Sorry for the long, rambling comment. After I wrote it I wasn't sure it added much, but since I invested so much time writing it I figured someone might find something in it useful so in that off chance I am posting it.

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Those were really interesting reads, and it's obvious to me that the author is well experienced even if I find myself at odds with some of the points and ultimate conclusion. To be explicit, there _are_ points which resonated strongly with me.

I am by no means an expert, and fairly middling in experience by any nominal measure, but I _have_ spent a significant portion of my professional experience scaling PostgreSQL so I thought I would throw out my $0.02. I have seen many of the common issues:

- Checkpoint bloat

- Autovacuum deficiencies

- Lock contention

- Write amplification

and even some less widely known (maybe even esoteric) issues like:

- Index miss resulting in seq scan (see "random_page_cost" https://www.postgresql.org/docs/13/runtime-config-query.html)

I originally scaled out Postgres 9.4 for a SaaS monitoring and analytics platform, which I can only describe as being a very "hands on" or a manual process. Mostly because many performance oriented features like:

- Parallel execution (9.6+) (originally limited in 9.6 and expanded in later releases)

- Vacuum and other parallelization/performance improvements (9.6+)

- Declarative partitioning (10.0) (Hash based partitions added in 11.0)

- Optional JIT compiling of some SQL to speed up expression evaluation (11.0)

- (and more added in 12 and 13)

Simply didn't exist yet. But even without all of that we were able to scale our PostgreSQL deployment to handle a few terabytes of data ingest a day by the time I left the project. The team was small, between 4-7 (average 5) full time team members over 3 years including product and QA. I think that it was possible--somewhat surprisingly--then, and has been getting steadily easier/better ever since.

I think the general belief that it is difficult to scale or requires a high level of specialization is at odds with my personal experience. I doubt anyone would consider me a specialist; I personally see myself as an average DB _user_ that has had the good fortune (or misfortune) to deal with data sets large enough to expose some less common challenges. Ultimately, I think most engineers would have come up with similar (if not the same) solutions after reading the same documentation we did. Another way to say this is I don't think there is much magic to scaling Postgres and it is actually more straight forward than common belief suggests; I believe there is a disproportionate amount of the fear of the unknown rather than PostgreSQL being intrinsically more difficult to scale than other RDBMS's.

The size and scope of the PostgreSQL feature set can make it somewhat difficult to figure out where to start, but I think this is a challenge for any feature-rich, mature tool and the quality of the PostgreSQL documentation is a huge help to actually figuring out a solution in my experience.

Also, with the relatively recent (last 5 years or so) rise of PostgreSQL horizontal-scale projects like Citus and TimescaleDB I think it is an even easier to scale PostgreSQL. Most recently, I used Citus to implement a single (sharded) storage/warehouse for my current project. I have been _very_ pleasantly surprised by how easy it was to create a hybrid data model which handles everything from OLTP single node data to auto-partitioned time series tables. There are some gotchas and lessons learned, but that's probably a blog post in it's own right so I'll just leave it as a qualification that it's not a magic bullet that completely abstracts the nuances of how to scale PostgreSQL (but it does a darned lot).

TL;DR: I think scaling PostgreSQL is easier than most believe and have done it with small teams (< 5) without deep PostgreSQL expertise. New features in PostgreSQL core and tangential projects like Citus and TimescaleDB have made it even easier.