cgimodel: CGI Programming Made Easy with Python
The Common Gateway Interface (CGI) is a way in which you can let others from all over the world execute a program that resides on your computer. CGI is dynamic, since it runs in real time. You can decorate the CGI output with HTML (Hyper Text Markup Language). Most of the time, CGI is used as a front end for existing applications. CGI can be easy or complex, depending on the complexity of your project. Most CGI developers know the frustration which comes with debugging the CGI programs.
We present a very simple and robust way of doing CGI programming with Python. Debugging your CGI is easy, since you can do it on the command line, and integrating existing applications to work with CGI is just one step.
For our work, we chose Python, an object-oriented scripting language with a clear syntax. It is very easy to use, widely available and is free software.
Our intended audience is both experienced and novice CGI programmers. We will use the words “function” and “method” interchangeably. Note that CGI can be written in any computer language.
There are two ways of invoking CGI programs: through a URL with all data included, or by submitting HTML forms.
The two methods defined in HTTP to send your data to the CGI are GET and POST. When the method is GET, the CGI program gets the input from the QUERY_STRING environment variable. When the method is POST, the CGI program gets the input from standard input (STDIN). In both cases, one has to parse the input to obtain the input argument name,value pairs.
CGI may or may not be complicated, but when you have a larger application with many features, you might have problems in testing, debugging, etc. This is true with all software projects. Debugging becomes problematic with CGIs. For example, when the method is GET, you have to set up environment variables QUERY_STRING and REQUEST_METHOD. When the method is POST, you must set up REQUEST_METHOD and CONTENT_LENGTH (number of bytes) to read from standard input (STDIN). Moreover, when your program crashes, it is not visible to your browser—you do not know what happened. The only message you get in this situation is the error report made by the web server.
You can use either of these methods (GET/POST) depending on your need. If you will be sending more data to CGI, use the POST method. When you have less data to be sent to CGI, use GET to put all the data inside the URL. For example, on one line, type:
<A HREF="/cgi/cgimodel.py?fun=DisplayFile&fileName= cgimodel.pycgimodel">cgimodel</A>
With HTML FORMS (for POST method), the same would be
<FORM METHOD="post" ACTION="/cgi-bin/cgimodel.py"> <INPUT TYPE=hidden name=fun value=DisplayFile> <INPUT TYPE=hidden name=fileName value=cgimodel.py> <INPUT TYPE=SUBMIT VALUE="cgimodel"> </FORM>We all know the difficulties of and have adopted different styles for debugging CGI programs. Our intention is to build CGI that does not work in the traditional way, but like other programs which work on the command line. This means you can test your CGI the way you test any other program on the command line. When it works on the command line, it is guaranteed to exhibit the same behavior on the Web.
Let us see how we can make life easier with cgimodel, which lets you integrate your existing application in an elegant way without much hassle. Basically it consists of two modules: cgimodel.py (see Listing 1) and cgidisp.py (see Listing 2).
cgimodel.py is a wrapper to Python's CGI module. It also encapsulates reading from the command line, so there is no real difference in invoking from HTML FORMS or a URL or the command line.
The CollectArgs function in the cgimodel.py module takes care of collecting arguments including name,value parameters from CGI or the command line. On the UNIX command line, you can supply the name,value parameters like this:
-name1 value1 -name2 value2
or like this:
name1 value1 name2 value2The same is true for both URL and FORMS.
You do not have to modify anything in cgimodel.py. You just have to use it. The main section of cgimodel contains the following lines:
d = Dispatcher() parDict = CollectArgs(parDict) print mime_html fun=parDict['fun'] if not fun: print "usage: cgimodel -fun functionName" d.ShowAvailableFunc() TraceIt(parDict) else: try: d.dispatch(fun,parDict) except: TraceIt(parDict)
cgimodel.py tries to call the function you have given as an argument to the parameter -fun.
When there is no such function available, it tells you the names of functions that can be called. If there is an exception (because of a syntax error, etc.) in the program, the exception will be traced back and reported. You can use this feature to e-mail the exception to yourself and make your CGI program more stable.
Practical Task Scheduling Deployment
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide