Why doesn't time.sleep run in parallel in a To

Question

When I run this handler in a simple Tornado app and make two requests to it with curl, it doesn't run in parallel. It prints out "1 2 3 4 5 1 2 3 4 5", when I want it to print "1 1 2 2 3 3 4 4 5 5".

class SleepHandler(RequestHandler):
    def get(self):
        for i in range(5):
            print(i)
            time.sleep(1)

What am I doing wrong?

Answer 1

The reason for this is that time.sleep is a blocking function: it doesn’t allow control to return to the IOLoop so that other handlers can be run.

Of course, time.sleep is often just a placeholder in these examples, the point is to show what happens when something in a handler gets slow. No matter what the real code is doing, to achieve concurrency blocking code must be replaced with non-blocking equivalents. This means one of three things:

• Find a coroutine-friendly equivalent. For time.sleep, use tornado.gen.sleep instead:

  class CoroutineSleepHandler(RequestHandler):
      @gen.coroutine
      def get(self):
          for i in range(5):
              print(i)
              yield gen.sleep(1)
  

  When this option is available, it is usually the best approach. See the Tornado wiki for links to asynchronous libraries that may be useful.

• Find a callback-based equivalent. Similar to the first option, callback-based libraries are available for many tasks, although they are slightly more complicated to use than a library designed for coroutines. These are typically used with tornado.gen.Task as an adapter:

  class CoroutineTimeoutHandler(RequestHandler):
      @gen.coroutine
      def get(self):
          io_loop = IOLoop.current()
          for i in range(5):
              print(i)
              yield gen.Task(io_loop.add_timeout, io_loop.time() + 1)
  

  Again, the Tornado wiki can be useful to find suitable libraries.

• Run the blocking code on another thread. When asynchronous libraries are not available, concurrent.futures.ThreadPoolExecutor can be used to run any blocking code on another thread. This is a universal solution that can be used for any blocking function whether an asynchronous counterpart exists or not:

  executor = concurrent.futures.ThreadPoolExecutor(8)
  
  class ThreadPoolHandler(RequestHandler):
      @gen.coroutine
      def get(self):
          for i in range(5):
              print(i)
              yield executor.submit(time.sleep, 1)
  

  See the Asynchronous I/O chapter of the Tornado user’s guide for more on blocking and asynchronous functions.