Programming Python (170 page)

PyMailGUI

This is a traditional “desktop” user-interface program: it
runs entirely on the local machine, calls out to an in-process GUI
API library to implement interfaces, and talks to the Internet
through sockets only when it has to (e.g., to load or send email
on demand). User requests are routed immediately to callback
handler method functions running locally and in-process, with
shared variables and memory that automatically retain state
between requests. As mentioned, because its memory is retained
between events, PyMailGUI can cache messages in memory—it loads
email headers and selected mails only once, fetches only newly
arrived message headers on future loads, and has enough
information to perform general inbox synchronization checks. On
deletions, PyMailGUI can simply refresh its memory cache of loaded
headers without having to reload from the server. Moreover,
because PyMailGUI runs as a single process on the local machine,
it can leverage tools such as multithreading to allow mail
transfers to overlap in time (you can send while a load is in
progress), and it can more easily support extra functionality such
as local mail file saves and opens.

PyMailCGI

Like all CGI systems, PyMailCGI consists of scripts that
reside and run on a server machine and generate HTML to interact
with a user’s web browser on the client machine. It runs only in
the context of a web browser or other HTML-aware client, and it
handles user requests by running CGI scripts on the web server.
Without manually managed state retention techniques such as a
server-side database system, there is no equivalent to the
persistent memory of PyMailGUI—each request handler runs
autonomously, with no memory except that which is explicitly
passed along by prior states as hidden form fields, URL query
parameters, and so on. Because of that, PyMailCGI currently must
reload all email headers whenever it needs to display the
selection list, naively reloads messages already fetched earlier
in the session, and cannot perform general inbox synchronization
tests. This can be improved by more advanced state-retention
schemes such as cookies and server-side databases, but none is as
straightforward as the persistent in-process memory of
PyMailGUI.

Performance costs

Networks are slower than CPUs
. As
implemented, PyMailCGI
isn’t nearly as fast or as complete as PyMailGUI. In
PyMailCGI, every time the user clicks a Submit button, the request
goes across the network (it’s routed to another program on the
same machine for “localhost,” but this setup is for testing, not
deployment). More specifically, every user request incurs a
network transfer overhead, every callback handler may take the
form of a newly spawned process or thread on most servers,
parameters come in as text strings that must be parsed out, and
the lack of state information on the server between pages means
that either mail needs to be
reloaded
often or state retention
options must be employed which are slower and more complex than
shared process memory.

In contrast, user clicks in PyMailGUI trigger in-process
function calls rather than network traffic and program executions,
and state is easily saved as Python in-process variables. Even
with an ultra-fast Internet connection, a server-side CGI system
is slower than a client-side program. To be fair, some tkinter
operations are sent to the underlying Tcl library as strings, too,
which must be parsed. This may change in time, but the contrast
here is with CGI scripts versus GUI libraries in general. Function
calls will probably always beat network transfers.

Some of these bottlenecks may be designed away at the cost
of extra program complexity. For instance, some web servers use
threads and process pools to minimize process creation for CGI
scripts. Moreover, as we’ve seen, some state information can be
manually passed along from page to page in hidden form fields,
generated URL parameters, and client-side cookies, and state can
be saved between pages in a concurrently accessible database to
minimize mail reloads. But there’s no getting past the fact that
routing events and data over a network to scripts is slower than
calling a Python function directly. Not every application must
care, but some do.

Complexity costs

HTML isn’t pretty
. Because PyMailCGI
must generate HTML to interact with the user in a web browser, it
is also more complex (or at least, less readable) than PyMailGUI.
In some sense, CGI scripts embed HTML code in Python; templating
systems such as PSP often take the opposite approach. Either way,
because the end result of this is a mixture of two very different
languages, creating an interface with HTML in a CGI script can be
much less straightforward than making calls to a GUI API such as
tkinter.

Witness, for example, all the care we’ve taken to escape
HTML and URLs in this chapter’s examples; such constraints are
grounded in the nature of HTML. Furthermore, changing the system
to retain loaded-mail list state in a database between pages would
introduce further complexities to the CGI-based solution (and,
most likely, yet another language such as SQL, even if it only
appears near the bottom of the software stack). And secure HTTP
would eliminate the manual encryption complexity but would
introduce new server configuration complexity.

Functionality limitations

HTML can say only so much
. HTML is a
portable way to specify simple pages and forms, but it is poor to
useless when it comes to describing more complex user interfaces.
Because CGI scripts create user interfaces by writing HTML back to
a browser, they are highly limited in terms of user-interface
constructs. For example, consider implementing an image-processing
and animation program as CGI scripts: HTML doesn’t easily apply
once we leave the domain of fill-out forms and simple
interactions.

It is possible to generate graphics in CGI scripts. They may
be created and stored in temporary files on the server, with
per-session filenames referenced in image tags in the generated
HTML reply. For browsers that support the notion, graphic images
may also be in-lined in HTML image tags, encoded in Base64 format
or similar. Either technique is substantially more complex than
using an image in the tkinter GUI library, though. Moreover,
responsive animation and drawing applications are beyond the scope
of a protocol such as CGI, which requires a network transaction
per interaction. The interactive drawing and animation scripts we
wrote at the end of
Chapter 9
, for example, could not
be implemented as normal server-side scripts.

This is precisely the limitation that Java applets were
designed to
address—
programs
that are stored on a server
but are pulled down to run on a client on demand and are given
access to a full-featured GUI API for creating richer user
interfaces. Nevertheless, strictly server-side programs are
inherently limited by the constraints of HTML.

Beyond HTML’s limitations, client-side programs such as
PyMailGUI also have access to tools such as multithreading which
are difficult to emulate in a CGI-based application (threads
spawned by a CGI script cannot outlive the CGI script itself, or
augment its reply once sent). Persistent process models for web
applications such as FastCGI may provide options here, but the
picture is not as clear-cut as on the client.

Although web developers make noble efforts at emulating
client-side
capabilities—
see
the discussion of RIAs and HTML 5 ahead—such efforts add
additional complexity, can stretch the server-side programming
model nearly to its breaking point, and account for much of the
plethora of divergent web techniques.

Portability benefits

All you need is a browser on clients
.
On the upside, because PyMailCGI runs over the Web, it can be run
on any machine with a web browser, whether that machine has Python
and tkinter installed or not. That is, Python needs to be
installed on only one computer—the web server machine where the
scripts actually live and run. In fact, this is probably the most
compelling benefit to the web application model. As long as you
know that the users of your system have an Internet browser,
installation is simple. You still need Python on the server, but
that’s easier to
guarantee
.

Python and tkinter, you will recall, are very portable,
too—they run on all major window systems (X11, Windows, Mac)—but
to run a client-side Python/tkinter program such as PyMailGUI, you
need Python and tkinter on the client machine itself. Not so with
an application built as CGI scripts: it will work on Macintosh,
Linux, Windows, and any other machine that can somehow render HTML
web pages. In this sense, HTML becomes a sort of portable GUI API
language in web scripts, interpreted by your web browser, which is
itself a kind of generalized GUI for rendering GUIs. You don’t
even need the source code or bytecode for the CGI scripts
themselves—they run on a remote server that exists somewhere else
on the Net, not on the machine running the browser.

Execution requirements

But you do need a browser
. That is, the
very nature of web-enabled systems can render them useless in some
environments. Despite the pervasiveness of the Internet, many
applications still run in settings that don’t have web browsers or
Internet access. Consider, for instance, embedded systems,
real-time systems, and secure government applications. While an
intranet
(a local network without external
connections) can sometimes make web applications feasible in some
such environments, I have worked at more than one company whose
client sites had no web browsers to speak of. On the other hand,
such clients may be more open to installing systems like Python on
local machines, as opposed to supporting an internal or external
network.

Administration requirements

You really need a server, too
. You
can’t write CGI-based systems at all without access to a web
server. Further, keeping programs on a centralized server creates
some fairly critical administrative overheads. Simply put, in a
pure client/server architecture, clients are simpler, but the
server becomes a critical path resource and a potential
performance bottleneck. If the centralized server goes down, you,
your employees, and your customers may be knocked out of
commission. Moreover, if enough clients use a shared server at the
same time, the speed costs of web-based systems become even more
pronounced. In production systems, advanced techniques such as
load balancing and fail-over servers help, but they add new
requirements
.

In fact, one could make the argument that moving toward a
web server architecture is akin to stepping backward in time—to
the time of centralized mainframes and dumb terminals. Some would
include the emerging
cloud computing
model in
this analysis, arguably in part a throwback to older computing
models. Whichever way we step, offloading and distributing
processing to client machines at least partially avoids this
processing
bottleneck.

Client-side solutions

Client- and server-side
programs can be mixed in many ways. For instance,
applet programs live on a server but are downloaded to and run as
client-side programs with access to rich GUI libraries.

Other technologies, such as embedding JavaScript or Python
directly in HTML code, also support client-side execution and
richer GUI possibilities. Such scripts live in HTML on the server
but run on the client when downloaded and access browser
components through an exposed object model to customize
pages.

The Dynamic HTML (DHTML) extensions provide yet another
client-side scripting option for changing web pages after they
have been constructed. And the newly emerging AJAX model offers
additional ways to add interactivity and responsiveness to web
pages, and is at the heart of the RIA model noted ahead. All of
these client-side technologies add extra complexities all their
own, but they ease some of the limitations imposed by straight
HTML.

State retention solutions

We discussed general
state retention options in detail in the prior
chapter, and we will study full-scale database systems for Python
in
Chapter 17
. Some web
application servers (e.g., Zope) naturally support state retention
between pages by providing concurrently accessible object
databases. Some of these systems have an explicit underlying
database component (e.g., Oracle and MySQL); others may use flat
files or Python persistent shelves with appropriate locking. In
addition, object relational mappers (ORMs) such as SQLObject allow
relational databases to be processed as Python classes.

Scripts can also pass state information around in hidden
form fields and generated URL parameters, as done in PyMailCGI, or
they can store it on the client machine itself using the standard
cookie protocol. As we learned in
Chapter 15
, cookies are strings of
information that are stored on the client upon request from the
server, and that are transferred back to the server when a page is
revisited (data is sent back and forth in HTTP header lines).
Cookies are more complex than program variables and are somewhat
controversial and optional, but they can offload some simple state
retention tasks.

Alternative models such as FastCGI and
mod_python
offer additional persistence
options—where supported, FastCGI applications may retain context
in long-lived processes, and
mod_python
provides session data within
Apache.

HTML generation solutions

Third-party extensions
can also take some of the complexity out of
embedding HTML in Python CGI scripts, albeit at some cost to
execution speed. For instance, the HTMLgen system and its
relatives let programs build pages as trees of Python objects that
“know” how to produce HTML. Other frameworks prove an object-based
interface to reply-stream generation (e.g., a reply object with
methods). When a system like this is employed, Python scripts deal
only with objects, not with the syntax of HTML itself.

For instance, systems such as PHP, Python Server Pages
(PSP), Zope’s DTML and ZPT, and Active Server Pages provide
server-side
templating languages, which allow scripting language
code to be embedded in HTML and executed on the server, to
dynamically generate or determine part of the HTML that is sent
back to a client in response to requests. The net result can
cleanly insulate Python code from the complexity of HTML code and
promote the separation of display format and business logic, but
may add complexities of its own due to the mixture of different
languages
.

Generalized user interface
development

To cover both bases, some systems attempt to separate logic
from display so much as to make the choice almost irrelevant—by
completely encapsulating display details, a single program can, in
principle, render its user interface as either a traditional GUI
or an HTML-based web page. Due to the vastly different
architectures, though, this ideal is difficult to achieve well and
does not address larger disparities between the client and server
platforms. Issues such as state retention and network interfaces
are much more significant than generation of windows and controls,
and may impact code more.

Other systems may try to achieve similar goals by
abstracting the display representation—a common XML
representation, for instance, might lend itself to both a GUI and
an HTML rendering. Again, though, this addresses only the
rendering of the display, not the fundamental architectural
differences of client- and server-side approaches.

Emerging technologies: RIAs and HTML
5

Finally, higher-level approaches such as the
RIA (Rich Internet Application) toolkits introduced
in Chapters
7
and
12
can
offer additional functionality that HTML lacks and can approach
the utility on GUI toolkits. On the other hand, they can also
complicate the web development story even further, and add yet
additional languages to the mix. Though this can vary, the net
result is often something of a Web-hosted Tower of Babel, whose
development might require simultaneously programming in Python,
HTML, SQL, JavaScript, a server-side templating language, an
object-relational mapping API, and more, and even nested and
embedded combinations of these. The resulting software stack can
be more complex than Python and a GUI toolkit.

Moreover, RIAs today inherit the inherent speed degradation
of network-based systems in general; although AJAX can add
interactivity to web pages, it still implies network access
instead of in-process function calls. Ironically, much like
desktop applications, RIAs may also still require installation of
a browser plug-in on the client to be used at all. The emerging
HTML 5 standard may address the plug-in constraint and ease the
complexity somewhat, but it brings along with it a grab bag of new
complexities all its own which we won’t describe here.

Suggested Reading: The PyErrata System

Now that I’ve
told you all the reasons you might not want to design
systems for the Web, I’m going to completely contradict myself and
refer you to a system that almost requires a web-based implementation.
The second edition of this book included a chapter that presented the
PyErrata website—a Python program that lets arbitrary people on
arbitrary machines submit book comments and bug reports (usually
called errata) over the Web, using just a web browser. Such a system
must store information on a server, so it can be read by arbitrary
clients.

Because of space concerns, that chapter was cut in this book’s
third edition. However, we’re making its original content available as
optional, supplemental reading. You can find this example’s code, as
well as the original chapter’s file in the directory
PP4E\Internet\Web\PyErrata
of the book examples
distribution tree (see the
Preface
for more on the
examples distribution).

PyErrata is in some ways simpler than the PyMailCGI case study
presented in this chapter. From a user’s perspective, PyErrata is more
hierarchical than linear: user interactions are shorter and spawn
fewer pages. There is also little state retention in the web pages
themselves in PyErrata—URL parameters pass state in only one isolated
case, and no hidden form fields are generated.

On the other hand, PyErrata introduces an entirely new
dimension: persistent data storage. State (error and comment reports)
is stored permanently by this system on the server, either in flat
pickle files or in a shelve-based database. Both raise the specter of
concurrent updates, since any number of users out in cyberspace may be
accessing the site at the same time, so PyErrata also introduces
file-locking techniques along the way.

I no longer maintain the website described by this extra
chapter, and the material itself is slightly out of date in some ways.
For instance, the
os.open
call is
preferred for file locking now; I would probably use a different data
storage system today, such as ZODB; the code and its chapter may still
be in Python 2.X form in the examples package; and this site might be
better implemented as a blog or wiki, concepts and labels that arose
after the site was developed.

Still, PyErrata provides an additional Python website case
study, and it more closely reflects websites that must store
information on the server.