Building Scalable Web Applications - Architecture Patterns for Growth

A monolithic architecture packages all functionality into a single application with one codebase, shared database, and unified deployment.

Advantages:

• Simpler development workflow
• Easier debugging and testing
• Lower initial operational complexity
• Better performance for smaller applications

Disadvantages:

• Becomes unwieldy as the application grows
• Harder to understand and modify over time
• Limited technology flexibility
• Scaling requires replicating the entire application

Microservices break the application into small, independent services that communicate via APIs. Each service:

• Has its own codebase
• Manages its own data
• Can be deployed independently
• Can scale independently

Advantages:

• Services can be developed, deployed, and scaled independently
• Multiple teams can work autonomously
• Technology flexibility for each service
• Resilience through isolation

Disadvantages:

• Increased operational complexity
• Distributed system challenges (network latency, eventual consistency)
• More complex testing and debugging
• Overhead for small applications

Many successful applications start as well-designed monoliths with clear internal boundaries, then evolve toward microservices as specific scaling challenges emerge.

A modular monolith:

• Maintains clear domain boundaries within a single codebase
• Uses well-defined interfaces between modules
• Prepares for potential future decomposition into microservices
• Delays the operational complexity of microservices until necessary

Regardless of architecture style, Domain-Driven Design (DDD) principles help create clear boundaries and models that align with business domains, making future scaling decisions more straightforward.

Increasing the resources (CPU, memory, storage) of a single database server. This approach:

• Is simpler to implement
• Works well up to certain limits
• Eventually hits physical or cost constraints
• May require downtime for upgrades

Distributing database workload across multiple servers through:

Read Replicas:

• Primary database handles writes
• Replicas handle read queries
• Reduces read load on primary
• Provides geographical distribution

Sharding:

• Divides data across multiple database instances
• Partitions typically based on tenant, geography, or data type
• Increases complexity significantly
• Requires careful planning of shard keys

NoSQL databases can offer better horizontal scaling for certain use cases:

• Document Databases (MongoDB, Firestore):

  • Ideal for semi-structured data
  • Good for content management, user profiles

• Key-Value Stores (Redis, DynamoDB):

  • Optimized for simple, high-volume lookups
  • Excellent for caching, session storage

• Wide-Column Stores (Cassandra, HBase):

  • Designed for massive scale
  • Good for time-series data, analytics

CQRS separates read and write operations, allowing them to be optimized independently:

• Write operations use normalized data models optimized for consistency
• Read operations use denormalized models optimized for query performance
• Different storage technologies can be used for each model
• Increases complexity but enables significant scaling advantages

Implemented in the browser through:

• HTTP cache headers
• Service Workers for offline capabilities
• Local Storage for user-specific data

This reduces server load and improves perceived performance.

Content Delivery Networks cache static assets and potentially API responses at edge locations around the world, providing:

• Reduced latency for users worldwide
• Lower origin server load
• Protection against traffic spikes

Implemented within your application servers using in-memory stores like Redis or Memcached for:

• API responses
• Computed values
• Session data
• Complex query results

Optimizations at the database level:

• Query result caches
• Database buffer caches
• Materialized views

Effective cache invalidation prevents stale data:

• Time-based expiration
• Event-based invalidation
• Version tagging

The application checks cache first, then falls back to the source:

async function getData(key) {
  // Try to get data from cache
  let data = await cache.get(key);
  
  // If cache miss, get from database and update cache
  if (!data) {
    data = await database.get(key);
    await cache.set(key, data, TTL);
  }
  
  return data;
}

Well-designed REST APIs enhance scalability through:

• Proper resource modeling
• Statelessness
• Cacheability
• Pagination for large result sets
• Proper HTTP method usage

GraphQL can improve efficiency by allowing clients to request exactly what they need, but requires careful implementation:

• Query complexity analysis
• Dataloader for batching and caching
• Persisted queries for performance
• Rate limiting strategies

API gateways provide a unified entry point to your services with:

• Rate limiting and throttling
• Authentication and authorization
• Request routing
• Response caching
• Analytics and monitoring
• Performance optimizations like response compression

Containers package applications with their dependencies, providing:

• Consistent environments across development and production
• Efficient resource utilization
• Fast startup times
• Isolation between applications

Orchestration platforms like Kubernetes automate deployment, scaling, and management of containerized applications:

• Automatic scaling based on metrics
• Self-healing capabilities
• Rolling updates with zero downtime
• Resource optimization
• Service discovery and load balancing

Managing infrastructure through code provides:

• Reproducible environments
• Version control for infrastructure
• Automated provisioning and scaling
• Consistent configuration across environments

Stateless architecture allows any server to handle any request, enabling horizontal scaling by:

• Eliminating server-bound sessions
• Making each request self-contained
• Allowing for simpler load balancing

For applications requiring sessions:

• Token-based authentication (JWT) with client-side storage
• Centralized session stores using Redis or similar technologies
• Client-side session storage with encryption for sensitive data

Decouple operations using message queues like RabbitMQ, Apache Kafka, or AWS SQS:

• Smooth out traffic spikes
• Ensure task execution even if services fail temporarily
• Enable independent scaling of producers and consumers
• Allow for retry mechanisms and dead letter queues

Design services to react to events rather than direct commands:

• Services publish events when state changes
• Interested services subscribe to relevant events
• Reduces direct dependencies between services
• Enables easier addition of new functionality

Before optimizing, establish baselines and identify bottlenecks through:

• Load testing
• Performance monitoring
• User experience metrics

Address scalability in order of impact:

1. Optimize existing code and queries
2. Implement caching at various levels
3. Scale out stateless components
4. Consider database scaling options
5. Decompose into services if necessary

Building scalable systems requires automation:

• CI/CD pipelines
• Infrastructure provisioning
• Monitoring and alerting
• Scaling policies