Building a Document-Oriented Database: A Step-by-Step Tutorial

As an experienced developer, who unquestionably worked with different database systems before, you might be familiar with MongoDB. It is a document-oriented non-relational database (NoSQL) famous for its scalability and flexibility. As we are about to recreate a primitive version of it, it's essential to know that the core of any document-oriented database, like MongoDB, lies in its documents and collections.

Imagine a document as an equivalent of a row in a SQL database, and a collection as a table. A document is a single record in a collection. A record contains data in fields, just like columns in a SQL database.

Database core setup

In Python, one of the ways we can represent this is using dictionaries and lists. Consider the setup shown in the Python script on your left. We start by implementing a DocumentDB class, which represents our database wrapper. Initially, our database (self.db) is an empty dictionary where keys represent collection names and values are lists of documents in those collections.

The insert method allows us to add documents to specific collections. If the collection doesn't exist, an empty list is added as a placeholder, then the document is appended to that collection.

The find method is a simple implementation of a search feature. It checks every document in a specific collection against a given criteria. If a document fulfils criteria, it is added to the results.

Although our database is extremely simplified, this implementation represents the basic core of a document-oriented database. However, in real production scenarios, other considerations such as concurrency, persistence, validation, etc. need to be taken into account. As we progress with our lessons, we'll keep enhancing this basic structure until we achieve a closer representation of MongoDB, learning key concepts along the way.

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if __name__ == "__main__":

  class DocumentDB:

    def __init__(self):

        self.db = {}

​

    def insert(self, collection, document):

        if collection not in self.db:

            self.db[collection] = []

        self.db[collection].append(document)

​

    def find(self, collection, criteria):

        results = []

        for document in self.db[collection]:

            for key, value in criteria.items():

                if document.get(key) != value:

                    break

            else:

                results.append(document)

        return results

In the field of financial data processing and AI, efficiency is the key. By incorporating indexing in our document-oriented database equivalent, we can significantly increase our search efficiency, bringing us closer to MongoDB's capabilities.

Consider the insert method in the displayed code. Apart from inserting the document into our database, it also populates an index, self.index.

An index in databases is akin to an index in a book. It's a data structure that improves the speed of operations in a database. self.index is a nested dictionary where the first key is the document field (equivalent to column in SQL), and the second key is the value of that field in the document. The value against this second key is a list of all documents that contain this field-value pair.

The indexed_find method then leverages this index to execute searches. It's a straightforward iteration over the criteria. For each field-value pair in the criteria, if the field and value exist in self.index, all matching documents are added to the results. This method is significantly faster than the find method for large collections, which must check every document fully against the criteria.

Next, we create a DocumentDB instance, add some documents into the 'products' collection, and execute both find and indexed_find methods to retrieve documents matching given criteria.

While Python's dictionaries are essentially hash tables with practically constant time complexity, for complex queries and large amounts of data, tabular databases would still suffer without the use of indices. This trade-off we experience here is commonly seen in real-world applications in finance and AI, where time complexity can significantly impact performance.

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    print(db.indexed_find('products', {'name': 'Turing Machine'}))

if __name__ == '__main__':

    class DocumentDB:

        def __init__(self):

            self.db = {}

            self.index = {}

​

        def insert(self, collection, document):

            if collection not in self.db:

                self.db[collection] = []

            self.db[collection].append(document)

​

            for key, value in document.items():

                if key not in self.index:

                    self.index[key] = {}

                if value not in self.index[key]:

                    self.index[key][value] = []

                self.index[key][value].append(document)

​

        def find(self, collection, criteria):

            results = []

            for document in self.db[collection]:

                for key, value in criteria.items():

                    if document.get(key) != value:

                        break

                else:

                    results.append(document)

​

        def indexed_find(self, collection, criteria):

            results = []

In the previous screens, we have established our collections and indexed them for efficient data retrieval. Now, we delve into the fundamentals of querying methods that make our database useful. For targeted data retrieval, we need to implement a find method in our database.

The find method is the basis of querying our document-oriented database. It checks each document in a specified collection against specified criteria, which is a dictionary where the key is the field we want to match and the value is the value we're matching against.

In the example provided, we define a 'products' collection containing documents that represent different fruits. We utilize the find method to extract fruits of a particular color. In the financial world, this is equivalent to querying all the transactions belonging to a particular party or of a specific type.

These 'simple queries', as they're often called in MongoDB, form the basis of our data retrieval logic. However, we can also implement more advanced querying tools such as sorting and filtering, which we will discuss later on.

To career-oriented data scientists and AI developers dealing with complex and large datasets, efficient data retrieval methods like this can be the key to successful and speedy data analysis.

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if __name__ == "__main__":

    class DocumentDB:

        def __init__(self):

            self.collections = {}

​

        def find(self, collection, criteria):

            if collection not in self.collections:

                return None

            results = []

            for document in self.collections[collection]:

                match = True

                for field, value in criteria.items():

                    if document[field] != value:

                        match = False

                        break

                if match:

                    results.append(document)

            return results

​

    db = DocumentDB()

    db.collections['products'] = [

        {'name': 'apple', 'color': 'red', 'price': 1.2},

        {'name': 'banana', 'color': 'yellow', 'price': 0.5},

    ]

    print(db.find('products', {'color': 'red'}))

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}}))

In order to enhance our database and come closer to MongoDB's functionality, we will consider adding two essential features - Schema Validation and Transaction Handling. These features are incumbent in large-scale applications, particularly in domains like finance and Artificial Intelligence.

Schema Validation is a way to ensure that your database only contains data that fits specific criteria. This could mean data types, lengths, or even semantic rules, improving the robustness of data quality and integrity.

Consider a situation where you're building a machine learning model using data from your document-oriented database. If there is no validation in place, you could end up training your model with incorrect or irrelevant data causing skewed results.

Transaction Handling ensures that your database remains in a consistent state, even in case of errors. In simpler terms, it follows the ACID principle - Atomicity, Consistency, Isolation, and Durability. Especially in finance, atomic transactions are critical. Failure in midway could lead from incorrect balances to potential legal issues.

To incorporate these features, we would extend our classes and functions. Let's start with schema validation, building on our existing Document and Collection classes.

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class Document:

    def __init__(self, doc_id, collection):

        self.doc_id = int(doc_id)

        self.collection = collection

    def validate_schema(self, data):

        # Code to validate the data against the pre-defined schema

        pass

​

class Collection:

    def __init__(self, name):

        self.name = name

        self.documents = {}

    def validate_transaction(self):

        # Transaction validation code

        pass

​

if __name__ == '__main__':

  transaction_data = {'type': 'purchase', 'amount': 100, 'currency': 'USD'}

  financial_collection = Collection('financialData')

  financial_doc = Document(1, financial_collection)

  # Implement the validate_schema method in the Document class

  financial_doc.validate_schema(transaction_data)

  # Implement the validate_transaction method in the Collection class

  financial_collection.validate_transaction()

Congratulations on enhancing your document-oriented database! You've made it majorly on par with systems like MongoDB, using Python. Reflecting on the journey, we've started with a basic database core, iteratively added indexing for search efficiency, advanced querying methods, and additional features like schema validation and transaction handling. Owing to their use-cases, these features say a lot about where such databases find application.

For instance, strong schema validation and transaction handling align directly with the requirements of AI and finance domain. Without proper schema validation, you can easily end up feeding improper data to your machine learning algorithms with disastrous consequences. Similarly, a weak transaction system could lead to inconsistent states in finance, causing legal issues.

However, building a database is a journey, not a destination. There are several areas where you can still improve and tailor your database even closer to your needs -

1) Abstraction: While we have a neat little system running, good software engineering principles suggest we abstract away common functionalities into generic methods. Experiment with this.

2) Scaling Capabilities: If your queries are getting slower with more data, consider adding sharding capabilities. If you require high availability, explore replication.

3) AI Applications: Databases carrying machine learning workloads need to be optimized for increasingly heavy workloads including transactions and complex analytical queries.

4) Financial Applications: Enhance your transaction capabilities further. Investigate Write-Ahead-Logging for crash recovery, connection pooling for efficient resource utilization.

With these points in mind, happy exploring and keep building!

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if __name__ == '__main__':

    # Reflect on the code and software engineering principles

​

    print('Great job on building an enhanced version of a document-oriented database!')

​

    print('Next, consider exploring abstraction and scaling capabilities. Delve into sharding, and replication for high availability and data integrity.')

​

    print('For AI applications, investigate how to optimize your database for mixed workload scenarios - transactions and analytical queries.')

​

    print('For financial applications, further enhance your transaction capabilities with functionalities like Write-Ahead-Logging for crash recovery.')