The Various Kinds of IO – Blocking, Non-Blocking, Multiplexed and Async
rubberducking.com
rubberducking.com
> Level-triggered notification: A file descriptor is considered to be ready if it is possible to perform an I/O system call without blocking.
> Edge-triggered notification: Notification is provided if there is I/O activity (e.g., new input) on a file descriptor since it was last monitored.
The two different types can affect the way the program is structured. For example, the same book says that for level-triggered notification, generally when a file descriptor is ready, only one read/write operation is performed, and then go back to the main loop. Whereas for edge-triggered, usually I/O is performed as much as possible, so we don't miss any more opportunities.
In practice, you usually want your file descriptors to be non-blocking, for many reasons (like writing a large enough buffer can nevertheless block even when the file descriptor is initially considered ready for writing), so even for level-triggered notifications you can read/write in a loop.
Personally I believe edge-triggered notifications can make program design slightly simpler, though I'm not exactly uncertain how much simpler. I'd appreciate if my comment would invite a more detailed and nuanced discussion about those two.
I think I know what you're saying, but in practice this is exactly backwards.
Usually you're doing I/O for some practical reason and want to do simple, well-defined, typically sequential processing on the results. Which is to say blocking is your friend and you shouldn't be mucking with parallel I/O paradigms if you can at all avoid it.
In principle yes, though it becomes annoying when your use-case evolves or you have to miss out on otherwise obvious optimization opportunities because they'd seriously complicate your program.
E.g., Your program is mostly sequential but in one step, you'd be able to do a bunch of requests in parallel.
I think paradigms like async/await are a step ahead to give you the best of both worlds here: You can write your programs as if your requests block, but it still uses async IO behind the scenes - and you can drop the pretense of blocking when it makes sense at any time.
Are you thinking of “green”/M:N threading (as found e.g. in Go)?
Async/await (as found e.g. in Python) is precisely what hinders the style you describe: If your brand new I/O routine is “async colored” to take advantage of non-blocking syscalls, you can’t easily call it from your regular “sync colored” code without “spilling the color” all around, i.e. considerable refactoring.
My context is that most of my recent years work was in high performance systems that handle thousands of streams concurrently and only have a limited amount of cores and a rather limited amount of processing for each io request have so the context switching cost becomes a high percentage compared to actual work done.
But I'm specifically talking about those that need sophisticated strategies to deal with multiplexed I/O (which is a topic of this article you're commenting on).
I've never understood this choice myself. It has always seemed unhelpful given that the read/write returns an error anyhow. Why is it a helpful default?
Basically it's a very sane robustness choice and one of the great ideas of classic unix. It's just surprising the first time you stumble over it.
The actual runtime difference for me was 40 mins -> 10 mins
"The way it works is simple, it uses blocking IO, but each blocking call is made in its own thread. Now depending on the implementation, it either takes a callback, or uses a polling model, like returning a Future."
It depends on the design of your concurrency model.
For event based systems, you may prefer level-triggered notifications. That's because you want to trigger the callback any time data is available.
For fiber/green-thread based systems, you may prefer edge-triggered notifications, and often typically one-shot (e.g. EONESHOT). That's because someone called `wait_until_readable` and when that function returns, they are done. If they want to wait again, they will call the function again.
* Event driven with callbacks (`on data`, `on error`, and so on).
* Stackless coroutines (async/await).
* Stackful coroutines (no direct semantic changes to code required).
Personally, I like stackful coroutines because the concurrency model has minimal effect on the structure of the code, i.e. it's possible to retrofit existing code without change.
I find stackful coroutines (ie, Go, Rust, Python Async, etc.) quite useful, they don't behave differently to normal functions and in most languages also don't require callbacks (you can exchange channels, generators, etc.).
Additionally, such actually threaded implementations of coroutines can take advantage of multiple CPU cores more easily, goroutines can swap quickly in and out of CPU threads. Any waiting action will suspend the goroutine until the IO is available. Unlike in Node.js it won't have to wait until the current synchronous action is done since it can be scheduled in another goroutine at any time (and goroutines automatically yield regularly or you put in manual yields).
There is nothing wrong with hiding the nature of the async under the hood of a sync function as long as the abstraction is clean, which in most cases it quite easily can be. For some cases you'll obviously need locks of course, but a simply RWMutex/RWLock can prevent deadlocks easily for 99% of situations where you will need it.
This is the question and I would disagree. Hiding the asynchronous nature of any given function is a dangerous illusion. Languages that promote it through stackful coroutines ultimately lead to programs that are difficult to reason about and often don't perform well because developers lack any real control over concurrency and are completely at the mercy of an opaque scheduler. (And frankly, even languages like Go which get this 'right' ... don't. Eventually even people who like Go realize Go channels are bad[1].)
The answer here I think is going to be higher kinded types a la Rust. Asynchronous is a "fundamental property of time and space" -- ignore it at your own risk. But if the type system can elegantly capture the difference between present-values and values-to-come then you can realize the best of both words: code that reads like a single-thread-of-control but is actually heavily asynchronous. This is why 'await' style stackless coroutines and Futures prove to be so popular.
Though I agree more research is needed here. I was disappointed to see the Rust developers (who apparently convinced people to pay them to research this stuff) converge so quickly on stackless coroutines.
[1] https://www.jtolio.com/2016/03/go-channels-are-bad-and-you-s...
I think of myself of an older generation so my approach to concurrency is to use locks and fancy data structures and fancy architecture to avoid race conditions.
I would suggest looking into how easy it is to write an HTTP handler in Go. That involves plenty of go routines. Each connection is a go routine. Each HTTP request also gets it's own (in HTTP/2 the former and later may not be the same).
I don't have to think about the blocking nature of an operation to, for a recent example, fetch and parse a remote webpage, I simply do it. The Go runtime will take care of scheduling the goroutines such that if a new request comes in while I'm a tight loop the HTTP server can continue to handle queries.
In JS land however, I can't write tight for loops without potentially blocking up the entire server.
You mentioned the solution yourself, hide the async nature of the code. My HTTP code doesn't read like async code until you hit global resources. That's what I essentially want.
I do not want to think about async until I need it and when I need it I should be able to pretend it's sync without cognitive overhead; that enables me to efficiently reason about the steps a routine takes before every resource a request uses is released again.
Go is fantastic about asynchronous programming and the fact that it hides it is one of Go's strengths. Channels are/were overused when I wrote that post but that's completely independent of Go's excellent async support.
My all time favorite blog post on the matter is http://journal.stuffwithstuff.com/2015/02/01/what-color-is-y... which is required reading about why a language hiding a program's async nature is actually the best thing it can do, and Rust's decision to not implement green threads at the language level was wrong, in my opinion.
I understand where you are coming from. However, in my experience, it's "turtles all the way down". Pre-emptive multi-tasking, NUMA, multiple CPUs, CISC, all add their level of asynchronicity to your program execution whether you are aware of it or not.
The right answer in this case is "hiding the nature of async under the hood". Yet, we've seen in a few cases recently, e.g. the recent CPU bugs, where this isn't entirely possible.
> ultimately lead to programs that are difficult to reason about and often don't perform well because developers lack any real control over concurrency
My experience has been the complete opposite. It's hard to make complex parallel programs and reason about execution, but concurrency primitives like fibers and reactors are a negative overhead abstraction which makes code simpler and easier to understand and therefore allows us to do more complex things with the same cognitive potential.
Python Async is "stackless coroutine", not "stackful" according to the taxonomy given by GP. They also behave quite differently from normal functions.
It illustrates decisively that a language that doesn't hide async behavior balkanizes libraries into (at least) two camps for everyone's detriment.
Fixing this and doing it correctly requires language support, yes (the language's mutexes must work right in the appropriate contexts) but it is much better for the ecosystem to hide the details of async.
Making the programmer deal with async nature (even with await, yield, etc) adds additional unnecessary details for the programmer to worry about and makes the program harder to reason about.
As for where the industry is headed, Go shows what it's like getting this right.
Can you see a problem with this approach?
I would imagine articles that focus on a small subpiece of concurrency are inadequate articles about concurrency in general, yes.
I want to point out though that the article is not claiming OS 1:1 threading is better than async/await/callbacks. OS-level 1:1 threading is too heavy, and it's obvious that many programmers reach for callbacks to handle high concurrency evented I/O. The article is claiming that languages that allow a blocking style of programming via green threads (to still get the same high concurrency) allow a more natural way to program.
Your point was the article was one of "the most inadequate articles on concurrency ever written." I agree. It wasn't trying to be a complete article on concurrency. It says nothing about the actor model.
Then you said it "Even managed to claim shared memory multithreading as superior to all other concurrency models." The article doesn't talk about all other concurrency models. It only talks about the programming styles that "shared memory multithreading" is better than.
I would say this is a subjective conclusion at best. We might ask instead which model facilitates collaboration. That is in a large, very concurrent program composed by many developers which model is going to lead to more robust code. The interesting thing about M:N programming is that large programs in 1:1 languages (Java, C#) tend to converge on to the M:N model. Many large concurrent Java programs therefore end up involving several carefully monitored threadpools with well-defined contracts that describe how work gets scheduled and distributed between the threadpools. But this is not an argument for M:N languages precisely because the "problem of scheduling" often tends to be very domain specific. Other programs have lots of success with the Reactive model because they're never truly blocking and waiting for information that hasn't arrived. Some find success with Actors. It's worth considering then that different concurrency problems call for very different solutions and saying one model is better than another is erroneous.
That article simply begs the question. Or rather, I think the author has missed the point. Advocates of Futures-based APIs for example don't regard the balkanization as a bad thing. That's the point -- asynchronous functions really are different, they really do accept and return different types of values in the form of callbacks and futures.
Threading and shared memory don't solve anything, btw. These are implementation details. This is not about implementation so much as it's about expressing contracts between modules. Go channels are, frankly, rather useless but still, to my point, they don't try to hide asynchronous behavior. Go could've gone whole hog and provided yield and subsumption but I think they wisely realized that concurrency at scale requires shared nothing message passing. Or rather the essential insight of CSP (and Actors, and PPP and so many other models) is that reifying events and the flow of events is a good thing.
(Though I've heard it proposed that this is just because the limited human mind is "event oriented". Super intelligent martians and AIs would use Dataflow[1] languages to talk about the illusion of time. They might see the world as a giant spreadsheet.)
> Making the programmer deal with async nature (even with await, yield, etc) adds additional unnecessary details for the programmer to worry about and makes the program harder to reason about.
Again, I think it's kind of wacky to think that the async nature is "unnecessary" or accidental for the programmer. The solution the programmer is trying to model is asynchronous. The whole point is to allow the end User to do stuff while you talk to the database or process multiple trades at the same time. You can't wish this asynchronous nature of the problem away because its asynchronous nature, rather than being an unnecessary detail, is the problem. A lot of very smart people have spent a lot of time trying to "solve" concurrency by making it invisible. All failed. The issues around concurrency are a problem that must be addressed head on.
Types are the abstraction that let us be very precise for describing how a system can change. Using the type system to bring the problems of concurrency to the forefront so that they can reasoned about and enforced by the compiler is absolutely absolutely a good thing. People don't like this though because concurrent APIs are "viral". One asynchronous function now requires all callers to either block or become asynchronous themselves. But this was always the case anyways. As Morpheus would say, "How would this be different from any other day?"
I'd argue that all code at some level is asynchronous. Once you start considering how the CPU executes code, and how OSes schedule threads/processes, it's not all that different. Yet, for some reason, we call that code synchronous.
It is, of course, possible to wrap all of them in a stackful coroutine interface - but that is no longer "retrofitting existing code without change". My impression is that the cumulative experience is to go either with event driven (in C / C++ / Python) or Stackless (in Python / JavaScript) or threaded (in Java), but stackful coroutines very rarely deliver on their promise.
The difference is that fibers stay on the thread they were created on, while green threads can transit between OS threads. There have also been projects to cooperatively schedule OS level threads to minimise latency, but that's a bit different.
I don't know that many decent green thread implementations [off the top of my head], but an interesting one is yahns: https://yhbt.net/yahns/design_notes.txt - to me, green threads have all the benefits of coroutines but all of the downsides of multi-threaded programming.
I implemented fiber based concurrency for C++ here: https://github.com/kurocha/async. An example would be https://github.com/kurocha/async-network/blob/d1d2bf12eb2d9b... - calling read yields the fiber if it would block as implemented here: https://github.com/kurocha/async/blob/2edef4d6990259cc60cc30...
I implemented almost exactly the same model for Ruby here: https://github.com/socketry/async and if you are interested in how it works, see these projects which build on it: https://github.com/socketry/async#see-also
A capabillity which represents a cpu core contains a queue of TSO's. If a thread blocks on something it can also move to a different queue which is used for thread synchronization, blocking on computations from other threads, io etc.
Im going to ask you a favor, because i love this comment so much based on how info-dense it is.
I challenge you to unpack it not as an ELI5 - but ELIInfant.
Please accept. Many shall learn from such.
However, he is an advocate for stackless coroutines. They are a good idea in some situations but they still have a semantic overhead unlike stackful coroutines. They may have slightly better performance but at the expense of semantic overhead (async/await, explicit futures/concurrency).
I like to think that I'm not advocating for a specific design choice, but for a design choice which makes it possible to write higher level abstractions without the need to expose the underlying concurrency models.
If the video doesn't answer all your questions, I'd be happy to answer specific questions.
On Windows, you have to put your thread to sleep to receive any callbacks [1]. If OS would suspend your thread at random point to execute a callback, that could lead to hard-to-detect/debug deadlocks and race conditions.
[1] https://msdn.microsoft.com/en-us/library/windows/desktop/aa3...
> That is, gone are the days of serial communication. In that sense, all communications between the CPU and peripherals is therefore asynchronous at a hardware level.
At a hardware level, 99.99% of modern peripherals are based on serial communication (e.g. USB, PCIe, I2C...), and these serial comminications are of course fully asynchronous. They implement concurrent communication at a higher level.
> Think of the simple case, where the CPU would ask the peripheral to read something, and it would then go in an infinite loop,
Many peripherals are logic blocks that doesn’t infinite loop internally. Infinite loop is a kind of software techniques and they don’t need to implement looping to do their job.
https://github.com/lettergram/IPC-examples
Lots of fun to play with
I also got the sense during the height of the Node craze that some of the people who were very excited about it had the impression that Node had some sort of unique access to a "non-blocking" kernel layer or something (of course, they just weren't thinking about the kernel layer at all) that no other languages were using. In reality, if you set something like a Go program to a single OS thread for execution, you could mechanically translate a Go program into something that executes in principle identically to a Node program at the assembler level, at least in terms of what events come in and what sort of high-level code gets executed in response (vast differences in the literal instruction stream, of course). The converted Go would have a lot more "event handlers" than you'd expect because Go programs also break at function calls and a few other places (as the compiler is doing it it's easy to have lots of "event break points" that you'd never write by hand), but the compiler essentially converts the "blocking code" that uses lots of threads into "non-blocking event-based code". The difference between the two is not at the execution layer; it's at the layer you're programming. In fact pretty much everything nowadays is "non-blocking event-based code" at the execution layer, because the high-powered kernel functions that make that efficient implement event-based code, so anything else you find at a programming-language-level must be converting that abstraction into the language's abstraction. Even using kernel threads still turns into non-blocking event-based code under the hood, if you dig far enough in.
(A more clear example of how critical the layer of abstraction you are looking at is IMHO is the distinction between immutable and mutable. You can implement an immutable abstraction layer on top of mutable storage, which given that our hardware is based on mutable storage, all immutability in programming languages necessarily comes from. You can implement a mutable abstraction on top of an immutable layer with a worst-case penalty of O(log n), as in the worst case you represent memory as a immutable tree of bytes and then mutate as you would normally. Whether or not something is "mutable" or "immutable" critically depends on layer of abstraction you are looking at; it does not have a non-contingent answer, or, if it does, since our hardware is mutable the answer is that immutability does not exist at all. But while true in a sense, that answer is not as useful as one that is contingent on the abstraction layer being examined.)