pure-Python asynchronous I/O using coroutines
Bluelet is a simple, pure-Python solution for writing intelligible asynchronous socket applications. It uses PEP 342 coroutines to make concurrent I/O look and act like sequential programming.
In this way, it is similar to the Greenlet green-threads library and its associated packages Eventlet and Gevent. Bluelet has a simpler, 100% Python implementation that comes at the cost of flexibility and performance when compared to Greenlet-based solutions. However, it should be sufficient for many applications that don’t need serious scalability; it can be thought of as a less-horrible alternative to asyncore or an asynchronous replacement for SocketServer (and more).
The “Echo” Server
An “echo” server is a canonical stupid example for demonstrating socket programming. It simply accepts connections, reads lines, and writes everything it reads back to the client.
Here’s an example using plain Python sockets:
import socket listener = socket.socket(socket.AF_INET, socket.SOCK_STREAM) listener.bind(('', 4915)) listener.listen(1) while True: sock, addr = listener.accept() while True: data = sock.recv(1024) if not data: break sock.sendall(data)
The code is very simple, but its synchronousness has a major problem: the server can accept only one connection at a time. This won’t do even for very small server applications.
One solution to this problem is to fork several operating system threads or processes that each run the same synchronous code. This, however, quickly becomes complex and makes the application harder to manage. Python’s asyncore module provides a way to write asynchronous servers that accept multiple connections in the same OS thread:
import asyncore import socket class Echoer(asyncore.dispatcher_with_send): def handle_read(self): data = self.recv(1024) self.send(data) class EchoServer(asyncore.dispatcher): def __init__(self): asyncore.dispatcher.__init__(self) self.create_socket(socket.AF_INET, socket.SOCK_STREAM) self.bind(('', 4915)) self.listen(1) def handle_accept(self): sock, addr = self.accept() handler = Echoer(sock) server = EchoServer() asyncore.loop()
Async I/O lets the thread run a single select() loop to handle all connections and send callbacks when events (such as accepts and data packets) occur. However, the code becomes much more complex: the execution of a simple echo server gets broken up into smaller methods and the control flow becomes hard to follow.
Bluelet (like other coroutine-based async I/O libraries) lets you write code that looks sequential but acts concurrent. Like so:
import bluelet def echoer(conn): while True: data = yield conn.recv(1024) if not data: break yield conn.sendall(data) bluelet.run(bluelet.server('', 4915, echoer))
Except for the yield keyword, note that this code appears very similar to our first, sequential version. (Bluelet also takes care of the boilerplate socket setup code.) This works because echoer is a Python coroutine: everywhere it says yield, it temporarily suspends its execution. Bluelet’s scheduler then takes over and waits for events, just like asyncore. When a socket event happens, the coroutine is resumed at the point it yielded. So there’s no need to break up your code; it can all appear as a single code block. Neat!
This repository also includes a few less-trivial examples of Bluelet’s programming model.
The httpd.py example implements a very simple Web server in less than 100 lines of Python. Start the program and navigate to http://127.0.0.1:8088/ in your Web browser to see it in action.
This example demonstrates the implementation of a network server that is slightly more complicated than the echo server described above. Again, the code for the server just looks like a sequential, one-connection-at-a-time program with yield expressions inserted – but it runs concurrently and can service many requests at the same time.
crawler.py demonstrates how Bluelet can be used for client code in addition to just servers. It implements a very simple asynchronous HTTP client and makes a series of requests for tweets from the Twitter API.
The crawler.py program actually implements the same set of requests four times to compare their performance:
- The sequential version makes one request, waits for the response, and then makes the next request.
- The “threaded” version spawns one OS thread per request and makes all the requests concurrently.
- The “processes” version uses Python’s multiprocessing module to make each request in a separate OS process. It uses the multiprocessing module’s convenient parallel map implementation.
- The Bluelet version runs each HTTP request in a Bluelet coroutine. The requests run concurrently but they use a single thread in a single process.
The sequential implementation will almost certainly be the slowest. The three other implementations are all concurrent and should have roughly the same performance. The thread- and process-based implementations incur spawning overhead; the multiprocessing implementation could see advantages by avoiding the GIL (but this is unlikely to be significant as the network latency is dominant); the Bluelet implementation has no spawning overhead but has some scheduling logic that may slow things down.
crawler.py reports the runtime of each implementation. On my machine, this is what I see:
sequential: 4.62 seconds threading: 0.81 seconds multiprocessing: 0.13 seconds bluelet: 0.20 seconds
The numbers are noisy and somewhat inconsistent across runs, but in general we see that Bluelet is competitive with the other two concurrent implementations and that the sequential version is much slower.
To get started with Bluelet, you just write a coroutine that yield Bluelet events and invoke it using bluelet.run:
import bluelet def coro(): yield bluelet.end() bluelet.run(coro())
bluelet.run takes a generator (a running coroutine) as an argument and runs it to completion. It’s the gateway into the Bluelet scheduling universe. Remember that, in Python, any “function” with a yield expression in it is a coroutine – that’s what makes coro special.
The key to programming with Bluelet is to use yield expressions where you would typically do anything that blocks or you need to interact with the Bluelet scheduler. Technically, every yield statement sends an “event” object to the Bluelet scheduler that’s running it, but you can usually get by without thinking about event objects at all. Here are some of the Bluelet yield expressions that make up Bluelet’s network socket API:
- conn = yield bluelet.connect(host, port): Connects to a network host and returns a “connection” object usable for communication.
- yield conn.send(data): Send a string of data over the connection. Returns the amount of data actually sent.
- yield conn.sendall(data): Send the string of data, continuously sending chunks of the data until it is all sent.
- data = yield conn.recv(bufsize): Receive data from the connection.
- data = yield conn.readline(delim="\n"): Read a line of data from the connection, where lines are delimited by delim.
- server = bluelet.Listener(host, port): Constructs a Bluelet server object that can be used to asynchronously wait for connections. (There’s no yield here; this just a constructor.)
- conn = yield server.accept(): Asynchronously wait for a connection to the server, returning a connection object as above.
These tools are enough to build asynchronous client and server applications with Bluelet. There’s also one convenient off-the-shelf coroutine, called bluelet.server, that helps you get off the ground with a server application quickly. This line:
bluelet.run(bluelet.server(host, port, handler_coro))
runs an asynchronous socket server, listening for concurrent connections. For each incoming connection conn, the server calls handler_coro(conn)) and adds that coroutine to the Bluelet scheduler.
Bluelet also provides some non-socket-related tools encapsulating generic green-threads capabilities:
- res = yield bluelet.call(coro()): Invokes another coroutine as a “sub-coroutine”, much like calling a function in ordinary Python code. Pedantically, the current coroutine is suspended and coro is started up; when coro finishes, Bluelet returns control to the current coroutine and returns the value returned by coro (see bluelet.end, below). The effect is similar to Python’s proposed “yield from” syntax.
- res = yield coro()): Shorthand for the above. Just yielding any generator object is equivalent to using bluelet.call.
- yield bluelet.spawn(coro()): Like call but makes the child coroutine run concurrently. Both coroutines remain in the thread scheduler. This is how you can build programs that, for example, handle multiple network connections at once (it’s used internally by bluelet.server).
- yield bluelet.join(coro): Suspends the current coroutine until a given thread, previously started with spawn, completes.
- yield bluelet.kill(coro): Aborts and unschedules a previously-spawned thread.
- yield bluelet.end(value=None): Terminate the current coroutine and, if the present coroutine was invoked by another one using bluelet.call, return the specified value to it. Analogous to return in ordinary Python.
- yield bluelet.sleep(duration): Suspend the current coroutine for approximately duration seconds, resuming it at the earliest opportunity after the interval has passed.
- yield bluelet.null(): Yield without doing anything special. This just makes it possible to let another coroutine run if one is waiting to. It’s useful if you have to do a long-running, blocking operation in a coroutine and want to give other green threads a chance to get work done.
Together, this small set of yield statements are enough to build any application that can benefit from simple, pure-Python collaborative multitasking.