Design a scalable resume search system

Q: Design a scalable resume search system

This question evaluates a candidate's competence in large-scale system architecture, data modeling, full-text search and indexing, asynchronous processing pipelines, storage and consistency trade-offs, API design, and security/privacy for handling uploaded documents.

Q: How do I approach System Design interview questions?

System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

Q: What difficulty level is this interview question?

This is a hard difficulty System Design question, commonly asked during Technical Screen rounds at MongoDB.

Q: What role is this question designed for?

This question is commonly asked for Software Engineer candidates at MongoDB during technical interviews.

Question

System Design: Resume Search Platform

Context

A recruiting company needs a platform where applicants can upload resumes and recruiters can search, filter, and view matching candidates. Build a scalable, secure system with robust search and asynchronous processing.

Requirements

Applicant uploads
- Accept PDF/Word resumes via web/mobile.
Resume Server
- Parse resumes into structured metadata.
- Generate thumbnails and resized previews.
- Store both metadata and files.
Recruiter search and view
- Provide search UI and APIs to retrieve matching resumes.
Specify in your design
- High-level architecture and components (applicant/recruiter interfaces, resume server services, data stores).
- Data model for resume metadata and file references; include schema fields such as name, contact, skills, education, experience, locations, uploaded_at, and versioning strategy.
- Storage choices for blobs vs metadata (e.g., object storage + document/relational DB) and trade-offs (consistency, transactions, cost).
- Asynchronous processing pipeline for parse/resize using a queue and horizontally scalable workers; address idempotency and retries.
- Public APIs for upload, search with multiple filters (skills, location, experience, education), and get-by-id; outline request/response shapes and pagination.
- Search strategy and indexing with a dedicated search engine; propose mappings/fields, ranking signals, and example queries (term, text, range filters).
- Scalability plan: sharding/partitioning, back-pressure, rate limiting, caching/CDN for previews, and data lifecycle policies.
- Security and privacy: HTTPS, authentication, RBAC for applicants vs recruiters, signed URLs for file access, PII handling, audit logs.
- Observability and reliability: metrics, logs, traces, SLOs, failure handling, dead-letter queues.
- Evolution: relevance tuning, typo tolerance, synonym handling, multi-tenant isolation, and cost optimization.
Capacity estimates
- Propose reasonable daily uploads, QPS, and data size, and justify design choices with trade-offs.

Design a scalable resume search system

Quick Overview

System Design: Resume Search Platform

Context

Requirements

Solution

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