What is lifter?

Lifter is a generic query engine, inspired by Django ORM and SQLAlchemy.

Its goal is to provide a unique interface to query any type of data, regardless of the underlying query language or data backend (SQL, REST, Python objects...).

Warning: This package is still in alpha state and a lot of work is still needed to make queries faster and efficient. Contributions are welcome :)

Useful links:

• Documentation is available at http://lifter.readthedocs.org
• Ask your programming questions on Stack Overflow using the tag python-lifter

Features

• Queryset API similar to Django and SQLAlchemy
• Lazy querysets
• Composable queries
• Lightweight: very little dependencies
• Tested and working on Python 2.7 to Python 3.5

Example usage

Consider the following list of users, returned from a REST API endpoint:

users = [
    {
        "is_active": True,
        "age": 35,
        "eye_color": "brown",
        "name": "Bernard",
        "gender": "male",
        "email": "[email protected]",
    },
    {
        "is_active": True,
        "age": 34,
        "eye_color": "brown",
        "name": "Manny",
        "gender": "male",
        "email": "[email protected]",
    },
    {
        "is_active": True,
        "age": 35,
        "eye_color": "brown",
        "name": "Fran",
        "gender": "female",
        "email": "[email protected]",
    },
    # And so on ...
]

Now, imagine you have to extract data from this list. Let's compare how you can do this using regular Python and lifter.

To use lifter in your project, you'll only need the following instructions:

import lifter.models
from lifter.backends.python import IterableStore

class User(lifter.models.Model):
    pass

store = IterableStore(users)
manager = store.query(User)

Getting all active 26 years old users:

# vanilla Python
results = [
    user for user in users
    if user['age'] == 26 and user['is_active']
]

# lifter
results = manager.filter(User.age == 26, User.is_active == True)

Getting names and emails of inactive users under 56:

# vanilla Python
results = [
    (user['name'], user['email']) for user in users
    if not user['is_active'] and user['age'] < 56
]

# lifter
results = manager.filter(User.is_active == False, User.age < 56)\
                 .values_list('name', 'email')

Getting all active users except the one with brown eyes and sort them by age:

# vanilla Python
raw_results = [
    user for user in users
    if user['is_active'] and not user['eye_color'] == 'brown'
]
results = sorted(raw_results, key=lambda v: v['age'])

# lifter
results = manager.filter(User.is_active == True)\
                 .exclude(User.eye_color == 'brown')\
                 .order_by('age')

Getting minimum and average women age:

# vanilla Python
from statistics import mean # Only in Python >=3.4
women_ages = [
    user['age'] for user in users
    if user['gender'] == 'female'
]
women_average_age = mean(women_ages)
minimum_woman_age = min(women_ages)

# lifter
results = manager.filter(User.gender == 'female')\
                 .aggregate((User.age, mean), (User.age, min))

As you can see, lifter's version is shorter and more readable than vanilla Python. It's also less error prone, especially if you're writing really complex queries, and quite familiar if you've already used an ORM.

Wanna know more? Have a look at the documentation!