Guide to DevSecOps

Appearance

Job Portal System Design ​

1. Business Requirements ​

Functional Requirements ​

  • User registration and authentication (job seekers, employers, admins)
  • Profile management for job seekers and employers
  • Job posting, editing, and removal by employers
  • Job search and filtering (by title, location, salary, skills, etc.)
  • Application submission and tracking
  • Resume upload and management
  • Messaging between job seekers and employers
  • Generate trends and analytics (e.g., most popular jobs, application rates)
  • Notifications (application status, new jobs, interview invites)
  • Mobile-ready responsive UI

Non-Functional Requirements ​

  • 99.9% availability (max ~8.76 hours downtime/year)
  • Scalability to support high traffic and spikes
  • Secure data storage and access control
  • Fast response times (<300ms for most requests)
  • Audit logging and monitoring
  • Backup and disaster recovery
  • GDPR/data privacy compliance

Out of Scope ​

  • Payroll or payment processing
  • Third-party job board integration
  • Video interview or conferencing features

2. Estimation & Back-of-the-Envelope Calculations ​

  • Users: 100,000 job seekers, 5,000 employers
  • Job posts: 50,000 active at any time
  • Daily transactions: ~20,000 (searches, applications, messages)
  • Peak concurrent users: ~2,000
  • Data size:
    • User profiles: 105,000 × 2 KB ≈ 210 MB
    • Job posts: 50,000 × 2 KB ≈ 100 MB
    • Applications: 2M × 0.5 KB ≈ 1 GB
    • Messages: 5M × 0.5 KB ≈ 2.5 GB
    • Resumes: 100,000 × 200 KB ≈ 20 GB
    • Total DB size: ~25 GB (excluding logs, backups)
  • Availability:
    • 99.9% = 8.76 hours/year downtime max
    • Use managed DB, multi-AZ deployment, health checks, auto-scaling

3. High Level Design (Mermaid Diagrams) ​

Component Diagram ​

mermaid
flowchart LR
  User[User (Web/Mobile)]
  LB[Load Balancer]
  App[Application Server]
  DB[(Database)]
  Cache[Cache (Redis)]
  Storage[Object Storage (Resumes)]
  Analytics[Analytics Engine]
  Notif[Notification Service]

  User --> LB --> App
  App --> DB
  App --> Cache
  App --> Storage
  App --> Notif
  App --> Analytics
  Analytics --> DB

Data Flow Diagram ​

mermaid
sequenceDiagram
  participant U as User
  participant A as App Server
  participant D as Database
  participant C as Cache
  participant S as Storage
  participant N as Notification

  U->>A: Apply for Job
  A->>C: Check Job Post Cache
  C-->>A: Hit/Miss
  A->>D: Create Application Record
  D-->>A: Success/Fail
  A->>S: Upload Resume
  S-->>A: Success/Fail
  A->>N: Send Notification
  A-->>U: Response

Key Design Decisions ​

  • Database: Relational DB (e.g., PostgreSQL) for transactional data, strong consistency
  • Cache: Redis for fast lookups (job posts, sessions)
  • Object Storage: For resumes and attachments (e.g., AWS S3, Azure Blob)
  • Analytics: Batch or streaming (e.g., Kafka + Spark, or managed cloud analytics)
  • Deployment: Cloud-based, multi-AZ, managed services for high availability
  • Notifications: Email/SMS via third-party service (e.g., SendGrid, Twilio)

4. Conceptual Design ​

Entities ​

  • User: id, name, email, role, profile, registration_date, status
  • Employer: id, company_name, contact_info, profile
  • JobPost: id, employer_id, title, description, location, salary, skills, status, posted_date
  • Application: id, user_id, job_id, resume_url, cover_letter, status, applied_date
  • Message: id, sender_id, receiver_id, content, sent_at
  • Notification: id, user_id, type, message, sent_at

Key Flows ​

  • Job Application:
    1. User applies for a job
    2. App checks cache for job post, falls back to DB
    3. Creates Application record, uploads resume to storage
    4. Sends notification to employer
  • Job Posting:
    1. Employer posts a job
    2. App creates JobPost record, updates cache
    3. Notifies job seekers (if opted in)
  • Analytics:
    • Periodic jobs aggregate data for trends (e.g., most applied jobs, active employers)

Security ​

  • Role-based access control (RBAC)
  • Input validation, rate limiting
  • Encrypted connections (HTTPS)
  • Regular backups and audit logs

5. Bottlenecks and Refinement ​

Potential Bottlenecks ​

  • Database contention (high read/write during peak):
    • Use read replicas, caching, and DB connection pooling
  • Resume storage:
    • Use scalable object storage, CDN for downloads
  • Analytics workload:
    • Offload to separate analytics engine, run during off-peak
  • Notification delays:
    • Use async queues for notification delivery
  • Single region failure:
    • Deploy across multiple availability zones/regions

Refinement ​

  • Monitor system metrics and auto-scale app servers
  • Regularly test failover and backup restores
  • Optimize queries and indexes for frequent operations
  • Consider sharding if user/job volume grows significantly

This design provides a scalable, highly available, and mobile-ready job portal with analytics and robust operational practices.