YohAsakura
Joined 7 years ago
Joined 7 years ago
Very interested in what data engineers think about this project!
I am not a developer, but the founders (including Yury Selivanov, Python Core Developer, see also https://github.com/MagicStack) and the fact that these people have been investing in the project for four years already, make me think that EdgeDB can be an important project for the database world!
EdgeQL is an explicit attempt to make not a new version of SQL, but a new language. It's about evolution, which is, again, explicitly noted. Saying "evolution is not needed" or "I don't want/need evolution" is strange to me. At the same time, giving constructive feedback is useful, as always.
I don't know how to organize wiki-like things for teams, but here are my two cents for the topic of personal knowledge bases.
First of all, regardless of software, I prefer less structured and less constrained approaches so that it doesn’t take too much time and effort to add things to my knowledge base. I try to minimize the total number of entities (lists, files, notes) and to avoid folders at all. Usually I achieve this by using tags.
Secondly, I am still in a search of a perfect approach, but here is one thing where I succeeded to some extent, namely collections of homogeneous items. I use AirTable for that, but one can use Notion. For example, here is a list of software, I started it several months ago: https://airtable.com/shr8Wd96FurJmiTLs/tbl0n17xFuXrs0kMS?blo... I have a similar list for scientific papers that I read. And I started two more lists for other things. It is important that 1) the number of lists is low (I have 4 now) 2) the lists have simple structure 3) I can always download them as csv and do whatever I want.
As for the rest, I use markdown notes (I edit them with https://typora.io) stored in a dropbox-like cloud for ideas-like stuff. I use Google Keep for super-simple lists and I also have there a temporary note that serves as a buffer for sudden thoughts and ideas, from where I move them later to more appropriate place. I use https://todoist.com for my tasks, they are not organized very well (because no tags in the free plan), but the app is really nice and the number of tasks is not that great, so for now this works fine :)
Could you clarify why the benchmarks show lower performance than vanilla Postgres? Is it because of less efficient generated SQL? (I am not a database engineer and probably missing something obvious)
Other links: - twitter thread: https://twitter.com/YuraFiveTwo/status/1684516633803358208 - reddit post: https://www.reddit.com/r/MachineLearning/comments/15azddj/r_...