Aggify Example

This is an example of using the “aggify” library with MongoDB and “mongoengine.”

Aggify is a Python library that helps you create aggregation pipelines for MongoDB using a more Pythonic syntax.

Usage

First, import the necessary modules and create MongoDB Document classes:

from aggify import Aggify, Q
from mongoengine import Document, fields

class AccountDocument(Document):
   username = fields.StringField()
   display_name = fields.StringField()
   phone = fields.StringField()
   is_verified = fields.BooleanField()
   disabled_at = fields.LongField()
   deleted_at = fields.LongField()
   banned_at = fields.LongField()

   meta = {
       'collection': 'account',
       'ordering': ['-_id'],
       'indexes': [
           'username', 'phone', 'display_name',
           'deleted_at', 'disabled_at', 'banned_at'
       ],
   }

class PostDocument(Document):
   owner = fields.ReferenceField('AccountDocument', db_field='owner_id')
   caption = fields.StringField()
   location = fields.StringField()
   comment_disabled = fields.BooleanField()
   stat_disabled = fields.BooleanField()
   hashtags = fields.ListField()
   archived_at = fields.LongField()
   deleted_at = fields.LongField()

   meta = {
       'collection': 'post'
   }

Create an Aggify instance with the base model (e.g., PostDocument):

query = Aggify(PostDocument)

The following examples demonstrate different aggregation pipeline scenarios:

1. Simple filter based on caption:

pprint(query.filter(caption__contains="hello").pipelines)

Output:
[{'$match': {'caption': {'$options': 'i', '$regex': '.*hello.*'}}}]

2. Filter with a lookup and unwind:

pprint(query.filter(caption__contains="hello", owner__deleted_at=None).pipelines)

Output:
[{'$match': {'caption': {'$options': 'i', '$regex': '.*hello.*'}},
 {'$lookup': {'as': 'owner',
              'foreignField': '_id',
              'from': 'account',
              'localField': 'owner_id'}},
 {'$unwind': {'path': '$owner', 'preserveNullAndEmptyArrays': True}},
 {'$match': {'owner.deleted_at': None}}]

3. Projection:

pprint(query.filter(caption__contains="hello").project(caption=1, deleted_at=0).pipelines)

Output:
[{'$match': {'caption': {'$options': 'i', '$regex': '.*hello.*'}},
 {'$project': {'caption': 1, 'deleted_at': 0}}]

4. Complex filter using Q objects:

pprint(
     query.filter(
         (Q(caption__contains=['hello']) | Q(location__contains='test')) & Q(deleted_at=None)
     ).pipelines
)

Output:
[{'$match': {'$and': [{'$or': [{'caption': {'$options': 'i',
                                    '$regex': ".*['hello'].*"}},
                               {'location': {'$options': 'i', '$regex': '.*test.*'}}]},
                     {'deleted_at': None}]}]

5. Slicing and ordering:

pprint(query.filter(caption='hello')[3:10].pipelines)

Output:
[{'$match': {'caption': 'hello'}}, {'$skip': 3}, {'$limit': 7}]

pprint(query.filter(caption='hello').order_by('-_id').pipelines)

Output:
[{'$match': {'caption': 'hello'}}, {'$sort': {'_id': -1}}]

This documentation provides examples of how to use the Aggify library to create MongoDB aggregation pipelines using Pythonic syntax.