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Filename : perlthrtut.pod
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=encoding utf8

=head1 NAME

perlthrtut - Tutorial on threads in Perl

=head1 DESCRIPTION

This tutorial describes the use of Perl interpreter threads (sometimes
referred to as I<ithreads>).  In this
model, each thread runs in its own Perl interpreter, and any data sharing
between threads must be explicit.  The user-level interface for I<ithreads>
uses the L<threads> class.

B<NOTE>: There was another older Perl threading flavor called the 5.005 model
that used the L<threads> class.  This old model was known to have problems, is
deprecated, and was removed for release 5.10.  You are
strongly encouraged to migrate any existing 5.005 threads code to the new
model as soon as possible.

You can see which (or neither) threading flavour you have by
running C<perl -V> and looking at the C<Platform> section.
If you have C<useithreads=define> you have ithreads, if you
have C<use5005threads=define> you have 5.005 threads.
If you have neither, you don't have any thread support built in.
If you have both, you are in trouble.

The L<threads> and L<threads::shared> modules are included in the core Perl
distribution.  Additionally, they are maintained as a separate modules on
CPAN, so you can check there for any updates.

=head1 What Is A Thread Anyway?

A thread is a flow of control through a program with a single
execution point.

Sounds an awful lot like a process, doesn't it? Well, it should.
Threads are one of the pieces of a process.  Every process has at least
one thread and, up until now, every process running Perl had only one
thread.  With 5.8, though, you can create extra threads.  We're going
to show you how, when, and why.

=head1 Threaded Program Models

There are three basic ways that you can structure a threaded
program.  Which model you choose depends on what you need your program
to do.  For many non-trivial threaded programs, you'll need to choose
different models for different pieces of your program.

=head2 Boss/Worker

The boss/worker model usually has one I<boss> thread and one or more
I<worker> threads.  The boss thread gathers or generates tasks that need
to be done, then parcels those tasks out to the appropriate worker
thread.

This model is common in GUI and server programs, where a main thread
waits for some event and then passes that event to the appropriate
worker threads for processing.  Once the event has been passed on, the
boss thread goes back to waiting for another event.

The boss thread does relatively little work.  While tasks aren't
necessarily performed faster than with any other method, it tends to
have the best user-response times.

=head2 Work Crew

In the work crew model, several threads are created that do
essentially the same thing to different pieces of data.  It closely
mirrors classical parallel processing and vector processors, where a
large array of processors do the exact same thing to many pieces of
data.

This model is particularly useful if the system running the program
will distribute multiple threads across different processors.  It can
also be useful in ray tracing or rendering engines, where the
individual threads can pass on interim results to give the user visual
feedback.

=head2 Pipeline

The pipeline model divides up a task into a series of steps, and
passes the results of one step on to the thread processing the
next.  Each thread does one thing to each piece of data and passes the
results to the next thread in line.

This model makes the most sense if you have multiple processors so two
or more threads will be executing in parallel, though it can often
make sense in other contexts as well.  It tends to keep the individual
tasks small and simple, as well as allowing some parts of the pipeline
to block (on I/O or system calls, for example) while other parts keep
going.  If you're running different parts of the pipeline on different
processors you may also take advantage of the caches on each
processor.

This model is also handy for a form of recursive programming where,
rather than having a subroutine call itself, it instead creates
another thread.  Prime and Fibonacci generators both map well to this
form of the pipeline model. (A version of a prime number generator is
presented later on.)

=head1 What kind of threads are Perl threads?

If you have experience with other thread implementations, you might
find that things aren't quite what you expect.  It's very important to
remember when dealing with Perl threads that I<Perl Threads Are Not X
Threads> for all values of X.  They aren't POSIX threads, or
DecThreads, or Java's Green threads, or Win32 threads.  There are
similarities, and the broad concepts are the same, but if you start
looking for implementation details you're going to be either
disappointed or confused.  Possibly both.

This is not to say that Perl threads are completely different from
everything that's ever come before. They're not.  Perl's threading
model owes a lot to other thread models, especially POSIX.  Just as
Perl is not C, though, Perl threads are not POSIX threads.  So if you
find yourself looking for mutexes, or thread priorities, it's time to
step back a bit and think about what you want to do and how Perl can
do it.

However, it is important to remember that Perl threads cannot magically
do things unless your operating system's threads allow it. So if your
system blocks the entire process on C<sleep()>, Perl usually will, as well.

B<Perl Threads Are Different.>

=head1 Thread-Safe Modules

The addition of threads has changed Perl's internals
substantially. There are implications for people who write
modules with XS code or external libraries. However, since Perl data is
not shared among threads by default, Perl modules stand a high chance of
being thread-safe or can be made thread-safe easily.  Modules that are not
tagged as thread-safe should be tested or code reviewed before being used
in production code.

Not all modules that you might use are thread-safe, and you should
always assume a module is unsafe unless the documentation says
otherwise.  This includes modules that are distributed as part of the
core.  Threads are a relatively new feature, and even some of the standard
modules aren't thread-safe.

Even if a module is thread-safe, it doesn't mean that the module is optimized
to work well with threads. A module could possibly be rewritten to utilize
the new features in threaded Perl to increase performance in a threaded
environment.

If you're using a module that's not thread-safe for some reason, you
can protect yourself by using it from one, and only one thread at all.
If you need multiple threads to access such a module, you can use semaphores and
lots of programming discipline to control access to it.  Semaphores
are covered in L</"Basic semaphores">.

See also L</"Thread-Safety of System Libraries">.

=head1 Thread Basics

The L<threads> module provides the basic functions you need to write
threaded programs.  In the following sections, we'll cover the basics,
showing you what you need to do to create a threaded program.   After
that, we'll go over some of the features of the L<threads> module that
make threaded programming easier.

=head2 Basic Thread Support

Thread support is a Perl compile-time option. It's something that's
turned on or off when Perl is built at your site, rather than when
your programs are compiled. If your Perl wasn't compiled with thread
support enabled, then any attempt to use threads will fail.

Your programs can use the Config module to check whether threads are
enabled. If your program can't run without them, you can say something
like:

    use Config;
    $Config{useithreads} or
        die('Recompile Perl with threads to run this program.');

A possibly-threaded program using a possibly-threaded module might
have code like this:

    use Config;
    use MyMod;

    BEGIN {
        if ($Config{useithreads}) {
            # We have threads
            require MyMod_threaded;
            import MyMod_threaded;
        } else {
            require MyMod_unthreaded;
            import MyMod_unthreaded;
        }
    }

Since code that runs both with and without threads is usually pretty
messy, it's best to isolate the thread-specific code in its own
module.  In our example above, that's what C<MyMod_threaded> is, and it's
only imported if we're running on a threaded Perl.

=head2 A Note about the Examples

In a real situation, care should be taken that all threads are finished
executing before the program exits.  That care has B<not> been taken in these
examples in the interest of simplicity.  Running these examples I<as is> will
produce error messages, usually caused by the fact that there are still
threads running when the program exits.  You should not be alarmed by this.

=head2 Creating Threads

The L<threads> module provides the tools you need to create new
threads.  Like any other module, you need to tell Perl that you want to use
it; C<use threads;> imports all the pieces you need to create basic
threads.

The simplest, most straightforward way to create a thread is with C<create()>:

    use threads;

    my $thr = threads->create(\&sub1);

    sub sub1 {
        print("In the thread\n");
    }

The C<create()> method takes a reference to a subroutine and creates a new
thread that starts executing in the referenced subroutine.  Control
then passes both to the subroutine and the caller.

If you need to, your program can pass parameters to the subroutine as
part of the thread startup.  Just include the list of parameters as
part of the C<threads-E<gt>create()> call, like this:

    use threads;

    my $Param3 = 'foo';
    my $thr1 = threads->create(\&sub1, 'Param 1', 'Param 2', $Param3);
    my @ParamList = (42, 'Hello', 3.14);
    my $thr2 = threads->create(\&sub1, @ParamList);
    my $thr3 = threads->create(\&sub1, qw(Param1 Param2 Param3));

    sub sub1 {
        my @InboundParameters = @_;
        print("In the thread\n");
        print('Got parameters >', join('<>',@InboundParameters), "<\n");
    }

The last example illustrates another feature of threads.  You can spawn
off several threads using the same subroutine.  Each thread executes
the same subroutine, but in a separate thread with a separate
environment and potentially separate arguments.

C<new()> is a synonym for C<create()>.

=head2 Waiting For A Thread To Exit

Since threads are also subroutines, they can return values.  To wait
for a thread to exit and extract any values it might return, you can
use the C<join()> method:

    use threads;

    my ($thr) = threads->create(\&sub1);

    my @ReturnData = $thr->join();
    print('Thread returned ', join(', ', @ReturnData), "\n");

    sub sub1 { return ('Fifty-six', 'foo', 2); }

In the example above, the C<join()> method returns as soon as the thread
ends.  In addition to waiting for a thread to finish and gathering up
any values that the thread might have returned, C<join()> also performs
any OS cleanup necessary for the thread.  That cleanup might be
important, especially for long-running programs that spawn lots of
threads.  If you don't want the return values and don't want to wait
for the thread to finish, you should call the C<detach()> method
instead, as described next.

NOTE: In the example above, the thread returns a list, thus necessitating
that the thread creation call be made in list context (i.e., C<my ($thr)>).
See L<< threads/"$thr->join()" >> and L<threads/"THREAD CONTEXT"> for more
details on thread context and return values.

=head2 Ignoring A Thread

C<join()> does three things: it waits for a thread to exit, cleans up
after it, and returns any data the thread may have produced.  But what
if you're not interested in the thread's return values, and you don't
really care when the thread finishes? All you want is for the thread
to get cleaned up after when it's done.

In this case, you use the C<detach()> method.  Once a thread is detached,
it'll run until it's finished; then Perl will clean up after it
automatically.

    use threads;

    my $thr = threads->create(\&sub1);   # Spawn the thread

    $thr->detach();   # Now we officially don't care any more

    sleep(15);        # Let thread run for awhile

    sub sub1 {
        my $count = 0;
        while (1) {
            $count++;
            print("\$count is $count\n");
            sleep(1);
        }
    }

Once a thread is detached, it may not be joined, and any return data
that it might have produced (if it was done and waiting for a join) is
lost.

C<detach()> can also be called as a class method to allow a thread to
detach itself:

    use threads;

    my $thr = threads->create(\&sub1);

    sub sub1 {
        threads->detach();
        # Do more work
    }

=head2 Process and Thread Termination

With threads one must be careful to make sure they all have a chance to
run to completion, assuming that is what you want.

An action that terminates a process will terminate I<all> running
threads.  die() and exit() have this property,
and perl does an exit when the main thread exits,
perhaps implicitly by falling off the end of your code,
even if that's not what you want.

As an example of this case, this code prints the message
"Perl exited with active threads: 2 running and unjoined":

    use threads;
    my $thr1 = threads->new(\&thrsub, "test1");
    my $thr2 = threads->new(\&thrsub, "test2");
    sub thrsub {
       my ($message) = @_;
       sleep 1;
       print "thread $message\n";
    }

But when the following lines are added at the end:

    $thr1->join();
    $thr2->join();

it prints two lines of output, a perhaps more useful outcome.

=head1 Threads And Data

Now that we've covered the basics of threads, it's time for our next
topic: Data.  Threading introduces a couple of complications to data
access that non-threaded programs never need to worry about.

=head2 Shared And Unshared Data

The biggest difference between Perl I<ithreads> and the old 5.005 style
threading, or for that matter, to most other threading systems out there,
is that by default, no data is shared. When a new Perl thread is created,
all the data associated with the current thread is copied to the new
thread, and is subsequently private to that new thread!
This is similar in feel to what happens when a Unix process forks,
except that in this case, the data is just copied to a different part of
memory within the same process rather than a real fork taking place.

To make use of threading, however, one usually wants the threads to share
at least some data between themselves. This is done with the
L<threads::shared> module and the C<:shared> attribute:

    use threads;
    use threads::shared;

    my $foo :shared = 1;
    my $bar = 1;
    threads->create(sub { $foo++; $bar++; })->join();

    print("$foo\n");  # Prints 2 since $foo is shared
    print("$bar\n");  # Prints 1 since $bar is not shared

In the case of a shared array, all the array's elements are shared, and for
a shared hash, all the keys and values are shared. This places
restrictions on what may be assigned to shared array and hash elements: only
simple values or references to shared variables are allowed - this is
so that a private variable can't accidentally become shared. A bad
assignment will cause the thread to die. For example:

    use threads;
    use threads::shared;

    my $var          = 1;
    my $svar :shared = 2;
    my %hash :shared;

    ... create some threads ...

    $hash{a} = 1;       # All threads see exists($hash{a})
                        # and $hash{a} == 1
    $hash{a} = $var;    # okay - copy-by-value: same effect as previous
    $hash{a} = $svar;   # okay - copy-by-value: same effect as previous
    $hash{a} = \$svar;  # okay - a reference to a shared variable
    $hash{a} = \$var;   # This will die
    delete($hash{a});   # okay - all threads will see !exists($hash{a})

Note that a shared variable guarantees that if two or more threads try to
modify it at the same time, the internal state of the variable will not
become corrupted. However, there are no guarantees beyond this, as
explained in the next section.

=head2 Thread Pitfalls: Races

While threads bring a new set of useful tools, they also bring a
number of pitfalls.  One pitfall is the race condition:

    use threads;
    use threads::shared;

    my $x :shared = 1;
    my $thr1 = threads->create(\&sub1);
    my $thr2 = threads->create(\&sub2);

    $thr1->join();
    $thr2->join();
    print("$x\n");

    sub sub1 { my $foo = $x; $x = $foo + 1; }
    sub sub2 { my $bar = $x; $x = $bar + 1; }

What do you think C<$x> will be? The answer, unfortunately, is I<it
depends>. Both C<sub1()> and C<sub2()> access the global variable C<$x>, once
to read and once to write.  Depending on factors ranging from your
thread implementation's scheduling algorithm to the phase of the moon,
C<$x> can be 2 or 3.

Race conditions are caused by unsynchronized access to shared
data.  Without explicit synchronization, there's no way to be sure that
nothing has happened to the shared data between the time you access it
and the time you update it.  Even this simple code fragment has the
possibility of error:

    use threads;
    my $x :shared = 2;
    my $y :shared;
    my $z :shared;
    my $thr1 = threads->create(sub { $y = $x; $x = $y + 1; });
    my $thr2 = threads->create(sub { $z = $x; $x = $z + 1; });
    $thr1->join();
    $thr2->join();

Two threads both access C<$x>.  Each thread can potentially be interrupted
at any point, or be executed in any order.  At the end, C<$x> could be 3
or 4, and both C<$y> and C<$z> could be 2 or 3.

Even C<$x += 5> or C<$x++> are not guaranteed to be atomic.

Whenever your program accesses data or resources that can be accessed
by other threads, you must take steps to coordinate access or risk
data inconsistency and race conditions. Note that Perl will protect its
internals from your race conditions, but it won't protect you from you.

=head1 Synchronization and control

Perl provides a number of mechanisms to coordinate the interactions
between themselves and their data, to avoid race conditions and the like.
Some of these are designed to resemble the common techniques used in thread
libraries such as C<pthreads>; others are Perl-specific. Often, the
standard techniques are clumsy and difficult to get right (such as
condition waits). Where possible, it is usually easier to use Perlish
techniques such as queues, which remove some of the hard work involved.

=head2 Controlling access: lock()

The C<lock()> function takes a shared variable and puts a lock on it.
No other thread may lock the variable until the variable is unlocked
by the thread holding the lock. Unlocking happens automatically
when the locking thread exits the block that contains the call to the
C<lock()> function.  Using C<lock()> is straightforward: This example has
several threads doing some calculations in parallel, and occasionally
updating a running total:

    use threads;
    use threads::shared;

    my $total :shared = 0;

    sub calc {
        while (1) {
            my $result;
            # (... do some calculations and set $result ...)
            {
                lock($total);  # Block until we obtain the lock
                $total += $result;
            } # Lock implicitly released at end of scope
            last if $result == 0;
        }
    }

    my $thr1 = threads->create(\&calc);
    my $thr2 = threads->create(\&calc);
    my $thr3 = threads->create(\&calc);
    $thr1->join();
    $thr2->join();
    $thr3->join();
    print("total=$total\n");

C<lock()> blocks the thread until the variable being locked is
available.  When C<lock()> returns, your thread can be sure that no other
thread can lock that variable until the block containing the
lock exits.

It's important to note that locks don't prevent access to the variable
in question, only lock attempts.  This is in keeping with Perl's
longstanding tradition of courteous programming, and the advisory file
locking that C<flock()> gives you.

You may lock arrays and hashes as well as scalars.  Locking an array,
though, will not block subsequent locks on array elements, just lock
attempts on the array itself.

Locks are recursive, which means it's okay for a thread to
lock a variable more than once.  The lock will last until the outermost
C<lock()> on the variable goes out of scope. For example:

    my $x :shared;
    doit();

    sub doit {
        {
            {
                lock($x); # Wait for lock
                lock($x); # NOOP - we already have the lock
                {
                    lock($x); # NOOP
                    {
                        lock($x); # NOOP
                        lockit_some_more();
                    }
                }
            } # *** Implicit unlock here ***
        }
    }

    sub lockit_some_more {
        lock($x); # NOOP
    } # Nothing happens here

Note that there is no C<unlock()> function - the only way to unlock a
variable is to allow it to go out of scope.

A lock can either be used to guard the data contained within the variable
being locked, or it can be used to guard something else, like a section
of code. In this latter case, the variable in question does not hold any
useful data, and exists only for the purpose of being locked. In this
respect, the variable behaves like the mutexes and basic semaphores of
traditional thread libraries.

=head2 A Thread Pitfall: Deadlocks

Locks are a handy tool to synchronize access to data, and using them
properly is the key to safe shared data.  Unfortunately, locks aren't
without their dangers, especially when multiple locks are involved.
Consider the following code:

    use threads;

    my $x :shared = 4;
    my $y :shared = 'foo';
    my $thr1 = threads->create(sub {
        lock($x);
        sleep(20);
        lock($y);
    });
    my $thr2 = threads->create(sub {
        lock($y);
        sleep(20);
        lock($x);
    });

This program will probably hang until you kill it.  The only way it
won't hang is if one of the two threads acquires both locks
first.  A guaranteed-to-hang version is more complicated, but the
principle is the same.

The first thread will grab a lock on C<$x>, then, after a pause during which
the second thread has probably had time to do some work, try to grab a
lock on C<$y>.  Meanwhile, the second thread grabs a lock on C<$y>, then later
tries to grab a lock on C<$x>.  The second lock attempt for both threads will
block, each waiting for the other to release its lock.

This condition is called a deadlock, and it occurs whenever two or
more threads are trying to get locks on resources that the others
own.  Each thread will block, waiting for the other to release a lock
on a resource.  That never happens, though, since the thread with the
resource is itself waiting for a lock to be released.

There are a number of ways to handle this sort of problem.  The best
way is to always have all threads acquire locks in the exact same
order.  If, for example, you lock variables C<$x>, C<$y>, and C<$z>, always lock
C<$x> before C<$y>, and C<$y> before C<$z>.  It's also best to hold on to locks for
as short a period of time to minimize the risks of deadlock.

The other synchronization primitives described below can suffer from
similar problems.

=head2 Queues: Passing Data Around

A queue is a special thread-safe object that lets you put data in one
end and take it out the other without having to worry about
synchronization issues.  They're pretty straightforward, and look like
this:

    use threads;
    use Thread::Queue;

    my $DataQueue = Thread::Queue->new();
    my $thr = threads->create(sub {
        while (my $DataElement = $DataQueue->dequeue()) {
            print("Popped $DataElement off the queue\n");
        }
    });

    $DataQueue->enqueue(12);
    $DataQueue->enqueue("A", "B", "C");
    sleep(10);
    $DataQueue->enqueue(undef);
    $thr->join();

You create the queue with C<Thread::Queue-E<gt>new()>.  Then you can
add lists of scalars onto the end with C<enqueue()>, and pop scalars off
the front of it with C<dequeue()>.  A queue has no fixed size, and can grow
as needed to hold everything pushed on to it.

If a queue is empty, C<dequeue()> blocks until another thread enqueues
something.  This makes queues ideal for event loops and other
communications between threads.

=head2 Semaphores: Synchronizing Data Access

Semaphores are a kind of generic locking mechanism. In their most basic
form, they behave very much like lockable scalars, except that they
can't hold data, and that they must be explicitly unlocked. In their
advanced form, they act like a kind of counter, and can allow multiple
threads to have the I<lock> at any one time.

=head2 Basic semaphores

Semaphores have two methods, C<down()> and C<up()>: C<down()> decrements the resource
count, while C<up()> increments it. Calls to C<down()> will block if the
semaphore's current count would decrement below zero.  This program
gives a quick demonstration:

    use threads;
    use Thread::Semaphore;

    my $semaphore = Thread::Semaphore->new();
    my $GlobalVariable :shared = 0;

    $thr1 = threads->create(\&sample_sub, 1);
    $thr2 = threads->create(\&sample_sub, 2);
    $thr3 = threads->create(\&sample_sub, 3);

    sub sample_sub {
        my $SubNumber = shift(@_);
        my $TryCount = 10;
        my $LocalCopy;
        sleep(1);
        while ($TryCount--) {
            $semaphore->down();
            $LocalCopy = $GlobalVariable;
            print("$TryCount tries left for sub $SubNumber "
                 ."(\$GlobalVariable is $GlobalVariable)\n");
            sleep(2);
            $LocalCopy++;
            $GlobalVariable = $LocalCopy;
            $semaphore->up();
        }
    }

    $thr1->join();
    $thr2->join();
    $thr3->join();

The three invocations of the subroutine all operate in sync.  The
semaphore, though, makes sure that only one thread is accessing the
global variable at once.

=head2 Advanced Semaphores

By default, semaphores behave like locks, letting only one thread
C<down()> them at a time.  However, there are other uses for semaphores.

Each semaphore has a counter attached to it. By default, semaphores are
created with the counter set to one, C<down()> decrements the counter by
one, and C<up()> increments by one. However, we can override any or all
of these defaults simply by passing in different values:

    use threads;
    use Thread::Semaphore;

    my $semaphore = Thread::Semaphore->new(5);
                    # Creates a semaphore with the counter set to five

    my $thr1 = threads->create(\&sub1);
    my $thr2 = threads->create(\&sub1);

    sub sub1 {
        $semaphore->down(5); # Decrements the counter by five
        # Do stuff here
        $semaphore->up(5); # Increment the counter by five
    }

    $thr1->detach();
    $thr2->detach();

If C<down()> attempts to decrement the counter below zero, it blocks until
the counter is large enough.  Note that while a semaphore can be created
with a starting count of zero, any C<up()> or C<down()> always changes the
counter by at least one, and so C<< $semaphore->down(0) >> is the same as
C<< $semaphore->down(1) >>.

The question, of course, is why would you do something like this? Why
create a semaphore with a starting count that's not one, or why
decrement or increment it by more than one? The answer is resource
availability.  Many resources that you want to manage access for can be
safely used by more than one thread at once.

For example, let's take a GUI driven program.  It has a semaphore that
it uses to synchronize access to the display, so only one thread is
ever drawing at once.  Handy, but of course you don't want any thread
to start drawing until things are properly set up.  In this case, you
can create a semaphore with a counter set to zero, and up it when
things are ready for drawing.

Semaphores with counters greater than one are also useful for
establishing quotas.  Say, for example, that you have a number of
threads that can do I/O at once.  You don't want all the threads
reading or writing at once though, since that can potentially swamp
your I/O channels, or deplete your process's quota of filehandles.  You
can use a semaphore initialized to the number of concurrent I/O
requests (or open files) that you want at any one time, and have your
threads quietly block and unblock themselves.

Larger increments or decrements are handy in those cases where a
thread needs to check out or return a number of resources at once.

=head2 Waiting for a Condition

The functions C<cond_wait()> and C<cond_signal()>
can be used in conjunction with locks to notify
co-operating threads that a resource has become available. They are
very similar in use to the functions found in C<pthreads>. However
for most purposes, queues are simpler to use and more intuitive. See
L<threads::shared> for more details.

=head2 Giving up control

There are times when you may find it useful to have a thread
explicitly give up the CPU to another thread.  You may be doing something
processor-intensive and want to make sure that the user-interface thread
gets called frequently.  Regardless, there are times that you might want
a thread to give up the processor.

Perl's threading package provides the C<yield()> function that does
this. C<yield()> is pretty straightforward, and works like this:

    use threads;

    sub loop {
        my $thread = shift;
        my $foo = 50;
        while($foo--) { print("In thread $thread\n"); }
        threads->yield();
        $foo = 50;
        while($foo--) { print("In thread $thread\n"); }
    }

    my $thr1 = threads->create(\&loop, 'first');
    my $thr2 = threads->create(\&loop, 'second');
    my $thr3 = threads->create(\&loop, 'third');

It is important to remember that C<yield()> is only a hint to give up the CPU,
it depends on your hardware, OS and threading libraries what actually happens.
B<On many operating systems, yield() is a no-op.>  Therefore it is important
to note that one should not build the scheduling of the threads around
C<yield()> calls. It might work on your platform but it won't work on another
platform.

=head1 General Thread Utility Routines

We've covered the workhorse parts of Perl's threading package, and
with these tools you should be well on your way to writing threaded
code and packages.  There are a few useful little pieces that didn't
really fit in anyplace else.

=head2 What Thread Am I In?

The C<threads-E<gt>self()> class method provides your program with a way to
get an object representing the thread it's currently in.  You can use this
object in the same way as the ones returned from thread creation.

=head2 Thread IDs

C<tid()> is a thread object method that returns the thread ID of the
thread the object represents.  Thread IDs are integers, with the main
thread in a program being 0.  Currently Perl assigns a unique TID to
every thread ever created in your program, assigning the first thread
to be created a TID of 1, and increasing the TID by 1 for each new
thread that's created.  When used as a class method, C<threads-E<gt>tid()>
can be used by a thread to get its own TID.

=head2 Are These Threads The Same?

The C<equal()> method takes two thread objects and returns true
if the objects represent the same thread, and false if they don't.

Thread objects also have an overloaded C<==> comparison so that you can do
comparison on them as you would with normal objects.

=head2 What Threads Are Running?

C<threads-E<gt>list()> returns a list of thread objects, one for each thread
that's currently running and not detached.  Handy for a number of things,
including cleaning up at the end of your program (from the main Perl thread,
of course):

    # Loop through all the threads
    foreach my $thr (threads->list()) {
        $thr->join();
    }

If some threads have not finished running when the main Perl thread
ends, Perl will warn you about it and die, since it is impossible for Perl
to clean up itself while other threads are running.

NOTE:  The main Perl thread (thread 0) is in a I<detached> state, and so
does not appear in the list returned by C<threads-E<gt>list()>.

=head1 A Complete Example

Confused yet? It's time for an example program to show some of the
things we've covered.  This program finds prime numbers using threads.

   1 #!/usr/bin/perl
   2 # prime-pthread, courtesy of Tom Christiansen
   3
   4 use v5.36;
   5
   6 use threads;
   7 use Thread::Queue;
   8
   9 sub check_num ($upstream, $cur_prime) {
  10     my $kid;
  11     my $downstream = Thread::Queue->new();
  12     while (my $num = $upstream->dequeue()) {
  13         next unless ($num % $cur_prime);
  14         if ($kid) {
  15             $downstream->enqueue($num);
  16         } else {
  17             print("Found prime: $num\n");
  18             $kid = threads->create(\&check_num, $downstream, $num);
  19             if (! $kid) {
  20                 warn("Sorry.  Ran out of threads.\n");
  21                 last;
  22             }
  23         }
  24     }
  25     if ($kid) {
  26         $downstream->enqueue(undef);
  27         $kid->join();
  28     }
  29 }
  30
  31 my $stream = Thread::Queue->new(3..1000, undef);
  32 check_num($stream, 2);

This program uses the pipeline model to generate prime numbers.  Each
thread in the pipeline has an input queue that feeds numbers to be
checked, a prime number that it's responsible for, and an output queue
into which it funnels numbers that have failed the check.  If the thread
has a number that's failed its check and there's no child thread, then
the thread must have found a new prime number.  In that case, a new
child thread is created for that prime and stuck on the end of the
pipeline.

This probably sounds a bit more confusing than it really is, so let's
go through this program piece by piece and see what it does.  (For
those of you who might be trying to remember exactly what a prime
number is, it's a number that's only evenly divisible by itself and 1.)

The bulk of the work is done by the C<check_num()> subroutine, which
takes a reference to its input queue and a prime number that it's
responsible for.  We create a new queue (line 11) and reserve a scalar
for the thread that we're likely to create later (line 10).

The while loop from line 12 to line 24 grabs a scalar off the input
queue and checks against the prime this thread is responsible
for.  Line 13 checks to see if there's a remainder when we divide the
number to be checked by our prime.  If there is one, the number
must not be evenly divisible by our prime, so we need to either pass
it on to the next thread if we've created one (line 15) or create a
new thread if we haven't.

The new thread creation is line 18.  We pass on to it a reference to
the queue we've created, and the prime number we've found.  In lines 19
through 22, we check to make sure that our new thread got created, and
if not, we stop checking any remaining numbers in the queue.

Finally, once the loop terminates (because we got a 0 or C<undef> in the
queue, which serves as a note to terminate), we pass on the notice to our
child, and wait for it to exit if we've created a child (lines 25 and
28).

Meanwhile, back in the main thread, we first create a queue (line 31) and
queue up all the numbers from 3 to 1000 for checking, plus a termination
notice.  Then all we have to do to get the ball rolling is pass the queue
and the first prime to the C<check_num()> subroutine (line 32).

That's how it works.  It's pretty simple; as with many Perl programs,
the explanation is much longer than the program.

=head1 Different implementations of threads

Some background on thread implementations from the operating system
viewpoint.  There are three basic categories of threads: user-mode threads,
kernel threads, and multiprocessor kernel threads.

User-mode threads are threads that live entirely within a program and
its libraries.  In this model, the OS knows nothing about threads.  As
far as it's concerned, your process is just a process.

This is the easiest way to implement threads, and the way most OSes
start.  The big disadvantage is that, since the OS knows nothing about
threads, if one thread blocks they all do.  Typical blocking activities
include most system calls, most I/O, and things like C<sleep()>.

Kernel threads are the next step in thread evolution.  The OS knows
about kernel threads, and makes allowances for them.  The main
difference between a kernel thread and a user-mode thread is
blocking.  With kernel threads, things that block a single thread don't
block other threads.  This is not the case with user-mode threads,
where the kernel blocks at the process level and not the thread level.

This is a big step forward, and can give a threaded program quite a
performance boost over non-threaded programs.  Threads that block
performing I/O, for example, won't block threads that are doing other
things.  Each process still has only one thread running at once,
though, regardless of how many CPUs a system might have.

Since kernel threading can interrupt a thread at any time, they will
uncover some of the implicit locking assumptions you may make in your
program.  For example, something as simple as C<$x = $x + 2> can behave
unpredictably with kernel threads if C<$x> is visible to other
threads, as another thread may have changed C<$x> between the time it
was fetched on the right hand side and the time the new value is
stored.

Multiprocessor kernel threads are the final step in thread
support.  With multiprocessor kernel threads on a machine with multiple
CPUs, the OS may schedule two or more threads to run simultaneously on
different CPUs.

This can give a serious performance boost to your threaded program,
since more than one thread will be executing at the same time.  As a
tradeoff, though, any of those nagging synchronization issues that
might not have shown with basic kernel threads will appear with a
vengeance.

In addition to the different levels of OS involvement in threads,
different OSes (and different thread implementations for a particular
OS) allocate CPU cycles to threads in different ways.

Cooperative multitasking systems have running threads give up control
if one of two things happen.  If a thread calls a yield function, it
gives up control.  It also gives up control if the thread does
something that would cause it to block, such as perform I/O.  In a
cooperative multitasking implementation, one thread can starve all the
others for CPU time if it so chooses.

Preemptive multitasking systems interrupt threads at regular intervals
while the system decides which thread should run next.  In a preemptive
multitasking system, one thread usually won't monopolize the CPU.

On some systems, there can be cooperative and preemptive threads
running simultaneously. (Threads running with realtime priorities
often behave cooperatively, for example, while threads running at
normal priorities behave preemptively.)

Most modern operating systems support preemptive multitasking nowadays.

=head1 Performance considerations

The main thing to bear in mind when comparing Perl's I<ithreads> to other threading
models is the fact that for each new thread created, a complete copy of
all the variables and data of the parent thread has to be taken. Thus,
thread creation can be quite expensive, both in terms of memory usage and
time spent in creation. The ideal way to reduce these costs is to have a
relatively short number of long-lived threads, all created fairly early
on (before the base thread has accumulated too much data). Of course, this
may not always be possible, so compromises have to be made. However, after
a thread has been created, its performance and extra memory usage should
be little different than ordinary code.

Also note that under the current implementation, shared variables
use a little more memory and are a little slower than ordinary variables.

=head1 Process-scope Changes

Note that while threads themselves are separate execution threads and
Perl data is thread-private unless explicitly shared, the threads can
affect process-scope state, affecting all the threads.

The most common example of this is changing the current working
directory using C<chdir()>.  One thread calls C<chdir()>, and the working
directory of all the threads changes.

Even more drastic example of a process-scope change is C<chroot()>:
the root directory of all the threads changes, and no thread can
undo it (as opposed to C<chdir()>).

Further examples of process-scope changes include C<umask()> and
changing uids and gids.

Thinking of mixing C<fork()> and threads?  Please lie down and wait
until the feeling passes.  Be aware that the semantics of C<fork()> vary
between platforms.  For example, some Unix systems copy all the current
threads into the child process, while others only copy the thread that
called C<fork()>. You have been warned!

Similarly, mixing signals and threads may be problematic.
Implementations are platform-dependent, and even the POSIX
semantics may not be what you expect (and Perl doesn't even
give you the full POSIX API).  For example, there is no way to
guarantee that a signal sent to a multi-threaded Perl application
will get intercepted by any particular thread.  (However, a recently
added feature does provide the capability to send signals between
threads.  See L<threads/THREAD SIGNALLING> for more details.)

=head1 Thread-Safety of System Libraries

Whether various library calls are thread-safe is outside the control
of Perl.  Calls often suffering from not being thread-safe include:
C<localtime()>, C<gmtime()>,  functions fetching user, group and
network information (such as C<getgrent()>, C<gethostent()>,
C<getnetent()> and so on), C<readdir()>, C<rand()>, and C<srand()>. In
general, calls that depend on some global external state.

If the system Perl is compiled in has thread-safe variants of such
calls, they will be used.  Beyond that, Perl is at the mercy of
the thread-safety or -unsafety of the calls.  Please consult your
C library call documentation.

On some platforms the thread-safe library interfaces may fail if the
result buffer is too small (for example the user group databases may
be rather large, and the reentrant interfaces may have to carry around
a full snapshot of those databases).  Perl will start with a small
buffer, but keep retrying and growing the result buffer
until the result fits.  If this limitless growing sounds bad for
security or memory consumption reasons you can recompile Perl with
C<PERL_REENTRANT_MAXSIZE> defined to the maximum number of bytes you will
allow.

=head1 Conclusion

A complete thread tutorial could fill a book (and has, many times),
but with what we've covered in this introduction, you should be well
on your way to becoming a threaded Perl expert.

=head1 SEE ALSO

Annotated POD for L<threads>:
L<https://web.archive.org/web/20171028020148/http://annocpan.org/?mode=search&field=Module&name=threads>

Latest version of L<threads> on CPAN:
L<https://metacpan.org/pod/threads>

Annotated POD for L<threads::shared>:
L<https://web.archive.org/web/20171028020148/http://annocpan.org/?mode=search&field=Module&name=threads%3A%3Ashared>

Latest version of L<threads::shared> on CPAN:
L<https://metacpan.org/pod/threads::shared>

Perl threads mailing list:
L<https://lists.perl.org/list/ithreads.html>

=head1 Bibliography

Here's a short bibliography courtesy of Jürgen Christoffel:

=head2 Introductory Texts

Birrell, Andrew D. An Introduction to Programming with
Threads. Digital Equipment Corporation, 1989, DEC-SRC Research Report
#35 online as
L<https://www.hpl.hp.com/techreports/Compaq-DEC/SRC-RR-35.pdf>
(highly recommended)

Robbins, Kay. A., and Steven Robbins. Practical Unix Programming: A
Guide to Concurrency, Communication, and
Multithreading. Prentice-Hall, 1996.

Lewis, Bill, and Daniel J. Berg. Multithreaded Programming with
Pthreads. Prentice Hall, 1997, ISBN 0-13-443698-9 (a well-written
introduction to threads).

Nelson, Greg (editor). Systems Programming with Modula-3.  Prentice
Hall, 1991, ISBN 0-13-590464-1.

Nichols, Bradford, Dick Buttlar, and Jacqueline Proulx Farrell.
Pthreads Programming. O'Reilly & Associates, 1996, ISBN 156592-115-1
(covers POSIX threads).

=head2 OS-Related References

Boykin, Joseph, David Kirschen, Alan Langerman, and Susan
LoVerso. Programming under Mach. Addison-Wesley, 1994, ISBN
0-201-52739-1.

Tanenbaum, Andrew S. Distributed Operating Systems. Prentice Hall,
1995, ISBN 0-13-219908-4 (great textbook).

Silberschatz, Abraham, and Peter B. Galvin. Operating System Concepts,
4th ed. Addison-Wesley, 1995, ISBN 0-201-59292-4

=head2 Other References

Arnold, Ken and James Gosling. The Java Programming Language, 2nd
ed. Addison-Wesley, 1998, ISBN 0-201-31006-6.

comp.programming.threads FAQ,
L<http://www.serpentine.com/~bos/threads-faq/>

Le Sergent, T. and B. Berthomieu. "Incremental MultiThreaded Garbage
Collection on Virtually Shared Memory Architectures" in Memory
Management: Proc. of the International Workshop IWMM 92, St. Malo,
France, September 1992, Yves Bekkers and Jacques Cohen, eds. Springer,
1992, ISBN 3540-55940-X (real-life thread applications).

Artur Bergman, "Where Wizards Fear To Tread", June 11, 2002,
L<http://www.perl.com/pub/a/2002/06/11/threads.html>

=head1 Acknowledgements

Thanks (in no particular order) to Chaim Frenkel, Steve Fink, Gurusamy
Sarathy, Ilya Zakharevich, Benjamin Sugars, Jürgen Christoffel, Joshua
Pritikin, and Alan Burlison, for their help in reality-checking and
polishing this article.  Big thanks to Tom Christiansen for his rewrite
of the prime number generator.

=head1 AUTHOR

Dan Sugalski E<lt>dan@sidhe.orgE<gt>

Slightly modified by Arthur Bergman to fit the new thread model/module.

Reworked slightly by Jörg Walter E<lt>jwalt@cpan.orgE<gt> to be more concise
about thread-safety of Perl code.

Rearranged slightly by Elizabeth Mattijsen E<lt>liz@dijkmat.nlE<gt> to put
less emphasis on yield().

=head1 Copyrights

The original version of this article originally appeared in The Perl
Journal #10, and is copyright 1998 The Perl Journal. It appears courtesy
of Jon Orwant and The Perl Journal.  This document may be distributed
under the same terms as Perl itself.

=cut