Asynchronous Socket Programming

Asynchronous Socket Programming

Asynchronous Socket Programming

What is 'asynchronous socket programming'?

a.k.a. event-driven programming or select()-based multiplexing, it's a solution to a network programming problem: How do I talk to bunch of different network connections at once, all within one process/thread?

Let's say you're writing a database server that accepts requests via a tcp connection. If you expect to have many simultaneous requests coming in, you might look at the following options:

• synchronous: you handle one request at a time, each in turn.
  pros: simple
  cons: any one request can hold up all the other requests
• fork: you start a new process to handle each request.
  pros: easy
  cons: does not scale well, hundreds of connections means hundreds of processes.
  fork() is the Unix programmer's hammer. Because it's available, every problem looks like a nail. It's usually overkill
• threads: start a new thread to handle each request.
  pros: easy, and kinder to the kernel than using fork, since threads usually have much less overhead
  cons: your machine may not have threads, and threaded programming can get very complicated very fast, with worries about controlling access to shared resources.

[Note: there is a similar discussion in the file SocketServer.py, in the standard python library. I had planned to integrate this software with the SocketServer classes, but haven't had the time.]

With async, or 'event-driven' programming, you cooperatively schedule the cpu or other resources you wish to apply to each connection. How you do this really depends on the application - and it's not always possible or reasonable to try. But if you can capture the state of any one connection, and divide the work it will do into relatively small pieces, then this solution might work for you. If your connections do not require much (or any) state, then this is an ideal approach.

pros:

• efficient and elegant
• scales well - hundreds of connections means only hundreds of socket/state objects, not hundreds of threads or processes.
• requires no interlocking for access to shared resources. If your database provides no interlocking of its own (as is the case for dbm, dbz, and berkeley db), than the need to serialize access to the database is provided trivially.
• integrates easily with event-driven window-system programming. GUI programs that use blocking network calls are not very graceful.

cons:

• more complex - you may need to build state machines.
• requires a fundamentally different approach to programming that can be confusing at first.

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How does it work?

Here's a good visual metaphor to help describe the advantages of multiplexed asynchronous I/O. Picture your program as a person, with a bucket in front of him, and a bucket behind him. The bucket in front of him fills with water, and his job is to wait until the bucket is full, and empty it into the bucket behind him. [which might have yet another person behind it...] The bucket fills sporadically, sometimes very quickly, and sometimes at just a trickle, but in general your program sits there doing nothing most of the time.

Now what if your program needs to talk to more than one connection (or file) at a time? Forking another process is the equivalent of bringing in another person to handle each pair of buckets. The typical server is written in just this style! A server may be handling 20 simultaneous clients, and in our metaphor that means a line of 20 people, sitting idle for 99% of the time, each waiting for his bucket to fill!

The obvious solution to this is to have a single person walk up and down the aisle of bucket pairs. When he comes to a bucket that's full, he dumps it into the other side, and then moves on. By walking up and down the aisle of buckets, one busy person does the job of 20 idle people.

The only time when this technique doesn't work well is when something other than just dumping one bucket into the next needs to be done - say, turning the water into gold first. If turning a bucket of water into a bucket of gold takes a long time, then the other buckets may not get processed in a timely fashion. For example, if your server program needs to crunch on the data it receives before responding.

Writing a single-process multiplexing socket program

Now how do we apply our bucket wisdom to network programming?

Depending on the operating system, there are several different ways to achieve our 'bucket-trickling' affect. By far the most common, and simplest mechanism uses the select() system call. The select() function takes (in effect) four arguments: three 'lists' of sockets, and a timeout. The three socket lists indicate interest in read, write, and exception events for each socket listed. The function will return whenever the indicated socket fires one of these events. If nothing happens within the timeout period, the function simply returns.

The result of the select() function is three lists telling you which sockets fired which events.

Your application will have a handler for each expected event type. This handler will perform different tasks depending on your application. If a connection has a need to keep state information, you'll probably end up writing a state machine to handle transitions between different behaviors. Diving back into the bucket [paradigm], these events might be the equivalent of adding little "I'm full now" mailbox-like flags to the buckets.

Animating the server using select() is easy: A simple event loop will suffice. I usually use a loop with a 30-second timeout. That way, I can do any necessary housekeeping tasks at least every 30 seconds.

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Sounds like a lot of work.

[Note: for those of you coming to this page through an Internet search engine, the following description is of a library written in Python.]

Well, lucky for you, there's a set of common code that makes writing these programs a snap. In fact, all you need to do is pick from two connection styles, and plug in your own event handlers. As an extra added bonus, the differences between Windows and Unix socket multiplexing have been abstracted - using the async base classes (asyncore.dispatcher and asynchat.async_chat) - you can write asynchronous programs that will work on Unix, Windows, or the Macintosh (in fact, it should work on any platform that correctly implements the socket and select modules).

The first class is the simpler one, 'asyncore.dispatcher'. This class manages the association between a socket descriptor (which is how the operating system refers to the socket) and your socket object. dispatcher is really a container for a system-level socket, but it's been wrapped to look as much like a socket as possible. The main difference is that creating the underlying socket operation is done by calling the create_socket method.

Aside from the default 'low-level' event handlers, (for read, write, and expt), there are a few extra implied events that are captured by the asyncore.dispatcher class for you.

• handle_read: called whenever the socket has more data to be read, meaning that recv() can be called with an expectation of success.
• handle_write: called whenever a socket is ready to be written to - a call to send() can be expected to succeed.
• handle_oob: called when out-of-band data is present.
• handle_accept: called when a new connection has been accepted on a listening socket.
• handle_connect: called when an outgoing connect() has succeeded.
• handle_close: called when the socket has closed.

The second class, asynchat.async_chat, provides support for typical command/response protocols like SMTP, NNTP, FTP, etc... It helps solve several problems for you:

• It lets you divide the stream of incoming data into blocks delimited by a 'terminator' of your own choosing. Almost all the internet protocols use a combination of '\r\n' and '\r\n.\r\n' as terminators, the latter being a common end-of-message delimiter. You can change the current terminator by calling the set_terminator method. Input is accumulated by calling your own collect_incoming_data method. When the terminator is located, the found_terminator method is called.
• It provides a mechanism for queueing output, using a simple fifo and the concept of a producer. A producer is an object that knows how to incrementally produce output for delivery to the socket. All output to the socket is in the form of a producer. When you call send(), async_chat first wraps the data in a simple producer, called (strangely enough) 'simple_producer':

class simple_producer:
    def __init__ (self, data):
        self.data = data

    def more (self):
        if len (self.data) > 512:
            result = self.data[:512]
            self.data = self.data[512:]
            return result
        else:
            result = self.data
            self.data = ''
            return result

Each producer must have a more() method, which is called whenever more output is needed. Note how the data is deliberately sent in fixed-size chunks: If you create a simple_producer with a 15-Megabyte long string (ghastly!), this will keep that one socket from monopolizing the whole program. When the producer is exhausted, it returns an empty string, like a file object signifying an end-of-file condition.

A producer can compute its output 'on-the-fly', if so desired. It can keep state information, too, like a file pointer, a database index, or a partial computation.

Each producer is filed into a queue (fifo), which is progressively emptied. The more method of the front-most element of the queue is called until it is exhausted, and then the producer is popped off the queue.

The combination of delimiting the input and scheduling the output with a fifo allows you to design a server that will correctly handle an impatient client. For example, some NNTP clients send a barrage of commands to the server, and then count out the responses as they are made (rather than sending a command, waiting for a response, etc...). If a call to recv() reveals a buffer full of these impatient commands, async_chat will handle the situation correctly, calling collect_incoming_data and found_terminator in sequence for each command.

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An Example

The following discussion assumes a basic familiarity with socket programming

We'll start with a simple async finger client. Finger is a trivial tcp protocol that's completely stateless. You just connect to the finger port, send a request line, and read until the connection closes. [This is very similar to HTTP, by the way]

import socket
import asyncore
import string



# simple demo of the asyncore dispatcher class.

class finger_client (asyncore.dispatcher):
    def __init__ (self, account, done_fun, long=1):
        self.name, self.host = tuple(string.split (account, '@'))
        self.done_fun = done_fun
        self.data = ''
        self.long = long
        self.create_socket (socket.AF_INET, socket.SOCK_STREAM)
        asyncore.dispatcher.__init__ (self)
        self.connect ((host, 79))

done_fun is a function that will be called when the finger server has sent all the data and closed the connection. [this programming style - passing functions around that represent an execution path - is called continuation-passing style] The call to create_socket will register the socket with the underlying event mechanism, enabling the following callback procedures.

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    # once connected, send the account name
    def handle_connect (self):
        self.log ('connected')
        if self.long:
            # this requests 'long' output.
            self.send ('/w %s\r\n' % self.name)
        else:
            self.send ('%s\r\n' % self.name)

This function is called when the socket has made a connection. This tells us that we can now send the finger request.

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    # collect some more finger server output.
    def handle_read (self):
        more = self.recv(512)
        if not more:
            self.handle_close()
        self.data = self.data + more

When data is available for reading on the socket, this callback will collect it into the member variable self.data

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    # the other side closed, we're done.
    def handle_close (self):
        print ''
        self.done_fun (self.data)
        self.close()

Now that we're all done, call the user's done_fun with the finger data as an argument.

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def demo_done_fun (stuff):
    print stuff

def demo (who='asynfingdemo@squirl.nightmare.com'):
    f = finger_client (who, demo_done_fun, long=0)
    asyncore.loop()

The call to asyncore.loop() kicks of the default select loop, one that will run until its list of channels becomes empty. Once the finger client connection closes, the loop will exit, returning control to the interpreter. [Go ahead and try this one, I'm counting my readership with a python script]

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Discussion of other examples

A few other examples are available:

pop3.py

An implementation of the Post Office Protocol (version 3), as described in RFC 1725. Uses the facilities of consumer.py.

ftp.py

An implementation of the File Transfer Protocol, as described in RFC 959.

ftpgrab.py

A client of ftp.py; shows how to use an async client class to build a useful program. This one allows you to simultaneously grab many files from different servers from the command line.

asynhttp.py

is very similar to the finger client described above. It's an asyncore.dispatcher-based http client. It's not much of an http client, though: it neither sends or processes HTTP headers - it merely sends a 'GET' command and collects the output.

servhttp.py

A more complete example. It's a bare-bones asynchronous http server, [the only one I've ever heard of], supporting only the 'GET' command and capable only of delivering files. Just like the HTTP server that is now part of the python distribution, though, it is easily extensible. What makes this server interesting is its performance: In a bit of informal testing against apache 0.6.2, it seems to be able to handle a substantially higher number of hits, with a much lower load on the machine. See the file 'abuse.py' for more information and timings.

Historical Note: Medusa grew, starting in March 1996, from experimentation with this sample program. Medusa is a full-fledged TCP/IP server platform built upon the principles (and library) described here.

It is possible to build complicated state machines for use with async_chat; I have built a fully RFC977-compliant (plus all common extensions) NNTP server, and several other sophisticated NNTP retrieval/filter engines. (If you'd like to see some of these as examples, drop me a note).

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A Platform Note

On Unix, the arguments to select() are not limited to tcp sockets, they can in fact be any file descriptor - including such objects as unix domain sockets, pipes and fifos. This means that the library can be used to communicate with these objects efficiently. To do this, it is necessary to make sure that these descriptors are placed in non-blocking mode. There are also a few API differences - the non-socket objects will need to use read() and write(), rather than recv() and send(). Simple support for this is provided in the class asyncore.py:file_dispatcher

First-Class Continuations, Coroutines...

Work with this library has taken me down an interesting path. One strange and exciting fork has been some experimentation in other languages with a feature called 'first-class continuations'. A first-class continuation is an object that captures the current state of a program in such a way that it can be stored away and invoked later, even invoked multiple times. This feature is an extremely powerful tool for changing the control flow of a program. Usually delivered to the user as a function, call-with-current-continuation (a.k.a. 'call/cc') in languages like Scheme and ML, it can be used to write asynchronous programs that look synchronous; in effect, call/cc allows one to write programs in a multi-threaded style, but without using an operating-system-supplied thread capability (the technical term is 'coroutines'). call/cc can also be used to synthesize all other known higher-level control structures, such as Python and C++-style exception handling.

There is some chance that Python will gain this wonderful feature, and I await the day anxiously. In the meanwhile, something along these lines has been written using the Gambit Scheme compiler. Here is some code that implements a portable socket interface to Gambit, and a coroutine-based socket-event 'scheduler'.

For more information on call/cc, see the Revised^4 Report on Scheme

A C++ Implementation

A straightforward translation of the asyncore and asynchat modules to C++ classes has been written (see the 'cpp' directory). It hasn't been tested extensively, but appears to work as advertised. The code makes heavy use of the up-and-coming 'Standard C++ Library', including the string classes and STL. It can be very difficult to compile, because most vendor's implementations of these features are in flux (i.e., buggy as hell - for instance, juggling the order and naming of include files can keep the source from compiling). The provided code has been tested with Microsoft Visual C++ Version 4.2 on Win32, and with the GNU C++ compiler and library version 2.7.2.

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Getting the Library

The Async Socket Library is available from nightmare.com as async.tar.gz

Sam Rushing / rushing@nightmare.com