This app is a solution for a small apparel retail store that needs to update prices of displayed items in response to the highly competitive pricing environment in its neighborhood. It works by creating a dummy data and pushing it into our MongoDB database for further manipulations.
MongoDB is the choice for this application because it is a highly scalable, document-oriented NoSQL database that can handle a large volume of data and provides flexible data modeling. It also allows for easy integration with other technologies, making it an ideal choice for modern web applications.
For quick data modelling and alterations to our database, we chose MongoDB-Compass. MongoDB-Compass is a GUI tool that provides a visual interface for managing and interacting with MongoDB databases, making it easier for users to navigate and work with the data.
We also use TKinter GUI to link our python code to our NoSQL DB (MongoDB in this case). With this interface, entering the appropriate MongoDB queries below this document will allow you manipulate the actual database.
We have also run this database on Docker in consideration of its advantages. Running MongoDB in a Docker container allows for easy deployment and management of the database, as well as providing a consistent and reproducible environment for the application. Containerization allows for better resource management and easier "horizontal" scaling of the application as the business grows.
The use of containerization also provides benefits such as improved portability, version control, and isolation, making it easier to maintain and deploy the application across different environments.
Using MongoDB, MongoDB Compass, and Docker provides a modern and scalable solution for the apparel retail store's pricing update needs.
MongoDB document databases provide high availability and easy scalability.
Our python version is 3.10.6
Operating system is Ubuntu 22.04 LTS
Retail_runner.py: This is the main file that contains the code that allows us run Tkinter GUI and use Mongo Interactively in it.
README.md: This is a markdown file that provides documentation and instructions on how to use the application.
docker-compose.yml: This is a YAML config file that defines the MongoDB database container that should be run in our Docker environment.
requirements.txt: This is a file that lists the Python packages that are required for our application to run. It contains some dependencies that pymongo requires to run.
pip install pymongo
pip install flask
pip install python3-tk
sudo apt-get install libkrb5-dev
'''GSSAPI authentication requires pykerberos on Unix or WinKerberos on Windows. The correct dependency can be installed automatically along with PyMongo:'''
```python3 -m pip install "pymongo[gssapi]"```
'''MONGODB-AWS authentication requires pymongo-auth-aws:'''
```python3 -m pip install "pymongo[aws]"```
'''OCSP (Online Certificate Status Protocol) requires PyOpenSSL, requests, service_identity and may require certifi:'''
```python3 -m pip install "pymongo[ocsp]"```
'''Also you can just install all of them like this'''
' python -m pip install "pymongo[gssapi,aws,ocsp,snappy,zstd,encryption]" '
docker pull mongo
Included in the main directory is our docker-compose.yml file for those who prefer to build it that way.
The mongoDB docker image version looks like this: - "GOSU_VERSION=1.16" - "JSYAML_VERSION=3.13.1" - "MONGO_PACKAGE=mongodb-org" - "MONGO_REPO=repo.mongodb.org" - "MONGO_MAJOR=6.0" - "MONGO_VERSION=6.0.5" - "HOME=/data/db"
In our docker-compose.yml file, you will notice that we mounted the Database container on a volume to achieve "persistence" of the data to be written and manipulated on our container.
Our volume details look like this: volumes: - "b4e8c304e3e0410b43f1acdaf13668c8e835fef15fd4300066e52eb17be4aedf:/data/configdb" - "3ac3575caa70f1b6b8bad599171a411b340ae490c932d1fcd2886268eb2a8800:/data/db"
docker run -d -p 27017:27017 --name mongodb mongo
'''After running the python app, if you need to run start the mongo daemon, enter the docker shell for the mongo container, then just type'''
mongosh
Create your mongodb using this link https://www.mongodb.com/basics/create-database:
use Datastream_db
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Download MongoDB Compass with: 'wget https://downloads.mongodb.com/compass/mongodb-compass_1.35.0_amd64.deb'
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Install MongoDB Compass: sudo dpkg -i mongodb-compass_1.35.0_amd64.deb
If you get an error with the above installation, then use this to fix any missing dependencies: sudo apt-get install -f
If it completes the fixing successfully, then proceed to retry: sudo dpkg -i mongodb-compass_1.35.0_amd64.deb
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Start MongoDB Compass: mongodb-compass
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Once mongodb-compass GUI Interface is initialized, enter this command to switch to our database: use Datastream_db
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Next, use the following commands to adjust or manipulate the fields accordingly:
To print and retrieve the contents of the entire document, use: collection.find({})
To retrieve all documents in the Data_collection collection and sort them by the "price" field in ascending order, using this command will help. for example, It will sort the documents in ascending order of the "price" field. That means the lowest prices will appear first, followed by higher prices.
collection.find().sort("price")
But If you want to sort the documents in descending order of the "price" field, we can use: collection.find().sort("price", -1)
collection.delete_many({})
collection.update_many({}, { "$set": { "merged_field": { "$concat": ["$price", "60.98", "$quantity"] } } })
collection.update_many({'category': {'$regex': 'Category 1'}}, {'$set': {'category': 'Category A'}})
collection.update_many({}, {'$unset': {'size': ''}})
collection.update_many({}, {'$set': {'size': 'Large'}})