Skip to main content

Asynchronous library for building and managing a hybrid database, by scheme of key-value.

Project description

Logo

Scruby (small shrub)

Asynchronous library for building and managing a hybrid database,
by scheme of key-value.

Build Status Docs PyPI pyversions PyPI status PyPI version fury.io
Types: Pyrefly Code style: Ruff Format PyPI Downloads License: MIT License: GPL v3

The library uses fractal-tree addressing and
the search for documents based on the effect of a quantum loop.
The database consists of collections.
The maximum size of the one collection is 16**8=4294967296 branches,
each branch can store one or more keys.
The value of any key in collection can be obtained maximum in 8 steps,
thereby achieving high performance.
The effectiveness of the search for documents based on a quantum loop,
requires a large number of processor threads.


List of plugins

Documentation

Requirements

Installation

uv add scruby

Run

# Run Development:
uv run python main.py
# Run Production:
uv run python -OOP main.py

Usage

Examples

"""Working with keys."""

import anyio
from datetime import datetime
from zoneinfo import ZoneInfo
from typing import Annotated
from pydantic import EmailStr, Field
from pydantic_extra_types.phone_numbers import PhoneNumber, PhoneNumberValidator
from scruby import Scruby, ScrubyModel, ScrubySettings

ScrubySettings.db_root = "ScrubyDB"  # By default = "ScrubyDB"
ScrubySettings.hash_reduce_left = 6  # By default = 6
ScrubySettings.max_workers = None  # By default = None
ScrubySettings.plugins = []  # By default = []

class User(ScrubyModel):
    """User model."""
    first_name: str = Field(strict=True)
    last_name: str = Field(strict=True)
    birthday: datetime = Field(strict=True)
    email: EmailStr = Field(strict=True)
    phone: Annotated[PhoneNumber, PhoneNumberValidator(number_format="E164")] = Field(frozen=True)
    # key is always at bottom
    key: str = Field(
        strict=True,
        frozen=True,
        default_factory=lambda data: data["phone"],
    )


async def main() -> None:
    """Example."""
    # Get collection `User`.
    user_coll = await Scruby.collection(User)

    user = User(
        first_name="John",
        last_name="Smith",
        birthday=datetime(1970, 1, 1, tzinfo=ZoneInfo("UTC")),
        email="John_Smith@gmail.com",
        phone="+447986123456",
    )

    await user_coll.add_doc(user)

    await user_coll.update_doc(user)

    await user_coll.get_doc("+447986123456")  # => user
    await user_coll.get_doc("key missing")  # => KeyError

    await user_coll.has_key("+447986123456")  # => True
    await user_coll.has_key("key missing")  # => False

    await user_coll.delete_doc("+447986123456")
    await user_coll.delete_doc("+447986123456")  # => KeyError
    await user_coll.delete_doc("key missing")  # => KeyError

    # Full database deletion.
    # Hint: The main purpose is tests.
    Scruby.napalm()


if __name__ == "__main__":
    anyio.run(main)
"""Find one document matching the filter.

The search is based on the effect of a quantum loop.
The search effectiveness depends on the number of processor threads.
"""

import anyio
from pydantic import Field
from scruby import Scruby, ScrubyModel, ScrubySettings
from pprint import pprint as pp

ScrubySettings.db_root = "ScrubyDB"  # By default = "ScrubyDB"
ScrubySettings.hash_reduce_left = 6  # By default = 6
ScrubySettings.max_workers = None  # By default = None
ScrubySettings.plugins = []  # By default = []


class Phone(ScrubyModel):
    """Phone model."""
    brand: str = Field(strict=True, frozen=True)
    model: str = Field(strict=True, frozen=True)
    screen_diagonal: float = Field(strict=True)
    matrix_type: str = Field(strict=True)
    # key is always at bottom
    key: str = Field(
        strict=True,
        frozen=True,
        default_factory=lambda data: f"{data['brand']}:{data['model']}",
    )


async def main() -> None:
    """Example."""
    # Get collection `Phone`.
    phone_coll = await Scruby.collection(Phone)

    # Create phone.
    phone = Phone(
        brand="Samsung",
        model="Galaxy A26",
        screen_diagonal=6.7,
        matrix_type="Super AMOLED",
    )

    # Add phone to collection.
    await phone_coll.add_doc(phone)

    # Find phone by brand.
    phone_details: Phone | None = await phone_coll.find_one(
        filter_fn=lambda doc: doc.brand == "Samsung",
    )
    if phone_details is not None:
        pp(phone_details)
    else:
        print("No Phone!")

    # Find phone by model.
    phone_details: Phone | None = await phone_coll.find_one(
        filter_fn=lambda doc: doc.model == "Galaxy A26",
    )
    if phone_details is not None:
        pp(phone_details)
    else:
        print("No Phone!")

    # Full database deletion.
    # Hint: The main purpose is tests.
    Scruby.napalm()


if __name__ == "__main__":
    anyio.run(main)
"""Find many documents matching the filter.

The search is based on the effect of a quantum loop.
The search effectiveness depends on the number of processor threads.
"""

import anyio
from pydantic import Field
from scruby import Scruby, ScrubyModel, ScrubySettings
from pprint import pprint as pp

ScrubySettings.db_root = "ScrubyDB"  # By default = "ScrubyDB"
ScrubySettings.hash_reduce_left = 6  # By default = 6
ScrubySettings.max_workers = None  # By default = None
ScrubySettings.plugins = []  # By default = []

class Car(ScrubyModel):
    """Car model."""
    brand: str = Field(strict=True, frozen=True)
    model: str = Field(strict=True, frozen=True)
    year: int = Field(strict=True)
    power_reserve: int = Field(strict=True)
    # key is always at bottom
    key: str = Field(
        strict=True,
        frozen=True,
        default_factory=lambda data: f"{data['brand']}:{data['model']}",
    )


async def main() -> None:
    """Example."""
    # Get collection `Car`.
    car_coll = await Scruby.collection(Car)

    # Create cars.
    for num in range(1, 10):
        car = Car(
            brand="Mazda",
            model=f"EZ-6 {num}",
            year=2025,
            power_reserve=600,
        )
        await car_coll.add_doc(car)

    # Find cars by brand and year.
    car_list: list[Car] | None = await car_coll.find_many(
        filter_fn=lambda doc: doc.brand == "Mazda" and doc.year == 2025,
    )
    if car_list is not None:
        pp(car_list)
    else:
        print("No cars!")

    # Find all cars.
    car_list: list[Car] | None = await car_coll.find_many()
    if car_list is not None:
        pp(car_list)
    else:
        print("No cars!")

    # For pagination output.
    car_list: list[Car] | None = await car_coll.find_many(
        filter_fn=lambda doc: doc.brand == "Mazda",
        limit_docs=5,
        page_number=2,
    )
    if car_list is not None:
        pp(car_list)
    else:
        print("No cars!")

    # Full database deletion.
    # Hint: The main purpose is tests.
    Scruby.napalm()


if __name__ == "__main__":
    anyio.run(main)

Changelog

MIT

GPL-3.0

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

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

scruby-0.35.7-py3-none-any.whl (33.9 kB view details)

Uploaded Python 3

File details

Details for the file scruby-0.35.7-py3-none-any.whl.

File metadata

  • Download URL: scruby-0.35.7-py3-none-any.whl
  • Upload date:
  • Size: 33.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.9.24 {"installer":{"name":"uv","version":"0.9.24","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Fedora Linux","version":"42","id":"","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}

File hashes

Hashes for scruby-0.35.7-py3-none-any.whl
Algorithm Hash digest
SHA256 770b2b76ebf7f1ea42e43d38d60393a8df6cb1a085332afc2d25474ab5ced658
MD5 f7d46a25c7c0159498bfa44b30fd957a
BLAKE2b-256 41258867c085c0df066673846d14a0179110b483219384761f6cf456fdf4b1ee

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