put your data in a bag
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databag
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README.rst
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README.rst

d bag

PUT YOUR DATA IN A BAG

Pretty simple library for just splatting stuff to disk and getting it back out with minimal fuss.

wait...

Yep - it's a nosql type, document oriented database wrapper on top of sqlite3.

features

  • Easy to use and quite efficient at accessing relatively large number of items (not talking big data here, but a couple of thousand items works well)
  • Requires no other libs, everything is python batteries included.
  • Built on top of sqlite3 so it's fast and stable (which is included in Python stdlib)
  • Easy to use - just create one and use it like a dictionary. Most dict methods supported. Also can add to it like a set by not specifying a key. One will be created on the fly.
  • Pretty well tested
  • Ideal for running on small vm instances. Doesn't require any other daemon to provide data access
  • Core code is about 400 lines - very easy to understand.
  • Automatically compresses data with bz2 in cases that benefit from it
  • You can always query the data with native sqlite3 libs from other languages if you need to. It's just strings in the database.
  • Since the underlying datafile is sqlite3, multiple processes can work with the same file (multiple read, write locks, etc)
  • Every object gets a ts object attached to it for convenience when it's saved. This is accessed via bag.when('key')

versioning

Simple versioning is possible. Just create your DataBag like::

>>> dbag = DataBag(versioned=True, fpath='/tmp/some.db')

and then you can do things like...:

>>> dbag['blah'] = 'blip'
>>> dbag['blah'] = 'new blip'
>>> dbag['blah'] = 'newer blip'
>>> dbag.get('blah', version=-2)
u'blip'
>>> dbag.get('blah', version=-1)
u'new blip'
>>> dbag.get('blah')
u'newer blip'
>>> dbag['blah']
u'newer blip'

The default is to keep 10 versions but that can be set with the history parameter when initializing your bag.

If you don't specify an fpath argument, the database is only created in memory. By specifying fpath, you specify the location of the file on the filesystem.

A bag.get(...) method works much like a dictionary's .get(...) but with an additional keyword argument of version that indicates how far back to go.

examples

>>> from databag import DataBag
>>> bag = DataBag() # will store sqlite db in memory
>>> bag['xyz'] = 'some string' # will save in the db
>>> s = bag['xyz'] # retrieves from db
>>> s
'some string'
>>> 'xyz' in bag # True
True
>>> bag['abc'] = {'x':22, 'y':{'a':'blah'}} # works
>>> bag['abc']
{u'y': {u'a': u'blah'}, u'x': 22}
>>> [k for k in bag]
['abc', 'xyz']
>>> bag.when('xyz')
datetime.datetime(2011, 12, 31, 2, 45, 47, 187621)
>>> del bag['xyz']
>>> 'xyz' in bag
False
>>> meh = DataBag(bag='other') # set name of storage table

DictBag example

>>> from databag import DictBag, Q
>>> d = DictBag()
>>> d.ensure_index(('name', 'age'))
>>> person1 = {'name':'joe', 'age':23}
>>> person2 = {'name':'sue', 'age':44}
>>> d.add(person1)
'fachVqv6RxsmCXAZgJMJ5p'
>>> d.add(person2)
'fpC7cAtx2ZQLadprQR7aa6'
>>> d.find(Q('age')>40).next()
(u'fpC7cAtx2ZQLadprQR7aa6', {u'age': 44, u'name': u'sue'})
>>> age = Q('age')
>>> [p for p in d.find(20 < age < 50) ]
[(u'fachVqv6RxsmCXAZgJMJ5p', {u'age': 23, u'name': u'joe'}),
    (u'fpC7cAtx2ZQLadprQR7aa6', {u'age': 44, u'name': u'sue'})]
>>>

Mongo Style Queries

>>> d.find( {'age':23}
-- or --
>>> d.find( {'age':{"$gt":20}} )

limitations

  • although a lot of the basic data types in python are supported for the values (lists, dictionaries, tuples, ints, strings)... datetime objects can be saved fine but they come out of the bag as an iso format string of the original datetime.
  • when saving a dictionary, the keys must be a string in the dictionary. If they are not, they will be when coming back from the bag
  • if using versioning, be sure to instantiate your DataBag object with versioning enabled and the same history size each time. Failure to do so will cause interesting things to happen, in particular, your databag will act unversioned and overwrite recent updates w/o cascading the historical change to records.

Further notes

The Schematics library makes an excellent compliment to creation of models that map and store quite nicely in DictBags. A contrib class is included for DictShield (precursor to Schematics) in databag that inherits from dictshield.document.Document and adds some helpers for storing/retrieving DictShield models from DictBags. A Schematics mixin is planned.