This is a python application that uses microservices to publish messages to subscribers. In order to allow this application to scale we employ the Inversion of Control (IoC) design pattern with dependency injection (DI). This allows the endpoints to access our message broker.
Endpoints are served by the Flask framework adjacent called "Quart".
$ pip install --upgrade pip
$ pip install poetry
$ git clone git@github.com:stav/notefly.git
$ poetry install
Enter the notefly directory:
$ cd notefly
Start two (2) microservices:
$ poetry run client$ poetry run broker
Subscribe to messages at http://localhost:5001/sub
Publish messages at http://localhost:5000/pub
- Log notifications
- Three types: SMS, e-mail, push
- Three categories: Finance, Movies, Sports
- Scalable: add more types of notifications
- Users can subscribe to categories
- Users can select which message types they want to receive
- Manage requests to the Server by RESTful APIs
- Architecture: software design patterns
- OOP: use object oriented programming techniques
- Testing: register at least 3 users with different configurations
- Log: store activity but don't actually send any actual notifications
- Admin: no need for administration, just use mocks
- Ordering: message order does not need to be guaranteed
Messaging platforms provide a way for microservices to communicate and exchange data reliably and efficiently. This reliable communication between services architecture is crucial for completing tasks in microservice, as each service is responsible for managing one specific function.
A message queuing system allows for asynchronous communication between different components of your notification system. It ensures that messages are not lost, even if the recipient is offline, and provides a buffer to handle bursts of traffic.
Message queuing, also known as point-to-point communication, involves sending messages to a queue, where they are stored until the intended recipient processes them. The sender and receiver do not need to be connected at the same time, facilitating asynchronous communication. The messages are stored in the queue until they can be delivered, and they are generally deleted after that.
Generally, queuing is appropriate for use cases where there’s no requirement for real-time delivery or processing, such as batch processing, order fulfillment, or offline data processing.