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goplt/docs/content/architecture/data-flow-patterns.md
2025-11-05 11:00:36 +01:00

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# Data Flow Patterns
## Purpose
This document describes how data flows through the Go Platform system, covering request/response flows, event flows, cache patterns, and observability data collection.
## Overview
Data flows through the platform in multiple patterns depending on the type of operation. Understanding these patterns helps in debugging, performance optimization, and system design decisions.
## Key Concepts
- **Request Flow**: Data flow from HTTP request to response
- **Event Flow**: Asynchronous data flow through event bus
- **Cache Flow**: Data flow through caching layers
- **Observability Flow**: Telemetry data collection and export
## Request/Response Data Flow
### Standard HTTP Request Flow
Complete data flow from HTTP request to response.
```mermaid
graph TD
Start[HTTP Request] --> Auth[Authentication]
Auth -->|Valid| Authz[Authorization]
Auth -->|Invalid| Error1[401 Response]
Authz -->|Authorized| Handler[Request Handler]
Authz -->|Unauthorized| Error2[403 Response]
Handler --> Service[Domain Service]
Service --> Cache{Cache Check}
Cache -->|Hit| CacheData[Return Cached Data]
Cache -->|Miss| Repo[Repository]
Repo --> DB[(Database)]
DB --> Repo
Repo --> Service
Service --> CacheStore[Update Cache]
Service --> EventBus[Publish Events]
Service --> Audit[Audit Log]
Service --> Metrics[Update Metrics]
Service --> Handler
Handler --> Response[HTTP Response]
CacheData --> Response
Error1 --> Response
Error2 --> Response
Response --> Client[Client]
style Auth fill:#ff6b6b,stroke:#c92a2a,stroke-width:2px,color:#fff
style Service fill:#50c878,stroke:#2e7d4e,stroke-width:2px,color:#fff
style Cache fill:#4a90e2,stroke:#2e5c8a,stroke-width:2px,color:#fff
```
### Request Data Transformation
How request data is transformed as it flows through the system.
```mermaid
sequenceDiagram
participant Client
participant Handler
participant Service
participant Repo
participant DB
Client->>Handler: HTTP Request (JSON)
Handler->>Handler: Parse JSON
Handler->>Handler: Validate request
Handler->>Handler: Convert to DTO
Handler->>Service: Business DTO
Service->>Service: Business logic
Service->>Service: Domain entity
Service->>Repo: Domain entity
Repo->>Repo: Convert to DB model
Repo->>DB: SQL query
DB-->>Repo: DB result
Repo->>Repo: Convert to domain entity
Repo-->>Service: Domain entity
Service->>Service: Business logic
Service->>Service: Response DTO
Service-->>Handler: Response DTO
Handler->>Handler: Convert to JSON
Handler-->>Client: HTTP Response (JSON)
```
## Event Data Flow
### Event Publishing Flow
How events are published and flow through the event bus.
```mermaid
graph LR
Publisher[Event Publisher] --> Serialize[Serialize Event]
Serialize --> Metadata[Add Metadata]
Metadata --> EventBus[Event Bus]
EventBus --> Topic[Kafka Topic]
Topic --> Subscriber1[Subscriber 1]
Topic --> Subscriber2[Subscriber 2]
Topic --> SubscriberN[Subscriber N]
Subscriber1 --> Process1[Process Event]
Subscriber2 --> Process2[Process Event]
SubscriberN --> ProcessN[Process Event]
style EventBus fill:#4a90e2,stroke:#2e5c8a,stroke-width:2px,color:#fff
style Topic fill:#7b68ee,stroke:#5a4fcf,stroke-width:2px,color:#fff
```
### Event Data Transformation
How event data is transformed during publishing and consumption.
```mermaid
sequenceDiagram
participant Publisher
participant EventBus
participant Kafka
participant Subscriber
Publisher->>Publisher: Domain event
Publisher->>EventBus: Publish(event)
EventBus->>EventBus: Serialize to JSON
EventBus->>EventBus: Add metadata:
- trace_id
- user_id
- timestamp
- source
EventBus->>Kafka: Send to topic
Kafka-->>EventBus: Acknowledged
Kafka->>Subscriber: Deliver event
Subscriber->>Subscriber: Deserialize JSON
Subscriber->>Subscriber: Extract metadata
Subscriber->>Subscriber: Domain event
Subscriber->>Subscriber: Process event
```
## Cache Data Flow
### Cache-Aside Pattern Flow
How data flows through cache using the cache-aside pattern.
```mermaid
graph TD
Start[Service Request] --> Check{Cache Hit?}
Check -->|Yes| GetCache[Get from Cache]
Check -->|No| GetDB[Query Database]
GetCache --> Deserialize[Deserialize Data]
Deserialize --> Return[Return Data]
GetDB --> DB[(Database)]
DB --> DBData[Database Result]
DBData --> Serialize[Serialize Data]
Serialize --> StoreCache[Store in Cache]
StoreCache --> Return
style Check fill:#4a90e2,stroke:#2e5c8a,stroke-width:2px,color:#fff
style StoreCache fill:#50c878,stroke:#2e7d4e,stroke-width:2px,color:#fff
```
### Cache Invalidation Flow
How cache is invalidated when data changes.
```mermaid
sequenceDiagram
participant Service
participant Repository
participant DB
participant Cache
Service->>Repository: Update entity
Repository->>DB: Update database
DB-->>Repository: Update complete
Repository->>Cache: Invalidate(key)
Cache->>Cache: Remove from cache
Cache-->>Repository: Invalidated
Repository-->>Service: Update complete
Note over Service,Cache: Next read will fetch from DB and cache
```
### Cache Write-Through Pattern
How data is written through cache to database.
```mermaid
sequenceDiagram
participant Service
participant Cache
participant Repository
participant DB
Service->>Cache: Write data
Cache->>Cache: Store in cache
Cache->>Repository: Write to database
Repository->>DB: Insert/Update
DB-->>Repository: Success
Repository-->>Cache: Write complete
Cache-->>Service: Data written
```
## Observability Data Flow
### Tracing Data Flow
How distributed tracing data flows through the system.
```mermaid
graph TD
Request[HTTP Request] --> Trace[Start Trace]
Trace --> Span1[HTTP Span]
Span1 --> Service[Service Call]
Service --> Span2[Service Span]
Span2 --> DB[Database Query]
DB --> Span3[DB Span]
Span2 --> gRPC[gRPC Call]
gRPC --> Span4[gRPC Span]
Span3 --> Aggregate[Collect Spans]
Span4 --> Aggregate
Aggregate --> Export[Export to Collector]
Export --> Collector[OpenTelemetry Collector]
Collector --> Backend[Backend Storage]
style Trace fill:#4a90e2,stroke:#2e5c8a,stroke-width:2px,color:#fff
style Aggregate fill:#50c878,stroke:#2e7d4e,stroke-width:2px,color:#fff
```
### Metrics Data Flow
How metrics are collected and exported.
```mermaid
sequenceDiagram
participant Service
participant MetricsRegistry
participant Exporter
participant Prometheus
participant Grafana
Service->>Service: Business operation
Service->>MetricsRegistry: Increment counter
Service->>MetricsRegistry: Record duration
Service->>MetricsRegistry: Set gauge
MetricsRegistry->>MetricsRegistry: Aggregate metrics
Prometheus->>Exporter: Scrape metrics
Exporter->>MetricsRegistry: Get metrics
MetricsRegistry-->>Exporter: Metrics data
Exporter-->>Prometheus: Prometheus format
Prometheus->>Prometheus: Store metrics
Grafana->>Prometheus: Query metrics
Prometheus-->>Grafana: Metrics data
Grafana->>Grafana: Render dashboard
```
### Log Data Flow
How logs flow through the system to various sinks.
```mermaid
graph TD
Service[Service] --> Logger[Logger]
Logger --> Format[Format Log]
Format --> Output[Output Log]
Output --> Stdout[stdout]
Output --> File[File]
Output --> LogCollector[Log Collector]
LogCollector --> Elasticsearch[Elasticsearch]
LogCollector --> CloudLogging[Cloud Logging]
Stdout --> Container[Container Logs]
style Logger fill:#4a90e2,stroke:#2e5c8a,stroke-width:2px,color:#fff
style LogCollector fill:#50c878,stroke:#2e7d4e,stroke-width:2px,color:#fff
```
## Audit Data Flow
How audit logs flow through the system.
```mermaid
sequenceDiagram
participant Service
participant AuditClient
participant AuditService
participant DB
participant Archive
Service->>Service: Security-sensitive action
Service->>AuditClient: Record audit log
AuditClient->>AuditClient: Build audit entry:
- actor
- action
- target
- metadata
- timestamp
AuditClient->>AuditService: Store audit log
AuditService->>AuditService: Validate entry
AuditService->>AuditService: Ensure immutability
AuditService->>DB: Insert audit log
DB-->>AuditService: Log stored
AuditService->>Archive: Archive old logs
Archive->>Archive: Long-term storage
Note over Service,Archive: Audit logs are immutable
```
## Cross-Service Data Flow
### Inter-Service Request Flow
How data flows when services communicate via service clients.
```mermaid
sequenceDiagram
participant ServiceA
participant ServiceClient
participant ServiceRegistry
participant ServiceB
participant DB
ServiceA->>ServiceClient: Call service method
ServiceClient->>ServiceRegistry: Discover service
ServiceRegistry-->>ServiceClient: Service endpoint
ServiceClient->>ServiceB: gRPC request
ServiceB->>ServiceB: Process request
ServiceB->>DB: Query data
DB-->>ServiceB: Data
ServiceB->>ServiceB: Business logic
ServiceB-->>ServiceClient: gRPC response
ServiceClient-->>ServiceA: Return data
```
### Service-to-Service Event Flow
How events flow between services.
```mermaid
graph LR
ServiceA[Service A] -->|Publish| EventBus[Event Bus]
EventBus -->|Route| ServiceB[Service B]
EventBus -->|Route| ServiceC[Service C]
ServiceB -->|Publish| EventBus
EventBus -->|Route| ServiceD[Service D]
ServiceC -->|Publish| EventBus
EventBus -->|Route| ServiceE[Service E]
style EventBus fill:#4a90e2,stroke:#2e5c8a,stroke-width:2px,color:#fff
```
## Data Flow Patterns Summary
### Request Flow Pattern
- **Path**: Client → HTTP → Handler → Service → Repository → Database
- **Response**: Database → Repository → Service → Handler → HTTP → Client
- **Side Effects**: Cache updates, event publishing, audit logging, metrics
### Event Flow Pattern
- **Path**: Publisher → Event Bus → Kafka → Subscribers
- **Characteristics**: Asynchronous, eventual consistency, decoupled
### Cache Flow Pattern
- **Read**: Cache → (miss) → Database → Cache
- **Write**: Service → Database → Cache invalidation
- **Characteristics**: Performance optimization, cache-aside pattern
### Observability Flow Pattern
- **Tracing**: Service → OpenTelemetry → Collector → Backend
- **Metrics**: Service → Metrics Registry → Prometheus → Grafana
- **Logs**: Service → Logger → Collector → Storage
## Integration Points
This data flow patterns document integrates with:
- **[System Behavior Overview](system-behavior.md)**: How data flows fit into system behavior
- **[Service Orchestration](service-orchestration.md)**: How data flows between services
- **[Module Integration Patterns](module-integration-patterns.md)**: How data flows through modules
- **[Operational Scenarios](operational-scenarios.md)**: Data flow in specific scenarios
- **[Component Relationships](component-relationships.md)**: Component-level data flow
## Related Documentation
- [System Behavior Overview](system-behavior.md) - System-level behavior
- [Service Orchestration](service-orchestration.md) - Service coordination
- [Module Integration Patterns](module-integration-patterns.md) - Module integration
- [Operational Scenarios](operational-scenarios.md) - Operational flows
- [Component Relationships](component-relationships.md) - Component dependencies
- [Architecture Overview](architecture.md) - System architecture