This month’s column is only peripherally related to the GNU Project, in that it describes a number of the GNU tools on you Linux system and how they might be used. What it’s really about is the “Software Tools” philosophy of program development and usage.
The Software Toolbox
The Software Tools philosophy was an important and integral concept in the initial design and development of Unix (of which Linux and GNU are essentially clones). Unfortunately, in the modern day press of Internetworking and flashy GUIs, it seems to have fallen by the wayside. This is a shame, since it provides a powerful mental model for solving many kinds of problems.
Many people carry a Swiss Army knife around in their pants pockets (or purse). A Swiss Army knife is a handy tool to have: it has several knife blades, a screwdriver, tweezers, toothpick, nail file, corkscrew, and perhaps a number of other things on it. For the everyday, small miscellaneous jobs where you need a simple, general purpose tool, it’s just the thing.
On the other hand, an experienced carpenter doesn’t build a house using a Swiss Army knife. Instead, he has a toolbox chock full of specialized tools—a saw, a hammer, a screwdriver, a plane, and so on. And he knows exactly when and where to use each tool; you won’t catch him hammering nails with the handle of his screwdriver.
The Unix developers at Bell Labs were all professional programmers and trained computer scientists. They had found that while a one-size-fits-all program might appeal to a user because there’s only one program to use, in practice such programs are: a) difficult to write, b) difficult to maintain and debug, and c) difficult to extend to meet new situations.
Instead, they felt that programs should be specialized tools. In short, each program “should do one thing well.” No more and no less. Such programs are simpler to design, write, and get right—they only do one thing.
Furthermore, they found that with the right machinery for hooking programs together, that the whole was greater than the sum of the parts. By combining several special purpose programs, you could accomplish a specific task that none of the programs was designed for, and accomplish it much more quickly and easily than if you had to write a special purpose program. We will see some (classic) examples of this further on in the column. (An important additional point was that, if necessary, take a detour and build any software tools you may need first, if you don’t already have something appropriate in the toolbox.)
Hopefully, you are familiar with the basics of I/O redirection in the shell, in particular the concepts of “standard input,” “standard output,” and “standard error”. Briefly, “standard input” is a data source, where data comes from. A program should not need to either know or care if the data source is a disk file, a keyboard, a magnetic tape, or even a punched card reader. Similarly, “standard output” is a data sink, where data goes to. The program should neither know nor care where this might be. Programs that only read their standard input, do something to the data, and then send it on, are called “filters”, by analogy to filters in a water pipeline.
With the Unix shell, it’s very easy to set up data pipelines:
program_to_create_data | filter1 | .... | filterN > final.pretty.data
We start out by creating the raw data; each filter applies some successive transformation to the data, until by the time it comes out of the pipeline, it is in the desired form.
This is fine and good for standard input and standard output. Where does the standard error come in to play? Well, think about filter1 in the pipeline above. What happens if it encounters an error in the data it sees? If it writes an error message to the standard output, it will just disappear down the pipeline into filter2‘s input, and the user will probably never see it. So programs need a place where they can send error messages so that the user will notice them. This is the standard error, and it is usually connected to your console or window, even if you have redirected the standard output of your program away from your screen.
For filter programs to work together, the format of the data has to be agreed upon. The most straightforward and easiest format to use is simply lines of text. Unix data files are generally just streams of bytes, with lines delimited by the ASCII LF (Line Feed) character, conventionally called a “newline” in the Unix literature. (This is ‘\n’ if you’re a C programmer.) This is the format used by all the traditional filtering programs. (Many earlier operating systems had elaborate facilities and special purpose programs for managing binary data. Unix has always shied away from such things, under the philosophy that it’s easiest to simply be able to view and edit your data with a text editor.)
OK, enough introduction. Let’s take a look at some of the tools, and then we’ll see how to hook them together in interesting ways. In the following discussion, we will only present those command line options that interest us. As you should always do, double check your system documentation for the full story.
The first program is the who command. By itself, it generates a list of the users who are currently logged in. Although I’m writing this on a single-user system, we’ll pretend that several people are logged in:
$ who arnold console Jan 22 19:57 miriam ttyp0 Jan 23 14:19 (:0.0) bill ttyp1 Jan 21 09:32 (:0.0) arnold ttyp2 Jan 23 20:48 (:0.0)
Here, the $ is the usual shell prompt, at which I typed who. There are three people logged in, and I am logged in twice. On traditional Unix systems, user names are never more than eight characters long. This little bit of trivia will be useful later. The output of who is nice, but the data is not all that particularly exciting.
The next program we’ll look at is the cut command. This program cuts out columns or fields of input data. For example, we can tell it to print just the login name and full name from the /etc/passwd file. The /etc/passwd file has seven fields, separated by colons:
arnold:xyzzy:2076:10:Arnold D. Robbins:/home/arnold:/bin/ksh
To get the first and fifth fields, we would use cut like this:
$ cut -d: -f1,5 /etc/passwd root:Operator ... arnold:Arnold D. Robbins miriam:Miriam A. Robbins ...
With the -c option, cut will cut out specific characters (i.e. columns) in the input lines. This command looks like it might be useful for data filtering.
Next we’ll look at the sort command. This is one of the most powerful commands on a Unix-style system; one that you will often find yourself using when setting up fancy data plumbing. The sort command reads and sorts each file named on the command line. It then merges the sorted data and writes it to standard output. It will read the standard input if no files are given on the command line (thus making it into a filter). The sort is based on the machine collating sequence (ASCII) or based on user-supplied ordering criteria.
Finally (at least for now), we’ll look at the uniq program. When sorting data, you will often end up with duplicate lines, lines that are identical. In general, all you need is one instance of each line. This is where uniq comes in. The uniq program reads its standard input, which it expects to be sorted. It only prints out one copy of each duplicated line. It does have several options. Later on, we’ll use the -c option, which prints each unique line, preceded by a count of the number of times that line occurred in the input.
Now, let’s suppose this is a large BBS system with dozens of users logged in. The management wants the SysOp to write a program that will generate a sorted list of logged in users. Furthermore, even if a user is logged in multiple times, his name should only show up in the output one time.
The SysOp could sit down with his system documentation and write a C program that did this. It would take him perhaps a couple of hundred lines of code and about two hours to write it, test it, and debug it. However, knowing his software toolbox, he starts out by generating just a list of logged on users:
$ who | cut -c1-8 arnold miriam bill arnold
Next, he sorts the list:
$ who | cut -c1-8 | sort arnold arnold bill miriam
Finally, he runs the sorted list through uniq, to weed out duplicates:
$ who | cut -c1-8 | sort | uniq arnold bill miriam
The sort command actually has a -u option that does what uniq does. However, uniq has other uses for which one cannot substitute sort -u.
The SysOp puts this pipeline into a shell script, and makes it available for all the users on the system:
# cat > /usr/local/bin/listusers who | cut -c1-8 | sort | uniq ^D # chmod +x /usr/local/bin/listusers
There are four major points to note here. First, with just four programs, on one command line, the SysOp was able to save himself about two hours worth of work. Furthermore, the shell pipeline is just about as efficient as the C program would be, and it is much more efficient in terms of programmer time. People time is much more expensive than computer time, and in our modern “there’s never enough time to do everything” society, saving two hours of programmer time is no mean feat.
Second, it is also important to emphasize that with the combination of the tools, it is possible to do a special purpose job never imagined by the authors of the individual programs.
Third, it is also valuable to build up your pipeline in stages, as we did here. This allows you to view the data at each stage in the pipeline, which helps you acquire the confidence that you are indeed using these tools correctly.
Finally, by bundling the pipeline in a shell script, other users can use your command, without having to remember the fancy plumbing you set up for them. In terms of how you run them, shell scripts and compiled programs are indistinguishable.
After the previous warm-up exercise, we’ll look at two additional, more complicated pipelines. For them, we need to introduce two more tools.
The first is the tr command, which stands for “transliterate.” The tr command works on a character-by-character basis, changing characters. Normally it is used for things like mapping upper case to lower case:
$ echo ThIs ExAmPlE HaS MIXED case! | tr '[A-Z]' '[a-z]' this example has mixed case!
There are several options of interest:
- -c work on the complement of the listed characters, i.e. operations apply to characters not in the given set
- -d delete characters in the first set from the output
- -s squeeze repeated characters in the output into just one character.
We will be using all three options in a moment.
The other command we’ll look at is comm. The comm command takes two sorted input files as input data, and prints out the files’ lines in three columns. The output columns are the data lines unique to the first file, the data lines unique to the second file, and the data lines that are common to both. The -1, -2, and -3 command line options omit the respective columns. (This is non-intuitive and takes a little getting used to.) For example:
$ cat f1 11111 22222 33333 44444 $ cat f2 00000 22222 33333 55555 $ comm f1 f2 00000 11111 22222 33333 44444 55555
A single dash as a file name tells comm to read the standard input instead of a regular file.
Now we’re ready to build a fancy pipeline. The first application is a word frequency counter. This helps an author determine if he or she is over-using certain words.
The first step is to change the case of all the letters in our input file to one case. “The” and “the” are the same word when doing counting.
$ tr '[A-Z]' '[a-z]' < whats.gnu | ...
The next step is to get rid of punctuation. Quoted words and unquoted words should be treated identically; it’s easiest to just get the punctuation out of the way.
$ tr '[A-Z]' '[a-z]' < whats.gnu | tr -cd '[A-Za-z0-9_ \012]' | ...
The second tr command operates on the complement of the listed characters, which are all the letters, the digits, the underscore, and the blank. The \012 represents the newline character; it has to be left alone. (The ASCII TAB character should also be included for good measure in a production script.)
At this point, we have data consisting of words separated by blank space. The words only contain alphanumeric characters (and the underscore). The next step is break the data apart so that we have one word per line. This makes the counting operation much easier, as we will see shortly.
$ tr '[A-Z]' '[a-z]' < whats.gnu | tr -cd '[A-Za-z0-9_ \012]' | < tr -s '[ ]' '\012' | ...
This command turns blanks into newlines. The -s option squeezes multiple newline characters in the output into just one. This helps us avoid blank lines. (The > is the shell’s “secondary prompt.” This is what the shell prints when it notices you haven’t finished typing in all of a command.)
We now have data consisting of one word per line, no punctuation, all one case. We’re ready to count each word:
$ tr '[A-Z]' '[a-z]' < whats.gnu | tr -cd '[A-Za-z0-9_ \012]' | > tr -s '[ ]' '\012' | sort | uniq -c | ...
At this point, the data might look something like this:
60 a 2 able 6 about 1 above 2 accomplish 1 acquire 1 actually 2 additional
The output is sorted by word, not by count! What we want is the most frequently used words first. Fortunately, this is easy to accomplish. The sort command takes two more options:
- -n do a numeric sort, not an ASCII one
- -r reverse the order of the sort
The final pipeline looks like this:
$ tr '[A-Z]' '[a-z]' < whats.gnu | tr -cd '[A-Za-z0-9_ \012]' | > tr -s '[ ]' '\012' | sort | uniq -c | sort -nr 156 the 60 a 58 to 51 of 51 and ...
Whew! That’s a lot to digest. Yet, the same principles apply. With six commands, on two lines (really one long one split for convenience), we’ve created a program that does something interesting and useful, in much less time than we could have written a C program to do the same thing.
A minor modification to the above pipeline can give us a simple spelling checker! To determine if you’ve spelled a word correctly, all you have to do is look it up in a dictionary. If it is not there, then chances are that your spelling is incorrect. So, we need a dictionary. If you have the Slackware Linux distribution, you have the file /usr/lib/ispell/ispell.words, which is a sorted, 38,400 word dictionary.
Now, how to compare our file with the dictionary? As before, we generate a sorted list of words, one per line:
$ tr '[A-Z]' '[a-z]' < whats.gnu | tr -cd '[A-Za-z0-9_ \012]' | > tr -s '[ ]' '\012' | sort -u | ...
Now, all we need is a list of words that are not in the dictionary. Here is where the comm command comes in.
$ tr '[A-Z]' '[a-z]' < whats.gnu | tr -cd '[A-Za-z0-9_ \012]' | > tr -s '[ ]' '\012' | sort -u | > comm -23 - /usr/lib/ispell/ispell.words
The -2 and -3 options eliminate lines that are only in the dictionary (the second file), and lines that are in both files. Lines only in the first file (the standard input, our stream of words), are words that are not in the dictionary. These are likely candidates for spelling errors. This pipeline was the first cut at a production spelling checker on Unix.
There are some other tools that deserve brief mention.
- grep search files for text that matches a regular expression
- egrep like grep, but with more powerful regular expressions
- wc count lines, words, characters
- tee a T-fitting for data pipes, copies data to files and to the standard output
- sed the stream editor, an advanced tool
- awk a data manipulation language, another advanced tool
The Software Tools philosophy also espoused the following bit of advice: “Let someone else do the hard part.” This means, take something that gives you most of what you need, and then massage it the rest of the way until it’s in the form that you want.
- 1. Each program should do one thing well. No more, no less.
- 2. Combining programs with appropriate plumbing leads to results where the whole is greater than the sum of the parts. It also leads to novel uses of programs that the authors might never have intended.
- 3. Programs should never print extraneous header or trailer data, since these could get sent on down a pipeline. (A point we didn’t mention earlier.)
- 4. Let someone else do the hard part.
- 5. Know your toolbox! Use each program appropriately. If you don’t have an appropriate tool, build one.
As of this writing, all the programs we’ve discussed are in the textutils-1.9 package, available via anonymous ftp from prep.ai.mit.edu in the /pub/gnu directory, file textutils-1.9.tar.gz.
None of what I have presented in this column is new. The Software Tools philosophy was first introduced in the book Software Tools by Brian Kernighan and P.J. Plauger (Addison-Wesley, ISBN 0-201-03669-X). This book showed how to write and use software tools. It was written in 1976, using a preprocessor for FORTRAN named ratfor (RATional FORtran). At the time, C was not as ubiquitous as it is now; FORTRAN was. The last chapter presented a ratfor to FORTRAN processor, written in ratfor. Ratfor looks an awful lot like C; if you know C, you won’t have any problem following the code.
In 1981, the book was updated and made available as Software Tools in Pascal (Addison-Wesley, ISBN 0-201-10342-7). Both books remain in print, and are well worth reading if you’re a programmer. They certainly made a major change in how I view programming.
Initially, the programs in both books were available (on 9-track tape) from Addison-Wesley. Unfortunately, this is no longer the case, although you might be able to find copies floating around the Internet. For a number of years, there was an active Software Tools Users Group, whose members had ported the original ratfor programs to essentially every computer system with a FORTRAN compiler. The popularity of the group waned in the middle ’80s as Unix began to spread beyond universities.
With the current proliferation of GNU code and other clones of Unix programs, these program now receive little attention; modern C versions are much more efficient and do more than these programs do. Nevertheless, as exposition of good programming style, and evangelism for a still-valuable philosophy, these books are unparalleled, and I recommend them highly.
Acknowledgement: I would like to express my gratitude to Brian Kernighan of Bell Labs, the original Software Toolsmith, for reviewing this column.
Questions and/or comments about this column can be addressed to the author via postal mail C/O Linux Journal, or via e-mail to email@example.com.