We have used AWS CodeDeploy for auto deployment of applications to instances and to update the applications as required. So we thought about the AWS Autoscaling and we have created Autoscaling Group, Launch Configuration and SNS to send notifications to our API Server. We had started multiple processes of Sidekiq on single API Server but it’s consumed more resources and not achieved the concurrency which we were looking for. This single API server instance takes 20 minutes to process 30000 jobs. Previously we had only one API server instance to process background jobs (Sidekiq integrated). When it happens, we increase or decrease our workers by adding or removing EC2 instances automatically depending upon the queue size. The size of our queues change drastically during the daytime. Problem Statement or Case studyįor one of our client who had outsourced software development to Cuelogic, we were using the sidekiq for background jobs processing. This article talks about the problem and the solution implemented. We encountered such an incident in one of the project. Apart from this there are some scenarios where scaling above resources does not solve the problem. The resources are like CPU, storage, memory and so on. With growing demands we need to scale the resources up and down – if you are on the cloud, using cloud migration services can solve the problem. They grow in terms of data volume, users and so on. In real-world scenarios, the applications grow.
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