AWS Auto Scaling vs. Django

It is well suited for scaling up our cloud virtual machines to handle the increase or decrease in workload. It really helps us to satisfy the demand because it doesn't take lot of time to spin up new machines. If there is unpredictability in the demand it is very useful. But it is a over kill if demand is consistent.

Django is really well suited for backend heavy work where logic is key and you want a solid foundation that can scale well. If you are comfortable with python and work with a lot of different third parties, it is an extremely easy decision to make. But for a quick prototype or front end heavy work or where the system has low resources, there are different frameworks available.

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• Set policies to add more instances when CPU utilization exceeds.
• Prevents downtime, as we don't need to stop the servers.
• Auto Scaling group can be setup for different types of apps.

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• Update code and configuration when the number of servers is always changing
• How will user-uploaded content make it to new servers

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We use AWS auto scaling for scaling up our cloud virtual machines to handle the increase or decrease in workload. It really helps us to satisfy the demand because it doesn't take lot of time to spin up new machines. I gave the rating 10 because it really does help you to handle the sudden spike in number of requests.

It is a bit harder than the other frameworks for new users and so for that reason some may find it harder to use than others. The Django admin is great but the UI is a bit dated and some functionality need explicit plumbing. Like how to handle related records within the admin panel.

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Used for the below reasons:

• Easy to implement and use
• Pay per use, cost savings
• Automate and build scaling plans
• Automate performance and workload

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I was unable to add Ruby on Rails, FastAPI which were also alternatives. The product was complicated and needed to be stable to high load. Django is battle tested and very stable and actively managed. The other python were relatively newer at the time. PHP and ruby were not the best language choices and so the choice of python drove down the actual alternatives to only a handful with Django being the most reliable, widely used among them

Incentivized

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• optimized the cost effectiveness
• save lot of money which help the organization to use it in effective areas
• Not applicable for all use cases.It is specific use cases and business problems