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111 lines
5.3 KiB
Markdown
111 lines
5.3 KiB
Markdown
---
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sidebar_position: 1
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---
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# Architecture
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Immich uses a traditional client-server design, with a dedicated database for data persistence. The frontend clients communicate with backend services over HTTP using REST APIs.
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## High Level Diagram
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The diagram shows clients communicating with the server via REST, as well as the flow of database between backend services.
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## Clients
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Immich has three main clients:
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1. Mobile app - Android, iOS
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2. Web app - Responsive website
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3. CLI - Command-line utility for bulk upload
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:::info
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All three clients use [OpenAPI](./open-api.md) to auto-generate rest clients for easy integration. For more information about this process, see [OpenAPI](./open-api.md).
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:::
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### Mobile App
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The mobile app is written in [Flutter](https://flutter.dev/). It uses [Isar Database](https://isar.dev/) for a local database and [Riverpod](https://riverpod.dev/) for state management.
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### Web Client
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The web app is a [TypeScript](https://www.typescriptlang.org/) project that uses [SvelteKit](https://kit.svelte.dev) and [Tailwindcss](https://tailwindcss.com/).
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### CLI
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The CLI is a [TypeScript](https://www.typescriptlang.org/) project that parses command line arguments to programmatically upload/import assets to an Immich server. See [Bulk Upload](/docs/features/bulk-upload.md) for more information about its usage.
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## Server
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The Immich backend is divided into several services, which are run as individual docker containers.
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1. `immich-server` - Handle and respond to REST API requests
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1. `immich-microservices` - Execute background jobs (thumbnail generation, metadata extraction, transcoding, etc.)
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1. `immich-machine-learning` - Execute machine learning models
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1. `postgres` - Persistent data storage
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1. `redis`- Queue management for `immich-microservices`
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1. `typesense`- Specialized database for search, specifically with vector comparison features
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### Immich Server
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The Immich Server is a [TypeScript](https://www.typescriptlang.org/) project written for [Node.js](https://nodejs.org/). It uses the [Nest.js](https://nestjs.com) framework, with [TypeORM](https://typeorm.io/) for database management. The server codebase also loosely follows the [Hexagonal Architecture](<https://en.wikipedia.org/wiki/Hexagonal_architecture_(software)>). Specifically, we aim to separate technology specific implementations (`infra/`) from core business logic (`domain/`).
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#### REST Endpoints
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The server is a list of HTTP endpoints and associated handlers (controllers). Each controller usually implements the following CRUD operations:
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- `POST` `/<type>` - **Create**
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- `GET` `/<type>` - **Read** (all)
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- `GET` `/<type>/:id` - **Read** (by id)
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- `PUT` `/<type>/:id` - **Updated** (by id)
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- `DELETE` `/<type>/:id` - **Delete** (by id)
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#### DTOs
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The server uses [Domain Transfer Objects](https://en.wikipedia.org/wiki/Data_transfer_object) as public interfaces for the inputs (query, params, and body) and outputs (response) for each endpoint. DTOs translate to [OpenAPI](./open-api.md) schemas and control the generated code used by each client.
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### Microservices
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The Immich Microservices image uses the same `Dockerfile` as the Immich Server, but with a different entrypoint. The Immich Microservices service mainly handles executing jobs, which include the following:
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- Thumbnail Generation
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- Metadata Extraction
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- Video Transcoding
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- Object Tagging
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- Facial Recognition
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- Storage Template Migration
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- Search (Typesense synchronization)
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- Sidecar (see [XMP Sidecars](/docs/features/xmp-sidecars.md))
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- Background jobs (file deletion, user deletion)
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:::info
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This list closely matches what is available on the [Administration > Jobs](/docs/administration/jobs.md) page, which provides some remote queue management capabilities.
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:::
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### Machine Learning
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The machine learning service is written in [Python](https://www.python.org/) and uses [FastAPI](https://fastapi.tiangolo.com/) for HTTP communication.
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All machine learning related operations have been externalized to this service, `immich-machine-learning`. Python is a natural choice for AI and machine learning. It also has some pretty specific hardware requirements. Running it as a separate container makes it possible to run the container on a separate machine, or easily disable it entirely.
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Machine learning models are also quite _large_, requiring _quite a bit_ of memory. We are always looking for ways to improve and optimize this aspect of this container specifically.
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### Postgres
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Immich persists data in Postgres, which includes information about access and authorization, users, albums, asset, sharing settings, etc.
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:::info
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See [Database Migrations](./database-migrations.md) for more information about how to modify the database to create an index, modify a table, add a new column, etc.
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:::
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### Redis
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Immich uses [Redis](https://redis.com/) via [BullMQ](https://docs.bullmq.io/) to manage job queues. Some jobs trigger subsequent jobs. For example, object detection relies on thumbnail generation and automatically run after one is generated.
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### Typesense
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Immich synchronizes some of the Postgres data into Typesense, so it can execute vector related queries in order to implement certain features including, facial recognition and CLIP search.
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<!-- - [NGINX](https://www.nginx.com/) for internal communication between containers and load balancing when scaling. -->
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