NAME
Parallel::Simple - the simplest way to run code blocks in parallel
SYNOPSIS
use Parallel::Simple qw( prun );
# style 1: simple list of code blocks
prun(
sub { print "$$ foo\n" },
\&my_bar_func,
) or die( Parallel::Simple::errplus() );
# style 1 with options
prun(
sub { print "$$ foo\n" },
\&my_bar_func,
{ use_return => 1 },
) or die( Parallel::Simple::errplus() );
# style 2: named code blocks (like the Benchmark module)
prun(
foo => sub { print "$$ foo\n" },
bar => \&my_bar_func,
) or die( Parallel::Simple::errplus() );
# getting fancy with arg binding
prun(
[ \&my_bar_func, @args ],
[ \&my_bar_func, @other_args ],
) or die( Parallel::Simple::errplus() );
DESCRIPTION
I generally write my scripts in a linear fashion. Do A, then B, then
C. However, I often have parts that don't depend on each other, and
therefore don't have to run in any particular order, or even linearly.
I could save time by running them in parallel - but I'm too lazy to
deal with forking, and reaping zombie processes, and other nastiness.
The goal of this module is to make it so mind-numbingly simple to run
blocks of code in parallel that there is no longer any excuse not to do
it, and in the process, drastically cut down the runtimes of many of
our applications, especially when running on multi-processor servers
(which are pretty darn common these days).
Parallel code execution is now as simple as calling "prun" and passing
it a list of code blocks to run, followed by testing the return value
for truth using the common "or die" perl idiom.
EXPORTS
By default, Parallel::Simple does not export any symbols. You gener-
ally want to export the "prun" function to save time:
use Parallel::Simple qw(prun);
prun( ... );
No other functions are exportable. You will have to full qualify calls
to them, like this:
print Parallel::Simple::errplus();
FUNCTIONS
prun
Runs multiple code blocks in parallel by forking a process for each
one and returns when all processes have exited. Returns true if
nothing went wrong, false otherwise. Therefore, returns true if no
code blocks are passed, since nothing can go wrong if there are no
code blocks to execute!
There are two styles of passing code blocks:
Style 1 - Simple List of Code Blocks
In its simplest form (which is what we're all about here), "prun"
takes a list of code blocks and then forks a process to run each
block. It returns true if all processes exited with exit value 0,
false otherwise. Example:
prun(
sub { print "$$ foo\n" },
\&my_bar_func,
) or die( Parallel::Simple::errplus() );
By default, the exit value will be 255 if the code block dies or
throws any exceptions, or 0 if it doesn't. You can exercise more
control over this by using the "use_return" option (documented
below) and returning values from your code block.
If "prun" returns false and you want to see what went wrong, try
the "err", "errplus", and "rv" functions documented below - espe-
cially "rv" which will tell you the exit values of the processes
that ran the code blocks.
Style 2 - Named Code Blocks
Alternatively, you can specify names for all of your code blocks by
using the common "named params" perl idiom. The only benefit you
get from this currently is an improved lookup method for code block
return values (see "rv" for more details). Example:
prun(
foo => sub { print "$$ foo\n" },
bar => \&my_bar_func,
) or die( Parallel::Simple::errplus() );
Other than looking nicer, this behaves identically to the Style 1
example.
Argument Binding
You can include arguments to be passed to the code block along with
the block itself by passing a listref containing the code block
followed by the args:
prun(
[ \&my_bar_func, @args ],
[ \&my_bar_func, @other_args ],
) or die( Parallel::Simple::errplus() );
This will fork two processes, one of which will call &my_bar_func
and pass it @args, the other of which will call &my_bar_func and
pass it @other_args. This works with both styles. Here a similar
example using named code blocks:
prun(
foo => sub { print "$$ foo\n" },
bar1 => [ \&my_bar_func, @args ],
bar2 => [ \&my_bar_func, @other_args ],
) or die( Parallel::Simple::errplus() );
Options
You can optionally pass a reference to a hash containing additional
options as the last argument. Example:
prun(
sub { print "$$ foo\n" },
\&my_bar_func,
{ use_return => 1 },
) or die( Parallel::Simple::errplus() );
There are currently only two options:
use_return
By default, the return values for the code blocks, which are
retrieved using the "rv" function, will be 0 if the code block
executed normally or 255 if the code block died or threw any
exceptions. By default, any value the code block returns is
ignored.
If you use the "use_return" option, then the return value of
the code block is used as the return value (unless the code
block dies or throws an exception, in which case the return
value will still be 255). This value is passed to the exit
function, so please please please use only number between 0 and
255!
Note: If you use the "abort_on_error" option, blocks may also
have a return value of -1 indicating that they were terminated
because another block failed.
abort_on_error
Normally, a call to "prun" will block until all code blocks
have exited, regardless of exit status. If you use the
"abort_on_error" option, all blocks will be terminated immedi-
ately as soon as any block exits unsuccessfully. Note that
terminated blocks will have a return value of -1.
err Returns a string describing the last error that occured, or undef
if there has not yet been any errors.
Currently, only two error messages are possible:
* if the call to "fork" fails, "err" returns the contents of $!
* if any blocks fail, "err" returns a message describing how many
blocks failed out of the total
errplus
Returns a string containing the return value of "err" plus a nicely
formatted version of the return value of "rv". I strongly recom-
mend using this function in the following idiom, because it is the
laziest way to present the greatest amount of failure/debugging
information to the user:
prun(
... # code blocks
) or die( Parallel::Simple::errplus() );
rv Returns different value types depending on whether or not you used
named code blocks:
Style 1 (not using named code blocks)
returns a reference to an array containing the return values of the
code blocks in the order they were passed to "prun"
Style 2 (using named code blocks)
returns a reference to a hash, where keys are the code block names,
and values are the return values of the respective code block
See the "use_return" option for the "prun" function for more details on
how to control return values.
PLATFORM SUPPORT
This module was developed and tested on Red Hat Linx 9.0, kernel
2.6.11, and perl v5.8.4 built for i686-linux-thread-multi. I have not
tested it anywhere else. This module is obviously limited to platforms
that have a working "fork" implementation.
I would appreciate any feedback from people using this module in dif-
ferent environments, whether it worked or not, so I can note it here.
FUTURE
The world could probably use a thread-based version of the "prun" func-
tion. I'm currently accepting applications from volunteer coders. :)
Also:
- capture stdout/stderr
- capture block die/exception messages
- capture entire 16 bit exit code, just in case
However, these all make Parallel::Simple less Simple.
SEE ALSO
Parallel::ForkControl, Parallel::ForkManager, and Parallel::Jobs are
all similarly themed, and offer different interfaces and advanced fea-
tures. I suggest you skim the docs on all three (in addition to mine)
before choosing the right one for you. Or you can foolishly trust the
executive summaries below:
Parallel::ForkControl
Only takes one subroutine reference to run, but provides wonderful
ways to control how many children are forked and keeping activity
below certain thresholds. Arguments to the "run" method will be
passed on to the subroutine you specified during construction, so
there's some run-time flexibility. It is not yet possible to learn
anything about what happened to the forked children, such as
inspecting return or exit values.
Conclusion: Best for repetitive, looped tasks, such as fetching web
pages or running a command across a cluster of machines in paral-
lel.
Incidentally, Parallel::ForkControl would be far more useful with
the following two changes:
* Support some kind of feedback - return/exit values at a mini-
mum, or even a single value summary, like the return value of
my prun function.
* Allow the user to specify the Code value in the run method
instead of during construction. Then Parallel::ForkControl
could do everything this module does and more, albeit with a
more sophisticated interface.
Parallel::ForkManager
Unique in the Parallel::* world in that it keeps the user somewhat
involved in the forking process. Rather than taking a code refer-
ence, you call the start() method to fork and test the return value
to determine whether you are now the parent or child... almost like
just calling fork yourself. :)
Provides control over how many child processes to allow, and blocks
new forks until some previous children have exited. Let's child
determine the process exit value. Provides a trigger mechanism to
run callbacks when certain events happen (child start, child exit,
and start blocking). You must supply a callback for the child exit
event to inspect the exit value of the child.
Conclusion: While also designed for repetitive, looped tasks, it is
far more flexible, being a thin wrapper around fork rather than
taking over child creation and management entirely. Useful mostly
if you want to limit child processes to a certain number at a time
and/or if the native system calls scare you.
Parallel::Jobs
Different in that it executes shell commands as opposed to subrou-
tines or code blocks. Provides all the features of the open3 func-
tion, including explicit control over "STDIN", "STDOUT", and
"STDERR" on all 'jobs'. Lets you monitor jobs for output and exit
events (with associated details for each event).
Conclusion: Great for shell commands! Not great for not shell com-
mands.
AUTHORS
Written by Ofer Nave <
[email protected]>. Sponsered by Shopzilla, Inc.
(formerly BizRate.com).
COPYRIGHT
Copyright 2005 by Shopzilla, Inc.
This program is free software; you can redistribute it and/or modify it
under the same terms as Perl itself.
See
http://www.perl.com/perl/misc/Artistic.html
