docs/PERFORMANCE_OPTIMIZATION.md

Performance Optimization Guide

Date: 2025-01-27 Purpose: Guide for optimizing performance across integrated system Status: Complete

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Overview

This guide provides strategies and best practices for optimizing performance across the integrated workspace.

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Application Performance

Code Optimization

TypeScript/JavaScript

• Use efficient algorithms
• Minimize object creation
• Cache expensive computations
• Use lazy loading

Database Queries

• Use indexes
• Avoid N+1 queries
• Use connection pooling
• Optimize joins

API Performance

• Implement caching
• Use compression
• Minimize payload size
• Batch requests

Caching Strategies

Application Cache

// In-memory cache
const cache = new Map();

function getCached(key: string) {
  if (cache.has(key)) {
    return cache.get(key);
  }
  const value = computeExpensive();
  cache.set(key, value);
  return value;
}

Redis Cache

import { Redis } from 'ioredis';

const redis = new Redis();

async function getCached(key: string) {
  const cached = await redis.get(key);
  if (cached) return JSON.parse(cached);

  const value = await computeExpensive();
  await redis.setex(key, 3600, JSON.stringify(value));
  return value;
}

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Infrastructure Performance

Resource Optimization

Right-Sizing

• Monitor actual usage
• Adjust resources based on metrics
• Use auto-scaling
• Optimize for cost

Load Balancing

• Distribute traffic evenly
• Health check optimization
• Session affinity when needed
• Geographic distribution

Database Performance

Connection Pooling

const pool = new Pool({
  max: 20,
  min: 5,
  idleTimeoutMillis: 30000,
  connectionTimeoutMillis: 2000,
});

Query Optimization

• Use prepared statements
• Index frequently queried fields
• Analyze slow queries
• Use query caching

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Monitoring & Profiling

Application Metrics

Track:

• Response times (p50, p95, p99)
• Throughput (requests/second)
• Error rates
• Resource usage (CPU, memory)

Profiling Tools

Node.js

• clinic.js - Performance profiling
• 0x - Flamegraph generation
• autocannon - Load testing

Database

• EXPLAIN ANALYZE - Query analysis
• Slow query logs
• Connection pool metrics

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Optimization Checklist

Code Level

• Profile before optimizing
• Identify bottlenecks
• Optimize hot paths
• Use efficient algorithms
• Minimize allocations

Infrastructure Level

• Right-size resources
• Enable caching
• Optimize database
• Use CDN for static assets
• Implement load balancing

Monitoring Level

• Set up performance monitoring
• Track key metrics
• Set up alerts
• Regular performance reviews
• Continuous optimization

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Performance Targets

Application

• API Response Time: < 200ms (p95)
• Page Load Time: < 2 seconds
• Database Query Time: < 100ms (p95)
• Cache Hit Rate: > 80%

Infrastructure

• CPU Usage: < 70% average
• Memory Usage: < 80% average
• Network Latency: < 50ms
• Disk I/O: Optimized

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Last Updated: 2025-01-27