This content originally appeared on HackerNoon and was authored by Mahesh Ganesamoorthi
System design refers to the process of defining and creating a high-level architecture that meets certain requirements related to performance, scalability, availability, maintainability, and more. Based on my learnings and experience so far as a Senior Software Engineering Leader, I have tried to summarize the key concepts of Software System Design. Here are some of the most important concepts you’ll encounter when designing large-scale systems:
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Scalability
The ability of a system to handle an increasing workload (either by scaling up or scaling out) without sacrificing performance.
- Vertical Scaling (Scale-Up): Adding more resources (CPU, RAM) to a single machine.
- Horizontal Scaling (Scale-Out): Adding more machines (servers, nodes) to the system.
- Key Trade-offs:
- Vertical scaling is limited by the maximum capacity of a single machine.
- Horizontal scaling introduces complexities like load balancing, sharding, and distributed systems coordination.
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Reliability and Availability
- Reliability: The probability that a system will run without failure over a given period.
- Availability: The proportion of time a system is up and running (e.g., “five nines” or 99.999% availability).
- Techniques to Improve:
- Redundancy: Running multiple instances (active-active or active-passive) to avoid a single point of failure.
- Replication: Storing the same data across multiple machines or data centers.
- Failover: Switching to a redundant or standby system component upon the failure of the currently active component.
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Latency and Throughput
- Latency: The time it takes for a request to travel through a system end-to-end and produce a response.
- Throughput: The number of requests or transactions a system can handle per unit of time.
- Trade-offs:
- Tuning for ultra-low latency can sometimes reduce overall throughput.
- Systems often need to balance the two based on use-case (e.g., real-time trading vs. batch processing).
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Load Balancing
Distributing incoming requests across multiple servers to avoid overloading a single machine.
- Common Algorithms: Round Robin, Least Connections, IP Hash, Weighted Round Robin.
- Approaches:
- Hardware Load Balancers: Specialized, often expensive appliances.
- Software Load Balancers: e.g., HAProxy, Nginx.
- DNS-based Load Balancing: Using DNS responses to distribute traffic.
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Data Storage and Databases
- SQL Databases: (e.g., PostgreSQL, MySQL) Provide strong consistency, ACID properties, relational schema. Good for structured data and complex queries.
- NoSQL Databases: (e.g., Cassandra, MongoDB, Redis) Offer flexible schemas, often higher scalability and better performance for large volumes of data but might sacrifice strong consistency for high availability.
- Sharding:
- Distributing data across multiple machines to handle larger datasets and higher throughput.
- Requires careful planning of shard keys to avoid hotspots.
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Caching
Reduce latency and offload requests from the primary data store by keeping frequently accessed data in memory or in a faster-access layer.
- Types:
- Client-Side (Browser) Caching: HTML, CSS, JS, and other static resources.
- Server-Side Caching: Application-level caching using tools like Redis or Memcached.
- Content Delivery Network (CDN): Caching static or dynamic content at geographically distributed edge locations to reduce latency for users.
- Invalidation Strategies:
- Time-based (TTL): Automatic expiration after a certain time.
- Event-based: Invalidating caches when data changes
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Asynchronous Processing and Messaging
Offloading certain tasks to be processed asynchronously can dramatically improve system responsiveness.
- Message Queues (e.g., RabbitMQ, Apache Kafka, AWS SQS):
- Decouple producers and consumers.
- Enable asynchronous processing, buffering, and smooth handling of spikes in workload.
- Background Workers: Long-running tasks (e.g., video encoding, data processing) can be queued and processed behind the scenes.
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CAP Theorem
In a distributed system, you can only guarantee “two out of three” in the below:
- Consistency: All reads see the latest written data or an error.
- Availability: The system continues to operate, returning a response (not necessarily the latest data) for every request.
- Partition Tolerance: The system continues to operate despite network partitions.
Implications: System designers often choose between Consistency and Availability when network failures (partitions) happen. This is why many NoSQL databases provide eventual consistency for high availability.
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Consistency Models
- Strong Consistency: All clients always see the same data, even if multiple replicas are used.
- Eventual Consistency: Replicas will eventually become consistent if no new writes occur.
- Causal Consistency: Operations that are causally related respect consistency; unrelated operations can be seen out of order.
- Choosing the Model: Based on application requirements—strict banking transactions need strong consistency; social media feeds often tolerate eventual consistency.
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Microservices vs. Monolithic Architecture
- Monolithic:
- All functionalities in a single codebase and process.
- Easier to start but can become difficult to maintain and scale as it grows.
- Microservices:
- Each service handles a single function or domain area.
- Easier to scale individual services, but introduces additional complexity around deployment, communication, and orchestration.
- Commonly use lightweight communication protocols (e.g., HTTP/REST, gRPC).
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Communication Patterns
- Synchronous (Request-Response): Traditional HTTP calls, direct and immediate response required.
- Asynchronous (Event-Driven): Emphasizes loose coupling, services publish events to a message bus, other services subscribe and handle them.
- Event Sourcing and CQRS: Store every state change as an event and maintain query/read models separately from write models.
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Observability and Monitoring
- Logging: Capturing records of events; helps diagnose and fix issues.
- Metrics: Exposing time-series data (e.g., CPU usage, requests per second, memory usage).
- Tracing: Tracking the flow of a request through multiple services (distributed tracing).
- Tools: Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana), Jaeger, Zipkin.
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Security
- Authentication and Authorization:
- OAuth, JWT, SAML, etc. for identity and access management.
- Data Encryption:
- Transport Layer: SSL/TLS for data in transit.
- At Rest: Encrypt data on disk (e.g., AES).
- Network Security:
- Firewalls, VLANs, API gateways, rate limiting.
- Application Security:
- Input validation, secure code practices, frequent security testing.
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CI/CD and DevOps
- Continuous Integration (CI): Merging code changes frequently with automated builds and tests.
- Continuous Delivery/Deployment (CD): Automated release processes that push changes into production safely and rapidly.
- Infrastructure as Code (IaC): Using code or configuration files to manage infrastructure (e.g., Terraform, AWS CloudFormation).
- Containerization and Orchestration:
- Containers: Docker for packaging and running applications.
- Orchestration: Kubernetes, ECS, or similar tools for managing containerized services at scale.
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Trade-offs and Design Principles
- Simplicity vs. Complexity: Complex architectures might solve scaling problems but can be harder to maintain. Aim for the simplest design that meets current needs with an eye toward future growth.
- Loosely Coupled, Highly Cohesive: Microservices or modular monolith structures that reduce interdependencies.
- Cost vs. Performance: Achieving ultra-low latency or very high availability can be expensive; balancing cost is crucial.
- Evolutionary Architecture: Start with a minimal viable system design and iterate as demands grow.
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Conclusion
System design is ALL about making informed compromises in areas like performance, consistency, reliability, complexity, and cost. Understanding these core concepts helps you evaluate trade-offs and architect a solution best suited to your application's current and future needs.
\ When preparing for system design interviews or planning a real-world system:
Start by gathering requirements (functional & non-functional).
Sketch a high-level architecture: data flow, major components, and integrations.
Dive into details: database choices, caching layers, load balancing, failover strategies, etc.
Monitor and adapt over time as system usage grows or requirements change.
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==By mastering these fundamentals, you’ll be better equipped to build systems that are efficient, scalable, maintainable, and resilient.==
This content originally appeared on HackerNoon and was authored by Mahesh Ganesamoorthi
Mahesh Ganesamoorthi | Sciencx (2025-03-19T10:37:46+00:00) A Senior Engineer’s Guide to Scalable & Reliable System Design. Retrieved from https://www.scien.cx/2025/03/19/a-senior-engineers-guide-to-scalable-reliable-system-design/
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