New comment by davismwfl in "Ask HN: What is your primary database?"

To be fair there are people far more of an expert to provide guidance on postgres tuning, but here is a slide share (near top google result) I have used for some high write ideas: https://www.slideshare.net/GrantMcAlister/tuning-postgresql-... . Not advocating everything said there, but it has quality points to utilize.

The idea, is for example on high write loads, I don't really need to index for read, and indexes in general will cause slower writes in any SQL system. In addition I might choose different types of disk to store data on depending on durability and load. High write, SSD optimized (with high IOPS) for write and lower durability is probably ideal, yet for high reliability needs, high durability magnetic slower disks is likely a better choice. Stuff like that makes a huge difference, in fairness AWS (and other cloud providers) make it easy to make the choice today, but it makes a huge difference in performance capabilities and so many people don't understand the details of the selection they make.

This also leads us to adjusting the parameters for the Postgres install, which of course if you use RDS or similar you are more limited on, but it is still critical to weigh.

If you have questions in specific I am happy to try and help or I will ask one of my teammates to respond that has more specific knowledge. As an example, my consulting team a few years back scaled a Wordpress install using MySQL to millions of requests but did so using EC2 instead of RDS and used way less resources because we tuned the database and queries to be efficient for the hardware. It goes back to the old adage, for a 10x increase in hardware, there is likely a 100x increase in software if you just look. Our client at the time kept upgrading the hardware on AWS to the next larger instance for RDS, and we instead moved off RDS to EC2 and then tuned the instance and MySQL install to be more efficient for their specific workload. Obviously highly tailored to their specific use case but we saved them a HUGE amount of money and still provided the same scalability and reliability.

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