - Explicitly remove references when done - Efficient data structures - Choose appropriate data types

Memory profiling tools: - Browser DevTools - Memory tab for heap snapshots - Node.js - Built-in memory usage monitoring - Application monitoring - New Relic, Datadog memory metrics - Language-specific tools - JProfiler (Java), Instruments (iOS)

When you'll use it: Any long-running application, especially those with dynamic content creation or event handling.

Performance Monitoring

What it is: Continuous measurement and analysis of application performance metrics to identify issues and optimization opportunities.

Why it matters: You can't improve what you don't measure. Performance monitoring helps identify bottlenecks, track improvements, and ensure good user experience.

Key performance metrics: - Response time - How long requests take to complete - Throughput - Number of requests processed per second - Error rate - Percentage of failed requests - Availability - Percentage of time system is operational - Resource utilization - CPU, memory, disk, network usage

Client-side metrics: - First Contentful Paint (FCP) - When first content appears - Largest Contentful Paint (LCP) - When main content loads - First Input Delay (FID) - Time to interactive - Cumulative Layout Shift (CLS) - Visual stability - Time to Interactive (TTI) - When page becomes fully interactive

Server-side metrics: - Database query time - How long database operations take - API response time - Time for API endpoints to respond - Background job processing - Queue processing times - Cache hit rates - Effectiveness of caching strategies

Performance monitoring tools: - Real User Monitoring (RUM) - Google Analytics, New Relic Browser - Synthetic monitoring - Pingdom, GTmetrix, WebPageTest - Application Performance Monitoring - New Relic, Datadog, AppDynamics - Open source - Prometheus + Grafana, ELK stack

When you'll use it: Every production application should have performance monitoring to ensure good user experience.

Database Indexing

What it is: Database optimization technique that creates additional data structures to speed up data retrieval operations.

Why it matters: Proper indexing can make database queries orders of magnitude faster, while poor indexing can severely degrade performance.

How indexes work: - Data structure - Usually B-trees or hash tables - Pointer system - Indexes point to actual data locations - Trade-offs - Faster reads but slower writes and more storage

Types of indexes: - Primary index - Usually on primary key (automatic) - Secondary index - Additional indexes on other columns - Composite index - Index on multiple columns - Unique index - Ensures uniqueness while optimizing lookups - Partial index - Index only subset of rows meeting condition - Full-text index - For text search operations

Indexing strategies: - Query analysis - Identify frequently used WHERE clauses - Composite index order - Most selective columns first - Covering indexes - Include all needed columns in index - Index maintenance - Regular analysis and optimization

Index performance considerations: - Selectivity - Indexes work best on columns with many unique values - Cardinality - High cardinality columns benefit more from indexing - Update frequency - Frequently updated columns have indexing overhead - Index size - Large indexes may not fit in memory

When you'll use it: Any database with performance requirements needs strategic indexing on frequently queried columns.