Chapter 66 – Flask Application Database Using MongoDB Atlas and Python to Better Big Data Management

The non-SQL database is more advanced and capable of handling big data with dynamic schema features. In the era of Big Data, the non-SQL server seems to have a bright future, although in some cases, end-to-end business data solutions are still suitable to utilize SQL databases.

In this article, I will go through how to leverage MongoDB Altas integrating with Flask Applications using Python. It’s right for you to apply this approach to application development.

Ingredients on Flask Application Database Using MongoDB Atlas & Python

Table of Contents on Web Application Database

• MongoDB Intro and Free Atlas Account Creation
• Create a Database & Collection
• Import MongoClient, Certifi
• MongoDB Config Connection Code
• insert_one(), find({},{field_data:bool})
• Full Python Script of Flask Application Database using MongoDB
• FAQ

MongoDB Intro and Free Atlas Account Creation

MongoDB is an open-source database that stores flexible JSON-like “documents,” which can have any number, name, or hierarchy of fields within, instead of rows of data as in a relational database. Python developers can think of MongoDB as a persistent, searchable repository of Python dictionaries (and, in fact, this is how PyMongo represents MongoDB documents).

MongoDB provides both free and paid service scope for developers to consider and adopt. No matter which one you are considering based on actual needs, I would say the offer is evenly friendly and reasonable, compared to avg. offerings in the market. Signing up for a free atlas account is super easy, which just requires your email address.

After having signed up for a free account, let’s create a new cluster. Basically, it means a new shared server with others. You can select a server by country, etc. For me, it’s almost the same in terms of a free service, although the loading speed should be faster if you select the closest to your target market.

Then, we can go to the connection section and copy and paste the server connection URL. Please be sure to remove pymongo from the given URL if we would import a mongo client from pymongo in the script. You might be able to learn more in a moment next paragraph.

Create a Database & Collection

Then, we go to the browse collection section and create a new database. The free account database provides some samples for your test. In terms of new database creation, we just need to create two things. One is the new database name, and the other is the new collection name. Please be sure to copy these names. We will use them in a moment.

Import MongoDB a& Certifi Modules

There are two modules we need to import for MongoDB integration. Python 3.9 or above version has already included these modules, which are pymongo and ceritifi

MongoDB Config Connection Code

For the password, all the special signal that existed in your password, such as #, $, ! and so on and so forth, needs to be converted into percent encodings, such as For more details, please google a free online converter and get your percent-encoded password.  

insert_one(), find({},{field_data:bool})

Okay, now it’s time to go through how to insert sign-up user data into the MongoDB database when the above deployments are ready in your flask application.

insert_one()

MongoDB uses a JSON-like data structure so that it’s super easy and friendly to insert or create a new dataset, such as handling the new user sign-up. Take this for example as well, this method can be applied to the user sign-up page that connects with MongoDB.

As we can see, we can create a block that includes any variable data set to shape a property. you can standardize the property or even customize the property based on actual membership diversification. On the whole, it’s super application-friendly.

Fetching only specific fields.  If you want to fetch only some fields then in the find method pass the first parameter as {} and the second parameter as 1 for the field that you want to fetch and 0 for those you don’t want to fetch.

This method must be one of the most common and popular usages, notably in user matching, API development, targeting settings, etc

Full Python Script of Flask Application Database using MongoDB

If you are interested in the Full Python Script of Chapter 66 – Flask Application Database Using MongoDB Atlas and Python to Better Big Data Management, please subscribe to our newsletter by adding the message “Chapter 66”. We would send you the script immediately to your mailbox.

I hope you enjoy reading Chapter 66 – Flask Application Database Using MongoDB Atlas and Python to Better Big Data Management. If you did, please support us by doing one of the things listed below, because it always helps out our channel.

• Support and Donate to our channel through PayPal (paypal.me/Easy2digital)
• Subscribe to my channel and turn on the notification bell Easy2Digital Youtube channel.
• Follow and like my page Easy2Digital Facebook page
• Share the article on your social network with the hashtag #easy2digital
• You sign up for our weekly newsletter to receive Easy2Digital latest articles, videos, and discount codes
• Subscribe to our monthly membership through Patreon to enjoy exclusive benefits (www.patreon.com/louisludigital)

FAQ:

Q1: What is Flask MongoDB Integration?

A: Flask MongoDB Integration is a feature that allows Flask, a popular web framework, to seamlessly integrate with MongoDB, a NoSQL database, for efficient data storage and retrieval.

Q2: Why should I use Flask MongoDB Integration?

A: Using Flask MongoDB Integration offers several benefits, including easy setup and configuration, flexible data modeling, high scalability, and improved performance for web applications.

Q3: How do I install Flask MongoDB Integration?

A: To install Flask MongoDB Integration, you need to have Flask and MongoDB already installed. You can then use pip, the Python package installer, to install the necessary Flask MongoDB package. Detailed installation instructions can be found in the Flask MongoDB Integration documentation.

Q4: Can Flask MongoDB Integration work with other databases?

A: No, Flask MongoDB Integration is specifically designed to work with MongoDB as the database. It provides seamless integration and optimization for MongoDB’s unique features.

Q5: What version of Flask is compatible with Flask MongoDB Integration?

A: Flask MongoDB Integration is compatible with Flask version 1.0 and above. It is recommended to use the latest version of Flask for optimal compatibility and performance.

Q6: Does Flask MongoDB Integration support authentication and authorization?

A: Yes, Flask MongoDB Integration provides built-in support for authentication and authorization using MongoDB’s authentication mechanisms. This allows you to secure your web application and control access to sensitive data.

Q7: Can Flask MongoDB Integration handle large datasets?

A: Yes, Flask MongoDB Integration is designed to handle large datasets efficiently. MongoDB’s scalability and flexible data modeling, combined with Flask’s lightweight and modular architecture, make it a suitable choice for applications dealing with significant amounts of data.

Q8: Is Flask MongoDB Integration suitable for small projects?

A: Yes, Flask MongoDB Integration is suitable for small projects as well. Its ease of use, quick setup, and flexible data modeling make it an excellent choice for both small and large web applications.

Q9: Are there any limitations to using Flask MongoDB Integration?

A: While Flask MongoDB Integration offers many advantages, it is important to note that it is specifically designed for MongoDB. If you require compatibility with other databases or have specific requirements that are not well-suited for MongoDB, alternative solutions may be more appropriate.

Q10: Where can I find documentation and resources for Flask MongoDB Integration?

A: You can find comprehensive documentation, tutorials, and other resources for Flask MongoDB Integration on the official Flask MongoDB Integration website. Additionally, the Flask and MongoDB documentation can also provide valuable information for integrating the two technologies.