Code Challenge: Python-Django Backend

A sample microservice project with Django, Redis and Kafka to test your familiarities with these tools.

Requirements

You need Docker installed on you machine to run Redis and Apache Kafka instances. Python is also needed for various scripts in all the steps.

Included in the Box

Sample price data for three fictional stocks needed in the next steps are stored in price_data.csv. In ./redis folder there is a docker-compose file for running a dockerized Redis database instance. Similarly, the docker-compose file for starting a dockerized instance of message queuing system Kafka is found in ./kafka folder. This docker-compose file initializes a topic named main_topic after startup.

Step 1: Price Updater Service

Write a price updater service with Python that accomplishes these tasks:

• Reads the price data in price_data.csv for Stock1, Stock2 and Stock3 starting from 9:00:00 to 9:59:47
• Connects to the Redis database
• For each data row, updates the time and price history of the corresponding stock

Note: Make sure Redis database is running and is accessible on localhost on default port 6379

Hint: The stock price history data in stored in Redis in key-value format. The key value for Stock1, Stock2 and Stock3 are stock1, stock2 and stock3 respectively. The value is in json format in which the time field is a list of timestamps hhmmss. The price field is the list of stock prices at respective timestamps.

Note: Price history is a minute-wise time series, so price values for each minute is required.

Finally, dockerize your script to be used as a standalone service.

Step 2: It's Message Queueing Time!

In this Step we want to integrate Apache Kafka in our project, so, instead of reading the data from the price_data.csv file, we will be listening to a Kafka topic and get the stock price data from there.

Note: Make sure Kafka is running and is accessible on localhost on port 9092 and the topic main_topic is available.

First you need to fill the Kafka main_topic with the price data given in price_data.csv. Create a KafkaProducer, then read the data from price_data.csv and send it to the main_topic.

Now that the data is saved in Kafka, repeat the task in Step 1, but use a KafkaConsumer to get the price data.

Step 3: Django and DRF

The goal in this step is to create a BuyStock API endpoint in Django and do a basic check on the request.

Start an empty Django project and make sure rest_framework is installed. Create an API endpoint named BuyStock which accepts a Post request from user. The body content is a valid json object in this form as an example:

{
	"user": "user2",
	"stockname": "stock1",
	"quantity" : 100
}

This request needs to be verified and a proper response needs to be sent back. First connect to the Redis database and get the user data for this user. If the user credit is enough to buy this stock in this quantity return Accept, otherwise return Deny.

Finally, write Unit Tests and Integration Tests for your API to make sure it's working correctly.

Step 4: Save and Retrieve

In this final step we want to use django ORM to save user orders and retrieve this data. First, create a model named Orders with fields

• user: either user1 or user2
• stock: either stock1 or stock2 or stock3
• status: either accepted or denied
• creation_date: date and time of the order creation
• price: positive Integer
• quantity: positive Integer

Returning back to Step 3, add the functionality to the BuyStock endpoint to save the received order.

Now implement a Django REST API endpoint using Django REST Framework to fetch orders that meet the following criteria: The status is either accepted or the order is created today. Exclude orders where the status is denied and the quantity is smaller than 10. Ensure the API endpoint returns the ordered results by the creation_date field in descending order.

Write unit tests for the API endpoint to verify its functionality.