> There is an efficiency penalty converting back to electricity; round-trip efficiency is 40%-45%, but sometimes the steady supply of electricity is worth it.
I was this person for my dad's care, heading back to Canada from California after his third ambulance ride to the hospital and subsequent discharge a few hours later. Turns out his first doctor at the ER had correctly identified the life-threatening condition he had developed, and when the shift changed the new physician ignored the handoff instructions and sent him home. If I hadn't pushed to get the ER to look again he would be dead now. I think the syndrome described is real, and enough doctors are bad enough at practicing medicine that it saves lives.
Pardon, but I dont think you fit the mold if you were right? Or if you were saying that you got lucky (right for the wrong reasons) then that wouldn't mean much about doctors?
I think the Daughter From California will be perceived and treated the same way by care providers regardless of whether they end up being right, and regardless of their actual vs perceived motivation, so from that perspective I think it still fits.
Doctors are just people - they don't appreciate it when somebody parachutes in to question their work, and they make mistakes like anybody else.
It might be worthwhile to expand a bit in the docs on the problems you see with JS concurrency (why you think it sucks, maybe some examples) and then show how ts-chan fixes those problems.
Honestly, I've been struggling to come up with examples that aren't extremely contrived, but are still self-contained enough to easily demonstrate. It might actually be easier to just document patterns, though.
I think y'all have very fair points, for the record.
Unfortunately it is a lot easier to write documentation for an audience that shares the same context / background / experience. The README was written with an audience in mind consisting primarily of those familiar with Go (or more convoluted "communicating sequential processes" implementations), who were frustrated that things that are very easy in Go, are so much harder in JS. It's not something I considered deeply, but I was imagining that it was unlikely that someone would be searching for "channels in JS" without a base level of understanding.
I work/have worked with some pretty talented people, but (in the past) I've found it difficult to convey the value of the sorts of patterns that `ts-chan` is intended to enable, to those without first-hand experience with such patterns.
I’m pretty sure you could articulate how exactly this “better” pattern works versus the default “bad” one. Right now the readme is a pile of illegible jargon to me as a non-go person.
I will say though, I personally get the impression that attempts at "concurrency" in JavaScript (in production code) are quite rare, which I attribute to how difficult it is.
That is to say, I don't know if there really _is_ a "default pattern".
Could you give a side by side comparison (with and without ts-chan) so we can better understand what kind of problem it is attempting to solve?
My understanding was that Go channels / CSP solves concurrency in a multithreaded environment where reads/writes need to coordinated, but since JavaScript is single threaded, I'm not sure I understand why they would be useful in JavaScript. In JavaScript concurrent tasks can simply communicate by writing/reading shared variables.
I am working on better examples, but they are going to take me a while, at the rate I'm currently going.
To be clear, `ts-chan` is not intended to target any use case already addressed by promises or async/await.
You mentioned CSP so I'll assume you've got context re: that topic. I believe I understand your point re: synchronisation between threads, which is fair, but I'd point out that race conditions still exist in JavaScript - I'd even say they are common, at least in my experience. It is easiest to maintain the integrity of the internal state of complex data structures when only a single logical process can mutate that state at a time.
Example in a similar vein: Firewall daemon that accepts commands over RPC, and performs system configuration, in a linear, blocking fashion, to avoid blowing things up (say it runs `iptables` and/or `nft` commands, under the hood). It would be trivial to have a select statement, with a channel per command (or just one, perhaps), receiving the input payload. In JS, the response would probably be via callback, rather than a ping-pong channel recv then send, or the like.
It wasn't a firewall daemon (although it did interact with firewalld and more), but that's exactly a pattern I've implemented in Go, for a past employer. I don't imagine anyone is keen to implement such a thing in JavaScript, but it's a pattern that applies to anything that mutates state, especially if that state is fragile or complex.
IME, race conditions are quite rare and pretty easy to solve in JS, because the flow of code execution is only susceptible to be interrupted at known locations (async function calls). Here's an example of how you could solve the problem you mentioned in a few lines of JavaScript:
function createRunExclusive() {
let runningTask = Promise.resolve();
return async (asyncFn) => {
runningTask = runningTask.then(async () => {
return await asyncFn();
});
return runningTask;
}
}
// Example usage:
// The idea is that any command that should not overlap should use the same "runExclusive" function
const runExclusive = createRunExclusive();
function handleIpTablesCommand() {
runExclusive(async () => {
await doSomethingWithIpTables();
})
}
Hey, that's a neat little trick to implement locking in JS, thanks.
I oversimplified my example perhaps - it also involved handling interruptions (certain system events), maintaining a lifecycle (set up and tear down), and scenarios where it allowed a certain subset of operations to be performed, while performing one of several operations. That last requirement was due to it using shell scripts to perform configuration of the system, and it needing to extract runtime and configuration information from the main daemon.
Still though, thanks very much for your comments, I've enjoyed reading them.
> It is easiest to maintain the integrity of the internal state of complex data structures when only a single logical process can mutate that state at a time.
I agree and this is exactly what js event loop provides. So I don’t understand ts-chan
An operation may take longer than a single tick of the event loop, and may have it's own rules regarding state transitions.
To be clear, I'm not saying "don't do any communication by sharing state", just that there are use cases where it's possible to make it much simpler to reason about.
As an example, you might control the state of "making a HTTP request to perform a search", within the frontend of a single page app that has a map, search filters, and results.
One strategy is to use a buffered channel (1 element), and, when the search filters are updated, drain then re-send the request to the channel.
The logic processing these requests would then just need to sit there, iterating on / receiving from the channel. It could also support cancellation, if that was desired.
(I'd imagine the results would be propagated via some other mechanism, e.g. to a store implementation)
It uses one of the patterns suggested in a comment chain above, which I think is pretty neat, and wasn't one that readily occurred to me: https://news.ycombinator.com/item?id=38163562
I'm not making a case for using ts-chan for any situation where a simple generator-based solution suffices. I wouldn't call the example solution (in my first link) simple, but it's something I'd personally be ok with maintaining. Like, I'd approve a PR containing something similar without significant qualms, _if_ there was a significant enough motivator, and it was sufficiently unit tested. I might suggest `ts-chan` as an alternative, to make it easier to maintain, but wouldn't be particularly concerned either way.
I think it would be useful to generally explain what these primitives do and how they interact with each other. A lot of JS/TS users haven’t used golang, but would appreciate a better solution if they understand it (me included).
Regarding the default vs better, a comparative example with a real concurrent task coded with/out your library would be my preferred way to understand it clearly.
I somewhat disagree with this take, as I felt the intro and "The microtask queue: a footgun" [1] section in the README does an adequate job of laying out the 'why' and the problems with JS's concurrency model. However, it does presume some understanding of Go's channels, so a more explicit example contrasting ts-chan with native JS concurrency could better clarify its benefits for those less familiar. Granted, there is an /example directory, but the benchmarking complexity muddles the readability. Regardless, upon a quick run-through, it looks to be an A-grade library that seems promising for practical use, plus well-referenced, composed, and quite thorough.
Not necessarily, and after giving it more thought, I somewhat retract my previous comment. You do make a good point; it's explained well, but not in concrete terms without assumptions. So, I'll take a stab at it: The core idea is that async functions A and B can communicate through Chan instances, with the Select class overseeing multiple Chan operations, waiting for one to be ready before proceeding. While ts-chan might seem unnecessary for just two async functions, what if you had to manage 8, 16, 32, or more? At some point, Promise.all won't cut it, and that's where ts-chan comes to the rescue. It defines a protocol for channels to better manage communication between asynchronous functions in JS, offering a structure similar to how goroutines communicate in Go.
I might be completely wrong, but the impression I got from your comment is that you haven't been exposed to many implementations using non-trivial concurrency :)
Fair call though, I guess. It doesn't really matter, but I'm certainly used to TypeScript and JavaScript.
Maybe because we probably shouldn’t be doing that in JavaScript. I primarily use Go and work with Go routines often but I’ve never wanted to do anything remotely close to it in JavaScript. If I wanted proper concurrency, I would be using Go, not JS
I'm curious which features highland provides which RxJS doesn't. From what I understand, composable streams from any data source with backpressure support is pretty much the definition of Rx.
RxJS advocates are unhappy with this comment so I'm going to qualify it a little. Apologies for any misunderstanding...
Rx doesn't handle automatic back-pressure (like Node Streams) but does have mechanisms to avoid overwhelming slow consumers. Rx also has delayed subscription which you can call lazy, but not by turning the stream into a pull-stream (allowing you to sequence actions in the way Highland does).
If any of the above needs further qualification or comment please weigh in on the issue by commenting here... but for now I'll leave it at that. I actually list RxJS in the blogpost because it's a good example!
Coming in 2.3, we will have full capabilities for backpressure. We already have window/buffer/throttle, etc. But, I think it's naive to have only one style of backpressure because many are valid.
Just an example of RxJS, and what can be done, which includes a style in which you can do several forms of backpressure, and yes, push to pull based models:
https://gist.github.com/mattpodwysocki/9010149
since `fastSource.map(slowThing)` automatically pauses the source while the slow thing is processing, how is this different from iteration? It also states that in the case of a non-pausable source it will buffer the data. How does it know when to pause vs when to buffer.
Is there some way to know what kind of source you've got, or are the the sources constructed in a way that chooses which behavior you get?
I disagree as fervently as possible; always use reference counting smart pointers. It is substantially harder to guarantee exception safety if you don't make use of them. You'll also be able to program more quickly without devoting extra mental cycles to make sure everything is cleaned up properly.
Using valgrind IN ADDITION is a good idea, but there is no reason to avoid smart pointer memory management.
The best way to deal with exceptions is to disable them. Arbitrary interruptions of control flow will screw up just about any algorithm. Otherwise, you will have to reason about everything using RAII semantics, which work well much of the time. Smart pointers have the same problem. You may believe that everything is cleaned up properly with your smart pointers, until a cyclic reference happens one day.
> There is an efficiency penalty converting back to electricity; round-trip efficiency is 40%-45%, but sometimes the steady supply of electricity is worth it.