Fundamental System Design Concepts

Understanding these core system design concepts is essential for any developer building scalable, distributed applications. These fundamentals form the building blocks of modern system architectures.

Latency vs Throughput - Latency is delay per request; throughput is how many you can handle per second.
Load Balancer - Distributes incoming traffic across multiple servers to avoid overload.
Caching - Stores frequently accessed data to reduce latency and database hits.
Database Indexing - Speeds up queries by avoiding full table scans.
Sharding - Splits data across multiple DBs to scale horizontally.
Replication - Copies data across DB nodes for availability and read scaling.
CAP Theorem - In distributed systems, you can only guarantee 2 of: Consistency, Availability, Partition Tolerance.
Consistency Models - Defines how and when changes become visible across systems (e.g., eventual, strong).
Rate Limiting - Controls how frequently users or services can make requests.
Backpressure - Mechanism to prevent a system from being overwhelmed by slowing down producers.
Message Queues - Decouple producers and consumers for asynchronous, reliable communication.
Idempotency - Ensures repeated operations have the same effect - crucial for APIs.
Service Discovery - Allows services to dynamically find each other in distributed environments.
Health Checks - Automated checks to determine if a service is up and running.
Circuit Breaker - Temporarily disables calls to a failing service to avoid cascading failures.
Retry Logic with Exponential Backoff - Retries failed operations with increasing delay to reduce load.
Data Partitioning - Splits large datasets across storage units for performance and scale.
Eventual Consistency - Guarantees data will sync across nodes - eventually, not instantly.
Distributed Transactions - Coordinated commits across multiple systems (often avoided due to complexity).
Horizontal vs Vertical Scaling - Add more machines (horizontal) vs more power (vertical).
Sticky Sessions - Route a user to the same server to maintain session state.
Content Delivery Network (CDN) - Distributes static content closer to the user for faster delivery.
Write Amplification - Cost of writing more data than needed - common in DBs and SSDs.
Cold Start Problem - Initial delay when a resource (e.g., serverless function) is spun up on demand.
Observability (vs Monitoring) - Observability helps explain why something broke, not just what broke.

System Architecture Patterns

Microservices Architecture

Decomposes applications into small, loosely coupled services
Each service focuses on a specific business capability
Benefits: Independent scaling, deployment, and technology choices
Challenges: Distributed systems complexity, service coordination

Event-Driven Architecture

Components communicate by producing and consuming events
Decouples systems by removing direct dependencies
Commonly implemented with message brokers like Kafka or RabbitMQ
Enables real-time processing and reactive systems

Layered Architecture

Organizes components into horizontal layers (presentation, business, data)
Each layer has a specific responsibility and depends only on layers below it
Benefits: Separation of concerns, maintainability
Challenges: May lead to monolithic deployments

Service-Oriented Architecture (SOA)

Services communicate via well-defined interfaces
Often uses enterprise service bus for communication
Precursor to microservices but typically more coarse-grained

Distributed Systems Challenges

Consistency and Consensus

Ensuring all nodes in a distributed system agree on data values
Algorithms like Paxos and Raft help achieve consensus
Trade-offs between consistency and availability/performance

Distributed Data Management

Strategies for storing and querying data across multiple nodes
Polyglot persistence: Using different database types for different data needs
Data sovereignty and compliance considerations

Failure Modes

Partial failures: Some components fail while others continue working
Network partitions: Network failures that isolate parts of the system
Byzantine failures: Components behaving erroneously or maliciously

Scalability Patterns

Stateless services for horizontal scaling
Database read replicas for scaling reads
Command Query Responsibility Segregation (CQRS) for separate read/write models
Event sourcing for capturing all changes as a sequence of events

Performance Optimization Strategies

Caching Strategies

Cache-aside: Application checks cache first, then data store
Read-through: Cache handles data fetching transparently
Write-through: Data written to cache and data store simultaneously
Write-behind: Data written to cache, then asynchronously to data store

Asynchronous Processing

Handles time-consuming operations in the background
Improves user experience by not blocking responses
Implemented using message queues, event buses, or background workers

Data Access Optimization

Query optimization and efficient indexing
Connection pooling to minimize overhead
Prepared statements to reduce parsing costs
Batch operations to reduce network overhead

Conclusion

Understanding these system design fundamentals is crucial for building scalable, resilient, and efficient applications. As systems grow more complex and distributed, having a solid grasp of these concepts becomes increasingly important for making informed architecture decisions and solving the challenges of modern software systems.