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Barry Warsaw

I'm writing a bunch of new code these days for Ubuntu Touch's Image Based Upgrade system.  Think of it essentially as Ubuntu Touch's version of upgrading the phone/tablet (affectionately called phablet) operating system in a bulk way rather than piecemeal apt-gets the way you do it on a traditional Ubuntu desktop or server.  One of the key differences is that a phone has to detour through a reboot in order to apply an upgrade since its Ubuntu root file system is mounted read-only during the user session.

Anyway, those details aren't the focus of this article.  Instead, just realize that because it's a pile of new code, and because we want to rid ourselves of Python 2, at least on the phablet image if not everywhere else in Ubuntu, I am prototyping all this in Python 3, and specifically 3.3.  This means that I can use all the latest and greatest cool stuff in the most recent stable Python release.  And man, is there a lot of cool stuff!

One module in particular that I'm especially fond of is contextlibContext managers are objects implementing the protocol behind the with statement, and they are typically used to guarantee that some resource is cleaned up properly, even in the event of error conditions.  When you see code like this:

with open(somefile) as fp:
    data = fp.read()

you are invoking a context manager.  Python was clever enough to make file objects support the context manager protocol so that you never have to explicitly close the file; that happens automatically when the with statement completes, regardless of whether the code inside the with statement succeeds or raises an exception.

It's also very easy to define your own context managers to properly handle other kinds of resources.  I won't go into too much detail here, because this is all well-established; the with statement has been, er, with us since Python 2.5.

You may be familiar with the contextlib module because of the @contextmanager decorator it provides.  This makes it trivial to define a new context manager without having to deal with all the intricacies of the protocol.  For example, here's how you would implement a context manager that temporarily changes the current working directory:

import os
from contextlib import contextmanager

@contextmanager
def chdir(dir):
    cwd = os.getcwd()
    try:
        os.chdir(dir)
        yield
    finally:
        os.chdir(cwd)

In this example, the yield cedes control back to the body of the with statement, and when that completes, the code after the yield is executed.  Because the yield is wrapped inside a try/finally, it is guaranteed that the original working directory is restored.  You would use this code like so:

with chdir('/tmp'):
    print(os.getcwd())

So far, so good, but this is nothing revolutionary.  Python 3.3 brings additional awesomeness to contextlib by way of the new ExitStack class.

The documentation for ExitStack is a bit dense, and even the examples didn't originally make it clear to me how amazing this new API is.  In my opinion, this is so powerful, it changes completely the way you think about deploying safe code.

So what is an ExitStack?  One way to think about it is as an extensible context manager.  It's used in with statements just like any other context manager:

from contextlib import ExitStack
with ExitStack() as stack:
    # do some magical stuff

Just like any other context manager, the ExitStack's "exit" code is guaranteed to be run at the end of the with statement.  It's the programmable extensibility of the ExitStack where the cool stuff happens.

The first interesting method of an ExitStack you might use is the callback() method.  Let's say for example that in your with statement, you are creating a temporary directory and you want to make sure that temporary directory gets deleted when the with statement exits.  You could do something like this:

import shutil, tempfile
with ExitStack() as stack:
    tempdir = tempfile.mkdtemp()
    stack.callback(shutil.rmtree, tempdir)

Now, when the with statement completes, it calls all of its callbacks, which includes removing the temporary directory.

So, what's the big deal?  Let's say you're actually creating three temporary directories and any of those calls could fail.  To guarantee that all successfully created directories are deleted at the end of the with statement, regardless of whether an exception occurred in the middle, you could do this:

with ExitStack() as stack:
    tempdirs = []
    for i in range(3):
        tempdir = tempfile.mkdtemp()
        stack.callback(shutil.rmtree, tempdir)
        tempdirs.append(tempdir)
    # Do something with the tempdirs

If you knew statically that you wanted three temporary directories, you could set this up with nested with statements, or a single with statement containing multiple backslash-separated targets, but that gets unwieldy very quickly.  And besides, that's impossible if you only know the number of directories you need dynamically at run time.  On the other hand, the ExitStack makes it easy to guarantee everything gets cleaned up and there are no leaks.

That's powerful enough, but it's not all you can do!  Another very useful method is enter_context().

Let's say that you are opening a bunch of files and you want the following behavior: if all of the files open successfully, you want to do something with them, but if any of them fail to open, you want to make sure that the ones that did get open are guaranteed to get closed.  Using ExitStack.enter_context() you can write code like this:

files = []
with ExitStack() as stack:
    for filename in filenames:
        # Open the file and automatically add its context manager to the stack.
        # enter_context() returns the passed in context manager, i.e. the 
        # file object.
        fp = stack.enter_context(open(filename))
        files.append(fp)
    # Capture the close method, but do not call it yet.
    close_all_files = stack.pop_all().close

(Note that the contextlib documentation contains a more efficient, but denser way of writing the same thing.)

So what's going on here?  First, the open(filename) does what it always does of course, it opens the file and returns a file object, which is also a context manager.  However, instead of using that file object in a with statement, we add it to the ExitStack by passing it to the enter_context() method.  For convenience, this method returns the passed in object.

So what happens if one of the open() calls fail before the loop completes?  The with statement will exit as normal and the ExitStack will exit all the context managers it knows about.  In other words, all the files that were successfully opened will get closed.  Thus, in an error condition, you will be left with no open files and no leaked file descriptors, etc.

What happens if the loop completes and all files got opened successfully?  Ah, that's where the next bit of goodness comes into play: the ExitStack's pop_all() method.

pop_all() creates a new ExitStack, and populates it from the original ExitStack, removing all the context managers from the original ExitStack.  So, after stack.pop_all() completes, the original ExitStack, i.e. the one used in the with statement, is now empty.  When the with statement exits, the original ExitStack contains no context managers so none of the files are closed.

Well, then, how do you close all the files once you're done with them?  That's the last bit of magic.  ExitStacks have a .close() method which unwinds all the registered context managers and callbacks and invokes their exit functionality.  So, after you're finally done with all the files and you want to clean everything up, you would just do:

close_all_files()

And that's it.

Hopefully that all makes sense.  I know it took a while to sink in for me, but now that it has, it's clear the enormous power this gives you.  You can write much safer code, in the sense that it's easier to ensure much better guarantees that your resources are cleaned up at the right time.

The real power comes when you have many different disparate resources to clean up for a particular operation.  For example, in the test suite for the Image Based Upgrader, I have a test where I need to create a temporary directory and start an HTTP server in a thread.  Roughly, my code looks like this:

@classmethod
def setUpClass(cls):
    cls._cleaner = ExitStack()
    try:
        cls._serverdir = tempfile.mkdtemp()
        cls._cleaner.callback(shutil.rmtree, cls._serverdir)
        # ...
        cls._stop = make_http_server(cls._serverdir)
        cls._cleaner.callback(cls._stop)
    except:
        cls._cleaner.pop_all().close()
        raise

@classmethod
def tearDownClass(cls):
    cls._cleaner.close()

Notice there's no with statement there at all. :)   This is because the resources must remain open until tearDownClass() is called, unless some exception occurs during the setUpClass().  If that happens, the bare except will ensure that all the context managers are properly closed, leaving the original ExitStack empty.  (The bare except is acceptable here because the exception is re-raised after the resources are cleaned up.)  Even though the exception will prevent the tearDownClass() from being called, it's still safe to do so in case it is called for some odd reason, because the original ExitStack is empty.

But if no exception occurs, the original ExitStack will contain all the context managers that need to be closed, and calling .close() on it in the tearDownClass() does exactly that.

I have one more example from my recent code.  Here, I need to create a GPG context (the details are unimportant), and then use that context to verify the detached signature of a file.  If the signature matches, then everything's good, but if not, then I want to raise an exception and throw away both the data file and the signature (i.e. .asc) file.  Here's the code:

with ExitStack() as stack:
    ctx = stack.enter_context(Context(pubkey_path))
    if not ctx.verify(asc_path, channels_path):
        # The signature did not verify, so arrange for the .json and .asc
        # files to be removed before we raise the exception.
        stack.callback(os.remove, channels_path)
        stack.callback(os.remove, asc_path)
        raise FileNotFoundError


Here we create the GPG context, which itself is a context manager, but instead of using it in a with statement, we add it to the ExitStack.  Then we verify the detached signature (asc_path) of a data file (channels_path), and only arrange to remove those files if the verification fails.  When the FileNotFoundError is raised, the ExitStack in the with statement unwinds, removing both files and closing the GPG context.  Of course, if the signature matches, only the GPG context is closed -- the channels_path and asc_path files are not removed.

You can see how an ExitStack actually functions as a fairly generic resource manager!

To me, this revolutionizes the management of external resources.  The new ExitStack object, and the methods and semantics it exposes, make it so much easier to manage those resources, guaranteeing that they get cleaned up at the right time, once and only once, regardless of whether errors occur or not.

ExitStack takes the already powerful concept of context managers and turns it up to 11.  There's more you can do, and it's worth spending some time reading the contextlib documentation in Python 3.3, especially the examples and recipes.

As I mentioned on Twitter, it's features like this that make using Python 2 seem downright barbaric.

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Barry Warsaw

UDS Update #1 - OAuth

For UDS-R for Raring (i.e. Ubuntu 13.04) in Copenhagen, I sponsored three blueprints.  These blueprints represent most of the work I will be doing for the next 6 months, as we're well on our way to the next LTS, Ubuntu 14.04.

I'll provide some updates to the other blueprints later, but for now, I want to talk about OAuth and Python 3.  OAuth is a protocol which allows you to programmatically interact with certain website APIs, in an authenticated manner, without having to provide your website password.  Essentially, it allows you to generate an authorization token which you can use instead, and it allows you to manage and share these tokens with applications, so that you can revoke them if you want, or decide how and which applications to trust to act on your behalf.

A good example of a site that uses OAuth is Launchpad, but many other sites also support OAuth, such as Twitter and Facebook.

There are actually two versions of OAuth out there.  OAuth version 1 is definitely the more prevelent, since it has been around for years, is relatively simple (at least on the client side), and enshrined in RFC 5849.  There are tons of libraries available that support OAuth v1, in a multitude of languages, with Python being no exception.

OAuth v2 is much less common, since it is currently only a draft specification, and has had its share of design-by-committee controversy.  Still, some sites such as Facebook do require OAuth v2.

One of the very earliest Python libraries to support OAuth v1, on both the client and server side, was python-oauth (I'll use the Debian package names in this post), and on the Ubuntu desktop, you'll find lots of scripts and libraries that use python-oauth.  There are major problems with this library though, and I highly recommend not using it.  The biggest problems are that the code is abandoned by its upstream maintainer (it hasn't be updated on PyPI since 2009), and it is not Python 3 compatible.  Because the OAuth v2 draft came after this library was abandoned, it provides no support for the successor specification.

For this reason, one of the blueprints I sponsored was specifically to survey the alternatives available for Python programmers, and make a decision about which one we would officially endorse for Ubuntu.  By "official endorsement" I mean promote the library to other Python programmers (hence this post!) and to port all of our desktop scripts from python-oauth to the agreed upon library.

After some discussion, it was unanimous by the attendees of the UDS session (both in-person and remotely), to choose the python-oauthlib as our preferred library.

python-oauthlib has a lot going for it.  It's Python 3 compatible, has an active upstream maintainer, supports both RFC 5849 for v1, and closely follows the draft for v2.  It's a well-tested, solid library, and it is available in Ubuntu for both Python 2 and Python 3.  Probably the only negative is that the library does not provide any support for the server side.  This is not a major problem for our immediate plans, since there aren't any server applications on the Ubuntu desktop requiring OAuth.  Eventually, yes, we'll need server side support, but we can punt on that recommendation for now.

Another cool thing about python-oauthlib is that it has been adopted by the python-requests library, meaning, if you want to use a modern replacement for the urllib2/httplib2 circus which supports OAuth out of the box, you can just use python-requests, provide the appropriate parameters, and you get request signing for free.

So, as you'll see from the blueprint, there are several bugs linked to packages which need porting to python-oauthlib for Ubuntu 13.04, and I am actively working on them, though contributions, as always, are welcome!  I thought I'd include a little bit of code to show you how you might port from python-oauth to python-oauthlib.  We'll stick with OAuth v1 in this discussion.

The first thing to recognize is that python-oauth uses different, older terminology that predates the RFC.  Thus, you'll see references to a token key and token secret, as well as a consumer key and consumer secret.  In the RFC, and in python-oauthlib, these terms are client key, client secret, resource owner key, and resource owner secret respectively.  After you get over that hump, the rest pretty much falls into place.  As an example, here is a code snippet from the piston-mini-client library which used the old python-oauth library:

class OAuthAuthorizer(object):
    """Authenticate to OAuth protected APIs."""
    def __init__(self, token_key, token_secret, consumer_key, consumer_secret,
                 oauth_realm="OAuth"):
        """Initialize a ``OAuthAuthorizer``.

        ``token_key``, ``token_secret``, ``consumer_key`` and
        ``consumer_secret`` are required for signing OAuth requests.  The
        ``oauth_realm`` to use is optional.
        """
        self.token_key = token_key
        self.token_secret = token_secret
        self.consumer_key = consumer_key
        self.consumer_secret = consumer_secret
        self.oauth_realm = oauth_realm

    def sign_request(self, url, method, body, headers):
        """Sign a request with OAuth credentials."""
        # Import oauth here so that you don't need it if you're not going
        # to use it.  Plan B: move this out into a separate oauth module.
        from oauth.oauth import (OAuthRequest, OAuthConsumer, OAuthToken,
                                 OAuthSignatureMethod_PLAINTEXT)
        consumer = OAuthConsumer(self.consumer_key, self.consumer_secret)
        token = OAuthToken(self.token_key, self.token_secret)
        oauth_request = OAuthRequest.from_consumer_and_token(
            consumer, token, http_url=url)
        oauth_request.sign_request(OAuthSignatureMethod_PLAINTEXT(),
                                   consumer, token)
        headers.update(oauth_request.to_header(self.oauth_realm))


The constructor is pretty simple, and it uses the old OAuth terminology.  The key thing to notice is the way the old API required you to create a consumer, a token, and then a request object, then ask the request object to sign the request.  On top of all the other disadvantages, this isn't a very convenient API.  Let's look at the snippet after conversion to python-oauthlib.

class OAuthAuthorizer(object):
    """Authenticate to OAuth protected APIs."""
    def __init__(self, token_key, token_secret, consumer_key, consumer_secret,
                 oauth_realm="OAuth"):
        """Initialize a ``OAuthAuthorizer``.

        ``token_key``, ``token_secret``, ``consumer_key`` and
        ``consumer_secret`` are required for signing OAuth requests.  The
        ``oauth_realm`` to use is optional.
        """
        # 2012-11-19 BAW: python-oauthlib requires unicodes for its tokens and
        # secrets.  Assume utf-8 values.
        # https://github.com/idan/oauthlib/issues/68
        self.token_key = _unicodeify(token_key)
        self.token_secret = _unicodeify(token_secret)
        self.consumer_key = _unicodeify(consumer_key)
        self.consumer_secret = _unicodeify(consumer_secret)
        self.oauth_realm = oauth_realm

    def sign_request(self, url, method, body, headers):
        """Sign a request with OAuth credentials."""
        # 2012-11-19 BAW: In order to preserve API backward compatibility,
        # convert empty string body to None.  The old python-oauth library
        # would treat the empty string as "no body", but python-oauthlib
        # requires None.
        if not body:
            body = None
        # Import oauthlib here so that you don't need it if you're not going
        # to use it.  Plan B: move this out into a separate oauth module.
        from oauthlib.oauth1 import Client, SIGNATURE_PLAINTEXT
        oauth_client = Client(self.consumer_key, self.consumer_secret,
                              self.token_key, self.token_secret,
                              signature_method=SIGNATURE_PLAINTEXT,
                              realm=self.oauth_realm)
        uri, signed_headers, body = oauth_client.sign(
            url, method, body, headers)
        headers.update(signed_headers)


See how much nicer this is?  You need only create a client object, essentially using all the same bits of information.  Then you ask the client to sign the request, and update the request headers with the signature.  Much easier.

Two important things to note.  If you are doing an HTTP GET, there is no request body, and thus no request content which needs to contribute to the signature.  In python-oauth, you could specify an empty body by using either None or the empty string.  piston-mini-client uses the latter, and this is embodied in its public API.  python-oauthlib however, treats the empty string as a body being present, so it would require the Content-Type header to be set even for an HTTP GET which has no content (i.e. no body).  This is why the replacement code checks for an empty string being passed in (actually, any false-ish value), and coerces that to None.

The second issue is that python-oauthlib requires the keys and secrets to be Unicode objects; they cannot be bytes objects.  In code ported straight from Python 2 however, these values are usually 8-bit strings, and so become bytes objects in Python 3.  python-oauthlib will raise a ValueError during signing if any of these are bytes objects.  Thus the use of the _unicodeify() function to decode these values to unicodes.

def _unicodeify(s):
    if isinstance(s, bytes):
        return s.decode('utf-8')
    return s


The above works in both Python 2 and Python 3.  Of course, we don't know for sure that the bytes values are UTF-8, but it's the only sane encoding to expect, and if a client of piston-mini-client were to be so insane as to use an incompatible encoding (US-ASCII is fine because it's compatible with UTF-8), it would be up to the client to just pass in unicodes in the first place.  At the time of this writing, this is under active discussion with upstream, but for now, it's not too difficult to work around.

Anyway, I hope this helps, and I encourage you to help increase the popularity of python-oauthlib on the Cheeseshop, so that we can one day finally kill off the long defunct python-oauth library.

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Barry Warsaw

Recently, as part of our push to ship only Python 3 on the Ubuntu 12.10 desktop, I've helped several projects update their internationalization (i18n) support.  I've seen lots of instances of suboptimal Python 2 i18n code, which leads to liberal sprinkling of cargo culted .decode() and .encode() calls simply to avoid the dreaded UnicodeErrors.  These get worse when the application or library is ported to Python 3 because then even the workarounds aren't enough to prevent nasty failures in non-ASCII environments (i.e. the non-English speaking world majority :).

Let's be honest though, the problem is not because these developers are crappy coders! In fact, far from it, the folks I've talked with are really really smart, experienced Pythonistas.  The fundamental problem is Python 2's 8-bit string type which doubles as a bytes type, and the terrible API of the built-in Python 2 gettext module, which does its utmost to sabotage your Python 2 i18n programs.  I take considerable blame for the latter, since I wrote the original version of that module.  At the time, I really didn't understand unicodes (this is probably also evident in the mess I made of the email package).  Oh, to really have access to Guido's time machine.

The good news is that we now know how to do i18n right, especially in a bilingual Python 2/3 world, and the Python 3 gettext module fixes the most egregious problems in the Python 2 version.  Hopefully this article does some measure of making up for my past sins.

Stop right here and go watch Ned Batchelder's talk from PyCon 2012 entitled Pragmatic Unicode, or How Do I Stop the Pain?  It's the single best description of the background and effective use of Unicode in Python you'll ever see.  Ned does a brilliant job of resolving all the FUD.

...

Welcome back.  Your Python application is multi-language friendly, right?  I mean, I'm as functionally monolinguistic as most Americans, but I love the diversity of languages we have in the world, and appreciate that people really want to use their desktop and applications in their native language.  Fortunately, once you know the tricks it's not that hard to write good i18n'd Python code, and there are many good FLOSS tools available for helping volunteers translate your application, such as Pootle, Launchpad translations, Translatewiki, Transifex, and Zanata.

So there really is no excuse not to i18n your Python application.  In fact, GNU Mailman has been i18n'd for many years, and pioneered the supporting code in Python's standard library, namely the gettext module.  As part of the Mailman 3 effort, I've also written a higher level library called flufl.i18n which makes it even easier to i18n your application, even in tricky multi-language contexts such as server programs, where you might need to get a German translation and a French translation in one operation, then turn around and get Japanese, Italian, and English for the next operation.

In one recent case, my colleague was having a problem with a simple command line program.  What's common about these types of applications is that you fire them up once, they run to completion then exit, and they only have to deal with one language during the entire execution of the program, specifically the language defined in the user's locale.  If you read the gettext module's documentation, you'd be inclined to do this at the very start of your application:

from gettext import gettext as _
gettext.textdomain(my_program_name)

then, you'd wrap translatable strings in code like this:

print _('Here is something I want to tell you')

What gettext does is look up the source string (i.e. the argument to the underscore function) in a translation catalog, returning the text in the appropriate language, which will then be printed.  There are some additional details regarding i18n that I won't go into here.  If you're curious, ask in the comments, and I'll try to fill things in.

Anyway, if you do write the above code, you'll be in for a heap of trouble, as my colleague soon found out.  Just running his program with --help in a French locale, he was getting the dreaded UnicodeEncodeError:

"UnicodeEncodeError: 'ascii' codec can't encode character"

I've also seen reports of such errors when trying to send translated strings to a log file (a practice which I generally discourage, since I think log messages usually shouldn't be translated).  In any case, I'm here to tell you why the above "obvious" code is wrong, and what you should do instead.

First, why is that code wrong, and why does it lead to the UnicodeEncodeErrors?  What might not be obvious from the Python 2 gettext documentation is that gettext.gettext() always returns 8-bit strings (a.k.a. byte strings in Python 3 terminology), and these 8-bit strings are encoded with the charset defined in the language's catalog file.

It's always best practice in Python to deal with human readable text using unicodes.  This is traditionally more problematic in Python 2, where English programs can cheat and use 8-bit strings and usually not crash, since their character range is compatible with ASCII and you only ever print to English locales.  As soon as your French friend uses your program though, you're probably going to run into trouble.  By using unicodes everywhere, you can generally avoid such problems, and in fact it will make your life much easier when you eventually switch to Python 3.

So the 8-bit strings that gettext.gettext() hands you have already sunk you, and to avoid the pain, you'd want to convert them back to unicodes before you use them in any way.  However, converting to unicodes makes the i18n APIs much less convenient, so no one does it until there's way too much broken code to fix.

What you really want in Python 2 is something like this:

from gettext import ugettext as _

which you'd think you should be able to do, the "u" prefix meaning "give me unicode".  But for reasons I can only describe as based on our misunderstandings of unicode and i18n at the time, you can't actually do that, because ugettext() is not exposed as a module-level function.  It is available in the class-based API, but that's a more advanced API that again almost no one uses.  Sadly, it's too late to fix this in Python 2.  The good news is that in Python 3 it is fixed, not by exposing ugettext(), but by changing the most commonly used gettext module APIs to return unicode strings directly, as it always should have done.  In Python 3, the obvious code just works:

from gettext import gettext as _

What can you do in Python 2 then?  Here's what you should use instead of the two lines of code at the beginning of this article:

_ = gettext.translation(my_program_name).ugettext

and now you can wrap all your translatable strings in _('Foo') and it should Just Work.

Perhaps more usefully, you can use the gettext.install() function to put _() into the built-in namespace, so that all your other code can just use that function without doing anything special.  Again, though we have to work around the boneheaded Python 2 API.  Here's how to write code which works correctly in both Python 2 and Python 3.

import sys, gettext
kwargs = {}
if sys.version_info[0] < 3:
    # In Python 2, ensure that the _() that gets installed into built-ins
    # always returns unicodes.  This matches the default behavior under Python
    # 3, although that keyword argument is not present in the Python 3 API.
    kwargs['unicode'] = True
gettext.install(my_program_name, **kwargs)

Or you can use the flufl.i18n API, which always uses returns unicode strings in both Python 2 and Python 3.

Also interesting was that I could never reproduce the crash when ssh'd into the French locale VM. It would only crash for me when I was logged into a terminal on the VM's graphical desktop.  The only difference between the two that I could tell was that in the desktop's terminal, locale(8) returned French values (e.g. fr_FR.UTF-8) for everything, but in the ssh console, it returned the French values for everything except the LC_CTYPE environment variable.  For the life of me, I could not get LC_CTYPE set to anything other than en_US.UTF-8 in the ssh context, so the reproducible test case would just return the English text, and not crash.  This happened even if I explicitly set that environment variable either as a separate export command in the shell, or as a prefix to the normally crashing command.  Maybe there's something in ssh that causes this, but I couldn't find it.

One last thing.  It's important to understand that Python's gettext module only handles Python strings, and other subsystems may be involved.  The classic example is GObject Introspection, the newest and recommended interface to the GNOME Object system.  If your Python-GI based project needs to translate strings too (e.g. in menus or other UI elements), you'll have to use both the gettext API for your Python strings, and set the locale for the C-based bits using locale.setlocale().  This is because Python's API does not set the locale automatically, and Python-GI exposes no other way to control the language it uses for translations.

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Barry Warsaw

So, now all the world now knows that my suggested code name for Ubuntu 12.10, Qwazy Quahog, was not chosen by Mark.  Oh well, maybe I'll have more luck with Racy Roadrunner.

In any case, Ubuntu 12.04 LTS is to be released any day now so it's time for my semi-annual report on Python plans for Ubuntu.  I seem to write about this every cycle, so 12.10 is no exception.  We've made some fantastic progress, but now it's time to get serious.

For Ubuntu 12.10, we've made it a release goal to have Python 3 only on the desktop CD images.  The usual caveats apply: Python 2.7 isn't going away; it will still probably always be available in the main archive.  This release goal also doesn't affect other installation CD images, such as server, or other Ubuntu flavors.  The relatively modest goal then only affects packages for the standard desktop CD images, i.e. the alternative installation CD and the live CD.

Update 20120425: To be crystal clear,  if you depend on Python 2.7, the only thing that changes for you is that after a fresh install from the desktop CD on a new machine, you'll have to explicitly apt-get install python2.7.  After that, everything else will be the same.

This is ostensibly an effort to port a significant chunk of Ubuntu to Python 3, but it really is a much wider, Python-community driven effort.  Ubuntu has its priorities, but I personally want to see a world where Python 3 rules the day, and we can finally start scoffing at Python 2 :).

Still, that leaves us with about 145 binary packages (and many fewer source packages) to port.  There are a few categories of packages to consider:

  • Already ported and available.  This is the good news, and covers packages such as dbus-python.  Unfortunately, there aren't too many others, but we need to check with Debian and make sure we're in sync with any packages there that already support Python 3 (python3-dateutil comes to mind).
  • Upstream supports Python 3, but it is not yet available in Debian or Ubuntu.  These packages should be fairly easy to port, since we have pretty good packaging guidelines for supporting both Python 2 and Python 3.
  • Packages with better replacements for Python 3.  A good example is the python-simplejson package.  Here, we might not care as much because Python 3 already comes with a json module in its standard library, so code which depends on python-simplejson and is required for the desktop CD, should be ported to use the stdlib json module.  python-gobject is another case where porting is a better option, since pygi (gobject-introspection) already supports Python 3.
  • Canonical is the upstream.  Many packages in the archive, such as python-launchpadlib and python-lazr.restfulclient are developed upstream by Canonical.  This doesn't mean you can't or shouldn't help out with the porting of those modules, it's just that we know who to lean on as a last resort.  By all means, feel free to contribute to these too!
  • Orphaned by upstream.  These are the most problematic, since there's essentially no upstream maintainer to contribute patches to.  An example is python-oauth.  In these cases, we need to look for alternatives that are maintained upstream, and open to porting to Python 3.  In the case of python-oauth, we need to investigate oauth2, and see if there are features we're using from the abandoned package that may not be available in the supported one.
  • Unknowns.  Well, this one's the big risky part because we don't know what we don't know.
We need your help!  First of all, there's no way I can personally port everything on our list, including both libraries and applications.  We may have to make some hard choices to drop some functionality from Ubuntu if we can't get it ported, and we don't want to have to do that.  So here are some ways you can contribute:
  • Fill in the spreadsheet with more information.  If you're aware of an upstream or Debian port to Python 3, let us know.  It may make it easier for someone else to enable the Python 3 version in Debian, or to shepherd the upstream patch to landing on their trunk.
  • Help upstream make a Python 3 port available.  There are lots of resources available to help you port some code, from quick references to in-depth guides.  There's also a mailing list (and Gmane newsgroup mirror) you can join to get help, report status, and have other related discussions. Some people have asked Python 3 porting questions on StackOverflow, using the tags #python, #python-3.x, and #porting
  • Join us on the #python3 IRC channel on Freenode.
  • Subscribe to the python-porting mailing list.
  • Get packages ported in Debian.  Once upstream supports Python 3, you can extend the existing Debian package to expose this support into Debian.  From there, you or we can make sure that gets sync'd into Ubuntu.
  • Spread the word!  Even if you don't have time to do any ports yourself, you can help publicize this effort through social media, mailing lists, and your local Python community.  This really is a Python-wide effort!
Python 3.3 is scheduled to be released later this year.  Please help make 2012 the year that Python 3 reached critical mass!

 -----------------------------

On a more personal note, I am also committed to making Mailman 3 a Python 3 application, but right now I'm blocked on a number of dependencies.  Here are the list of dependencies from the setup.py file, and their statuses.  I would love it if you help get these ported too!
Of course, these are only the direct dependencies.  Others that get pulled in include:


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Barry Warsaw

Lessons in porting to Python 3

Yesterday, I completed my port of dbus-python to Python 3, and submitted my patch upstream.  While I've yet to hear any feedback from Simon about my patch, I'm fairly confident that it's going in the right direction.  This version should allow existing Python 2 applications to run largely unchanged, and minimizes the differences that clients will have to make to use the Python 3 version.

Some of the changes are specific to the dbus-python project, and I included a detailed summary of those changes and my rationale behind them.  There are lots of good lessons learned during this porting exercise that I want to share with you, have a discussion about, and see if there aren't things we core Python developers can do in Python 3.3 to make it even easier to migrate to Python 3.

First, some background.  D-Bus is a freedesktop.org project for same-system interprocess communication, and it's an essential component of any Linux desktop.  The D-Bus system and C API are mature and well-defined, and there are bindings available for many programming language, Python included of course.  The existing dbus-python package is only compatible with Python 2, and most recommendations are to use the Gnome version of Python bindings should you want to use D-Bus with Python 3.  For us in Ubuntu, this isn't acceptable though because we must have a solution that supports KDE and potentially even non-UI based D-Bus Python servers.  Several ports of dbus-python to Python 3 have been attempted in the past, but none have been accepted upstream, so naturally I took it as a challenge to work on a new version of the port.  After some discussion with the upstream maintainer Simon McVittie, I had a few requirements in mind:

  • One code base for both Python 2 and Python 3.  It's simply too difficult to support multiple development branches, so one branch must be compilable in both versions of Python.  Because dbus-python is not setuptools-based, I not to rely on 2to3 to auto-convert the Python layer.  This is more difficult, but given the next requirement, entirely possible.
  • Minimum Python versions to support are 2.6 and 3.2 (Python 2.7 is also supported).  Python 2.6 contains almost everything you need to do a high quality port of both the Python layer and the C extension layer with a single code base.  Python 2.7 has one or two additional helpers, but they aren't important enough to count Python 2.6 out.  For dbus-python, this specifically means dropping support for Python 2.5, which is more than 5 years old at the time of this writing.  Also, it makes no sense to support Python 3.0 or 3.1 as neither of those are in wide-spread use.
  • Minimize any API changes seen by Python 2 code, and minimize the changes needed to port clients to Python 3.  For the former, this means everything from keeping Python APIs unchanged to keeping the inheritance hierarchy the same.  Python 2 programs will see a few small changes after the application of my patches; I'll describe them below but they should be inconsequential for the vast majority of Python 2 applications.  While it's unavoidable that Python 3 applications will see a different API, these differences have been minimized.
There are two main issues that had to be sorted out for this port, and in general for most ports to Python 3: bytes vs. strings, and ints vs. longs.  For the latter, you probably know that where Python 2 has two integer types, Python 3 has only one. In Python 3, all integers are longs, and there is no L suffix for integer literals.  This turned out to be trickier in the dbus-python case because dbus supports a numeric stack of various integer widths, and in Python 2 these are implemented as subclasses of the built-in int and long types.  Because there are only longs in Python 3, the inheritance hierarchy a Python application will see changes between Python 2 and Python 3.  This is unavoidable.

I also made the decision to change some object types to longs in both versions of Python, where I thought it was highly unlikely that Python clients would care.  Specifically, many dbus objects have a variant_level attribute, which is usually zero, but can be any positive integer.  For implementation simplicity, I changed these to longs in Python 2 also.

Ah, bytes vs. strings is always where things get interesting when porting to Python 3.  It's the single most brain hurty exercise you will have to go through.  Remember that Python 2 lets you cheat.  If you not sure whether the entity you're dealing with is some bytes, or some (usually ASCII-encoded) string, just use a Python 2 str type (a.k.a. 8-bit string) and let Python's automatic conversion rules change it to a unicode when the two types meet.  You can't get away with this in Python 3 though, for very good reasons - it's error prone, and can lead to data corruption or the annoyingly ubiquitous and hard to predict UnicodeErrors.

In Python 3, you must be clear about what are bytes and what are strings (i.e. unicodes), and you must be explicit when converting between the two.  Yes, this can be painful at times but in my opinion, it's crucial that you do so.  It's that important to eliminate UnicodeErrors that you can't defend against and your users won't understand or be able to correct.  Once you're clear in your own mind as to which are strings and which are bytes, it's usually not that hard to reflect that clearly in your code, especially if you leave Python 2.5 and anything earlier behind, which I highly recommend.

dbus-python presented an interesting challenge here.  It has several data types in its C API that are defined as UTF-8 encoded char*'s.  At first blush, it seemed to me that these should be reflected in Python 3 as bytes objects to simplify the conversion in the extension module to and from char*'s.  It turns out that this was a bad idea from an implementation stand point, and dbus-python's upstream maintainer had already expressed his opinion that these data types should be exposed as unicodes in Python 3.  After having failed at my initial attempts at making them bytes, I now agree that they must be unicodes, both for implementation simplicity and for minimal impact on porting user code.

The biggest problem I ran into with the choice of bytes is that the callback dispatch code in dbus-python is complex, difficult to understand and debug, driven by external data, and written with a deep assumption of operating on strings.  For example, when the dbus C API receives a signal, it must determine whether there is a Python function registered to handle that signal, and it does this by comparing a number of client-registered parameters, such as the method name, the interface, and the object path.  If the dbus C API was turning these parameters into bytes, but the clients had registered strings, then the comparisons in the callback dispatch routines would fail, either loudly with an exception, or silently with failing comparisons.  The former were relatively easy to track down and fix, by explicitly decoding client-registered strings to bytes.  But the latter, silent failures, were nearly impossible to debug.  Add to that the fact that there were so many roads into the registration system, that it was also very difficult to coerce all incoming data early enough so that coercion wasn't necessary at comparison time.  I was left with the unappealing alternative of forcing all client code to also change their data from using strings to using bytes, which I realized would be much too high a burden on clients porting their applications to Python 3.  Simon was right, but it was a useful exercise to fail at anyway.

(By way of comparison, it took me the better part of a week and a half to try to get the test suite passing when these objects were bytes, which I was ultimately unable to do, and about a day to get them passing when everything was unicodes.  That's gotta tell you something right there, and hopefully not that "I suck" :).

Let's look at some practical advice that may help you in your own porting efforts.

  • Target nothing older than Python 2.6 or Python 3.2.  I mentioned this before, but it's really going to make your life easier.  Specifically, drop Python 2.5 and earlier and you will thank yourself[1].  If you absolutely cannot do this, consider waiting to port to Python 3.  Note that while Python 2.7 has a few additional conveniences for supporting both Python 2 and Python 3 in a single code base, I did not find them compelling enough to drop Python 2.6 support.
  • Where you have C types with reprs, make those reprs return unicodes in both versions.  Many dbus-python types have somewhat complicated reprs because they return different strings depending on whether their variant_levels are zero or non-zero.  #ifdef'ing all of these was just too much work. Because most code probably doesn't care about the specific type of the repr, and because Python 2 allows unicode reprs, and because I have a very clever hack for this[2], I decided to make all reprs return unicodes in both versions of Python.
  • Include the following __future__ imports in your Python code: print_function, absolute_import, and unicode_literals.  In Python 2.6 and 2.7, these enable features that are the default in Python 3, and so make it easier to support both with one codebase.  Specifically, change all your print statements to print() functions, and remove all your u'' prefixes from your unicode literals.  Be sure to b'' prefix all your byte literals[3].
  • Wherever possible, in your extension modules, change all your PyInts to PyLongs.  In dbus-python, this means that the variant_level attributes are longs in both Python versions, as are values that represent such things as UNIX file descriptors.  The only place where I kept PyInts in Python 2 (and their requisite #ifdefs to use PyLongs in Python 3) was in the numeric stack inheritance hierarchy, mostly so that Python 2 code which cares about such things would not have to change.
  • Define a Python variable and a C macro for determining whether you're running in Python 2 or Python 3.  The former is used in dbus-python because under Python 3, there is no UTF8String type any more, among other subtle differences.  The latter is used to simply the #ifdef tests where they're needed[4].
  • In your C code, #include <bytesobject.h> .  This header exposes aliases for all PyString calls so that you can use the Python 3 idiom of PyBytes.  Then globally replace all PyString_Foo() calls with PyBytes_Foo() and the code will look clean and be compilable under both versions of Python.  You may need to add explicit PyUnicode calls where you need to discern between bytes and strings, but again, this code will be completely portable between Python 2 and Python 3.
  • Try to write your functions to accept both unicodes and bytes, but always normalize them to one type or the other for internal use, and choose one or the other to return.  Some Python stdlib methods are polymorphic in that they return bytes when handed bytes, and unicodes when handed unicodes.  This can be convenient in some cases, but problematic in others.  Choose carefully when porting your APIs.
  • Don't use trailing-L long literals if you can help it.
  • Switch to using Py_TYPE() everywhere instead of de-references ob_type explicitly.  The structures are laid out differently between Python 2 and Python 3, and this Python-supplied macro hides the ugliness from you.
Here are a few other miscellaneous issues you should be aware of:

Metaclasses are defined differently in Python 2 and Python 3, and you cannot write any Python code snippet that is even compilable between the two.  That's because the syntax for defining a class that derives from a metaclass in Python 3 is illegal syntax in Python 2. Your module simply won't compile.  My solution was to use exec() on a string.  For this reason, I suggest keeping metaclass subclasses as simple as possible, so that string is nice and small.


Get rid of all your uses of iteritems(), iterkeys(), itervalues(), and xrange().  You probably don't need the optimization these provide, and they do not exist in Python 3.  You can conditionalize around them, but I think in most cases it's not worth it.  If you really need the optimization, then you'll have to figure out a way around the missing names in Python 3.  But note that Python 3 is already more efficient for the first three, since you get back dictview objects instead of concrete lists.


PyArg_Parse() and friends lack a 'y' code in Python 2.  In Python 3, these return bytes objects.  Where I absolutely needed bytes in Python 3 and strs in Python 2, I just #ifdef'd around the PyArg_Parse() calls.  In Python 3, there's no equivalent of 'z' for bytes objects (which accept Nones and set the output variable to NULL in that case).  If this is important to you, you might need to write an O& converter.


Watch out for next() vs. __next__() when writing iterators.  Python 2 uses the former while Python 3 uses the latter.  Best to define the method once, and then support compatibility via `next = __next__` in your class definition.


operator.isSequenceType() is gone in Python 3.  Here's the code I use for compatibility:


def is_sequence(obj):
    try:
        from collections import Sequence
    except ImportError:
        from operator import isSequenceType
        return operator.isSequenceType(obj)
    else:
        return isinstance(obj, Sequence)


If you by chance use PyCObjects in your extension module, you'll have to switch these to PyCapsules for Python 3.  If you're lucky enough to be able to drop Python 2.6, you can use PyCapsules everywhere, since they are available in Python 2.7.


Let me close by saying that you shouldn't be frightened off by the prospect either of porting your code to Python 3, or supporting both Python 2 and Python 3 in a single code base.  It's definitely doable, and we in the Python community are gaining more experience at it every day.  I strongly feel that we are well on the track of Guido's original goal of mainstream Python 3 acceptance within 5 years of Python 3's release.  I think we're soon going to see a critical mass of Python 3 ports, after which time, you'll just seem old and creaky if you don't port to Python 3.


There are some other excellent references for helping you port out there on the 'net, and for the most part, I've tried not to duplicate their information.  Here are some useful places to start:
Enjoy!

Footnotes:
    [1] It is not impossible to support both Python 3 and versions of Python 2 earlier than 2.6, just more difficult.  Michael Foord has had success doing this for libraries of his such as mock.  I just think it's more trouble than it's worth in most cases.


    [2] Here's the clever hack, but first a set-up.  The reprs of many of the dbus-python objects are conditional on whether the variant_level is zero or not.  The variant_level is only included in the repr when it is greater than zero (with zero being the typical value).  This just means there are usually two calls to PyUnicode_FromFormat() in each C repr implementation, and #ifdef'ing them to use PyString_FromFormat() in Python 2 would just double the pain.  In addition, the reprs all include the repr of their parent objects, i.e. their base class repr.  The problem is that these base-class reprs will be PyBytes in Python 2 and PyUnicodes in Python 3, and there's nothing we can do about that.  As it turns out, Python 2.6 and Python 3.2 have a %V format with some very interesting semantics.  %V consumes two arguments, a PyObject* and a char*, but it only uses one of them.  When the first argument is not NULL, it uses that and ignores the second argument.  But when the first argument is NULL, it will use the second argument.


    How can this help produce portable code?  I define the following macro and use this everywhere the %V format code is given:
    #define REPRV(obj) \
        (PyUnicode_Check(obj) ? (obj) : NULL), \
        (PyUnicode_Check(obj) ? NULL : PyBytes_AS_STRING(obj))
    which would be used at a call site something like this:

    return PyUnicode_FromFormat("...%V...", REPRV(parent_repr));

    In Python 2, where parent_repr is a PyBytes, REPRV() will return NULL as the first argument, and via PyBytes_AS_STRING(), a char* in the second argument. In Python 3, where parent_repr is a PyUnicode, the first argument will just be the object and the second argument will be NULL (but it is ignored by Python).  As long as parent_repr is either a PyUnicode or a PyBytes (a.k.a. PyString), this works perfectly, and keeps the call sites simple and sane.  Beware though because if parent_repr can be any other type, this will crash your program.  Fortunately, Python doesn't allow for arbitrary repr types - they must be bytes or unicodes, so in practice this is pretty safe.


    [3] A recent thread in python-dev points out that this recommendation may not be practical if you're building PEP 3333-compliant WSGI applications.  My take on it is that PEP 3333's definition of "native strings" is a mistake, but sadly one that we have to live with for now.


    [4] Here's what my Python-level flag looks like:
    import sys
    is_py3 = getattr(sys.version_info, 'major', sys.version_info[0]) == 3
    Now I can use this in other code to switch behavior between Python 2 and Python 3.  For example, in dbus-python to import the UTF8String type in Python 2 only:
    from dbus import is_py3
    if is_py3:
       from _dbus_bindings import UTF8String
    This is much easier and less error prone then doing the sys.version_info test everywhere.  The other problem is that sys.version_info is a namedtuple only in Python 2.7, so in Python 2.6, it has no attribute called 'major'.


    The C-level macro looks like this:
    #if PY_MAJOR_VERSION >= 3
    #define PY3K
    #endif
     So now C code only needs to do:

    #ifdef PY3K
    /* Do something Python 3-ish */
    #else
    /* Do something Python 2-ish */
    #endif

    You might also find the six package to be useful here, at least for writing portable Python code.

    Read more
    Barry Warsaw

    sbuild is an excellent tool for locally building Ubuntu and Debian packages.  It fits into roughly the same problem space as the more popular pbuilder, but for many reasons, I prefer sbuild.  It's based on schroot to create chroot environments for any distribution and version you might want.  For example, I have chroots for Ubuntu Oneiric, Natty, Maverick, and Lucid, Debian Sid, Wheezy, and Squeeze, for both i386 and amd64.  It uses an overlay filesystem so you can easily set up the primary snapshot with whatever packages or prerequisites you want, and the individual builds will create a new session with an overlaid temporary filesystem on top of that, so the build results will not affect your primary snapshot.  sbuild can also be configured to save the session depending on the success or failure of your build, which is fantastic for debugging build failures.  I've been told that Launchpad's build farm uses a customized version of sbuild, and in my experience, if you can get a package to build locally with sbuild, it will build fine in the main archive or a PPA.

    Right out of the box, sbuild will work great for individual package builds, with very little configuration or setup.  The Ubuntu Security Team's wiki page has some excellent instructions for getting started (you can stop reading when you get to UMT :).

    One thing that sbuild doesn't do very well though, is help you build a stack of packages.  By that I mean, when you have a new package that itself has new dependencies, you need to build those dependencies first, and then build your new package based on those dependencies.  Here's an example.

    I'm working on bug 832864 and I wanted to see if I could build the newer Debian Sid version of the PySide package.  However, this requires newer apiextractor, generatorrunner, and shiboken packages (and technically speaking, debhelper too, but I'm working around that), so you have to arrange for the chroot to have those newer packages when it builds PySide, rather than the ones in the Oneiric archive.  This is something that PPAs do very nicely, because when you build a package in your PPA, it will use the other packages in that PPA as dependencies before it uses the standard archive.  The problem with PPAs though is that when the Launchpad build farm is overloaded, you might have to wait several hours for your build.  Those long turnarounds don't help productivity much. ;)

    What I wanted was something like the PPA dependencies, but with the speed and responsiveness of a local build.  After reading the sbuild manpage, and "suffering" through a scan of its source code (sbuild is written in Perl :), I found that this wasn't really supported by sbuild.  However, sbuild does have hooks that can run at various times during the build, which seemed promising.  My colleague Kees Cook was a contributor to sbuild, so a quick IRC chat indicated that most people create a local repository, populating it with the dependencies as you build them.  Of course, I want to automate that as much as possible.  The requisite googling found a few hints here and there, but nothing to pull it all together.  With some willful hackery, I managed to get it working.

    Rather than post some code that will almost immediately go out of date, let me point you to the bzr repository where you can find the code.  There are two scripts: prep.sh and scan.sh, along with a snippet for your ~/.sbuildrc file to make it even easier.  sbuild will call scan.sh first, but here's the important part: it calls that outside the chroot, as you (not root). You'll probably want to change $where though; this is where you drop the .deb and .dsc files for the dependencies.  Note too, that you'll need to add an entry to your /etc/schroot/default/fstab file so that your outside-the-chroot repo directory gets mapped to /repo inside the chroot.  For example:

    # Expose local apt repository to the chroot
    /home/barry/ubuntu/repo    /repo    none   rw,bind  0 0
    An apt repository needs a Packages and Packages.gz file for binary packages, and a Sources and Sources.gz file for the source packages.  Secure APT also requires a Release and Release.gpg file signed with a known key.  The scan.sh file sets all this up, using the apt-ftparchive command.  The first apt-ftparchive call creates the Sources and Sources.gz file.  It scans all your .dsc files and generates the proper entries, then creates a compressed copy, which is what apt actually "downloads".  The tricky thing here is that without changing directories before calling apt-ftparchive, your outside-the-chroot paths will leak into this file, in the form of Directory: headers in Sources.gz.  Because that path won't generally be available inside the chroot, we have to get rid of those headers.  I'm sure there's an apt-ftparchive option to do this, but I couldn't find it.  I accidentally discovered that cd'ing to the directory with the .dsc files was enough to trick the command into omitting the Directory: headers.

    The second call to apt-ftparchive creates the Packages and Packages.gz files.  As with the source files, we get some outside-the-chroot paths leaking in, this time as path prefixes to the Filename: header value.  Again, we have to get rid of these prefixes, but cd'ing to the directory with the .deb files doesn't do the trick.  No doubt there's some apt-ftparchive magical option for this too, but sed'ing out the paths works well enough.

    The third apt-ftparchive file creates the Release file.  I shameless stole this from the security team's update_repo script.  The tricky part here is getting Release signed with a gpg key that will be available to apt inside the chroot.  sbuild comes with its own signing key, so all you have to do is specify its public and private keys when signing the file.  However, because the public file from
    /var/lib/sbuild/apt-keys/sbuild-key.pub
    won't be available inside the chroot, the script copies it to what will be /repo inside the chroot.  You'll see later how this comes into play.

    Okay, so now we have the repository set up well enough for sbuild to carry on.  Later, before the build commences, sbuild will call prep.sh, but this script gets called inside the chroot, as the root user.  Of course, at this point /repo is mounted in the chroot too.  All prep.sh needs to do is add a sources.list.d entry so apt can find your local repository, and it needs to add the public key of the sbuild signing key pair to apt's keyring.  After it does this, it needs to do one more apt-get update.  It's useful to know that at the point when sbuild calls prep.sh, it's already done one apt-get update, so this does add a duplicate step, but at least we're fortunate enough that prep.sh gets called before sbuild installs all the build dependencies.  Once prep.sh is run, the chroot will have your overriding dependent packages, and will proceed with a normal build.

    Simple, huh?

    Besides getting rid of the hackery mentioned above, there are a few things that could be done better:
    • Different /repo mounts for each different chroot
    • A command line switch to disable the /repo
    • Automatically placing .debs into the outside-the-chroot repo directory

    Anyway, it all seems to hang together.  Please let me know what you think, and if you find better workarounds for the icky hacks.
     

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    Barry Warsaw

    Ignore at your own peril

    On Monday, I lost my home directory on my primary development machine.  I'd had this machine for a couple of years but it was still beefy enough to be an excellent development box.  I've upgraded it several times with each new Ubuntu release, and it was running Natty.  I had decent sbuild and pbuilder environments, and a bunch of virtual machines for many different flavors of OS.

    I'd also encrypted my home directory when I did the initial install.  Under Ubuntu, this creates an ecryptfs and does some mount magic after you successfully log in.  It's as close to FileVault as you can get on Ubuntu, and I think it does a pretty good job without incurring much noticeable overhead.  Plus, with today's Ubuntu desktop installers, enabling an encrypted home directory is just a trivial checkbox away.

    To protect your home directory, ecryptfs creates a random hex passphrase that is used to decrypt the contents of your home directory.  To protect this passphrase, it encrypts it with your login password.  ecryptfs stores this "wrapped" passphrase on disk in the ~/.ecryptfs/wrapped-passphrase file.

    When you log in, ecryptfs uses your login password to decrypt wrapped-passphrase, and then uses the crazy long hex number inside it to decrypt your real home directory.  Usually, this works seamlessly and you never really see the guts of what's going on.  The problem of course is that if you ever lose your wrapped-passphrase file, you're screwed because without that long hex number, your home directory cannot be decrypted.  Yay for security, boo for robustness!

    When you do your initial installation and choose to encrypt your home directory, you will be prompted to write down the long hex number, i.e. your unwrapped passphrase.  Here's the moral of the story.  1) You should do this; 2) You should remember where you put that magic piece of paper!

    I did #1 but failed to do #2.

    I'm not exactly sure how my ~/.ecryptfs directory got corrupted, but somehow the wrapped-passphrase file got deleted.  I'm fairly certain that I didn't do it accidentally, but you never know.  I suspect that there was some bad interaction with sbuild because the only thing I'd done right before the corruption, was to update my sbuild environment, and build a package in it.  Note that if you follow the Ubuntu Security Team's instructions for setting up your sbuild (which I highly recommend), your unencrypted home directory will be exposed to sbuild's chroots.  This is very convenient for normal work-a-day package building.  However, it's possible that some bug in sbuild caused my ~/.ecryptfs directory to get corrupted.  That's only a wild guess though; I'm now rebuilding my dev box on a fresh 1.5T drive starting with Oneiric alpha 2, so we'll see if this bug bites me again.

    This time, I'm prepared.  I stumbled upon a clever and secure way to never lose that piece of paper again.  This technique should also allow you to store and share other securely encrypted data across any of your Ubuntu machines.

    The first step is to set up Ubuntu One.  You get 2GB of data free, so why not use it!?  I have U1 set up on all my Ubuntu desktops, and it's a great place to stash things you might need everywhere.  I've been considering storing things like my Firefox, Chromium, and Claws-Mail configs in U1 because I always want to keep them in sync.  I already store my Timelog Tracker data there, so I can enter my time and accomplishments from any desktop.

    (I generally do not keep most of my configuration files in U1 since I keep them under Bazaar.)

    Now, it would be a very simple matter of just creating a subdirectory under ~/Ubuntu One for each machine that has an encrypted home directory, and then just copy the contents of ~/.ecyptfs to that.  You need a separate subdirectory per machine because even if your login password is the same everywhere, that random hex number will be different.

    You can do this today, but it may not be secure as you'd like, since the hex number that unlocks your encrypted home directory is "only" protected by your login password.  Crack the password, and if there's ever a security hole in U1 (or some other legal or illegal machinations) that manages to expose your wrapped-passphrase file, then your entire home directory is vulnerable.  For a little extra security, we can use encfs and fuse to create an unencrypted front-end to an encrypted directory inside U1.  You would then copy your ~/.ecryptfs files to the unencrypted mount, and it would get stored with an additional level of encryption (and obfuscation) to U1.  Now someone would have to crack two passphrases to get to your home directory.

    The instructions on how to do this couldn't be simpler, and are outlined in this wiki article, and this one.  For the lazy (like myself), here are the steps:

    $ sudo apt-get install encfs fuse-utils
    $ sudo modprobe fuse
    $ sudo adduser <your-login> fuse
    $ sudo sh -c "echo fuse >> /etc/modules"
    $ mkdir ~/safe  # or whatever, contents are plaintext
    $ mkdir "~/Ubuntu One/safe" # or whatever, contents are encrypted
    $ encfs "~/Ubuntu One/safe" ~/safe
    # You will be prompted for a new encfs passphrase, this is your extra security
    $ mkdir ~/safe/<your-machine>
    $ cp -a ~/.ecryptfs/* ~/safe/<your-machine>

    And now just let U1 do its synchronization magic.

    Read more
    Barry Warsaw

    So, yesterday (June 21, 2011), six talented and motivated Python hackers from the Washington DC area met at Panera Bread in downtown Silver Spring, Maryland to sprint on PEP 382. This is a Python Enhancement Proposal to introduce a better way for handling namespace packages, and our intent is to get this feature landed in Python 3.3. Here then is a summary, from my own spotty notes and memory, of how the sprint went.

    First, just a brief outline of what the PEP does. For more details please read the PEP itself, or join the newly resurrected import-sig for more discussions. The PEP has two main purposes. First, it fixes the problem of which package owns a namespace's __init__.py file, e.g. zope/__init__.py for all the Zope packages. In essence, it eliminate the need for these by introducing a new variant of .pth files to define a namespace package. Thus, the zope.interfaces package would own zope/zope-interfaces.pth and the zope.components package would own zope/zope-components.pth.  The presence of either .pth file is enough to define the namespace package.  There's no ambiguity or collision with these files the way there is for zope/__init__.py.  This aspect will be very beneficial for Debian and Ubuntu.

    Second, the PEP defines the one official way of defining namespace packages, rather than the multitude of ad-hoc ways currently in use.  With the pre-PEP 382 way, it was easy to get the details subtly wrong, and unless all subpackages cooperated correctly, the packages would be broken.  Now, all you do is put a * in the .pth file and you're done.

    Sounds easy, right?  Well, Python's import machinery is pretty complex, and there are actually two parallel implementations of it in Python 3.3, so gaining traction on this PEP has been a hard slog.  Not only that, but the PEP has implications for all the packaging tools out there, and changes the API requirements for PEP 302 loaders.  It doesn't help that import.c (the primary implementation of the import machinery) has loads of crud that predates PEP 302.

    On the plus side, Martin von Loewis (the PEP author) is one of the smartest Python developers around, and he's done a very good first cut of an implementation in his feature branch, so there's a great place to start.

    Eric Smith (who is the 382 BDFOP, or benevolent dictator for one pep), Jason Coombs, and I  met once before to sprint on PEP 382, and we came away with more questions than answers.  Eric, Jason, and I live near each other so it's really great to meet up with people for some face-to-face hacking.  This time, we made a wider announcement, on social media and the BACON-PIG mailing list, and were joined by three other local Python developers.  The PSF graciously agreed to sponsor us, and while we couldn't get our first, second, third, or fourth choices of venues, we did manage to score some prime real-estate and free wifi at Panera.

    So, what did we accomplish?  Both a lot, and a little.  Despite working from about 4pm until closing, we didn't commit much more than a few bug fixes (e.g. an uninitialized variable that was crashing the tests on Fedora), a build fix for Windows, and a few other minor things.  However, we did come away with a much better understanding of the existing code, and a plan of action to continue the work online.  All the gory details are in the wiki page that I created.


    One very important thing we did was to review the existing test suite for coverage of the PEP specifications.  We identified a number of holes in the existing test suite, and we'll work on adding tests for these.  We also recognized that importlib (the pure-Python re-implementation of the import machinery) wasn't covered at all in the existing PEP 382 tests, so Michael worked on that.  Not surprisingly, once that was enabled, the tests failed, since importlib has not yet been modified to support PEP 382.


    We also came up with a number of questions where we think the PEP needs clarification.  We'll start discussion about these on the relevant mailing lists.


    Finally, Eric brought up a very interesting proposal.  We all observed how difficult it is to make progress on this PEP, and Eric commented on how there's a lot of historical cruft in import.c, much of which predates PEP 302.  That PEP defines an API for extending the import machinery with new loaders and finders.  Eric proposed that we could simplify import.c by removing all the bits that could be re-implemented as PEP 302 loaders, specifically the import-from-filesystem stuff.  The other neat thing is that the loaders could probably be implemented in pure-Python without much of a performance hit, since we surmise that the stat calls dominate. If that's true, then we'd be able to refactor importlib to share a lot of code with the built-in C import machinery.  This could have the potential to greatly simplify import.c so that it contains just the PEP 302 machinery, with some bootstrapping code.  It may even be possible to move most of the PEP 382 implementation into the loaders.  At the sprint we did a quick experiment with zipping up the standard library and it looked promising, so Eric's going to take a crack at this.


    This is briefly what we accomplished at the sprint.  I hope we'll continue the enthusiasm online, and if you want to join us, please do subscribe to the import-sig!

    Read more
    Barry Warsaw

    TL;DR: Ubuntu 12.04 LTS will contain only Python 2.7 and 3.2, while Ubuntu 11.10 will contain Python 3.2, 2.7 and possibly 2.6, but possibly not.

    Last week, I attended the Ubuntu Developer Summit in Budapest, Hungary. These semi-annual events are open to everyone, and hundreds of people participate both in person and remotely. Budapest's was called UDS-O, where the 'O' stands for Oneiric Ocelot, the code name for Ubuntu 11.10, which will be released in October 2011. This is where we did the majority of planning for what changes, new features, and other developments you'll find in the next version of Ubuntu. UDS-P will be held at the end of the year in Orlando, Florida and will cover the as yet unnamed 12.04 release, which will be a Long Term Support release.

    LTS releases are special, because we make longer guarantees for official support: 3 years on the desktop and 5 years on the server. Because of this, we're making decisions now to ensure that 12.04 LTS is a stable, confident platform for years to come.

    I attended many sessions, and there is a lot of exciting stuff coming, but I want to talk in some detail about one area that I'm deeply involved in. What's going to happen with Python for Oneiric and 12.04 LTS?

    First, a brief summary of where we are today. Natty Narwhal is the code name for Ubuntu 11.04, which was released back in April and is the most recent stable release. It is not an LTS though; the last LTS was Ubuntu 10.04 Lucid Lynx, release back in October 2010. In Lucid, the default Python (i.e. /usr/bin/python) is 2.6 and Python 2.7 is not officially supported or available. Python 3.1 is available for Lucid, but not installed by default.

    In Natty, the default Python is 2.7 with 2.6 still being officially supported. This means that you can have both Python 2.6 and 2.7 on your Natty machine, and where possible, packages were built for both Python versions. Where this was not possible, you'll almost always find a package for Python 2.7 instead of 2.6. Natty also has Python 3.2 and 3.1 available, with 3.2 being the default.

    Two more bits of background are useful to know. In Ubuntu (inherited from Debian, where most packages are initially developed), we separate Python 2 support and Python 3 support into separate "stacks", meaning entirely separate binary packages even if the source packages are the same. This has many benefits, including allowing a system administrator to only install the Python 2 stack, or only the Python 3 stack if they want. It also makes for our eventual transition to Python 3 much easier, because packages don't need to be renamed. So for example, if you see a package named "python-foo" you know this is the Foo package for Python 2. You might also see a "python3-foo" which would be the Python 3 version of Foo.

    Also, many packages are built for all supported versions in a particular stack. So for example, if we want to make Foo available for both Python 2.6 and 2.7, we'll include support for both in a single python-foo package. Pure Python source code is generally easily shared, so this reduces the duplication (more on this in another blog posting), however extension modules, which are usually implemented in C, must be compiled twice, and both shared libraries must be included in the same binary package. This means if we are supporting package Example which contains an extension module, for both Python 2.6 and 2.7, the binary package will contain two shared libraries, effectively doubling the disk consumption for extension module support.

    Keep all that in mind as I describe what comes next!

    To understand our plans for Oneiric, it's first useful to explain our goals for the next LTS, since we'll be using 11.10 as a transitional cycle. For 12.04 LTS, we want to support just one Python 2 version and just one Python 3 version. Because Python 2.6 is in security-fix only mode in upstream Python, we want to drop support for it in 12.04 LTS. This will also allow us to reclaim some space on the installation CDs because we won't need to include extension modules compiled for both Python 2.6 and 2.7. Last cycle we calculated the savings at about 10MiB, which is not insignificant on a standard CD.

    For 12.04 LTS, the only Python 3 version we want to support is Python 3.2. Our thinking here is that there really isn't much code out there that depends on Python 3 yet, and Python 3.2 has many very useful features that make it (IMO) the first Python 3 to start basing production quality code on. We're going to put our money where our mouth is here, and I'll write more on that later too.

    The decision to drop Python 3.1 support for 12.04 LTS is, as far as I know, completely uncontroversial, so this will happen in Oneiric. And because Python 3.3 will not be released before 12.04 LTS, we will be making that change very soon, so as to provide the longest possible period of stabilization and porting between now and April 2012. If you've been holding off on developing for Python 3, now is a great time to jump in!

    Dropping Python 2.6 is somewhat more controversial for several reasons. First, in Ubuntu, we rely very heavily on Debian for the majority of packages, and we strongly encourage our developers to submit patches and new packages in Debian first, with requests for syncing to Ubuntu once they're available in Debian. My take on this relationship is that, because Ubuntu has strictly timed releases while Debian has a "release-when-ready" policy, we can often use Ubuntu's development cycle to blaze a trail (sometimes on the bleeding edge ;) but it's always critical to ensure that wherever possible, Debian contains the authoritative versions of our packages. Now that Debian has released Squeeze and is working on its Wheezy release, it's time for us to push our Ubuntu changes back into Debian, and work on getting the latest upstream versions into Debian, while syncing back to Ubuntu. For this reason, we just don't want to get too far ahead of Debian in our Python support. Our plan therefore is to continue to support Python 2.6 until Debian has completed their transition to Python 2.7 as the default version (they already support both, but Python 2.6 is still the default).

    Our timeline therefore is to make a final decision on Python 2.6's fate for Oneiric by feature freeze. If Debian still hasn't completed their transition by then, we'll keep Python 2.6 for Oneiric and drop it as soon as the archive opens for 12.04 LTS. This should be pretty low risk for us, and it helps us better align ourselves with Debian, which is always a good thing! If you feel so inclined, you can help by working on some of the blocker bugs for Debian's transition to Python 2.7, as we will also be doing.

    Another reason to be cautious about dropping Python 2.6 is because many of the services in our own data center are not yet ported to Python 2.7. Probably the biggest of such services is Launchpad. Our data center machines always run the previous LTS, and Lucid does not have Python 2.7, so this makes for a kind of Catch-22 for the Launchpad team. To address this, we've created a quasi-official PPA into which we'll backport Python 2.7 and many dependent modules. The Launchpad team can then use this PPA to work on their own port to Python 2.7 in plenty of time for 12.04 LTS. Anybody else out there who wants to do the same can also use our PPA, and if they need additional modules backported, they can create their own PPA which depends on ours for the base support.

    So, that's what's happening, and why. Feedback is of course invited here, on the ubuntu-devel mailing list, or to me directly. If you want to follow along, you can take a look at the blueprint describing these changes, and more.

    In the next articles, I plan to discuss how we're going to get to Python 3 only on the Ubuntu CDs, and how we're going to help with the migration to dh_python2. Cheers!

    Read more
    Barry Warsaw

    Ubuntu 11.04 (code name: Natty Narwhal) beta 2 was just released and the final release is right around the corner. Canonical internal policy is that we upgrade to the latest in-development release as soon as it goes beta, to help with bug fixing, test, and quality assurance.

    Now, I've been running Natty on my primary desktops (my two laptops) since before alpha 1, and I've been very impressed with the stability of the core OS. One of my laptops cannot run Unity though, so I've mostly been a classic desktop user until recently. My other laptop can run Unity, but compiz and the wireless driver were too unstable to be usable, that is until just before beta 1. Still, I diligently updated both machines daily and at least on the classic desktop, Natty was working great. (Now that beta 1 is out, the wireless and compiz issues have been cleared up and it's working great too.)

    The real test is my beefy workstation. This is a Dell Studio XPS 435MT 12GB, quad-core i7-920, with an ATI Radeon HD 4670 graphics card, running dual-headed into two Dell 20" 1600x1200 flat panel displays. During the Maverick cycle I was a little too aggressive in upgrading it, because neither the free nor the proprietary drivers were ready to handle this configuration yet. I ended up with a system that either couldn't display any graphics, or didn't support the dual heads. This did eventually all get resolved before the final release, but it was kind of painful.

    So this time, I was a little gun shy and wanted to do more testing before I committed to upgrading this machine. Just before Natty beta 1, I dutifully downloaded the daily liveCD ISO, and booted the machine from CD. On the surface, things seemed promising. I had compiz and Unity, but no dual-head. Running from the liveCD is fairly transient though; it doesn't save enough state between reboots to be a fair test of the machine.

    How could I get a true test of Natty that would give me my normal development environment, run natively on the hardware, and yet be easily discarded if it turned out to not be ready yet? Here's where USB hard drives and virtual machines come in.

    I'm a very heavy user of virtual machines. With plenty of disk space on this 1.5TB drive, I have maybe a dozen VMs. This let's me run the stable Ubuntu release as a host, and have VMs for several versions of Debian, several older versions of Ubuntu, and even flavors of Windows, Solaris, FreeBSD, and Fedora. (Let me know if you've successfully made a libvirt guest running as a hackintosh for OS X!). Some day I'll post about safe, copy-on-write backing disks.

    Now, the cool thing about these VMs is that I bridge the network and give all of them unique IP addresses on my internal network, so for all intents and purposes, they are real machines. Most of them I access only through ssh, but virt-manager gives a nice graphical desktop when you need it. So I can mostly test just about anything on any x86 or amd64 operating system, with my full normal development environment.

    So here was my thinking: create a Natty VM that wouldn't use the normal virtual disk living in a file on the host file system. Instead, its disk would actually be on a 320GB USB external drive that I had laying around. I'd install Natty to this VM using the daily liveCD, get my full development environment up and running, then I'd shut down the host, and reboot it to the USB drive. This would give me a persistent Natty running natively on the hardware, and if it didn't work out, I'd just reboot back to the internal drive. No fuss, no muss.

    Let's cut to the chase: I eventually got this to work, and spent a day putting Natty through its paces. After few pre-beta-1 updates, I was satisfied that all my hardware was working great, including full resolution dual-head with Unity. That gave me the confidence to upgrade the host OS running on the internal drive, and I've been happily using it since then.

    Of course, all was not smooth sailing. I did run into a few hitches along the way.

    When the primary host operating system mounted the USB drive, it often got different /dev device assignments. Sometimes it would come up as /dev/sdc and sometimes as /dev/sdg. The problem with this is that libvirt wants to use the /dev name as the device for the VM's file system, so when this changed, I'd have to go into the VM's configuration and fix the storage device path. This is kind of a pain through the virt-manager U/I since the storage device has to be deleted and then re-added. You can't just change the path (I'll bet it can be done by editing the .xml configuration file directly).

    When creating the storage device for the VM, you have several options for the disk type. At first, I naturally chose USB disk, since in fact, the physical device was a USB drive. But this tended to cause the Ubiquity installer no end of trouble. It hung very early in the installation process. I never did investigate that or get it working, but I did realize that if you use a virtio disk and point it at the USB device path (see above), the installer worked perfectly.

    You have to be sure your host machine's BIOS can boot off of a USB drive. Luckily this is a new enough machine that it works fine, but older machines may not be able to boot off a USB drive. I think you could probably just do everything I've done above, but installing that spare drive internally and it would work just as well. But of course opening up the case is a PITA. :)

    Grub was a little finicky. I first tried this just a few days before beta 1, and every time I installed the OS to the USB drive, then booted off the drive I'd immediately be dropped into a grub> prompt, with no way to complete the boot process. It was like grub was not getting installed correctly, but at the time I also wasn't sure whether this idea could even work. Note that I did not want to add the USB drive to the chain loader on the primary internal drive, I just wanted to hit F12 during the BIOS phase and select the USB drive to boot off of. I thought this *should* work, but it didn't and I thought maybe something about the virtual environment caused grub to fail when run natively. I asked around on our internal tech list, but I think this was really a transient problem with the pre-beta ISOs. I tried again a day or so later with a new liveCD and everything worked perfectly.

    Of course, when booting off the USB drive natively, a few things are different. The virtual machine has a different MAC address, and thus a different host name and IP address than the native host. So after booting off the USB drive, you have to fiddle with a few things to get that to line up, depending on how you configure your machine. E.g. /etc/hosts was incorrect. These minor problems didn't really slow me down though.

    Ignoring the transient, inexplicable grub issue, this turned out to be a very nice way to test drive the new Ubuntu version on the native hardware, in a completely non-destructive way. I'll bet it would work to test drive other operating systems as well, and if one of my fantastic readers gives me a clue about how to build a hackintosh in a VM, I'll give that a try for the fun of it. And of course I'll let you know how it goes!

    Read more
    Barry Warsaw

    For the last couple of days I've been debugging a fun problem in the Ubuntu tool called Jockey. Jockey is a tool for managing device drivers on Ubuntu. It actually contains both a command-line and a graphical front-end, and a dbus backend service that does all the work (with proper authentication, since it modifies your system). None of that is terribly relevant to the problem, although the dbus bit will come back to haunt us later.


    What is important is that Jockey is a Python application, written using many Python modules interfacing to low-level tools such as apt and dbus. The original bug report was mighty confusing. Aside from not being reproducible by myself and others, the actual exception made no fricken sense! Basically, it was code like this that was throwing a TypeError:

    _actions = []
    # _actions gets appended to at various times and later...
    for item in _actions[:]:
    # do something

    Everyone who reported the problem said the TypeError was getting thrown on the for-statement line. The exception message indicated that Python was getting some object that it was trying to convert to an integer, but was failing. How could you possible get that exception when either making a copy of a list or iterating over that copy? Was the list corrupted? Was it not actually a list but some list-like object that was somehow returning non-integers for its min and max indexes?

    To make matters worse, this little code snippet was in Python's standard library, in the subprocess module. A quick search of Python's bug database did reveal some recent threads about changes here, made to ensure that popen objects got properly cleaned up by the garbage collector if they weren't cleaned up explicitly by the program. Note that we're using Python 2.7 here, and after some reading of the tracker issues and the Python subprocess.py code, I just couldn't see any problem here, or at least none that could possibly lead to the error being seen.

    When I originally looked at the Launchpad bug report, that was about as far as I got. I couldn't see any possible way this bug could happen, and I couldn't reproduce it, so I set the bug to Incomplete. Unfortunately, it kept hitting Ubuntu Natty beta testers so it wasn't going to go away. Fortunately, Martin Pitt found a test recipe, with which I could reproduce the bug 100% of the time. Yay! At least it probably wasn't going to be a race condition.

    How to debug this though? Normally, I'd just attach gdb to the thing and start tracing, but the problem was that when I set the Jockey dbus backend to use the debug version of Python, the error went away (or rather morphed into something that was really unrelated). Here's where I started to sharpen up my favorite yak shaving blade.

    The parable about shaving yaks is so entirely appropriate to problems like this that I'll take a quick detour. One of my (and my brother's) favorite shows in the 90's was Ren and Stimpy. That show was groundbreaking, and you can see elements of it in just about every NickToons cartoon on cable today. Some of the episodes are brilliant and others are horrible but as a whole, Ren and Stimpy are an undeniable classic of American animation. In one particular awesome episode, Ren and Stimpy celebrate Kilted Yaks-eve where a Yak comes up from the tub drain and shaves his stubble, leaving behind a present of the highly desired shaving scum for Stimpy in the morning. Based on this episode (most likely!) the programming term "yak shaving" typically means having to take detour after meaningless detour before you can actually solve the problem you're having.

    In the context of this bug, the attempt to use the debug build of Python in the Jockey dbus backend was the first yak to shave. Because I wanted to get more information out of the process, I tried to attach to a running backend process, but this proved to be quite difficult. I was debugging this in a 64bit virtual machine, and gdb+debug-python was just not cooperating.

    Now the question is: do you debug that problem (or at least get to a place where you can decide to file a bug or not), or do you punt and try a different approach? The former is most definitely shaving the yak; it gets you no closer to fixing the original problem, but certain wastes enough of your time to make you seem productive. Of course, it's inevitable that if you do follow that secondary bug, it'll lead to a third one, and a fourth one, and so on until you're deep in the shaving scum and no closer to a solution to the original problem. You need to be constantly on guard against these types of yak, er, rabbit holes.

    I'll spare you the gory details about hitting bugs in my text editor, bugs in building Python on a multiarch system, and other paths that all lead to smooth chinned yaks, but not to happy Pythons. When I punted on trying to attach to the Jockey backend, I actually decided to try to figure out exactly where the TypeError was occurring by using a twist on old-fashioned print statements. By grepping the Python source I found about a dozen occurrences of the error message "an integer is required". Which one was getting tripped? I added a little marker text to every such occurrence and re-built a Python package to aid in debugging.

    Here's where I did detour to yak shaving. The Ubuntu source package for Python itself, runs the full test suite on every build. That's great for ensuring a high quality Python package, but it's terrible for turnaround time when experimentally hacking Python. Building the package with DEB_BUILD_OPTIONS="nocheck nobench" is supposed to work, but for some reason did not with my sbuild environment. I guess debugging that would have been like shaving the yak's left check, but instead it was so much easier to shave his right check. So I spent some time with my razor chopping out huge sections of the debian/rules file so that Python 2.7 would build as fast as possible. While a seemingly meaningless task, this did actually help quite a bit since I was able to try out ideas with a much shorter turnaround.

    Anyway, with many iterations on the exception marking idea, I finally nailed down the culprit. It was in the Python C API function PyInt_AsLong(), but even here I was unsure which arm of the conditional was getting triggered. Another round of hack-build-scp-reset-test and I found what I suspected: PyInt_AsLong() was getting passed an object that could not be turned into an integer. But what was that object?

    So now back to the original problem with gdb. To solve this I downloaded and built a 32bit virtual machine, and was able to reproduce the bug there. Fortunately, in the 32bit environment I was much more successful in attaching to a running backend Jockey dbus process, and even though I didn't have the Python source available (and no, I won't talk about where that particular yak shaving detour led), I could fairly easily print the objects in the debugger at the offending code, where I learned that PyInt_AsLong() was getting called with None as its argument. And yeah, you can't turn None into an integer in Python!

    But, how was this function getting called with None? Popping up the call stack led me eventually to PyArg_Parse(), an older C API function that parses a Python tuple into a set of objects based on some format flags. This is used when implementing Python functions in C, to parse an argument list. Popping up the stack again led me into some python-apt code. python-apt is a C++ library that exposes the APT system to Python programs. It's fairly mature and robust, but I wasn't as familiar with all its dark corners as I was with Python.

    Now, my first instinct is never that I've found a bug in Python. That's not to say they don't exist, but just that Python has been around so long, is so well tested, and used so extensively that I'm always suspicious in cases like this (i.e. strange, inexplicable bugs that make no sense) of extension modules and third party code. And indeed, my sleuthing has led me to python-apt, exactly the kind of complex Python extension module that can have odd lurking bugs in it. Still, the problem I was now faced with was this: the call stack led me into a code path that had nothing to do with iterating over a list, or copying that list. So, what gives?

    Well, it's useful to know how Python exceptions work at the C level. In general terms, when some C code raises an exception, it basically sets some global state, and then returns error codes up the stack until either something catches it and deals with it, or it percolates up to Python's top eval loop. The key thing here is that there are usually two states that exist: the global exception value currently in effect, and an error code that gets returned up the C call stack. Usually this return code is a zero or one, but it can also be NULL or -1 in certain cases. The Python C API documentation is very good at describing these.

    So now that I looked closely at the python-apt code, I could see what what going on and it all began to make sense! The python-apt package was pulsing a progress meter, set up as a callback by the client of the python-apt code. Meaning, python-apt couldn't really control what this callback would return. python-apt expected the callback to return either Python's True or False, but it could return anything, including nothing! In Python, True can be coerced into the integer 1 and False into the integer 0, and python-apt wanted an integer, so indeed the call stack lead right to a call to PyArg_Parse() to turn the callback's return object into an integer. What happens if the callback didn't return something that could be turned into an integer, or worse, didn't return anything at all?

    In Python, a function always returns something, even if there is no explicit return statement. In those cases, None is implicitly returned. Yes, you see it now. And if not, there was this clue in the python-apt code: "most of the time the user who subclasses the pulse() method forgot to add a return {True,False} so we just assume he wants a True." Translated: Hey guy! You forgot to add a "return True" or "return False" to your pulse() method, and it probably fell of the end, giving us None, which we dutifully passed to PyArg_Parse().

    PyArg_Parse() did its thing when getting None, by correctly setting the global exception state to the TypeError, and returning a zero code to indicate an error occurred. But looking at the python-apt code, it recognizes the error code, but forgets that some global exception state was set! Meaning, even though python-apt was ignoring the exception, Python still knew about it. But because control wasn't being passed on up to the Python eval loop, the exception was just lurking there like a angry unshaven yak, waiting to be discovered. And in fact, the next time Python itself checked the exception state was in, yep, the for-loop iterating over the perfectly fine list object. Python hits the for-loop, finds this orphaned TypeError lurking there and raises it, in a place that has literally nothing to do with the original exception.

    The fix is a one-liner. In python-apt, where its ignoring any exception returned by PyArg_Parse(), it must both swallow the error code (which it was doing), and clear the global exception state (which it was notdoing). By adding a call to PyErr_Clear(), python-apt was keeping the interpreter's state consistent, and properly ignoring the argument parsing error, thus fixing the bug.

    As I mentioned to my colleague Colin Watson, this did turn out to be a fun one to debug, though not as "fun" as one he recently worked on.

    Happy Yak Shaving.

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    Barry Warsaw

    What We Do

    My friends and family often ask me what I do at my job. It's easy to understand when my one brother says he's a tax accountant, but not so easy to explain the complex world of open source software development I live in. Sometimes I say something to the effect: well, you know what Windows is, and you know what the Mac is right? We're building a third alternative called Ubuntu that is free, Linux-based and in most cases, much better. Mention that you won't get viruses and it can easily breathe new life into that old slow PC you shudder to turn on, and people at least nod their heads enthusiastically, even if they don't fully get it.

    I've been incredibly fortunate in my professional career, to have been able to share the software I write with the world for almost 30 years. I started working for a very cool research lab with the US Federal government while still in high school. We had a UUCP connection and were on the early Arpanet, and because we were funded by the US taxpayer, our software was not subject to copyright. This meant that we could share our code with other people on Usenet and elsewhere, collaborate with them, accept their suggestions and improvements, and hopefully make their lives a little better, just as others around the world did for us. It was free and open source software before such terms were coined.


    I've never had a "real job" in the sense of slaving away in a windowless cube writing solely proprietary software that would never see the light of day. Even the closed source shops I've worked have been invested somehow in free software, and with varying degrees of persuasion, have both benefited from and contributed to the free and open source ecosystem. Thus, in many ways, my current position at Canonical feels like the perfect fit and ultimate destination for my background, skills, and passion. Canonical is open source to its core. Its central mission, as articulated by our founder Mark Shuttleworth is "to bring free software to the widest possible audience, powered by services rather than licenses, in tune with a world that was moving to services as the core economic model of a digital world."

    To me, the free and open source ethos goes much deeper than just the software we write. It's about collaboration, community, sharing, learning, teaching, and having a truly positive impact on the world. It's about empowering individuals to realize their full potential, to give them the opportunity to build a merit based reputation, to carve out their own areas of interest and expertise, and relate that to the larger society, to know that they make a difference, and that their opinions and contributions matter. Open source is about having the courage of your convictions, but embracing humility to admit when you're wrong and someone else has a better idea. To encourage and actively seek out consensus, but also to cultivate a thoughtful and compassionate process for making the hard decisions when consensus can't be reached. It's about spreading enthusiasm and rallying others to your side sometimes, and at other times humbly and joyfully embracing other points of view.

    I could go on with all the mushy goodness, but let's look at a few areas where the work I do for Canonical directly contributes to the broader free and open source ecosystem.

    Python

    I've been a core Python developer since about 1995, and have served several times as release manager for major new versions. As part of my job on the Ubuntu Platform Foundations team, I'm keenly concerned with issues involving Python's deployment on Ubuntu and its upstream ancestor Debian. One of the distinction between Ubuntu/Debian and other Linux distributions is that Ubuntu/Debian often provides more than one Python version in a release. For example, in Natty Narwhal, it's likely that Ubuntu will officially support Python 2.6, 2.7, 3.1, and 3.2. The reason for this is that it does take some porting effort for applications to upgrade their version of Python, and this eases the transition for those applications. But it's complicated by the fact that upstream Python doesn't really support multiple installed versions out of the box. Recent work I've done on PEP 3147 and PEP 3149 improve this situation greatly, by allowing multiple versions of Python to more peacefully coexist. As is typical, I try to push my changes needed for the Ubuntu platform as far upstream as possible, because that benefits the most users of the open source software. In addition, a huge number of users get their Python interpreter from their operating system vendor (this is at least the case on all Linux variants as well as Mac OS X), so work done to improve the Python experience on Ubuntu directly and positively impacts users on Debian and other Linux distributions, as well as the general Python community at large.

    GNU Mailman and Launchpad

    I've been the lead developer for GNU Mailman since the late 90's, and I've been overwhelmed to see it become the predominant free software mailing list manager. When I was working on the Launchpad team at Canonical, my primary responsibility for the first few years was to integrate mailing lists with Launchpad, and of course Mailman was the obvious choice for underlying technology. At first, Launchpad was closed source software, but the intent was always to release it as open source, and it was with much joy that we saw Launchpad released under a free software license in the summer of 2009. It was not a design goal of the original Mailman software to be easily integrated with external systems, so a good bit of the work I did on Launchpad at the time directly benefited upstream Mailman users by making it easier for ourselves, and others, to do this type of integration. Now that I'm no longer on the Launchpad team, I work on Mailman 3 exclusively in my spare time. My experiences with that integration effort had a direct influence on the architecture of Mailman 3, and I have a goal of swapping the current Mailman 2.1 technology in Launchpad with Mailman 3. All the relevant work to do this is fed back into the upstream project, to the benefit of the larger free software community, since many other people also want a better way to integrate mailing list functionality with their web sites.

    My Mailman and Launchpad work also saw many spin-offs of helpful utilities and libraries which can be used by the much wider community of open source developers. The lazr suite of Python libraries has applicability outside Launchpad and I use many of them in my extra-curricular projects, evangelizing them along the way. Many GNU Mailman utilities have also been spun off into their own libraries, and these are now or will soon be available on the Debian and Ubuntu platforms, just as they are now available on the Python Package Index. The work I'm doing, along with my Canonical and Ubuntu colleagues to reduce the barriers to opportunistic participation in projects is key to this ecosystem. We're making it much easier for others to find and participate in the projects that interest them, at whatever level they choose.

    UDD

    Along those lines, as part of my work on Ubuntu Platform Foundations, I've become quite enthusiastic about technology we're calling Ubuntu Distributed Development. When I moved to the Platform team, I knew next to nothing about the art of packaging, which is how upstream projects are turned into installable and manageable chunks that people can put on their Ubuntu (and Debian) machines. Packaging has a rich tradition, but it's pretty esoteric and to most people who don't package, it's a black art. Even now that I know how to package software, I still think it's way too magical. UDD is a set of tools and procedures that aim to bring packaging to the masses, by reducing the magic and exposing the important parts in much more familiar tools. Every open source developer sooner or later (hopefully much sooner!) learns how to use a revision control system. Such systems allow developers to manage their code in a principled and rigorous way, by keeping detailed records about the changes to their software over time. Revision control systems are an absolutely fundamental and required tool in the open source developer's toolbox.

    The Bazaar distributed revision control system was written by Canonical, but is now a community-driven free software project. It's ease of use, superior quality, and high degree of flexibility and extensibility make it a very attractive choice for projects looking to use the next generation in revision control systems. Bazaar also integrates very nicely with Launchpad. UDD are a set of extensions to bring packaging to the Bazaar suite, so that the very tool that upstream software developers use dozens of times a day, are also the tools they will use to create packages for making their software available to the mass of Ubuntu users. I've been using Bazaar for years now, and have written a few plugins and patches. I'll soon be helping to lead an effort to improve the UDD workflows and more widely expose the benefits of UDD to more and more Ubuntu developers.

    Platform

    I love my work on Platform because it allows me to have a small hand in lots of different upstream free and open source projects. After so many years of hacking on such projects of all different stripes, I'm fairly good at looking at and understanding good open source code, debugging and fixing problems, and interacting with the upstream communities to get those patches pushed higher up the stack. Bug trackers, mailing lists, IRC, and revision control systems are the core technologies needed for this work, and I'm comfortable interacting on all of them. Part of our job as Platform developers is, in my opinion, to engage the upstream projects, so that the fixes and changes we need to make in order to provide the absolute best experience to Ubuntu users, are available even to those folks who don't get their software from Ubuntu. This to me is the the true meaning of being a free and open source developer for a company whose mission is to make free and open source available to the mass of computer users. Our aim is not just to make software that competes on price, or liberty, but also on quality and "sex appeal" - we want our users to flock to Ubuntu not just because it costs nothing, but because it's so compelling that you can't help but love it.

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    Barry Warsaw

    I'm doing some work these days on trying to get Python 2.7 as the default Python in the next version of Ubuntu, Maverick Meerkat (10.10). This work will occasionally require me to break my machine by installing experimental packages. That's a good and useful thing because I want to test various potentially disruptive changes before I think about unleashing them on the world. This is where virtual machines really shine!


    To be efficient, I need a really fast turnaround from known good state, to broken state, back to known good state. In the past, I've used VMware Fusion on my Mac to create a VM, then take a live snapshot of the disk before making my changes. It was really easy then to revert to the last known good snapshot, try something else and iterate.

    But lately Fusion has sprouted a nasty habit of freezing the host OS, such that a hard reboot is necessary. This will inevitably cause havoc on the host, by losing settings, trashing mail, corrupting VMs, etc. VMware can't reproduce the problem but it happens every time to me, and it hurts, so I'm not doing that any more :).

    Back to my Lucid host and libvirt/kvm and the sanctuary of FLOSS. It's really easy to create new VMs, and there are several ways of doing it, from virt-manager to vmbuilder to straight up kvm (thanks Colin for some recipes). The problem is that none of these are exactly fast to go from bare metal to working Maverick VM with all the known good extras I need (like openssh-server and bzr, plus my comfortable development environment).

    I didn't find a really good fit for vmbuilder or the kvm commands, and I'm not smart enough to use the libvirt command line tools, but I think I've figured out a hack using virt-manager that will work well enough.

    1. Create a disk for the baseline VM (named 'scars' in my case :) manually
    % qemu-img create -f qcow2 scars.qcow2 20G

    2. Create the baseline VM using virt-manager
    * I use dhcp internally, so I give this thing a mac address, assign it 1GB
    of RAM and 1 processor.
    * For storage, I tell it to use the scars.qcow2 file I created above
    * Boot from the maverick ISO of your choice, install everything you want,
    and get your development environment in place
    * Shut this machine down

    3. Clone your baseline VM
    * In the virt-manager Manager window, right click on your baseline VM and
    select Clone
    * You will not be given an opportunity to select a disk or a mac address,
    so for now just go with the defaults.
    * Do not start your clone

    4. Create an 'overlay' disk that is a backed by your baseline disk.
    % qemu-img create -f qcow2 -b scars.qcow2 scars.ovl

    5. Edit your clone
    * Delete the disk given to your clone by default
    * Create a new virtio storage that points to scars.ovl
    * Delete the nic given to your clone by default
    * Create a new virtio network device with the mac address of your
    baseline. You'll get a warning about a mac address collision, but this
    can be ignored (see below).

    6. Boot your clone

    At this point you'll have a baseline which is your known good system, and a clone/overlay which you can break to your heart's content. When it's time to iterate back to a known good state, shut down your clone, delete the overlay disk, and create a new one from the baseline qcow2 disk. This is pretty fast, and your turn around time is not much more than the time it takes to shutdown one machine and boot another. It actually feels a lot faster by the wall clock than Fusion ever was to snapshot and restore.

    One downside is that you cannot run both VMs at the same time. I think mostly this is because of the MAC address collision, but also because creating the overlay requires that both machines be powered off.

    The other downside seems to be that if you want to update your known good baseline, say by installing more packages or apt-get update/upgrade, you will have to recreate your overlay disk for your next experiment. Changes to the underlying disk do not seem to propagate to the overlay automatically. Maybe that's intentional; I can't find much documentation on it. (Note too that the manpage for qemu-img does not describe the -b option.)

    I guess the last downside is that I spent way too much time trying to figure all this out. The Googles were not a lot of help but did give me the qemu-img clue. But at least now you don't have to! :)

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    Barry Warsaw

    My friend Tim is working on a very cool Bazaar-backed wiki project and he asked me to package it up for Ubuntu. I'm getting pretty good at packaging Python projects, but I always like the practice because each time it gets a little smoother. This one I managed to package in about 10 minutes so I thought I'd outline the very easy process.


    First of all, you want to have a good setup.py, and if you like to cargo cult, you can start with this one. I highly recommend using Distribute instead of setuptools, and in fact the former is what Ubuntu gives you by default. I really like adding the distribute_setup.py which gives you nice features like being able to do python setup.py test and many other things. See lines 18 and 19 in the above referenced setup.py file.

    The next thing you'll want is Andrew Straw's fine stdeb package, which you can get on Ubuntu with sudo apt-get install python-stdeb. This package is going to bootstrap your debian/ directory from your setup.py file. It's not perfectly suited to the task (yet, Andrew assures me :), but we can make it work!

    These days, I host all of my packages in Bazaar on Launchpad, which is going to make some of the following steps really easy. If you use a different hosting site or a different version control system, you will have to build your Ubuntu package using more traditional means. That's okay, once you have your debian/ directory, it'll be fairly easy (but not as easy as described here ). If you do use Bazaar, you'll just want to make sure you have the bzr-builddeb. Just do sudo apt-get install bzr-builddeb on Ubuntu and you should get everything you need.

    Okay, so now you have the requisite packages, and a setup.py, let's build us a deb and upload it to our personal package archive so everyone on Debian and Ubuntu can easily try it out.

    First, let's create the debian directory. Here's the first little icky bit:

    % python setup.py --command-packages=stdeb.command sdist_dsc

    Notice that this leaves us with a deb_dist/ directory, not the debian/ directory we want. The latter is in there, just buried a bit. Let's dig it out:

    % mv deb_dist/wikkid-0.1/debian .
    % rm -rf deb_dist
    % bzr add debian
    % bzr commit -m'Debianize'

    Note that "wikkid-0.1" will be replaced by the name of your package. In order to build the .deb package, you need an "orig.tar.gz" file. Packaging sort of assumes that you've got an original upstream tarball somewhere and you're just adding the necessary Debian goo to package the thing. In this case, we don't have an upstream tarball, although we could easily create one, and upload it to the Cheeseshop or Launchpad or wherever. However, that just slows us down so let's skip that for now! (Aside: if you do have an upstream tarball somewhere, you'll want to add a debian/watch which points to it; that'll eliminate the need to do the next step, by downloading the tarball instead).

    Let's create the tarball right now and copy it to where the following step will expect it:

    % python setup.py sdist
    % mv dist/Wikkid-0.1.tar.gz ../wikkid_0.1.orig.tar.gz

    Here's the second icky bit. Building a Debian source package imposes a very specific naming convention on the tarball. Wikkid's setup.py happens to build a tarball with an incompatible name, while the sdist command leaves it in a place where the next step can't find it. The rename just gets everything into the proper place. YMMV.

    Now we can build the Debian source package. It's the source package that we'll upload to our Launchpad PPA. Launchpad will then automatically (if we've done everything right) build the binary package from the uploaded source package, from which Ubuntu and Debian users can easily install.

    Oops! Before we do this, please edit your debian/changelog file and change unstable to lucid. You should also change the version number by adding a ~ppa1 to the end of it. Yeah, more ickiness.

    Alright now we're ready to build our source package:

    % bzr bd -S

    Now let's upload it (assuming you've enabled a PPA):

    % cd ..
    % dput ppa:barry/python wikkid_0.1-1~ppa1_source.changes

    That's it! If you've done everything successfully, you'll have the package in your PPA in 5 minutes or so. Then anybody who's added your PPA can just apt-get install wikkid (or whatever your package is called).

    I do hope to work with the appropriate developers to make some of the ickiness go away. Please do contact me if you want to help!

    Addendum (2010-06-10)

    Let's say you publish your tarball on the Cheeseshop or Launchpad, and you don't want to have to build a different tarball locally in order to package it. Here's what I think works:

    Create a debian/watch file that points to the download location you publish to. If your package is not yet available in Debian or Ubuntu, then use this command to build your source package:

    bzr bd -S -- -sa

    The bit at the end tells the Debian packaging primitives to include your tarball when your source package is uploaded. The debian/watch file is used to download your published tarball and automatically renamed to the required .orig.tar.gz name. When you dput your package, your tarball will be uploaded too, and everything should build properly.

    Oh, and don't forget to look carefully at the lintian output. Try to make this as clean as possible. The Debian and Ubuntu packaging guides can help here.

    Addendum 2 (2010-06-10)

    Andrew Straw has added a debianize command to his stdeb package, which makes things much nicer. With this you can create the debian/ directory right next to your setup.py. AFAIK, this version of stdeb isn't released yet, so you need to install his git head in a virtualenv, and it has a few minor buglets, but it does seem like the best-of-breed solution. I'll post another article with a more detailed follow up later.

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    Barry Warsaw

    Gentoo No More

    Today I finally swapped my last Gentoo server for an Ubuntu 10.04 LTS server. Gentoo has served me well over these many years, but with my emerge updates growing to several pages (meaning, I was waaaay behind on updates with almost no hope of catching up) it was long past time to switch. I'd moved my internal server over to Ubuntu during the Karmic cycle, but that was a much easier switch. This one was tougher because I had several interdependent externally facing services: web, mail, sftp, and Mailman.


    The real trick to making this go smoothly was to set up a virtual machine in which to install, configure and progressively deploy the new services. My primary desktop machine is a honkin' big i7-920 quad-core Dell with 12GB of RAM, so it's perfectly suited for running lots of VMs. In fact, I have several Ubuntu, Debian and even Windows VMs that I use during my normal development of Ubuntu and Python. However, once I had the new server ready to go, I wanted to be able to quickly swap it into the real hardware. So I purchased a 160GB IDE drive (since the h/w it was going into was too old to support SATA, but still perfectly good for a simple Linux server!) and a USB drive enclosure. I dropped the new disk into the enclosure, mounted it on the Ubuntu desktop and created a virtual machine using the USB drive as its virtio storage.

    It was then a pretty simple matter of installing Ubuntu 10.04 on this USB drive-backed VM, giving the VM an IP address on my local network, and installing all the services I wanted. I could even register the VM with Landscape to easily keep it up-to-date as I took my sweet time doing the conversion. There were a few tricking things to keep in mind:

    • I use a port forwarding border router to forward packets from my static external IP address to the appropriate server on my internal network. As I prepared to move each service, I first shut the service off on the old server, twiddled the port forwarding to point to a bogus IP, then tested the new service internally before pointing the real port forward to the new service. This way for example, I had reasonably good confidence that my SMTP server was configured properly before loosing the fire hose of the intarwebs on it.
    • I host several domains on my server so of course my Apache uses NameVirtualHosts. The big downside here is that the physical IP address is used, so I had to edit all the configs over to the temporary IP address of the VM, then back again to the original IP of the server, once the switch was completed.
    • My old server used a fairly straightforward iptables configuration, but in Ubuntu, UFW seems to be the norm. Again, I use IP addresses in the configuration, so these had to be changed twice during the migration.
    • /etc/hosts and /etc/hostname both had to be tweaked after the move since while living in a VM, the host was called something different than when in its final destination. Landscape also had to be reconfigured (see landscape-config(8)).
    You get the picture. All in all, just tedious if not very difficult. One oddness was that when the machine was a Gentoo box, the ethernet port was eth0 but after the conversion to Ubuntu, it became eth1. Once I figured that out, it was easy to fix networking. There were a few other little things like updates to my internal DNS, pointing my backup server to the new locations of the web and Mailman data on the new server (Ubuntu is more FHS-compliant than my ancient Gentoo layout), and I'm sure a few other little things I forgot to take notes on.

    Still, the big lesson here was that by bouncing the services to a USB-drive backed VM, I was able to fairly easy drop the new disk into the old server for a quick and seamless migration to an entirely new operating system.

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