perspective VS arrow-julia

Why do you want to render to canvas?

Perspective seems to be the most performant html table. It is more focused on extremely fast updates than styling, although it looks good.

Glide is a newcomer that also renders to canvas.

https://github.com/finos/perspective

https://github.com/glideapps/glide-data-grid

SQL workbench also uses https://perspective.finos.org/ for tables. It's a WASM table library which pairs nicely with duckdb and works well with large tables.

Yes. We are working on adding support for aggregation and pivoting using https://github.com/finos/perspective

I haven't looked extensively at react-datasheet. It looks like it is trying to build more of a full product than the other data tables.

I have used ag-grid extensively, its an impressive product. Some pieces are a little awkward to use, particularly auto-sizing. But generally ag-grid has thought of most functionality and has a solution. The creator of ag-grid had a great interview on Javascript Jabber [1].

The other serious data table component that I have seen is FinOS Perspective [2]. This is extremely high performance, also more specialized and probably harder to customize. I think Perspective renders to a canvas element from Rust/C++ compiled to WASM (not 100% sure). It is also made for streaming updates.

AG-Grid supports streaming updates... but only in the commercial version.

Eventually the data model for these types of tables becomes tricky. I will be investigating parquet-wasm for my use case. Hit me up if you want to collaborate.

[1] https://blog.ag-grid.com/javascript-jabber-podcast/

[2] https://perspective.finos.org/

Something like https://github.com/finos/perspective ? We use an OLAP(-y) WASM engine to provide query-ability to our data visualization tool, and doing the calculations in the browser is cheaper and simpler than a server-side database for datasets that fit in browser memory.

For some examples of people porting existing C++ Fortran libraries to julia, you should check out https://github.com/JuliaLinearAlgebra/Octavian.jl, https://github.com/dgleich/GenericArpack.jl, https://github.com/apache/arrow-julia (just off the top of my head). These are all ports of C++ or Fortran libraries that match (or exceed) performance of the original, and in the case of Arrow.jl is faster, more general, and 10x less code.

I guess current preferred format not feather, but arrow: https://github.com/JuliaData/Arrow.jl

Excited to see this release's official inclusion of the pure Julia Arrow implementation [1]!

It's so cool to be able mmap Arrow memory and natively manipulate it from within Julia with virtually no performance overhead. Since the Julia compiler can specialize on the layout of Arrow-backed types at runtime (just as it can with any other type), the notion of needing to build/work with a separate "compiler for fast UDFs" is rendered obsolete.

It feels pretty magical when two tools like this compose so well without either being designed with the other in mind - a testament to the thoughtful design of both :) mad props to Jacob Quinn for spearheading the effort to revive/restart Arrow.jl and get the package into this release.

[1] https://github.com/JuliaData/Arrow.jl