System Design Interview

Scale From Zero To Millions Of Users

Start with a single server: Begin with a simple architecture where everything runs on one server
Separate database and web server: Move the database to a separate server as traffic grows
Add a load balancer: Implement a load balancer to distribute traffic across multiple web servers
Scale horizontally: Add more web servers to handle increased traffic
Implement database replication: Set up primary-replica replication for read/write splitting
Add caching layers: Implement memory caching to reduce database load
Use CDNs for static content: Offload static content delivery to Content Delivery Networks
Implement data partitioning: Shard databases as they grow larger
Plan for multiple data centers: Design for geographical distribution to improve reliability and performance
Continuously monitor scalability: Identify bottlenecks early and address them proactively

Back-of-the-Envelope Estimation

Learn key numbers: Memorize powers of 2, latency numbers, and availability calculations
Apply proper units: Use appropriate units (KB, MB, GB, TB) for clarity
Practice QPS calculations: Calculate queries per second based on daily active users
Estimate storage requirements: Calculate storage needs based on data volume and growth
Consider peak vs. average load: Design for peak traffic, not just average workloads
Factor in future growth: Add appropriate growth multipliers to your estimates
Break down complex estimates: Divide large problems into smaller, more manageable calculations
Validate with common sense: Use reality checks to verify your estimates are reasonable
Round numbers for simplicity: Use round numbers to make calculations easier
Document assumptions clearly: Make your estimation assumptions explicit

A Framework For System Design Interviews

Understand the problem thoroughly: Ask clarifying questions to define requirements
Establish scope: Determine what features to include in the initial design
Define API interfaces: Design the public interfaces for your system
Define data models: Establish the key entities and their relationships
Create a high-level design: Outline the major components and their interactions
Design for scale: Address how the system will handle growth
Identify and address bottlenecks: Discuss potential performance issues and solutions
Refine based on feedback: Incorporate the interviewer’s feedback throughout the process
Discuss trade-offs: Explain the pros and cons of your design decisions
Prioritize simplicity: Start with a simple design and add complexity as needed

Design A Rate Limiter

Understand rate limiting algorithms: Know when to use token bucket, leaky bucket, fixed window, or sliding window
Choose appropriate rate limiting granularity: Limit by IP, user ID, API key, or other identifiers
Design for distributed environments: Consider synchronization in multi-server deployments
Select proper storage mechanisms: Choose between in-memory and persistent storage options
Implement effective client communication: Return appropriate HTTP status codes and headers
Address rate limiter bypass attempts: Prevent circumvention through multiple identities
Design for configurability: Allow for easy adjustment of rate limiting rules
Create monitoring and alerting: Track rate limiting events to identify problems
Handle edge cases: Address clock drift and synchronization issues
Minimize performance impact: Ensure rate limiting doesn’t significantly impact latency

Design Consistent Hashing

Understand the problems with simple modulo hashing: Recognize why modulo-based hashing fails during server changes
Implement the hash ring concept: Map both servers and data onto a conceptual circle
Add virtual nodes: Use multiple virtual nodes per physical server to improve distribution
Balance data distribution: Ensure equal data distribution across servers
Handle server addition gracefully: Minimize data redistribution when adding servers
Manage server removal efficiently: Redistribute data appropriately when servers are removed
Choose appropriate hash functions: Select hash functions with good distribution properties
Consider practical implementations: Understand how to implement consistent hashing in code
Test distribution properties: Verify that your implementation provides good balance
Apply to real-world use cases: Understand applications in caching, data partitioning, and load balancing

Design A Key-Value Store

Define clear requirements: Understand the specific needs for your key-value store
Choose appropriate data structures: Select structures that optimize for your access patterns
Implement efficient storage engines: Design for on-disk and in-memory storage
Design for consistency guarantees: Determine what consistency level is required
Plan for horizontal scaling: Create a strategy for distributing data across nodes
Implement replication: Design for data redundancy and availability
Create effective partitioning: Develop a strategy for dividing data across nodes
Address node failures: Design protocols for handling node outages
Implement efficient read/write paths: Optimize critical data access paths
Design for operational simplicity: Create systems that are manageable in production

Design A Unique ID Generator

Understand ID requirements: Determine if IDs need to be numeric, sortable, or have other properties
Consider database auto-increment limitations: Recognize scaling issues with database sequences
Implement UUID when appropriate: Use UUIDs for simplicity when their properties fit requirements
Design ticket server approaches: Create centralized ID generation services when needed
Implement Twitter Snowflake-like solutions: Combine timestamps with machine IDs for sortable IDs
Address synchronization requirements: Determine if distributed coordination is necessary
Plan for clock drift: Handle server time synchronization issues
Optimize for performance: Ensure ID generation isn’t a bottleneck
Plan for failure recovery: Design for continuity during service disruptions
Test throughput capacity: Verify that the system can meet peak demand

Design A URL Shortener

Define clear requirements: Understand traffic volume, URL length, and feature requirements
Design efficient URL encoding: Create algorithms to convert between long and short URLs
Implement collision handling: Address hash collisions appropriately
Create scalable storage: Design database schema for billions of URLs
Optimize for read performance: Create caching strategies for popular URLs
Implement analytics capabilities: Design for tracking clicks and usage patterns
Address security concerns: Prevent abuse and security vulnerabilities
Design for high availability: Ensure the service remains available during component failures
Plan for global distribution: Design for low latency access worldwide
Create URL expiration mechanisms: Implement expiration for temporary short URLs

Design A Web Crawler

Define clear crawl policies: Establish rules for what to crawl and how often
Design URL frontier: Create systems to manage the queue of URLs to be crawled
Implement politeness protocols: Avoid overloading target websites
Create efficient HTML fetching: Design for parallel downloading of web pages
Implement robust parsing: Extract links and content from various HTML formats
Design for duplicate detection: Avoid crawling the same content repeatedly
Create storage systems: Design for storing different types of crawled data
Implement distributed coordination: Coordinate work across multiple crawler instances
Design for fault tolerance: Handle failures gracefully without losing data
Create continuous crawling systems: Design for ongoing refreshing of content

Design A Notification System

Support multiple channels: Design for push notifications, SMS, email, and in-app notifications
Create scalable delivery: Design for millions of notifications per day
Implement queuing mechanisms: Buffer notifications during traffic spikes
Design for reliability: Ensure notifications are delivered even during partial system failures
Implement rate limiting: Prevent overwhelming users with too many notifications
Create user preference management: Allow users to control what notifications they receive
Implement template management: Design systems for creating and managing notification templates
Create analytics tracking: Track delivery, open rates, and engagement
Design for global distribution: Handle notifications across different time zones
Implement retry mechanisms: Handle notification delivery failures gracefully

Design A News Feed System

Define feed composition rules: Establish how content is selected and ranked
Design efficient data models: Create schemas that support fast feed generation
Implement fan-out mechanisms: Choose between push, pull, or hybrid approaches
Create caching strategies: Cache feeds for fast retrieval
Design for personalization: Implement systems that tailor feeds to user interests
Address high-cardinality problems: Handle users with millions of followers
Create feed pagination: Implement efficient “load more” functionality
Design for content diversity: Ensure feeds aren’t dominated by single sources
Implement ranking algorithms: Create systems to rank content by relevance
Design for media content: Support images, videos, and other media

Design A Chat System

Define message delivery guarantees: Determine reliability requirements for messages
Create real-time delivery mechanisms: Design for immediate message delivery
Implement presence indicators: Show online/offline status and typing indicators
Design for offline users: Handle message delivery to temporarily offline users
Create group chat capabilities: Design efficient group messaging
Implement message synchronization: Sync messages across multiple devices
Design for media sharing: Support images, videos, and files
Create message persistence: Store message history appropriately
Implement end-to-end encryption: Design for message security and privacy
Address mobile considerations: Optimize for mobile network constraints

Design A Search Autocomplete System

Define clear requirements: Understand the scope and scale of the autocomplete system
Design efficient data structures: Implement tries or similar structures for prefix matching
Create ranking mechanisms: Design algorithms to rank suggestions by relevance
Implement caching strategies: Cache common prefixes for fast response
Design for real-time updates: Update suggestions based on trending searches
Create personalization features: Tailor suggestions to user history and preferences
Implement error tolerance: Handle typos and misspellings gracefully
Design for scale: Support millions of users and queries
Create data refreshing mechanisms: Update suggestion data regularly
Optimize for low latency: Ensure suggestions appear instantaneously

Design YouTube

Design content upload workflow: Create efficient video upload and processing pipelines
Implement transcoding systems: Convert videos to multiple formats and resolutions
Create effective storage strategies: Store videos optimally based on popularity and access patterns
Design content delivery: Implement CDNs and edge caching for fast video streaming
Create recommendation systems: Design algorithms to suggest relevant videos
Implement search functionality: Create systems to search across millions of videos
Design for monetization: Implement advertising and subscription capabilities
Create analytics systems: Track views, engagement, and user behavior
Implement content protection: Design for copyright protection and content filtering
Address global scale: Design for worldwide usage with varying network conditions

Design Google Drive

Design file storage architecture: Create systems for storing and retrieving files
Implement synchronization mechanisms: Design for syncing files across devices
Create sharing capabilities: Implement permissions and sharing models
Design for concurrent edits: Handle multiple users editing the same document
Implement versioning: Create systems to track file versions and history
Design for reliability: Ensure files are never lost or corrupted
Create efficient conflict resolution: Handle edit conflicts appropriately
Implement search functionality: Design for searching file contents and metadata
Create space management: Implement quotas and storage management
Design for different file types: Support various file formats and preview capabilities

Key Takeaways

Start with requirements: Always begin by clarifying functional and non-functional requirements
Estimate properly: Use back-of-the-envelope calculations to validate design feasibility
Begin simple, then scale: Start with a simple design and evolve it to handle larger scale
Design for appropriate guarantees: Match consistency, availability, and reliability to specific needs
Address data partitioning: Create effective strategies for distributing data as systems grow
Implement caching strategically: Add caching at various levels to improve performance
Design for failures: Assume components will fail and plan accordingly
Consider latency requirements: Design systems to meet specific latency goals
Make trade-offs explicit: Clearly articulate the pros and cons of your design decisions
Focus on bottlenecks: Identify and address the most critical limitations in your system