Most System Design interviews don’t go wrong because the candidate forgot a load balancer. They go wrong because the candidate started building before they understood what needed to be built and what “good” means. Requirements are the difference between a coherent design and a pile of boxes.
The trick is that requirements come in two flavors that must work together: what the system does and how well it must do it. When you can separate those, prioritize them, and turn vague goals into numbers, you control the interview and you design better systems in real work.
This guide explains functional vs non functional requirements as a repeatable method you can use under time pressure: ask the right questions, quantify targets, map requirements to architecture, and validate with metrics.
Interviewer tip: Requirements are not a warm-up. They are the input to every decision you make after.
| Requirement type | What it answers | Why it matters |
| Functional | “What must the system do?” | Defines APIs, data model, core flows |
| Non-functional | “How well must it do it?” | Drives scaling, reliability, cost, operability |
The core distinction, without making it academic
Functional requirements describe behavior: create a short link, send a message, fetch history, deliver a notification. They define scope and correctness in terms of features. If you get these wrong, you build the wrong product.
Non-functional requirements describe quality attributes: latency, throughput, availability, durability, consistency, privacy, cost, and operational constraints. If you get these wrong, you build the right product that fails under real conditions.
In interviews, candidates often treat non-functional requirements as optional extras. In practice, those “extras” are what decide the architecture. A design that doesn’t state latency or availability targets can’t justify caching, replication, or queues; it can only hand-wave.
Common pitfall: Listing features and calling it requirements, while never stating what “fast,” “reliable,” or “scalable” means.
| Example system | Functional requirement examples | Non-functional requirement examples |
| URL shortener | Create link, redirect, optional analytics | p95 redirect latency, cache hit rate, availability |
| Chat | Send message, fetch history, presence (optional) | Ordering, delivery guarantees, tail latency |
| Payments | Charge, refund, ledger | Strong consistency, auditability, durability |
| Search | Index docs, query | p99 latency, freshness SLO, cost constraints |
After the explanation, a short summary helps:
- Functional defines scope and behavior.
- Non-functional defines quality and constraints.
- Both are mandatory to design well.
Requirements state machine for interviews: clarify → prioritize → quantify → design → validate
Treat the interview like a controlled flow rather than a brainstorm. You start by clarifying scope, then you prioritize what matters most, then you quantify the non-functional targets, then you design, and finally you validate with metrics and failure behavior.
This “state machine” keeps you from two common traps: asking endless questions and never building, or drawing immediately and later discovering you built the wrong thing. It also gives the interviewer confidence that you can run a design discussion in real life.
You don’t need to be rigid. The point is to make your output visible at each step: a tight problem statement, a ranked requirement list, measurable targets, a baseline diagram, and a validation plan.
Interviewer tip: A strong candidate “checks in” after each state: “Here’s my scope, here’s what I’m optimizing, here are the numbers I’ll design to.”
| Step | What you do | What you produce |
| Clarify | Define users and core actions | A tight scope statement |
| Prioritize | Pick the top few must-haves | A ranked list of requirements |
| Quantify | Convert vague goals to numbers | SLO-style targets |
| Design | Build baseline, then evolve | APIs, data model, architecture |
| Validate | Tie behavior to metrics/failures | SLIs, dashboards, degradation plan |
How to ask requirement questions like an interviewer
The best requirement questions are not “what else do you want?” They are structured and high-leverage: who uses it, what actions they take, how usage scales, and what constraints can’t be violated. This keeps the discussion grounded and prevents the “feature wishlist” spiral.
A repeatable script works well in interviews. Start with users and actions to lock scope. Then ask about scale (read/write mix, traffic spikes, size of data). Finally, ask about constraints that force major design choices: latency targets, availability expectations, consistency guarantees, compliance, and abuse prevention.
If the interviewer won’t provide details, you can propose reasonable defaults and move forward. The key is to make your assumptions explicit and tie them to design choices later.
Most common mistake: Skipping requirements and drawing too early. A design built on guessed requirements is hard to defend because every trade-off becomes arbitrary.
| Question | Why it matters | Example answers | What it unlocks in design |
| Who are the users? | Defines UX and auth needs | “Anonymous + logged-in” | Identity, rate limits, quotas |
| What are the core actions? | Sets scope and APIs | “Create, read, delete” | Endpoint list and data model |
| What is the read/write mix? | Predicts bottlenecks | “90% reads” | Cache strategy, replicas |
| What is the latency target? | Shapes hot path | “p95 < 200 ms” | Fewer hops, caching, async |
| What consistency is needed? | Forces storage choices | “Read-your-writes” | Leader reads, session stickiness |
| What failure is acceptable? | Defines degradation | “Reads must stay up” | Load shedding, graceful fallback |
After the explanation, a compact summary fits:
- Users → actions → scale → constraints.
- Assume safely when details are missing.
- Reuse your answers to justify later architecture choices.
Turning non-functional requirements into numbers
Non-functional requirements are only useful when they are measurable. “Fast” becomes a latency SLO. “Scalable” becomes a throughput target at peak. “Reliable” becomes availability and durability targets, plus a failure budget. “Cheap” becomes a cost envelope or per-request budget.
In interviews, you don’t need perfect numbers. You need defensible ones and a plan to measure them. Pick p95 or p99 latency targets, define peak QPS, specify an availability target, and decide what durability means (for example, “writes are acknowledged only after durable persistence”).
This is also where you connect requirements to observability. If you can name the metric, you can design for it and validate it. If you can’t name the metric, you’re designing in the dark.
If you can’t measure it, you can’t design for it. A good non-functional requirement has a number, a metric, and a clear trade-off.
| Requirement | Measurable target | How to measure | Typical trade-off |
| Latency | p95 < 200 ms | p95 latency per endpoint | Cost vs fewer hops |
| Throughput | 5k QPS peak | QPS, saturation | Over-provisioning cost |
| Availability | 99.9% monthly | Error budget, uptime | Complexity of redundancy |
| Durability | “No acknowledged write lost” | Commit semantics, audit checks | Write latency and complexity |
| Consistency | “Read-your-writes” | Staleness tests | Leader load or routing |
| Cost | <$X per million requests | Cost dashboards | Feature limits, compression |
Requirements that change the architecture
Some requirements don’t just tweak the design, they pivot it. Real-time expectations push you toward long-lived connections. Ordering requirements push you toward sequencing strategies. At-least-once delivery pushes you toward idempotency and dedup. Multi-region availability pushes you toward replication strategy and conflict handling. Compliance pushes you toward audit logs and data retention controls.
The key interview skill is to recognize these pivots early, explain them clearly, and make a call. You can say “it depends” briefly, but you must conclude with a default and a condition that would change it. This is where you demonstrate judgment.
When you name pivots, connect them to benefits and risks. Adding a queue protects the hot path but introduces duplicates. Adding a cache improves latency but introduces staleness. Adding sharding scales writes but complicates transactions. The requirement justifies the cost.
Make a clear call. “It depends” is not an answer unless you also say what you’d pick by default and why.
| Requirement trigger | Architecture change | Benefit | Risk |
| Read-heavy load | Cache hot reads | Lower p95 latency | Staleness, invalidation |
| Spiky traffic | Async queue + workers | Smooth bursts | Queue lag, retries |
| Duplicate-prone ingestion | Idempotency + dedup store | Correctness under retries | Extra storage and logic |
| Ordering matters | Sequence numbers per entity | Predictable ordering | Coordination at write source |
| Multi-region | Replication strategy + routing | Availability | Conflicts, lag |
| Compliance | Audit log + retention | Traceability | Storage cost, access controls |
Requirements-to-design mapping: turn statements into components
A good way to stay grounded is to explicitly map each key requirement to a design component, the reason it’s needed, and the metric that validates it. This prevents “architecture theater,” where you add components without a requirement-driven justification.
This mapping also helps you communicate trade-offs. If the requirement changes, the component choice may change. For example, if read-your-writes is mandatory, you might route reads to the leader for a session window. If it’s not mandatory, you might read from followers or caches for better scale.
This section is where functional vs non functional requirements becomes a practical translation tool: each requirement leads to a concrete decision and a measurable outcome.
Interviewer tip: If you can point to a requirement and say “that’s why this component exists,” your design will feel intentional.
| Requirement | Design component | Reason | Metric |
| Fast reads | Cache | Reduce DB reads | cache hit rate, p95 latency |
| High write durability | Commit policy | Prevent lost writes | write loss audits, error rate |
| Burst handling | Queue | Smooth spikes | queue lag, drop rate |
| Ordering per conversation | Sequencer/sequence column | Avoid timestamp ordering bugs | out-of-order rate |
| Abuse prevention | Rate limits | Protect core | throttled requests, saturation |
| Operability | Dashboards + alerts | Detect issues early | deploy failure rate, error budget burn |
Walkthrough 1: “Design a URL shortener” requirements to design
Start by extracting functional requirements: create a short URL, redirect from short code to long URL, and optionally support custom aliases or link expiration. Those define your APIs and core data model: a mapping from short_code → long_url.
Then add non-functional targets. Redirect is the hot path, so latency matters. The system is often read-heavy, so the read path should be optimized. Availability matters because redirects are user-visible; durability matters for created links. These targets push you toward caching and a simple key-based lookup.
Finally, show how requirements change the design. If you need very low redirect latency, you cache aggressively. If custom aliases are required, you enforce uniqueness and handle conflicts. If analytics is needed but redirect latency is strict, you push analytics to an async pipeline.
What great answers sound like: “Redirect is the core path, so I’ll optimize it for p95 latency with a cache, and I’ll keep analytics async so it doesn’t slow redirects.”
| Requirement type | Requirement | Design implication |
| Functional | Create short link | POST /urls, code generation |
| Functional | Redirect | GET /{code} hot path |
| Non-functional | p95 redirect < 200 ms | Cache in front of DB |
| Non-functional | High availability | Replication + stateless app |
| Non-functional | Durable links | Persist before acknowledging |
End-to-end interview flow
- Clarify scope: custom aliases, expiration, analytics.
- Prioritize: redirect speed and correctness first.
- Quantify: p95 latency, QPS, availability target.
- Design: stateless service + DB + cache; async analytics if needed.
- Validate: p95 latency, cache hit rate, error rate, saturation.
Walkthrough 2: “Design a chat system” requirements to architecture
Chat looks simple until you ask about real-time expectations and guarantees. Functional requirements include sending messages, fetching history, and possibly presence. Non-functional requirements include latency, delivery guarantee, and ordering expectations.
Real-time expectations change the architecture. If polling is acceptable, you can start with simple endpoints. If near real-time is required, you introduce persistent connections and a fan-out mechanism. Delivery guarantees also matter: at-least-once delivery is practical, but it implies duplicates, so you need idempotency keys and dedup.
Ordering is where many candidates stumble. Timestamps can fail because clocks drift and events can arrive late or concurrently. If ordering matters per conversation, sequence numbers per conversation (assigned at the write source) are safer and easier to reason about.
Interviewer tip: When you introduce at-least-once delivery, say “duplicates are expected” and immediately state your idempotency and dedup plan.
| Requirement | Default choice | Why | Trade-off |
| Real-time | Persistent connections (if required) | Low latency delivery | Connection management complexity |
| Delivery | At-least-once | Durable and practical | Requires idempotency/dedup |
| Ordering | Sequence per conversation | Predictable ordering | Coordination at write source |
| History | Append-only storage | Efficient writes | Needs indexes for reads |
End-to-end interview flow
- Clarify: real-time vs polling, group size, history retention.
- Prioritize: send/receive correctness over presence features.
- Quantify: message QPS, p95 delivery latency, retention.
- Design: write path to storage; async fan-out; dedup with client message IDs.
- Validate: queue lag, fan-out success rate, out-of-order rate, error rate.
Walkthrough 3: Curveball “traffic spike + partial outage” requirements drive degradation
This curveball is about prioritization under stress. The functional behavior stays the same, but non-functional requirements decide what survives: which operations must remain available and which can degrade. A strong answer starts by defining the “core” and “non-core” paths.
During spikes and partial outages, systems fail because resources saturate and retries amplify load. Requirements should specify acceptable degradation: can we serve stale reads, drop optional work, or shed some traffic to protect the core? Those requirements drive concrete mechanisms: rate limits, backpressure, load shedding, and circuit breakers.
You also need validation metrics. You watch p95 latency, error rate, saturation, queue lag, cache hit rate, and drop or sampling rates. If you have fan-out, you monitor fan-out success rate. If you rely on control-plane levers like feature flags or quotas, control-plane propagation latency becomes important because slow propagation extends incidents.
What great answers sound like: “I’ll protect the core path with rate limits and timeouts, degrade non-core features first, and use metrics to detect overload before the SLO is breached.”
| Requirement | Degradation choice | Why | Metric |
| Keep core reads up | Serve stale cache reads briefly | Maintains usability | cache hit rate, p95 latency |
| Prevent cascades | Tight timeouts + circuit breakers | Stops resource exhaustion | timeout rate, error rate |
| Handle spikes | Backpressure + shedding | Protects core | saturation, drop rate |
| Async work can lag | Queue priority + delayed processing | Avoids hot path slowdown | queue lag |
| Safe changes during incident | Feature flags/quotas | Rapid mitigation | control-plane propagation latency |
End-to-end interview flow
- Clarify what must stay up (core) vs what can degrade (non-core).
- Prioritize protections: timeouts, rate limits, and backpressure first.
- Quantify: SLOs and error budget, max queue lag, acceptable staleness.
- Design: degrade non-core, shed load, bypass failing dependencies safely.
- Validate: p95 latency, error rate, saturation, queue lag, drop rate.
What a strong interview answer sounds like
A strong answer is structured and measurable. You don’t just list features; you translate them into scope, then translate quality goals into targets, then show how the design changes. This is where you can use functional vs non functional requirements as your narrative frame without getting stuck in definitions.
The best candidates also connect requirements to guarantees. If retries exist, they mention idempotency and dedup. If ordering matters, they choose sequence numbers over timestamps and explain why. If recovery matters, they mention durability and replay from a log or queue and how they validate correctness.
Sample 30–60 second outline: “I’ll start by clarifying the users and core actions, then I’ll list the functional requirements and pick the top few must-haves. Next I’ll convert non-functional goals into measurable targets like p95 latency, peak QPS, availability, and durability expectations. With those in place, I’ll propose a baseline design and evolve it as requirements demand, adding caching for read latency, queues for burst handling, and idempotency/dedup if at-least-once delivery is involved. I’ll close by validating the design with concrete SLIs and a degradation plan for spikes and partial outages.”
Checklist after the explanation:
- Clarify users, actions, and scale first.
- Prioritize must-haves before nice-to-haves.
- Turn quality goals into measurable targets.
- Tie components to requirements and metrics.
- Make guarantees explicit (dedup, ordering, replay).
- End with validation and degradation.
Closing: use requirements as your steering wheel
Once you build the habit, requirements become your steering wheel. They keep you from overbuilding, they make trade-offs defensible, and they let you communicate clearly under time pressure. In interviews, this is the skill that makes your designs feel deliberate rather than improvised.
In real engineering work, the same habits prevent painful rework. Clear functional scope stops you from building the wrong thing, and quantified non-functional targets stop you from discovering “performance requirements” after launch.
If you internalize this approach, functional vs non functional requirements becomes a practical method you can reuse on almost any System Design prompt.
Happy learning!
