What Are Some Good Python Orm Solutions

What are some good Python ORM solutions?

SQLAlchemy is more full-featured and powerful (uses the DataMapper pattern). Django ORM has a cleaner syntax and is easier to write for (ActiveRecord pattern). I don't know about performance differences.

SQLAlchemy also has a declarative layer that hides some complexity and gives it a ActiveRecord-style syntax more similar to the Django ORM.

I wouldn't worry about Django being "too heavy." It's decoupled enough that you can use the ORM if you want without having to import the rest.

That said, if I were already using CherryPy for the web layer and just needed an ORM, I'd probably opt for SQLAlchemy.

Architecting from scratch in Python: what to use?

Frameworks

OK, so I'm a little biased here as I currently make extensive use of Django and organise the Django User Group in London so bear that in mind when reading the following.

Start with Django because it's a great gateway drug. Lots of documentation and literature, a very active community of people to talk to and lots of example code around the web.

That's a completely non-technical reason. Pylons is probably purer in terms of Python philosophy (being much more a collection of discrete bits and pieces) but lots of the technical stuff is personal preference, at least until you get into Python more. Compare the very active Django tag on Stack Overflow with that of pylons or turbogears though and I'd argue getting started is simply easier with Django irrespective of anything to do with code.

Personally I default to Django, but find that an increasing amount of time I actually opt for writing using simpler micro frameworks (think Sinatra rather than Rails). Lots of things to choose from (good list here, http://fewagainstmany.com/blog/python-micro-frameworks-are-all-the-rage). I tend to use MNML (because I wrote parts of it and it's tiny) but others are actively developed. I tend to do this for small, stupid web services which are then strung together with a Django project in the middle serving people.

Worth noting here is appengine. You have to work within it's limitations and it's not designed for everything but it's a great way to just play with Python and get something up and working quickly. It makes a great testbed for learning and experimentation.

Mongo/ORM

On the MongoDB front you'll likely want to look at the basic python mongo library ( http://api.mongodb.org/python/ ) first to see if it has everything you need. If you really do want something a little more ORM like then mongoengine (http://hmarr.com/mongoengine/) might be what you're looking for. A bunch of folks are also working on making Django specifically integrate more seamlessly with nosql backends. Some of that is for future Django releases, but django-norel ( http://www.allbuttonspressed.com/projects/django-nonrel) has code now.

For relational data SQLAlchemy ( http://www.sqlalchemy.org/) is good if you want something standalone. Django's ORM is also excellent if you're using Django.

API

The most official Oauth library is python-oauth2 ( http://github.com/simplegeo/python-oauth2), which handily has a Django example as part of it's docs.

Piston ( http://bitbucket.org/jespern/django-piston/wiki/Home) is a Django app which provides lots of tools for building APIs. It has the advantage of being pretty active and well maintained and in production all over the place. Other projects exist too, including Dagny ( http://zacharyvoase.github.com/dagny/) which is an early attempt to create something akin to RESTful resources in Rails.

In reality any Python framework (or even just raw WSGI code) should be reasonably good for this sort of task.

Testing

Python has unittest as part of it's standard library, and unittest2 is in python 2.7 (but backported to previous versions too http://pypi.python.org/pypi/unittest2/0.1.4). Some people also like Nose ( http://code.google.com/p/python-nose/), which is an alternative test runner with some additional features. Twill ( http://twill.idyll.org/) is also nice, it's a "a simple scripting language for Web browsing", so handy for some functional testing. Freshen ( http://github.com/rlisagor/freshen) is a port of cucumber to Python. I haven't yet gotten round to using this in anger, but a quick look now suggests it's much better than when I last looked.

I actually also use Ruby for high level testing of Python apps and apis because I love the combination of celerity and cucumber. But I'm weird and get funny looks from other Python people for this.

Message Queues

For a message queue, whatever language I'm using, I now always use RabbitMQ. I've had some success with stompserver in the past but Rabbit is awesome. Don't worry that it's not itself written in Python, neither is PostgresSQL, Nginx or MongoDB - all for good reason. What you care about are the libraries available. What you're looking for here is py-amqplib ( http://barryp.org/software/py-amqplib/) which is a low level library for talking amqp (the protocol for talking to rabbit as well as other message queues). I've also used Carrot ( http://github.com/ask/carrot/), which is easier to get started with and provides a nicer API. Think bunny in Ruby if you're familiar with that.

Environment

Whatever bits and pieces you decide to use from the Python ecosystem I'd recommend getting to who pip and virtualenv ( http://clemesha.org/blog/2009/jul/05/modern-python-hacker-tools-virtualenv-fabric-pip/ - note that fabric is also cool, but not essential and these docs are out of date on that tool). Think about using Ruby without gem, bundler or rvm and you'll be in the right direction.

Python ORM for massive data set

I thought that ORM solutions had to do with DQL (Data Query Language), not DDL (Data Definition Language). You don't use ORM to add, alter, or remove columns at runtime. You'd have to be able to add, alter, or remove object attributes and their types at the same time.

ORM is about dynamically generating SQL and developer's lift, not what you're alluding to.

Fast, thread-safe Python ORM?

If you are looking for something thats high performance, and based on one of your comments "something that can handle >5k queries per second". You need to keep in mind that an ORM is not built specifically for speed and performance, it is built for maintainability and ease of use. If the data is so basic that even SqlAlchemy might be overkill, and your mostly doing writes, it might be easier to just do straight inserts and skip the ORM altogether.

Python libraries to construct classes from a relational database? (ORM in reverse)

SQLAlchemy extension to create a python code model from an existing database

Django-like abstract database API for non-Django projects

What you're looking for is an object-relational mapper (ORM). Django has its own, built-in.

To use Django's ORM by itself:

• Using the Django ORM as a standalone component
• Use Django ORM as standalone
• Using settings without setting DJANGO_SETTINGS_MODULE

If you want to use something else:

• What are some good Python ORM solutions?

python orm

The Django book covers this issue in Chapter 5, near the end of the chapter (or bottom of the page, in the web edition). Basically, the rules are:

• When adding a field, first add it to the database manually (using, e.g., ALTER TABLE) and then add the field to the model. (You can use manage.py sqlall to see what SQL statement to execute.)
• When removing a field, remove it from your model and then execute the appropriate SQL statement to remove the column (e.g., an ALTER TABLE command), and any join tables that were created.
• Renaming a field is basically a combination of adding/removing fields, as well as copying data.

So to answer your question, in Django's case, no, the ORM will not handle modifications for you -- but they're not that hard to do. See that chapter of the book (linked above) for more info.

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