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PERLPERF(1)	       Perl Programmers Reference Guide		   PERLPERF(1)

NAME
       perlperf - Perl Performance and Optimization Techniques

DESCRIPTION
       This is an introduction to the use of performance and optimization
       techniques which can be used with particular reference to perl
       programs.  While many perl developers have come from other languages,
       and can use their prior knowledge where appropriate, there are many
       other people who might benefit from a few perl specific pointers.  If
       you want the condensed version, perhaps the best advice comes from the
       renowned Japanese Samurai, Miyamoto Musashi, who said:

	   "Do Not Engage in Useless Activity"

       in 1645.

OVERVIEW
       Perhaps the most common mistake programmers make is to attempt to
       optimize their code before a program actually does anything useful -
       this is a bad idea.  There's no point in having an extremely fast
       program that doesn't work.  The first job is to get a program to
       correctly do something useful, (not to mention ensuring the test suite
       is fully functional), and only then to consider optimizing it.  Having
       decided to optimize existing working code, there are several simple but
       essential steps to consider which are intrinsic to any optimization
       process.

   ONE STEP SIDEWAYS
       Firstly, you need to establish a baseline time for the existing code,
       which timing needs to be reliable and repeatable.  You'll probably want
       to use the "Benchmark" or "Devel::DProf" modules, or something similar,
       for this step, or perhaps the unix system "time" utility, whichever is
       appropriate.  See the base of this document for a longer list of
       benchmarking and profiling modules, and recommended further reading.

   ONE STEP FORWARD
       Next, having examined the program for hot spots, (places where the code
       seems to run slowly), change the code with the intention of making it
       run faster.  Using version control software, like "subversion", will
       ensure no changes are irreversible.  It's too easy to fiddle here and
       fiddle there - don't change too much at any one time or you might not
       discover which piece of code really was the slow bit.

   ANOTHER STEP SIDEWAYS
       It's not enough to say: "that will make it run faster", you have to
       check it.  Rerun the code under control of the benchmarking or
       profiling modules, from the first step above, and check that the new
       code executed the same task in less time.  Save your work and repeat...

GENERAL GUIDELINES
       The critical thing when considering performance is to remember there is
       no such thing as a "Golden Bullet", which is why there are no rules,
       only guidelines.

       It is clear that inline code is going to be faster than subroutine or
       method calls, because there is less overhead, but this approach has the
       disadvantage of being less maintainable and comes at the cost of
       greater memory usage - there is no such thing as a free lunch.  If you
       are searching for an element in a list, it can be more efficient to
       store the data in a hash structure, and then simply look to see whether
       the key is defined, rather than to loop through the entire array using
       grep() for instance.  substr() may be (a lot) faster than grep() but
       not as flexible, so you have another trade-off to access.  Your code
       may contain a line which takes 0.01 of a second to execute which if you
       call it 1,000 times, quite likely in a program parsing even medium
       sized files for instance, you already have a 10 second delay, in just
       one single code location, and if you call that line 100,000 times, your
       entire program will slow down to an unbearable crawl.

       Using a subroutine as part of your sort is a powerful way to get
       exactly what you want, but will usually be slower than the built-in
       alphabetic "cmp" and numeric "<=>" sort operators.  It is possible to
       make multiple passes over your data, building indices to make the
       upcoming sort more efficient, and to use what is known as the "OM"
       (Orcish Maneuver) to cache the sort keys in advance.  The cache lookup,
       while a good idea, can itself be a source of slowdown by enforcing a
       double pass over the data - once to setup the cache, and once to sort
       the data.  Using "pack()" to extract the required sort key into a
       consistent string can be an efficient way to build a single string to
       compare, instead of using multiple sort keys, which makes it possible
       to use the standard, written in "c" and fast, perl "sort()" function on
       the output, and is the basis of the "GRT" (Guttman Rossler Transform).
       Some string combinations can slow the "GRT" down, by just being too
       plain complex for it's own good.

       For applications using database backends, the standard "DBIx" namespace
       has tries to help with keeping things nippy, not least because it tries
       to not query the database until the latest possible moment, but always
       read the docs which come with your choice of libraries.	Among the many
       issues facing developers dealing with databases should remain aware of
       is to always use "SQL" placeholders and to consider pre-fetching data
       sets when this might prove advantageous.	 Splitting up a large file by
       assigning multiple processes to parsing a single file, using say "POE",
       "threads" or "fork" can also be a useful way of optimizing your usage
       of the available "CPU" resources, though this technique is fraught with
       concurrency issues and demands high attention to detail.

       Every case has a specific application and one or more exceptions, and
       there is no replacement for running a few tests and finding out which
       method works best for your particular environment, this is why writing
       optimal code is not an exact science, and why we love using Perl so
       much - TMTOWTDI.

BENCHMARKS
       Here are a few examples to demonstrate usage of Perl's benchmarking
       tools.

   Assigning and Dereferencing Variables.
       I'm sure most of us have seen code which looks like, (or worse than),
       this:

	   if ( $obj->{_ref}->{_myscore} >= $obj->{_ref}->{_yourscore} ) {
	       ...

       This sort of code can be a real eyesore to read, as well as being very
       sensitive to typos, and it's much clearer to dereference the variable
       explicitly.  We're side-stepping the issue of working with object-
       oriented programming techniques to encapsulate variable access via
       methods, only accessible through an object.  Here we're just discussing
       the technical implementation of choice, and whether this has an effect
       on performance.	We can see whether this dereferencing operation, has
       any overhead by putting comparative code in a file and running a
       "Benchmark" test.

       # dereference

	   #!/usr/bin/perl

	   use strict;
	   use warnings;

	   use Benchmark;

	   my $ref = {
		   'ref'   => {
		       _myscore	   => '100 + 1',
		       _yourscore  => '102 - 1',
		   },
	   };

	   timethese(1000000, {
		   'direct'	  => sub {
		       my $x = $ref->{ref}->{_myscore} . $ref->{ref}->{_yourscore} ;
		   },
		   'dereference'  => sub {
		       my $ref	= $ref->{ref};
		       my $myscore = $ref->{_myscore};
		       my $yourscore = $ref->{_yourscore};
		       my $x = $myscore . $yourscore;
		   },
	   });

       It's essential to run any timing measurements a sufficient number of
       times so the numbers settle on a numerical average, otherwise each run
       will naturally fluctuate due to variations in the environment, to
       reduce the effect of contention for "CPU" resources and network
       bandwidth for instance.	Running the above code for one million
       iterations, we can take a look at the report output by the "Benchmark"
       module, to see which approach is the most effective.

	   $> perl dereference

	   Benchmark: timing 1000000 iterations of dereference, direct...
	   dereference:	 2 wallclock secs ( 1.59 usr +	0.00 sys =  1.59 CPU) @ 628930.82/s (n=1000000)
	       direct:	1 wallclock secs ( 1.20 usr +  0.00 sys =  1.20 CPU) @ 833333.33/s (n=1000000)

       The difference is clear to see and the dereferencing approach is
       slower.	While it managed to execute an average of 628,930 times a
       second during our test, the direct approach managed to run an
       additional 204,403 times, unfortunately.	 Unfortunately, because there
       are many examples of code written using the multiple layer direct
       variable access, and it's usually horrible.  It is, however, miniscully
       faster.	The question remains whether the minute gain is actually worth
       the eyestrain, or the loss of maintainability.

   Search and replace or tr
       If we have a string which needs to be modified, while a regex will
       almost always be much more flexible, "tr", an oft underused tool, can
       still be a useful.  One scenario might be replace all vowels with
       another character.  The regex solution might look like this:

	   $str =~ s/[aeiou]/x/g

       The "tr" alternative might look like this:

	   $str =~ tr/aeiou/xxxxx/

       We can put that into a test file which we can run to check which
       approach is the fastest, using a global $STR variable to assign to the
       "my $str" variable so as to avoid perl trying to optimize any of the
       work away by noticing it's assigned only the once.

       # regex-transliterate

	   #!/usr/bin/perl

	   use strict;
	   use warnings;

	   use Benchmark;

	   my $STR = "$$-this and that";

	   timethese( 1000000, {
		   'sr'	 => sub { my $str = $STR; $str =~ s/[aeiou]/x/g; return $str; },
		   'tr'	 => sub { my $str = $STR; $str =~ tr/aeiou/xxxxx/; return $str; },
	   });

       Running the code gives us our results:

	   $> perl regex-transliterate

	   Benchmark: timing 1000000 iterations of sr, tr...
		   sr:	2 wallclock secs ( 1.19 usr +  0.00 sys =  1.19 CPU) @ 840336.13/s (n=1000000)
		   tr:	0 wallclock secs ( 0.49 usr +  0.00 sys =  0.49 CPU) @ 2040816.33/s (n=1000000)

       The "tr" version is a clear winner.  One solution is flexible, the
       other is fast - and it's appropriately the programmers choice which to
       use in the circumstances.

       Check the "Benchmark" docs for further useful techniques.

PROFILING TOOLS
       A slightly larger piece of code will provide something on which a
       profiler can produce more extensive reporting statistics.  This example
       uses the simplistic "wordmatch" program which parses a given input file
       and spews out a short report on the contents.

       # wordmatch

	   #!/usr/bin/perl

	   use strict;
	   use warnings;

	   =head1 NAME

	   filewords - word analysis of input file

	   =head1 SYNOPSIS

	       filewords -f inputfilename [-d]

	   =head1 DESCRIPTION

	   This program parses the given filename, specified with C<-f>, and displays a
	   simple analysis of the words found therein.	Use the C<-d> switch to enable
	   debugging messages.

	   =cut

	   use FileHandle;
	   use Getopt::Long;

	   my $debug   =  0;
	   my $file    = '';

	   my $result = GetOptions (
	       'debug'	       => \$debug,
	       'file=s'	       => \$file,
	   );
	   die("invalid args") unless $result;

	   unless ( -f $file ) {
	       die("Usage: $0 -f filename [-d]");
	   }
	   my $FH = FileHandle->new("< $file") or die("unable to open file($file): $!");

	   my $i_LINES = 0;
	   my $i_WORDS = 0;
	   my %count   = ();

	   my @lines = <$FH>;
	   foreach my $line ( @lines ) {
	       $i_LINES++;
	       $line =~ s/\n//;
	       my @words = split(/ +/, $line);
	       my $i_words = scalar(@words);
	       $i_WORDS = $i_WORDS + $i_words;
	       debug("line: $i_LINES supplying $i_words words: @words");
	       my $i_word = 0;
	       foreach my $word ( @words ) {
		   $i_word++;
		   $count{$i_LINES}{spec} += matches($i_word, $word, '[^a-zA-Z0-9]');
		   $count{$i_LINES}{only} += matches($i_word, $word, '^[^a-zA-Z0-9]+$');
		   $count{$i_LINES}{cons} += matches($i_word, $word, '^[(?i:bcdfghjklmnpqrstvwxyz)]+$');
		   $count{$i_LINES}{vows} += matches($i_word, $word, '^[(?i:aeiou)]+$');
		   $count{$i_LINES}{caps} += matches($i_word, $word, '^[(A-Z)]+$');
	       }
	   }

	   print report( %count );

	   sub matches {
	       my $i_wd	 = shift;
	       my $word	 = shift;
	       my $regex = shift;
	       my $has = 0;

	       if ( $word =~ /($regex)/ ) {
		   $has++ if $1;
	       }

	       debug("word: $i_wd ".($has ? 'matches' : 'does not match')." chars: /$regex/");

	       return $has;
	   }

	   sub report {
	       my %report = @_;
	       my %rep;

	       foreach my $line ( keys %report ) {
		   foreach my $key ( keys %{ $report{$line} } ) {
		       $rep{$key} += $report{$line}{$key};
		   }
	       }

	       my $report = qq|
	   $0 report for $file:
	   lines in file: $i_LINES
	   words in file: $i_WORDS
	   words with special (non-word) characters: $i_spec
	   words with only special (non-word) characters: $i_only
	   words with only consonants: $i_cons
	   words with only capital letters: $i_caps
	   words with only vowels: $i_vows
	   |;

	       return $report;
	   }

	   sub debug {
	       my $message = shift;

	       if ( $debug ) {
		   print STDERR "DBG: $message\n";
	       }
	   }

	   exit 0;

   Devel::DProf
       This venerable module has been the de-facto standard for Perl code
       profiling for more than a decade, but has been replaced by a number of
       other modules which have brought us back to the 21st century.  Although
       you're recommended to evaluate your tool from the several mentioned
       here and from the CPAN list at the base of this document, (and
       currently Devel::NYTProf seems to be the weapon of choice - see below),
       we'll take a quick look at the output from Devel::DProf first, to set a
       baseline for Perl profiling tools.  Run the above program under the
       control of "Devel::DProf" by using the "-d" switch on the command-line.

	   $> perl -d:DProf wordmatch -f perl5db.pl

	   <...multiple lines snipped...>

	   wordmatch report for perl5db.pl:
	   lines in file: 9428
	   words in file: 50243
	   words with special (non-word) characters: 20480
	   words with only special (non-word) characters: 7790
	   words with only consonants: 4801
	   words with only capital letters: 1316
	   words with only vowels: 1701

       "Devel::DProf" produces a special file, called tmon.out by default, and
       this file is read by the "dprofpp" program, which is already installed
       as part of the "Devel::DProf" distribution.  If you call "dprofpp" with
       no options, it will read the tmon.out file in the current directory and
       produce a human readable statistics report of the run of your program.
       Note that this may take a little time.

	   $> dprofpp

	   Total Elapsed Time = 2.951677 Seconds
	     User+System Time = 2.871677 Seconds
	   Exclusive Times
	   %Time ExclSec CumulS #Calls sec/call Csec/c	Name
	    102.   2.945  3.003 251215	 0.0000 0.0000	main::matches
	    2.40   0.069  0.069 260643	 0.0000 0.0000	main::debug
	    1.74   0.050  0.050	     1	 0.0500 0.0500	main::report
	    1.04   0.030  0.049	     4	 0.0075 0.0123	main::BEGIN
	    0.35   0.010  0.010	     3	 0.0033 0.0033	Exporter::as_heavy
	    0.35   0.010  0.010	     7	 0.0014 0.0014	IO::File::BEGIN
	    0.00       - -0.000	     1	      -	     -	Getopt::Long::FindOption
	    0.00       - -0.000	     1	      -	     -	Symbol::BEGIN
	    0.00       - -0.000	     1	      -	     -	Fcntl::BEGIN
	    0.00       - -0.000	     1	      -	     -	Fcntl::bootstrap
	    0.00       - -0.000	     1	      -	     -	warnings::BEGIN
	    0.00       - -0.000	     1	      -	     -	IO::bootstrap
	    0.00       - -0.000	     1	      -	     -	Getopt::Long::ConfigDefaults
	    0.00       - -0.000	     1	      -	     -	Getopt::Long::Configure
	    0.00       - -0.000	     1	      -	     -	Symbol::gensym

       "dprofpp" will produce some quite detailed reporting on the activity of
       the "wordmatch" program.	 The wallclock, user and system, times are at
       the top of the analysis, and after this are the main columns defining
       which define the report.	 Check the "dprofpp" docs for details of the
       many options it supports.

       See also "Apache::DProf" which hooks "Devel::DProf" into "mod_perl".

   Devel::Profiler
       Let's take a look at the same program using a different profiler:
       "Devel::Profiler", a drop-in Perl-only replacement for "Devel::DProf".
       The usage is very slightly different in that instead of using the
       special "-d:" flag, you pull "Devel::Profiler" in directly as a module
       using "-M".

	   $> perl -MDevel::Profiler wordmatch -f perl5db.pl

	   <...multiple lines snipped...>

	   wordmatch report for perl5db.pl:
	   lines in file: 9428
	   words in file: 50243
	   words with special (non-word) characters: 20480
	   words with only special (non-word) characters: 7790
	   words with only consonants: 4801
	   words with only capital letters: 1316
	   words with only vowels: 1701

       "Devel::Profiler" generates a tmon.out file which is compatible with
       the "dprofpp" program, thus saving the construction of a dedicated
       statistics reader program.  "dprofpp" usage is therefore identical to
       the above example.

	   $> dprofpp

	   Total Elapsed Time =	  20.984 Seconds
	     User+System Time =	  19.981 Seconds
	   Exclusive Times
	   %Time ExclSec CumulS #Calls sec/call Csec/c	Name
	    49.0   9.792 14.509 251215	 0.0000 0.0001	main::matches
	    24.4   4.887  4.887 260643	 0.0000 0.0000	main::debug
	    0.25   0.049  0.049	     1	 0.0490 0.0490	main::report
	    0.00   0.000  0.000	     1	 0.0000 0.0000	Getopt::Long::GetOptions
	    0.00   0.000  0.000	     2	 0.0000 0.0000	Getopt::Long::ParseOptionSpec
	    0.00   0.000  0.000	     1	 0.0000 0.0000	Getopt::Long::FindOption
	    0.00   0.000  0.000	     1	 0.0000 0.0000	IO::File::new
	    0.00   0.000  0.000	     1	 0.0000 0.0000	IO::Handle::new
	    0.00   0.000  0.000	     1	 0.0000 0.0000	Symbol::gensym
	    0.00   0.000  0.000	     1	 0.0000 0.0000	IO::File::open

       Interestingly we get slightly different results, which is mostly
       because the algorithm which generates the report is different, even
       though the output file format was allegedly identical.  The elapsed,
       user and system times are clearly showing the time it took for
       "Devel::Profiler" to execute it's own run, but the column listings feel
       more accurate somehow than the ones we had earlier from "Devel::DProf".
       The 102% figure has disappeared, for example.  This is where we have to
       use the tools at our disposal, and recognise their pros and cons,
       before using them.  Interestingly, the numbers of calls for each
       subroutine are identical in the two reports, it's the percentages which
       differ.	As the author of "Devel::Proviler" writes:

	   ...running HTML::Template's test suite under Devel::DProf shows output()
	   taking NO time but Devel::Profiler shows around 10% of the time is in output().
	   I don't know which to trust but my gut tells me something is wrong with
	   Devel::DProf.  HTML::Template::output() is a big routine that's called for
	   every test. Either way, something needs fixing.

       YMMV.

       See also "Devel::Apache::Profiler" which hooks "Devel::Profiler" into
       "mod_perl".

   Devel::SmallProf
       The "Devel::SmallProf" profiler examines the runtime of your Perl
       program and produces a line-by-line listing to show how many times each
       line was called, and how long each line took to execute.	 It is called
       by supplying the familiar "-d" flag to Perl at runtime.

	   $> perl -d:SmallProf wordmatch -f perl5db.pl

	   <...multiple lines snipped...>

	   wordmatch report for perl5db.pl:
	   lines in file: 9428
	   words in file: 50243
	   words with special (non-word) characters: 20480
	   words with only special (non-word) characters: 7790
	   words with only consonants: 4801
	   words with only capital letters: 1316
	   words with only vowels: 1701

       "Devel::SmallProf" writes it's output into a file called smallprof.out,
       by default.  The format of the file looks like this:

	   <num> <time> <ctime> <line>:<text>

       When the program has terminated, the output may be examined and sorted
       using any standard text filtering utilities.  Something like the
       following may be sufficient:

	   $> cat smallprof.out | grep \d*: | sort -k3 | tac | head -n20

	   251215   1.65674   7.68000	 75: if ( $word =~ /($regex)/ ) {
	   251215   0.03264   4.40000	 79: debug("word: $i_wd ".($has ? 'matches' :
	   251215   0.02693   4.10000	 81: return $has;
	   260643   0.02841   4.07000	128: if ( $debug ) {
	   260643   0.02601   4.04000	126: my $message = shift;
	   251215   0.02641   3.91000	 73: my $has = 0;
	   251215   0.03311   3.71000	 70: my $i_wd  = shift;
	   251215   0.02699   3.69000	 72: my $regex = shift;
	   251215   0.02766   3.68000	 71: my $word  = shift;
	    50243   0.59726   1.00000	 59:  $count{$i_LINES}{cons} =
	    50243   0.48175   0.92000	 61:  $count{$i_LINES}{spec} =
	    50243   0.00644   0.89000	 56:  my $i_cons = matches($i_word, $word,
	    50243   0.48837   0.88000	 63:  $count{$i_LINES}{caps} =
	    50243   0.00516   0.88000	 58:  my $i_caps = matches($i_word, $word, '^[(A-
	    50243   0.00631   0.81000	 54:  my $i_spec = matches($i_word, $word, '[^a-
	    50243   0.00496   0.80000	 57:  my $i_vows = matches($i_word, $word,
	    50243   0.00688   0.80000	 53:  $i_word++;
	    50243   0.48469   0.79000	 62:  $count{$i_LINES}{only} =
	    50243   0.48928   0.77000	 60:  $count{$i_LINES}{vows} =
	    50243   0.00683   0.75000	 55:  my $i_only = matches($i_word, $word, '^[^a-

       You can immediately see a slightly different focus to the subroutine
       profiling modules, and we start to see exactly which line of code is
       taking the most time.  That regex line is looking a bit suspicious, for
       example.	 Remember that these tools are supposed to be used together,
       there is no single best way to profile your code, you need to use the
       best tools for the job.

       See also "Apache::SmallProf" which hooks "Devel::SmallProf" into
       "mod_perl".

   Devel::FastProf
       "Devel::FastProf" is another Perl line profiler.	 This was written with
       a view to getting a faster line profiler, than is possible with for
       example "Devel::SmallProf", because it's written in "C".	 To use
       "Devel::FastProf", supply the "-d" argument to Perl:

	   $> perl -d:FastProf wordmatch -f perl5db.pl

	   <...multiple lines snipped...>

	   wordmatch report for perl5db.pl:
	   lines in file: 9428
	   words in file: 50243
	   words with special (non-word) characters: 20480
	   words with only special (non-word) characters: 7790
	   words with only consonants: 4801
	   words with only capital letters: 1316
	   words with only vowels: 1701

       "Devel::FastProf" writes statistics to the file fastprof.out in the
       current directory.  The output file, which can be specified, can be
       interpreted by using the "fprofpp" command-line program.

	   $> fprofpp | head -n20

	   # fprofpp output format is:
	   # filename:line time count: source
	   wordmatch:75 3.93338 251215: if ( $word =~ /($regex)/ ) {
	   wordmatch:79 1.77774 251215: debug("word: $i_wd ".($has ? 'matches' : 'does not match')." chars: /$regex/");
	   wordmatch:81 1.47604 251215: return $has;
	   wordmatch:126 1.43441 260643: my $message = shift;
	   wordmatch:128 1.42156 260643: if ( $debug ) {
	   wordmatch:70 1.36824 251215: my $i_wd  = shift;
	   wordmatch:71 1.36739 251215: my $word  = shift;
	   wordmatch:72 1.35939 251215: my $regex = shift;

       Straightaway we can see that the number of times each line has been
       called is identical to the "Devel::SmallProf" output, and the sequence
       is only very slightly different based on the ordering of the amount of
       time each line took to execute, "if ( $debug ) { " and "my $message =
       shift;", for example.  The differences in the actual times recorded
       might be in the algorithm used internally, or it could be due to system
       resource limitations or contention.

       See also the DBIx::Profiler which will profile database queries running
       under the "DBIx::*" namespace.

   Devel::NYTProf
       "Devel::NYTProf" is the next generation of Perl code profiler, fixing
       many shortcomings in other tools and implementing many cool features.
       First of all it can be used as either a line profiler, a block or a
       subroutine profiler, all at once.  It can also use sub-microsecond
       (100ns) resolution on systems which provide "clock_gettime()".  It can
       be started and stopped even by the program being profiled.  It's a one-
       line entry to profile "mod_perl" applications.  It's written in "c" and
       is probably the fastest profiler available for Perl.  The list of
       coolness just goes on.  Enough of that, let's see how to it works -
       just use the familiar "-d" switch to plug it in and run the code.

	   $> perl -d:NYTProf wordmatch -f perl5db.pl

	   wordmatch report for perl5db.pl:
	   lines in file: 9427
	   words in file: 50243
	   words with special (non-word) characters: 20480
	   words with only special (non-word) characters: 7790
	   words with only consonants: 4801
	   words with only capital letters: 1316
	   words with only vowels: 1701

       "NYTProf" will generate a report database into the file nytprof.out by
       default.	 Human readable reports can be generated from here by using
       the supplied "nytprofhtml" (HTML output) and "nytprofcsv" (CSV output)
       programs.  We've used the unix sytem "html2text" utility to convert the
       nytprof/index.html file for convenience here.

	   $> html2text nytprof/index.html

	   Performance Profile Index
	   For wordmatch
	     Run on Fri Sep 26 13:46:39 2008
	   Reported on Fri Sep 26 13:47:23 2008

		    Top 15 Subroutines -- ordered by exclusive time
	   |Calls |P |F |Inclusive|Exclusive|Subroutine				 |
	   |	  |  |	|Time	  |Time	    |					 |
	   |251215|5 |1 |13.09263 |10.47692 |main::		 |matches	 |
	   |260642|2 |1 |2.71199  |2.71199  |main::		 |debug		 |
	   |1	  |1 |1 |0.21404  |0.21404  |main::		 |report	 |
	   |2	  |2 |2 |0.00511  |0.00511  |XSLoader::		 |load (xsub)	 |
	   |14	  |14|7 |0.00304  |0.00298  |Exporter::		 |import	 |
	   |3	  |1 |1 |0.00265  |0.00254  |Exporter::		 |as_heavy	 |
	   |10	  |10|4 |0.00140  |0.00140  |vars::		 |import	 |
	   |13	  |13|1 |0.00129  |0.00109  |constant::		 |import	 |
	   |1	  |1 |1 |0.00360  |0.00096  |FileHandle::	 |import	 |
	   |3	  |3 |3 |0.00086  |0.00074  |warnings::register::|import	 |
	   |9	  |3 |1 |0.00036  |0.00036  |strict::		 |bits		 |
	   |13	  |13|13|0.00032  |0.00029  |strict::		 |import	 |
	   |2	  |2 |2 |0.00020  |0.00020  |warnings::		 |import	 |
	   |2	  |1 |1 |0.00020  |0.00020  |Getopt::Long::	 |ParseOptionSpec|
	   |7	  |7 |6 |0.00043  |0.00020  |strict::		 |unimport	 |

	   For more information see the full list of 189 subroutines.

       The first part of the report already shows the critical information
       regarding which subroutines are using the most time.  The next gives
       some statistics about the source files profiled.

		   Source Code Files -- ordered by exclusive time then name
	   |Stmts  |Exclusive|Avg.   |Reports			  |Source File	       |
	   |	   |Time     |	     |				  |		       |
	   |2699761|15.66654 |6e-06  |line   .	  block	  .    sub|wordmatch	       |
	   |35	   |0.02187  |0.00062|line   .	  block	  .    sub|IO/Handle.pm	       |
	   |274	   |0.01525  |0.00006|line   .	  block	  .    sub|Getopt/Long.pm      |
	   |20	   |0.00585  |0.00029|line   .	  block	  .    sub|Fcntl.pm	       |
	   |128	   |0.00340  |0.00003|line   .	  block	  .    sub|Exporter/Heavy.pm   |
	   |42	   |0.00332  |0.00008|line   .	  block	  .    sub|IO/File.pm	       |
	   |261	   |0.00308  |0.00001|line   .	  block	  .    sub|Exporter.pm	       |
	   |323	   |0.00248  |8e-06  |line   .	  block	  .    sub|constant.pm	       |
	   |12	   |0.00246  |0.00021|line   .	  block	  .    sub|File/Spec/Unix.pm   |
	   |191	   |0.00240  |0.00001|line   .	  block	  .    sub|vars.pm	       |
	   |77	   |0.00201  |0.00003|line   .	  block	  .    sub|FileHandle.pm       |
	   |12	   |0.00198  |0.00016|line   .	  block	  .    sub|Carp.pm	       |
	   |14	   |0.00175  |0.00013|line   .	  block	  .    sub|Symbol.pm	       |
	   |15	   |0.00130  |0.00009|line   .	  block	  .    sub|IO.pm	       |
	   |22	   |0.00120  |0.00005|line   .	  block	  .    sub|IO/Seekable.pm      |
	   |198	   |0.00085  |4e-06  |line   .	  block	  .    sub|warnings/register.pm|
	   |114	   |0.00080  |7e-06  |line   .	  block	  .    sub|strict.pm	       |
	   |47	   |0.00068  |0.00001|line   .	  block	  .    sub|warnings.pm	       |
	   |27	   |0.00054  |0.00002|line   .	  block	  .    sub|overload.pm	       |
	   |9	   |0.00047  |0.00005|line   .	  block	  .    sub|SelectSaver.pm      |
	   |13	   |0.00045  |0.00003|line   .	  block	  .    sub|File/Spec.pm	       |
	   |2701595|15.73869 |	     |Total			  |
	   |128647 |0.74946  |	     |Average			  |
	   |	   |0.00201  |0.00003|Median			  |
	   |	   |0.00121  |0.00003|Deviation			  |

	   Report produced by the NYTProf 2.03 Perl profiler, developed by Tim Bunce and
	   Adam Kaplan.

       At this point, if you're using the html report, you can click through
       the various links to bore down into each subroutine and each line of
       code.  Because we're using the text reporting here, and there's a whole
       directory full of reports built for each source file, we'll just
       display a part of the corresponding wordmatch-line.html file,
       sufficient to give an idea of the sort of output you can expect from
       this cool tool.

	   $> html2text nytprof/wordmatch-line.html

	   Performance Profile -- -block view-.-line view-.-sub view-
	   For wordmatch
	   Run on Fri Sep 26 13:46:39 2008
	   Reported on Fri Sep 26 13:47:22 2008

	   File wordmatch

	    Subroutines -- ordered by exclusive time
	   |Calls |P|F|Inclusive|Exclusive|Subroutine	 |
	   |	  | | |Time	|Time	  |		 |
	   |251215|5|1|13.09263 |10.47692 |main::|matches|
	   |260642|2|1|2.71199	|2.71199  |main::|debug	 |
	   |1	  |1|1|0.21404	|0.21404  |main::|report |
	   |0	  |0|0|0	|0	  |main::|BEGIN	 |

	   |Line|Stmts.|Exclusive|Avg.	 |Code						 |
	   |	|      |Time	 |	 |						 |
	   |1	|      |	 |	 |#!/usr/bin/perl				 |
	   |2	|      |	 |	 |						 |
	   |	|      |	 |	 |use strict;					 |
	   |3	|3     |0.00086	 |0.00029|# spent 0.00003s making 1 calls to strict::	 |
	   |	|      |	 |	 |import					 |
	   |	|      |	 |	 |use warnings;					 |
	   |4	|3     |0.01563	 |0.00521|# spent 0.00012s making 1 calls to warnings::	 |
	   |	|      |	 |	 |import					 |
	   |5	|      |	 |	 |						 |
	   |6	|      |	 |	 |=head1 NAME					 |
	   |7	|      |	 |	 |						 |
	   |8	|      |	 |	 |filewords - word analysis of input file	 |
	   <...snip...>
	   |62	|1     |0.00445	 |0.00445|print report( %count );			 |
	   |	|      |	 |	 |# spent 0.21404s making 1 calls to main::report|
	   |63	|      |	 |	 |						 |
	   |	|      |	 |	 |# spent 23.56955s (10.47692+2.61571) within	 |
	   |	|      |	 |	 |main::matches which was called 251215 times,	 |
	   |	|      |	 |	 |avg 0.00005s/call: # 50243 times		 |
	   |	|      |	 |	 |(2.12134+0.51939s) at line 57 of wordmatch, avg|
	   |	|      |	 |	 |0.00005s/call # 50243 times (2.17735+0.54550s) |
	   |64	|      |	 |	 |at line 56 of wordmatch, avg 0.00005s/call #	 |
	   |	|      |	 |	 |50243 times (2.10992+0.51797s) at line 58 of	 |
	   |	|      |	 |	 |wordmatch, avg 0.00005s/call # 50243 times	 |
	   |	|      |	 |	 |(2.12696+0.51598s) at line 55 of wordmatch, avg|
	   |	|      |	 |	 |0.00005s/call # 50243 times (1.94134+0.51687s) |
	   |	|      |	 |	 |at line 54 of wordmatch, avg 0.00005s/call	 |
	   |	|      |	 |	 |sub matches {					 |
	   <...snip...>
	   |102 |      |	 |	 |						 |
	   |	|      |	 |	 |# spent 2.71199s within main::debug which was	 |
	   |	|      |	 |	 |called 260642 times, avg 0.00001s/call: #	 |
	   |	|      |	 |	 |251215 times (2.61571+0s) by main::matches at	 |
	   |103 |      |	 |	 |line 74 of wordmatch, avg 0.00001s/call # 9427 |
	   |	|      |	 |	 |times (0.09628+0s) at line 50 of wordmatch, avg|
	   |	|      |	 |	 |0.00001s/call					 |
	   |	|      |	 |	 |sub debug {					 |
	   |104 |260642|0.58496	 |2e-06	 |my $message = shift;				 |
	   |105 |      |	 |	 |						 |
	   |106 |260642|1.09917	 |4e-06	 |if ( $debug ) {				 |
	   |107 |      |	 |	 |print STDERR "DBG: $message\n";		 |
	   |108 |      |	 |	 |}						 |
	   |109 |      |	 |	 |}						 |
	   |110 |      |	 |	 |						 |
	   |111 |1     |0.01501	 |0.01501|exit 0;					 |
	   |112 |      |	 |	 |						 |

       Oodles of very useful information in there - this seems to be the way
       forward.

       See also "Devel::NYTProf::Apache" which hooks "Devel::NYTProf" into
       "mod_perl".

SORTING
       Perl modules are not the only tools a performance analyst has at their
       disposal, system tools like "time" should not be overlooked as the next
       example shows, where we take a quick look at sorting.  Many books,
       theses and articles, have been written about efficient sorting
       algorithms, and this is not the place to repeat such work, there's
       several good sorting modules which deserve taking a look at too:
       "Sort::Maker", "Sort::Key" spring to mind.  However, it's still
       possible to make some observations on certain Perl specific
       interpretations on issues relating to sorting data sets and give an
       example or two with regard to how sorting large data volumes can effect
       performance.  Firstly, an often overlooked point when sorting large
       amounts of data, one can attempt to reduce the data set to be dealt
       with and in many cases "grep()" can be quite useful as a simple filter:

	   @data = sort grep { /$filter/ } @incoming

       A command such as this can vastly reduce the volume of material to
       actually sort through in the first place, and should not be too lightly
       disregarded purely on the basis of it's simplicity.  The "KISS"
       principle is too often overlooked - the next example uses the simple
       system "time" utility to demonstrate.  Let's take a look at an actual
       example of sorting the contents of a large file, an apache logfile
       would do.  This one has over a quarter of a million lines, is 50M in
       size, and a snippet of it looks like this:

       # logfile

	   188.209-65-87.adsl-dyn.isp.belgacom.be - - [08/Feb/2007:12:57:16 +0000] "GET /favicon.ico HTTP/1.1" 404 209 "-" "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1)"
	   188.209-65-87.adsl-dyn.isp.belgacom.be - - [08/Feb/2007:12:57:16 +0000] "GET /favicon.ico HTTP/1.1" 404 209 "-" "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1)"
	   151.56.71.198 - - [08/Feb/2007:12:57:41 +0000] "GET /suse-on-vaio.html HTTP/1.1" 200 2858 "http://www.linux-on-laptops.com/sony.html" "Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US; rv:1.8.1.1) Gecko/20061204 Firefox/2.0.0.1"
	   151.56.71.198 - - [08/Feb/2007:12:57:42 +0000] "GET /data/css HTTP/1.1" 404 206 "http://www.rfi.net/suse-on-vaio.html" "Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US; rv:1.8.1.1) Gecko/20061204 Firefox/2.0.0.1"
	   151.56.71.198 - - [08/Feb/2007:12:57:43 +0000] "GET /favicon.ico HTTP/1.1" 404 209 "-" "Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US; rv:1.8.1.1) Gecko/20061204 Firefox/2.0.0.1"
	   217.113.68.60 - - [08/Feb/2007:13:02:15 +0000] "GET / HTTP/1.1" 304 - "-" "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1)"
	   217.113.68.60 - - [08/Feb/2007:13:02:16 +0000] "GET /data/css HTTP/1.1" 404 206 "http://www.rfi.net/" "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1)"
	   debora.to.isac.cnr.it - - [08/Feb/2007:13:03:58 +0000] "GET /suse-on-vaio.html HTTP/1.1" 200 2858 "http://www.linux-on-laptops.com/sony.html" "Mozilla/5.0 (compatible; Konqueror/3.4; Linux) KHTML/3.4.0 (like Gecko)"
	   debora.to.isac.cnr.it - - [08/Feb/2007:13:03:58 +0000] "GET /data/css HTTP/1.1" 404 206 "http://www.rfi.net/suse-on-vaio.html" "Mozilla/5.0 (compatible; Konqueror/3.4; Linux) KHTML/3.4.0 (like Gecko)"
	   debora.to.isac.cnr.it - - [08/Feb/2007:13:03:58 +0000] "GET /favicon.ico HTTP/1.1" 404 209 "-" "Mozilla/5.0 (compatible; Konqueror/3.4; Linux) KHTML/3.4.0 (like Gecko)"
	   195.24.196.99 - - [08/Feb/2007:13:26:48 +0000] "GET / HTTP/1.0" 200 3309 "-" "Mozilla/5.0 (Windows; U; Windows NT 5.1; fr; rv:1.8.0.9) Gecko/20061206 Firefox/1.5.0.9"
	   195.24.196.99 - - [08/Feb/2007:13:26:58 +0000] "GET /data/css HTTP/1.0" 404 206 "http://www.rfi.net/" "Mozilla/5.0 (Windows; U; Windows NT 5.1; fr; rv:1.8.0.9) Gecko/20061206 Firefox/1.5.0.9"
	   195.24.196.99 - - [08/Feb/2007:13:26:59 +0000] "GET /favicon.ico HTTP/1.0" 404 209 "-" "Mozilla/5.0 (Windows; U; Windows NT 5.1; fr; rv:1.8.0.9) Gecko/20061206 Firefox/1.5.0.9"
	   crawl1.cosmixcorp.com - - [08/Feb/2007:13:27:57 +0000] "GET /robots.txt HTTP/1.0" 200 179 "-" "voyager/1.0"
	   crawl1.cosmixcorp.com - - [08/Feb/2007:13:28:25 +0000] "GET /links.html HTTP/1.0" 200 3413 "-" "voyager/1.0"
	   fhm226.internetdsl.tpnet.pl - - [08/Feb/2007:13:37:32 +0000] "GET /suse-on-vaio.html HTTP/1.1" 200 2858 "http://www.linux-on-laptops.com/sony.html" "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1)"
	   fhm226.internetdsl.tpnet.pl - - [08/Feb/2007:13:37:34 +0000] "GET /data/css HTTP/1.1" 404 206 "http://www.rfi.net/suse-on-vaio.html" "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1)"
	   80.247.140.134 - - [08/Feb/2007:13:57:35 +0000] "GET / HTTP/1.1" 200 3309 "-" "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; .NET CLR 1.1.4322)"
	   80.247.140.134 - - [08/Feb/2007:13:57:37 +0000] "GET /data/css HTTP/1.1" 404 206 "http://www.rfi.net" "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; .NET CLR 1.1.4322)"
	   pop.compuscan.co.za - - [08/Feb/2007:14:10:43 +0000] "GET / HTTP/1.1" 200 3309 "-" "www.clamav.net"
	   livebot-207-46-98-57.search.live.com - - [08/Feb/2007:14:12:04 +0000] "GET /robots.txt HTTP/1.0" 200 179 "-" "msnbot/1.0 (+http://search.msn.com/msnbot.htm)"
	   livebot-207-46-98-57.search.live.com - - [08/Feb/2007:14:12:04 +0000] "GET /html/oracle.html HTTP/1.0" 404 214 "-" "msnbot/1.0 (+http://search.msn.com/msnbot.htm)"
	   dslb-088-064-005-154.pools.arcor-ip.net - - [08/Feb/2007:14:12:15 +0000] "GET / HTTP/1.1" 200 3309 "-" "www.clamav.net"
	   196.201.92.41 - - [08/Feb/2007:14:15:01 +0000] "GET / HTTP/1.1" 200 3309 "-" "MOT-L7/08.B7.DCR MIB/2.2.1 Profile/MIDP-2.0 Configuration/CLDC-1.1"

       The specific task here is to sort the 286,525 lines of this file by
       Response Code, Query, Browser, Referring Url, and lastly Date.  One
       solution might be to use the following code, which iterates over the
       files given on the command-line.

       # sort-apache-log

	   #!/usr/bin/perl -n

	   use strict;
	   use warnings;

	   my @data;

	   LINE:
	   while ( <> ) {
	       my $line = $_;
	       if (
		   $line =~ m/^(
		       ([\w\.\-]+)	       # client
		       \s*-\s*-\s*\[
		       ([^]]+)		       # date
		       \]\s*"\w+\s*
		       (\S+)		       # query
		       [^"]+"\s*
		       (\d+)		       # status
		       \s+\S+\s+"[^"]*"\s+"
		       ([^"]*)		       # browser
		       "
		       .*
		   )$/x
	       ) {
		   my @chunks = split(/ +/, $line);
		   my $ip      = $1;
		   my $date    = $2;
		   my $query   = $3;
		   my $status  = $4;
		   my $browser = $5;

		   push(@data, [$ip, $date, $query, $status, $browser, $line]);
	       }
	   }

	   my @sorted = sort {
	       $a->[3] cmp $b->[3]
		       ||
	       $a->[2] cmp $b->[2]
		       ||
	       $a->[0] cmp $b->[0]
		       ||
	       $a->[1] cmp $b->[1]
		       ||
	       $a->[4] cmp $b->[4]
	   } @data;

	   foreach my $data ( @sorted ) {
	       print $data->[5];
	   }

	   exit 0;

       When running this program, redirect "STDOUT" so it is possible to check
       the output is correct from following test runs and use the system
       "time" utility to check the overall runtime.

	   $> time ./sort-apache-log logfile > out-sort

	   real	   0m17.371s
	   user	   0m15.757s
	   sys	   0m0.592s

       The program took just over 17 wallclock seconds to run.	Note the
       different values "time" outputs, it's important to always use the same
       one, and to not confuse what each one means.

       Elapsed Real Time
	   The overall, or wallclock, time between when "time" was called, and
	   when it terminates.	The elapsed time includes both user and system
	   times, and time spent waiting for other users and processes on the
	   system.  Inevitably, this is the most approximate of the
	   measurements given.

       User CPU Time
	   The user time is the amount of time the entire process spent on
	   behalf of the user on this system executing this program.

       System CPU Time
	   The system time is the amount of time the kernel itself spent
	   executing routines, or system calls, on behalf of this process
	   user.

       Running this same process as a "Schwarzian Transform" it is possible to
       eliminate the input and output arrays for storing all the data, and
       work on the input directly as it arrives too.  Otherwise, the code
       looks fairly similar:

       # sort-apache-log-schwarzian

	   #!/usr/bin/perl -n

	   use strict;
	   use warnings;

	   print

	       map $_->[0] =>

	       sort {
		   $a->[4] cmp $b->[4]
			   ||
		   $a->[3] cmp $b->[3]
			   ||
		   $a->[1] cmp $b->[1]
			   ||
		   $a->[2] cmp $b->[2]
			   ||
		   $a->[5] cmp $b->[5]
	       }
	       map  [ $_, m/^(
		   ([\w\.\-]+)		   # client
		   \s*-\s*-\s*\[
		   ([^]]+)		   # date
		   \]\s*"\w+\s*
		   (\S+)		   # query
		   [^"]+"\s*
		   (\d+)		   # status
		   \s+\S+\s+"[^"]*"\s+"
		   ([^"]*)		   # browser
		   "
		   .*
	       )$/xo ]

	       => <>;

	   exit 0;

       Run the new code against the same logfile, as above, to check the new
       time.

	   $> time ./sort-apache-log-schwarzian logfile > out-schwarz

	   real	   0m9.664s
	   user	   0m8.873s
	   sys	   0m0.704s

       The time has been cut in half, which is a respectable speed improvement
       by any standard.	 Naturally, it is important to check the output is
       consistent with the first program run, this is where the unix system
       "cksum" utility comes in.

	   $> cksum out-sort out-schwarz
	   3044173777 52029194 out-sort
	   3044173777 52029194 out-schwarz

       BTW. Beware too of pressure from managers who see you speed a program
       up by 50% of the runtime once, only to get a request one month later to
       do the same again (true story) - you'll just have to point out your
       only human, even if you are a Perl programmer, and you'll see what you
       can do...

LOGGING
       An essential part of any good development process is appropriate error
       handling with appropriately informative messages, however there exists
       a school of thought which suggests that log files should be chatty, as
       if the chain of unbroken output somehow ensures the survival of the
       program.	 If speed is in any way an issue, this approach is wrong.

       A common sight is code which looks something like this:

	   logger->debug( "A logging message via process-id: $$ INC: " . Dumper(\%INC) )

       The problem is that this code will always be parsed and executed, even
       when the debug level set in the logging configuration file is zero.
       Once the debug() subroutine has been entered, and the internal $debug
       variable confirmed to be zero, for example, the message which has been
       sent in will be discarded and the program will continue.	 In the
       example given though, the \%INC hash will already have been dumped, and
       the message string constructed, all of which work could be bypassed by
       a debug variable at the statement level, like this:

	   logger->debug( "A logging message via process-id: $$ INC: " . Dumper(\%INC) ) if $DEBUG;

       This effect can be demonstrated by setting up a test script with both
       forms, including a "debug()" subroutine to emulate typical "logger()"
       functionality.

       # ifdebug

	   #!/usr/bin/perl

	   use strict;
	   use warnings;

	   use Benchmark;
	   use Data::Dumper;
	   my $DEBUG = 0;

	   sub debug {
	       my $msg = shift;

	       if ( $DEBUG ) {
		   print "DEBUG: $msg\n";
	       }
	   };

	   timethese(100000, {
		   'debug'	 => sub {
		       debug( "A $0 logging message via process-id: $$" . Dumper(\%INC) )
		   },
		   'ifdebug'  => sub {
		       debug( "A $0 logging message via process-id: $$" . Dumper(\%INC) ) if $DEBUG
		   },
	   });

       Let's see what "Benchmark" makes of this:

	   $> perl ifdebug
	   Benchmark: timing 100000 iterations of constant, sub...
	      ifdebug:	0 wallclock secs ( 0.01 usr +  0.00 sys =  0.01 CPU) @ 10000000.00/s (n=100000)
		       (warning: too few iterations for a reliable count)
		debug: 14 wallclock secs (13.18 usr +  0.04 sys = 13.22 CPU) @ 7564.30/s (n=100000)

       In the one case the code, which does exactly the same thing as far as
       outputting any debugging information is concerned, in other words
       nothing, takes 14 seconds, and in the other case the code takes one
       hundredth of a second.  Looks fairly definitive.	 Use a $DEBUG variable
       BEFORE you call the subroutine, rather than relying on the smart
       functionality inside it.

   Logging if DEBUG (constant)
       It's possible to take the previous idea a little further, by using a
       compile time "DEBUG" constant.

       # ifdebug-constant

	   #!/usr/bin/perl

	   use strict;
	   use warnings;

	   use Benchmark;
	   use Data::Dumper;
	   use constant
	       DEBUG => 0
	   ;

	   sub debug {
	       if ( DEBUG ) {
		   my $msg = shift;
		   print "DEBUG: $msg\n";
	       }
	   };

	   timethese(100000, {
		   'debug'	 => sub {
		       debug( "A $0 logging message via process-id: $$" . Dumper(\%INC) )
		   },
		   'constant'  => sub {
		       debug( "A $0 logging message via process-id: $$" . Dumper(\%INC) ) if DEBUG
		   },
	   });

       Running this program produces the following output:

	   $> perl ifdebug-constant
	   Benchmark: timing 100000 iterations of constant, sub...
	     constant:	0 wallclock secs (-0.00 usr +  0.00 sys = -0.00 CPU) @ -7205759403792793600000.00/s (n=100000)
		       (warning: too few iterations for a reliable count)
		  sub: 14 wallclock secs (13.09 usr +  0.00 sys = 13.09 CPU) @ 7639.42/s (n=100000)

       The "DEBUG" constant wipes the floor with even the $debug variable,
       clocking in at minus zero seconds, and generates a "warning: too few
       iterations for a reliable count" message into the bargain.  To see what
       is really going on, and why we had too few iterations when we thought
       we asked for 100000, we can use the very useful "B::Deparse" to inspect
       the new code:

	   $> perl -MO=Deparse ifdebug-constant

	   use Benchmark;
	   use Data::Dumper;
	   use constant ('DEBUG', 0);
	   sub debug {
	       use warnings;
	       use strict 'refs';
	       0;
	   }
	   use warnings;
	   use strict 'refs';
	   timethese(100000, {'sub', sub {
	       debug "A $0 logging message via process-id: $$" . Dumper(\%INC);
	   }
	   , 'constant', sub {
	       0;
	   }
	   });
	   ifdebug-constant syntax OK

       The output shows the constant() subroutine we're testing being replaced
       with the value of the "DEBUG" constant: zero.  The line to be tested
       has been completely optimized away, and you can't get much more
       efficient than that.

POSTSCRIPT
       This document has provided several way to go about identifying hot-
       spots, and checking whether any modifications have improved the runtime
       of the code.

       As a final thought, remember that it's not (at the time of writing)
       possible to produce a useful program which will run in zero or negative
       time and this basic principle can be written as: useful programs are
       slow by their very definition.  It is of course possible to write a
       nearly instantaneous program, but it's not going to do very much,
       here's a very efficient one:

	   $> perl -e 0

       Optimizing that any further is a job for "p5p".

SEE ALSO
       Further reading can be found using the modules and links below.

   PERLDOCS
       For example: "perldoc -f sort".

       perlfaq4.

       perlfork, perlfunc, perlretut, perlthrtut.

       threads.

   MAN PAGES
       "time".

   MODULES
       It's not possible to individually showcase all the performance related
       code for Perl here, naturally, but here's a short list of modules from
       the CPAN which deserve further attention.

	   Apache::DProf
	   Apache::SmallProf
	   Benchmark
	   DBIx::Profiler
	   Devel::AutoProfiler
	   Devel::DProf
	   Devel::DProfLB
	   Devel::FastProf
	   Devel::GraphVizProf
	   Devel::NYTProf
	   Devel::NYTProf::Apache
	   Devel::Profiler
	   Devel::Profile
	   Devel::Profit
	   Devel::SmallProf
	   Devel::WxProf
	   POE::Devel::Profiler
	   Sort::Key
	   Sort::Maker

   URLS
       Very useful online reference material:

	   http://www.ccl4.org/~nick/P/Fast_Enough/

	   http://www-128.ibm.com/developerworks/library/l-optperl.html

	   http://perlbuzz.com/2007/11/bind-output-variables-in-dbi-for-speed-and-safety.html

	   http://en.wikipedia.org/wiki/Performance_analysis

	   http://apache.perl.org/docs/1.0/guide/performance.html

	   http://perlgolf.sourceforge.net/

	   http://www.sysarch.com/Perl/sort_paper.html

	   http://www.unix.org.ua/orelly/perl/prog/ch08_03.htm

AUTHOR
       Richard Foley <richard.foley@rfi.net> Copyright (c) 2008

perl v5.10.1			  2009-05-14			   PERLPERF(1)
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