Fable: F# |> BABEL VS crux

This conversation could be referring to https://fable.io/

Other than that, the question is indeed strange and I agree with your statements.

NOTE: For larger codebases with more history it is likely that the Program.fs file will have a lot of orchestration and logic as well. given that it is often where everything clashes and starts, for example the Fable Entrypoint is in Entry.fs and it contains a lot of code. The best you can do always is to start at the bottom of the file and work your way up. Remember: Everything at the bottom uses what has already been defined at the top so there are no circular dependencies or random functions/types at the bottom that can trip you off, everything comes from the top!

I am a big fan of going with web components + plain (build-less) javascript whenever possible, so it is not surprising that I often favor things like the Fable Compiler, where I can target my F# code directly to javascript and be as close to the native JS experience as possible, both for interop concerns and for ecosystem integration.

Fable compiler - https://fable.io/

The F# community is very friendly (these sub-communities as well), and they have plenty of good issues/opportunities to contribute OSS work to across any skill level.

Phosphor isn't hiring right now, but we expect to begin a search for FE/interface engineers over the next few month. Email [email protected] for anyone interested.

Naturally I’d recommend using a better language such as ReScript or Elm or PureScript or F#‘s Fable + Elmish, but “React” is the king right now and people perceive TypeScript as “less risky” for jobs/hiring, so here we are.

Stuff like this: https://github.com/fable-compiler/Fable/issues/1822

It just seems like an incredibly ambitious project that appears to have very little equal but is mainly worked on by a handful of people but no corporate backing. I get the feeling that if you want to use it, you'll either be the only one doing what you're doing or among just a few people. I already use F# and feel this way about the core language itself.

F# is also part of the OCaml family, has a great to-JS transpiler (https://fable.io/) and F# code can also be used in .NET projects.

I have heard many good things about https://fable.io/ Fable converts F# code to JavaScript. There are currently 407 packages available for interacting with existing JavaScript packages and frameworks.

Hi, a couple of my colleagues spent some time working on this integration with our open source database product (https://opencrux.com), and I'm curious to know - has anyone done similar things to connect Corda with a secondary off-the-shelf query engine?

For more details, see the release notes.

The context is that I work on on https://opencrux.com, which offers a bi-temporal Datalog query layer (as well as SQL) that more or less addresses the intersection of the two, since Datalog is great for expressing recursive queries.

I suppose another somewhat important distinction, once again performance related, is that graph databases will typically track index statistics to aid with query planning. For example, Crux uses stored knowledge of attribute-value cardinalities (recently via HyperLogLog) to optimise the join order of a query - this can make a big difference when attempting to traverse large graphs efficiently.

Agreed, recursive querying & bitemporal modelling in SQL are non-trivial problems, and the combination of the two is harder still. For an alternative perspective on tackling such problems I'd suggest looking at Datalog, which makes recursion a breeze, and a database with first-class bitemporality - both of which feature in https://opencrux.com (which I happen to work on :))

I work on Crux so can share a few details about our implementation of Datalog. The query is compiled into a kind of Worst-Case Optimal Join algorithm [0] which means that certain types of queries (e.g. cyclic graph-analytical queries, like counting triangles) are generally more efficient than what is possible with a non-WCOJ query execution strategy. However, the potency of this approach relies on the query planner calculating a good ordering of variables for the join order, and this is a hard problem in itself.

Crux is usually very competent at selecting a sensible variable ordering but when it makes a bad choice your query will take an unnecessary performance hit. The workaround for these situations is to break your query into smaller queries (since we don't wish to support any kind of hinting). Over the longer term we will be continuing to build more intelligent heuristics that make use of advanced population statistics. For instance we are about to merge a PR that uses HyperLogLog to inform attribute selectivity: https://github.com/juxt/crux/pull/1472

[0] https://cs.stanford.edu/people/chrismre/papers/paper49.Ngo.p...