Best Practices for System Design

System design interviews can be a daunting part of the hiring process, but being prepared with the right knowledge makes all the difference. This System Design Cheat Sheet covers essential concepts that every engineer should know when tackling these types of questions. Key Areas to Focus On: 1. Data Management:    - Cache: Boost read operation speeds with caching mechanisms like Redis or Memcached.    - Blob/Object Storage: Efficiently handle large, unstructured data using systems like S3.    - Data Replication: Ensure data reliability and fault tolerance through replication.    - Checksums: Safeguard data integrity during transmission by detecting errors. 2. Database Selection:    - RDBMS/SQL: Best for structured data with strong consistency (ACID properties).    - NoSQL: Ideal for large volumes of unstructured or semi-structured data (MongoDB, Cassandra).    - Graph DB: For interconnected data like social networks and recommendation engines (Neo4j). 3. Scalability Techniques:    - Database Sharding: Partition large datasets across multiple databases for scalability.    - Horizontal Scaling: Scale out by adding more servers to distribute the load.    - Consistent Hashing: A technique for efficient distribution of data across nodes, essential for load balancing.    - Batch Processing: Use when handling large amounts of data that can be processed in chunks. 4. Networking:    - CDN: Distribute content globally for faster access and lower latency (e.g., Cloudflare, Akamai).    - Load Balancer: Spread traffic across multiple servers to ensure high availability.    - Rate Limiter: Prevent overloading by controlling the rate of incoming requests.    - Redundancy: Design systems to avoid single points of failure by duplicating components. 5. Protocols & Queues:    - Message Queues: Asynchronous communication between microservices, ideal for decoupling services (RabbitMQ, Kafka).    - API Gateway: Control API traffic, manage rate limiting, and provide a single point of entry for your services.    - Gossip Protocol: Efficient communication in distributed systems by periodically exchanging state information.    - Heartbeat Mechanism: Monitor the health of nodes in distributed systems. 6. Modern Architecture:    - Containerization (Docker): Package applications and dependencies into containers for consistency across environments.    - Serverless Architecture: Run functions in the cloud without managing servers, focusing entirely on the code (e.g., AWS Lambda).    - Microservices: Break down monolithic applications into smaller, independently scalable services.    - REST APIs: Build lightweight, maintainable services that interact through stateless API calls. 7. Communication:    - WebSockets: Real-time, bi-directional communication between client and server, commonly used in chat applications, live updates, and collaborative tools. Save this post and use it as a quick reference for your next system design challenge!

You can memorize patterns and still build systems that fall apart. Because real system design comes in levels. ⬆️level 0 Fundamentals: • Clients send requests • Servers handle logic • Databases store data You learn HTTP methods, status codes, and what a REST API is. You pick between SQL and NoSQL without really knowing why. You're not a backend dev until you've panic-fixed a 500 error in production caused by a missing null check. ⬆️level 1 Master the building blocks: • Load balancers for traffic distribution • Caches (Redis, Memcached) to reduce DB pressure • Background workers for async jobs • Queues (RabbitMQ, SQS, Kafka) for decoupling • Relational vs Document DBs; use cases, not just syntax differences You realize reads and writes scale differently. You learn that consistency, availability, and partition tolerance don't always play nice. You stop asking "SQL or NoSQL?" and start asking “What are the access patterns?” ⬆️level 2 Architect for complexity: • Separate read and write paths • Use circuit breakers, retries, and timeouts • Add rate limiting and backpressure to avoid overload • Design idempotent endpoints You start drawing sequence diagrams before writing code. You stop thinking in services and start thinking in boundaries. ⬆️level 3 Design for reliability and observability: • Add structured logging, metrics, and traces • Implement health checks, dashboards, and alerts • Use SLOs to define what “good enough” means • Write chaos tests to simulate failure • Add correlation IDs to trace issues across services At this level, you care more about mean time to recovery than mean time between failures. You understand that invisible systems are the most dangerous ones. ⬆️level 4 Design for scale and evolution: • Break monoliths into services only when needed • Use event-driven patterns to reduce coupling • Support versioning in APIs and messages • Separate compute from storage • Think in terms of contracts, not code • Handle partial failures in distributed systems You design for change, not perfection. You embrace trade-offs. You know when to keep it simple and when to go all in. What’s one system design lesson you learned the hard way?

I have taken 100+ System Design interviews in my 2 decades in the industry. This master template will help you clear any System Design Interview: 1. Golden Rules. ➥ Read heavy? Use cache ↳ Speed up data retrieval for frequently accessed data. ➥ Write heavy? Use queue ↳ Handle write requests asynchronously to avoid slowdowns. ➥ Need speed? Use cache & CDN ↳ Deliver content blazingly fast to users worldwide. 2. Choosing the Right Tech. ➥ Reliable & structured data? Use SQL Database ↳ Think banking, customer info, etc. ➥ Unstructured data? Use NoSQL database ↳ Perfect for flexible data like social media posts. ➥ Big files & images? Use blob storage ↳ Efficiently store and manage large objects. ➥ User-to-user communication?  ↳  Use WebSockets 3. Scaling & Performance. ➥ Massive SQL database? Shard it ↳ Distribute data across multiple servers for better performance. ➥ High traffic? Use a load balancer ↳ Spread user requests evenly across your servers. ➥ Global reach? Use CDN ↳ Deliver content from servers closest to users for faster loading. 4. Advanced Techniques. ➥ Graph data? Use the graph database ↳ Powerful for analyzing connections and relationships. ➥ Horizontal scaling? Scale it out ↳ Add more servers to handle the increasing load. ➥ Fast queries? Use DB indexing ↳ Improve database search speeds significantly. 5. Bonus Tips. ➥ Break down big jobs ↳ Batch processing makes data handling more efficient. ➥ Prevent overloads ↳ Use rate limiters to stop denial-of-service attacks. ➥ Use API gateway ↳ Manage communication between services. ➥Redundancy is key ↳ Ensure your system keeps running even if parts fail. – Save it, share it, and grow together. You can follow me here for more content Raman Walia

I've been interviewing data candidates in system design for 10 years! I’ll teach you the key concepts in just 5m: 1. Clearly define the requirements Without clarity on the output, everything else falls apart. * Define the expected output precisely. * Ensure all stakeholders agree on what “success” looks like. Miss this, and no amount of skill will save the project. 2. Understand the data you have access to Knowing your inputs is essential to shaping your outputs. * Identify the business entities (e.g., customers, products). * Map the business transactions (e.g., orders, payments). * Define relationships between entities and transactions. * Know which datasets contribute to specific outputs. This mapping builds the foundation for your system design. 3. Understand how data is modeled Inputs generally fall into categories that dictate how they are used. * Facts: Core data points (e.g., purchase, checkout). * Dimensions: Descriptive attributes (e.g., product category). * Rollups: Aggregated views (e.g., total sales). * Joins: Understand the keys to use to join fact and dimensions Understanding data types and their relationships sets you up for transformation success. 4. Transform data effectively Use SQL to turn inputs into meaningful outputs. * Write functional SQL first; perfection comes later. * Don’t over-optimize in the early stages. Transformation is where the magic happens, but clarity and simplicity matter most at first. 5. Define data quality checks Poor data quality leads to poor decisions – safeguard against it. * Focus on key metrics (e.g., revenue, MAU, DAU). * Set constraints: PK, FK, NOT NULL, ENUMs. * Check for key metric skews, outliers, and reconciliation issues. Quality isn’t a “nice-to-have” – it’s a must-have for reliable systems. 6. Optimize performance through partitioning Partitioning data helps speed up access and processing. * Identify common filters (e.g., date, type). * Use low-cardinality columns for partitioning. * Process data in parallel for large-scale pulls. Partitioning is your first step toward scaling performance. 7. Advanced optimizations – Clustering & Ordering Fine-tune your data layout for even greater efficiency. * Use clustering for high-cardinality columns (e.g., timestamps). * Sort columns for range queries to minimize scan times. * Consider Z-order for multi-column range queries. These optimizations can make the difference between “good enough” and “great.” 8. Reduce data movement in the cluster Data shuffling is the silent killer of distributed performance. * Use filters and avoid unnecessary operations. * Know which actions trigger shuffling (e.g., group by, joins). * Leverage database engine optimizations (e.g., Spark’s AQE). Reducing shuffle equals faster processing and lower costs. - Like this post? Let me know your thoughts in the comments, and follow me for more actionable insights on data engineering and system design. #dataengineering #data

𝐌𝐀𝐒𝐓𝐄𝐑 𝐒𝐘𝐒𝐓𝐄𝐌 𝐃𝐄𝐒𝐈𝐆𝐍 2026 → 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 • Start with the basics before jumping into advanced architecture. • Strong fundamentals make every design decision better. → 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 • Clarify the problem first. • Estimate scale, define requirements, and design step by step. • Always explain trade offs clearly. → 𝐂𝐨𝐫𝐞 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 • Learn how load balancers, app servers, databases, and message queues work together. • These are the building blocks of most systems. → 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬 • Understand DNS, HTTP, and TCP. • These protocols power communication across distributed systems. → 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 • Know when to choose SQL or NoSQL. • Understand indexing, replication, and sharding for performance and scale. → 𝐂𝐚𝐜𝐡𝐢𝐧𝐠 𝐁𝐚𝐬𝐢𝐜𝐬 • Caching reduces latency and lowers database load. • Learn caching layers and when to use them. → 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 • Study microservices, event driven systems, serverless, and API first design. • Each pattern solves different business and technical needs. → 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 • Compare vertical and horizontal scaling. • Explore auto scaling, sharding, and replication for growth. → 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 • Build systems with redundancy, health checks, retries, and failover. • Reliability is what keeps systems running under stress. → 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 • Use async processing and rate limiting to improve speed and stability. • Performance tuning is critical for user experience. → 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 • Monitoring, logging, tracing, and alerting help teams understand system health. • You cannot fix what you cannot see. → 𝐂𝐀𝐏 𝐚𝐧𝐝 𝐓𝐫𝐚𝐝𝐞 𝐎𝐟𝐟𝐬 • Learn the balance between consistency, availability, and partition tolerance. • Every architecture choice comes with compromise. → 𝐅𝐢𝐧𝐚𝐥 𝐌𝐢𝐧𝐝𝐬𝐞𝐭 • Great system design is not about memorizing diagrams. • It is about thinking clearly, choosing wisely, and designing for real world scale. Follow Umair Ahmad for more insights

1 million+ engineers prepare for system design interviews every year. 100 tips. 10 chapters. Save this before your next one: _______ btw, 12 system design cheatsheets: https://dub.sh/12-patterns _______ 0. Requirements Clarify read vs write ratio first. Get scale targets: DAU, QPS, storage. Separate functional from non-functional. Define SLA before touching architecture. Strong or eventual consistency? Single-region or global? Timebox to 5 min. Success metrics upfront. 1. Capacity Estimation QPS = DAU × actions / 86400. Writes are typically 1-10% of reads. Storage = rows × avg size × retention. Bandwidth = QPS × avg payload. Show reasoning not precision. Flag the bottleneck before designing. 2. Data Modeling SQL for relational. NoSQL for high-write flex schemas. Normalize first, denormalize for reads. Index every FK and query predicate. Partition by time or hash. Soft deletes for audit trails. Shard keys must minimize cross-shard queries. 3. API Design Start REST, justify gRPC/GraphQL. POST creates. PUT replaces. PATCH updates. 4xx = client. 5xx = server. Paginate all lists, cursor-based at scale. Version from day one: /v1/endpoint. Idempotency keys for retryable POSTs. Rate limit every public endpoint. 4. Read Scaling Redis cache before read replicas. Cache-aside for most use cases. Set TTLs, stale data beats no cache. CDN for static and edge responses. Fan-out on write for read-heavy feeds. Materialized views for complex aggregates. Hit rate below 80% = wrong strategy. 5. Write Scaling Queues decouple write bursts. Batch writes to reduce round trips. Async writes for non-critical paths. Avoid sync writes to multiple systems. Connection pooling to prevent exhaustion. Shard by user or tenant ID. Find the bottleneck before sharding. 6. Caching Cache reads, not writes. Cache-aside: app manages misses. Write-through: consistency, higher latency. LRU eviction for general use. Stampede: probabilistic early expiration. Thundering herd: warm before old expires. Redis Cluster for horizontal scaling. 7. Consistency Strong: every read sees the latest write Eventual: reads may lag 2PC is slow, use Sagas instead Idempotency prevents double-processing Optimistic locking for low-conflict writes CAP: pick 2. PACELC adds latency tradeoffs Consistency per operation, not system 8. Fault Tolerance Every component will fail, design for it Retries with backoff and jitter Circuit breakers stop cascading failures Degrade gracefully over hard failure Serve stale data when DB is down Blue/green for zero-downtime releases Chaos eng before prod does it for you 9. Observability Logs: structured JSON, not strings Metrics: percentiles, not averages Traces: follow requests across services Alert on symptoms, not causes p99 reveals what averages hide Errors spike before latency, alert them first Dashboard: QPS, errors, latency, saturation Postmortems: only way to learn from prod What did I miss?

One of the best perks of working at Google? You get to learn from some of the most talented engineers in the world.  Here’s the advice that changed how I approached system design:  ○ 1. Start with the Systems You Already Work On  You don’t need to build the next Google-scale system to learn system design. Start with what’s in front of you. - Study the architecture of the systems your team maintains.   - Ask senior engineers why certain design decisions were made.   - Try to understand trade-offs, +why was SQL chosen over NoSQL?  +Why is there a cache?  ○ 2. Get Involved in Design Discussions  Even if you’re not leading the discussions, listen, observe, and ask questions.  - How does the team decide on a database?   - What factors go into choosing between microservices vs. a monolith?   - What happens when something fails, and how is it mitigated?  ○ 3. Learn to Think in Trade-offs  System design is never about finding a perfect solution, it’s about understanding trade-offs.  - High availability vs. consistency   - Performance vs. cost   - Scalability vs. simplicity  Engineers who understand why something works a certain way are the ones who stand out.  ○ 4. Read and Reverse-Engineer Real Systems  Reading about distributed systems is great, but reverse-engineering existing architectures is even better.   - Read engineering blogs from Netflix, Meta, Uber, and Stripe.   - Study open-source projects to understand their system design.   - Look at case studies of real-world scaling challenges.  ○ 5. Build Small Projects and Simulate Failures  Designing a system on paper is one thing, running one is another.    - Try deploying your own API and scaling it.   - Experiment with load balancing, caching, and database sharding.   - Simulate failures: What happens if a server crashes? How does the system recover?  ○ 6. Teach Others What You Learn   The fastest way to internalize system design concepts? Explain them to others.  - Write blog posts breaking down technical concepts.   - Mentor junior engineers and help them understand architecture decisions.   - Share what you learn with your team in tech talks or discussions.  You don’t gain experience in system design by just watching YouTube videos or reading books.   You gain it by getting involved, asking questions, experimenting, and building. 

𝗛𝗼𝘄 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗪𝗼𝗿𝗸𝘀, 𝗮𝗻𝗱 𝗛𝗼𝘄 𝘁𝗼 𝗗𝗼 𝗶𝘁 𝗪𝗲𝗹𝗹 Most developers jump into building features. Few stop to think and ask: 🔹What happens when 10,000 or 1 million users show up at once? That’s when system design starts to matter. 🔹Here’s a practical way to start designing better systems from day one: 𝟭. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗥𝗲𝗮𝗹 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 𝗙𝗶𝗿𝘀𝘁 Before thinking in services or tech: • What exactly are we building? • What’s the expected load (users, reads/writes per second)? • What’s the latency budget? • What’s the tolerance for downtime or data loss? A good system design starts with questions, not code. 𝟮. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗛𝗶𝗴𝗵-𝗟𝗲𝘃𝗲𝗹 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 Draw the simplest version of the system: Client → Load Balancer → App Server → Database This is your baseline. Now ask: • Do we need caching for reads? • Do we need message queues for asynchronous processing? • Where does horizontal scaling make sense? 𝟯. 𝗕𝗿𝗲𝗮𝗸 𝗜𝘁 𝗗𝗼𝘄𝗻 𝗜𝗻𝘁𝗼 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗮𝗻𝗱 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 Each component should have: • A clear boundary of responsibility • Loose coupling to other parts • The ability to scale independently Monoliths are fine at the start. But once complexity grows, separation becomes your best tool. 𝟰. 𝗣𝗹𝗮𝗻 𝗳𝗼𝗿 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗦𝘁𝗮𝗿𝘁 Systems will fail. That’s not a risk, it’s a guarantee. Build with: • Graceful degradation (show cached data, retry later) • Timeouts and retry logic • Circuit breakers • Logging and observability A system that “mostly works” under load isn’t scalable, it’s a liability. 𝟱. 𝗨𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗧𝗼𝗼𝗹𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗝𝗼𝗯 There’s no one-size-fits-all stack. But here’s what modern systems often include: • Load balancers (NGINX, AWS ALB) • Relational DBs (PostgreSQL, MySQL) • NoSQL DBs (MongoDB, DynamoDB) • Caching layers (Redis, Memcached) • Queues & brokers (Kafka, RabbitMQ, SQS) • File/object storage (S3, GCS) • CDNs (Cloudflare, Akamai) • Monitoring (Prometheus, Grafana, Datadog) 𝟲. 𝗔𝗹𝘄𝗮𝘆𝘀 𝗧𝗵𝗶𝗻𝗸 𝗶𝗻 𝗧𝗲𝗿𝗺𝘀 𝗼𝗳 𝗧𝗿𝗮𝗱𝗲𝗼𝗳𝗳𝘀 Design is about choosing what to optimize and what to sacrifice temporarily: • Latency vs Durability • Speed vs Cost • Simplicity vs Flexibility • Availability vs Consistency There’s no perfect system—only good tradeoff decisions made deliberately. 𝟳. 𝗧𝗲𝘀𝘁 𝗬𝗼𝘂𝗿 𝗔𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 Launch early with: • Load testing tools (k6, JMeter) • Logging everything • Metrics dashboards • Feature flags and controlled rollouts Design is not done on a whiteboard, it’s proven in production. 𝗔 𝗯𝗲𝘁𝘁𝗲𝗿 𝘀𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻 𝗱𝗼𝗲𝘀𝗻’𝘁 𝘀𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗱𝗶𝗮𝗴𝗿𝗮𝗺𝘀. 𝗜𝘁 𝘀𝘁𝗮𝗿𝘁𝘀 𝘄𝗶𝘁𝗵 𝗺𝗶𝗻𝗱𝘀𝗲𝘁. If your app works for 5 users, you’ve built a prototype. If it works for 500,000 users and fails gracefully at 5 million, you’ve built a system.

Long-running agentic systems rarely fail outright they drift. Outputs remain syntactically valid and pass local checks, but semantic alignment with the original objective decays over time due to state inconsistency and context loss. This is a systems design problem requiring explicit control over state, context propagation, and execution constraints. 1. A stable system starts with context integrity. Inputs are almost always incomplete or contradictory. If this state is not resolved upfront, errors propagate silently. Best practice: enforce a pre-task context audit schema validation, dependency checks, and contradiction resolution before execution begins. 2. Next is planning discipline. Treat planning as a search space, not a single decision. Agents that lock into the first viable path optimize for speed, not durability. Best practice: generate multiple candidate plans and score them on maintainability, composability, and system impact. Select the cleanest path, not the fastest. 3. Execution introduces context pressure. As workflows grow, context becomes noisy and agents compensate by approximating or skipping steps. Best practice: use structured context compaction information-dense handoffs that preserve intent while removing noise. Combine this with task atomization (small, bounded units) to keep execution deterministic and verifiable. 4. Drift accelerates when agents deviate from plans. Best practice: enforce continuous plan adherence checks. Execution should behave like a constrained state machine, not open-ended generation. 5. Verification must be independent. When agents validate their own work, they confirm approximations rather than actual outcomes. Best practice: use fresh-context agents for end-to-end validation and system cleanup resolving inconsistencies, updating artifacts, and removing dead code. 6. Finally, stability depends on continuous telemetry. Tracing decision lineage, context changes, and outcome variance makes deviations observable and correctable. Feedback becomes both diagnostic and recovery. As agents operate across enterprise systems, new primitives: negotiation, context federation, policy enforcement, and trust evaluation become essential. At scale, agents become stateful, policy-bound execution units. Autonomy is no longer a model property; it’s a systems invariant enforced by the harness.

I read 100+ system design guides, postmortems, and interview rubrics this month. Here’s what actually moves you from “𝐈 𝐤𝐢𝐧𝐝 𝐨𝐟 𝐠𝐞𝐭 𝐢𝐭” to “𝐈 𝐜𝐚𝐧 𝐝𝐞𝐬𝐢𝐠𝐧 𝐢𝐭.” 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐞𝐬𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐦𝐚𝐬𝐭𝐞𝐫 𝐟𝐢𝐫𝐬𝐭: 🔹Mental models → CAP, latency budgets, throughput math, fallacies of distributed systems. 🔹Data flows → read/write paths, fan-out, fan-in, cold vs warm paths, backpressure. 🔹State → cache tiers, TTL + invalidation, idempotency, exactly-once is a trap. 🔹Queues → protect downstreams, smooth bursts, enable async retries and dead letters. 🔹Storage trade-offs → SQL for consistency & joins; NoSQL for scale & partitions; hybrid for reality. 🔹Resilience → timeouts, hedged requests, circuit breakers, bulkheads. 🔹Observability → RED + USE, tracing budgets, SLOs with error budgets. 𝐘𝐨𝐮𝐫 𝟑𝟎-𝐝𝐚𝐲 𝐩𝐚𝐭𝐡 (𝐬𝐚𝐯𝐞 𝐭𝐡𝐢𝐬): 🔹Week 1: Foundations — CAP, consistency models, latency/throughput math, partitioning + replication. 🔹Week 2: Building blocks — LB → API → cache → queue → storage; design a URL shortener & feed. 🔹Week 3: Hard parts — hot keys, cache stampede, thundering herd, out-of-order events, exactly-once myth. 🔹Week 4: Production thinking — SLOs, backfills, migrations, incident drills, cost guardrails. 𝐅𝐫𝐞𝐞 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐚𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞: 🔹Starter reads & walkthroughs: https://lnkd.in/g7zs2psR 🔹Build along: pick any public spec (e.g., URL shortener) and implement stubs with metrics & SLOs. 🔹Practice: explain your design on a single whiteboard snapshot—no hand-waving, every box earns its keep. Comment the one system-design topic that still trips you up (e.g., cache invalidation, feed ranking, idempotency). I’ll pick a few and post full breakdowns with diagrams. #SystemDesign #SoftwareEngineering #DistributedSystems #SRE #TechInterviews