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.

(The photo was taken by Matthew Ratzloff and is licensed under CC BY-NC-ND 2.0.)

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 worker 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'}]

If you are using PostgreSQL with jsonb columns, you can use a helper function to serialize the parameter values:

>>> from databank.utils import serialize_params
>>> serialize_params({"member": "Ringo", "song": ["Don't Pass Me By", "Octopus's Garden"]})
{'member': 'Ringo', 'song': '["Don\'t Pass Me By", "Octopus\'s Garden"]'}

Executing Queries in the Background

For both execute() and execute_many() you can pass an in_background keyword argument (which is by default False). If set to True, the query will be executed in the background in another thread and the method will return immediately the Thread object (i.e. non-blocking). You can call join() on that object to wait for the query to finish or just do nothing and go on:

>>> db.execute("INSERT INTO beatles (id, member) VALUES (:id, :member);", {"id": 4, "member": "Klaus"}, in_background=True)
<Thread(Thread-1 (_execute), started 140067398776512)>

Beware that if you are using in_background=True, you have to make sure that the connection pool size is large enough to handle the number of concurrent queries and that your program is running long enough if you are not explicitly waiting for the thread to finish. Also note that this might lead to a range of other issues like locking, reduced performance or even deadlocks. You also might want to set an explicit timeout for queries by passing e.g. {"options": "-c statement_timeout=60000"} for PostgreSQL when initializing the Database object to kill all queries taking longer than 60 seconds.

Query Collection

You can also organize SQL queries in an SQL file and load them into a QueryCollection:

/* @name insert_data */
INSERT INTO beatles (id, member) VALUES (:id, :member);

/* @name select_all_data */
SELECT * FROM beatles;

This idea is borrowed from PgTyped

A query must have a header comment with the name of the query. If a query name is not unique, the last query with the same name will be used. You can parse that file and load the queries into a QueryCollection:

>>> from databank import QueryCollection
>>> queries = QueryCollection.from_file("queries.sql")

and access the queries like in a dictionary:

>>> queries["insert_data"]
'INSERT INTO beatles (id, member) VALUES (:id, :member);'
>>> queries["select_all_data"]
'SELECT * FROM beatles;'