ORM for the dataset library
Project description
Fast Active Record ORM for the dataset library
Why?
The dataset library is a great and easy tool to work with any SQL database. Unfortunately, it lacks an object mapper (ORM) - if you need one you are left with the complexity that is sqlalchemy.
Enter dataset-orm
How?
Installation
$ pip install dataset-orm
Define classes that define a dataset.Table:
from dataset_orm import Model, connect
class User(Model):
# in some dbs, unique strings must be limited in length
username = Column(types.string(length=100), unique=True)
data = Column(types.json)
connect('sqlite:///mydb.sqlite')
Then create rows directly from Python objects:
user = User(username='dave', data={'sports': ['football', 'tennis']'})
user.save()
user = User.objects.find_one(username='dave')
user.data
=>
{'sports': ['football', 'tennis']'}
Query exiting tables, ORM-style:
User = Model.from_table(db['customer'])
User.objects.all()
=>
[ User(pk=1), User(pk=2), User(pk=3)]
user = User.objects.find_one(name='John Walker')
print(user.pk, user.name)
=>
1 John Walker
Update and delete
user = User.objects.find_one(name='John Walker')
user.place = 'New York'
user.save()
users = User.objects.find(place='London')
users.delete()
Store and access any data types, including json and binary values
class User(Model):
# in some dbs, unique strings must be limited in length
username = Column(types.string(length=100), unique=True)
picture = Column(types.binary)
user = User.objects.get(name='Dave')
with open('image.png', 'rb') as fimg:
user.picture = fimg.read()
user.save()
Use the file column type for transparently storing binary data:
class Image(Model):
imagefile = Column(types.file)
Usage:
img = Image()
with open('/path/to/image') as f:
img.imagefile.write(f)
img.save()
data = img.imagefile.read()
The imagefile field is the filename of a dataset_orm.files.DatasetFile. The file's data is split in chunks and written to the database in multiple parts. On reading back, the chunks are retrieved from the db in parallel, in order to improve performance for large files. Tests indicate a 25% speed up is possible v.v. a single BLOB.
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