Storm is an object-relational mapper (ORM) for Python developed at Canonical.
Storm is an Object Relational Mapper for Python developed at Canonical. API docs, a manual, and a tutorial are available from:
The project was in development for more than a year for use in Canonical projects such as Launchpad and Landscape before being released as free software on July 9th, 2007.
- Clean and lightweight API offers a short learning curve and long-term maintainability.
- Storm is developed in a test-driven manner. An untested line of code is considered a bug.
- Storm needs no special class constructors, nor imperative base classes.
- Storm is well designed (different classes have very clear boundaries, with small and clean public APIs).
- Designed from day one to work both with thin relational databases, such as SQLite, and big iron systems like PostgreSQL.
- Storm is easy to debug, since its code is written with a KISS principle, and thus is easy to understand.
- Designed from day one to work both at the low end, with trivial small databases, and the high end, with applications accessing billion row tables and committing to multiple database backends.
- It’s very easy to write and support backends for Storm (current backends have around 100 lines of code).
- Storm is fast.
- Storm lets you efficiently access and update large datasets by allowing you to formulate complex queries spanning multiple tables using Python.
- Storm allows you to fallback to SQL if needed (or if you just prefer), allowing you to mix “old school” code and ORM code
- Storm handles composed primary keys with ease (no need for surrogate keys).
- Storm doesn’t do schema management, and as a result you’re free to manage the schema as wanted, and creating classes that work with Storm is clean and simple.
- Storm works very well connecting to several databases and using the same Python types (or different ones) with all of them.
- Storm can handle obj.attr = <A SQL expression> assignments, when that’s really needed (the expression is executed at INSERT/UPDATE time).
- Storm handles relationships between objects even before they were added to a database.
- Storm works well with existing database schemas.
- Storm will flush changes to the database automatically when needed, so that queries made affect recently modified objects.
Copyright (C) 2006-2020 Canonical, Ltd. All contributions must have copyright assigned to Canonical.
This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
On Ubuntu systems, the complete text of the GNU Lesser General Public Version 2.1 License is in /usr/share/common-licenses/LGPL-2.1
SHORT VERSION: If you are running ubuntu, or probably debian, the following should work. If not, and for reference, the long version is below.
$ dev/ubuntu-deps $ make develop $ make check
The following instructions describe the procedure for setting up a development environment and running the test suite.
The following instructions assume that you’re using Ubuntu. The same procedure will probably work without changes on a Debian system and with minimal changes on a non-Debian-based linux distribution. In order to run the test suite, and exercise all supported backends, you will need to install PostgreSQL, along with the related Python database drivers:
- $ sudo apt-get install
- postgresql pgbouncer build-essential
These will take a few minutes to download (its a bit under 200MB all together).
The Python dependencies for running tests can mostly be installed with apt-get:
- $ apt-get install
- python-fixtures python-psycopg2 python-testresources python-transaction python-twisted python-zope.component python-zope.security
Two modules - pgbouncer and timeline - are not yet packaged in Ubuntu. These can be installed from PyPI:
Alternatively, dependencies can be downloaded as eggs into the current directory with:
$ make develop
This ensures that all dependencies are available, downloading from PyPI as appropriate.
Setting up database users and access security
PostgreSQL needs to be setup to allow TCP/IP connections from localhost. Edit /etc/postgresql/8.3/main/pg_hba.conf and make sure the following line is present:
host all all 127.0.0.1/32 trust
This will probably (with PostgresSQL 8.4) entail changing ‘md5’ to ‘trust’.
In order to run the two-phase commit tests, you will also need to change the max_prepared_transactions value in postgres.conf to something like
max_prepared_transactions = 200
Now save and close, then restart the server:
$ sudo /etc/init.d/postgresql-8.4 restart
Lets create our PostgreSQL user now. As noted in the Ubuntu PostgreSQL documentation, the easiest thing is to create a user with the same name as your username. Run the following command to create a user for yourself (if prompted for a password, leave it blank):
$ sudo -u postgres createuser –superuser $USER
Creating test databases
The test suite needs some local databases in place to exercise PostgreSQL functionality. Run:
$ createdb storm_test
Running the tests
Finally, its time to run the tests! Go into the base directory of the storm branch you want to test, and run:
$ make check
They’ll take a while to run. All tests should pass: failures mean there’s a problem with your environment or a bug in Storm.
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