The first library I’ll show here is the python-cloudfiles. I recommend using the latest one from Github, since the one that you can get for example from Ubuntu repositories does not support Swift and the one you can get from Python Package Index had a bug that made it not work with Swift.

Here I’ll show you how you can connect to your local Swift instance using the authurl parameter and how you can create containers and objects using python-cloudfiles.

from cloudfiles.connection import Connection
 
conn = Connection("test:test", "test", authurl="http://127.0.0.1:11000/v1.0")
 
container = conn.create_container("test")
 
obj = container.create_object("test.txt")
obj.content_type = "text/plain"
obj.write("test")

Pretty straightforward… right?

Next, I’ll show you another library that works with Swift called cferl. It’s a Erlang library for Cloudfiles and I made some simple patches to it to make it work with Swift.

Here’s how you can do the same things as in previous example using cferl.

ibrowse:start().
{ok, Connection} = cferl:connect("test:test", "test", "http://127.0.0.1:11000/v1.0").
 
{ok, Container} = Connection:create_container(<<"test">>).
 
{ok, Object} = Container:create_object(<<"test.txt">>).
ok = Object:write_data(<<"test">>, <<"text/plain">>).

Ok, that’s it. Now you can start playing with Swift and storing petabytes of data in it.

Ok, I could just simply do this…

time.sleep(0.5)

… to make my server wait half a second before returning, but since Tornado is using asynchronous networking and hence runs in a single thread, this would block all other requests made to my server. Not good…

I need to do this asynchronously. Here is an example on how this can be done with Tornado without blocking.

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import time
 
import tornado.web
import tornado.ioloop
 
class RequestHandler(tornado.web.RequestHandler):
 
    def _finish_request(self):
        self.finish()
 
    @tornado.web.asynchronous
    def get(self):
        ioloop = tornado.ioloop.IOLoop.instance()
        ioloop.add_timeout(time.time() + 0.5, self._finish_request)
        self.add_header("Content-Type", "text/plain")
        self.write("Hello, world")

So, this is an example of an asynchronous Tornado request handler that will wait for half a second before returning and will not block other requests while doing that. Here we are using decorator tornado.web.asynchronous on line 11 to tell Tornado that this request should not be returned immediately and we need to call tornado.web.RequestHandler.finish() on our own. The timeout is implemented by calling tornado.ioloop.IOLoop.add_timeout() method which is given a callback method that will finish the request.

Now, the problem with this is that, if I need other request handlers to do the same thing, I would need to copy paste this peace of code all over the place. And I don’t like that. We can do this bit more elegantly by using Python decorators. By writing a decorator we can avoid duplicating the same code to every request handler. This is how the same example will look using a decorator.

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import tornado.web
import tornado.decorators
 
class RequestHandler(tornado.web.RequestHandler):
 
    @tornado.decorators.wait_for(milliseconds=500)                                               
    def get(self):                                                            
        self.set_header("Content-Type", "text/plain")                         
        self.write("Hello, world")

Looks nice and clean. Doesn’t it? Well, the complex part has been moved now to the decorator tornado.decorators.wait_for. Let’s look now how we can implement that.

tornado/decorators.py

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import time
 
from functools import partial
 
from tornado.web import asynchronous
from tornado.ioloop import IOLoop
 
def wait_for(milliseconds=0):
    def _finish_request(request, start_time):
        timeout = (time.time() - start_time) * 1000.0
	request.write("\n\nServer waited for %.3f ms" % timeout)
        request.finish()
    def _decorator(func):
	func = asynchronous(func)
        def _wrapper(*args, **kwargs):
            ioloop = IOLoop.instance()
            callback = partial(_finish_request, args[0], time.time())
            ioloop.add_timeout(time.time() + milliseconds / 1000.0, callback)
            func(*args, **kwargs)
	return _wrapper
    return _decorator

Here I have implemented the decorator wait_for that takes the number of milliseconds to wait as a parameter. Let’s start from line 13 where the actual decorator is implemented. Decorator’s parameter is always the function that is being decorated. You can think of this…

@my_decorator
def my_function():
    // do something

…being same as this…

def my_function():
    // do something
my_function = my_decorator(my_function)

Now, on line 14 decorate the function with Tornado’s tornado.web.asynchronous decorator. Just like we did in the first example. So that our request does not return before we call tornado.web.RequestHandler.finish(). Then on line 15 we write a wrapper method that adds the timeout and callback to tornado.ioloop.IOLoop and after that calls the original function that we are decorating. The trickiest part here probably is that we need to give our callback function some parameters and Tornado’s add_timeout method only takes the callback function as the parameter. For that we use Python’s functools.partial to generate the callback and give some parameters to it on line 17.

To conclude the blog post here is a complete example of a script that you can test this with. You need to create the tornado/decorators.py using the code above for this to work.

http_server.py

import time
 
import tornado.httpserver
import tornado.ioloop
import tornado.web
 
from tornado.decorators import wait_for
 
 
class MainHandler(tornado.web.RequestHandler):
 
    @wait_for(milliseconds=500)
    def get(self):
        self.set_header("Content-Type", "text/plain")
        self.write("Hello, world")
 
application = tornado.web.Application([(r"/", MainHandler)])
 
if __name__ == "__main__":
    http_server = tornado.httpserver.HTTPServer(application)
    http_server.listen(8080)
    tornado.ioloop.IOLoop.instance().start()

If everything goes right your server should return something like this…

Hello, world
 
Server waited for 500.371 ms

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get_random_string(Length, AllowedChars) ->
    lists:foldl(fun(_, Acc) ->
                        [lists:nth(random:uniform(length(AllowedChars)),
                                   AllowedChars)]
                            ++ Acc
                end, [], lists:seq(1, Length)).

Ok, Erlang is not the most readable language in the world and a simple thing such as generating a random string can look pretty tedious. No worries. I’ll go through the method line by line.

I’m using the lists:foldl method here. What it does is that it goes through a list (from left to right) and calls a function that has as it’s parameter a value from that list and the result form the previous iteration. The result of the method is the result of the last call to the function. The list I give as a parameter to lists:foldl is a sequence of numbers from one to the length of the resulting random string. For that I use the lists:seq method. This is how we define how many times we loop.

I’ll explain the fun() that is the first parameter of lists:foldl. Here is what it looks like separate from the whole code.

fun(_, Acc) ->
     [lists:nth(random:uniform(length(AllowedChars)), AllowedChars)]
          ++ Acc
end

The first parameter of the function is the value from the given list ([1, 2, 3, 4,..., N]) and we don’t use it (hence the underscore). The second parameter Acc is called the accumulator that is the result from the previous iteration. To achieve our goal of producing random strings we use lists:nth and random:uniform method calls to pick a random character from the AllowedChars string. Note that the lists:nth returns the integer value of that character so that is why the method call is wrapped in square brackets making the result a string (in Erlang strings are lists of integers). What we do then is that we add the Acc (the result of the previous iteration) to the result and this way build our random string.

There is also a third parameter for the lists:foldl method that you probably have guessed already. Naturally, you also have to give the value of the accumulator for the first iteration, which in this case is empty list [] or empty string since strings in Erlang are actually lists.

Here is an example of the result that the method produces.

test:get_random_string(32, "qwertyQWERTY1234567890").     
"8qttW01wQET1qRTt1r4tr2T392QY94Re"

First of all I should mention that as I write this blog entry I’ve not yet had time to test the new Python for Series 60 release. So I apologize beforehand for all false comments I’m about to make.

It seems that they are giving a lot more focus on the ease of development in this new release. Which is a really good thing I must say. What pleases me the most is that they are planning on improving the Python runtime deployment so that the end-user or the guy that is installing Python applications should not have to worry too much about having or not having the Python runtime installed on his phone. Definitely a good thing. The new release includes a packaging tool that is not part of the S60 SDK and is basically ensymble with added GUI. Ensymble is an excellent tool and it is very nice to see it included in the official PyS60 release.

Like mentioned already, the new release includes the version 2.5 of the Python interpreter and most of it’s standard libraries. Ok, the word “most” does not sound good here. I’ll focus first on the good things. First, the new release has Expat XML parser in it. Definitely a good thing since XML is something that pretty much every application out there uses and so far people have had to make ugly regexp XML parsers or use 3rd party package of expat to be able to parse XML in their PyS60 applications. However, I would say that including JSON parser as the official Python 2.6 release did lately, would probably be a good idea too.

Also, inclusion of asyncore and more compliant socket module sounds nice. I can’t wait to try Twisted on PyS60 and see how it works. One could do some crazy things with that on a mobile phone.

Then to the things that are not included from the standard Python 2.5 libraries. Now, I don’t have too much information on this since, like said before, I haven’t tried the new release yet. However, to my disappointment I noticed that sqlite3 is not included. SQLite should probably be a platform component in S60 because it is kind of becoming a de facto standard in mobile platforms since both iPhone and Android platforms use it. I don’t know what is the equivalent in S60 or does such exist but having some kind of storage other than just plain text file for PyS60 applications would be extremely nice thing to have.

Ok, I have to try out the new PyS60 release soon. I’m hoping that I will be pleasantly surprised. I’ll definitely write more about it later.