Achieving Feature Parity

Often, when designing and implementing data stores from scratch, we aim to achieve a 'feature parity' with existing, more complex systems like PostgreSQL or MongoDB. This means that we want our data store to have the same functionality and features as the professional versions.

Achieving feature parity doesn't mean we have to implement every single function of these elaborate data stores. Instead, we focus on the most important features that define the functionality of the data store.

For instance, MongoDB is a document-oriented database system, meaning that it stores data as 'documents' in a semi-structured manner. If we were to build a simplified version of MongoDB from scratch, we'd want to ensure our system can store data as documents as well.

In Python, a simple way to implement a document store is by using dictionaries. The code example here demonstrates how we can create a very basic document store using Python's built-in defaultdict function. Each document is represented by another dictionary, with key-value pairs representing the fields of the document.

By reaching a feature parity, we ensure our system can be utilized in similar ways as the more established versions. However, the goal isn't to just clone these data stores, but to understand the core principles, mechanisms, and algorithms that allow them to function as they do.

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if __name__ == '__main__':

    from collections import defaultdict

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    document_store = defaultdict(dict)

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    document_store['doc1'] = {'title': 'Data Science', 'content': 'Data Science is cool'}

    document_store['doc2'] = {'title': 'AI in finance', 'content': 'AI is transforming the financial industry'}

​

    print(document_store['doc1'])

    print(document_store['doc2'])