This continues the introduction started here. You can find an index to the entire series here.
Introduction
The servers we have written so far have just run in a terminal window, with output going to the screen via print statements. This works alright for development, but it’s hardly a way to deploy services in production. A well-behaved production server ought to:
Run as a daemon process, unconnected with any terminal or user session. You don’t want a service to shut down just because the administrator logs out.
Send debugging and error output to a set of rotated log files, or to the syslog service.
Drop excessive privileges, e.g., switching to a lower-privileged user before running.
Record its pid in a file so that the administrator can easily send signals to the daemon.
We can get all of those features using the twistd script provided by Twisted. But first we’ll have to change our code a bit.
This continues the introduction started here. You can find an index to the entire series here.
Introduction
We’ve written a lot of code in our exploration of Twisted, but so far we’ve neglected to write something important — tests. And you may be wondering how you can test asynchronous code using a synchronous framework like the unittest package that comes with Python. The short answer is you can’t. As we’ve discovered, synchronous and asynchronous code do not mix, at least not readily.
This continues the introduction started here. You can find an index to the entire series here.
Introduction
In this part we’re going to learn another aspect of the Deferred class. To motivate the discussion, we’ll add one more server to our stable of poetry-related services. Suppose we have a large number of internal clients who want to get poetry from the same external server. But this external server is slow and already over-burdened by the insatiable demand for poetry across the Internet. We don’t want to contribute to that poor server’s problems by sending all our clients there too.
This continues the introduction started here. You can find an index to the entire series here.
Introduction
Recall poetry client 5.1 from Part 10.The client used a Deferred to manage a callback chain that included a call to a poetry transformation engine. In client 5.1, the engine was implemented as a synchronous function call implemented in the client itself.
Now we want to make a new client that uses the networked poetry transformation service we wrote in Part 12. But here’s the wrinkle: since the transformation service is accessed over the network, we’ll need to use asynchronous I/O. And that means our API for requesting a transformation will have to be asynchronous, too. In other words, the try_to_cummingsify callback is going to return a Deferred in our new client.
This continues the introduction started here. You can find an index to the entire series here.
One More Server
Alright, we’ve written one Twisted server so let’s write another, and then we’ll get back to learning some more about Deferreds.
In Parts 9 and 10 we introduced the idea of a poetry transformation engine. The one we eventually implemented, the cummingsifier, was so simple we had to add random exceptions to simulate a failure. But if the transformation engine was located on another server, providing a network “poetry transformation service”, then there is a much more realistic failure mode: the transformation server is down.
This continues the introduction started here. You can find an index to the entire series here.
A Twisted Poetry Server
Now that we’ve learned so much about writing clients with Twisted, let’s turn around and re-implement our poetry server with Twisted too. And thanks to the generality of Twisted’s abstractions, it turns out we’ve already learned almost everything we need to know. Take a look at our Twisted poetry server located in twisted-server-1/fastpoetry.py. It’s called fastpoetry because this server sends the poetry as fast as possible, without any delays at all. Note there’s significantly less code than in the client!
This continues the introduction started here. You can find an index to the entire series here.
Client 5.0
Now we’re going to add some transformation logic to our poetry client, along the lines suggested in Part 9. But first, I have a shameful and humbling confession to make: I don’t know how to write the Byronification Engine. It is beyond my programming abilities. So instead, I’m going to implement a simpler transformation, the Cummingsifier. The Cummingsifier is an algorithm that takes a poem and returns a new poem like the original but written in the style of e.e. cummings. Here is the Cummingsifier algorithm in its entirety:
This continues the introduction started here. You can find an index to the entire series here.
More Consequence of Callbacks
We’re going to pause for a moment to think about callbacks again. Although we now know enough about deferreds to write simple asynchronous programs in the Twisted style, the Deferred class provides more features that only come into play in more complex settings. So we’re going to think up some more complex settings and see what sort of challenges they pose when programming with callbacks. Then we’ll investigate how deferreds address those challenges.
This continues the introduction started here. You can find an index to the entire series here.
Client 4.0
Now that we have know something about deferreds, we can rewrite our Twisted poetry client to use them. You can find client 4.0 in twisted-client-4/get-poetry.py.
Our get_poetry function no longer needs callback or errback arguments. Instead, it returns a new deferred to which the user may attach callbacks and errbacks as needed.
This continues the introduction started here. You can find an index to the entire series here.
Callbacks and Their Consequences
In Part 6 we came face-to-face with this fact: callbacks are a fundamental aspect of asynchronous programming with Twisted. Rather than just a way of interfacing with the reactor, callbacks will be woven into the structure of any Twisted program we write. So using Twisted, or any reactor-based asynchronous system, means organizing our code in a particular way, as a series of “callback chains” invoked by a reactor loop.