System Design Patterns and Backend Architecture

Scalability Patterns

Horizontal vs Vertical Scaling

• Vertical Scaling (Scaling Up): Adding more power (CPU, RAM) to an existing server.
• Horizontal Scaling (Scaling Out): Adding more servers to your pool of resources.

Load Balancing Strategies

• Round Robin: Requests are distributed sequentially.
• Least Connections: Requests go to the server with the fewest active connections.
• IP Hash: The client's IP address determines which server receives the request.

Data Consistency

CAP Theorem

• Consistency: Every read receives the most recent write or an error.
• Availability: Every request receives a (non-error) response.
• Partition Tolerance: The system continues to operate despite an arbitrary number of messages being dropped or delayed by the network between nodes. Note: You can only pick two.

ACID vs BASE

• ACID (RDBMS): Atomicity, Consistency, Isolation, Durability.
• BASE (NoSQL): Basically Available, Soft state, Eventual consistency.

Communication Patterns

Synchronous (REST, gRPC)

• Client waits for a response.
• Good for immediate feedback but can lead to cascading failures.

Asynchronous (Message Queues, Pub/Sub)

• Client sends a message and continues its work.
• Decouples services and improves system resilience.

Resiliency Patterns

Circuit Breaker

Prevents a network or service failure from cascading to other services.

Bulkhead

Isolates elements of an application into pools so that if one fails, the others will continue to function.

CQRS (Command Query Responsibility Segregation)

Separates read and update operations for a data store.

Caching Strategies

Cache-Aside

The application code is responsible for loading data into the cache and keeping it in sync with the database.

Write-Through

The application writes data to the cache, which then synchronously updates the database.

Write-Behind (Write-Back)

The application writes data to the cache, and the cache asynchronously updates the database after a delay.