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How long did you spend on the coding test below? What would you add to your solution if you had more time?

  1. I spent one month on the coding test and approximately 30 working hours in total. This was due to challenge combining it with my heavy responsibilities at work. I am strong with ReactJS and Flask, but had to quickly ramp up on Django and Vue.js for the test. Deployment also caused me a lot of troubles and time. Glad to have learnt!

  2. If I had more time, I would add the following features:

    • Implement recycle bin for deleted favourites/categories
    • Implement functionality to restore deleted favourites/categories from recycle bin
    • Implement pagination when retrieving favorites
    • Integrate elastic search in backend and refactor implemented functionality to search favourites by category correspondingly
    • Implement logging for keeping track of, and easier debugging of production issues when they occur
    • Implement Continuous Deployment using Circleci to automatically deploy github master branch to AWS

What was the most useful feature that was added to the latest version of your chosen language? Please include a snippet of code that shows how you've used it.

Answer: Python asyncio library for asynchronous processing in Python 3.7

    import asyncio
    import time


    async def say_after(delay, what):
        await asyncio.sleep(delay)
        return what


    async def main():
        print(f"started at {time.strftime('%X')}")

        # Wait until the three tasks are completed
        # Should take around 2 seconds instead of 6 seconds for the three calls
        [task1, task2, task3] = await asyncio.gather(
            say_after(2, 'hello'), say_after(2, 'world'), say_after(2, 'anaeze'))

        print(task1, task2, task3)
        
        print(f"finished at {time.strftime('%X')}")


    asyncio.run(main())

How would you track down a performance issue in production? Have you ever had to do this?

  1. How would you track down a performance issue in production?
    • Implement logging for keeping track of, and easier debugging of production issues when they occur
    • Define complex user behavior with multiple steps and transactions, loops, conditionals and custom code, using load testing tools like Locust (python) and Artilery (nodejs)
    • Swarm the system with millions of simultaneous requests, to replicate production user behaviour.
    • Use the statistical result presented (times/latency percentiles, requests per second, concurrency, throughput, etc) to track down performance of individual endpoints tested
    • Use assertions and expectations on the responses to track custom application-specific metrics using javascript/python code
  2. Have you ever had to do this?
    • Partially. I used winston to log http requests, events and exceptions/errors that happen in production
    • I then implemented a JSON endpoint: GET /logs for retrieval of logs for futher analysis and debugging of production issues