Free account

Save course progress and unlock the starter library.

Join free
CodesBlock
Become a member
Interactive AI learning

CRACK THE SYSTEM DESIGN INTERVIEW

Prepare for the System Design Interview by learning how to design scalable, reliable, and high-performance systems from the ground up. Master the frameworks and patterns behind real-world architectures, practice common system design interview questions, and learn to reason through trade-offs with confidence. Then take it further with modern system design—from distributed systems and event-driven architectures to RAG, AI agents, tool calling, vector databases, and the infrastructure powering today’s AI applications.

New 0 views 3 Lessons Updated October 5, 2026 6 weeks / 30-36 hours 0 users learning

What You'll Learn

  • Break vague System Design questions into clear requirements, constraints, and success criteria
  • Estimate traffic, storage, bandwidth, and infrastructure capacity with confidence
  • Choose databases, caches, load balancers, queues, CDNs, and services from real constraints
  • Explain scalability, availability, reliability, latency, consistency, and security trade-offs
  • Draw high-level architectures and improve them as traffic grows
  • Handle interviewer follow-ups with a clear, structured design narrative
  • Practice with real systems and receive guided feedback on your reasoning
  • Design a modern AI product with cost, latency, safety, and evaluation in mind

About this course

Course overview

System Design Interview Lab is a hands-on course for developers who want to reason like an architect under interview pressure. You will progress from fundamentals and capacity math to reusable distributed-system building blocks, real product designs, and mock interviews. Read, sketch, question, test your choices, and practise defending the trade-offs that make a design credible.

Course Material

Build a system design answer you can explain, draw, and defend.

System Design Interview Lab turns vague prompts into a calm, repeatable method. You will clarify the problem, estimate the scale, draw a complete first architecture, choose components from constraints, and defend the trade-offs that matter. The goal is not to memorize finished diagrams. It is to build the judgment to design a system in front of another engineer.

Learn deeply, without getting stuck

Read the lesson, form your own answer, then use the study companion when a sentence or decision needs a clearer explanation. Highlight a concept to unpack it, ask for a trade-off, test yourself with an interviewer-style follow-up, or return to your notes and architecture sketch.

What you will leave with

  • A reusable interview framework for open-ended design prompts
  • An estimation and architecture playbook you can adapt to new systems
  • Hands-on practice with core building blocks and full product designs
  • Clear notes on scalability, reliability, consistency, cost, and operational trade-offs
  • Mock-interview preparation and a capstone architecture review

Course Syllabus

  • What System Design is and why it matters
  • Who this course is for: prerequisites and a beginner readiness check
  • What interviews test at junior, mid, senior and staff levels
  • Functional requirements: users, use cases and scope
  • Non-functional requirements: latency, availability, durability and cost
  • Clarifying questions, assumptions and design constraints
  • Bits, bytes, powers of two and useful reference numbers
  • Estimate average and peak QPS from user behaviour
  • Estimate storage, bandwidth, cache capacity and growth
  • Latency budgets, percentiles and Little’s Law intuition
  • Core entities, data modelling and access patterns
  • API contracts: resources, errors, pagination and versioning
  • Trace the critical path and separate asynchronous work
  • SCALE-45: structure a 45-minute design conversation
  • Communicate trade-offs and record architectural decisions
  • Checkpoint: scope and estimate a photo-sharing system
  • CPU, memory, disks and the bottlenecks behind a request
  • Client-server architecture: trace one request end to end
  • DNS, IP addresses, ports and connection setup
  • TCP vs UDP: reliability, ordering and transport trade-offs
  • HTTP, HTTPS and TLS: methods, status codes and connection reuse
  • REST vs gRPC; polling, SSE and WebSockets
  • Start with one server; monoliths vs microservices
  • Vertical vs horizontal scaling; stateless vs stateful services
  • Load balancing: L4/L7, algorithms, health checks and sticky sessions
  • API gateways, reverse proxies and service discovery
  • Relational modelling, SQL joins and schema evolution
  • Indexes, query plans and B-tree vs LSM-tree intuition
  • Transactions, ACID, isolation levels and concurrent writes
  • SQL vs key-value, document, wide-column and graph databases
  • Replication, read replicas and the read-after-write problem
  • Partitioning, sharding, consistent hashing and rebalancing
  • Cache-aside, read-through, write-through and write-back
  • Cache keys, TTLs, eviction, invalidation and stampedes
  • CDNs, object storage, signed URLs and media delivery
  • Queues vs pub/sub vs event logs: choosing the right model
  • Workers, consumer groups, ordering and delivery guarantees
  • Batch vs stream processing; backpressure and dead-letter queues
  • Checkpoint: evolve one server through three traffic milestones
  • Latency, throughput, scalability and saturation
  • Availability, reliability, durability and fault tolerance
  • CAP during a partition; consistency vs availability choices
  • Strong, eventual, causal and session consistency
  • Replication lag, quorums and conflict resolution
  • Leader election, split brain and consensus intuition
  • Clocks, ordering, logical timestamps and distributed IDs
  • Timeout budgets, bounded retries, jitter and retry storms
  • Circuit breakers, bulkheads and load shedding
  • Idempotency keys, deduplication and at-least-once processing
  • Locks, leases, optimistic concurrency and fencing tokens
  • Distributed transactions, sagas and the transactional outbox
  • Hot keys, skew, consistent hashing and uneven partitions
  • Rate limiting: token bucket, sliding window and distributed limits
  • Authentication, authorization, encryption and secrets
  • Multitenancy, isolation, quotas and data lifecycle
  • Metrics, logs, traces and actionable alerting
  • SLIs, SLOs, error budgets and capacity planning
  • Multi-zone and multi-region design; failover and data residency
  • Backups, restore tests, RPO and RTO
  • Safe deployments, migrations and rollback strategies
  • Failure lab: recover from a slow dependency and a region outage
  • Lab method: scope, estimate, sketch, break and defend
  • Design a URL shortener: IDs, redirects and abuse controls
  • Design a distributed rate limiter: fairness and race conditions
  • Design a key-value store: replication, partitions and durability
  • Design a distributed ID generator: uniqueness and clock rollback
  • Design a web crawler: frontier, politeness and deduplication
  • Design search and autocomplete: indexes, ranking and freshness
  • Design notifications: channels, preferences, retries and duplicates
  • Design chat: ordering, delivery receipts and offline users
  • Design presence: heartbeats, expiry and fan-out
  • Design a news feed: push vs pull and celebrity hot spots
  • Design ticket booking: inventory holds and overselling
  • Design payments: idempotency, ledgers, webhooks and reconciliation
  • Design cloud file sync: chunks, conflicts and resumable uploads
  • Design video streaming: transcoding, manifests and CDN delivery
  • Design nearby search and ride matching: geospatial indexes
  • Design collaborative documents: OT vs CRDT and convergence
  • Design a leaderboard: ranking, ties and real-time updates
  • Design metrics and event aggregation: windows and late events
  • Constraint ladder: 10× traffic, one hot tenant and one failed region
  • Checkpoint: compare two valid architectures and defend your choice
  • LLM fundamentals for architects: inference, tokens and context
  • Model APIs vs self-hosted inference: capacity and deployment trade-offs
  • Latency, throughput, batching and time-to-first-token
  • Streaming responses: cancellation, partial output and backpressure
  • Structured outputs: schemas, validation and repair limits
  • Tool calling: contracts, execution and result handling
  • Context assembly, conversation state and token budgets
  • Prompt and model versioning; rollout and rollback
  • Design an AI gateway: provider abstraction and policy enforcement
  • Model routing, timeouts, fallbacks and independent capacity
  • Request and token rate limits; tenant quotas and admission control
  • Cost budgets, usage attribution and cost per successful task
  • Exact vs semantic caching: privacy, invalidation and correctness
  • Graceful degradation, deterministic fallbacks and when to avoid AI
  • Lab: design an AI chatbot and defend its gateway architecture
  • When to use RAG, fine-tuning or a deterministic lookup
  • Document ingestion: connectors, parsing and provenance
  • Chunking: boundaries, overlap, metadata and hierarchy
  • Embeddings: similarity, dimensions and model changes
  • Vector indexes: exact search, ANN, HNSW and recall trade-offs
  • Lexical search, BM25 and hybrid retrieval
  • Query rewriting, metadata filters and retrieval strategies
  • Reranking: relevance, latency and cost
  • Context assembly: deduplication, citations and token allocation
  • Incremental indexing, freshness, deletions and re-embedding
  • Permissions-aware retrieval and tenant isolation
  • Grounded answers, abstention and conflicting sources
  • Retrieval evaluation: recall, ranking and labelled test sets
  • End-to-end RAG evaluation: faithfulness and answer usefulness
  • Latency and cost budgets across the RAG pipeline
  • Lab: enterprise knowledge search under ACL and freshness constraints
  • Workflow vs agent: decide whether autonomy is needed
  • Agent control loops: planning, execution and termination
  • Tool schemas, registries, discovery and validation
  • Keep business rules in deterministic services
  • MCP fundamentals: hosts, clients, servers, tools and resources
  • MCP service integration: authentication and trust boundaries
  • Short-term context vs durable memory and retrieval
  • Checkpoints, durable workflows and crash recovery
  • Retries and idempotency for side-effecting tool calls
  • Human approval, least privilege and action auditing
  • Sandboxes, untrusted code and restricted tool execution
  • Budgets for steps, time, tokens and tool costs
  • Multi-agent coordination: handoffs, shared state and when to avoid it
  • Lab: coding assistant with repo retrieval and sandboxed execution
  • Lab: bounded support agent with approval and recovery
  • Define quality, safety, latency and cost objectives
  • Golden datasets: coverage, leakage and representative cases
  • Offline evals, regression tests and release gates
  • Online evaluation, user feedback and human review
  • Trace prompts, retrieval, model calls and tool execution
  • Diagnose hallucinations and separate retrieval from generation failures
  • Prompt injection in user input, documents and tool results
  • Poisoned retrieval, data leakage and authorization boundaries
  • Unsafe actions, excessive agency and output validation
  • Abuse prevention, red teaming and incident response
  • Cost per request vs cost per successful task
  • Provider outages, overload, queues and fallback testing
  • Canary rollouts, model drift and version regressions
  • Lab: evaluate and harden a RAG or agent service
  • Audio fundamentals: sampling, codecs and streaming chunks
  • STT → LLM → TTS vs speech-to-speech
  • WebRTC vs WebSockets: transport and session trade-offs
  • ICE, STUN and TURN: connectivity behind NAT
  • Voice activity detection and endpointing
  • Barge-in, cancellation and echo handling
  • Conversation state, reconnects and session recovery
  • Tool use during conversation: latency and approval
  • Latency budgets: first audio, turn completion and jitter
  • Concurrent-call capacity, admission control and regional routing
  • Privacy, consent, retention and safe transcript handling
  • Voice quality evaluation, accessibility and degraded connections
  • Lab: design a voice AI interviewer under latency constraints
  • Build your playbook: estimates, patterns and decision records
  • Classic architecture mock: scope and build from a blank canvas
  • Senior reliability mock: failures, migrations and operational depth
  • RAG architecture mock: retrieval quality, freshness and permissions
  • Agent architecture mock: safe actions and durable execution
  • Voice architecture mock: interruptions and latency
  • Constraint ladders: 10× scale, budget cuts and dependency failures
  • Assess your answer: requirements, estimates, data flow and trade-offs
  • Capstone brief: choose users, constraints and measurable goals
  • Capstone design: APIs, schemas, diagrams and capacity estimates
  • Capstone pressure test: failure modes, security and cost model
  • Capstone defence: alternatives, trade-offs and architecture report
  • Revision plan: identify gaps and repeat the weakest mock

Course Guide

Find your way through the course.

Use the interactive guide for the course summary, prerequisites, pricing, and curriculum details.