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