1. A Beginner’s Guide to System Design — Aritra Sen A structured, beginner-friendly walkthrough of system design interview preparation and key topics. (Medium)

2. System Design was HARD until I Learned these 30 Concepts — Ashish Pratap Singh A hands-on article listing 30 essential system design concepts (with explanations). (AlgoMaster Blog)

3. Netflix Tech Blog Real-world stories of scaling, architecture, and distributed systems from Netflix engineers. (Netflix Tech Blog)

4. AWS Architecture Blog Deep dives into designing systems on AWS: cloud architectures, best practices, case studies. (Amazon Web Services, Inc.)

5. 10 engineering blogs to become a System Design Hero (free list) — Dev.to post A curated list of engineering blogs focused on system design topics. (DEV Community)

6. 40 Must-Read Engineering Blogs to Learn System Design A list of 40 engineering blogs with content on system design and architecture. (Medium)

7. Engineering Blogs Every Software Architect Should Read A blog article recommending engineering / architecture blogs worth following. (Vedcraft)

If you like, I can pull out 5 best examples of blog posts with strong style, structure, visuals (for system design) and send you links + short analysis (what makes them good). That way you can use them as models for your own writing. Do you want me to fetch that?

System design is about creating software systems that meet functional and non-functional requirements (scale, latency, availability, cost, maintainability). Good design balances trade-offs and uses patterns to achieve reliability, performance, and simplicity.

• Functional requirements: What must the system do? (API endpoints, features)
• Non-functional requirements (NFRs): Scale (QPS, users), latency targets, availability (SLA), durability, cost, consistency, security, compliance.
• Constraints & assumptions: Budget, timeline, team skillset, legacy systems.
• Success metrics: e.g., 99.95% uptime, <200ms p95 latency, support 1M DAU.

1. Clarify requirements — ask/assume specifics and state them.
2. Define API & data model — endpoints, payloads, main entities.
3. Estimate scale — users, QPS, data growth. Convert to reads/writes per second.
4. Choose high-level architecture — monolith vs microservices; synchronous vs async.
5. Design components — load balancer, stateless app servers, DB, cache, queues, storage, CDNs.
6. Data modeling & storage — relational vs NoSQL, schema, indexes, partitions.
7. Data partitioning & replication — sharding strategy, replication factor, leader/follower.
8. Caching & CDNs — caching layers and cache invalidation strategy.
9. Asynchrony & queues — for long tasks, retries, backpressure.
10. Consistency & failure modes — CAP tradeoffs and fallback plans.
11. Scaling strategy — horizontal vs vertical, autoscaling triggers.
12. Monitoring, logging & alerts — metrics, tracing, dashboards, SLOs.
13. Security & compliance — auth, encryption, rate limiting, data privacy.
14. Bottleneck analysis & tradeoffs — where to optimize and why.
15. Walk through failure scenarios — how the system behaves under partial failure.

• Load Balancer (LB) — distribute traffic, health checks, TLS termination (e.g., HAProxy, ELB).
• Stateless Application Servers — scale horizontally; keep state out of memory (use DB/redis).
• Relational DB (SQL) — strong consistency, complex queries, transactions (e.g., Postgres, MySQL).
• NoSQL DB — high throughput, flexible schema, horizontal scaling (e.g., Cassandra, DynamoDB, Mongo).
• Cache (in-memory) — reduce DB load, lower latency (Redis / Memcached). Choose eviction policy thoughtfully.
• Message Queue / Stream — decouple components, async processing, buffering (Kafka, RabbitMQ, SQS).
• Object Storage — large binary blobs, media (S3, GCS).
• CDN — serve static assets globally (images, JS, CSS).
• Search Engine — full-text or complex query (Elasticsearch, OpenSearch).
• API Gateway — auth, routing, rate limiting.
• Monitoring & Tracing — Prometheus, Grafana, Jaeger / Zipkin.
• Feature Flags & Config Service — runtime toggles, progressive rollout.

• Stateless services — keep horizontal scaling simple.
• Circuit Breaker — protect system from cascading failures.
• Bulkhead — isolate failures to small partitions.
• Backpressure — throttle producers when consumers are overloaded.
• CQRS (Command Query Responsibility Segregation) — separate reads/writes for scalability.
• Event Sourcing — store events as the source of truth (use carefully).
• Leader Election — coordinate distributed tasks safely.
• Cache Aside vs Write-Through vs Write-Back — choose per consistency/cost.
• Consistent Hashing — shard keys without huge data movement.
• Idempotency — for safe retries of operations.

• Sharding (horizontal partitioning): choose shard key that evenly distributes load and supports queries.

• Replication: master-slave (primary/replica) vs multi-master — tradeoffs in write availability and conflict resolution.

• Consistency models:

  • Strong (e.g., relational in single DC) — simpler correctness, potentially higher latency.
  • Eventual (e.g., many NoSQL stores) — higher availability and scale, need conflict resolution.

• CAP theorem: In a partition, you choose between Consistency and Availability.

• Cache-Aside (lazy): app reads cache, on miss reads DB and populates cache. Good common pattern.
• Write-Through: write to cache and DB synchronously.
• Write-Back: write to cache and persist asynchronously (complex).
• Invalidation: TTLs, explicit invalidation on writes, versioning, or use of publish/subscribe for cache updates.
• Consider hot keys and cache stampede mitigation (locks, request coalescing, randomized TTLs).

• Prefer round-robin or least connections depending on session stickiness and request characteristics.
• Use health checks and circuit breakers to avoid sending traffic to unhealthy nodes.
• Autoscale on multiple signals: CPU, queue length, latency p95.
• Use multi-AZ / multi-region for higher availability (watch replication lag).

• Define SLOs (e.g., 99.9% requests < 200ms). Derive alert thresholds.
• Track metrics: latency (p50/p95/p99), error rates, throughput, resource utilization, queue depth.
• Distributed tracing to pinpoint latency sources.
• Design graceful degradation — fallbacks, cached responses, reduced features under load.

• Authentication & Authorization: OAuth / JWT; least privilege.
• Transport & Rest encryption: TLS everywhere.
• Data protection: encryption at rest, key management.
• Rate limiting & throttling: per user/API key.
• DDoS protection: WAF, edge rate limits, CDN.
• Secrets management: vaults, environment segregation.
• Backups & DR: backup frequency, RTO/RPO targets, test restores.

1. Restate and confirm requirements and constraints.
2. Draw a high-level diagram first (LB → App → DB, Cache, Queue, Storage).
3. Show data model for key entities.
4. Show read and write flow; highlight bottlenecks.
5. Explain scaling — both reads and writes.
6. Discuss data partitioning and consistency.
7. Explain caching, cache invalidation plan.
8. Add async paths and retries for long tasks.
9. Mention monitoring, alerts, and failure modes.
10. Discuss trade-offs and alternatives.

Requirements: Create short URLs, redirect quickly, support 100M URLs, 10k QPS reads, 500 writes/sec.

Design summary:

• API: POST /shorten {url} → returns short code; GET /{code} → redirect.
• DB: Sharded NoSQL (e.g., DynamoDB/Cassandra) or SQL with sharding; primary key = short code.
• ID generation: Base62 from auto-increment + salted hash OR generate unique random 6-8 char strings with collision detection (use Redis set to check+reserve).
• Cache: CDN/edge cache for redirects (cache popular short codes).
• Scaling: stateless app servers behind LB, autoscale, DB shards per prefix.
• Consistency: eventual ok for propagation; ensure uniqueness during write (use optimistic retry or central ID service).
• Analytics: async write of click events to Kafka → batch process → analytics DB.

Requirements: Personalized feed, 10M DAU, low read latency, new posts visible quickly.

Options:

• Fan-out on write: push new post to followers' feed lists (fast reads, heavy writes).
• Fan-out on read: compute feed at request time (cheap writes, heavy reads).
• Hybrid: push to active followers, compute for others.

Components:

• Write path: Post service → store post in object storage (media) + DB for metadata → fan-out job via queue to populate feeds (Redis lists or Cassandra per user).
• Read path: Read from per-user feed store (Redis for hot users, Cassandra for cold storage), fallback to on-the-fly aggregation.
• Search & ML: separate services for ranking, recommendations (use offline ML pipelines).
• CDN for media; presigned URLs; thumbnails.

Notes: Prioritize availability; handle backpressure in fan-out with batching; support eventual consistency for less active users.

Requirements: Real-time messaging, 1M concurrent connections, ordered delivery in room, message persistence.

Architecture:

• Use WebSockets (or MQTT / WebRTC for P2P needs).
• Connection routers: LB → gateway cluster that holds WebSocket connections (stateless if you use sticky LB or a connection registry).
• Message broker: Publish messages to topics/rooms (Kafka, Redis Streams, or custom broker).
• Delivery guarantees: at-least-once with dedupe on recipients (message IDs), optional ack/receipt flows.
• Persistence: Messages appended to durable store (Cassandra for high writes) and archived to cold storage.
• Presence & typing: short TTL entries in Redis.
• Scaling: shard rooms by room id using consistent hashing; ensure message ordering per room via single partitioning key.
• Offline delivery: store undelivered messages; on reconnect, fetch missed messages.

Concerns: connection spikes, backpressure, graceful reconnect, encryption (end-to-end if required).

• SQL vs NoSQL: SQL for strong ACID needs; NoSQL for scale and flexible schema. Explain why you chose one.
• Caching: improves latency but complicates consistency; explain TTLs and invalidation strategy.
• Synchronous vs Asynchronous: sync gives immediacy; async gives resiliency and throughput.
• Complexity vs simplicity: prefer simple architecture that meets SLAs; add complexity only for clear needs.

• Always design for observability from day 1.
• Think about operational cost when choosing premium features.
• Watch out for hot partitions/keys; use hashing or composite keys.
• Test disaster recovery and scale tests; simulate network partitions.
• Idempotency matters for retries — design request IDs for critical ops.
• Document APIs and SLAs for cross-team clarity.

(Books / topics to search)

• “Designing Data-Intensive Applications” — storage, replication, partitioning concepts.
• System design interview resources (practical whiteboard approaches).
• Read docs of Kafka, Redis, Postgres, Cassandra, Nginx for concrete details.

If you want, I can:

• Create a one-page system design template (diagram + checklist + sample metrics) you can reuse in interviews.
• Build a detailed whiteboard walkthrough for one of the examples (URL shortener, social feed, or chat) with diagrams and component-level choices.
• Compare two specific technologies for a component (e.g., Cassandra vs Postgres vs DynamoDB) for a particular use case.