In the world of finance and AI, searching through massive data is a common requirement. You have already implemented the find method to perform simple queries, which is similar to searching for transactions based on a party's name or type. Now, let's dive into more advanced querying methods like aggregation, sorting, and filtering.

These advanced methods emulate MongoDB's querying flexibilities and bring more acuity to data analysis. Here are some common scenarios:

1. Aggregation: Consider working on a massive dataset of stock prices. To perform a yearly analysis, you might need to aggregate the prices month-wise or quarter-wise. This is where aggregation plays a significant role.

2. Sorting: While implementing trading algorithms, you might want to sort stocks based on their recent price changes. Sorting provides a way to arrange data in an ordered manner, ascending or descending, which assists in drawing insights with ease.

3. Filtering: In the realm of AI, say you are training a model with a huge dataset. You might want to filter out some data based on specific conditions to make the training set more robust. Filtering enables you to fine-tune your dataset as per your needs.

We will implement these methods in our document-oriented database by extending the find method to accept additional parameters for aggregation, sorting, and filtering.

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if __name__ == "__main__":

  class Database:

    def __init__(self):

      self.collections = {}

​

    def create_collection(self, name):

      self.collections[name] = []

​

    def insert(self, collection, document):

      self.collections[collection].append(document)

​

    def find(self, collection, criteria=None, aggregate=None, sort=None, filter=None):

      if not criteria:

        return self.collections[collection]

      else:

        # Add logic for aggregation, sorting, and filtering

        pass

  # Usage

  db = Database()

  db.create_collection('stocks')

  db.insert('stocks', {'name': 'stock1', 'price': 100})

  db.insert('stocks', {'name': 'stock2', 'price': 200})

  db.insert('stocks', {'name': 'stock3', 'price': 150})

​

  # Implement aggregation, sorting and filtering here

  print('Aggregation:', db.find('stocks', aggregate={}))

  print('Sorting:', db.find('stocks', sort='price'))

  print('Filtering:', db.find('stocks', filter={'price': {'$gt': 150}}))