databank 0.1.2

Databank is an easy-to-use Python library for making raw SQL queries in a multi-threaded environment.

Databank

Databank is an easy-to-use Python library for making raw SQL queries in a multi-threaded environment.

No ORM, no frills. Only raw SQL queries and parameter binding. Thread-safe. Built on top of SQLAlchemy.

Installation

You can install the latest stable version from PyPI:

$ pip install databank

Adapters not included. Install e.g. psycopg2 for PostgreSQL:

$ pip install psycopg2

Usage

Connect to the database of your choice:

>>> from databank import Database
>>> db = Database("postgresql://user:password@localhost/db", pool_size=2)

The keyword arguments are passed directly to SQLAlchemy's create_engine() function. Depending on the database you connect to, you have options like the size of connection pools.

If you are using databank in a multi-threaded environment (e.g. in a web application), make sure the pool size is at least the number of threads.

Let's create a simple table:

>>> db.execute("CREATE TABLE beatles (id SERIAL PRIMARY KEY, member TEXT NOT NULL);")

You can insert multiple rows at once:

>>> params = [
...     {"id": 0, "member": "John"},
...     {"id": 1, "member": "Paul"},
...     {"id": 2, "member": "George"},
...     {"id": 3, "member": "Ringo"}
... ]
>>> db.execute_many("INSERT INTO beatles (id, member) VALUES (:id, :member);", params)

Fetch a single row:

>>> db.fetch_one("SELECT * FROM beatles;")
{'id': 0, 'member': 'John'}

But you can also fetch n rows:

>>> db.fetch_many("SELECT * FROM beatles;", n=2)
[{'id': 0, 'member': 'John'}, {'id': 1, 'member': 'Paul'}]

Or all rows:

>>> db.fetch_all("SELECT * FROM beatles;")
[{'id': 0, 'member': 'John'},
 {'id': 1, 'member': 'Paul'},
 {'id': 2, 'member': 'George'},
 {'id': 3, 'member': 'Ringo'}]