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Parallel::Iterator(3)	       User Contributed Perl Documentation	    Parallel::Iterator(3)

NAME
       Parallel::Iterator - Simple parallel execution

VERSION
       This document describes Parallel::Iterator version 1.00

SYNOPSIS
	   use Parallel::Iterator qw( iterate );

	   # A very expensive way to double 100 numbers...

	   my @nums = ( 1 .. 100 );

	   my $iter = iterate( sub {
	       my ( $id, $job ) = @_;
	       return $job * 2;
	   }, \@nums );

	   my @out = ();
	   while ( my ( $index, $value ) = $iter->() ) {
	       $out[$index] = $value;
	   }

DESCRIPTION
       The "map" function applies a user supplied transformation function to each element in a
       list, returning a new list containing the transformed elements.

       This module provides a 'parallel map'. Multiple worker processes are forked so that many
       instances of the transformation function may be executed simultaneously.

       For time consuming operations, particularly operations that spend most of their time
       waiting for I/O, this is a big performance win. It also provides a simple idiom to make
       effective use of multi CPU systems.

       There is, however, a considerable overhead associated with forking, so the example in the
       synopsis (doubling a list of numbers) is not a sensible use of this module.

   Example
       Imagine you have an array of URLs to fetch:

	   my @urls = qw(
	       http://google.com/
	       http://hexten.net/
	       http://search.cpan.org/
	       ... and lots more ...
	   );

       Write a function that retrieves a URL and returns its contents or undef if it can't be
       fetched:

	   sub fetch {
	       my $url = shift;
	       my $resp = $ua->get($url);
	       return unless $resp->is_success;
	       return $resp->content;
	   };

       Now write a function to synthesize a special kind of iterator:

	   sub list_iter {
	       my @ar = @_;
	       my $pos = 0;
	       return sub {
		   return if $pos >= @ar;
		   my @r = ( $pos, $ar[$pos] );  # Note: returns ( index, value )
		   $pos++;
		   return @r;
	       };
	   }

       The returned iterator will return each element of the array in turn and then undef.
       Actually it returns both the index and the value of each element in the array. Because
       multiple instances of the transformation function execute in parallel the results won't
       necessarily come back in order. The array index will later allow us to put completed items
       in the correct place in an output array.

       Get an iterator for the list of URLs:

	   my $url_iter = list_iter( @urls );

       Then wrap it in another iterator which will return the transformed results:

	   my $page_iter = iterate( \&fetch, $url_iter );

       Finally loop over the returned iterator storing results:

	   my @out = ( );
	   while ( my ( $index, $value ) = $page_iter->() ) {
	       $out[$index] = $value;
	   }

       Behind the scenes your program forked into ten (by default) instances of itself and
       executed the page requests in parallel.

   Simpler interfaces
       Having to construct an iterator is a pain so "iterate" is smart enough to do that for you.
       Instead of passing an iterator just pass a reference to the array:

	   my $page_iter = iterate( \&fetch, \@urls );

       If you pass a hash reference the iterator you get back will return key, value pairs:

	   my $some_iter = iterate( \&fetch, \%some_hash );

       If the returned iterator is inconvenient you can get back a hash or array instead:

	   my @done = iterate_as_array( \&fetch, @urls );

	   my %done = iterate_as_hash( \&worker, %jobs );

   How It Works
       The current process is forked once for each worker. Each forked child is connected to the
       parent by a pair of pipes. The child's STDIN, STDOUT and STDERR are unaffected.

       Input values are serialised (using Storable) and passed to the workers.	Completed work
       items are serialised and returned.

   Caveats
       Parallel::Iterator is designed to be simple to use - but the underlying forking of the
       main process can cause mystifying problems unless you have an understanding of what is
       going on behind the scenes.

       Worker execution enviroment

       All code apart from the worker subroutine executes in the parent process as normal. The
       worker executes in a forked instance of the parent process. That means that things like
       this won't work as expected:

	   my %tally = ();
	   my @r = iterate_as_array( sub {
	       my ($id, $name) = @_;
	       $tally{$name}++;       # might not do what you think it does
	       return reverse $name;
	   }, @names );

	   # Now print out the tally...
	   while ( my ( $name, $count ) = each %tally ) {
	       printf("%5d : %s\n", $count, $name);
	   }

       Because the worker is a closure it can see the %tally hash from its enclosing scope; but
       because it's running in a forked clone of the parent process it modifies its own copy of
       %tally rather than the copy for the parent process.

       That means that after the job terminates the %tally in the parent process will be empty.

       In general you should avoid side effects in your worker subroutines.

       Serialization

       Values are serialised using Storable to pass to the worker subroutine and results from the
       worker are again serialised before being passed back. Be careful what your values refer
       to: everything has to be serialised. If there's an indirect way to reach a large object
       graph Storable will find it and performance will suffer.

       To find out how large your serialised values are serialise one of them and check its size:

	   use Storable qw( freeze );
	   my $serialized = freeze $some_obj;
	   print length($serialized), " bytes\n";

       In your tests you may wish to guard against the possibility of a change to the structure
       of your values resulting in a sudden increase in serialized size:

	   ok length(freeze $some_obj) < 1000, "Object too bulky?";

       See the documetation for Storable for other caveats.

       Performance

       Process forking is expensive. Only use Parallel::Iterator in cases where:

       the worker waits for I/O
	   The case of fetching web pages is a good example of this. Fetching a page with
	   LWP::UserAgent may take as long as a few seconds but probably consumes only a few
	   milliseconds of processor time. Running many requests in parallel is a huge win - but
	   be kind to the server you're talking to: don't launch a lot of parallel requests
	   unless it's your server or you know it can handle the load.

       the worker is CPU intensive and you have multiple cores / CPUs
	   If the worker is doing an expensive calculation you can parallelise that across
	   multiple CPU cores. Benchmark first though. There's a considerable overhead associated
	   with Parallel::Iterator; unless your calculations are time consuming that overhead
	   will dwarf whatever time they take.

INTERFACE
   "iterate( [ $options ], $worker, $iterator )"
       Get an iterator that applies the supplied transformation function to each value returned
       by the input iterator.

       Instead of an iterator you may pass an array or hash reference and "iterate" will convert
       it internally into a suitable iterator.

       If you are doing this you may wish to investigate "iterate_as_hash" and
       "iterate_as_array".

       Options

       A reference to a hash of options may be supplied as the first argument.	The following
       options are supported:

       "workers"
	   The number of concurrent processes to launch. Set this to 0 to disable forking.
	   Defaults to 10 on systems that support fork and 0 (disable forking) on those that do
	   not.

       "nowarn"
	   Normally "iterate" will issue a warning and fall back to single process mode on
	   systems on which fork is not available. This option supresses that warning.

       "batch"
	   Ordinarily items are passed to the worker one at a time. If you are processing a large
	   number of items it may be more efficient to process them in batches. Specify the batch
	   size using this option.

	   Batching is transparent from the caller's perspective. Internally it modifies the
	   iterators and worker (by wrapping them in additional closures) so that they pack,
	   process and unpack chunks of work.

       "adaptive"
	   Extending the idea of batching a number of work items to amortize the overhead of
	   passing work to and from parallel workers you may also ask "iterate" to heuristically
	   determine the batch size by setting the "adaptive" option to a numeric value.

	   The batch size will be computed as

	       <number of items seen> / <number of workers> / <adaptive>

	   A larger value for "adaptive" will reduce the rate at which the batch size increases.
	   Good values tend to be in the range 1 to 2.

	   You can also specify lower and, optionally, upper bounds on the batch size by passing
	   an reference to an array containing ( lower bound, growth ratio, upper bound ). The
	   upper bound may be omitted.

	       my $iter = iterate(
		   { adaptive => [ 5, 2, 100 ] },
		   $worker, \@stuff );

       "onerror"
	   The action to take when an error is thrown in the iterator. Possible values are 'die',
	   'warn' or a reference to a subroutine that will be called with the index of the job
	   that threw the exception and the value of $@ thrown.

	       iterate( {
		   onerror => sub {
		       my ($id, $err) = @_;
		       $self->log( "Error for index $id: $err" );
		   },
		   $worker,
		   \@jobs
	       );

	   The default is 'die'.

   "iterate_as_array"
       As "iterate" but instead of returning an iterator returns an array containing the
       collected output from the iterator. In a scalar context returns a reference to the same
       array.

       For this to work properly the input iterator must return (index, value) pairs. This allows
       the results to be placed in the correct slots in the output array. The simplest way to do
       this is to pass an array reference as the input iterator:

	   my @output = iterate_as_array( \&some_handler, \@input );

   "iterate_as_hash"
       As "iterate" but instead of returning an iterator returns a hash containing the collected
       output from the iterator. In a scalar context returns a reference to the same hash.

       For this to work properly the input iterator must return (key, value) pairs. This allows
       the results to be placed in the correct slots in the output hash. The simplest way to do
       this is to pass a hash reference as the input iterator:

	   my %output = iterate_as_hash( \&some_handler, \%input );

CONFIGURATION AND ENVIRONMENT
       Parallel::Iterator requires no configuration files or environment variables.

DEPENDENCIES
       None.

INCOMPATIBILITIES
       None reported.

BUGS AND LIMITATIONS
       No bugs have been reported.

       Please report any bugs or feature requests to "bug-parallel-iterator@rt.cpan.org", or
       through the web interface at <http://rt.cpan.org>.

AUTHOR
       Andy Armstrong  "<andy@hexten.net>"

THANKS
       Aristotle Pagaltzis for the END handling suggestion and patch.

LICENCE AND COPYRIGHT
       Copyright (c) 2007, Andy Armstrong "<andy@hexten.net>". All rights reserved.

       This module is free software; you can redistribute it and/or modify it under the same
       terms as Perl itself. See perlartistic.

DISCLAIMER OF WARRANTY
       BECAUSE THIS SOFTWARE IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY FOR THE SOFTWARE,
       TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE
       COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE SOFTWARE "AS IS" WITHOUT WARRANTY OF
       ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
       WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO
       THE QUALITY AND PERFORMANCE OF THE SOFTWARE IS WITH YOU. SHOULD THE SOFTWARE PROVE
       DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR, OR CORRECTION.

       IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT
       HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR REDISTRIBUTE THE SOFTWARE AS PERMITTED BY
       THE ABOVE LICENCE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL,
       INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE
       SOFTWARE (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR
       LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE SOFTWARE TO OPERATE WITH ANY
       OTHER SOFTWARE), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
       SUCH DAMAGES.

perl v5.16.3				    2014-06-10			    Parallel::Iterator(3)
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