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Search Engine System Design ​

1. Business Requirements ​

Functional Requirements ​

  • User registration and authentication (optional, for personalization)
  • Web/mobile search interface (text, voice)
  • Web crawler for data collection
  • Indexing and ranking of web pages/documents
  • Query processing and result ranking
  • Real-time indexing for urgent content
  • Alerts/notifications (urgent system issues, trending topics)
  • Analytics and reporting (search trends, popular queries)
  • Mobile-ready responsive UI and API
  • Role-based access control for admin/ops

Non-Functional Requirements ​

  • 99.9% availability (max ~8.76 hours downtime/year)
  • Scalability to handle millions of queries and documents
  • Fast response times (<300ms for most queries)
  • Secure data storage and access control
  • Audit logging and monitoring
  • Backup and disaster recovery
  • GDPR/data privacy compliance
  • Mobile responsiveness

Out of Scope ​

  • Paid ad serving (unless specified)
  • Deep web/dark web crawling
  • Built-in content moderation/AI filtering

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

  • Users: 1M
  • Indexed documents: 1B
  • Daily queries: 10M
  • Peak concurrent users: ~100,000
  • Data size:
    • Index: 1B × 2 KB ≈ 2 TB
    • Raw crawled data: 1B × 10 KB ≈ 10 TB
    • User data: 1M × 2 KB ≈ 2 GB
    • Logs/analytics: 100M × 0.2 KB ≈ 20 GB/day
    • Total DB size: ~12 TB (excluding logs, backups)
  • Availability:
    • 99.9% = 8.76 hours/year downtime max
    • Use distributed index, 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[Search API Server]
  Index[Search Index Cluster]
  DB[(Metadata DB)]
  Crawler[Web Crawler]
  Ingest[Ingestion Pipeline]
  Alert[Alert/Notification Service]
  Analytics[Analytics Engine]

  User --> LB --> App
  App --> Index
  App --> DB
  App --> Alert
  App --> Analytics
  Crawler --> Ingest --> Index
  Analytics --> DB

Data Flow Diagram ​

mermaid
sequenceDiagram
  participant U as User
  participant A as Search API
  participant I as Index Cluster
  participant D as Metadata DB
  participant C as Crawler
  participant P as Ingestion
  participant L as Alert Service

  U->>A: Search Query
  A->>I: Query Index
  I-->>A: Results
  A->>D: Log Query
  A->>L: Send Alert (if needed)
  A-->>U: Return Results
  C->>P: Crawl Data
  P->>I: Update Index
  P->>D: Update Metadata

Key Design Decisions ​

  • Index Database: Distributed search engine (e.g., Elasticsearch, OpenSearch, Solr) for fast, scalable search
  • Metadata DB: Relational DB (e.g., PostgreSQL) for user data, logs, analytics
  • Web Crawler: Distributed, scalable crawler for data collection
  • Ingestion Pipeline: For cleaning, parsing, and indexing crawled data
  • Alerting/Notifications: Email/SMS/push via third-party service (e.g., Twilio, Firebase)
  • Analytics: Batch or streaming (e.g., Kafka + Spark, or managed cloud analytics)
  • Deployment: Cloud-based, multi-AZ, managed services for high availability
  • API: REST/GraphQL for search and admin operations

4. Conceptual Design ​

Entities ​

  • User: id, name, email, password_hash, preferences, registration_date, status
  • Document: id, url, title, content, metadata, indexed_at, rank
  • Query: id, user_id, query_text, timestamp, results_count
  • Alert: id, type (urgent/system/trend), message, created_at, status
  • Analytics: id, metric, value, period, created_at
  • AuditLog: id, user_id, action, entity, entity_id, timestamp

Key Flows ​

  • Search Query:
    1. User submits query
    2. API queries index cluster, returns ranked results
    3. Logs query and triggers alert if needed
  • Crawling/Indexing:
    1. Crawler collects data
    2. Ingestion pipeline cleans and parses
    3. Index cluster updates index, metadata DB updated
  • Alerts:
    • System triggers urgent alerts for failures, trending topics, or system events
  • Analytics:
    • Periodic jobs aggregate query, usage, and trend data

Security ​

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

5. Bottlenecks and Refinement ​

Potential Bottlenecks ​

  • Index cluster scaling:
    • Use sharding, replication, and auto-scaling
  • Crawler throughput:
    • Use distributed crawling and rate limiting
  • Ingestion pipeline:
    • Use parallel processing and queueing
  • Query latency:
    • Use caching for popular queries, optimize index structure
  • Alert delivery:
    • Use async queues for urgent notifications
  • Single region failure:
    • Deploy across multiple availability zones/regions

Refinement ​

  • Monitor system metrics and auto-scale index/crawler/app servers
  • Regularly test failover and backup restores
  • Optimize queries and index structure for frequent operations
  • Consider sharding and multi-cluster index if data/query volume grows significantly

This design provides a scalable, highly available, and mobile-ready search engine with robust urgent alerts, analytics, and operational best practices.