Fast, lightweight Python database toolkit for SQLite, built with Cython.
Switch branches/tags
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.
_pysqlite 0.2.0, complete rewrite. Feb 22, 2017
sweepea.pyx Add travis config, small nogil tweaks Jul 17, 2018


Fast, lightweight Python database toolkit for SQLite, built with Cython.

Like it's cousin peewee <>_, swee'pea is comprised of a database connection abstraction and query-building / execution APIs. This project is a pet project of mine, so tailor expectations accordingly.


  • Implemented in Cython for performance and to expose advanced features of the SQLite database library.
  • Composable and consistent APIs for building queries using Python.
  • Layered APIs allow you to work as close to the database as you want.
  • No magic.
  • No bullshit.

Issue tracker and code are hosted on GitHub:

Documentation hosted on RTFD:



This project is designed to work with the standard library sqlite3 driver, or alternatively, the latest version of pysqlite2.


First install Cython and ensure that you have a SQLite library (standard library sqlite3 or pysqlite <>_). Then:

$ pip install sweepea


$ pip install -e git+

Database Helper

Dynamic Tables

SQLite makes it easy to define scalar and aggregate functions, but it is more challenging to create functions that return multiple values. Scalar functions accept zero or more parameters and return a single value. Aggregate functions accept parameters from any number of input rows, and then generate a final scalar value.

To create functions that return multiple values, it is necessary to create a virtual table. SQLite has the concept of "eponymous" virtual tables, which are virtual tables that can be called like a function and do not require explicit creation using DDL statements.

The vtfunc module abstracts away the complexity of creating an eponymous virtual table, allowing you to write your own multi-value SQLite functions in Python.


Suppose we want to create a function that, given a regular expression and an input string, returns all matching subgroups in the input string. For instance, if our regex was '[0-9]+' and our input string was '123 xxx 456 yyy 789 zzz 0', the function should return four rows:

  • 123
  • 456
  • 789
  • 0

With the vtab module it is very easy to implement this:

import re

from vtfunc import TableFunction

class RegexSearch(TableFunction):
    params = ['regex', 'search_string']
    columns = ['match']
    name = 'regex_search'

    def initialize(self, regex=None, search_string=None):
        self._iter = re.finditer(regex, search_string)

    def iterate(self, idx):
        # We do not need `idx`, so just ignore it.
        return (next(self._iter).group(0),)

To use our function, we need to register the module with a SQLite connection, then call it using a SELECT query:

import sqlite3

conn = sqlite3.connect(':memory:')  # Create an in-memory database.

RegexSearch.register(conn)  # Register our module.

query_params = ('[0-9]+', '123 xxx 456 yyy 789 zzz 0')
cursor = conn.execute('SELECT * FROM regex_search(?, ?);', query_params)
print cursor.fetchall()

Let's say we have a table that contains a list of arbitrary messages and we want to capture all the e-mail addresses from that table. This is also easy using our table-valued function. We will query the messages table and pass the message body into our table-valued function. Then, for each email address we find, we'll return a row containing the message ID and the matching email address:

email_regex = '[\w]+@[\w]+\.[\w]{2,3}'  # Stupid simple email regex.
query = ('SELECT, regex_search.match '
         'FROM messages, regex_search(?, messages.body)')
cursor = conn.execute(query, (email_regex,))

The resulting rows will look something like:

message id |         email
     1     |
     1     |
     1     |
     3     |
     3     |
    ...    |         ...

Important note

In the above example you will note that the parameters for our query actually change (because each row in the messages table has a different search string). This means that for this particular query, the RegexSearch.initialize() function will be called once for each row in the messages table.

How it works

Behind-the-scenes, vtfunc is creating a Virtual Table and filling in the various callbacks with wrappers around your user-defined function. There are two important methods that the wrapped virtual table implements:

  • xBestIndex
  • xFilter

When SQLite attempts to execute a query, it will call the xBestIndex method of the virtual table (possibly multiple times) trying to come up with the best query plan. The vtfunc module optimizes for those query plans which include values for all the parameters of the user-defined function. Since some user-defined functions may have optional parameters, query plans with only a subset of param values will be slightly penalized.

Since we have no visibility into what parameters the user actually passed in, and we don't know ahead of time which query plan SQLite suggests will be best, vtfunc just does its best to optimize for plans with the highest number of usable parameter values.

If you encounter a situation where you pass your function multiple parameters, but it doesn't receive all of them, it's the case that a less-than-optimal plan was used.

After the plan is chosen by calling xBestIndex, the query will execute by calling xFilter (possibly multiple times). xFilter has access to the actual query parameters, and it's responsibility is to initialize the cursor and call the user's initialize() callback with the parameters passed in.

Query Builder

The query builder is designed to allow users to construct queries with reusable Python objects. Instead of working with string query fragments, you can construct queries using more Pythonic components which are then compiled into SQL.

The query-builder is designed with consistency and composability as the primary goals. Consistency enables one to learn once, then apply everywhere, while composability ensures that large systems can be built one piece at a time, from the bottom-up.

Example of constructing a simple query:

db = Database('app.db')

Employee = Table('employees', ('id', 'name', 'start_date', 'manager_id'))

# Get list of employees and their manager's name, sorted by tenure.
Manager = Employee.alias('manager')
query = (Employee
         .join(Manager, JOIN.LEFT_OUTER, on=(Employee.manager_id ==

for row in query.execute(db):
    print row['name'], row['start_date'], (row['manager_name'] or 'no mgr')

Unlike an ORM, the query builder has no opinions on your data-model, nor does it encourage inefficiency (e.g. the n+1 problem). The query builder can be integrated into an already-running system with hardly any code needing to be written.