Alexander Miranda

If I had a system design interview tomorrow at Google, this is exactly how I’d approach it.   (This framework helped me crack 3 FAANG+ companies in the past, including Amazon.)  Most engineers fail system design interviews not because they lack knowledge but because they lack structure.  You don’t need to memorize 100 architectures. You need a structured and easy-to-apply approach that works every time.  Here’s how I break it down:   1/ Clarify the Problem Before Writing Anything   - System design interviews aren’t about throwing buzzwords—they’re about trade-offs.   - Start with scoping → Are we designing just one feature or the entire system?   - Ask constraints upfront → How many users? Read/write ratio? Latency requirements?   - Define success criteria → What matters most? Scalability? Cost? Low-latency?  Most candidates assume things and jump into solutions.   I make sure I know what we’re solving before I even start.   2/ Define Functional & Non-Functional Requirements Clearly   - Functional: What features does the system need?   - Non-functional: What are the performance expectations?   - What’s the biggest technical challenge? (This helps guide the discussion.)  Example: If we’re designing YouTube, is the focus on video uploads, recommendations, or live streaming?   Each has a different set of constraints.   3/ Estimate the Scale & Plan Capacity Like an Engineer   - Users per second? Requests per second?   - Storage needs? If we store 10MB per user and have 100M users, what does that mean?   - Throughput? Can a single database handle the load, or do we need sharding?  Most candidates throw random numbers.   I do quick, back-of-the-envelope calculations to validate my assumptions.   4/ Break the System into Core Components (High-Level Design)   - Define the major building blocks → API Gateway, Load Balancer, Service Layers, Databases.   - Don’t overcomplicate. Simple and scalable always wins.   - Clearly define the interactions between services.  If I’m designing a messaging app, I break it down into:   — User Service (auth, profiles)   — Messaging Service (storing chats)   — Notification Service (real-time updates)   — Media Storage (for images, videos)  Each has different constraints, so I build around what’s most important.  Continued Here: https://lnkd.in/eiHQs-qT P.S. If you’re preparing for tech interviews or appearing soon for one as a SWE. Check out my book Awesome Tech Interviews. It will help you: — Learn techniques to win behavioral interviews — Learn DSA with a detailed 6-month roadmap — Build your foundations of System Design - all in one place. Along with 300+ free online resources. Digital copy: https://lnkd.in/efc7u85w Paperback (Available on Amazon internationally): https://lnkd.in/ePWCr74g

3,442

120 Comments

Software Engineers are some of the smartest individuals I've had the pleasure of interacting with. However, I still find myself scratching my head when startups make offers and engineers use flawed logic to weigh one offer against another. Higher equity dollar value does not = better offer. I don't care if Startup A offers you $500k in equity, and Startup B offers you $100k in equity. If Startup A isn't around a year from now, that $500k is worth nothing. Z-E-R-O. It means nothing. Some put such a heavy emphasis on total compensation, that they forget the basics: -Is the startup's business model viable long-term? -Can the company survive in a market downturn? -Can the leadership team can deliver through the good times + the bad? -Do they have an exit strategy? -Do their customers actually like the product compared to their competitors? -Are the people you interviewed with excited about the outlook of the company? ^more of these types of questions will give you better odds of hitting the 'startup lottery'. not pie in the sky numbers on a spreadsheet

45

3 Comments

A candidate interviewing for a Senior Engineer @ Meta was asked to design a rate limiter. Another candidate at Google's L5 loop got hit with the same question. I've been asked this three times across different companies. Rate-limiting questions look simple until you add one layer of complexity: – Add distributed rate limiting? Now you're dealing with race conditions and clock skew. – Add multiple rate limit tiers? Welcome to priority queues and quota management. – Add per-user, per-IP, and per-API-key limits? Your Redis bill just exploded. Here's my personal checklist of 15 things you must get right when building rate limiters: 1. Always do rate limiting on the server, not the client → Client-side limits are useless. They’re easily bypassed, so always enforce limits on your backend. 2. Choose the right placement → For most web APIs, place the rate limiter at the API gateway or load balancer (the “edge”) for global protection and minimal added latency. 3. Identify users correctly → Use a combination of user ID, API key, and IP address. Apply stricter limits for anonymous/IP-only clients, higher for authenticated or premium users. 4. Support multiple rule types → Allow per-user, per-IP, and per-endpoint limits. Make rules configurable, not hardcoded. 5. Pick an algorithm that fits your needs → Know the pros/cons: –  Fixed Window: Easy, but suffers from burst issues. –  Sliding Log: Accurate, but memory-heavy. –  Sliding Window Counter: Good balance, small memory footprint. – Token Bucket: Handles bursts and steady rates, an industry standard for distributed systems. 6. Store rate limit state in a fast, shared store → Use an in-memory cache like Redis or Memcached. Every gateway instance must read and write to this store, so limits are enforced globally. 7. Make every check atomic → Use atomic operations (e.g., Redis Lua scripts or MULTI/EXEC) to avoid race conditions and double-accepting requests. 8. Shard your cache for scale → Don’t rely on a single Redis instance. Use Redis Cluster or consistent hashing to scale horizontally and handle millions of users/requests. 9. Build in replication and failover → Each cache node should have replicas. If a primary fails, replicas take over. This keeps the system available and fault-tolerant. 10. Decide your “failure mode” → Fail-open (let all requests through if the cache is down) = risk of backend overload. Fail-closed (block all requests) = user-facing downtime. For critical APIs, prefer fail-closed to protect backend. 11. Return proper status codes and headers → Use HTTP 429 for “Too Many Requests.” Include headers like: – X-RateLimit-Limit,  – X-RateLimit-Remaining,  – X-RateLimit-Reset, Retry-After This helps clients know when to back off. 12. Use connection pooling for cache access → Avoid reconnecting to Redis on every check.  Pool connections to minimize latency. Continued in Comments...

2,222

71 Comments

"Engineers need very specific requirements." I overheard this today, thankfully not from someone at my company. But it pissed me off. This statement implies that software engineers can't think through complex and ambiguous customer problems, that they need someone to carefully prepare very specific widget designs for them to build. That they can't figure out the very specific requirements themselves, if necessary. I suppose if the requirements weren't specific enough, they might ask questions! Questions that only the customer can answer! And maybe not even them! And then maybe they would have to figure it out for themselves and iterate on an idea until the customer is satisfied! Oh no! I'm sure the person who said this was acting in good faith, but siloing engineers off and only feeding them "requirements" robs them of the opportunity to understand and solve the real problems and have real impact. It also wastes time with unnecessary feedback loops. Worst of all, it holds them back from growing into more product and business-oriented directions, which can have a negative impact: on their careers. The next time someone tells you that engineers need very specific requirements, don't argue. Just ask to invite the engineers to the next meeting to discuss the requirements with them directly. Bring them into the process. If you get pushback, go to the engineers directly. The best software engineers I know would appreciate it.

103

24 Comments