Expense Tracker API

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design a personal expense tracking app with categories, budgets, recurring expenses, and monthly reports.
• •Design for a peak load target around 100 RPS (including burst headroom).
• •Registered users: ~25,000
• •Daily active users: ~15,000
• •Expenses per user/month: ~100
• •API response time: < 200 ms
• •Report generation: < 1 second

2) Capacity Planning Method

• •Convert traffic and growth constraints into request rate, storage growth, and concurrency budgets.
• •Keep at least 2-3x safety margin per tier (ingress, compute, storage, async workers).
• •Reserve explicit latency budgets per hop so p95 can be defended in review.

3) Architecture Decisions

• •Databases: Define a clear system-of-record and design read/write paths separately before adding optimizations.
• •API Design: Standardize API boundaries, idempotency keys, pagination, and error contracts first.
• •Auth: Centralize identity verification and keep authorization checks close to domain resources.

4) Reliability and Failure Strategy

• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Apply strict input validation and backward-compatible versioning.
• •Use short-lived tokens and secure key rotation workflows.

5) Validation Plan

• •Run one peak-load test, one dependency-degradation test, and one failover test.
• •Verify idempotency for all retried writes and async consumers.
• •Track user-facing SLOs first: p95 latency, error rate, and successful throughput.

6) Trade-offs to Call Out in Interviews

• •Databases: SQL gives stronger transactional guarantees; NoSQL often gives better write scaling and flexibility.
• •API Design: Rich APIs improve developer speed but can create long-term compatibility burden.
• •Auth: Central auth simplifies policy, but makes auth service availability/security critical.

Practical Notes

• •A relational database with proper indexes on (user_id, date, category) handles all queries efficiently.
• •Pre-compute monthly aggregates whenever a new expense is added - store in a summary table for instant report loading.
• •Recurring expenses: a scheduled job creates new expense entries at the start of each period.

Why This Solution Works

Request path: The solution keeps ingress, service logic, and stateful dependencies separated so each layer can scale independently.

Reference flow: Web Clients -> API Gateway -> API Service -> Auth Service -> Primary SQL DB

Design strengths

• •Security controls are enforced at ingress to protect downstream capacity.

Interview defense

• •This design makes bottlenecks explicit (ingress, core compute, persistence, async workers).
• •It supports progressive scaling without re-architecting the core request path.
• •It keeps correctness-sensitive state changes in durable systems while offloading background work asynchronously.