Ultimate Global System Design Cheatsheet: Architecting Scalable Distributed Systems

Introduction to Global System Design

Global System Design refers to the art and science of architecting large-scale distributed systems that can reliably serve users across the globe while maintaining performance, availability, and consistency. These systems power the applications and services used by millions or billions of users daily, from social media platforms and e-commerce sites to financial systems and content delivery networks. Unlike traditional system design, global system design specifically addresses the challenges of geographical distribution, massive scale, and diverse regional requirements, making it essential for modern internet-scale applications.

Core Concepts and Principles

Fundamental Design Principles

Key Architectural Patterns

• Microservices Architecture: Breaking down applications into loosely coupled, independently deployable services
• Event-Driven Architecture: Building systems where components communicate through events
• Layered Architecture: Organizing components into horizontal layers with specific responsibilities
• Service-Oriented Architecture (SOA): Designing based on services that provide discrete business functions
• Serverless Architecture: Building applications that rely on third-party cloud services (BaaS/FaaS)
• Domain-Driven Design (DDD): Organizing systems based on the core business domain

CAP Theorem and Trade-offs

The CAP theorem states that distributed systems can only provide two of these three guarantees simultaneously:

• Consistency: All nodes see the same data at the same time
• Availability: Every request receives a response (success or failure)
• Partition Tolerance: System continues to operate despite network partitions

System Design Methodology

Step-by-Step Design Process

1. Clarify Requirements and Constraints

   • Functional requirements (what the system should do)
   • Non-functional requirements (performance, scalability, reliability)
   • Scale estimation (users, traffic, data volume)
   • Technical constraints (budget, tech stack, existing systems)

2. Define System Interface

   • API definitions
   • Data models
   • Service contracts

3. Design High-Level Architecture

   • Major components and their interactions
   • Data flow through the system
   • Global distribution strategy

4. Scale the Design

   • Horizontal vs. vertical scaling strategies
   • Sharding and partitioning approaches
   • Replication strategies

5. Address Specific Challenges

   • Data consistency models
   • Failure handling mechanisms
   • Cache strategy and CDN usage
   • Regional compliance requirements

6. Evolve and Iterate

   • Performance bottleneck identification
   • Capacity planning
   • Monitoring and observability integration

Scale Estimation Techniques

• Traffic Estimation: QPS = DAU × (Actions per User per Day) ÷ 86,400
• Storage Estimation: Total Storage = Objects × Average Size × (1 + Replication Factor)
• Bandwidth Estimation: Bandwidth = QPS × Average Response Size
• Memory Estimation: Cache Memory = QPS × Data Size × Cache Time

Core Building Blocks and Components

Data Storage Solutions

Communication Patterns

Caching Strategies

• Cache-Aside (Lazy Loading): Application checks cache first, then database if cache miss
• Write-Through: Writes go to cache and database simultaneously
• Write-Back (Write-Behind): Writes go to cache first, then asynchronously to database
• Read-Through: Cache handles retrieving data from database on cache miss

Global Distribution Techniques

• Content Delivery Networks (CDNs): Distribute static content closer to users
• Edge Computing: Process data closer to where it’s generated
• Regional Deployment: Deploy complete application stacks in multiple regions
• Global Load Balancing: Route users to the nearest or healthiest region
• Data Replication: Synchronize data across regions
• Sharding by Geography: Partition data based on user location

Scaling and Performance Optimization

Horizontal vs. Vertical Scaling

Data Partitioning Strategies

• Horizontal Partitioning (Sharding)

  • Range-based: Partition by data range (e.g., user IDs 1-1M, 1M-2M)
  • Hash-based: Distribute using hash function (user_id % num_shards)
  • Directory-based: Maintain lookup service for shard location
  • Geographically-based: Partition by user location

• Vertical Partitioning

  • Split table columns across servers
  • Separate frequently accessed columns
  • Group related columns together

Load Balancing Algorithms

Rate Limiting Techniques

• Token Bucket: Accumulate tokens at fixed rate, each request consumes a token
• Leaky Bucket: Process requests at constant rate, queue or reject excess
• Fixed Window: Count requests in fixed time windows
• Sliding Window: Count requests in rolling time windows
• Sliding Window with Counter: Combine count with timestamp weighting

Reliability and Resilience

Fault Tolerance Patterns

Consistency Models

Disaster Recovery Strategies

RPO = Recovery Point Objective (data loss), RTO = Recovery Time Objective (downtime)

Security Considerations

Authentication and Authorization

• Authentication Methods:

  • Password-based: Email/password with strong policies
  • Token-based: JWT, OAuth tokens
  • Certificate-based: mTLS
  • Multi-factor: Combine multiple methods

• Authorization Models:

  • Role-Based Access Control (RBAC)
  • Attribute-Based Access Control (ABAC)
  • Discretionary Access Control (DAC)
  • Mandatory Access Control (MAC)

Data Protection

• Encryption Types:

  • In-transit: TLS/SSL, VPN
  • At-rest: Full disk encryption, field-level encryption
  • In-use: Homomorphic encryption, secure enclaves

• Key Management:

  • Hardware Security Modules (HSMs)
  • Key Rotation Policies
  • Secrets Management Services (AWS KMS, HashiCorp Vault)

Common Attack Vectors and Mitigations

Observability and Monitoring

Monitoring Dimensions

• System Metrics: CPU, memory, disk I/O, network
• Application Metrics: Request rates, error rates, latencies
• Business Metrics: User engagement, conversion rates, revenue
• Synthetic Monitoring: Simulated user journeys
• Real User Monitoring: Actual user experience metrics

Logging Best Practices

• Use structured logging (JSON)
• Include contextual information (request ID, user ID, etc.)
• Implement appropriate log levels
• Centralize log storage and processing
• Establish log retention policies

Instrumentation Tools

Common Challenges and Solutions

Handling High Traffic

Challenge: Managing sudden traffic spikes without service degradation

Solutions:

• Implement autoscaling based on traffic metrics
• Use CDNs to absorb static content load
• Employ caching at multiple levels
• Implement graceful degradation for non-critical features
• Use rate limiting and traffic prioritization
• Design stateless services where possible

Ensuring Global Consistency

Challenge: Maintaining data consistency across distributed regions

Solutions:

• Choose appropriate consistency models based on business requirements
• Implement conflict resolution strategies (CRDTs, vector clocks)
• Use distributed consensus algorithms for critical data
• Consider eventual consistency with clear reconciliation patterns
• Leverage specialized global databases (Spanner, Cosmos DB)
• Implement change data capture (CDC) for asynchronous replication

Managing Costs

Challenge: Controlling expenses while scaling globally

Solutions:

• Implement right-sizing for resources
• Use spot/preemptible instances for non-critical workloads
• Leverage serverless for variable workloads
• Implement caching to reduce database load
• Define clear data retention and archiving policies
• Use data tiering to move less accessed data to cheaper storage
• Monitor and alert on unusual spending patterns

Handling Regional Requirements

Challenge: Addressing diverse regulatory and performance needs across regions

Solutions:

• Implement data residency controls
• Design for regional isolation when needed
• Create region-specific feature flags
• Develop customizable compliance controls
• Use geofencing for restricted features or content
• Implement multi-region deployment pipelines
• Monitor region-specific metrics and SLAs

Best Practices and Tips

System Design Principles

• Start Simple: Begin with the simplest design that meets requirements
• Design for Scale from Day One: Even if you don’t implement it all immediately
• Embrace Failure: Design assuming components will fail
• Prefer Isolation: Limit failure domains through proper isolation
• Use Proven Technologies: Favor battle-tested solutions for critical components
• Make Conscious Trade-offs: Explicitly document design decisions and their rationales
• Design for Operability: Consider monitoring, debugging, and maintenance from the start

Performance Optimization

• Reduce Round Trips: Minimize client-server communication
• Optimize Critical Paths: Focus on high-traffic and user-facing operations
• Compress Data: Reduce payload sizes where appropriate
• Use Connection Pooling: Reuse expensive connections
• Implement Pagination: Break large responses into manageable chunks
• Right-size Infrastructure: Match resource allocation to workload needs
• Profile Regularly: Identify and address bottlenecks early

Data Management

• Design for Data Evolution: Schema changes will happen
• Plan for Data Growth: Consider future scale in storage design
• Implement Data Lifecycle Management: Archive, summarize, or delete old data
• Use Data Tiering: Store data based on access patterns and importance
• Protect Sensitive Data: Implement encryption and access controls
• Backup Strategically: Balance completeness with recovery speed
• Test Restores: Verify backup effectiveness regularly

Global Deployment

• Deploy in Phases: Roll out to regions progressively
• Use Canary Deployments: Test changes on small traffic portions
• Implement Blue-Green Deployments: Maintain parallel environments
• Automate Everything: Infrastructure as code, CI/CD pipelines
• Design for Minimal Global Dependencies: Reduce inter-region communication
• Consider Regional Service Differences: Cloud services vary by region
• Monitor Regional Performance: Ensure consistent experience globally

Resources for Further Learning

Books

• “Designing Data-Intensive Applications” by Martin Kleppmann
• “System Design Interview” by Alex Xu
• “Fundamentals of Software Architecture” by Mark Richards & Neal Ford
• “Building Microservices” by Sam Newman
• “Release It!” by Michael T. Nygard
• “Site Reliability Engineering” by Google

Online Courses

• MIT 6.824: Distributed Systems
• Stanford CS348: Computer Networks
• Grokking the System Design Interview (educative.io)
• AWS/Azure/GCP Architecture Certification Courses

Blogs and Websites

• High Scalability Blog (highscalability.com)
• Netflix Tech Blog (netflixtechblog.com)
• AWS Architecture Blog (aws.amazon.com/blogs/architecture)
• System Design Primer (GitHub)
• Martin Fowler’s Blog (martinfowler.com)
• InfoQ Architecture Section (infoq.com/architecture-design)

Tools and Frameworks

• Diagramming: draw.io, Lucidchart, Miro
• Load Testing: JMeter, Gatling, Locust
• Infrastructure as Code: Terraform, CloudFormation, Pulumi
• Container Orchestration: Kubernetes, Docker Swarm
• Monitoring: Prometheus, Grafana, ELK Stack
• API Gateway: Kong, AWS API Gateway, Apigee

Communities

• Stack Overflow
• Reddit r/devops, r/programming
• Discord servers for specific technologies
• Cloud provider communities
• GitHub discussions on major open-source projects