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Now, let’s suppose this is a large ISP server system with dozens of users logged in. The management wants the system administrator 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 or her name should only show up in the output once.
The administrator could sit down with the system documentation and write a C program that did this. It would take perhaps a couple of hundred lines of code and about two hours to write it, test it, and debug it. However, knowing the software toolbox, the administrator can instead start out by generating just a list of logged on users:
$ who | cut -c1-8 -| arnold -| miriam -| bill -| arnold
Next, sort the list:
$ who | cut -c1-8 | sort -| arnold -| arnold -| bill -| miriam
Finally, run the sorted list through
uniq, to weed out duplicates:
$ who | cut -c1-8 | sort | uniq -| arnold -| bill -| miriam
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 administrator puts this pipeline into a shell script, and makes it
all the users on the system (‘#’ is the system administrator,
# 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 administrator was able to save 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.”
tr command works on a character-by-character basis, changing
characters. Normally it is used for things like mapping upper case to
$ echo ThIs ExAmPlE HaS MIXED case! | tr '[:upper:]' '[:lower:]' -| this example has mixed case!
There are several options of interest:
work on the complement of the listed characters, i.e., operations apply to characters not in the given set
delete characters in the first set from the output
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
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
The file name - tells
comm to read 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 '[:upper:]' '[:lower:]' < 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 '[:upper:]' '[:lower:]' < whats.gnu | tr -cd '[:alnum:]_ \n' | ...
tr command operates on the complement of the listed
characters, which are all the letters, the digits, the underscore, and
the blank. The ‘\n’ 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 '[:upper:]' '[:lower:]' < whats.gnu | tr -cd '[:alnum:]_ \n' | > tr -s ' ' '\n' | ...
This command turns blanks into newlines. The -s option squeezes multiple newline characters in the output into just one, removing 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 '[:upper:]' '[:lower:]' < whats.gnu | tr -cd '[:alnum:]_ \n' | > tr -s ' ' '\n' | 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,
with the help of two more
do a numeric sort, not a textual one
reverse the order of the sort
The final pipeline looks like this:
$ tr '[:upper:]' '[:lower:]' < whats.gnu | tr -cd '[:alnum:]_ \n' | > tr -s ' ' '\n' | sort | uniq -c | sort -n -r -| 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. The conventional location for a dictionary is /usr/dict/words. On my GNU/Linux system,6 this is a sorted, 45,402 word dictionary.
Now, how to compare our file with the dictionary? As before, we generate a sorted list of words, one per line:
$ tr '[:upper:]' '[:lower:]' < whats.gnu | tr -cd '[:alnum:]_ \n' | > tr -s ' ' '\n' | 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 '[:upper:]' '[:lower:]' < whats.gnu | tr -cd '[:alnum:]_ \n' | > tr -s ' ' '\n' | sort -u | > comm -23 - /usr/dict/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 (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.
search files for text that matches a regular expression
count lines, words, characters
a T-fitting for data pipes, copies data to files and to standard output
the stream editor, an advanced tool
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.
All the programs discussed are available as described in GNU core utilities.
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
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 looks an awful
lot like C; if you know C, you won’t have any problem following the
In 1981, the book was updated and made available as Software Tools in Pascal (Addison-Wesley, ISBN 0-201-10342-7). Both books are still in print and are well worth reading if you’re a programmer. They certainly made a major change in how I view programming.
The programs in both books are available from
Brian Kernighan’s home page.
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 1980s
as Unix began to spread beyond universities.
With the current proliferation of GNU code and other clones of Unix programs, these programs 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.
Acknowledgment: I would like to express my gratitude to Brian Kernighan of Bell Labs, the original Software Toolsmith, for reviewing this column.
Redhat Linux 6.1, for the November 2000 revision of this article.
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