The C10K problem

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转载:https://rtoax.blog.csdn.net/article/details/117317900

译文:https://rtoax.blog.csdn.net/article/details/108723496

目录

The C10K problem

Contents

Related Sites

Book to Read First

I/O frameworks

I/O Strategies

1. Serve many clients with each thread, and use nonblocking I/O and level-triggered readiness notification

2. Serve many clients with each thread, and use nonblocking I/O and readiness change notification

3. Serve many clients with each server thread, and use asynchronous I/O

4. Serve one client with each server thread

5. Build the server code into the kernel

Bring the TCP stack into userspace

Comments

Limits on open filehandles

Limits on threads

Java issues

Other tips

Other limits

Kernel Issues

Measuring Server Performance

Examples

Interesting select()-based servers

Interesting /dev/poll-based servers

Interesting epoll-based servers

Interesting kqueue()-based servers

Interesting realtime signal-based servers

Interesting thread-based servers

Interesting in-kernel servers

Translations

Changelog


The C10K problem

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It's time for web servers to handle ten thousand clients simultaneously, don't you think? After all, the web is a big place now.

And computers are big, too. You can buy a 1000MHz machine with 2 gigabytes of RAM and an 1000Mbit/sec Ethernet card for $1200 or so. Let's see - at 20000 clients, that's 50KHz, 100Kbytes, and 50Kbits/sec per client. It shouldn't take any more horsepower than that to take four kilobytes from the disk and send them to the network once a second for each of twenty thousand clients. (That works out to $0.08 per client, by the way. Those $100/client licensing fees some operating systems charge are starting to look a little heavy!) So hardware is no longer the bottleneck.

In 1999 one of the busiest ftp sites, cdrom.com, actually handled 10000 clients simultaneously through a Gigabit Ethernet pipe. As of 2001, that same speed is now being offered by several ISPs, who expect it to become increasingly popular with large business customers.

And the thin client model of computing appears to be coming back in style -- this time with the server out on the Internet, serving thousands of clients.

With that in mind, here are a few notes on how to configure operating systems and write code to support thousands of clients. The discussion centers around Unix-like operating systems, as that's my personal area of interest, but Windows is also covered a bit.

Contents

 

Related Sites

See Nick Black's execellent Fast UNIX Servers page for a circa-2009 look at the situation.

In October 2003, Felix von Leitner put together an excellent web page and presentation about network scalability, complete with benchmarks comparing various networking system calls and operating systems. One of his observations is that the 2.6 Linux kernel really does beat the 2.4 kernel, but there are many, many good graphs that will give the OS developers food for thought for some time. (See also the Slashdot comments; it'll be interesting to see whether anyone does followup benchmarks improving on Felix's results.)

Book to Read First

If you haven't read it already, go out and get a copy of Unix Network Programming : Networking Apis: Sockets and Xti (Volume 1) by the late W. Richard Stevens. It describes many of the I/O strategies and pitfalls related to writing high-performance servers. It even talks about the 'thundering herd' problem. And while you're at it, go read Jeff Darcy's notes on high-performance server design.

(Another book which might be more helpful for those who are *using* rather than *writing* a web server is Building Scalable Web Sites by Cal Henderson.)

I/O frameworks

Prepackaged libraries are available that abstract some of the techniques presented below, insulating your code from the operating system and making it more portable.

  • ACE, a heavyweight C++ I/O framework, contains object-oriented implementations of some of these I/O strategies and many other useful things. In particular, his Reactor is an OO way of doing nonblocking I/O, and Proactor is an OO way of doing asynchronous I/O.
  • ASIO is an C++ I/O framework which is becoming part of the Boost library. It's like ACE updated for the STL era.
  • libevent is a lightweight C I/O framework by Niels Provos. It supports kqueue and select, and soon will support poll and epoll. It's level-triggered only, I think, which has both good and bad sides. Niels has a nice graph of time to handle one event as a function of the number of connections. It shows kqueue and sys_epoll as clear winners.
  • My own attempts at lightweight frameworks (sadly, not kept up to date):
    • Poller is a lightweight C++ I/O framework that implements a level-triggered readiness API using whatever underlying readiness API you want (poll, select, /dev/poll, kqueue, or sigio). It's useful for benchmarks that compare the performance of the various APIs. This document links to Poller subclasses below to illustrate how each of the readiness APIs can be used.
    • rn is a lightweight C I/O framework that was my second try after Poller. It's lgpl (so it's easier to use in commercial apps) and C (so it's easier to use in non-C++ apps). It was used in some commercial products.
  • Matt Welsh wrote a paper in April 2000 about how to balance the use of worker thread and event-driven techniques when building scalable servers. The paper describes part of his Sandstorm I/O framework.
  • Cory Nelson's Scale! library - an async socket, file, and pipe I/O library for Windows

I/O Strategies

Designers of networking software have many options. Here are a few:

  • Whether and how to issue multiple I/O calls from a single thread
    • Don't; use blocking/synchronous calls throughout, and possibly use multiple threads or processes to achieve concurrency
    • Use nonblocking calls (e.g. write() on a socket set to O_NONBLOCK) to start I/O, and readiness notification (e.g. poll() or /dev/poll) to know when it's OK to start the next I/O on that channel. Generally only usable with network I/O, not disk I/O.
    • Use asynchronous calls (e.g. aio_write()) to start I/O, and completion notification (e.g. signals or completion ports) to know when the I/O finishes. Good for both network and disk I/O.
  • How to control the code servicing each client
    • one process for each client (classic Unix approach, used since 1980 or so)
    • one OS-level thread handles many clients; each client is controlled by:
      • a user-level thread (e.g. GNU state threads, classic Java with green threads)
      • a state machine (a bit esoteric, but popular in some circles; my favorite)
      • a continuation (a bit esoteric, but popular in some circles)
    • one OS-level thread for each client (e.g. classic Java with native threads)
    • one OS-level thread for each active client (e.g. Tomcat with apache front end; NT completion ports; thread pools)
  • Whether to use standard O/S services, or put some code into the kernel (e.g. in a custom driver, kernel module, or VxD)

The following five combinations seem to be popular:

  1. Serve many clients with each thread, and use nonblocking I/O and level-triggered readiness notification
  2. Serve many clients with each thread, and use nonblocking I/O and readiness change notification
  3. Serve many clients with each server thread, and use asynchronous I/O
  4. serve one client with each server thread, and use blocking I/O
  5. Build the server code into the kernel

1. Serve many clients with each thread, and use nonblocking I/O and level-triggered readiness notification

... set nonblocking mode on all network handles, and use select() or poll() to tell which network handle has data waiting. This is the traditional favorite. With this scheme, the kernel tells you whether a file descriptor is ready, whether or not you've done anything with that file descriptor since the last time the kernel told you about it. (The name 'level triggered' comes from computer hardware design; it's the opposite of 'edge triggered'. Jonathon Lemon introduced the terms in his BSDCON 2000 paper on kqueue().)

Note: it's particularly important to remember that readiness notification from the kernel is only a hint; the file descriptor might not be ready anymore when you try to read from it. That's why it's important to use nonblocking mode when using readiness notification.

An important bottleneck in this method is that read() or sendfile() from disk blocks if the page is not in core at the moment; setting nonblocking mode on a disk file handle has no effect. Same thing goes for memory-mapped disk files. The first time a server needs disk I/O, its process blocks, all clients must wait, and that raw nonthreaded performance goes to waste.
This is what asynchronous I/O is for, but on systems that lack AIO, worker threads or processes that do the disk I/O can also get around this bottleneck. One approach is to use memory-mapped files, and if mincore() indicates I/O is needed, ask a worker to do the I/O, and continue handling network traffic. Jef Poskanzer mentions that Pai, Druschel, and Zwaenepoel's 1999 Flash web server uses this trick; they gave a talk at Usenix '99 on it. It looks like mincore() is available in BSD-derived Unixes like FreeBSD and Solaris, but is not part of the Single Unix Specification. It's available as part of Linux as of kernel 2.3.51, thanks to Chuck Lever.

But in November 2003 on the freebsd-hackers list, Vivek Pei et al reported very good results using system-wide profiling of their Flash web server to attack bottlenecks. One bottleneck they found was mincore (guess that wasn't such a good idea after all) Another was the fact that sendfile blocks on disk access; they improved performance by introducing a modified sendfile() that return something like EWOULDBLOCK when the disk page it's fetching is not yet in core. (Not sure how you tell the user the page is now resident... seems to me what's really needed here is aio_sendfile().) The end result of their optimizations is a SpecWeb99 score of about 800 on a 1GHZ/1GB FreeBSD box, which is better than anything on file at spec.org.

There are several ways for a single thread to tell which of a set of nonblocking sockets are ready for I/O:

  • The traditional select()
    Unfortunately, select() is limited to FD_SETSIZE handles. This limit is compiled in to the standard library and user programs. (Some versions of the C library let you raise this limit at user app compile time.)

    See Poller_select (cch) for an example of how to use select() interchangeably with other readiness notification schemes.

     

  • The traditional poll()
    There is no hardcoded limit to the number of file descriptors poll() can handle, but it does get slow about a few thousand, since most of the file descriptors are idle at any one time, and scanning through thousands of file descriptors takes time.

    Some OS's (e.g. Solaris 8) speed up poll() et al by use of techniques like poll hinting, which was implemented and benchmarked by Niels Provos for Linux in 1999.

    See Poller_poll (cchbenchmarks) for an example of how to use poll() interchangeably with other readiness notification schemes.

     

  • /dev/poll
    This is the recommended poll replacement for Solaris.

    The idea behind /dev/poll is to take advantage of the fact that often poll() is called many times with the same arguments. With /dev/poll, you get an open handle to /dev/poll, and tell the OS just once what files you're interested in by writing to that handle; from then on, you just read the set of currently ready file descriptors from that handle.

    It appeared quietly in Solaris 7 (see patchid 106541) but its first public appearance was in Solaris 8according to Sun, at 750 clients, this has 10% of the overhead of poll().

    Various implementations of /dev/poll were tried on Linux, but none of them perform as well as epoll, and were never really completed. /dev/poll use on Linux is not recommended.

    See Poller_devpoll (cch benchmarks ) for an example of how to use /dev/poll interchangeably with many other readiness notification schemes. (Caution - the example is for Linux /dev/poll, might not work right on Solaris.)

     

  • kqueue()
    This is the recommended poll replacement for FreeBSD (and, soon, NetBSD).

    See below. kqueue() can specify either edge triggering or level triggering.

2. Serve many clients with each thread, and use nonblocking I/O and readiness change notification

Readiness change notification (or edge-triggered readiness notification) means you give the kernel a file descriptor, and later, when that descriptor transitions from not ready to ready, the kernel notifies you somehow. It then assumes you know the file descriptor is ready, and will not send any more readiness notifications of that type for that file descriptor until you do something that causes the file descriptor to no longer be ready (e.g. until you receive the EWOULDBLOCK error on a send, recv, or accept call, or a send or recv transfers less than the requested number of bytes).

When you use readiness change notification, you must be prepared for spurious events, since one common implementation is to signal readiness whenever any packets are received, regardless of whether the file descriptor was already ready.

This is the opposite of "level-triggered" readiness notification. It's a bit less forgiving of programming mistakes, since if you miss just one event, the connection that event was for gets stuck forever. Nevertheless, I have found that edge-triggered readiness notification made programming nonblocking clients with OpenSSL easier, so it's worth trying.

[Banga, Mogul, Drusha '99] described this kind of scheme in 1999.

There are several APIs which let the application retrieve 'file descriptor became ready' notifications:

3. Serve many clients with each server thread, and use asynchronous I/O

This has not yet become popular in Unix, probably because few operating systems support asynchronous I/O, also possibly because it (like nonblocking I/O) requires rethinking your application. Under standard Unix, asynchronous I/O is provided by the aio_ interface (scroll down from that link to "Asynchronous input and output"), which associates a signal and value with each I/O operation. Signals and their values are queued and delivered efficiently to the user process. This is from the POSIX 1003.1b realtime extensions, and is also in the Single Unix Specification, version 2.

AIO is normally used with edge-triggered completion notification, i.e. a signal is queued when the operation is complete. (It can also be used with level triggered completion notification by calling aio_suspend(), though I suspect few people do this.)

glibc 2.1 and later provide a generic implementation written for standards compliance rather than performance.

Ben LaHaise's implementation for Linux AIO was merged into the main Linux kernel as of 2.5.32. It doesn't use kernel threads, and has a very efficient underlying api, but (as of 2.6.0-test2) doesn't yet support sockets. (There is also an AIO patch for the 2.4 kernels, but the 2.5/2.6 implementation is somewhat different.) More info:

Suparna also suggests having a look at the the DAFS API's approach to AIO.

Red Hat AS and Suse SLES both provide a high-performance implementation on the 2.4 kernel; it is related to, but not completely identical to, the 2.6 kernel implementation.

In February 2006, a new attempt is being made to provide network AIO; see the note above about Evgeniy Polyakov's kevent-based AIO.

In 1999, SGI implemented high-speed AIO for Linux. As of version 1.1, it's said to work well with both disk I/O and sockets. It seems to use kernel threads. It is still useful for people who can't wait for Ben's AIO to support sockets.

The O'Reilly book POSIX.4: Programming for the Real World is said to include a good introduction to aio.

A tutorial for the earlier, nonstandard, aio implementation on Solaris is online at Sunsite. It's probably worth a look, but keep in mind you'll need to mentally convert "aioread" to "aio_read", etc.

Note that AIO doesn't provide a way to open files without blocking for disk I/O; if you care about the sleep caused by opening a disk file, Linus suggests you should simply do the open() in a different thread rather than wishing for an aio_open() system call.

Under Windows, asynchronous I/O is associated with the terms "Overlapped I/O" and IOCP or "I/O Completion Port". Microsoft's IOCP combines techniques from the prior art like asynchronous I/O (like aio_write) and queued completion notification (like when using the aio_sigevent field with aio_write) with a new idea of holding back some requests to try to keep the number of running threads associated with a single IOCP constant. For more information, see Inside I/O Completion Ports by Mark Russinovich at sysinternals.com, Jeffrey Richter's book "Programming Server-Side Applications for Microsoft Windows 2000" (AmazonMSPress), U.S. patent #06223207, or MSDN.

4. Serve one client with each server thread

... and let read() and write() block. Has the disadvantage of using a whole stack frame for each client, which costs memory. Many OS's also have trouble handling more than a few hundred threads. If each thread gets a 2MB stack (not an uncommon default value), you run out of *virtual memory* at (2^30 / 2^21) = 512 threads on a 32 bit machine with 1GB user-accessible VM (like, say, Linux as normally shipped on x86). You can work around this by giving each thread a smaller stack, but since most thread libraries don't allow growing thread stacks once created, doing this means designing your program to minimize stack use. You can also work around this by moving to a 64 bit processor.

The thread support in Linux, FreeBSD, and Solaris is improving, and 64 bit processors are just around the corner even for mainstream users. Perhaps in the not-too-distant future, those who prefer using one thread per client will be able to use that paradigm even for 10000 clients. Nevertheless, at the current time, if you actually want to support that many clients, you're probably better off using some other paradigm.

For an unabashedly pro-thread viewpoint, see Why Events Are A Bad Idea (for High-concurrency Servers) by von Behren, Condit, and Brewer, UCB, presented at HotOS IX. Anyone from the anti-thread camp care to point out a paper that rebuts this one? :-)

LinuxThreads

LinuxTheads is the name for the standard Linux thread library. It is integrated into glibc since glibc2.0, and is mostly Posix-compliant, but with less than stellar performance and signal support.

NGPT: Next Generation Posix Threads for Linux

NGPT is a project started by IBM to bring good Posix-compliant thread support to Linux. It's at stable version 2.2 now, and works well... but the NGPT team has announced that they are putting the NGPT codebase into support-only mode because they feel it's "the best way to support the community for the long term". The NGPT team will continue working to improve Linux thread support, but now focused on improving NPTL. (Kudos to the NGPT team for their good work and the graceful way they conceded to NPTL.)

NPTL: Native Posix Thread Library for Linux

NPTL is a project by Ulrich Drepper (the benevolent dict^H^H^H^Hmaintainer of glibc) and Ingo Molnar to bring world-class Posix threading support to Linux.

As of 5 October 2003, NPTL is now merged into the glibc cvs tree as an add-on directory (just like linuxthreads), so it will almost certainly be released along with the next release of glibc.

The first major distribution to include an early snapshot of NPTL was Red Hat 9. (This was a bit inconvenient for some users, but somebody had to break the ice...)

NPTL links:

Here's my try at describing the history of NPTL (see also Jerry Cooperstein's article):

In March 2002, Bill Abt of the NGPT team, the glibc maintainer Ulrich Drepper, and others met to figure out what to do about LinuxThreads. One idea that came out of the meeting was to improve mutex performance; Rusty Russell et al subsequently implemented fast userspace mutexes (futexes)), which are now used by both NGPT and NPTL. Most of the attendees figured NGPT should be merged into glibc.

Ulrich Drepper, though, didn't like NGPT, and figured he could do better. (For those who have ever tried to contribute a patch to glibc, this may not come as a big surprise :-) Over the next few months, Ulrich Drepper, Ingo Molnar, and others contributed glibc and kernel changes that make up something called the Native Posix Threads Library (NPTL). NPTL uses all the kernel enhancements designed for NGPT, and takes advantage of a few new ones. Ingo Molnar described the kernel enhancements as follows:

While NPTL uses the three kernel features introduced by NGPT: getpid() returns PID, CLONE_THREAD and futexes; NPTL also uses (and relies on) a much wider set of new kernel features, developed as part of this project.

Some of the items NGPT introduced into the kernel around 2.5.8 got modified, cleaned up and extended, such as thread group handling (CLONE_THREAD). [the CLONE_THREAD changes which impacted NGPT's compatibility got synced with the NGPT folks, to make sure NGPT does not break in any unacceptable way.]

The kernel features developed for and used by NPTL are described in the design whitepaper, http://people.redhat.com/drepper/nptl-design.pdf ...

A short list: TLS support, various clone extensions (CLONE_SETTLS, CLONE_SETTID, CLONE_CLEARTID), POSIX thread-signal handling, sys_exit() extension (release TID futex upon VM-release), the sys_exit_group() system-call, sys_execve() enhancements and support for detached threads.

There was also work put into extending the PID space - eg. procfs crashed due to 64K PID assumptions, max_pid, and pid allocation scalability work. Plus a number of performance-only improvements were done as well.

In essence the new features are a no-compromises approach to 1:1 threading - the kernel now helps in everything where it can improve threading, and we precisely do the minimally necessary set of context switches and kernel calls for every basic threading primitive.

One big difference between the two is that NPTL is a 1:1 threading model, whereas NGPT is an M:N threading model (see below). In spite of this, Ulrich's initial benchmarks seem to show that NPTL is indeed much faster than NGPT. (The NGPT team is looking forward to seeing Ulrich's benchmark code to verify the result.)

FreeBSD threading support

FreeBSD supports both LinuxThreads and a userspace threading library. Also, a M:N implementation called KSE was introduced in FreeBSD 5.0. For one overview, see www.unobvious.com/bsd/freebsd-threads.html.

On 25 Mar 2003, Jeff Roberson posted on freebsd-arch:

... Thanks to the foundation provided by Julian, David Xu, Mini, Dan Eischen, and everyone else who has participated with KSE and libpthread development Mini and I have developed a 1:1 threading implementation. This code works in parallel with KSE and does not break it in any way. It actually helps bring M:N threading closer by testing out shared bits. ...

And in July 2006, Robert Watson proposed that the 1:1 threading implementation become the default in FreeBsd 7.x:

I know this has been discussed in the past, but I figured with 7.x trundling forward, it was time to think about it again. In benchmarks for many common applications and scenarios, libthr demonstrates significantly better performance over libpthread... libthr is also implemented across a larger number of our platforms, and is already libpthread on several. The first recommendation we make to mysql and other heavy thread users is "Switch to libthr", which is suggestive, also! ... So the strawman proposal is: make libthr the default threading library on 7.x.

NetBSD threading support

According to a note from Noriyuki Soda:

Kernel supported M:N thread library based on the Scheduler Activations model is merged into NetBSD-current on Jan 18 2003.

For details, see An Implementation of Scheduler Activations on the NetBSD Operating System by Nathan J. Williams, Wasabi Systems, Inc., presented at FREENIX '02.

Solaris threading support

The thread support in Solaris is evolving... from Solaris 2 to Solaris 8, the default threading library used an M:N model, but Solaris 9 defaults to 1:1 model thread support. See Sun's multithreaded programming guide and Sun's note about Java and Solaris threading.

Java threading support in JDK 1.3.x and earlier

As is well known, Java up to JDK1.3.x did not support any method of handling network connections other than one thread per client. Volanomark is a good microbenchmark which measures throughput in messsages per second at various numbers of simultaneous connections. As of May 2003, JDK 1.3 implementations from various vendors are in fact able to handle ten thousand simultaneous connections -- albeit with significant performance degradation. See Table 4 for an idea of which JVMs can handle 10000 connections, and how performance suffers as the number of connections increases.

Note: 1:1 threading vs. M:N threading

There is a choice when implementing a threading library: you can either put all the threading support in the kernel (this is called the 1:1 threading model), or you can move a fair bit of it into userspace (this is called the M:N threading model). At one point, M:N was thought to be higher performance, but it's so complex that it's hard to get right, and most people are moving away from it.

5. Build the server code into the kernel

Novell and Microsoft are both said to have done this at various times, at least one NFS implementation does this, khttpd does this for Linux and static web pages, and "TUX" (Threaded linUX webserver) is a blindingly fast and flexible kernel-space HTTP server by Ingo Molnar for Linux. Ingo's September 1, 2000 announcement says an alpha version of TUX can be downloaded from ftp://ftp.redhat.com/pub/redhat/tux, and explains how to join a mailing list for more info.
The linux-kernel list has been discussing the pros and cons of this approach, and the consensus seems to be instead of moving web servers into the kernel, the kernel should have the smallest possible hooks added to improve web server performance. That way, other kinds of servers can benefit. See e.g. Zach Brown's remarks about userland vs. kernel http servers. It appears that the 2.4 linux kernel provides sufficient power to user programs, as the X15 server runs about as fast as Tux, but doesn't use any kernel modifications.

Bring the TCP stack into userspace

See for instance the netmap packet I/O framework, and the Sandstorm proof-of-concept web server based on it.

 

Comments

Richard Gooch has written a paper discussing I/O options.

In 2001, Tim Brecht and MMichal Ostrowski measured various strategies for simple select-based servers. Their data is worth a look.

In 2003, Tim Brecht posted source code for userver, a small web server put together from several servers written by Abhishek Chandra, David Mosberger, David Pariag, and Michal Ostrowski. It can use select(), poll(), epoll(), or sigio.

Back in March 1999, Dean Gaudet posted:

I keep getting asked "why don't you guys use a select/event based model like Zeus? It's clearly the fastest." ...

His reasons boiled down to "it's really hard, and the payoff isn't clear". Within a few months, though, it became clear that people were willing to work on it.

Mark Russinovich wrote an editorial and an article discussing I/O strategy issues in the 2.2 Linux kernel. Worth reading, even he seems misinformed on some points. In particular, he seems to think that Linux 2.2's asynchronous I/O (see F_SETSIG above) doesn't notify the user process when data is ready, only when new connections arrive. This seems like a bizarre misunderstanding. See also comments on an earlier draftIngo Molnar's rebuttal of 30 April 1999Russinovich's comments of 2 May 1999a rebuttal from Alan Cox, and various posts to linux-kernel. I suspect he was trying to say that Linux doesn't support asynchronous disk I/O, which used to be true, but now that SGI has implemented KAIO, it's not so true anymore.

See these pages at sysinternals.com and MSDN for information on "completion ports", which he said were unique to NT; in a nutshell, win32's "overlapped I/O" turned out to be too low level to be convenient, and a "completion port" is a wrapper that provides a queue of completion events, plus scheduling magic that tries to keep the number of running threads constant by allowing more threads to pick up completion events if other threads that had picked up completion events from this port are sleeping (perhaps doing blocking I/O).

See also OS/400's support for I/O completion ports.

There was an interesting discussion on linux-kernel in September 1999 titled "> 15,000 Simultaneous Connections" (and the second week of the thread). Highlights: