Tech Stack

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Fluvius Framework is built on modern Python technologies and best practices.

Core Technologies

Python

Version: Python 3.12+

Why Python 3.12+?

• Modern type hints
• Improved async/await
• Better performance
• Pattern matching support

Features Used:

• Type hints
• Async/await
• Dataclasses
• Enums

FastAPI

Purpose: REST API framework

Why FastAPI?

• High performance
• Automatic API documentation
• Type validation with Pydantic
• Async support

Usage:

from fastapi import FastAPI
from fluvius.fastapi import FluviusFastAPI

app = FluviusFastAPI()

Pydantic

Purpose: Data validation and serialization

Why Pydantic?

• Type validation
• JSON serialization
• Data models
• Settings management

Usage:

from pydantic import BaseModel

class User(BaseModel):
    name: str
    email: str

SQLAlchemy / Alembic

Purpose: Database ORM and migrations

Why SQLAlchemy?

• Database abstraction
• Query builder
• Migration support
• Multiple database support

Usage:

from sqlalchemy import create_engine
from alembic import alembic

Database Technologies

PostgreSQL

Purpose: Primary relational database

Why PostgreSQL?

• ACID compliance
• JSON support
• Full-text search
• Extensions

Usage:

from fluvius.data import PostgreSQLDriver

driver = PostgreSQLDriver(connection_string)

MongoDB

Purpose: Document database support

Why MongoDB?

• Document storage
• Flexible schema
• Horizontal scaling
• JSON-like documents

Usage:

from fluvius.data import MongoDBDriver

driver = MongoDBDriver(connection_string)

SQLite

Purpose: Development and testing

Why SQLite?

• Zero configuration
• File-based
• Fast for development
• Easy testing

Usage:

from fluvius.data import SQLiteDriver

driver = SQLiteDriver(database_path)

Async Technologies

asyncio

Purpose: Async runtime

Why asyncio?

• Native Python async
• High concurrency
• Non-blocking I/O
• Event loop

aiohttp / httpx

Purpose: Async HTTP client

Why aiohttp/httpx?

• Async HTTP requests
• Connection pooling
• Timeout handling
• SSL support

Background Processing

ARQ

Purpose: Redis-based job queue

Why ARQ?

• Async job processing
• Task scheduling
• Retry logic
• Redis backend

Usage:

from fluvius.worker import Worker, task

@task
async def process_payment(payment_id: str):
    # Background processing
    pass

Redis

Purpose: Job queue backend

Why Redis?

• Fast in-memory store
• Pub/sub support
• Job queue
• Caching

Authorization

Casbin

Purpose: Policy-based access control

Why Casbin?

• RBAC support
• ABAC support
• Policy engine
• Multiple backends

Usage:

from fluvius.casbin import CasbinEnforcer

enforcer = CasbinEnforcer()

Testing

pytest

Purpose: Testing framework

Why pytest?

• Simple syntax
• Fixtures
• Parametrization
• Plugins

Usage:

import pytest

@pytest.mark.asyncio
async def test_create_user():
    # Test code
    pass

pytest-asyncio

Purpose: Async test support

Why pytest-asyncio?

• Async test support
• Fixture support
• Event loop management

Development Tools

Ruff

Purpose: Fast Python linter

Why Ruff?

• Fast performance
• Multiple rules
• Auto-fix support
• Modern Python

mypy

Purpose: Static type checking

Why mypy?

• Type checking
• IDE integration
• Error detection
• Type inference

Black (Optional)

Purpose: Code formatter

Why Black?

• Consistent formatting
• Auto-formatting
• PEP 8 compliance

Deployment

Docker

Purpose: Containerization

Why Docker?

• Consistent environments
• Easy deployment
• Isolation
• Scalability

Docker Compose

Purpose: Multi-container orchestration

Why Docker Compose?

• Local development
• Service orchestration
• Environment setup

Kubernetes (Optional)

Purpose: Container orchestration

Why Kubernetes?

• Production deployment
• Auto-scaling
• Service mesh
• Load balancing

Monitoring & Observability

Structured Logging

Purpose: Application logging

Why Structured Logging?

• JSON logs
• Searchable
• Parseable
• Integration with log aggregators

Prometheus (Optional)

Purpose: Metrics collection

Why Prometheus?

• Time-series metrics
• Alerting
• Query language
• Integration

OpenTelemetry (Optional)

Purpose: Distributed tracing

Why OpenTelemetry?

• Request tracing
• Performance monitoring
• Service maps
• Standard protocol

Package Management

pip

Purpose: Package installation

Standard Python package manager

uv (Optional)

Purpose: Fast package manager

Why uv?

• Faster than pip
• Better dependency resolution
• Lock file support

Version Control

Git

Purpose: Version control

Standard version control system

CI/CD

GitHub Actions

Purpose: Continuous integration

Why GitHub Actions?

• Integrated with GitHub
• Easy configuration
• Free for open source

GitLab CI (Optional)

Purpose: Alternative CI/CD

Why GitLab CI?

• Integrated with GitLab
• Flexible pipelines

Technology Choices Summary

Category	Technology	Purpose
Language	Python 3.12+	Core language
Web Framework	FastAPI	REST API
Validation	Pydantic	Data validation
Database ORM	SQLAlchemy	Database abstraction
Migrations	Alembic	Schema migrations
Async Runtime	asyncio	Async support
Job Queue	ARQ	Background jobs
Cache/Queue	Redis	Caching & queues
Authorization	Casbin	Access control
Testing	pytest	Test framework
Linting	Ruff	Code quality
Type Checking	mypy	Static analysis
Containerization	Docker	Deployment
Orchestration	Kubernetes	Production scaling

Compatibility

Python Versions

• Minimum: Python 3.12
• Recommended: Python 3.12+
• Tested: Python 3.12, 3.13

Operating Systems

• Linux: Fully supported
• macOS: Fully supported
• Windows: Supported (with WSL recommended)

Database Versions

• PostgreSQL: 12+
• MongoDB: 5.0+
• SQLite: 3.35+

Next Steps

• Learn about Design Patterns
• Explore Core Components
• Check Getting Started