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databag-1.5.0
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Put your data in a bag and get it back out again
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | databag-1.5.0 |
| نام | databag |
| نسخه کتابخانه | 1.5.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jeremy Kelley |
| ایمیل نویسنده | jeremy@33ad.org |
| آدرس صفحه اصلی | https://github.com/nod/databag |
| آدرس اینترنتی | https://pypi.org/project/databag/ |
| مجوز | - |
<img src="https://github.com/nod/databag/raw/master/misc/dbag.png" />
# PUT YOUR DATA IN A BAG
Pretty simple library for splatting stuff to disk and getting it back out
with minimal fuss.
It's sort of a long term file based dictionary with enhanced range type
filtering.
**updated for python3**
## 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
- offers versioned records if you so choose
- 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
Simplified versioning is provided. Just create your DataBag like:::
```Python console
>>> dbag = DataBag(versioned=True, fpath='/tmp/some.db')
```
and then you can do things like...
```Python console
>>> 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
```Python console
>>> 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
```Python console
>>> 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'})]
```
There's also some syntactic sugar that lets you also use a Q object directly
if the key name is a proper symbol name in python.
```
>>> [p for p in d.find(20 < Q.age < 50) ]
[(u'fachVqv6RxsmCXAZgJMJ5p', {u'age': 23, u'name': u'joe'}),
(u'fpC7cAtx2ZQLadprQR7aa6', {u'age': 44, u'name': u'sue'})]
>>>
```
## Mongo Style Queries
```Python console
>>> d.find({'age':23})
>>> 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.
# DataBag ORM
There are times an ORM makes life a little easier.
```python3
from databag.orm.model import set_db_path, Model, Field, IntField, Q
set_db_path(':memory:')
# define one
class SomeThing(Model):
thingname = Field(str)
num = IntField()
# make and save one
mything = SomeThing(thingname='oobleck', num=23).save()
# use one
print(mything.name)
# get it from db again
k = mything.key
samething = SomeThing.grab(k)
# or search for it with the same syntax as DictBag, but get obj instead
otherthing = SomeThing.find_one(num=23) # just one
# returns a generator, so list gets all of them
things = list(SomeThing.find(Q.num > 19))
```
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl databag-1.5.0:
pip install databag-1.5.0.whl
نصب پکیج tar.gz databag-1.5.0:
pip install databag-1.5.0.tar.gz