Linux 3.9 introduced a new way of writing socket servers
freeprogrammersblog.vhex.net
freeprogrammersblog.vhex.net
That is not true. It is an often repeated misconception. It makes it sound like Python creators were just incompetent and just stuck threads in there even though they are completely useless. In fact Python's threads work well for IO concurrency. I used them and saw great speedup when accepting and handling simultaneous socket connections. Yes you won't get CPU concurrency, but if your server is not CPU bound you might not notice much of a difference.
IO concurrency is real concurrency. In 8 years using Python for fun and professionally I probably wrote more IO concurrent code than CPU concurrent code. Even then for CPU concurrent code I would have had to drop into C using an extension (and there you can release the GIL anyway).
Now, the obvious follow up is that in case of IO concurrency you are often better of using gevent or eventlet. You get lighter weight threads (memory wise) and less chances of synchronizations bugs (since greenlet based green threads will switch only on IO concurrency points, socket reads, sleep and explicit waits on green semaphores and locks).
Now this is IO concurrency but it is real concurrency. Adding CPU concurrency would be very nice. It might speed things up a bit, or it might not. It really depends.
As an example consider haproxy. The little proxy that could. It handles large amounts of concurrent connection in parallel and it is single threaded in its default configuration. I've heard of 100k connections. It deals with IO concurrency. Chances are, making it multi-threaded might not dramatically improve its performance (it might even slow it down).
Of course, does this really make a difference for network IO? Almost always the answer is no. The difference will be on the order of microseconds, maybe milliseconds.
* Concurrency is a property of relationships between tasks in the problem (or the algorithm). Is fetching one page for example independent of another one. If could be so it is concurrent, but it also might not be true, if it is a child page. You have to fetch one page, look at links and then fetch those pages. To the tasks have hard coded sequence so those are not concurrent.
* Parallelism is how that algorithm or problem is solved or executed. It could be that you can execute all concurrent units at the same time so you achieve parallelism, which is great. Or it could be that due to a particular architecture or other reasons you execute it serially. Maybe you just have a while loop and fetch one page, wait fetch another one. The problem is concurrent but it is not run in parallel.
Notice my definition doesn't include CPU or IO in there. In real world there is both. CPU concurrency interleaved with IO concurrency. That you can then end up running none, one or both in parallel when you execute.
Concurrent tasks complete in the same, overlapping time period.
Parallel tasks run at literally the same time.
I don't really agree. The heart of the problem of concurrency is non-determinism, but it's perfectly possible to have deterministic parallel algorithms. Normally the key is not letting the parallel operations interact with one another.
So to me, a useful (for discussion) definition of concurrency involves multiple logical tasks, overlapping in time, and interacting with one another, in a non-deterministic way.
Whereas parallelism is concerned with taking advantage of physical hardware that can do more than one thing simultaneously.
And concurrency does not imply parallelism, nor does parallelism imply concurrency, under my understanding. In particular, data parallelism like SIMD or CUDA is not concurrent.
Hmm, I would think it would be the opposite, they're concurrent precisely because they don't have to interact. They can run independently. 2 requests from a server are concurrent because they don't have to know about each other and don't have to interact with each. This is a property of the problem domain (idealized web requests) this doesn't tell us anything about how they'll run (in parallel or not).
> Whereas parallelism is concerned with taking advantage of physical hardware that can do more than one thing simultaneously.
I agree with that.
> And concurrency does not imply parallelism, nor does parallelism imply concurrency, under my understanding. In particular, data parallelism like SIMD or CUDA is not concurrent.
Don't quite agree with that and don't see why SIMD algorithms have to be a special case. Maybe you compute a dot product between 2 vectors. If you write the algorithm down you have a bunch of multiplications and a sum. You notice that it has a lot of concurrency (the algorithm). If you don't have SIMD you could spawn a thread to multiply out each pair and then to sum. That would be silly. But you'd run in parallel. You could just do it sequentially with a for loop. But if you have SIMD, it know how to run those concurrent algorithmic steps in parallel.
In computer science, concurrency is a property of systems in which several computations are executing simultaneously, and potentially interacting with each other.
But it doesn't really matter, what matters is whether people understand each other, not whether they're using the "correct" words.
What annoys me is when half of the comments is about form, not about substance. It's understandable, people (including me) love to correct mistakes of other people, but it still annoys me.
I like to avoid confusion whenever possible.
1. Concurrent means having two cups of water, one in each hand, and drinking(think CPU computation) a little bit from one, then switch to the other. While you drink from a cup someone is filling up the other (think socket IO)
2. Parallel means having two cups, one in each hand,and lifting them up and drinking from them at the same exact time.
http://en.wiktionary.org/wiki/concurrent
http://en.wikipedia.org/wiki/Concurrency_(computer_science)
To state the obvious, you're attempting to make distinctions that either don't exist, or do not have a consensus. You need to find new words. Your definition of concurrent is just... wrong.
http://existentialtype.wordpress.com/2011/03/17/parallelism-... http://blog.golang.org/concurrency-is-not-parallelism http://ghcmutterings.wordpress.com/2009/10/06/parallelism-co...
Concurrent and parallel, in their literal senses can be synonyms – and sure enough, if you read the first source you've just linked, at 2a you'll see concurrent defined as "in parallel". In the case of programming, a concurrent program is not parallel, though it could be if you have multiple cores.
I suspect we're actually in agreement that ideally "concurrent" would be a description of capability and "parallel" a manifestation of that capability, but the fact is you're never going to get everyone to agree on (or remember) that[1]. So, again, new words are needed.
[1] Edit: I just found the later post of yours where you said this:
"I like to imagine that concurrent processes, concur (agree) on how they should share the time slices of the CPU, and parallel processes don't ever give a damn about each other, cause each has its own core, and like parallel lines, they never meet."
So, yet another novel definition of "concurrent". And yet you think other people are wrong. Heh.
The fact that it's so hard to remember which is supposed to be "concurrency" and which "parallelism" is an indicator of how weakly these words are bound to those meanings.
I like to imagine that concurrent processes, concur (agree) on how they should share the time slices of the CPU, and parallel processes don't ever give a damn about each other, cause each has its own core, and like parallel lines, they never meet.
Now, for clarity purposes I would add that in fact concurrent processes don't "choose" per se when to run, that's the schedulers job.
I think the whole concurrent/parallel confusion is worsened even more by that fact that on a multi-core system concurrent processes can in fact be executed in parallel.
Maybe that is why they stick with Latin when practicing law. Each term then is in a separate language and less likely to cause confusion or collisions with the English language.
What ithkuil pointed out is that the root "cur" in "current" comes from the Latin for "to run". Thus "concurrent" does not literally mean "at the same time"—what I said was wrong. It literally means "running together". There's not any piece of that word that technically refers to time, so it's not an oxymoron to use it to describe interleaved timeslicing.
Perhaps it would be clearer if we spoke of "concurrent vs. simultaneous" processes rather than "concurrent vs. parallel". I'm not sure; I still don't think everyone is talking about the same things.
Context switches without the compiler being explicitly aware of when this happens can yield similar issues whether the context switch is done in software or if memory accesses are interleaved because the code is genuinely running on multiple execution units.
The problem stems from the fact that both the compiler and the processor might perform memory access in a different order than what you'd expect. I'd suggest an interesting read about it at http://ridiculousfish.com/blog/posts/barrier.html
Asynchronous programming allows to process effectively one event at a time, where things happen exactly as defined by a simple programming model, and the compiler can know what it can safely be done to produce the requested side effects.
If the grain of the events is fine enough you can reach the same effect as being concurrent, from the point of view of task being performed, while actually there is nothing really concurrent from the point of view of the actual code that is running.
Thus, it's not about the definition of concurrency per se, but about what is being concurrent in the system.
http://joearms.github.io/2013/04/05/concurrent-and-parallel-...
For example, are parallel processes always also concurrent? It's hard to imagine a more elementary question, yet the different definitions don't all answer it the same way. That alone casts some doubt on how well-defined these terms are to begin with.
concurrency is a property of the algorithm, parallelism is a property of the execution environment
To expand on the above. This means that a particular problem can be talked about in terms of smaller sub problems. Example, you are serving a site. Sub problems are handling each client request. Another example of problem "crack password via brute-force method", sub problem is "try one particular password". Here is where discussion comes about whether there are concurrent sub-problems or not. We are not sure about how they'll run yet.
>For example, are parallel processes always concurrent?
Not sure what you mean by that. Are these processes solving one particular problem. Concurrency and parallelism make sense for a particular problem or algorithm. How are these processes related? Do they just happen to run on the same machine but otherwise are solving separate problems. Then maybe it doesn't even make sense to talk about either concurrency or parallelism.
Now you can turn this on its head an look at it from the point of view of a kernel designer. His very simplified algorithm is "fairly schedule processes and IO" for all the users. So his problem now deals with any two processes but these are now all part of a problem.
I guess I am trying to say that some questions just don't make sense to ask.
It'd be nice if new terms were used entirely, really.
I thought that by definition this is impossible under the GIL. Not completely sure, but would love to know. I have written thousands of lines using gevent and eventlet but have only achieved peaks of 10 Mb/s (on servers that have at least 100), and I'm sure that truly concurrent languages could fully take advantage of that throughput -- currently in the process of migrating from Python.
Python's GIL won't let you execute Python code in parallel like say you start multiplying numbers in one thread and another. You won't multiply twice as many numbers because of the GIL. But for IO concurrency you should achieve parallelism (unless you have a string CPU consuming part in there as well).
I'd be curious to see if your migration does allow better bandwidth peaks, though.
So long as you don't have any CPU bound threads competing for the GIL ;)
If you have a CPU bound thread, it may be worth to pay the performance penalty of separating some of the program flow in different processes.
That is a common misconception. And it seems to me nowadays most concurrency people deal with (at least when it comes to server and web back-end world) is heavier IO bound. Yet everyone automatically default to their CS 102 -- algorithms class when they think about solving graph problems in parallel or multiplying matrices. So concurrency automatically is implied to be CPU concurrency.
That's not true. Most applications, with the possible exceptions of proxies, are also CPU bound.
Take for example a web service that receives JSON documents. The act of parsing JSON documents is CPU bound. The act of creating a response is CPU bound. In between you can also have IO bound operations, like fetching data from a MySQL database or a Memcached instance, however in the process of creating the final response you also need to transform the data received and that's also CPU bound.
As a real world example, I worked on a web-service written in Scala and running on the JVM. Initially it was running on only 8 Heroku dynos and these instances were receiving over 30,000 requests per second of real traffic. These Heroku instances are of course under-powered, because on my modest laptop the same web server is able to handle more than 10,000 requests per second.
And yes, asynchronous I/O lets you easily have 100,000 connections per server. But if you need throughput, then the CPU starts being a bottleneck.
Of course, my problem with Python and why I migrated away from it is that in truth Python sucks for asynchronous I/O too. But that's another story.
JSON is parsed in C with CPython, or in assembly with PyPy.
As a real world example, I've done realtime image processing of a gigabyte per second worth of data on a single machine with asynchronous python. It was IO bound, we had more CPU to spare. Hell, we even had some GPUs sitting there not doing anything because they weren't needed.
If you're doing real performance computing, then taking advantage of GPUs/DSP or other hardware is where it is at anyway. Python is quite good at a glue language for interfacing to these things.
This is an example of a major downfall with free software: a developer decides he needs a feature so he implements it without taking any effort to see what has been done before – and more importantly, why.
It leads to the project sprouting thousands of new features while nothing achieves the polish and completeness of the original idea because the developer moved on to something newer and shinier.
I can't find the original blog post where I read the idea, but I did find one on Coding Horror: http://www.codinghorror.com/blog/2008/01/the-magpie-develope...
The Linux kernel solves this by having Linus, who has the long term perspective and the commitment to keep the project moving forward. I'm not claiming he's perfect, just that having him is the correct solution to the problem. Obviously here is someone who thinks the 3.9 kernel has a new feature he needs all the while ignoring past socket work.
Reinventing the wheel is certainly a common flaw of developers, but I don't see what it has to do with free software. Are you suggesting that it's less present in non-open-source software development?
Perhaps I could clarify by defining a cathedral/bazaar axis, and an open/closed axis.
A centralized effort can achieve the original idea more quickly than ad-hoc distributed effort.
Free software has this common downfall: a developer wants to reinvent the wheel, and nobody takes the time to educate him why that's a bad idea. It's easier to just accept his patch and forget about it.
What happens to closed software is completely invisible to the community, so I consider it irrelevant but perhaps worse than what happens to free software.
Here's a demo in Python: https://gist.github.com/bdarnell/1073945
With FD passing, you can have multiple processes, related or unrelated, pulling incoming connections from the same socket. You use the FD passing to share the listening socket.
It could even work as expected for a while (since the kernel gets to arbitrarily decide what port to deliver incoming requests to) only to intermittently fail later.
I would however like SO_REUSEPORT to run experiments: Right now we use iptables/tc to direct some traffic at "new versions" of some of our systems so we can run tests with live data, but connection tracking for localhost is lame. I'd much rather use SO_REUSEPORT.
Only if it has the same uid as the other one. It'd also be trivial to check whether the other processes listening to your port are "friendly" (as in "you don't want both Apache and Nginx listening on port 80").
It's also not possible to occasionally listen and unlisten.. that causes the hash modulus to change, sending traffic to the wrong sockets and (most likely) resetting all existing connections
Does the kernel use some sort of round-robin approach to assigning client sockets to processes waiting on accept()? This is one area where I'd imagine a dedicated master process would be beneficial, as it could implement "smarter" load balancing based on the health and response times of its child processes.
See Appendix C to his December 15, 1993 book on TCP/IP.
1993.
[0] http://en.wikipedia.org/wiki/Thundering_herd_problem [1] http://stackoverflow.com/questions/15636319/why-is-accept-mu... [2] http://uwsgi-docs.readthedocs.org/en/latest/articles/Seriali...
Implementing the prefork model by spawning unrelated processes (by opposition to forking from a common parent process) is likely to consume more memory: each process is unrelated, and do not share copy on write memory pages with other processes.
Let's say that we are running a server on a port which uses this option to allow multiple processes to bind to it. What's to prevent a rogue process, perhaps with malicious intent, from starting up and siphoning off requests willy nilly? Sounds like a great way to implement a hard to detect MITM attack.
What would be nicer, I think, is if socket reusing was bound not only to the same uid but also to the process listening to it.
In fact I have seen issues where gunicorn failed miserably simply because it did not handle a bad import in a child process. Tornado as of the latest version I had used (2.0 I think) did not have any ability to check for dead child processes. I am sure there are more examples of this done wrong than right.
This is an interesting option for several use cases but you still need a parent process to monitor things. Perhaps at some point upstart or systemd will get good enough to monitor multiple processes per daemon in real time. Until then, meh.
Edit: actually, one cool thing you can do with this is code reloading. You simply have your parent process start more workers that attach to the same socket, then kill the old ones. That way the idea of code or config reloading doesn't need to be baked into every part of the worker.
The article suggests you let http://supervisord.org/ (or similar) take care of these things.
If I understand SO_REUSEPORT right you let the kernel decide everything - access control, receiving process, timing, etc in exchange for not having your own process doing the same thing. Since that simplistic approach is the kind of thing that can be implemented in about 100 lines of user-space code doing file-descriptor sharing with sendmsg/recvmg via AF_UNIX sockets, I don't see the benefit of pushing that complexity into the kernel. Especially since if you want to exercise any greater level of control you'll just have to roll your own AF_UNIX based code anyway.
You can use setrlimit to prevent that. Plus, your application is likely to have direct control over forking anyway.
That being said, this option can simplify things -- removing the necessity of having some moving part to distribute connections across completely independent processes.
Because this is a Linux kernel feature involving sharing a socket amongst multiple OS processes, and is therefore only interesting to talk about if you are using multiple OS processes. It's not a generalized primer on all techniques of handling IO.
(Btw, there is another interesting forking-for-client-connection pattern in Erlang. Instead of forking off and handling the client connection in a separate process, instead handle the client connection in the accepting process but fork-off another process to continue accepting. In general, just a process pool, that should be easier to set up with this new feature).
And as a result, the user can configure the prefetch pool.
Most networking servers should be dealing with hundreds or thousands of concurrent connections.
Threads / processes:
* Run some code from A
* Save state, context switch
* Run some code from B
* Save state, context switch
* Deal with locking, synchronisation, etc
* Run some code.
The problem is that once you understand what lies behind your glib "run some code", you understand what the problem is. I mean, for one thing, the idea that in a busy server switching to a different event handler which has neither its code nor its data in any processor cache is not itself a "context switch" is a use of the term not necessarily connected to any reality, even if one might pass Computer Science 302 with that answer. Alas, we can not convince our CPUs or RAM to go any faster by arguing at them that they aren't making a "context switch".
But, you know, it's an open benchmark, and the benchmarks themselves aren't all that complicated. Do feel free to submit your event-based handlers that blow the socks off the competition. Bearing in mind that is the standard you've set here. Merely competitive means you've still lost. Nor do I see any "but benchmarks don't mean anything" wiggle room in your statements, because what you're talking about is exactly what is being benchmarked.
Event based system can be more performant in some cases and slow in another cases. If there is not much opportunity for CPU to do any work, then event based system will often outperform threads. One example is proxies. I already gave haproxy as an example, so I'll repeat it here as well. It is single threaded event based by default. It is certainly performant. Why? Because in a simplified model it just shuffles data from one socket to another. Pretty straight forward. Introducing multiple threads and context switches might just thrash caches around and actually make it worse (I have seen that happen).
Now add some CPU work in there. Say make each connection compute something, serialize some JSON. Like in those benchmarks, they use a DB driver get a row, serialize it and return. Ok there is some work. Now it is more likely that multi-threaded will help. But again one can surely tweak CPU affinities, thread pool sizes, hyper-threading BIOS settings, db driver types to really change things up. Threads take up memory. Not an insignificant amount. Now I like green threads, Erlang's processes, Go's goroutines because they are lightweight. (At least Erlang's processes map N:M to CPUs for parallel execution on the host machine).
So I guess my point is you are right that event based are not always and strictly more performant. But I also think in certain cases it can beat multi-threaded code (thread memory size, context switches, cache thrashing). That benchmark there, I wouldn't take it too seriously just like I wouldn't take Language Shootout too seriously.
Sure. But he's replying to a zealot, so he's using zealot-comprehensible statements.
Taking benchmarks too seriously is a problem; dismissing them too cavalierly is a problem, too. Those benchmarks may reflect the truth to seven significant digits... but based on what I see in there, I suspect they reflect the truth to about one and a half digits.
I've got some event-based code I manage at work, because it was the best choice. But it wasn't the best choice because of performance, or code complexity, or any of the other putative advantages of event-based systems, it was the best choice due to the local language-use landscape pushing me into a language in which event-based systems are the only credible choice. You know that comment that "design patterns show a weakness in your language?" I don't 100% agree with that, but it's true here; event-based server loops are a sign of a weakness in your language, not a good idea.
[1]: Here defined to a first approximation as "shared little-to-nothing" threading models, rather than the old-school approaches that produced enormous program-state-space complexity.
Modern machines are different than those 10-15 years ago. Caches and SMP typologies sometimes play serious roles in what could be an outcome of a benchmark. Threads are often heavyweight memory-wise. That is why the 10K problem had started to be solved better by event based systems.
Even looking at your benchmarks link, I would say more on the top are actually event based. "cpoll" ones look like event based centered around a polling loop. So is openresty -- which is a set of Lua modules working in nginx, also an evented server (but it is also mixed with a set of worker processes from what I understand).
And I like what you said about even if they are the same threaded ones are better. Yes. Not only that, for me it is 10x. Even if threaded ones are 10x slower and that is tolerable, the I would rather pick that. Why? Because code is clearer and matches better which the intuitive breakdown of a problem domain. That is why I like Erlang, Go, Rust and Akka -- actor models just model the world better (a single request is sequential there are clear steps that work in one after another to process it, but there is concurrency between each requests). An actor models that perfectly and I like that.
I also, like you, dealt with an evented promises/futures based system for years and it wasn't fun. It works great for little benchmarks and examples, once it grows it becomes a set of tangled slinkies that only the original writer (me in this case) knows how it works.