Productive oodbms for python?

Question

can anyone tell me and describe what oodbms implementation can be used for productive web application that stores all persistent data on it.

Answer 1

Maybe you should give ZODB a look. It is easy pretty easy to use, even if the user community is not as big as those of some other DBMSs.

Answer 2

After some research and benchmark. i found the MongoDB productive, because:

MongoDB (from "humongous") is a scalable, high-performance, open source NoSQL database. Written in C++, MongoDB features:

1. Document-oriented storage » JSON-style documents with dynamic schemas offer simplicity and power.

2. Full Index Support » Index on any attribute, just like you're used to.

3. Replication & High Availability » Mirror across LANs and WANs for scale and peace of mind.

4. Auto-Sharding » Scale horizontally without compromising functionality.

5. Querying » Rich, document-based queries.

6. Fast In-Place Updates » Atomic modifiers for contention-free performance.

7. Map/Reduce » Flexible aggregation and data processing.

8. GridFS » Store files of any size without complicating your stack.

9. Commercial Support » Enterprise class support, training, and consulting available.

EDIT 1:

Packages that provides mongodb in python:

• Humongolus Humongolus is a lightweight ORM framework for Python and MongoDB. The name comes from the combination of MongoDB and Homunculus (the concept of a miniature though fully formed human body). Humongolus allows you to create models/schemas with robust validation. It attempts to be as pythonic as possible and exposes the pymongo cursor objects whenever possible. The code is available for download at github. Tutorials and usage examples are also available at GitHub.

• MongoKit The MongoKit framework is an ORM-like layer on top of PyMongo. There is also a MongoKit google group.

• Ming Ming (the Merciless) is a library that allows you to enforce schemas on a MongoDB database in your Python application. It was developed by SourceForge in the course of their migration to MongoDB. See the introductory blog post for more details.

• MongoAlchemy MongoAlchemy is another ORM-like layer on top of PyMongo. Its API is inspired by SQLAlchemy. The code is available on github; for more information, see the tutorial.

• MongoEngine MongoEngine is another ORM-like layer on top of PyMongo. It allows you to define schemas for documents and query collections using syntax inspired by the Django ORM. The code is available on github; for more information, see the tutorial.

• Minimongo minimongo is a lightweight, pythonic interface to MongoDB. It retains pymongo’s query and update API, and provides a number of additional features, including a simple document-oriented interface, connection pooling, index management, and collection & database naming helpers. The source is on github.