System Architecture

The Real-Time Data Processing System is designed as a distributed, fault-tolerant architecture capable of processing millions of events per second with sub-second latency. The system follows modern microservices principles and implements industry best practices for scalability and reliability.

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Data Sources  │    │   Load Balancer │    │   API Gateway   │
│                 │    │                 │    │                 │
│ • Web Apps      │───▶│ • Rate Limiting │───▶│ • Authentication│
│ • Mobile Apps   │    │ • Health Checks │    │ • Request Routing│
│ • APIs          │    │ • SSL Termination│   │ • Monitoring    │
│ • External      │    │                 │    │                 │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                                        │
                                                        ▼
┌─────────────────────────────────────────────────────────────────┐
│                    Event Ingestion Layer                        │
│                                                                 │
│ ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐ │
│ │ HTTP Endpoints  │  │ WebSocket       │  │ Batch Processor │ │
│ │                 │  │ Connections     │  │                 │ │
│ │ • Single Events │  │ • Real-time     │  │ • Bulk Ingestion│ │
│ │ • Validation    │  │ • Streaming     │  │ • File Uploads  │ │
│ │ • Rate Limiting │  │ • Low Latency   │  │ • ETL Pipelines │ │
│ └─────────────────┘  └─────────────────┘  └─────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
                                │
                                ▼
┌─────────────────────────────────────────────────────────────────┐
│                    Stream Processing Engine                     │
│                                                                 │
│ ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐ │
│ │ Event Router    │  │ Processing      │  │ Aggregation     │ │
│ │                 │  │ Instances       │  │ Engine          │ │
│ │ • Partitioning  │  │ • Windowing     │  │ • Time Windows  │ │
│ │ • Load Balancing│  │ • Filtering     │  │ • Metrics Calc  │ │
│ │ • Fault Tolerance│ │ • Transformation│  │ • Statistics    │ │
│ └─────────────────┘  └─────────────────┘  └─────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
                                │
                                ▼
┌─────────────────────────────────────────────────────────────────┐
│                      Storage Layer                              │
│                                                                 │
│ ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐ │
│ │ Time-Series DB  │  │ Metrics Store   │  │ Cache Layer     │ │
│ │                 │  │                 │  │                 │ │
│ │ • Event Storage │  │ • Performance   │  │ • Hot Data      │ │
│ │ • Partitioning  │  │ • Health Checks │  │ • Query Cache   │ │
│ │ • Compression   │  │ • Aggregates    │  │ • Session Store │ │
│ └─────────────────┘  └─────────────────┘  └─────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
                                │
                                ▼
┌─────────────────────────────────────────────────────────────────┐
│                   Monitoring & Analytics                        │
│                                                                 │
│ ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐ │
│ │ Real-time       │  │ Health          │  │ Alerting        │ │
│ │ Dashboard       │  │ Monitoring      │  │ System          │ │
│ │                 │  │                 │  │                 │ │
│ │ • Live Metrics  │  │ • System Health │  │ • Notifications │ │
│ │ • Visualizations│  │ • Performance   │  │ • Escalation    │ │
│ │ • User Interface│  │ • Error Tracking│  │ • Integration   │ │
│ └─────────────────┘  └─────────────────┘  └─────────────────┘ │
└─────────────────────────────────────────────────────────────────┘

Purpose: High-throughput event collection with validation and rate limiting.

Components:

• HTTP Endpoints: REST API for single event ingestion
• WebSocket Handlers: Real-time streaming connections
• Batch Processors: Bulk event processing
• Rate Limiters: Prevent system overload
• Validators: Ensure data quality

Key Features:

• 10,000+ events/second per instance
• Automatic event ID generation
• Partition key assignment
• Input validation and sanitization
• Backpressure handling

Purpose: Real-time event processing with windowing and aggregation.

Components:

• Event Router: Distributes events across processing instances
• Processing Instances: Parallel event processing workers
• Windowing Engine: Time-based event grouping
• Aggregation Engine: Real-time statistics calculation

Key Features:

• Sub-second processing latency
• Horizontal scaling (1-100+ instances)
• Fault tolerance with automatic recovery
• Dynamic load balancing
• Event ordering guarantees

Purpose: Persistent storage optimized for time-series data and high-speed queries.

Components:

• Time-Series Database: Event storage with time-based partitioning
• Metrics Store: Performance and health metrics
• Cache Layer: Hot data and query result caching

Key Features:

• Time-based partitioning for optimal query performance
• Automatic data compression
• Configurable retention policies
• ACID compliance for critical operations
• Read replicas for query scaling

Purpose: Real-time system observability and performance monitoring.

Components:

• Real-time Dashboard: Live system metrics visualization
• Health Monitoring: System health checks and status
• Alerting System: Automated notifications and escalation

Key Features:

• Real-time metrics updates via WebSocket
• Interactive charts and visualizations
• Comprehensive health checks
• Performance trend analysis
• Custom alerting rules

1. Ingestion

   Event → Validation → Rate Check → Normalization → Storage
   

2. Processing

   Event → Partitioning → Windowing → Aggregation → Metrics Update
   

3. Monitoring

   Metrics → Collection → Storage → Dashboard → Alerts
   

1. Reception: Event received via HTTP/WebSocket
2. Validation: Schema validation and sanitization
3. Enrichment: ID generation, timestamp assignment, partition key
4. Storage: Persistent storage in time-series database
5. Processing: Real-time processing and aggregation
6. Monitoring: Metrics collection and health checks
7. Visualization: Real-time dashboard updates

• Processing Instances: Auto-scale from 1 to 100+ instances
• Database Sharding: Partition data across multiple nodes
• Load Balancing: Distribute load across available instances
• Cache Distribution: Distributed caching for hot data

• Memory Optimization: Efficient data structures and algorithms
• CPU Optimization: Parallel processing and async operations
• Storage Optimization: Compression and indexing strategies
• Network Optimization: Connection pooling and batching

• Multi-Instance Deployment: No single points of failure
• Data Replication: Multiple copies of critical data
• Health Checks: Continuous system monitoring
• Automatic Failover: Seamless instance replacement

• Circuit Breakers: Prevent cascade failures
• Retry Logic: Exponential backoff for transient failures
• Graceful Degradation: Reduced functionality during issues
• State Recovery: Restore processing state after failures

• Target: 1M+ events/second
• Achieved: Scales linearly with instances
• Bottlenecks: Database write capacity, network bandwidth
• Optimization: Batching, compression, connection pooling

• Target: <100ms processing time
• P50: <50ms typical processing
• P95: <100ms under normal load
• P99: <200ms under high load

• Target: 99.9% uptime
• MTTR: <5 minutes for automatic recovery
• MTBF: >30 days between failures
• Monitoring: Real-time health checks

• Encryption: TLS for data in transit
• Sanitization: Input validation and cleaning
• Access Control: Role-based permissions
• Audit Logging: Complete operation tracking

• Rate Limiting: Prevent abuse and DoS attacks
• Input Validation: Prevent injection attacks
• Network Security: Firewall and VPN protection
• Monitoring: Security event detection

• Runtime: Deno with TypeScript
• Web Framework: Hono for HTTP handling
• Database: SQLite with time-series optimization
• WebSockets: Native Deno WebSocket support

• Framework: React 18 with TypeScript
• Styling: TailwindCSS for responsive design
• Charts: Chart.js for data visualization
• Real-time: WebSocket for live updates

• Deployment: Val Town platform
• Monitoring: Custom metrics collection
• Logging: Structured logging with correlation IDs
• Alerting: Webhook-based notifications