Building Scalable Systems at Amazon

As a Delivery Consultant at Amazon Web Services, I've had the opportunity to work on systems that serve millions of users. Here are some key lessons I've learned about building scalable, reliable software.

The Importance of Design

Before writing a single line of code, we spend significant time on design documents. This upfront investment pays dividends:

• Clarity - Everyone understands the system goals
• Feedback - Catch issues before implementation
• Documentation - Built-in reference for future engineers

Distributed Systems Principles

CAP Theorem

In distributed systems, you can only guarantee two of three properties:

1. Consistency - All nodes see the same data
2. Availability - Every request receives a response
3. Partition Tolerance - System continues despite network failures

Most systems choose AP (Availability + Partition Tolerance) or CP (Consistency + Partition Tolerance) based on requirements.

Example: Handling Failures

Here's a simple retry mechanism with exponential backoff:

import time
import random

def retry_with_backoff(func, max_retries=3, base_delay=1):
    """
    Retry a function with exponential backoff.
    
    Args:
        func: Function to retry
        max_retries: Maximum number of retry attempts
        base_delay: Initial delay in seconds
    
    Returns:
        Result of the function call
    """
    for attempt in range(max_retries):
        try:
            return func()
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            
            # Exponential backoff with jitter
            delay = base_delay * (2 ** attempt)
            jitter = random.uniform(0, delay * 0.1)
            time.sleep(delay + jitter)
            
            print(f"Retry attempt {attempt + 1} after {delay:.2f}s")

Monitoring and Observability

You can't improve what you don't measure. Key metrics we track:

| Metric | Description | Target | |--------|-------------|--------| | Latency | Response time (p99) | < 100ms | | Availability | Uptime percentage | 99.99% | | Error Rate | Failed requests | < 0.1% | | Throughput | Requests per second | Variable |

Logging Best Practices

// Good: Structured logging with context
logger.info('Order processed', {
  orderId: order.id,
  userId: user.id,
  amount: order.total,
  duration: processingTime,
  timestamp: new Date().toISOString()
});

// Bad: Unstructured logging
console.log('Order processed: ' + order.id);

Code Quality Matters

Testing Pyramid

Our testing strategy follows the testing pyramid:

        /\
       /  \      E2E Tests (Few)
      /____\
     /      \    Integration Tests (Some)
    /________\
   /          \  Unit Tests (Many)
  /____________\

Property-Based Testing

Instead of testing specific examples, test properties that should always hold:

// Example with fast-check
import fc from 'fast-check';

describe('Array sorting', () => {
  it('should maintain array length', () => {
    fc.assert(
      fc.property(fc.array(fc.integer()), (arr) => {
        const sorted = [...arr].sort((a, b) => a - b);
        return sorted.length === arr.length;
      })
    );
  });
  
  it('should be idempotent', () => {
    fc.assert(
      fc.property(fc.array(fc.integer()), (arr) => {
        const sorted1 = [...arr].sort((a, b) => a - b);
        const sorted2 = [...sorted1].sort((a, b) => a - b);
        return JSON.stringify(sorted1) === JSON.stringify(sorted2);
      })
    );
  });
});

Architecture Patterns

Microservices Communication

We use several patterns for service-to-service communication:

1. Synchronous - REST APIs, gRPC
2. Asynchronous - Message queues (SQS, SNS)
3. Event-Driven - Event streams (Kinesis, Kafka)

Example API client with circuit breaker:

class ServiceClient {
  private failureCount = 0;
  private lastFailureTime = 0;
  private readonly threshold = 5;
  private readonly timeout = 60000; // 1 minute
  
  async call(endpoint: string): Promise<Response> {
    // Check if circuit is open
    if (this.isCircuitOpen()) {
      throw new Error('Circuit breaker is open');
    }
    
    try {
      const response = await fetch(endpoint);
      this.onSuccess();
      return response;
    } catch (error) {
      this.onFailure();
      throw error;
    }
  }
  
  private isCircuitOpen(): boolean {
    if (this.failureCount >= this.threshold) {
      const timeSinceLastFailure = Date.now() - this.lastFailureTime;
      return timeSinceLastFailure < this.timeout;
    }
    return false;
  }
  
  private onSuccess(): void {
    this.failureCount = 0;
  }
  
  private onFailure(): void {
    this.failureCount++;
    this.lastFailureTime = Date.now();
  }
}

Performance Optimization

Caching Strategies

Different caching layers serve different purposes:

• CDN - Static assets, edge caching
• Application Cache - Redis, Memcached
• Database Cache - Query results, computed values
• Client Cache - Browser cache, service workers

Database Optimization

-- Bad: N+1 query problem
SELECT * FROM orders WHERE user_id = 123;
-- Then for each order:
SELECT * FROM items WHERE order_id = ?;

-- Good: Join to fetch everything at once
SELECT 
  o.*,
  i.*
FROM orders o
LEFT JOIN items i ON i.order_id = o.id
WHERE o.user_id = 123;

Key Takeaways

Here's what I've learned about building systems at scale:

• Design first - Invest time in planning
• Measure everything - You can't improve what you don't measure
• Fail gracefully - Systems will fail; plan for it
• Test thoroughly - Unit, integration, and property-based tests
• Keep it simple - Complexity is the enemy of reliability

Looking Forward

The field of distributed systems continues to evolve. I'm particularly excited about:

• Serverless architectures
• Edge computing
• WebAssembly for performance
• AI/ML integration in traditional systems

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What challenges have you faced building scalable systems? I'd love to hear your experiences!