# Scalability Patterns and Architectures

## Overview

Scalability is the ability of a system to handle increased load by adding resources. This guide covers horizontal and vertical scaling patterns, distributed systems, and scalability architectures.

## Scaling Strategies

### Vertical Scaling (Scale Up)

**Increase Resource Capacity:**
- Add more CPU, RAM, or storage to existing servers
- Simpler to implement
- Limited by hardware constraints
- Single point of failure

**When to Use:**
- Small to medium workloads
- Applications that can't be distributed
- Quick performance improvements
- Cost-effective for low traffic

### Horizontal Scaling (Scale Out)

**Add More Servers:**
- Distribute load across multiple servers
- Better fault tolerance
- Virtually unlimited capacity
- More complex to manage

**When to Use:**
- High traffic applications
- Need for high availability
- Geographic distribution
- Cost-effective at scale

## Load Balancing

### Load Balancer Types

**Layer 4 (Transport Layer):**
- Routes based on IP and port
- Faster, less overhead
- No application awareness

**Layer 7 (Application Layer):**
- Routes based on content
- More intelligent routing
- SSL termination
- Higher overhead

### Load Balancing Algorithms

**Round Robin:**
- Distribute requests evenly
- Simple and fair
- Doesn't consider server load

**Least Connections:**
- Route to server with fewest connections
- Better for long-lived connections
- More complex

**Weighted Round Robin:**
- Assign weights to servers
- Route more to powerful servers
- Manual configuration

**IP Hash:**
- Route based on client IP
- Session affinity
- Uneven distribution possible

## Database Scaling

### Read Replicas

**Distribute Read Load:**
- Master for writes
- Replicas for reads
- Reduce master load
- Eventual consistency

**Example Architecture:**
```
Write → Master Database
Read → Replica 1, Replica 2, Replica 3
```

### Database Sharding

**Partition Data:**
- Split data across multiple databases
- Shard by user ID, region, or date
- Each shard independent
- Complex queries across shards

**Sharding Strategies:**
- **Range-based**: Partition by value ranges
- **Hash-based**: Partition by hash function
- **Directory-based**: Lookup table for shard location

### Caching Layer

**Reduce Database Load:**
- Cache frequently accessed data
- Use Redis, Memcached
- Reduce database queries
- Improve response times

## Microservices Architecture

### Service Decomposition

**Break into Services:**
- Single responsibility per service
- Independent deployment
- Technology diversity
- Team autonomy

**Service Patterns:**
- API Gateway
- Service Mesh
- Event-Driven Architecture
- Saga Pattern

### API Gateway

**Single Entry Point:**
- Route requests to services
- Authentication/authorization
- Rate limiting
- Request/response transformation

### Service Discovery

**Dynamic Service Location:**
- Services register themselves
- Clients discover services
- Health checking
- Load balancing

## Caching Strategies

### CDN (Content Delivery Network)

**Distribute Static Content:**
- Cache at edge locations
- Reduce latency
- Offload origin servers
- Geographic distribution

### Application Caching

**Multi-Level Caching:**
- Browser cache
- CDN cache
- Application cache
- Database cache

### Cache Patterns

**Cache-Aside:**
- Application manages cache
- Check cache, then database

**Write-Through:**
- Write to cache and database
- Consistent but slower

**Write-Back:**
- Write to cache first
- Async database write
- Faster but risk of data loss

## Message Queues

### Asynchronous Processing

**Decouple Components:**
- Producer/consumer pattern
- Handle traffic spikes
- Background processing
- Improved responsiveness

**Queue Types:**
- **Task Queues**: Process jobs asynchronously
- **Message Queues**: Event-driven communication
- **Pub/Sub**: Publish-subscribe pattern

### Queue Patterns

**Priority Queues:**
- Process high-priority items first
- Urgent tasks get priority

**Dead Letter Queues:**
- Store failed messages
- Retry logic
- Error handling

## Stateless Design

### Stateless Services

**No Server-Side State:**
- Any server can handle any request
- Easier horizontal scaling
- Better fault tolerance
- Use external state storage

**State Storage:**
- Database
- Cache (Redis)
- External session store
- Client-side (JWT)

## Database Optimization

### Connection Pooling

**Reuse Connections:**
- Reduce connection overhead
- Limit connection count
- Better resource utilization

### Query Optimization

**Efficient Queries:**
- Use indexes
- Avoid N+1 queries
- Use pagination
- Optimize JOINs

### Database Partitioning

**Split Large Tables:**
- Partition by date, region, or hash
- Improve query performance
- Easier maintenance

## Monitoring and Auto-Scaling

### Metrics to Monitor

**Key Metrics:**
- CPU utilization
- Memory usage
- Request rate
- Response time
- Error rate
- Queue depth

### Auto-Scaling

**Automatic Scaling:**
- Scale based on metrics
- Scale up during high load
- Scale down during low load
- Cost optimization

**Scaling Policies:**
- Target tracking
- Step scaling
- Scheduled scaling

## Best Practices

1. **Design for Scale**: Plan scalability from start
2. **Horizontal First**: Prefer horizontal scaling
3. **Stateless Services**: Avoid server-side state
4. **Cache Aggressively**: Use caching at all levels
5. **Monitor Everything**: Track all metrics
6. **Test at Scale**: Load test before production
7. **Fail Gracefully**: Handle failures elegantly
8. **Document Architecture**: Keep architecture docs updated

## Scalability Patterns

### Circuit Breaker

**Prevent Cascading Failures:**
- Stop calling failing services
- Fail fast
- Automatic recovery

### Bulkhead

**Isolate Failures:**
- Separate resource pools
- Prevent resource exhaustion
- Isolate critical services

### Throttling

**Limit Request Rate:**
- Protect backend services
- Fair resource allocation
- Prevent abuse

## References

- [Scalability Patterns](https://www.oreilly.com/library/view/designing-data-intensive-applications/9781491903063/)
- [System Design Primer](https://github.com/donnemartin/system-design-primer)
- [AWS Well-Architected Framework](https://aws.amazon.com/architecture/well-architected/)

