System Design
The System Design course teaches software engineers core scalability principles, caching strategies, data storage choices, distributed patterns, and networking fundamentals, enabling them to design robust, high‑performance services.
Who Should Take This
It is ideal for mid‑level engineers who have built production code and now aim to ace system‑design interviews or lead architecture decisions. Learners should be comfortable with core programming concepts and want to deepen their understanding of trade‑offs, capacity planning, and distributed system trade‑offs.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
60 learning goals
1
Scalability Fundamentals
3 topics
Scaling Strategies
- Describe the difference between horizontal scaling (adding more machines) and vertical scaling (adding more resources) and explain the practical limits and cost implications of each
- Implement back-of-envelope capacity estimation including QPS calculations, storage requirements, bandwidth needs, and memory sizing for a given system's expected traffic patterns
- Analyze system bottlenecks using Amdahl's Law and evaluate how the ratio of parallelizable to sequential work limits the scalability benefits of adding more resources
Load Balancing
- Describe load balancing algorithms including round-robin, weighted round-robin, least connections, IP hash, and consistent hashing and explain their suitability for different traffic patterns
- Describe Layer 4 versus Layer 7 load balancing and explain how each operates at different network layers with different capabilities for routing, SSL termination, and content inspection
- Implement health check mechanisms including active probes, passive monitoring, and graceful degradation to remove unhealthy backends from the load balancer pool
- Analyze session affinity (sticky sessions) trade-offs and evaluate how they impact horizontal scalability, failover behavior, and stateless service design
Capacity Planning
- Implement storage estimation calculations including data size per record, daily growth rate, replication factor, and total storage requirements over multi-year time horizons
- Implement bandwidth estimation including peak versus average traffic, read/write ratios, and network throughput requirements for data-intensive applications
- Analyze common latency numbers including memory access, SSD read, HDD seek, network round-trip, and cross-continent latency and evaluate their impact on system design decisions
2
Caching
3 topics
Caching Strategies
- Describe caching patterns including cache-aside (lazy loading), write-through, write-back (write-behind), and read-through and explain the consistency guarantees of each pattern
- Describe cache eviction policies including LRU, LFU, FIFO, and TTL-based expiration and explain how each policy affects cache hit rates for different access patterns
- Implement a multi-layer caching architecture including browser cache, CDN cache, application-level cache, and database query cache for optimal response time reduction
Cache Challenges
- Analyze cache invalidation strategies including TTL, event-driven invalidation, and versioned keys and evaluate why cache invalidation is considered one of the hardest problems in computing
- Analyze cache failure modes including cache stampede (thundering herd), cache penetration, and cache avalanche and evaluate mitigation strategies for each scenario
Distributed Caching
- Describe distributed cache architectures including client-side caching, shared cache clusters, and replicated caches and explain how consistent hashing distributes keys across cache nodes
- Implement cache-aside pattern with TTL-based expiration for a read-heavy application and explain how to handle cache warm-up after cache node failures
3
Data Storage and Databases
4 topics
Database Scaling
- Describe database replication topologies including leader-follower, leader-leader, and multi-region replication and explain consistency and availability trade-offs of each
- Describe database sharding strategies including range-based, hash-based, and directory-based partitioning and explain how each distributes data across multiple database instances
- Analyze the challenges of database sharding including cross-shard queries, rebalancing, hotspot mitigation, and referential integrity and evaluate when sharding is worth the complexity
CAP Theorem and Consistency
- Describe the CAP theorem including consistency, availability, and partition tolerance and explain why distributed systems must choose between CP and AP during network partitions
- Describe consistency models including strong consistency, eventual consistency, causal consistency, and read-your-writes and explain appropriate use cases for each model
- Analyze real-world system design scenarios and evaluate whether CP or AP characteristics are more appropriate based on business requirements and data correctness needs
SQL versus NoSQL
- Describe NoSQL database categories including key-value stores, document databases, wide-column stores, and graph databases and explain the data model and access patterns each optimizes for
- Analyze SQL versus NoSQL selection criteria including ACID requirements, schema flexibility, query complexity, horizontal scalability, and data relationship depth for specific use cases
Data Storage Patterns
- Describe blob storage and object storage concepts including immutability, content addressing, metadata tagging, and CDN integration for large file serving at scale
- Implement data partitioning strategies for time-series data including time-based partitioning, hot/cold data separation, and retention policies for cost-effective storage management
- Analyze the CQRS pattern (Command Query Responsibility Segregation) and evaluate how separating read and write models enables independent scaling and optimization of each path
4
Distributed System Patterns
4 topics
Message Queues and Event-Driven Architecture
- Describe message queue concepts including producers, consumers, topics, partitions, delivery guarantees (at-most-once, at-least-once, exactly-once), and dead letter queues
- Implement asynchronous processing patterns using message queues for task offloading, event sourcing, and pub/sub communication between decoupled services
- Analyze the trade-offs between synchronous request-response and asynchronous event-driven communication and evaluate when message queues add necessary decoupling versus unnecessary complexity
Consistent Hashing
- Describe consistent hashing including the hash ring, virtual nodes, and how it minimizes key redistribution when nodes are added or removed from a distributed system
- Implement consistent hashing with virtual nodes for load distribution across cache nodes and explain how it solves the rehashing problem of modular hashing during scaling events
Resilience Patterns
- Describe the circuit breaker pattern including closed, open, and half-open states and explain how it prevents cascading failures across dependent services
- Implement retry strategies with exponential backoff and jitter for transient failure recovery and explain how they prevent retry storms from overwhelming recovering services
- Implement rate limiting algorithms including token bucket, leaky bucket, fixed window, and sliding window and explain how each controls request throughput with different burst characteristics
- Analyze graceful degradation strategies including feature toggles, fallback responses, and bulkhead isolation and evaluate how they maintain partial functionality during component failures
Distributed Locking and Coordination
- Describe distributed locking mechanisms including database-based locks, Redis-based locks (Redlock), and ZooKeeper-based locks for coordinating access to shared resources across services
- Analyze the challenges of distributed locking including clock skew, lock expiration, split-brain scenarios, and evaluate fencing tokens as a correctness mechanism for lock holders
5
Networking and Content Delivery
3 topics
DNS and CDN
- Describe DNS resolution including recursive and iterative queries, record types (A, AAAA, CNAME, MX), TTL, and how DNS-based load balancing distributes traffic geographically
- Describe CDN architecture including edge nodes, origin servers, cache hierarchies, and cache control headers and explain how CDNs reduce latency for geographically distributed users
- Implement CDN caching strategies including static asset caching, dynamic content caching with vary headers, and cache purging mechanisms for content update propagation
API Design
- Describe RESTful API design principles including resource naming, HTTP method semantics, status codes, idempotency, and HATEOAS and explain how they enable scalable client-server interaction
- Implement API pagination strategies including offset-based, cursor-based, and keyset pagination and explain the performance and consistency characteristics of each for large datasets
- Implement API versioning strategies including URL path versioning, header versioning, and query parameter versioning and evaluate their impact on backward compatibility
- Analyze API gateway patterns including request routing, authentication, rate limiting, response aggregation, and protocol translation for microservice architectures
Communication Protocols
- Describe inter-service communication protocols including REST, gRPC, and GraphQL and explain how each protocol's characteristics affect latency, bandwidth, and developer experience
- Describe WebSocket and Server-Sent Events for real-time communication and explain when each is appropriate compared to polling and long-polling for push notification systems
- Analyze the trade-offs between synchronous RPC and asynchronous messaging for inter-service communication and evaluate coupling, latency, and reliability implications
6
Architecture Patterns
3 topics
Microservices versus Monolith
- Describe microservice architecture principles including single responsibility, independent deployment, decentralized data management, and service boundary definition based on business domains
- Analyze the trade-offs between microservices and monolithic architectures including operational complexity, team autonomy, data consistency, network latency, and deployment independence
- Describe service discovery mechanisms including client-side discovery, server-side discovery, and service registry patterns for dynamic service location in distributed environments
- Evaluate the strangler fig pattern for incremental monolith-to-microservice migration and analyze how to identify service boundaries using domain-driven design bounded contexts
System Design Methodology
- Implement a structured system design approach including requirements clarification, capacity estimation, high-level design, detailed component design, and bottleneck identification
- Implement system design for a URL shortener including hash generation, collision handling, redirect flow, analytics tracking, and storage requirements estimation
- Implement system design for a social media feed including fan-out strategies (push vs pull vs hybrid), timeline assembly, ranking algorithms, and read-heavy optimization
- Analyze design trade-offs in real-world systems and evaluate how constraints such as latency requirements, consistency needs, and cost budgets drive architectural decisions
Data Consistency Patterns
- Describe the saga pattern for distributed transactions including choreography-based and orchestration-based implementations and explain how compensating transactions handle failures
- Implement event sourcing by storing state changes as an immutable event log and explain how event replay enables temporal queries, auditing, and system recovery
- Analyze idempotency requirements for distributed operations and evaluate how idempotency keys prevent duplicate processing in payment systems and order management
Hands-On Labs
5 labs
~125 min total
Console Simulator
Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.
Scope
Included Topics
- Scalability patterns including horizontal and vertical scaling, load balancing algorithms, caching strategies (write-through, write-back, cache-aside, LRU eviction), database partitioning (sharding, replication, read replicas), and the CAP theorem with consistency models
- Distributed system components including message queues, event-driven architectures, API gateway patterns, rate limiting, CDN concepts, DNS resolution, consistent hashing, and back-of-envelope capacity estimation techniques
- Architectural patterns including microservices versus monolith trade-offs, service discovery, circuit breaker pattern, API design principles (REST, pagination, versioning), data storage selection (SQL vs NoSQL), and system design interview methodology
Not Covered
- Specific cloud provider implementations and services
- Coding and algorithm implementation
- Network protocol internals below the application layer
- Database query optimization and indexing internals
- DevOps tooling and deployment automation
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