Because hives are for ants and bees.

Snakepit is a horizontally partitioned, scalable, database access library. It manages the partitioning for you, while trying to be light and staying out of the way -- in concrete terms, you tell it what record id you're interested in, and it hands you an SQLAlchemy engine pointing to the node storing that record. From there on, you're free to access the record, and all things you know to be stored on the same node; e.g. a user and their blog entries could always be on the same node.

It's licensed under the MIT license.

You can get snakepit via git by saying:

git clone git://eagain.net/snakepit.git

And install it via:

python setup.py install

Though you may want to use e.g. --prefix=. For Debian/Ubuntu users, the source will probably be debianized later (TODO).

HiveDB docs aren't in a great shape either, and that project has a lot more experience with the database schema. I'm briefly summarizing what I know, and then you're on your own:

• a hive is the central point of coordination (and often a SPOF), it stores information about the nodes in the system
• a directory stores the id to node mapping, it can reside in the same database as the hive
• a node is a database that actually stores the data

Records are partitioned to nodes based on partitioning dimensions. This can happen by primary key or secondary key. For example, users could be partitioned to nodes and their blog posts could be stored on the same node. In such a schema, user_id would be mapped to a node in the directory, and blogpost_id would be mapped to user_id, via a secondary index, also in the directory.

There's a couple of sample apps: demo.sh and demo.py.

Actual use of the library is still.. shall we say.. finding it's path.

Good luck on your adventures. You can reach me at tv@eagain.net, or as Tv on irc.freenode.net etc.