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Introduction to System Design: A Comprehensive Overview of Core Concepts and Thinking Frameworks

WCSee, May 7, 2025May 17, 2025

System design is the process of defining the architecture, components, modules, interfaces, and data for a system to satisfy specified requirements. It serves as a blueprint for building scalable, maintainable, and efficient software systems.

1. What is System Design?

System design is the high-level process by which we plan and define the structure of a software solution. It involves:

  • Breaking down complex problems.
  • Making strategic choices about technology and architecture.
  • Ensuring the system meets performance, reliability, and scalability needs.

2. Types of System Design

  • High-Level Design (HLD): Focuses on the overall system architecture, components, data flow, and how different subsystems communicate.
  • Low-Level Design (LLD): Focuses on class diagrams, detailed logic, database schema, and API definitions.

3. Key Concepts in System Design

  • Scalability: Ability to handle growing amounts of traffic/data.
  • Availability: Ensuring the system is operational most of the time.
  • Reliability: Ensuring the system behaves correctly even in the face of failures.
  • Maintainability: Ease of updating and fixing the system.
  • Latency & Throughput: Performance metrics for responsiveness and data processing capability.

4. Common System Design Components

  • Load Balancers
  • Databases (SQL/NoSQL)
  • Caching (Redis, Memcached)
  • Message Queues (Kafka, RabbitMQ)
  • Microservices vs Monoliths
  • APIs (REST, gRPC)
  • CDN, DNS, etc.

5. Steps in System Design

  1. Requirements Gathering – Functional and non-functional.
  2. Capacity Estimation – Users, QPS, storage, etc.
  3. Define APIs – Inputs/outputs for communication.
  4. Design Database Schema – Choosing between relational and non-relational.
  5. Component Design – Services, storage, queues, etc.
  6. System Architecture – Putting it all together with diagrams.
  7. Scalability and Fault Tolerance – Plan for high availability.
  8. Bottleneck Identification – Plan for future load and improvements.

6. Popular System Design Interview Examples

  • Design a URL Shortener (e.g., TinyURL)
  • Design an Online Bookstore (e.g., Amazon)
  • Design a Ride-Sharing System (e.g., Uber)
  • Design a Social Media Feed (e.g., Twitter)

Following are deeper dive into key System Design topics, starting with core concepts critical for building real-world systems:

🔹 1. Scalability

➤ Definition:

Scalability is the ability of a system to handle increasing loads without degrading performance.

➤ Types:

  • Vertical Scaling (Scale-Up): Add more resources (CPU, RAM) to a single machine.
  • Horizontal Scaling (Scale-Out): Add more machines to distribute the load.

➤ Strategies:

  • Load Balancers to distribute traffic.
  • Partitioning (sharding) databases.
  • Stateless services that are easier to replicate.

🔹 2. Availability

➤ Definition:

The ability of a system to remain operational over time (often measured in “9s” like 99.9%).

➤ Techniques:

  • Redundancy: Multiple instances across zones/regions.
  • Failover Systems: Automatic switch to backup servers.
  • Health checks and auto-restart for services.

🔹 3. Reliability

➤ Definition:

The ability of a system to operate correctly and consistently.

➤ How to Achieve:

  • Data replication and consistency checks.
  • Retry mechanisms and circuit breakers.
  • Monitoring + alerts for anomalies.

🔹 4. Maintainability

➤ Definition:

Ease with which a system can be modified, extended, or fixed.

➤ Best Practices:

  • Modular codebases (microservices architecture).
  • Clear documentation and API contracts.
  • Use of CI/CD pipelines for safer deployments.

🔹 5. Latency vs Throughput

MetricDescription
LatencyTime to process a single request (e.g. ms)
ThroughputNumber of requests processed per second

Example:
An HTTP server might have low latency but if it can only handle 10 RPS (requests per second), it has low throughput.


🔹 6. Caching

➤ Purpose:

Reduce load on databases and improve performance by storing frequently accessed data.

➤ Tools:

  • Redis, Memcached

➤ Strategies:

  • Write-through Cache: Data is written to cache and DB simultaneously.
  • Write-back Cache: Data is written to cache, and DB is updated asynchronously.
  • Cache Invalidation: Ensuring stale data gets cleared.

🔹 7. Database Design

➤ SQL vs NoSQL:

  • SQL (Relational): Structured schema, supports joins (e.g., PostgreSQL, MySQL)
  • NoSQL (Document, Key-Value): Flexible schema, scalable (e.g., MongoDB, DynamoDB)

➤ Sharding:

Splitting a large DB into smaller chunks across servers.


🔹 8. Message Queues

➤ Purpose:

Decouple producers and consumers to handle asynchronous workloads.

➤ Tools:

  • Kafka, RabbitMQ, Amazon SQS

➤ Use Cases:

  • Order processing
  • Email notifications
  • Logging & analytics pipelines

🔹 9. Microservices vs Monoliths

FeatureMonolithMicroservices
DeploymentSingle unitIndependently deployable units
ScalabilityScale whole appScale specific services
CodebaseCentralizedDecentralized
ComplexityEasier to build initiallyHarder to manage, but flexible

🔹 10. API Design

➤ REST:

  • Stateless, resource-oriented (GET, POST, PUT, DELETE).
  • Standard HTTP methods and status codes.

➤ gRPC:

  • High-performance RPC framework by Google.
  • Uses Protocol Buffers (compact and fast).

Summary

System design is crucial in building scalable, reliable, and maintainable software systems. It transforms high-level requirements into a structured architecture that guides development and ensures the system performs well under real-world conditions.

System design is the foundation for building robust, future-proof systems that meet both functional and non-functional requirements.

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