Asynchronous and non-Blocking I/O

Real-time web features require a long-lived mostly-idle connection per user. In a traditional synchronous web server, this implies devoting one thread to each user, which can be very expensive.

To minimize the cost of concurrent connections, Tornado uses a single-threaded event loop. This means that all application code should aim to be asynchronous and non-blocking because only one operation can be active at a time.

The terms asynchronous and non-blocking are closely related and are often used interchangeably, but they are not quite the same thing.


A function blocks when it waits for something to happen before returning. A function may block for many reasons: network I/O, disk I/O, mutexes, etc. In fact, every function blocks, at least a little bit, while it is running and using the CPU (for an extreme example that demonstrates why CPU blocking must be taken as seriously as other kinds of blocking, consider password hashing functions like bcrypt, which by design use hundreds of milliseconds of CPU time, far more than a typical network or disk access).

A function can be blocking in some respects and non-blocking in others. In the context of Tornado we generally talk about blocking in the context of network I/O, although all kinds of blocking are to be minimized.


An asynchronous function returns before it is finished, and generally causes some work to happen in the background before triggering some future action in the application (as opposed to normal synchronous functions, which do everything they are going to do before returning). There are many styles of asynchronous interfaces:

  • Callback argument
  • Return a placeholder (Future, Promise, Deferred)
  • Deliver to a queue
  • Callback registry (e.g. POSIX signals)

Regardless of which type of interface is used, asynchronous functions by definition interact differently with their callers; there is no free way to make a synchronous function asynchronous in a way that is transparent to its callers (systems like gevent use lightweight threads to offer performance comparable to asynchronous systems, but they do not actually make things asynchronous).

Asynchronous operations in Tornado generally return placeholder objects (Futures), with the exception of some low-level components like the IOLoop that use callbacks. Futures are usually transformed into their result with the await or yield keywords.


Here is a sample synchronous function:

from tornado.httpclient import HTTPClient

def synchronous_fetch(url):
    http_client = HTTPClient()
    response = http_client.fetch(url)
    return response.body

And here is the same function rewritten asynchronously as a native coroutine:

from tornado.httpclient import AsyncHTTPClient

async def asynchronous_fetch(url):
    http_client = AsyncHTTPClient()
    response = await http_client.fetch(url)
    return response.body

Or for compatibility with older versions of Python, using the tornado.gen module:

from tornado.httpclient import AsyncHTTPClient
from tornado import gen

def async_fetch_gen(url):
    http_client = AsyncHTTPClient()
    response = yield http_client.fetch(url)
    raise gen.Return(response.body)

Coroutines are a little magical, but what they do internally is something like this:

from tornado.concurrent import Future

def async_fetch_manual(url):
    http_client = AsyncHTTPClient()
    my_future = Future()
    fetch_future = http_client.fetch(url)
    def on_fetch(f):
    return my_future

Notice that the coroutine returns its Future before the fetch is done. This is what makes coroutines asynchronous.

Anything you can do with coroutines you can also do by passing callback objects around, but coroutines provide an important simplification by letting you organize your code in the same way you would if it were synchronous. This is especially important for error handling, since try/except blocks work as you would expect in coroutines while this is difficult to achieve with callbacks. Coroutines will be discussed in depth in the next section of this guide.