Writing CGI Scripts in Python
The Python Reference Manual abstract describes Python as:
A simple yet powerful, interpreted programming language that bridges the gap between C and shell programming, and is thus ideally suited for “throw-away programming” and rapid prototyping. Its syntax is put together from constructs borrowed from a variety of other languages; most prominent are influences from ABC, C Modula-3 and Icon ... Python is available for various operating systems, amongst which are several flavors of Unix (including Linux), the Apple Macintosh O.S., MS-DOS, MS Windows 3.1, Windows NT, and OS/2.
It should also be noted that Python is an object-oriented language. You can write classes like in C++ or Java. I use Python every time the common guy would use Perl [“common guy”? Sheesh!—Ed, who is a die-hard Perl fan]. It has about the same functionality, while being far more readable. But it should not be restricted to a scripting language—a lot of people are using it for complete applications. It's also a perfect glue language, like Tcl, because it's easy to add new modules (written in C) to it. It can also be embedded in C applications.
Listing 1 shows my very first Python script. It's still used on a file server at the office. It deletes ~*.tmp left everywhere by buggy MS Windows applications. It's not really the common Hello World program (we'll see one later), and maybe it's not the most efficient way to do the job, but it demonstrates several features of the language:
Variables: Variables don't have to be declared.
Recursivity: See the ScanDir() function.
Platform independence: The os module provides constants for current directory, parent directory, and so on. See os.curdir, os.pardir...
for statement and lists: In Python, for works differently than in C. os.listdir() returns a list of files. For example, in Listing 1, at each iteration of:
for p in files
p will become the value of the next element in the list. So, if files is a triplet with values:
['lib', 'include', 'src' ]
The first time, p will be 'lib'. At the second iteration, it will be 'include', and so on, until it has gone through the whole list.
Arrays and indices: An array can be referred with one or two indices. One index is used to get a single element from the array (like in C). Two indices can be used to get a subset of the array. The first index gives the from element, while the second one gives the to element. For example, if a is an array containing the values 'abcdef', a[ 2 : 4 ] will return 'cd'. There are defaults for both indices: they default to from the start and to to the end respectively. Examples: a[ 2 : ] will return 'cdef'. Negative indices can be used to count from the end; a[ -2 ] will return 'e'. See the Python Tutorial at the Python web site (http://www.Python.org/) to learn more about arrays.
Blocks: Unlike C or Pascal-derived languages, there are no Start-Block or End-Block separators. Python works only with indentations (and the “:” character).
One of my colleagues in the firmware department recently had some problems debugging a TCP/IP application he is writing. There is a server application running in an embedded system, and a client application running on a PC. He was stuck for two days with a protocol problem, and didn't even know if the problem came from the client or from the server. Every test version meant recompiling, eventually downloading the code in the embedded system, and so on. In addition, it's not always easy to debug a device that doesn't even have a screen—you get the point.
So, after we discussed his problems, I decided to write little Python test programs to test his applications. In less than a quarter of an hour, I had tested his server application. This included writing a Python script and running it on a console of a Linux box, concurrently to tcpdump. Since the problem didn't come from the server, I wrote another program to test his client application. This script masqueraded the server, and we immediately discovered the problem. My colleague was very impressed by the short time it took me to write those two scripts, so I gave him a copy of the Python Tutorial.
Some simple scripts using sockets can be found in Listing 2a and2b. They are from the Python Library Reference.
My company sells time and attendance software in a client/server environment. Supported platforms include Unix and NT. The biggest problem with time and attendance is that, although general functionalities are the same for all our customers, they all have special specific rules. That's why the software department is considering the inclusion of the Python interpreter in their software. It would allow on-site customization, and it is available on all our platforms.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
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.
Join Linux Journal's Mike Diehl and Pat Cameron of Help Systems.
Free to Linux Journal readers.Register Now!
- SUSE LLC's SUSE Manager
- Murat Yener and Onur Dundar's Expert Android Studio (Wrox)
- My +1 Sword of Productivity
- Non-Linux FOSS: Caffeine!
- Managing Linux Using Puppet
- Doing for User Space What We Did for Kernel Space
- SuperTuxKart 0.9.2 Released
- Parsing an RSS News Feed with a Bash Script
- Google's SwiftShader Released
- Rogue Wave Software's Zend Server
With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
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