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=head1 NAME perlperf - Perl Performance and Optimization Techniques =head1 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. =head1 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 I<correctly> do something B<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. =head2 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 C<Benchmark> or C<Devel::NYTProf> modules, or something similar, for this step, or perhaps the Unix system C<time> utility, whichever is appropriate. See the base of this document for a longer list of benchmarking and profiling modules, and recommended further reading. =head2 ONE STEP FORWARD Next, having examined the program for I<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 C<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 B<really> was the slow bit. =head2 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 B<same task> in I<less time>. Save your work and repeat... =head1 GENERAL GUIDELINES The critical thing when considering performance is to remember there is no such thing as a C<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 I<alphabetic> C<cmp> and I<numeric> C<E<lt>=E<gt>> 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 C<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 C<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<c> and fast, perl C<sort()> function on the output, and is the basis of the C<GRT> (Guttman Rossler Transform). Some string combinations can slow the C<GRT> down, by just being too plain complex for its own good. For applications using database backends, the standard C<DBIx> namespace has tries to help with keeping things nippy, not least because it tries to I<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 C<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 C<POE>, C<threads> or C<fork> can also be a useful way of optimizing your usage of the available C<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. =head1 BENCHMARKS Here are a few examples to demonstrate usage of Perl's benchmarking tools. =head2 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 C<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 C<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 C<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, minusculy faster. The question remains whether the minute gain is actually worth the eyestrain, or the loss of maintainability. =head2 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, C<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 C<tr> alternative might look like this: $str =~ tr/aeiou/xxxxx/ We can put that into a test file which we can run to check which approach is the fastest, using a global C<$STR> variable to assign to the C<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 C<tr> version is a clear winner. One solution is flexible, the other is fast - and it's appropriately the programmer's choice which to use. Check the C<Benchmark> docs for further useful techniques. =head1 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 C<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; =head2 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 L<Devel::NYTProf> seems to be the weapon of choice - see below), we'll take a quick look at the output from L<Devel::DProf> first, to set a baseline for Perl profiling tools. Run the above program under the control of C<Devel::DProf> by using the C<-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 C<Devel::DProf> produces a special file, called F<tmon.out> by default, and this file is read by the C<dprofpp> program, which is already installed as part of the C<Devel::DProf> distribution. If you call C<dprofpp> with no options, it will read the F<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 C<dprofpp> will produce some quite detailed reporting on the activity of the C<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 C<dprofpp> docs for details of the many options it supports. See also C<L<Apache::DProf>> which hooks C<Devel::DProf> into C<mod_perl>. =head2 Devel::Profiler Let's take a look at the same program using a different profiler: C<Devel::Profiler>, a drop-in Perl-only replacement for C<Devel::DProf>. The usage is very slightly different in that instead of using the special C<-d:> flag, you pull C<Devel::Profiler> in directly as a module using C<-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 C<Devel::Profiler> generates a tmon.out file which is compatible with the C<dprofpp> program, thus saving the construction of a dedicated statistics reader program. C<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 C<Devel::Profiler> to execute its own run, but the column listings feel more accurate somehow than the ones we had earlier from C<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 C<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 C<L<Devel::Apache::Profiler>> which hooks C<Devel::Profiler> into C<mod_perl>. =head2 Devel::SmallProf The C<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 C<-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 C<Devel::SmallProf> writes it's output into a file called F<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 C<L<Apache::SmallProf>> which hooks C<Devel::SmallProf> into C<mod_perl>. =head2 Devel::FastProf C<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 C<Devel::SmallProf>, because it's written in C<C>. To use C<Devel::FastProf>, supply the C<-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 C<Devel::FastProf> writes statistics to the file F<fastprof.out> in the current directory. The output file, which can be specified, can be interpreted by using the C<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 C<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, C<if ( $debug ) { > and C<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 L<DBIx::Profile> which will profile database queries running under the C<DBIx::> namespace. =head2 Devel::NYTProf C<Devel::NYTProf> is the B<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 I<line> profiler, a I<block> or a I<subroutine> profiler, all at once. It can also use sub-microsecond (100ns) resolution on systems which provide C<clock_gettime()>. It can be started and stopped even by the program being profiled. It's a one-line entry to profile C<mod_perl> applications. It's written in C<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 C<-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 C<NYTProf> will generate a report database into the file F<nytprof.out> by default. Human readable reports can be generated from here by using the supplied C<nytprofhtml> (HTML output) and C<nytprofcsv> (CSV output) programs. We've used the Unix system C<html2text> utility to convert the F<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 I<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 F<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: $message\n"; \| \|108 \| \| \| \|} \| \|109 \| \| \| \|} \| \|110 \| \| \| \| \| \|111 \|1 \|0.01501 \|0.01501\|exit 0; \| \|112 \| \| \| \| \| Oodles of very useful information in there - this seems to be the way forward. See also C<L<Devel::NYTProf::Apache>> which hooks C<Devel::NYTProf> into C<mod_perl>. =head1 SORTING Perl modules are not the only tools a performance analyst has at their disposal, system tools like C<time> should not be overlooked as the next example shows, where we take a quick look at sorting. Many books, theses and articles, have been written about efficient sorting algorithms, and this is not the place to repeat such work, there's several good sorting modules which deserve taking a look at too: C<Sort::Maker>, C<Sort::Key> spring to mind. However, it's still possible to make some observations on certain Perl specific interpretations on issues relating to sorting data sets and give an example or two with regard to how sorting large data volumes can effect performance. Firstly, an often overlooked point when sorting large amounts of data, one can attempt to reduce the data set to be dealt with and in many cases C<grep()> can be quite useful as a simple filter: @data = sort grep { /$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 its simplicity. The C<KISS> principle is too often overlooked - the next example uses the simple system C<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 C<STDOUT> so it is possible to check the output is correct from following test runs and use the system C<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 C<time> outputs, it's important to always use the same one, and to not confuse what each one means. =over 4 =item Elapsed Real Time The overall, or wallclock, time between when C<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. =item 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. =item 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. =back Running this same process as a C<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 C<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 you're only human, even if you are a Perl programmer, and you'll see what you can do... =head1 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 I<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: $$ INC: " . Dumper(\%INC) ) The problem is that this code will always be parsed and executed, even when the debug level set in the logging configuration file is zero. Once the debug() subroutine has been entered, and the internal C<$debug> variable confirmed to be zero, for example, the message which has been sent in will be discarded and the program will continue. In the example given though, the C<\%INC> hash will already have been dumped, and the message string constructed, all of which work could be bypassed by a debug variable at the statement level, like this: logger->debug( "A logging message via process-id: $$ INC: " . Dumper(\%INC) ) if $DEBUG; This effect can be demonstrated by setting up a test script with both forms, including a C<debug()> subroutine to emulate typical C<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 C<Benchmark> makes of this: $> perl ifdebug Benchmark: timing 100000 iterations of constant, sub... ifdebug: 0 wallclock secs ( 0.01 usr + 0.00 sys = 0.01 CPU) @ 10000000.00/s (n=100000) (warning: too few iterations for a reliable count) debug: 14 wallclock secs (13.18 usr + 0.04 sys = 13.22 CPU) @ 7564.30/s (n=100000) In the one case the code, which does exactly the same thing as far as outputting any debugging information is concerned, in other words nothing, takes 14 seconds, and in the other case the code takes one hundredth of a second. Looks fairly definitive. Use a C<$DEBUG> variable BEFORE you call the subroutine, rather than relying on the smart functionality inside it. =head2 Logging if DEBUG (constant) It's possible to take the previous idea a little further, by using a compile time C<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: $> perl ifdebug-constant Benchmark: timing 100000 iterations of constant, sub... constant: 0 wallclock secs (-0.00 usr + 0.00 sys = -0.00 CPU) @ -7205759403792793600000.00/s (n=100000) (warning: too few iterations for a reliable count) sub: 14 wallclock secs (13.09 usr + 0.00 sys = 13.09 CPU) @ 7639.42/s (n=100000) The C<DEBUG> constant wipes the floor with even the C<$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 C<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 C<DEBUG> constant: zero. The line to be tested has been completely optimized away, and you can't get much more efficient than that. =head1 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: I<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 C<p5p>. =head1 SEE ALSO Further reading can be found using the modules and links below. =head2 PERLDOCS For example: C<perldoc -f sort>. L<perlfaq4>. L<perlfork>, L<perlfunc>, L<perlretut>, L<perlthrtut>. L<threads>. =head2 MAN PAGES C<time>. =head2 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::Profile 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 =head2 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 =head1 AUTHOR Richard Foley <richard.foley@rfi.net> Copyright (c) 2008 =cut Back

Edit File : perlperf.pod | Size : 48.71 KB

=head1 NAME

perlperf - Perl Performance and Optimization Techniques

=head1 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.

=head1 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 I<correctly> do something B<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.

=head2 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
C<Benchmark> or C<Devel::NYTProf> modules, or something similar, for this step,
or perhaps the Unix system C<time> utility, whichever is appropriate.  See the
base of this document for a longer list of benchmarking and profiling modules,
and recommended further reading.

=head2 ONE STEP FORWARD

Next, having examined the program for I<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 C<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 B<really> was the slow bit.

=head2 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 B<same task> in
I<less time>.  Save your work and repeat...

=head1 GENERAL GUIDELINES

The critical thing when considering performance is to remember there is no such
thing as a C<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 I<alphabetic> C<cmp> and
I<numeric> C<E<lt>=E<gt>> 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 C<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 C<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<c> and fast, perl C<sort()> function on the output,
and is the basis of the C<GRT> (Guttman Rossler Transform).  Some string
combinations can slow the C<GRT> down, by just being too plain complex for its
own good.

For applications using database backends, the standard C<DBIx> namespace has
tries to help with keeping things nippy, not least because it tries to I<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
C<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 C<POE>, C<threads> or C<fork> can also be a
useful way of optimizing your usage of the available C<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.

=head1 BENCHMARKS

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

=head2  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 C<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 C<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 C<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, minusculy faster.  The question remains whether the minute gain is
actually worth the eyestrain, or the loss of maintainability.

=head2  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, C<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 C<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 C<$STR> variable to assign to the C<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 C<tr> version is a clear winner.  One solution is flexible, the other is
fast - and it's appropriately the programmer's choice which to use.

Check the C<Benchmark> docs for further useful techniques.

=head1 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
C<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;

=head2 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 L<Devel::NYTProf> seems to be the
weapon of choice - see below), we'll take a quick look at the output from
L<Devel::DProf> first, to set a baseline for Perl profiling tools.  Run the
above program under the control of C<Devel::DProf> by using the C<-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

C<Devel::DProf> produces a special file, called F<tmon.out> by default, and
this file is read by the C<dprofpp> program, which is already installed as part
of the C<Devel::DProf> distribution.  If you call C<dprofpp> with no options,
it will read the F<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

C<dprofpp> will produce some quite detailed reporting on the activity of the
C<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 C<dprofpp> docs for details of the many options it supports.

See also C<L<Apache::DProf>> which hooks C<Devel::DProf> into C<mod_perl>.

=head2 Devel::Profiler

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

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

<...multiple lines snipped...>

C<Devel::Profiler> generates a tmon.out file which is compatible with the
C<dprofpp> program, thus saving the construction of a dedicated statistics
reader program.  C<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 C<Devel::Profiler> to execute its own run, but
the column listings feel more accurate somehow than the ones we had earlier
from C<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 C<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 C<L<Devel::Apache::Profiler>> which hooks C<Devel::Profiler>
into C<mod_perl>.

=head2 Devel::SmallProf

The C<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
C<-d> flag to Perl at runtime.

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

<...multiple lines snipped...>

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

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 C<L<Apache::SmallProf>> which hooks C<Devel::SmallProf> into
C<mod_perl>.

=head2 Devel::FastProf

C<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
C<Devel::SmallProf>, because it's written in C<C>.  To use C<Devel::FastProf>,
supply the C<-d> argument to Perl:

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

<...multiple lines snipped...>

C<Devel::FastProf> writes statistics to the file F<fastprof.out> in the current
directory.  The output file, which can be specified, can be interpreted by using
the C<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 C<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, C<if ( $debug ) { > and C<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 L<DBIx::Profile> which will profile database queries running
under the C<DBIx::*> namespace.

=head2 Devel::NYTProf

C<Devel::NYTProf> is the B<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 I<line> profiler, a I<block> or a I<subroutine>
profiler, all at once.  It can also use sub-microsecond (100ns) resolution on
systems which provide C<clock_gettime()>.  It can be started and stopped even
by the program being profiled.  It's a one-line entry to profile C<mod_perl>
applications.  It's written in C<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 C<-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

C<NYTProf> will generate a report database into the file F<nytprof.out> by
default.  Human readable reports can be generated from here by using the
supplied C<nytprofhtml> (HTML output) and C<nytprofcsv> (CSV output) programs.
We've used the Unix system C<html2text> utility to convert the
F<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 I<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 F<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 C<L<Devel::NYTProf::Apache>> which hooks C<Devel::NYTProf> into
C<mod_perl>.

=head1  SORTING

Perl modules are not the only tools a performance analyst has at their
disposal, system tools like C<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: C<Sort::Maker>, C<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 C<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 its simplicity.  The C<KISS> principle is too often
overlooked - the next example uses the simple system C<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 C<STDOUT> so it is possible to check the
output is correct from following test runs and use the system C<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 C<time> outputs, it's important to always use the same one, and to not
confuse what each one means.

=over 4

=item Elapsed Real Time

The overall, or wallclock, time between when C<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.

=item 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.

=item 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.

=back

Running this same process as a C<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;

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 C<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 you're only human, even if you are a
Perl programmer, and you'll see what you can do...

=head1 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 I<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 C<$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 C<\%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 C<debug()> subroutine to emulate typical C<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 C<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 C<$DEBUG> variable BEFORE you call the
subroutine, rather than relying on the smart functionality inside it.

=head2  Logging if DEBUG (constant)

It's possible to take the previous idea a little further, by using a compile
time C<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 C<DEBUG> constant wipes the floor with even the C<$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 C<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 C<DEBUG> constant: zero.  The line to be tested has been
completely optimized away, and you can't get much more efficient than that.

=head1 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: I<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 C<p5p>.

=head1 SEE ALSO

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

=head2 PERLDOCS

For example: C<perldoc -f sort>.

L<perlfaq4>.

L<perlfork>, L<perlfunc>, L<perlretut>, L<perlthrtut>.

L<threads>.

=head2 MAN PAGES

C<time>.

=head2 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::Profile
 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

=head2 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

=head1 AUTHOR

=cut

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