Collections

Grouping datastores into datastore collections can significantly simplify complex access patterns. For example, caching datastores can be checked before accessing more costly datastores, or a group of equivalent datastores can act as shards containing large data sets.

As shims, datastore collections also derive from datastore, and must implement the four datastore operations (get, put, delete, query).

DatastoreCollection

Collections may derive from DatastoreCollection

TieredDatastore

Example:

>>> import pymongo
>>> import datastore.core
>>>
>>> from datastore.mongo import MongoDatastore
>>> from datastore.pylru import LRUCacheDatastore
>>> from datastore.filesystem import FileSystemDatastore
>>>
>>> conn = pymongo.Connection()
>>> mongo = MongoDatastore(conn.test_db)
>>>
>>> cache = LRUCacheDatastore(1000)
>>> fs = FileSystemDatastore('/tmp/.test_db')
>>>
>>> ds = datastore.TieredDatastore([cache, mongo, fs])
>>>
>>> hello = datastore.Key('hello')
>>> ds.put(hello, 'world')
>>> ds.contains(hello)
True
>>> ds.get(hello)
'world'
>>> ds.delete(hello)
>>> ds.get(hello)
None

ShardedDatastore

Example:

>>> import datastore.core
>>>
>>> shards = [datastore.DictDatastore() for i in range(0, 10)]
>>>
>>> ds = datastore.ShardedDatastore(shards)
>>>
>>> hello = datastore.Key('hello')
>>> ds.put(hello, 'world')
>>> ds.contains(hello)
True
>>> ds.get(hello)
'world'
>>> ds.delete(hello)
>>> ds.get(hello)
None