E-Commerce Product Search

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design a product search engine with faceted filters, autocomplete, spell correction, and relevance ranking for 5 M products.
• •Design for a peak load target around 5,000 RPS (including burst headroom).
• •Products: 5,000,000
• •Search QPS (peak): ~10,000
• •Search latency (P95): < 200 ms
• •Autocomplete latency: < 100 ms
• •Facets updated per query: ~10 filter categories

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

• •Search: Use primary store for writes and async index updates for search relevance + scale.
• •Databases: Define a clear system-of-record and design read/write paths separately before adding optimizations.
• •Caching: Put cache on hot read paths first and pick cache-aside or write-through explicitly.
• •API Design: Standardize API boundaries, idempotency keys, pagination, and error contracts first.

4) Reliability and Failure Strategy

• •Track indexing lag and support reindex from source of truth.
• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Bound staleness with TTL + invalidation hooks for critical entities.
• •Apply strict input validation and backward-compatible versioning.

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

• •Search: Search index gives rich querying but introduces eventual consistency and index ops overhead.
• •Databases: SQL gives stronger transactional guarantees; NoSQL often gives better write scaling and flexibility.
• •Caching: Higher hit rate cuts latency/cost, but stale data and invalidation bugs become primary risks.
• •API Design: Rich APIs improve developer speed but can create long-term compatibility burden.

Practical Notes

• •Elasticsearch is the standard choice: inverted index for full-text, aggregations for facets, suggesters for autocomplete.
• •Keep the search index in sync with the product catalog via Change Data Capture (CDC) or a message queue.
• •Autocomplete: use an edge n-gram tokenizer to match partial words as the user types.

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 -> Load Balancer -> API Gateway -> API Service -> Primary SQL DB -> Redis Cache -> Search Index

Design strengths

• •Cache sits on the read path to absorb repeated queries and keep DB pressure stable.

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.