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Analytics Service System Design ​

1. Business Requirements ​

Functional Requirements ​

  • Ingest data from multiple sources (web, mobile, APIs)
  • Real-time and batch analytics processing
  • Dashboard for visualizing metrics and trends
  • Customizable alerts (thresholds, anomalies)
  • User management and authentication (analysts, admins)
  • Mobile-ready responsive UI
  • Export analytics reports (CSV, PDF)
  • API for programmatic access to analytics data
  • Role-based access control

Non-Functional Requirements ​

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

Out of Scope ​

  • Data monetization or marketplace features
  • Built-in machine learning model training (unless specified)
  • Third-party BI tool integration

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

  • Data sources: 100+ (websites, apps, APIs)
  • Events per day: 10M (page views, clicks, transactions)
  • Peak concurrent users: ~2,000 (dashboard/API)
  • Data size:
    • Raw events: 10M × 0.5 KB ≈ 5 GB/day
    • 1 year: 5 GB × 365 ≈ 1.8 TB
    • Aggregated metrics: 100K × 0.5 KB ≈ 50 MB/day
    • User data: 10,000 × 2 KB ≈ 20 MB
    • Total DB size: ~2 TB/year (raw + aggregates, 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
  Source[Data Sources (Web/Mobile/API)]
  Ingest[Ingestion Service]
  Stream[Stream Processor]
  Batch[Batch Processor]
  DB[(Analytics DB)]
  Cache[Cache (Redis)]
  Alert[Alert/Notification Service]
  Dash[Dashboard (Web/Mobile)]
  API[Analytics API]

  Source --> Ingest
  Ingest --> Stream
  Ingest --> Batch
  Stream --> DB
  Batch --> DB
  Dash --> Cache
  Dash --> API
  API --> DB
  API --> Cache
  DB --> Alert
  Dash --> Alert

Data Flow Diagram ​

mermaid
sequenceDiagram
  participant S as Data Source
  participant I as Ingestion
  participant SP as Stream Processor
  participant BP as Batch Processor
  participant D as Analytics DB
  participant C as Cache
  participant A as Alert Service
  participant U as User (Dashboard/API)

  S->>I: Send Event Data
  I->>SP: Real-time Processing
  SP->>D: Store Aggregates
  I->>BP: Batch Processing
  BP->>D: Store Aggregates
  D->>A: Trigger Alert (if needed)
  U->>C: Query Metrics
  C-->>U: Hit/Miss
  U->>D: Query Metrics (if miss)
  D-->>U: Response

Key Design Decisions ​

  • Database: Columnar DB (e.g., ClickHouse, Amazon Redshift) for fast analytics queries; may use PostgreSQL for metadata
  • Cache: Redis for fast dashboard/API responses
  • Stream Processing: Apache Kafka + Apache Flink/Spark Streaming for real-time analytics
  • Batch Processing: Apache Spark or managed cloud ETL
  • Deployment: Cloud-based, multi-AZ, managed services for high availability
  • Alerting/Notifications: Email/SMS/push via third-party service (e.g., Twilio, Firebase)
  • API: REST/GraphQL for analytics data access

4. Conceptual Design ​

Entities ​

  • User: id, name, email, password_hash, role, registration_date, status
  • DataSource: id, name, type, config, status
  • Event: id, source_id, event_type, payload, timestamp
  • Metric: id, name, description, aggregation_type, created_at
  • Aggregate: id, metric_id, value, period, timestamp
  • Alert: id, user_id, metric_id, type (threshold/anomaly), message, created_at, status
  • AuditLog: id, user_id, action, entity, entity_id, timestamp

Key Flows ​

  • Data Ingestion:
    1. Data sources send events to ingestion service
    2. Stream processor computes real-time aggregates, stores in DB
    3. Batch processor computes periodic aggregates, stores in DB
  • Alerting:
    • System triggers alerts based on thresholds/anomalies in metrics
  • Dashboard/API:
    • Users query metrics via dashboard/API, using cache for performance

Security ​

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

5. Bottlenecks and Refinement ​

Potential Bottlenecks ​

  • Ingestion throughput:
    • Use scalable, distributed ingestion (Kafka, cloud pub/sub)
  • Analytics DB query load:
    • Use columnar DB, partitioning, and caching
  • Alert delivery:
    • Use async queues for notifications
  • Dashboard/API latency:
    • Use cache and optimize queries
  • Single region failure:
    • Deploy across multiple availability zones/regions

Refinement ​

  • Monitor system metrics and auto-scale ingestion/processors
  • Regularly test failover and backup restores
  • Optimize queries and indexes for frequent operations
  • Consider sharding or multi-cluster DB if data volume grows significantly

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