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Comment by dondraper36 | Hacker News Reader

There's another reason for that. Deep in my heart, I would love to be part of a team that works on truly data-intensive applications (as Martin Kleppmann would call them) where all the complexity is justified.

For example, I am more of the "All you need is Postgres" kind of software engineer. But reading all those fancy blog posts on how some team at Discord works with 1 trillion messages with Cassandra and ScyllaDB makes me envious.

Also, it seems that to be hired by such employers you need to prove that you already have such experience, which is a bit of a catch-22 situation.

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I feel like the phrase "all you need is Postgres" has the (often unspoken) continuation of "until you actually get to a trillion messages".

In other words, the developers you're envious of didn't start with Cassandra and ScyllaDB, they started with the problem of too many messages. That's not an architectural choice, that's product success.

Absolutely. To put it differently, unfortunately not everyone has a chance to be part of a product's organic evolution from "all we need is Postgres" to "holy crap, we're a success, what is Cassandra by the way?"

As a data point, I've been at two data-intensive startups where they eventually needed to pull (some) of their table-like data out of postgres, and for both that was past a $100MM valuation.

This varies by domain of course, but non-postgres solutions are generally built for very specific problems – they're worse than postgres at everything except one or two cases.

Only places that are making good money can afford to have overengineering.

Overengineering is more prevalent the more money a company makes and companies who overengineers will pay good money to keep the overengineering working.

Something about my old CTO and VP of Eng I respected is they were still technical enough to call out this kind of thing. For as big as that company was they really held down complexity and overengineering to a real minimum.

Unfortunately the rest of the executive has leaned on them so hard about AI boosting productivity they aren’t able to avoid thst becoming a mess

In other words they believed in principles other than increasing personal power

It is a shame that so many companies try to scale by just hiring a lot of people, the more people you have in a single project the more overengineering you will end up with.

Some of it is consequence of managing so many individual contributors, I still believe a lot of companies use microservice stuff as a way to scale to more teams than to more scalability/reliability/observability.

Some of it is just people coming up with clever solutions (and leaving after the fact) and a lot from resume-driven development.

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