I have worked with engineers who treated their work as glueing together various services to get the results required. They were absolutely shit engineers, whose approach to building services for massive scale were simply
1. Glue together whatever services are available and seem to make sense.
2. Throw increasing amounts of traffic at it and check what fails
3. Try to patch around failure point
4. blame the service provider for "not scaling"
5. Ask for more and more machines
6. Exclaim how great their design is on the basis of the cool class hierarchies and interfaces they wrote. Teach "junior devs" their "OOP design skills and patterns"
7. Go to step 2
They think of themselves as great engineers who scale things. The reality is they are absolute shit engineers who have no clue what they are building, frequently have crazy O(n^2) loops in their code, dont recognize a graph problem if its staring them in their face.
Examples
1. They needed a way to rerun map reduce jobs to regenerate data for previous 30 days. It was a straight forward, graph topological sort problem. Instead they built a crazy solution using multiple instances of Airbnb airflow. One server ran out of memory for these airflow instances, so they allocated a whole damn cluster of machines to run 30 instances of airflow! The job of the cluster? Issuing map reduce jobs to the hadoop cluster. They needed a cluster of machines just to issue commands to hadoop. They gave an elaborate tech talk about "scaling to 30X capacity" to process 30 days of data. Meanwhile, my team was processing 3 years of data. We considered hamake for our data rebuild process. Eventually, I wrote a 70 line scala topological sort routine that would do the job for us, because it gave us more flexibility than hamake and linked to our code libraries.
2. Some guy wrote a data pipeline to summarize a days results that took 24 hours to run, thanks to 40 different joins. I rewrote it to use a single group by. The code ran in 10s of minutes. He didn't know how hadoop worked or external sorting, beyond writing arbitrary sql. So much for looking up algorithms when required.
3. Guys presents beautiful OOP code that has major race conditions in a distributed environment. I point out the race condition. Keeps proposing hacks to "solve the problem". Does not understand transaction processing or locking algorithms or what options are available to him. Hadn't heard if read write locks. What are the pros or cons. Doesn't understand the difference between local locks and distributed fault tolerant locks. I explain it to him. He eventually comes up with a solution using zookeeper that will need zookeeper to process 1000s of updates per second. I give up.
"DSA, what DSA? They are for kids. I will look up an algorithm when I need it".