PostgreSQL for Everything

I'm waiting for the followup contrarian shitpost: "Firebird for Everything".

How do you implement HA in postgres, i found MySQL HA stack pretty straight forward with Innodb cluster, MySQL router and Shell.

Years ago I used PostgreSQL under Django to drive an industrial test and inspection robotic cell (which I also designed and built) at a major technology company. It worked very well. PostgreSQL maintained machine state, path planning, sensor readings, faults, operator input, etc.

I wanted to see how far I could push that toolset. It worked surprisingly well. Django's capabilities meant such things as multi-user login pages, access controls and remote monitoring were very easy.

isn't the process per connection restriction pretty heavyweight though?

Hardware is so far nowadays to make people with little systems knowledge confident to make such claims at least from their use cases. However it is neither generally reasonable nor efficient.

In my experience, it still kinda sucks if you want to store blobs. Anything on this front?

Except horizontal scaling.

But a lot of companies are trying to solve that, notably multigres, neki and even pgdog.

the more ive coded the more this is true

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For the graph database idea in Postgres, PG 19 has native support for property graphs [0]. You can set up your tables and their relationships as nodes/edges, then query against them using Cypher-esque [1] syntax.

0: https://www.postgresql.org/docs/19/ddl-property-graphs.html

1: https://www.postgresql.org/docs/19/queries-graph.html

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I really want a daemonless PostgreSQL, in the style of SQLite.

> My tip: Start with PostgreSQL as a queueing system. Only when that does no longer perform well switch to other systems like Kafka, RabbitMQ or SQS.

My tip: store your company's source code on a samba file server. Only when that no longer performs well, switch to other systems like Git.

i love postgresql but once we added ai-generated dashboard to our homegrown analytics tool [0] some of the crazy (amazing) dashboards that the ops team was building began accumulating horrendously slow db queries. I considered dynamically adding indexes or alerting around postgres slow queries but also quickly prototyped mirroring the postgres data in clickhouse. At first could not believe how fast clickhouse was on arbitrary analytics queries - like 60s to 0.5s for some gnarly queries. truly amazing software that just works without any tuning for this kind of exploratory analytics workload.

so yes, i'm still a postgres maximalist (worker queues still in pg [1]) but (especially in the age of quick LLM prototypes) it's always worth measuring the more purpose-built approach.

[0]: https://setoku.com

[1]: https://worker.graphile.org