README.markdown

This package exposes Redis backed datastructures which bring together standard Python syntax with persistent in-memory storage. Redis operations are all atomic as well, meaning that a thoughtful approach can use them concurrently on multiple machines.

Utimately, a trivial wrapper around Redis-Py.

Dictionary via Redis Strings

Using the entire Redis cluster as a dictionary:

>>> import redis_dict
>>> x = redis_dict.RedisDict()
>>> x
{}
>>> x['a'] = 100
>>> x
{'a': '100'}
>>> x['a']
'100'
>>> x['b']
>>> len(x)
1

Dictionaries via Redis Hashes

Using Redis hashes we can store multiple dictionaries in one Redis server.

>>> import redis_hash_dict
>>> x = redis_hash_dict.RedisHashDict("some_hash_key")
>>> x
{}
>>> x['a'] = 100
>>> x
{'a': '100'}
>>> x['a']
'100'
>>> x['b']
>>> len(x)
1

Lists via Redis Lists

We also have a kind-of-sort-off implementation of a list which certainly doesn't have the full flexibility of a Python list, but is persistent, synchronized and sharable.

>>> import redis_list
>>> x = redis_list.RedisList("my-list")
>>> x
RedisList([])
>>> x.append("a")
1
>>> x.append("b")
2
>>> x
RedisList(['a', 'b'])
>>> x[0]
'a'
>>> x.pop()
'b'
>>> x
RedisList(['a'])

It also provides access to blocking versions of pop, which with a little creativity you can use to create a message queue with workers.

>>> x.pop(blocking=True)
'a'

Woohoo.