Skip to content

Instantly share code, notes, and snippets.

@pancudaniel7

pancudaniel7/system-design-thinking.md

Last active September 5, 2024 08:59
Show Gist options
  • Save pancudaniel7/84036368def70dfbe49a3df222d80077 to your computer and use it in GitHub Desktop.
Save pancudaniel7/84036368def70dfbe49a3df222d80077 to your computer and use it in GitHub Desktop.
Download ZIP
System Design Thinking
Raw
system-design-thinking.md

System Design Thinking Guide

1. Requirements Gathering

  • Clarify the requirements: Ask clarifying questions to understand both functional and non-functional requirements. This will help ensure that you focus on the correct aspects of the system.
    • Functional Requirements: Core features (e.g., upload a file, search for a user).
    • Non-functional Requirements: Scalability, performance, latency, availability, consistency, and security.
  • Example Questions:
    • What’s the expected user base? How many daily active users?
    • Is there a focus on low-latency or high throughput?
    • Are there specific SLAs (Service Level Agreements) for uptime or performance?

2. High-Level Design

  • Break down the system into components: Identify key parts of the system (e.g., client, server, databases, caches).
  • Define the relationships and interactions between components: Understand how these components will communicate with each other (e.g., API calls, message queues, REST/gRPC).
  • Database Design: Choose between SQL vs. NoSQL based on the type of data (structured/unstructured) and access patterns (transactional, analytical).
    • SQL: Use for transactional consistency (ACID).
    • NoSQL: Use for scalability, flexible schema, and high availability (BASE).

Tip: Draw a high-level architecture diagram, even if it’s just verbal, to show you understand the flow of data.

3. Component Design

  • Dive deeper into each component: Go through each system component and design it in detail.
    • Database Design: Will you use SQL or NoSQL? How will you scale the database?
    • Cache: Where will you use caching (e.g., Redis, Memcached)? How will you handle cache invalidation?
    • Load Balancer: How will you distribute traffic across servers?
    • Message Queue: If asynchronous processing is required, how will you implement it (e.g., Kafka, RabbitMQ)?

Tip: Show that you understand each component’s trade-offs and justify your choices.

4. Scaling and Performance

  • Identify bottlenecks and hot spots: Focus on what parts of the system need scaling based on traffic estimates.
  • Vertical vs. Horizontal Scaling:
    • Vertical Scaling: Increasing the resources of a single machine (more CPU, RAM).
    • Horizontal Scaling: Adding more machines to handle load (e.g., more web servers).
  • Sharding/Partitioning: If applicable, show how you would split the database or workload across multiple servers.
  • Example:
    • How will you scale the system if it grows from 1,000 to 1 million users?
    • Introduce concepts like data replication, sharding, and caching to ensure performance doesn’t degrade.

5. Reliability and Fault Tolerance

  • Redundancy: Design the system to handle hardware or software failures (e.g., using multiple data centers, replication).
  • Failover: What happens when one component fails? Is there a backup or failover mechanism?
  • Data Replication: How will you ensure that data is replicated and backed up properly across regions (active-active or active-passive configurations)?
  • Example: For critical systems, how will you design for high availability (99.99% uptime)?

6. Security and Compliance

  • Authentication & Authorization: Implement secure user access with OAuth2, OpenID, or custom token systems.
  • Encryption:
    • In-transit: Use TLS/SSL for data encryption during transport.
    • At-rest: Ensure data is encrypted in databases or storage.
  • Rate Limiting & DDoS Protection: How will you protect the system from abuse or attacks (rate limiting, IP blocking)?
  • Data Privacy & Compliance: Are there regulations (GDPR, HIPAA) that need to be followed?

7. Monitoring, Logging, and Maintenance

  • Monitoring: Use tools like Prometheus, Grafana, or CloudWatch to monitor system health.
  • Alerting: Set up alerts for critical system failures, resource exhaustion, or high error rates.
  • Logging: Ensure centralized logging (ELK stack or Splunk) for debugging and auditability.
  • Auto-scaling: Implement automatic scaling based on CPU or memory usage to handle sudden traffic spikes.
  • Example: How will you detect if a service fails or if resource consumption becomes too high?

8. Trade-offs and Justifications

  • Latency vs. Throughput: Trade-off between low latency for quick response times and high throughput for processing large amounts of data.
  • Consistency vs. Availability (CAP Theorem): Discuss whether the system should prioritize consistency or availability in case of a network partition.
  • Cost vs. Performance: Show awareness of the cost implications (cloud infrastructure, operational complexity) vs. the performance improvements.
  • Complexity vs. Simplicity: Aim for a simple design unless complexity is required for scalability.

Tip: Be clear in explaining your design decisions and how they meet the problem’s requirements. Every choice involves trade-offs!

9. Final Review and Summary

  • Recap: Provide a brief summary of the high-level architecture and how the design meets the requirements.
  • Address bottlenecks or scaling limits: Mention any parts of the design that might become problematic at extreme scale and suggest improvements.
  • Prepare for edge cases: Discuss potential failure points or edge cases (e.g., sudden traffic spike, distributed data consistency issues) and how you would address them.
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment