# virtual424/video-streaming- architecture

> Architecture diagram of the GitHub repository virtual424/video-streaming-, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-06-15). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/virtual424/video-streaming-
- Repository: https://github.com/virtual424/video-streaming-

## Overview

This repository currently presents a backend-oriented video streaming platform rather than a full documented system. From the README, the core stack is Spring Boot for the application service, Kafka for asynchronous event handling, Redis for fast in-memory access, and AWS S3 for durable video asset storage.

The main application boundary is the Spring Boot service. It likely owns the public API surface for video-related actions such as upload, metadata access, playback authorization, and user-facing workflow orchestration.

AWS S3 is the primary media storage layer. Large binary video files are best treated as an external object store boundary, separating heavyweight content from the application runtime and allowing horizontal scaling of the service tier.

Kafka provides the event backbone for decoupled processing. In a system like this, it typically carries events such as upload completion, processing requests, indexing updates, or playback-related notifications, letting expensive work happen asynchronously.

Redis serves as the low-latency data plane. It is usually used here for caching hot metadata, session/state acceleration, playback tokens, or rate/usage counters so the service can respond quickly under load.

A likely high-level flow is: client request enters Spring Boot, metadata/state is checked or written with Redis support, video files are stored in S3, and downstream processing or notifications are emitted to Kafka for asynchronous consumers.

The important architectural boundary is between control plane and media plane. Spring Boot and Redis handle request/response and coordination, while S3 holds the actual media payloads and Kafka handles background propagation of changes.

Scalability is mainly achieved by keeping the application stateless where possible, pushing large objects to S3, offloading background work to Kafka consumers, and using Redis to reduce repeated reads and coordination latency.

No additional project structure is visible in the provided file tree, so this is a stack-level architecture rather than a component-by-component code map. A more detailed diagram would need the source tree to confirm producers, consumers, controllers, storage adapters, and any transcoding or CDN layers.

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_app["Application"]
  node_api["Video API<br/>spring boot service"]
  node_orchestration{{"Workflow<br/>service orchestration"}}
end

subgraph group_infra["Infrastructure"]
  node_redis[("Redis<br/>cache/state")]
  node_kafka["Kafka<br/>event bus"]
  node_s3[("S3 Storage<br/>object store")]
  node_processing{{"Async Work<br/>background processing"}}
end

node_client(("Client<br/>external actor"))

node_client -->|"requests"| node_api
node_api -->|"dispatches"| node_orchestration
node_orchestration -->|"reads/writes"| node_redis
node_orchestration -->|"stores media"| node_s3
node_orchestration -->|"emits events"| node_kafka
node_kafka -->|"feeds"| node_processing
node_processing -->|"updates assets"| node_s3
node_processing -->|"refreshes state"| node_redis
node_api -->|"fast path"| node_redis

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_api,node_orchestration toneBlue
class node_redis,node_kafka,node_s3,node_processing toneAmber
class node_client toneNeutral
```

## Components

### Application

Spring Boot service boundary and its coordination logic

- **Video API**: spring boot service · Public request surface for video upload, metadata, and playback actions
- **Workflow**: service orchestration · Coordinates request handling and backend interactions

### Infrastructure

Shared backing services for storage, caching, and async work

- **Redis**: cache/state · Low-latency cache and fast state store
- **Kafka**: event bus · Asynchronous event backbone for downstream processing
- **S3 Storage**: object store · Durable storage for large video assets
- **Async Work**: background processing · Represents downstream consumers for processing and notification tasks

### Other components

- **Client**: external actor · Uploads, fetches, and triggers video-related actions

## Connections

- Client → Video API: requests · User-facing video operations enter the service
- Video API → Workflow: dispatches · API hands off to internal workflow handling
- Workflow → Redis: reads/writes · Uses cache and fast state for hot metadata and coordination
- Workflow → S3 Storage: stores media · Persists large video objects in durable object storage
- Workflow → Kafka: emits events · Publishes upload and processing events asynchronously
- Kafka → Async Work: feeds · Downstream consumers react to published events
- Async Work → S3 Storage: updates assets · Background work may read or mutate stored media artifacts
- Async Work → Redis: refreshes state · Derived state or counters can be updated after async work
- Video API → Redis: fast path · Some requests may consult cache before heavier backend access