Performance Optimization: Profiling, Caching, and Latency Reduction

Measuring What Matters

Before optimizing, measure. You can't improve what you don't measure.

Key Metrics

Latency Breakdown

Where Time Goes

Typical Latencies

Caching Strategies

The Cache Hierarchy

Cache Patterns

Cache Invalidation

Database Optimization

Indexing Strategies

Query Optimization

-- Bad: SELECT *
SELECT * FROM orders WHERE user_id = 123;

-- Good: SELECT specific columns
SELECT id, total, status, created_at 
FROM orders 
WHERE user_id = 123 
AND status = 'completed'
LIMIT 10;

Denormalization Trade-offs

Network Optimization

Connection Pooling

HTTP/2 and HTTP/3 Benefits

Compression

Code-Level Optimization

Algorithm Complexity

The inefficient version uses nested loops causing O(n²) complexity. The optimized version sorts the data first (O(n log n)), then iterates once, improving overall complexity to O(n log n).

Avoiding N+1 Queries

The N+1 query problem occurs when fetching users then making a separate query for each user's posts. The improved version uses a single JOIN query to fetch all data at once, reducing database round trips from n+1 to 1.

Async I/O

The blocking approach waits for each HTTP request sequentially. The async approach uses aiohttp with asyncio.gather to fetch all URLs concurrently, dramatically reducing total wait time from the sum of all latencies to the slowest single request.

Monitoring and Profiling

Application Performance Monitoring (APM)

Popular Tools

What to Remember for Interviews

1. Measure first: Optimize based on data, not assumptions
2. Cache aggressively: Memory is cheaper than compute
3. Database tuning: Index wisely, avoid N+1, consider denormalization
4. Network efficiency: Use HTTP/2+, compress, keep connections alive
5. P99 latency: Some slow requests affect all users