Skip to main content

A collection of reusable utilities across projects I work on.

Project description

Jays Tools

Utilities for data persistence and application architecture that I use in my projects.

Quick Start

pip install jays-tools

Overview

Need Tool
App config/state JsonDatabase — Single-entity JSON storage with validation
Multiple entities JsonCollection — File-based distributed storage
Complex queries SQLDatabase — Type-safe SQLite with filtering
Code structure Architecture Framework — Five disciplined layers
Service lifecycle Service Management — Startup/shutdown coordination

Read PHILOSOPHY.md to understand the design reasoning.

Features

JsonDatabase

Single-entity JSON storage with Pydantic validation and automatic migrations.

from jays_tools.json_database import JsonDatabase
from jays_tools.json_database.models import MigratableModel

class AppSettings(MigratableModel):
    debug: bool = False

db = JsonDatabase("settings.json", AppSettings)
settings = db.get_database()
settings.debug = True
db.update_database(settings)

JsonCollection

Directory-based entity collections for distributed file storage and faster reads.

from jays_tools.json_collection import JsonCollection
from jays_tools.json_database.models import MigratableModel

class User(MigratableModel):
    name: str = ""
    email: str = ""

collection = JsonCollection("data/users", model=User)
collection.update("user_123", User(name="Alice", email="alice@example.com"))

SQLDatabase

Async SQLite database with type-safe row models and automatic migrations.

from jays_tools.sql_database import SQLDatabase, EqualTo

db = SQLDatabase("app.db", [User])
await db.initialize()
await db.insert(User, User(name="Alice", email="alice@example.com"))
result = await db.select_one(User, filter=EqualTo("email", "alice@example.com"))

Clean Architecture Framework

Five disciplined layers with type-safe dependency injection. Separate concerns into:

  • Service: Business logic (validation, calculations)
  • Repository: Data access (I/O, persistence)
  • Adapter: External integrations (APIs)
  • DomainUseCase: Workflow orchestration
  • UseCase: Entry-point formatting (HTTP, CLI)
from jays_tools.architecture import Service, Repository, DomainUseCase

class CreateUserUseCase(DomainUseCase):
    async def execute(self, name: str, email: str):
        if not self.services["validation"].validate_email(email):
            raise ValueError("Invalid email")
        await self.repos["users"].save_user(name, email)
        return {"name": name, "email": email}

Service Management

Lifecycle management for long-running services with coordinated startup and shutdown.

Structure:

server/
├── __init__.py
└── service.py

main.py

In server/service.py:

from jays_tools.services import Service, ReadinessSignal

def main(readiness_signal: ReadinessSignal):
    print("Starting server...")
    readiness_signal.set()

def shutdown():
    print("Shutting down server...")

def ServerService() -> Service:
    return Service(
        name="ServerService",
        start=main,
        stop=shutdown,
    )

In main.py:

from jays_tools.services import start_services, join_services, stop_services
from server.service import ServerService

if __name__ == "__main__":
    services = [ServerService()]
    start_services(services)
    join_services(services)
    stop_services(services)

Core Principles

Type Safety: All utilities leverage Pydantic for validation. Your editor knows what fields exist and their types.

Automatic Migrations: Update schemas without migration scripts. Old data automatically transforms to new versions.

Async-First: All I/O operations are async-first for non-blocking execution.

Minimal APIs: Simple, predictable interfaces. No complex query languages or hidden magic.

Documentation

Common Patterns

Layered Architecture

Organize code with clear separation: Repositories handle I/O, Services handle logic, UseCases coordinate.

# Repository: I/O
class FileRepository(Repository):
    async def read_csv(self, path: str) -> str:
        with open(path) as f:
            return f.read()

# Service: Logic
class CSVParsingService(Service):
    def parse_csv(self, raw: str) -> list[dict]:
        lines = raw.strip().split('\n')
        headers = lines[0].split(',')
        return [dict(zip(headers, line.split(','))) for line in lines[1:]]

# DomainUseCase: Orchestration
class ImportUseCase(DomainUseCase):
    async def execute(self, path: str):
        raw = await self.repos["files"].read_csv(path)
        parsed = self.services["parsing"].parse_csv(raw)

Database Queries

Use filter objects to query SQLDatabase:

from jays_tools.sql_database import EqualTo, Like, GreaterThan

# Simple filters
user = await db.select_one(User, filter=EqualTo("email", "alice@example.com"))
users = await db.select_all(User, filter=Like("name", "Al%"))

# Combined filters
active_users = await db.select_all(
    User, 
    filter=EqualTo("status", "active") & GreaterThan("user_id", 100)
)

Testing

Each layer is independently testable without complex mocks:

# Service test (pure function)
def test_validate_email():
    service = ValidationService()
    assert service.validate_email("test@example.com")

# UseCase integration test
@pytest.mark.asyncio
async def test_create_user_flow():
    usecase = CreateUserUseCase.init()
    result = await usecase.execute("Alice", "alice@example.com")
    assert result["email"] == "alice@example.com"

Project Structure Example

my_app/
├── src/
│   ├── models/
│   │   ├── domain.py        # DomainModel, AggregateRoot
│   │   ├── requests.py      # RequestDTO
│   │   ├── responses.py     # ResponseDTO
│   │   └── database.py      # AdapterModel, MigratableRow
│   ├── services/
│   │   └── user.py          # UserService
│   ├── repositories/
│   │   └── user.py          # UserRepository
│   ├── adapters/
│   │   └── payment.py       # PaymentAdapter
│   ├── domain_usecases/
│   │   └── create_user.py   # CreateUserUseCase
│   ├── usecases/
│   │   └── http/
│   │       └── create_user.py  # HTTP entry point
│   └── main.py
└── tests/
    ├── test_services.py
    ├── test_repositories.py
    ├── test_usecases.py
    └── test_integration.py

License

MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

jays_tools-3.2.5.tar.gz (42.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

jays_tools-3.2.5-py3-none-any.whl (23.7 kB view details)

Uploaded Python 3

File details

Details for the file jays_tools-3.2.5.tar.gz.

File metadata

  • Download URL: jays_tools-3.2.5.tar.gz
  • Upload date:
  • Size: 42.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.13

File hashes

Hashes for jays_tools-3.2.5.tar.gz
Algorithm Hash digest
SHA256 6273fd62e141bc0777758ed7207106029a70681bc28f1fa93a892fd96adf96e9
MD5 3d0eefe46d1a0ee0884cd253d2936eb2
BLAKE2b-256 7d9b0a0604636b20e97df4545a3bfdd2cdf9342391a035ee8d8951266711c8c1

See more details on using hashes here.

File details

Details for the file jays_tools-3.2.5-py3-none-any.whl.

File metadata

  • Download URL: jays_tools-3.2.5-py3-none-any.whl
  • Upload date:
  • Size: 23.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.13

File hashes

Hashes for jays_tools-3.2.5-py3-none-any.whl
Algorithm Hash digest
SHA256 69c200c4c6d77c6fd7a09f2f804655ded967b061b17586281806e064a787ed20
MD5 348453811e6501d92e1f143445ce0df9
BLAKE2b-256 1b1702b92d5e8e6fd3834177ca9ed6b877296d7f322016850816bb61cbf5cd64

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page