Skip to main content

asynchronous MongoDB ORM

Project description

Mongey

Mongey is a successor of Glasskit MongoDB ORM based on Motor MongoDB driver. Unlike GlassKit, Mongey is a stand-alone module detached from any web framework.

Mongey is based on Motor AsyncIO, meaning it is fully asynchronous.

Example

import asyncio
from mongey.context import ctx
from mongey.models import StorableModel
from mongey.models.fields import StringField, IntField, DictField
from mongey.decorators import api_field


class User(StorableModel):
    COLLECTION = "users"
    KEY_FIELD = "username"

    username = StringField(required=True)
    first_name = StringField(required=True)
    last_name = StringField(required=True)
    age = IntField(default=None)
    user_settings = DictField(default=dict)

    @api_field
    def full_name(self) -> str:
        return f"{self.first_name} {self.last_name}"


async def run():
    ctx.setup_db({"meta": {"uri": "mongodb://127.0.0.1:27017/mydb", "kwargs": {}}, "shards": {}})
    user = User({"username": "superuser", "first_name": "Joe", "last_name": "White"})
    await user.save()


if __name__ == "__main__":
    asyncio.run(run())

Context

Mongey context is a global variable holding configuration bits and the global database object. Global context allows Mongey to behave more like ActiveRecord rather than Django ORM or SQLAlchemy. In other words you do user.save() instead of db.save(user).

Configuration

The global context object is created on import but stays not configured until you do so explicitly.

Logging and caching have their default versions and are pre-configured for you while db does not.

Use ctx.setup_db(...) to configure the database when your application starts, accessing the db property prior to database configuration will raise a ConfigurationError exception.

Caching

Persistent models, like StorableModel have cache_get method along with the original get. This method fetches the model and if it succeeds, the model is stored to level1+level2 caches.

L1 cache is usually request local while the L2 is more "persistent", e.g. stored in memcached.

If you're developing a web app, this allows Mongey to get the same model multiple times within one web request quickly and "for free" from your app memory, while for new requests the L2 cache will be used.

Cache invalidation is a complex topic being considered one of the main problems in coding (along with naming of variables) so this is to be covered in a full documentation which is currently WIP.

Computed fields

@api_field decorator can be added to arg-less sync or async methods of your model which will expose the method to the sync to_dict() and async to_dict_ext() methods of the model which are the primary methods for further model serialization.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mongey-0.1.5.tar.gz (28.8 kB view details)

Uploaded Source

Built Distribution

mongey-0.1.5-py3-none-any.whl (36.7 kB view details)

Uploaded Python 3

File details

Details for the file mongey-0.1.5.tar.gz.

File metadata

  • Download URL: mongey-0.1.5.tar.gz
  • Upload date:
  • Size: 28.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.5

File hashes

Hashes for mongey-0.1.5.tar.gz
Algorithm Hash digest
SHA256 c99ad763dbac86e794c3ef7117ce2dae990fcb0e8cfc9189c441522803f315e0
MD5 f74a99d39d711bc783f1a04c2c29df4e
BLAKE2b-256 996e165173fedac6c9a643eb51c5bebc5122045a9c8f79ee686770b9c7f8448f

See more details on using hashes here.

File details

Details for the file mongey-0.1.5-py3-none-any.whl.

File metadata

  • Download URL: mongey-0.1.5-py3-none-any.whl
  • Upload date:
  • Size: 36.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.5

File hashes

Hashes for mongey-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 d6fe6131b3e99257a1a83af7b50dd44c995ac7f0beb3126ebe68c528eb684866
MD5 c3b5f0c2541094203655125fbb7d1af7
BLAKE2b-256 71a276b82e4a29750aa0296756ca2f904c79e4b9905bbea42261a9634d444ca5

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page