System Design Interview Framework & API Choices Guide

System Design Interview Cheat Sheet

Interview Framework

Step 1: Understand the Problem (10 min)

• Gather more information about the system requirements and constraints.
• Thought: Achieving a clear understanding early on will help in shaping the entire design process.

Step 2: High-Level Design (10 min)

• Explain how each part of the system works together. Start by defining APIs.
• APIs create the foundation of the architecture.

Step 3: Deep-Dive (10 min)

• Examine system components in detail.
• Be prepared to discuss specific areas or request to explore particular components.

Step 4: Improve the Design (10 min)

• Analyze bottlenecks and scalability.
• Identify potential areas for optimization and improvement.

Step 5: Wrap Up (5 min)

• Summarize the requirements, justify decisions, suggest alternatives, and answer any questions.

API Design Choices

Comparison Table

Synchronous

• Client and Service interactions.
• HTTP sync between services ensures quick response times but requires both ends to be available simultaneously.

Async Messaging

• Messages are queued and processed asynchronously.
• Suitable for systems that handle varying loads.

Publish-Subscribe

• Producer publishes to a topic, and subscribers consume messages.
• Useful for decoupling components, ensuring scalable and distributed processing.

Scalability

Replication

• Data Replication:
  • Copying data to ensure high availability.
• Active Data: Data currently being accessed or updated.
• Mirrored Data: Backup data, identical to active data.

Partitioning

• Dividing a large database into smaller, more manageable parts.
• Horizontal Partitioning: Rows of data are spread across multiple tables.
• Vertical Partitioning: Columns of data are spread across multiple tables.

Sharding

• Splitting data across multiple machines to increase storage and performance.
• Example: A 1TB database is split into four 256GB shards.

Load Balancing

• Distributes incoming traffic across multiple servers.
• Helps in managing large scale web services efficiently.

Caching

In-memory Cache

• Faster access as data is stored in memory.
• Example: Redis, Memcached.
• Uses: Storing frequently accessed data to reduce load on databases.

Distributed Cache

• Data cached across multiple machines.
• Ensures high availability and consistency.
• Example: Amazon Elasticache, Google Cloud Memorystore.

Caching Eviction Policies

• LRU (Least Recently Used): Removes least recently accessed items.
• LFU (Least Frequently Used): Removes least frequently accessed items.
• FIFO (First In, First Out): Removes oldest data first.
• Random Eviction: Randomly removes items.

Tips for Effective Caching

• Use for session storage, communication between microservices, and database query results.

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