Performance Patterns

This section covers performance optimization patterns and design principles for building high-performance, scalable systems.

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🚀 Caching Patterns

• Cache-Aside Pattern - Application-managed caching

• Write-Through/Write-Behind - Database caching strategies

• Distributed Caching - Redis and Memcached patterns

• CDN Strategies - Content delivery optimization

• Browser Caching - Client-side performance optimization

⚡ Load Balancing Patterns

• Round-Robin Load Balancing - Even distribution strategies

• Weighted Load Balancing - Priority-based routing

• Health Check Patterns - Ensuring healthy service routing

• Session Affinity - Sticky session management

• Geographic Load Balancing - Location-based routing

📊 Database Performance Patterns

• Database Sharding - Horizontal partitioning strategies

• Read Replicas - Scaling read operations

• Connection Pooling - Efficient database connections

• Query Optimization - SQL performance tuning

• Database Indexing - Strategic index design

🔄 Asynchronous Processing

• Message Queues - Decoupling with queues

• Background Jobs - Asynchronous task processing

• Event Streaming - Real-time data processing

• Worker Patterns - Parallel task execution

• Batch Processing - Efficient bulk operations

🎯 API Performance Patterns

• API Gateway Optimization - Request routing and transformation

• GraphQL Federation - Efficient data fetching

• Pagination Strategies - Large dataset handling

• Rate Limiting - Protecting against overload

• Response Compression - Bandwidth optimization

📱 Frontend Performance Patterns

• Code Splitting - Lazy loading strategies

• Image Optimization - Efficient media delivery

• Progressive Web Apps - Enhanced user experience

• Service Workers - Offline and caching strategies

• Bundle Optimization - Minimizing JavaScript payloads

Key Metrics

Response Time Metrics

• Latency - Time to first byte (TTFB)

• Response Time - End-to-end request processing

• Percentiles - P50, P95, P99 performance analysis

• Throughput - Requests per second capacity

Resource Utilization

• CPU Usage - Processing efficiency

• Memory Consumption - RAM optimization

• Network Bandwidth - Data transfer efficiency

• Storage I/O - Disk performance optimization

Scalability Metrics

• Concurrent Users - Maximum supported load

• Scalability Factor - Performance under increased load

• Resource Scaling - Horizontal vs vertical scaling efficiency

Technology Stack

Performance Monitoring

• APM Tools: New Relic, Datadog, AppDynamics

• Profiling: JProfiler, .NET Profiler, py-spy

• Load Testing: JMeter, k6, Artillery

• Monitoring: Prometheus, Grafana, CloudWatch

Caching Technologies

• In-Memory: Redis, Memcached, Hazelcast

• CDN: CloudFront, Cloudflare, Fastly

• Database: ElastiCache, Azure Cache, Google Memorystore

• Application: Ehcache, Caffeine, Guava Cache

Database Optimization

• SQL Optimization: Query analyzers, execution plans

• NoSQL: MongoDB, Cassandra, DynamoDB optimization

• Search: Elasticsearch, Solr performance tuning

• Time Series: InfluxDB, TimescaleDB optimization

Best Practices

• Measure First - Establish baseline performance metrics

• Identify Bottlenecks - Profile and analyze performance issues

• Optimize Incrementally - Make small, measurable improvements

• Test Under Load - Simulate production conditions

• Monitor Continuously - Real-time performance tracking

• Plan for Scale - Design for future growth requirements