Stern warning:

No seriously, it’s a very light wrapper around boto.dynamodb.layer2. If you stray much beyond the usage examples below, you’d do best to be familiar with Boto’s DynamoDB API. The docs are excellent. Reading duo’s source may also be helpful. It’s kept short for that reason.

Usage:

duo is made up of one module:

>>> import duo

The module isn’t very big (at the time of this writing, ~700 lines). If you want to know how something works, you should read it.

Pre-create your tables in the AWS console, then write simple classes to access them. duo.Table Sub-classes are automatically registered with the db:

>>> class MyHashKeyTable(duo.Table):
...     table_name = 'my_hashkey_table'
...     hash_key_name = 'slug'
...     range_key_name = None  # Implicit default

duo.Item is a thin wrapper around boto.dynamodb.items.Item, with lots of syntactic sugar. duo.Item sub-classs are automatically registered with the db:

>>> import datetime

>>> class MyHashKeyItem(duo.Item):
...     table_name = 'my_hashkey_table'
...     hash_key_name = 'slug'
...
...     slug = duo.UnicodeField()
...     my_field = duo.UnicodeField(default='foo')
...     on_this_date = duo.DateField(default=lambda o: datetime.date.today())

Databases and Tables use dict-like access syntax:

>>> db = duo.DynamoDB(key='access_key', secret='secret_key')

>>> # The correct Table sub-class is matched by table name:
>>> table = duo.DynamoDB['my_hashkey_table']

>>> # The correct Item sub-class is matched by table name:
>>> item = table['new-item']

>>> # Items are actually dict subclasses, but that's not where the
>>> # fun is. They can only store unicode strings and integers:
>>> item['slug']
'new-item'

Specify a field on an Item sub-class to get useful data types:

>>> item.is_new
True

>>> # A field doesn't exist initially...
>>> item['my_field']
Traceback (most recent call last):
  File "...", line 1, in <module>
    item['my_field']
KeyError: 'my_field'

>>> # But we specified a default.
>>> item.my_field
'foo'

>>> # The default, once accessed, gets populated:
>>> item['my_field']
'foo'

>>> # Or we can set our own value...
>>> item.my_field = 'bar'

>>> item['my_field']
'bar'

>>> # Finally, we save it to DynamoDB.
>>> item.put()

>>> item.is_new
False

Caching:

Duo integrates with any cache that implements a python-memcached-compatible interface, namely, the following:

import pylibmc
cache = pylibmc.Client(['127.0.0.1'])
cache.get(<keyname>)
cache.set(<keyname>, <duration-in-seconds>)
cache.delete(<keyname>)

Integrate caching by passing the cache to the db constructor:

>>> import duo
>>> db = duo.DynamoDB(key='access_key', secret='secret_key', cache=cache)

You can also specify a cache object on a per-table or per-item basis:

>>> class MyHashKeyTable(duo.Table):
 ...     cache = pylibmc.Client(['127.0.0.1'])
 ...
 ...     table_name = 'my_hashkey_table'
 ...     hash_key_name = 'slug'
 ...     range_key_name = None  # Implicit default

Caching is turned off by default, but you can turn it on by specifying a cache_duration as an integer (0 is forever):

>>> class MyHashKeyItem(duo.Item):
...     cache_duration = 30  # 30 seconds
...
...     table_name = 'my_hashkey_table'
...     hash_key_name = 'slug'
...
...     slug = duo.UnicodeField()
...     my_field = duo.UnicodeField(default='foo')
...     on_this_date = duo.DateField(default=lambda o: datetime.date.today())

Cache keys are determined by hash key, range key, and a cache prefix (set on the Table). By default, the cache prefix is the table name:

>>> table = duo.DynamoDB['my_hashkey_table']
>>> item = table['new-item']
>>> item.cache_prefix is None
True
>>>item._cache_key
'my_hashkey_table_new-item'
>>> MyHashKeyTable.cache_prefix = 'hello_world'
>>> item._get_cache_key()
'hello_world_new-item'

CHANGELOG