There is a trap in distributed system design - seeking to scale-up from a single-host perspective. An example - we have apache and want to scale it up, so we pu

There is a trap in distributed system design - seeking to scale-up from a single-host perspective. An example - we have apache and want to scale it up, so we put it in a container and generate its configuration so we can run several of them in parallel. This leads to unnecessarily heavy systems - you do not need a container to host a server socket. Industry puts algorithms and Big O on a pedestal. Most software projects start as someone building algorithms, with deployment and interactions only getting late attention. This is a bit like building the kitchen and bathroom before laying the foundations. Algorithm centric design creates mathematically elegant algorithms that move gigabytes of io across the network for every minor transaction. Teams wrap commodity resource schedulers around carefully tuned worker nodes, and discover their performance is awful because the scheduler can’t deal in the domain language of the big picture problem. I think it is interesting that the culture of Big O interviews and k8s both came out of Google.