Data Flow & Request Lifecycle

4. Data Flow / Request Lifecycle

This document describes the end-to-end journey of key interactions through the ProgNetwork system, showing how requests flow between components and how data is processed across the architecture.

Request Lifecycle Overview

Synchronous Request Flow Pattern

External Request → API Gateway → Service → Database → Response

Asynchronous Event Flow Pattern

Service Action → Event Publishing → Event Streaming → Event Consumption → Side Effects

Real-Time Update Flow Pattern

Service Action → Event Publishing → Platform API (HTTP) → Client Updates

Key User Journey Flows

1. User Registration and Authentication

Registration Flow

Key Components:

  • Auth Client: Collects registration data and handles UI
  • API Gateway: Validates request, adds tenant context
  • User Service: Creates user with multi-tenant isolation
  • Event Streaming: Coordinates verification email
  • Email Service: Sends welcome/verification emails

Data Transformations:

  • Registration form data → User record with tenant isolation
  • User creation → Email verification event
  • Event routing → Email delivery task

Authentication Flow

Security Measures:

  • Password hashing with bcrypt
  • JWT token generation with expiration
  • Session caching in Redis
  • Login event publishing for monitoring

2. Multi-Tenant Organization Setup

Tenant Creation Flow

Data Isolation:

  • Tenant record with unique identifier
  • User record linked to specific tenant
  • Billing configuration scoped to tenant
  • Complete data separation at database level

3. Payment Processing

Subscription Payment Flow

Payment Security:

  • PCI-compliant payment processing
  • Webhook verification for payment events
  • Secure token handling
  • Payment event publishing for audit trails

4. Real-Time Updates

HTTP Platform API and Updates

Real-Time Features:

  • HTTP request/response for commands and queries
  • Event subscription management via API
  • Client-side state synchronization

Event-Driven Workflows

Event Sourcing Pattern

User Profile Updates

Event Benefits:

  • Complete audit trail of all changes
  • Event replay for debugging
  • Decoupled event consumers
  • Reliable event ordering

CQRS Pattern Implementation

User Query vs Command Operations

CQRS Benefits:

  • Optimized read performance with replicas
  • Independent scaling of read/write operations
  • Event-driven consistency across read models
  • Complex query optimization

Saga Orchestration

Distributed Transaction Example (User + Payment)

Saga Features:

  • Distributed transaction coordination
  • Automatic rollback on failures
  • Event-driven compensation actions
  • Cross-service consistency guarantees

Background Processing Flows

Email Delivery Pipeline

Queue Processing:

  • Asynchronous email processing
  • Retry logic with exponential backoff
  • Dead letter queue for failed deliveries
  • Delivery tracking and analytics

Monitoring and Alerting Pipeline

Monitoring Flow:

  • Real-time metric collection
  • Automated anomaly detection
  • Alert routing and escalation
  • Notification delivery coordination

Data Consistency Patterns

Eventual Consistency via Events

1. Service A updates data → Publishes ChangeEvent
2. Event Streaming routes event to interested services
3. Service B consumes event → Updates local read models
4. Service C consumes event → Triggers side effects
5. System reaches eventual consistency

Strong Consistency via Distributed Transactions

1. Saga Coordinator starts transaction
2. Multiple services execute local transactions
3. Coordinator monitors completion
4. Success: Commit all changes
5. Failure: Execute compensating actions

Performance Optimization Flows

Caching Strategy

Caching Benefits:

  • Reduced database load
  • Improved response times
  • Better user experience
  • Configurable TTL per resource type

CDN and Asset Delivery

CDN Benefits:

  • Global asset delivery optimization
  • Reduced latency for static content
  • Offloaded traffic from origin servers
  • Improved scalability

Error Handling and Resilience Patterns

Circuit Breaker Pattern

Resilience Features:

  • Automatic failure detection
  • Fast failure for unavailable services
  • Gradual recovery testing
  • Comprehensive error metrics

Retry and Backoff Strategy

Retry Benefits:

  • Handles transient failures
  • Reduces error rates
  • Improves system reliability
  • Configurable retry policies

Monitoring and Observability Data Flow

Distributed Tracing

Tracing Benefits:

  • End-to-end request visibility
  • Performance bottleneck identification
  • Service dependency mapping
  • Error correlation across services

This comprehensive data flow documentation provides the foundation for understanding how data moves through the ProgNetwork system, enabling effective debugging, optimization, and maintenance of the platform.