Show HN: 1 Million Rows
1mrows.pages.dev
1mrows.pages.dev
This is such that only what's seen or about to be seen is put on the page. The rest is kept ready on the server on local memory depending on what the user is doing.
This allows for a scalable solution that allows you to view thousands of records and interact with them
https://github.com/bvaughn/react-virtualized https://github.com/bvaughn/react-window https://tanstack.com/virtual/latest
1M rows in memory, with pagination or infiniscroll, is interesting only if we load that data and go offline, and all of filtering and sorting is up to the browser. I'd say that it's a niche use case. Furthermore 1M row x 1kB each is 1GB so we enter an order of magnitude ridden with troubles.
This reads like it's a lazy loading library, but then the roadmap has features like
> Assign Items to Users
> Kanban View
> Collaborative Editing
Which read like something you'd have in a project management solution. How do these two concepts form a cohesive product and who is the target audience? I've seen my fair share of Jira and Trello hellscapes, but I doubt Kanban boards with more entries than memory can handle are very common.
I did some quick benchmarks a couple years back. It's been a while but I want to say that Chrome was drawing 10k rows of a decent size each (10 columns of real world data and about 500b/row iirc) in about 300ms on a 10 year old MBP.
I'll do a little benchmarking later today if I get a chance.
The thing that make performance complicated for a no-code database is when you have 30 interconnected tables, some tables with 200 fields, containing many formulas or other computed fields like lookups or rollups. Updating a single cell, can result in thousands of other rows that must be updated across different tables. If there are 30 users making constant changes, locking PostgreSQL rows under the hood while the formulas are recalculated, and then a couple of n8n workflows making a many API requests to those tables, that's when things get interesting. Especially in combination with features like webhooks, real-time updates, 100+ filters, grouping, 26 field types, date dependencies, aggregations, importing/exporting whole databases.
When implementing a new feature, I've heard users say that's not complicated because it's just adding a checkbox. Making to run it at scale and keeping things performant is what's making it complicated.
Streams in Elixir are lazy, chunked, and backpressure-friendly, so you can process any size dataset without loading it all into memory...whether it's a million or a trillion rows. The trick is you never try to render them all in the browser (that's where virtualization comes in).
So yeah...neat work, but battle-tested versions of this have been around for a while.
tldr: “show me the demo”
The column text length too should be trimmed to a uniform max-length except when clicked on. You could make it pup out on the page with CSS.
A better color scheme too won't hurt.
Had the same idea when I saw https://github.com/rowyio/rowy.
Stumbled on an idea while reading a HN entry a few days back and now I will merge them into a niche product idea.
└── Dey well
Coincidentally I worked on a large table renderer too this weekend: https://github.com/markwylde/react-massive-table
I noticed you didn't quite get to a million rows. For me, it cut off at 671088.
Same thing happened when I built my one.
I came across the same thing. In the end I just manual made the rows appear at their absolute position. Seemed to work well.
It seems this is just a minimum implementation of a ‘virtual list’.
To whom it may concern: I scrolled a bit with the scrollbar on iOS and the page immediately crashed.
Terrible from the front-end side of implementation: - performs worse than your average arbitrary-amount-of-rows-that-won't-fit-on-the-screen library (it should perform the same no matter if its 1k, 1m or 1mm rows) - is seemingly buggy - is pointless on its own, because THIS demo is a client-side demo, and no one loads that much data on the client-side.
Revisit this when this demo is performant AND data is loaded from the backend.
Ignoring that, every front-end JS developer should explore these kinds of libs and also try to implement them themselves, because they're basically front-end 101.