I want to build the best database possible. While Postgres is great, there are a lot of core issues that have been around for over a decade. We're working hard to get pgrust production-ready, and it will definitely be production-ready in the near future. I wouldn't be putting hundreds of thousands of dollars into this project if I didn't think we could build a production-ready database.
What is your motivation? What you are doing is very impressive for sure, but the longer term life of this project seems incertain to me. While you might be able to accomplish your goal of production readyness, you'll only have version 1.0 at that point.
Its unlikely an AI written project is one which people will want to maintain even if it "works", which means AI has to maintain it. Maintaining a code base of a production database is going to continue costing 100s of thousands, if its even possible at all with AI. Its also unlikely to find users even if its almost perfect, because a bug in a database is not something users can accept.
Postgres is postgres because its like 30 years old and battle tested. Sure its missing some modern features, but its incredibly stable, which is part of its popularity.
I'm guessing it's something like MongoDB TTL index[0]. It's useful for huge append-only tables where you want a row to be automatically removed after a period of time. The postgres approach is usually to partition your table by the event/insertion timestamp, and use something like pg_partman[1] to automatically drop entire partitions over time.
I want to build the best database possible. While Postgres is great, there are a lot of core issues that have been around for over a decade. We're working hard to get pgrust production-ready, and it will definitely be production-ready in the near future. I wouldn't be putting hundreds of thousands of dollars into this project if I didn't think we could build a production-ready database.
What is your motivation? What you are doing is very impressive for sure, but the longer term life of this project seems incertain to me. While you might be able to accomplish your goal of production readyness, you'll only have version 1.0 at that point.
Its unlikely an AI written project is one which people will want to maintain even if it "works", which means AI has to maintain it. Maintaining a code base of a production database is going to continue costing 100s of thousands, if its even possible at all with AI. Its also unlikely to find users even if its almost perfect, because a bug in a database is not something users can accept.
Postgres is postgres because its like 30 years old and battle tested. Sure its missing some modern features, but its incredibly stable, which is part of its popularity.
I'm certain pgrust can find a long term home somewhere
If you are at that, add a native TTL feature as well. I think it’s been requested for decades.
What do you mean by native TTL? Would that be when rows are automatically deleted if they aren't touched after a certain period of time?
I'm guessing it's something like MongoDB TTL index[0]. It's useful for huge append-only tables where you want a row to be automatically removed after a period of time. The postgres approach is usually to partition your table by the event/insertion timestamp, and use something like pg_partman[1] to automatically drop entire partitions over time.
[0] https://www.mongodb.com/docs/manual/core/index-ttl/
[1] https://github.com/pgpartman/pg_partman