Interview Prep: System Design Rounds
Master the frameworks, trade-offs, and communication patterns that top-tier engineering interviews demand. You'll design scalable systems from scratch — URL shorteners, rate limiters, distributed caches, and feed systems — using the same approach engineers at FAANG use.
Course Content
6 modules · 30 lessonsSystem Design Interview Framework
Students can structure any system design answer using a repeatable 4-step framework that impresses interviewers.
How to approach a system design interview question
interview structure, requirements clarification, functional vs non-functional requirements, scope definition, time management
How to gather and clarify system design requirements
functional requirements, non-functional requirements, read/write ratio, DAU estimation, SLA definition, scope bounding
Back-of-envelope estimation for system design interviews
DAU to QPS conversion, storage estimation, bandwidth calculation, memory estimation, powers of two cheat sheet
How to draw a high-level system design diagram
component identification, data flow, client-server model, load balancer placement, service decomposition, database selection
How to communicate trade-offs in system design interviews
CAP theorem basics, consistency vs availability, latency vs throughput, cost vs performance, SQL vs NoSQL trade-offs, communicating uncertainty
Scalability and Load Balancing
Students can design horizontally scalable systems and explain how load balancers, stateless services, and consistent hashing work together.
Horizontal vs vertical scaling — which to choose and when
vertical scaling limits, horizontal scaling, stateless vs stateful services, auto-scaling, cost trade-offs, single point of failure
How load balancers work and which algorithm to use
round robin, least connections, IP hash, layer 4 vs layer 7 load balancing, health checks, sticky sessions
Consistent hashing explained for system design interviews
hash ring, virtual nodes, node addition and removal, cache sharding, load distribution, hotspot prevention
How DNS and CDN work in large-scale systems
DNS resolution, CDN caching, edge nodes, cache-control headers, origin server, TTL, geographic routing
How to design a scalable API gateway
API gateway role, rate limiting at gateway, authentication offloading, request routing, protocol translation, service discovery
Databases and Storage Systems
Students can select the right database for any system design scenario and explain replication, sharding, and indexing trade-offs under interview pressure.
SQL vs NoSQL — when to use which in system design
ACID properties, BASE model, schema flexibility, horizontal scaling, joins vs denormalization, document vs column vs key-value stores
Database replication — primary-replica and multi-master patterns
synchronous vs asynchronous replication, read replicas, replication lag, multi-master conflicts, failover, write scalability
Database sharding strategies for large-scale systems
horizontal sharding, shard key selection, range vs hash sharding, cross-shard queries, hotspot shards, resharding complexity
How database indexes work and when to use them
B-tree index, composite indexes, index selectivity, covering indexes, write overhead, full-table scan vs index scan
How to choose between object storage, block storage, and file systems
object storage, block storage, file storage, S3 vs EBS vs EFS, metadata access, cost comparison, WORM storage, CDN integration
Caching Systems
Students can design multi-layer caching strategies and explain cache invalidation, eviction policies, and consistency trade-offs in system design interviews.
Cache-aside vs write-through vs write-back — which caching pattern to use
cache-aside pattern, write-through cache, write-back cache, read-through cache, cache population, consistency trade-offs
Cache eviction policies — LRU, LFU, and TTL explained
LRU eviction, LFU eviction, TTL expiry, FIFO, random eviction, cache size limits, eviction policy trade-offs
How to handle cache invalidation in distributed systems
cache invalidation strategies, TTL expiry, event-driven invalidation, write-through invalidation, stampede problem, cache warming
Redis vs Memcached — which cache to use
Redis data structures, Memcached simplicity, persistence options, clustering, pub/sub, atomic operations, multi-threading
How to design a distributed cache system
cache sharding, cache cluster topology, replication for availability, hot key problem, local vs remote cache, cache hierarchy
Designing Real Systems
Students can design complete, production-grade systems from scratch — URL shorteners, rate limiters, notification systems, and news feed systems — using all prior module concepts.
How to design a URL shortener like bit.ly
base62 encoding, ID generation, redirect architecture, analytics tracking, custom aliases, database schema design, read-heavy optimization
How to design a rate limiter
token bucket algorithm, leaky bucket, fixed window, sliding window, distributed rate limiting, Redis atomic operations, rate limit headers
How to design a notification system
push vs pull, notification fanout, message queues, multi-channel delivery, retry logic, notification templates, user preference management
How to design a news feed system like Twitter or Instagram
fanout on write, fanout on read, hybrid fanout, celebrity problem, feed ranking, timeline storage, Redis sorted sets
How to design a distributed message queue like Kafka
topics and partitions, consumer groups, offset management, message retention, producer acknowledgment, at-least-once delivery, ordering guarantees
Advanced Distributed Systems Concepts
Students can explain consensus, distributed transactions, and observability patterns in system design interviews at senior and staff engineer level.
CAP theorem — what it actually means for system design
consistency definition, availability definition, partition tolerance, CA vs CP vs AP systems, network partition behavior, CAP in practice
Distributed transactions — two-phase commit and sagas explained
distributed transaction problem, two-phase commit, 2PC coordinator failure, saga pattern, choreography vs orchestration, compensating transactions
Leader election and consensus in distributed systems
split-brain problem, Raft consensus, Paxos overview, Zookeeper coordination, etcd leader election, fencing tokens, lease-based leader election
How to design for observability — metrics, logging, and tracing
three pillars of observability, structured logging, distributed tracing, RED metrics, SLO vs SLA, alerting on symptoms not causes
How to design for failure — circuit breakers and bulkheads
circuit breaker pattern, closed/open/half-open states, bulkhead isolation, retry with backoff, timeout configuration, cascading failure prevention