Metadata-Version: 2.1
Name: que-py
Version: 1.1.0
Summary: Que: SQL for Sneks 🐍
Home-page: https://github.com/seandstewart/que
Author: Sean Stewart
Author-email: sean_stewart@me.com
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: SQL
Classifier: Topic :: Database
Classifier: Topic :: Database :: Front-Ends
Classifier: Topic :: Software Development :: Code Generators
Classifier: Topic :: Utilities
Requires-Python: >=3.7
Description-Content-Type: text/markdown

Que: SQL for Sneks 🐍
================
[![image](https://img.shields.io/pypi/v/que-py.svg)](https://pypi.org/project/que-py/)
[![image](https://img.shields.io/pypi/l/que-py.svg)](https://pypi.org/project/que-py/)
[![image](https://img.shields.io/pypi/pyversions/que-py.svg)](https://pypi.org/project/que-py/)
[![image](https://img.shields.io/github/languages/code-size/seandstewart/que.svg?style=flat)](https://github.com/seandstewart/que)
[![image](https://img.shields.io/travis/seandstewart/que.svg)](https://travis-ci.org/seandstewart/que)
[![codecov](https://codecov.io/gh/seandstewart/que/branch/master/graph/badge.svg)](https://codecov.io/gh/seandstewart/que)

Que allows you to get generate your SQL queries on the fly, without the
overhead of a fully-fledged ORM.

Motivations
--------
Que was born out of a need for dynamically generated SQL for an ASGI web
service. I found my self wishing for the convenience of dynamic querying
with an ORM such as SQLAlchemy, but the performance of a fully
asynchronous database client. Que attempts to fill this void. Choose the
connection client you prefer and let Que worry about the SQL.


What Is It?
---------
Que looks to solve a single purpose: generate SQL-compliant queries in 
pure-Python. Que has absolutely no hard dependendencies and does not
enforce the use of a specific database client or dialect.

Still want to use SQLAlchemy for your connection? Go for it. Want to use
PyMySQL or psycopg2? Que won't stop you. Want to use an asyncio
framework such as aiopg? You have excellent taste! This library was
written just for you.


Design
-----
The focus of Que is *simplicity*, just look at what it takes for a 
simple `SELECT`:

```python
>>> import que
>>> select = que.Select(table='foo')
>>> select
Select(table='foo', schema=None, filters=FilterList([]), fields=FieldList([]))
>>> sql, args = select.to_sql()
>>> print(sql)
SELECT
  *
FROM
  foo

```

Que works with the DBAPI client of your choice by parametrizing your sql
and formatting your arguments for you:

```python
>>> import que
>>> fields = [que.Field('bar')]
>>> filters = [que.Filter(que.Field('id', 1))]
>>> select = que.Select(table='foo', filters=filters, fields=fields)
>>> sql, args = select.to_sql()
>>> print(sql)
SELECT
  bar
FROM
  foo
WHERE
  id = :1

>>> args
[1]
>>> sql, args = select.to_sql(style=que.NameParamStyle.NAME)
>>> print(sql)
SELECT
  bar
FROM
  foo
WHERE
  id = :id

>>> args
{'id': 1}

```

Que works to normalize the API for your SQL operations, so that 
initializing an `INSERT` or `UPDATE` is functionally the same as
initializing a `SELECT`:

```python
>>> import que
>>> import dataclasses
>>> import datetime
>>>
>>> @dataclasses.dataclass
... class Foo:
...     bar: str
...     id: int = None
...     created: datetime.datetime = None
... 
>>> new_foo = Foo('blah')
>>> fields = que.data_to_fields(new_foo, exclude=None)
>>> insert = que.Insert(table='foo', fields=fields)
>>> sql, args = insert.to_sql(que.NameParamStyle.NAME)
>>> print(sql)
INSERT INTO
  foo (:colbar)
VALUES
  (:valbar)

>>> args
{'colbar': 'bar', 'valbar': 'blah'}

```

QuickStart
--------
Que has no dependencies and is exceptionally light-weight (currently
only ~30Kb!), comprising of only a few hundred lines of code.
Installation is as simple as `pip3 install que-py`.

Then you're good to go! `import que` and rock on 🤘


Examples
-------
A simple client for generating your SQL and inserting new entries:
```python
import dataclasses
import sqlite3

import que

@dataclasses.dataclass
class Spam:
    flavor: str
    id: int = None
    created_on: int = None


class SpamClient:
    """A database client for tracking spam flavors."""

    def __init__(self):
        self.conn = sqlite3.connect('sqlite://spam.db')

    def insert_spam(self, spam: Spam):
        fields = que.data_to_fields(spam, exclude=None)
        insert = que.Insert('spam', fields=fields)
        sql, args = insert.to_sql()
        return self.conn.execute(sql, args)

    def get_spam(self, **kwargs):
        fields = que.data_to_fields(kwargs)
        filters = [que.Filter(x) for x in fields]
        select = que.Select('spam', filters=filters)
        return self.conn.execute(*select.to_sql())

    def update_spam(self, spam: Spam):
        fields = [que.Field('flavor', spam.flavor)]
        filters = [que.Filter(que.Field('id', spam.id))]
        update = que.Update('spam', filters=filters, fields=fields)
        return self.conn.execute(*update.to_sql())

    def delete_spam(self, spam: Spam):
        filters = [que.Filter(que.Field('id', spam.id))]
        delete = que.Delete('spam', filters=filters)
        return self.conn.execute(*delete.to_sql())
```

Documentation
----------
Full documentation coming soon!

Happy Querying 🐍


How to Contribute
-----------------
1.  Check for open issues or open a fresh issue to start a discussion
    around a feature idea or a bug. 
2.  Create a branch on Github for your issue or fork 
    [the repository](https://github.com/seandstewart/que) on GitHub to
    start making your changes to the **master** branch.
3.  Write a test which shows that the bug was fixed or that the feature
    works as expected.
4.  Send a pull request and bug the maintainer until it gets merged and
    published. :)



