[squid3]

Unless I misread the article, but the writer makes a claim of having to support 100 req/sec with about 50 instances. "For Fargate, this meant deploying 50 instances of my container with pretty beefy settings — 8 GB of memory and 4 full CPU units per container instance.". If the api service was just returning some data with a few manupilations, then that is quite inefficient. At NodeChef ( https://www.nodechef.com ), there are users running over 1000 req/sec with just around 12 containers each with 512 MB ram and 2 CPUs.

[abd12]

Original author here.

The two sentences right before what you quoted are helpful:

"I’m not a Docker or Flask performance expert, and that’s not the goal of this exercise. To remedy this, I decided to bump the specs on my deployments.

The general goal for this bakeoff is to get a best-case outcome for each of these architectures, rather than an apples-to-apples comparison of cost vs performance."

I wasn't trying to squeeze out every ounce of performance and determine the minimum number of instances to handle 100 req/sec. I was trying to normalize across the three patterns as much as possible to see best-case performance. I didn't want resource constraints to be an excuse.

[CaveTech]

I think there’s a pretty big flaw in your benchmark because that failure rate is _insane_ and you shouldn’t need anywhere near that hardware to accomplish this. I don’t think your data is credible as a result.