System Design Interview Guide for FAANG Companies

System design interviews are a critical component of the hiring process at FAANG companies (Facebook/Meta, Amazon, Apple, Netflix, Google), especially for senior engineering roles. These interviews assess your ability to design scalable, reliable systems and demonstrate your understanding of distributed systems architecture.

Understanding FAANG System Design Interviews

Who Participates and When

System design interviews typically occur after initial coding rounds but before specialized technical and behavioral interviews. They're mandatory for senior positions and optional for some junior roles, lasting 45-60 minutes and focusing on your architectural thinking rather than coding implementation.

What FAANG Evaluates

FAANG interviewers judge candidates across several key dimensions:

Scalability: Can your design handle millions of users or requests? Systems should scale horizontally without performance degradation.

Reliability: Does the system consistently perform its intended function with fault tolerance built-in?

Availability: The system remains operational when needed, often measured in uptime percentages with redundancies to minimize downtime.

Performance/Efficiency: Low-latency, high-throughput responses with optimized resource utilization.

Maintainability: Clean, modular design that's easy to understand and modify for future engineers.

Security: Data privacy and protection considerations when applicable.

The FAANG System Design Framework

Step 1: Requirements Clarification (5-10 minutes)

Never assume requirements - ask clarifying questions:

What's the expected user scale? (thousands vs millions)
What features are in/out of scope?
What are the performance requirements?
Any specific constraints (security, compliance, budget)?
What's the read/write ratio?

Step 2: Capacity Estimation (5 minutes)

Perform back-of-the-envelope calculations:

Daily/monthly active users
Requests per second (peak and average)
Storage requirements
Bandwidth needs

Example calculation: 100M users, 10% daily active = 10M DAU. If each user makes 10 requests/day = 100M requests/day ≈ 1,200 requests/second average, 6,000 peak.

Step 3: High-Level Design (10-15 minutes)

Create a simple, working architecture first:

Client applications (web, mobile)
Load balancers
Application servers
Databases
Caching layers
External services

Draw boxes and arrows showing data flow and component interactions.

Step 4: Database Design (5-10 minutes)

Define your data model:

Choose between SQL vs NoSQL based on requirements
Design key tables/collections
Consider relationships and access patterns
Plan for data consistency needs

Step 5: API Design (5 minutes)

Define key APIs:

POST /api/users/{userId}/posts
GET /api/posts/{postId}
GET /api/users/{userId}/feed?limit=20&offset=0

Step 6: Detailed Component Design (10-15 minutes)

Dive deeper into critical components:

Caching strategies (Redis, Memcached)
Message queues (Kafka, RabbitMQ)
Search systems (Elasticsearch)
CDN for static content
Microservices architecture

Step 7: Scalability and Reliability (5-10 minutes)

Address scaling challenges:

Horizontal scaling: Add more servers
Database sharding: Partition data across multiple databases
Caching: Multiple levels (browser, CDN, application, database)
Load balancing: Distribute traffic efficiently
Replication: Master-slave database setup
Fault tolerance: Circuit breakers, retry mechanisms

Most Common FAANG System Design Questions

Based on analysis of over 350 questions from FAANG interviews:

Product Design Questions

Design a social media app (Twitter/Facebook/Instagram)
Design a messaging app (WhatsApp/Slack)
Design a video streaming service (YouTube/Netflix)
Design a ride-hailing app (Uber/Lyft)
Design a URL shortening service (bit.ly)
Design an e-commerce platform (Amazon)
Design a search engine (Google)
Design a file sharing system (Dropbox/Google Drive)
Design a gaming platform
Design an API gateway

Infrastructure Questions

Design a distributed cache
Design a load balancer
Design a monitoring system
Design a notification service
Design a payment system

Key Technical Concepts to Master

Scalability Patterns

Load Balancing: Distribute incoming requests across multiple servers using round-robin, least connections, or weighted algorithms.

Caching: Implement multiple cache levels:

Browser cache
CDN (CloudFlare, AWS CloudFront)
Application cache (Redis, Memcached)
Database query cache

Database Scaling:

Vertical scaling: Upgrade hardware (limited)
Horizontal scaling: Add read replicas
Sharding: Partition data across multiple databases
Federation: Split databases by function

Data Storage Solutions

SQL Databases: Use for ACID compliance, complex queries, structured data

Examples: PostgreSQL, MySQL
Best for: Financial systems, user management

NoSQL Databases: Use for flexibility, horizontal scaling

Document: MongoDB (user profiles, content)
Key-Value: Redis, DynamoDB (caching, sessions)
Column-family: Cassandra (time-series data)
Graph: Neo4j (social networks, recommendations)

System Architecture Patterns

Microservices: Break monolith into small, independent services

Benefits: Independent scaling, technology diversity
Challenges: Network complexity, data consistency

Event-Driven Architecture: Services communicate through events

Use message queues: Apache Kafka, AWS SQS
Enables loose coupling and scalability

CQRS (Command Query Responsibility Segregation): Separate read and write operations for better performance.

Advanced System Design Concepts

CAP Theorem

You can only guarantee two of three properties:

Consistency: All nodes see the same data simultaneously
Availability: System remains operational
Partition Tolerance: System continues despite network failures

Examples:

CP System: Traditional RDBMS (sacrifice availability for consistency)
AP System: DNS, web caches (sacrifice consistency for availability)

Consistency Models

Strong Consistency: All reads receive the most recent write Eventual Consistency: System will become consistent over time Weak Consistency: No guarantees about when data will be consistent

Monitoring and Observability

Metrics: CPU, memory, disk usage, request latency Logging: Centralized logging with ELK stack (Elasticsearch, Logstash, Kibana) Tracing: Distributed tracing with tools like Jaeger Alerting: Set up alerts for critical metrics

Machine Learning System Design

For ML engineering roles, FAANG companies assess additional areas:

Problem Exploration

Understanding business context and metrics
Developing technical strategy
Risk assessment and mitigation

Data Strategy

Data collection and labeling approaches
Data quality assurance
Cold start problem solutions
Feature engineering and selection

Model Architecture

Model selection with clear rationale
Training and inference pipelines
A/B testing frameworks
Online vs offline evaluation strategies

ML Infrastructure

Model serving architecture
Real-time vs batch processing
Model monitoring and drift detection
Feedback loops and continuous learning

Best Practices for FAANG Interviews

Communication Excellence

Structure Your Response: Follow the 8-step framework consistently. Start with requirements, move to high-level design, then drill down into details.

Think Out Loud: Verbalize your thought process. Interviewers want to understand your reasoning, not just the final answer.

Ask for Feedback: Periodically check with the interviewer: "Does this approach make sense?" or "Should I dive deeper into this component?"

Technical Depth

Justify Decisions: Explain why you chose specific technologies. For example: "I'm using Redis for caching because it supports complex data structures and has excellent performance for read-heavy workloads".

Discuss Trade-offs: Acknowledge limitations and alternatives. "While NoSQL offers better scalability, we lose ACID guarantees, which might be important for financial transactions".

Show Breadth: Demonstrate knowledge across different domains - databases, networking, security, monitoring.

Time Management

Start Simple: Get to a working solution first, then iterate. Don't over-engineer initially.

Use Diagrams: Start drawing around 15 minutes in. Visual aids help communicate complex architectures.

Prioritize: Focus on the most critical components first. You can always mention other aspects and return to them later.

Common Pitfalls to Avoid

Technical Mistakes

Jumping into solutions without understanding requirements
Over-engineering the initial design
Ignoring scalability from the start
Not considering failure scenarios
Choosing technologies without justification

Communication Issues

Not asking clarifying questions
Designing in silence without explaining reasoning
Not engaging with interviewer feedback
Spending too much time on minor details
Not managing time effectively

Process Errors

Starting with low-level details instead of high-level architecture
Not performing capacity estimation
Forgetting to address non-functional requirements
Not discussing monitoring and alerting
Avoiding trade-off discussions

Preparation Strategy

Study Timeline (3-4 weeks)

Week 1: Master fundamental concepts

Scalability patterns
Database types and when to use them
Caching strategies
Load balancing techniques

Week 2: Learn system architecture patterns

Microservices vs monolith
Event-driven architecture
CQRS and event sourcing
CAP theorem implications

Week 3: Practice common questions

Design 2-3 systems daily
Focus on different domains (social media, e-commerce, streaming)
Time yourself (45-60 minutes per question)

Week 4: Mock interviews and refinement

Practice with peers or experts
Record yourself and review
Focus on communication and time management

Essential Resources

Books:

"Designing Data-Intensive Applications" by Martin Kleppmann
"System Design Interview" by Alex Xu
"Building Microservices" by Sam Newman

Online Platforms:

Grokking the System Design Interview
System Design Primer (GitHub)
High Scalability blog

Practice Platforms:

Pramp for peer interviews
InterviewBit system design section
LeetCode system design questions

Sample Interview Walkthrough: Design Twitter

Requirements Clarification

300M monthly active users, 150M daily active users
Users can post tweets (280 characters), follow others, see timeline
Read-heavy system (100:1 read/write ratio)
Real-time timeline updates not required

Capacity Estimation

150M DAU × 2 tweets/day = 300M tweets/day ≈ 3,500 tweets/second
Timeline reads: 150M users × 50 reads/day = 7.5B reads/day ≈ 87K reads/second
Storage: 300M tweets × 280 chars × 2 bytes = 168GB/day

High-Level Architecture

[Mobile/Web Clients] → [Load Balancer] → [API Gateway] 
                                            ↓
[Tweet Service] [Timeline Service] [User Service] [Notification Service]
       ↓               ↓                ↓              ↓
[Tweet DB]      [Timeline Cache]   [User DB]    [Message Queue]

Database Design

Users: user_id, username, email, created_at
Tweets: tweet_id, user_id, content, created_at
Follows: follower_id, followee_id, created_at

Detailed Design

Tweet Service: Handle tweet creation, use database sharding by user_id
Timeline Service: Pre-compute timelines for active users, use Redis cache
User Service: Manage user data and relationships
CDN: Cache media content and static assets
Search Service: Elasticsearch for tweet search functionality

Scalability Solutions

Database sharding: Partition tweets by user_id
Timeline generation: Push model for celebrities, pull model for regular users
Caching: Multi-level caching (Redis for timelines, CDN for media)
Load balancing: Geographic distribution with regional data centers

This comprehensive approach demonstrates the depth and breadth expected in FAANG system design interviews, covering both technical architecture and communication skills essential for success.