Can you design the architecture for a temporary email service?

What the interviewer wants to learn

  • Your ability to break down a large, open‑ended problem into manageable components.
  • How you reason about scalability, reliability, security, and cost.
  • Whether you understand trade‑offs between different storage, messaging, and compute choices.
  • Your communication style: can you explain complex ideas clearly and justify decisions.

How to structure your answer

1. **Clarify requirements** – ask about functional (send/receive, expiry, API) and non‑functional (throughput, latency, durability, privacy) needs.

2. **Identify main components** – front‑end/API gateway, mail generation service, storage, expiry/cleanup, DNS, spam/abuse controls.

3. **Choose technologies & justify** – e.g., stateless API servers behind a load balancer, object storage for email bodies, a NoSQL store for metadata, a message queue for async processing.

4. **Describe data flow** – step through a “create address → receive mail → fetch mail → delete after TTL” sequence.

5. **Address scalability & fault tolerance** – sharding, replication, caching, auto‑scaling groups.

6. **Consider security & privacy** – TLS, rate limiting, encryption at rest, GDPR‑friendly deletion.

7. **Wrap up with cost & monitoring** – metrics, alerts, log aggregation.

Example answer

“Sure, let’s start by nailing down the core requirements. We need an API that lets a user create a disposable address, receive inbound mail, and retrieve it via a REST endpoint, with messages automatically deleted after, say, 24 hours. We also want to support thousands of addresses per second, keep latency under 200 ms, and ensure no one can harvest the addresses for spam.

I’d break the system into four layers. First, a stateless API tier behind an AWS ALB that handles address creation and message retrieval; we can run it in containers on ECS with auto‑scaling. Second, a mail‑processing service that receives SMTP traffic—this can be a small fleet of EC2 instances behind a load balancer, writing inbound messages to an S3 bucket for the raw content and to DynamoDB for metadata (address, timestamp, size). Third, a cleanup worker that scans DynamoDB for expired entries and deletes the corresponding S3 objects; we can trigger it with a scheduled Lambda. Finally, a DNS layer that points any sub‑domain like `abc123.tmpmail.com` to our mail‑processing service.

For durability we use S3 versioning and DynamoDB’s built‑in replication across AZs. To keep costs low we store only the metadata in DynamoDB (cheap reads/writes) and the actual email bodies in S3, which is cheap for large objects. Rate limiting and CAPTCHA on address creation stop abuse, and all traffic is encrypted with TLS. Monitoring is done via CloudWatch metrics and alerts for queue depth, error rates, and latency.”

Mistakes to avoid

  • **Jumping straight into a diagram** without first clarifying scope; you’ll appear to ignore requirements.
  • **Over‑engineering** (e.g., proposing a full micro‑service mesh) for a simple MVP; it shows poor cost awareness.
  • **Ignoring security** – no mention of TLS, rate limiting, or data deletion can be a red flag.
  • **Being vague about trade‑offs**; always explain why you chose one storage or messaging option over another.
  • **Leaving out monitoring/ops** – interviewers expect you to think about production readiness.

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