DevOps Real Time Scenarios Interview Questions | DevOps Real Time Challenges | DevOps Real Time

DevOps Real Time Scenarios Interview Questions | DevOps Real Time Challenges | DevOps Real Time | #10

Hey Folks,

"This question was asked in an interview, and was in an AWS certification, as well"

Welcome back to another video in the DevOps Real Time Scenarios
series or DevOps Real Time Issues. In today's video, we are going to talk about the following:

Problem Statement:-

Food delivery service
Growing rapidly
The order processing system, scaling problems during peak hours

The order collection process occurs quickly, but the order fulfillment process can take longer. How to solve this?

Condition - Data must not be lost because of a scaling event.

Solution:-

1) Provision two Amazon Simple Queue Service
2) One for order collection
3) One for order fulfillment.
4) Configure the EC2 instances to poll their respective queue.
5) Create a metric based on a backlog per instance calculation.
6) Scale the ASG based on the metric discussed in point #4

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[verp]

Really Helpful. Your Explanation is great🙏in every video

[RanjitCh]

Hello..Ravish bro.. questions are very interesting, please keep posting more n more videos until we become Solution Architects 😇👍

[akashlodhi]

What you are doing for us, its huge motivation for us :)

[praveensirvi]

amazing real time scenario question for interview preparation. This will be very helpful. Thank you so much Ravi Bhaiya .

[adityapawar]

Great explanation and very nice example .. please do make more videos like this

[sachinnagar]

Such an amazing way of explaining real time problems. I really appreciate your work towards us. Thank you for videos like these. 🙏🏻🙏🏻

[AnujYadav-dfuw]

Amazing video. This is exactly what I was looking for.

[jalandharbehera]

Its amazing video.... really its superb sir

[kasunperera]

Why we need sqs for order collection even though it works perfectly is it for future proof

[SunitaSonawane]

Can we use 2 SQS queue (so that Data lost will not happen) and use predictable scaling during peak hours.