As you're an experienced engineer, you know that once we've defined our database and the type of documents it'll store, the next step is storing these documents.

In a Document-Oriented Database, storing a document involves adding it to the collection of documents. For this, we'll usually be implementing a method named add or insert. This method will receive a document (a dictionary, in our Python case) and add it to the database, usually generating an ID to reference it later.

On the other hand, since our documents are dictionary-like objects, they can contain nested dictionaries, thus introducing hierarchical data. This is an advantage over traditional SQL databases where storing such hierarchical data can be a little bit complex.

Let's consider an example where we store our earlier created profile of a software engineer to our Document-Oriented Database:

In the Python code snippet below we define our 'database' as a dictionary. We then present a function add_document(db, document) which adds a document to the database and returns a generated ID for the inserted document.

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import uuid

​

if __name__ == '__main__':

  # Python logic here

  profile = {

    'name': 'John Doe',

    'age': 30,

    'profession': 'Software Engineer',

    'languages': ['Python', 'JavaScript', 'C++'],

    'interests': ['AI', 'Finance']

  }

  database = {}

​

  def add_document(db, document):

    doc_id = str(uuid.uuid4())

    db[doc_id] = document

    return doc_id

​

  doc_id = add_document(database, profile)

  print(f'Document inserted with ID: {doc_id}')

​

  print('Database content:')

  for key, value in database.items():

    print(f'ID: {key}, Document: {value}')