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Hi there 👋
This week we're going deep on System Design — the skill that separates mid-level engineers from senior ones. Whether you're prepping for a FAANG interview or designing real production systems, knowing how to reason about scale, reliability, and trade-offs is non-negotiable.
We cover the canonical case studies (URL shorteners, rate limiters, notification systems), the foundational concepts (CAP theorem, consistent hashing, database sharding), and the architectural patterns (CQRS, event sourcing, saga pattern) that power systems at companies like Netflix, Uber, and Twitter.
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THIS WEEK'S FOCUS
System Design: From Concepts to Case Studies
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🏗️ Scalability • Reliability • Consistency • Performance • Trade-offs
The complete system design toolkit every backend engineer needs
System design interviews test whether you can architect real-world solutions at scale. But more importantly, these skills matter every day on the job — when you're deciding between SQL and NoSQL, choosing a caching strategy, or figuring out how to handle 10x traffic spikes without rewriting everything.
| ✅ CAP theorem & consistency models |
✅ Horizontal vs vertical scaling |
| ✅ Database sharding & replication |
✅ Consistent hashing |
| ✅ Caching strategies (Redis, CDN) |
✅ Load balancing algorithms |
| ✅ Message queues & event streaming |
✅ Rate limiting patterns |
| ✅ Distributed transactions & saga |
✅ API gateway & service mesh |
Explore System Design Articles →
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Core Concepts Deep-Dives
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🌐
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Scalability Fundamentals
Horizontal vs vertical scaling trade-offs, stateless service design, consistent hashing for distributed systems, load balancing algorithms (round-robin, least-connections, IP-hash), and auto-scaling triggers. When to scale out vs scale up — and how to avoid premature scaling.
Read Scalability Guide →
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🗄️
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Database Design at Scale
SQL vs NoSQL decision framework, database sharding strategies (range, hash, directory), read replicas and replication lag, eventual consistency in practice, polyglot persistence, and when to use PostgreSQL, Cassandra, DynamoDB, or MongoDB for different access patterns.
Read Database Scaling Guide →
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Caching: Redis, CDN & Beyond
Cache-aside, write-through, and write-behind patterns — when to use each. Redis data structures for real use cases, cache invalidation strategies, CDN edge caching, distributed caching with consistent hashing, and the thundering herd problem with solutions.
Read Caching Deep-Dive →
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ARCHITECTURE CASE STUDIES
Classic System Design Problems, Solved
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🔗 Case Study
Design a URL Shortener
Base62 encoding, key generation strategies, database choice (SQL vs KV store), read-heavy caching with Redis, analytics tracking, and handling 100M+ URLs with sub-10ms redirect latency.
Read the Architecture →
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🚦 Case Study
Design a Rate Limiter
Token bucket, leaky bucket, fixed window, sliding window log, and sliding window counter algorithms — trade-offs, Redis-based distributed implementation, and where to place rate limiting in your stack.
Read the Architecture →
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🔔 Case Study
Design a Notification System
Multi-channel delivery (push, email, SMS), priority queues with Kafka, idempotency guarantees, retry logic with exponential backoff, user preference management, and handling 1M notifications/minute.
Read the Architecture →
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📰 Case Study
Design a Social News Feed
Fan-out on write vs fan-out on read, celebrity user problem, timeline generation with Redis sorted sets, pagination strategies, and how Instagram and Twitter solve feed at 500M daily users.
Read the Architecture →
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PATTERNS & PRINCIPLES
5 System Design Patterns Every Developer Must Know
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CQRS — Command Query Responsibility Segregation
Separate your read model from your write model. Write side handles commands and emits events; read side maintains optimised projections. Pairs perfectly with event sourcing and enables independent scaling of reads (which dominate 90%+ of most systems).
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Saga Pattern — Distributed Transactions Without 2PC
Replace distributed ACID transactions with a sequence of local transactions and compensating actions. Choreography-based (event-driven) vs orchestration-based (central coordinator) — when to pick each, and how to handle failures and rollbacks at the service boundary.
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Circuit Breaker — Fail Fast, Recover Gracefully
Wrap downstream calls in a circuit breaker (Resilience4j, Hystrix). Three states: Closed (normal), Open (fail fast), Half-Open (probe). Prevents cascade failures when a single dependency degrades. Combined with retry + timeout + bulkhead for production resilience.
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Event Sourcing — The Append-Only Audit Log
Store state as a sequence of events, not current values. Replay events to rebuild any past state, build multiple projections from the same event stream, and get a complete audit trail for free. Pairs with Kafka, EventStoreDB, and DynamoDB Streams.
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Outbox Pattern — Guaranteed Event Delivery
Avoid the dual-write problem (DB commit + message publish). Write the event to an outbox table in the same transaction as your domain change, then poll or use CDC (Debezium) to publish reliably. Exactly-once semantics without distributed transactions.
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🧠 This Week's Concept Corner
CAP Theorem: What It Actually Means in 2026
Every distributed system must choose two of three guarantees: Consistency (every read gets the latest write), Availability (every request gets a response), and Partition Tolerance (system works despite network splits).
The catch: network partitions always happen eventually, so the real choice is CP vs AP. CP systems (ZooKeeper, HBase, etcd) sacrifice availability to stay consistent. AP systems (Cassandra, DynamoDB, CouchDB) stay available but allow stale reads.
Most real-world systems don't pick a clean binary — they tune consistency per operation. Cassandra's QUORUM reads give you tunable consistency. DynamoDB's strongly consistent reads cost 2x. Understanding this lets you make intentional trade-offs rather than accidental ones.
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INTERVIEW PREP
How to Structure a System Design Interview Answer
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Step 1 — Clarify requirements (5 min): Functional requirements (what it does) vs non-functional (scale, latency, availability). Ask: DAU, read:write ratio, data size, SLA. Don't jump to design.
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Step 2 — Back-of-the-envelope estimates (3 min): QPS, storage per day, bandwidth. Shows you can reason about scale. 100M users × 10 requests/day = ~1,000 QPS. Write this down visibly.
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Step 3 — High-level design (10 min): Draw the boxes — client, load balancer, API servers, cache, database, CDN, message queue. Walk through a request end-to-end before going deep.
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Step 4 — Deep-dive components (15 min): Pick 2-3 hard parts (the interviewer usually guides you). Database schema, sharding key, cache invalidation, API design. This is where you differentiate.
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Step 5 — Identify bottlenecks & trade-offs (5 min): What breaks first under load? What did you sacrifice? Strong consistency vs availability, cost vs performance, simplicity vs flexibility. Interviewers want to see you reason, not recite.
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REAL WORLD ARCHITECTURE
How Top Companies Handle Scale
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🎥 Netflix
Chaos Engineering (Chaos Monkey), microservices on AWS, Zuul API gateway, Eureka service discovery, Hystrix circuit breakers, Cassandra for viewing history, and EVCache (Memcached) for session data across 190 countries.
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🚗 Uber
Geospatial indexing with H3 hexagons, dispatch on Kafka event streams, surge pricing as a real-time stream processing pipeline, MySQL + Schemaless (Cassandra-backed), and GRAIL for dynamic pricing ML.
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💬 Discord
Moved from MongoDB to Cassandra for messages (billions/day), then to ScyllaDB for lower latency. Read/write amplification solved with data bucketing. Elixir for real-time presence. Rust services for CPU-bound work.
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