fibers.texi

\input texinfo   @c -*-texinfo-*-
@c %**start of header
@setfilename fibers.info
@settitle Fibers
@c %**end of header

@set VERSION 1.2.0
@set UPDATED 22 December 2022

@copying
This manual is for Fibers (version @value{VERSION}, updated
@value{UPDATED})

Copyright 2016 Andy Wingo
Copyright 2021 Maxime Devos
Copyright 2022 Aleix Conchillo Flaqué

@quotation
@c For more information, see COPYING.docs in the fibers
@c distribution.
Permission is granted to copy, distribute and/or modify this document
under the terms of the GNU Free Documentation License, Version 1.3 or
any later version published by the Free Software Foundation; with no
Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
@end quotation
@end copying

@dircategory The Algorithmic Language Scheme
@direntry
* Fibers: (fibers.info).  Lightweight concurrency for Guile.
@end direntry

@titlepage
@title Fibers
@subtitle version @value{VERSION}, updated @value{UPDATED}
@author Andy Wingo
@page
@vskip 0pt plus 1filll
@insertcopying
@end titlepage

@ifnottex
@node Top
@top Fibers


@insertcopying
@menu
* Introduction::          What's this all about?
* Reference::             API reference.
* Pitfalls::              Stay on the happy path.
* Examples::              Starting points for a hack.
* Status::                Fibers is a work in progress.
@end menu

@end ifnottex

@iftex
@shortcontents
@end iftex

@node Introduction
@chapter Introduction

Fibers is a facility for lightweight concurrency in Guile.

@menu
* Context::               How do other systems handle concurrency?
* Design::                Fibers' point in the design space.
* Parallelism::           Faster throughput via more cores.
@end menu

@node Context
@section A brief history of language facilities for concurrency

Modern machines have the raw capability to serve hundreds of thousands
of simultaneous long-lived connections, but it's often hard to manage
this at the software level.  Fibers tries to solve this problem in a
nice way.  Before discussing the approach taken in Fibers, it's worth
spending some time on history to see how we got here.

One of the most dominant patterns for concurrency these days is
``callbacks'', notably in the Twisted library for Python and the
Node.js run-time for JavaScript.  The basic observation in the
callback approach to concurrency is that the efficient way to handle
tens of thousands of connections at once is with low-level operating
system facilities like @code{poll}, @code{epoll} or @code{kqueue}.  By
default, Fibers will try to use the native events system facility
implementation (e.g. @code{epoll} on Linux systems) but
@code{libevent} is also supported for portability reasons.  You add
all of the file descriptors that you are interested in to a ``poll
set'' and then ask the operating system which ones are readable or
writable, as appropriate.  Once the operating system says ``yes, file
descriptor 7145 is readable'', you can do something with that socket;
but what?  With callbacks, the answer is ``call a user-supplied
closure'': a callback, representing the continuation of the
computation on that socket.

Building a network service with a callback-oriented concurrency system
means breaking the program into little chunks that can run without
blocking.  Wherever a program could block, instead of just continuing
the program, you register a callback.  Unfortunately this requirement
permeates the program, from top to bottom: you always pay the mental
cost of inverting your program's control flow by turning it into
callbacks, and you always incur run-time cost of closure creation,
even when the particular I/O could proceed without blocking.  It's a
somewhat galling requirement, given that this contortion is required
of the programmer, but could be done by the compiler.  We Schemers
demand better abstractions than manual, obligatory
continuation-passing-style conversion.

Callback-based systems also encourage unstructured concurrency, as in
practice callbacks are not the only path for data and control flow in
a system: usually there is mutable global state as well.  Without
strong patterns and conventions, callback-based systems often exhibit
bugs caused by concurrent reads and writes to global state.

Some of the problems of callbacks can be mitigated by using
``promises'' or other library-level abstractions; if you're a Haskell
person, you can think of this as lifting all possibly-blocking
operations into a monad.  If you're not a Haskeller, that's cool,
neither am I!  But if your typey spidey senses are tingling, it's for
good reason: with promises, your whole program has to be transformed
to return promises-for-values instead of values anywhere it would
block.

An obvious solution to the control-flow problem of callbacks is to use
threads.  In the most generic sense, a thread is a language feature
which denotes an independent computation.  Threads are created by
other threads, but fork off and run independently instead of returning
to their caller.  In a system with threads, there is implicitly a
scheduler somewhere that multiplexes the threads so that when one
suspends, another can run.

In practice, the concept of threads is often conflated with a
particular implementation, @dfn{kernel threads}.  Kernel threads are
very low-level abstractions that are provided by the operating system.
The nice thing about kernel threads is that they can use any CPU that
is the kernel knows about.  That's an important factor in today's
computing landscape, where Moore's law seems to be giving us more
cores instead of more gigahertz.

However, as a building block for a highly concurrent system, kernel
threads have a few important problems.

One is that kernel threads simply aren't designed to be allocated in
huge numbers, and instead are more optimized to run in a
one-per-CPU-core fashion.  Their memory usage is relatively high for
what should be a lightweight abstraction: some 10 kilobytes at least
and often some megabytes, in the form of the thread's stack.  There
are ongoing efforts to reduce this for some systems but we cannot
expect wide deployment in the next 5 years, if ever.  Even in the best
case, a hundred thousand kernel threads will take at least a gigabyte
of memory, which seems a bit excessive for book-keeping overhead.

Kernel threads can be a bit irritating to schedule, too: when one
thread suspends, it's for a reason, and it can be that user-space
knows a good next thread that should run.  However because kernel
threads are scheduled in the kernel, it's rarely possible for the
kernel to make informed decisions.  There are some ``user-mode
scheduling'' facilities that are in development for some systems, but
again only for some systems.

The other significant problem is that building non-crashy systems on
top of kernel threads is hard to do, not to mention ``correct''
systems.  It's an embarrassing situation.  For one thing, the
low-level synchronization primitives that are typically provided with
kernel threads, mutexes and condition variables, are not composable.
Also, as with callback-oriented concurrency, one thread can silently
corrupt another via unstructured mutation of shared state.  It's worse
with kernel threads, though: a kernel thread can be interrupted at any
point, not just at I/O.  And though callback-oriented systems can
theoretically operate on multiple CPUs at once, in practice they
don't.  This restriction is sometimes touted as a benefit by
proponents of callback-oriented systems, because in such a system, the
callback invocations have a single, sequential order.  With multiple
CPUs, this is not the case, as multiple threads can run at the same
time, in parallel.

Kernel threads can work.  The Java virtual machine does at least
manage to prevent low-level memory corruption and to do so with high
performance, but still, even Java-based systems that aim for maximum
concurrency avoid using a thread per connection because threads use
too much memory.

In this context it's no wonder that there's a third strain of
concurrency: shared-nothing message-passing systems like Erlang.
Erlang isolates each thread (called @dfn{processes} in the Erlang
world), giving each it its own heap and ``mailbox''.  Processes can
spawn other processes, and the concurrency primitive is
message-passing.  A process that tries receive a message from an empty
mailbox will ``block'', from its perspective.  In the meantime the
system will run other processes.  Message sends never block, oddly;
instead, sending to a process with many messages pending makes it more
likely that Erlang will pre-empt the sending process.  It's a strange
trade off, but it makes sense when you realize that Erlang was designed
for network transparency: the same message send/receive interface can
be used to send messages to processes on remote machines as well.

No network is truly transparent, however.  At the most basic level,
the performance of network sends should be much slower than local
sends.  Whereas a message sent to a remote process has to be written
out byte-by-byte over the network, there is no need to copy immutable
data within the same address space.  The complexity of a remote
message send is O(n) in the size of the message, whereas a local
immutable send is O(1).  This suggests that hiding the different
complexities behind one operator is the wrong thing to do.  And
indeed, given byte read and write operators over sockets, it's
possible to implement remote message send and receive as a process
that serializes and parses messages between a channel and a byte sink
or source.  In this way we get cheap local channels, and network shims
are under the programmer's control.  This is the approach that the Go
language takes, and is the one we use in Fibers.

Structuring a concurrent program as separate threads that communicate
over channels is an old idea that goes back to Tony Hoare's work on
``Communicating Sequential Processes'' (CSP).  CSP is an elegant tower
of mathematical abstraction whose layers form a pattern language for
building concurrent systems that you can still reason about.
Interestingly, it does so without any concept of time at all, instead
representing a thread's behavior as a @dfn{trace} of instantaneous
events.  Threads themselves are like functions that unfold over the
possible events to produce the actual event trace seen at run-time.

This view of events as instantaneous happenings extends to
communication as well.  In CSP, one communication between two threads
is modelled as an instantaneous event, partitioning the traces of the
two threads into ``before'' and ``after'' segments.

Practically speaking, this has ramifications in the Go language, which
was heavily inspired by CSP.  You might think that a channel is just a
an asynchronous queue that blocks when writing to a full queue, or
when reading from an empty queue.  That's a bit closer to the Erlang
conception of how things should work, though as we mentioned, Erlang
simply slows down writes to full mailboxes rather than blocking them
entirely.  However, that's not what Go and other systems in the CSP
family do; sending a message on a channel will block until there is a
receiver available, and vice versa.  The threads are said to
``rendezvous'' at the event.

Unbuffered channels have the interesting property that you can
@code{select} between sending a message on channel @var{a} or channel
@var{b}, and in the end only one message will be sent; nothing happens
until there is a receiver ready to take the message.  In this way
messages are really owned by threads and never by the channels
themselves.  You can of course add buffering if you like, simply by
making a thread that waits on either sends or receives on a channel,
and which buffers sends and makes them available to receives.  It's
also possible to add explicit support for buffered channels, as Go
does, which can reduce the number of context switches as there is no
explicit buffer thread.

Whether to buffer or not to buffer is a tricky choice.  It's possible
to implement singly-buffered channels in a system like Erlang via an
explicit send/acknowledge protocol, though it seems difficult to
implement completely unbuffered channels.  As we mentioned, it's
possible to add buffering to an unbuffered system by the introduction
of explicit buffer threads.  In the end though in Fibers we follow
CSP's lead so that we can implement the nice @code{select} behavior
that we mentioned above.

As a final point, @code{select} is OK but is not a great language
abstraction.  Say you call a function and it returns some kind of
asynchronous result which you then have to @code{select} on.  It could
return this result as a channel, and that would be fine: you can add
that channel to the other channels in your @code{select} set and you
are good.  However, what if what the function does is receive a
message on a channel, then do something with the message?  In that
case the function should return a channel, plus a continuation (as a
closure or something).  If @code{select} results in a message being
received over that channel, then we call the continuation on the
message.  Fine.  But, what if the function itself wanted to
@code{select} over some channels?  It could return multiple channels
and continuations, but that becomes unwieldy.

What we need is an abstraction over asynchronous operations, and that
is the main idea of a CSP-derived system called ``Concurrent ML''
(CML).  Originally implemented as a library on top of Standard ML of
New Jersey by John Reppy, CML provides this abstraction, which in
Fibers is called an @dfn{operation}@footnote{CML uses the term
@dfn{event}, but we find this to be a confusing name.}.  Calling
@code{send-operation} on a channel returns an operation, which is just
a value.  Operations are like closures in a way; a closure wraps up
code in its environment, which can be later called many times or not
at all.  Operations likewise can be @dfn{performed}@footnote{In CML,
@dfn{synchronized}.} many times or not at all; performing an operation
is like calling a function.  The interesting part is that you can
compose operations via the @code{wrap-operation} and
@code{choice-operation} combinators.  The former lets you bundle up an
operation and a continuation.  The latter lets you construct an
operation that chooses over a number of operations.  Calling
@code{perform-operation} on a choice operation will perform one and
only one of the choices.  Performing an operation will call its
@code{wrap-operation} continuation on the resulting values.

While it's possible to implement Concurrent ML in terms of Go's
channels and baked-in @code{select} statement, it's more expressive to
do it the other way around, as that also lets us implement other
operations types besides channel send and receive, for example
timeouts and condition variables.

@node Design
@section Fibers design

In Fibers, the unit of computation is the @dfn{fiber}, a lightweight
thread managed by Guile.  A fiber communicates with the outside world
via normal Guile ports: @code{get-bytevector}, @code{put-string}, and
all that.  Within a single Guile process fibers communicate by sending
and receiving Scheme values over @dfn{channels}.

Whenever a fiber tries to read but no data is available, or tries to
write but no data can be written, Guile will suspend the fiber and
arrange for it to be resumed when the port or channel operation can
proceed.  In the meantime, Guile will run other fibers.  When no fiber
is runnable, Guile will use efficient system facilities to sleep until
input or output can proceed.

When a fiber would block, it suspends to the scheduler from the
current thread.  The scheduler will arrange to re-start the fiber when
the port or channel becomes readable or writable, as appropriate.  For
ports, the scheduler adds the file descriptor associated with the port
as a pending event.  In either case, the scheduler remembers which
fibers are waiting and for what, so that the user can inspect the
state of their system.

Currently in Fibers there is no ambient scheduler running; an error is
signalled if a user calls @code{spawn-fiber} while not inside a
@code{run-fibers} invocation.  However it is possible to communicate
with fibers via channels or other Concurrent ML-like operations, even
outside of a @code{run-fibers} invocation.  If an operation would
block, it suspends the entire kernel thread until the operation can
proceed.

On the Scheme level, a fiber is a delimited continuation.  When a
scheduler runs a fiber, it does so within a prompt; when the fiber
suspends, it suspends to the prompt.  The scheduler saves the
resulting continuation as part of the fiber's state.  In this way the
per-fiber computational state overhead is just the size of the pending
stack frames of the fiber, which can be just a handful of words.

By default, Fibers takes advantage of all available cores on your
system.  @xref{Parallelism}, for full details.

Ports are how fibers communicate with the world; channels are how
fibers communicate with each other.  Channels are meeting places
between fibers, or between threads.  A fiber or thread that goes to
send a message over a channel will block until there is a fiber or
thread ready to receive the message, and vice versa.  Once both
parties are ready, the message is exchanged and both parties resume.
There can be multiple fibers and threads waiting to read and write on
a channel, allowing channels to express not only pipelines but also
common concurrency patterns such as fan-in and fan-out.

Unlike Erlang channels, channels in Fibers are purely local and do not
attempt to provide the illusion of network transparency.  This does
have the positive advantage that we are able to provide better
backpressure support than Erlang, blocking when no receiver is
available to handle a message instead of letting the sender keep
sending many messages.

To avoid starvation, a fiber can only run once within a ``turn''.
Each turn starts with a poll on file descriptors of interest and marks
the associated fibers as runnable.  If no fiber is runnable at the
start of the poll, the poll call will ask the kernel to wait for a
runnable descriptor.  Otherwise the poll call will still check for
runnable file descriptors, but also ask the kernel to return
immediately.  There is an additional FD added to the poll set that is
used to interrupt a blocking poll, for example if a fiber becomes
runnable due to I/O on a channel from a separate kernel thread while
the first scheduler was still polling.

If a fiber runs for too long (by default, 10 milliseconds), it will be
@dfn{preempted}: interrupted and rescheduled for the next turn.  The
preemption frequency can be tuned by the user or turned off for a
fully cooperative scheduling model.

To enable expressive cross-kernel-thread communications, channel sends
and receives are atomic and thread-safe.

@node Parallelism
@section Parallelism

By default, Fibers will take advantage of all CPU cores available to
it.  The degree of parallelism is controlled by the
@code{#:parallelism} keyword argument to @code{run-fibers}, which
defaults to @code{(current-processor-count)}.
@xref{Threads,,,guile.info,Guile Reference Manual}, for more
information on @code{current-processor-count}.  Pass a different
argument to @code{#:parallelism} to choose a different degree of
parallelism, for example @code{1} for single-threaded operation.  To
allocate specific cores to a Guile process, use the @code{taskset}
command-line utility.

A newly spawned fiber will be scheduled on the kernel thread in which
it was created, unless @code{#:parallel? #t} was passed to the
@code{spawn-fiber} invocation, in which case its initial kernel thread
will be selected at random.  In this way the default is to preserve
locality of memory access and minimize cross-thread coordination.

Additionally, after a scheduler has exhausted its run queue for the
current turn, if it has nothing scheduled for the next turn it will
try to steal work from other schedulers.  This @dfn{work stealing}
allows a set of parallel schedulers to automatically rebalance and
burn through the current global run queue as fast as possible.

After processing its current run queue, possibly including stolen work
if its next run queue was empty, a scheduler will then ask the
operating system for any file descriptors that have pending activity.
The scheduler puts a time limit on this sleep phase if there are
pending timeouts, but otherwise the sleep will only wake up when a
file descriptor becomes readable or writable, or if another thread
wakes up the scheduler.  Schedulers that are sleeping do not
participate in work stealing.  For this reason there is another source
of work rebalancing in Fibers, @dfn{work sharing}.  As mentioned
above, to schedule a fiber on a random remote scheduler, use
@code{spawn-fiber} with the @code{#:parallel? #t} keyword argument.

The specifics of the scheduling algorithm may change, and it may be
that there is no global ``best scheduler''.  We look forward to
experimenting and finding not only a good default algorithm, but also
a library that you can use to find your own local maximum in the
scheduling space.

As far as performance goes, we have found that computationally
intensive tasks parallelize rather well.  Expect near-linear speedup
as you make more cores available to fibers.

On the other hand, although allocation rate improves with additional
cores, it currently does not scale linearly, and works best when all
cores are on the same NUMA node.  This is due to details about how
Guile manages its memory.

In general there may be many bottlenecks that originate in Guile,
Fibers, and in your application, and these bottlenecks constrain the
ability of an application to scale linearly.

Probably the best way to know if Fibers scales appropriately for your
use case is to make some experiments.  To restrict the set of cores
available to Guile, run Guile from within @code{taskset -c}.  See
@code{taskset}'s manual page.  For machines with multiple sockets you
will probably want to use @code{numactl --membind} as well.  Then to
test scalability on your machine, run @code{./env guile
tests/speedup.scm} from within your Fibers build directory, or
benchmark your application directly.  In time we should be able to
develop some diagnostic facilities to help the Fibers user determine
where a scaling bottleneck is in their application.

@node Reference
@chapter API reference

Fibers is a library built on Guile.  It consists of a public
interface, base support for asynchronous operations, implementations
of operations for channels and timers, and an internals interface.

@menu
* Using Fibers::         User-facing interface to fibers
* Operations::           Composable abstractions for concurrency.
* Channels::             Share memory by communicating.
* Timers::               Operations on time.
* Conditions::           Waiting for simple state changes.
* Port Readiness::       Waiting until a port is ready for I/O.
* REPL Commands::        Experimenting with Fibers at the console.
* Schedulers and Tasks:: Fibers are built from lower-level primitives.
@end menu

@node Using Fibers
@section Using Fibers

The public interface of fibers right now is quite minimal.  To use it,
import the @code{(fibers)} module:

@example
(use-modules (fibers))
@end example

To create a new fibers scheduler and run it in the current Guile
thread, use @code{run-fibers}.

@defun run-fibers [init-thunk=@code{#f}] @
       [#:install-suspendable-ports?=@code{#t}] @
       [#:scheduler=@code{#f}] @
       [#:parallelism=@code{(current-processor-count)}] @
       [#:cpus=@code{(getaffinity 0)}] @
       [#:hz=@code{100}] [#:drain?=@code{#f}]
Run @var{init-thunk} within a fiber in a fresh scheduler, blocking
until @var{init-thunk} returns.  Return the value(s) returned by the
call to @var{init-thunk}.

For example:
@example
(run-fibers (lambda () 1))
@result{} 1

(run-fibers
 (lambda ()
   (spawn-fiber (lambda () (display "hey!\n"))))
 #:drain? #t)
@print{} hey!
@end example

Calling @code{run-fibers} will ensure that Guile's port implementation
allows fibers to suspend if a read or a write on a port would block.
@xref{Non-Blocking I/O,,,guile.info,Guile Reference Manual}, for more
details on suspendable ports.  If for some reason you want port reads
or writes to prevent other fibers from running, pass @code{#f} as the
@code{#:install-suspendable-ports?} keyword argument.

By default, @code{run-fibers} will create a fresh scheduler, and
destroy it after @code{run-fibers} finishes.  If you happen to have a
pre-existing scheduler (because you used the low-level scheduler
interface to create one), you can pass it to @code{run-fibers} using
the @code{#:scheduler} keyword argument.  In that case the scheduler
will not be destroyed when @code{run-fibers} finishes.

@code{run-fibers} will return when the @var{init-thunk} call returns.
To make it additionally wait until there are no more runnable fibers
or pending timeouts, specify the @code{#:drain? #t} keyword argument.

If @code{run-fibers} creates a scheduler on your behalf, it will
arrange for a number of ``peer'' schedulers to also be created, up to
a total scheduler count controlled by the @var{parallelism} keyword
argument.  These peer schedulers will be run in separate threads and
will participate in work rebalancing.  The fibers will be run on the
CPUs specified by @var{cpus}.  @xref{Parallelism}.

By default @var{hz} is 100, indicating that running fibers should be
preempted 100 times per every second of CPU time (not wall-clock
time).  Note that preemption will only occur if the fiber can actually
be suspended; @xref{Barriers}, for more information.  Pass @code{0}
for @var{hz} to disable preemption, effectively making scheduling
fully cooperative.
@end defun

@defun spawn-fiber thunk [scheduler=@code{(require-current-scheduler)}] @
       [#:parallel?=@code{#f}]
Spawn a new fiber that will run @var{thunk}.  Return the new fiber.
The new fiber will run concurrently with other fibers.

The fiber will be added to the current scheduler, which is usually
what you want.  It's also possible to spawn the fiber on a specific
scheduler, which is useful to ensure that the fiber runs on a
different kernel thread.  In that case, pass the optional
@code{scheduler} argument.

If @var{parallel?} is true, the fiber will be started not
(necessarily) on @var{scheduler}, but on a random member of the peer
set of @var{scheduler}.  @xref{Parallelism}.  Note that every
scheduler is a member of its own peer set.

The fiber will inherit the fluid--value associations (the dynamic
state) in place when @code{spawn-fiber} is called.  Any
@code{fluid-set!} or parameter set within the fiber will not affect
fluid or parameter bindings outside the fiber.
@end defun

@defun sleep seconds
Wake up the current fiber after @var{seconds} of wall-clock time have
elapsed.  This definition will replace the binding for @code{sleep} in
the importing module, effectively overriding Guile's ``core''
definition.
@end defun

@node Operations
@section Operations

Operations are first-class abstractions for asynchronous events.
There are primitive operation types, such as waiting for a timer
(@pxref{Timers}) or waiting for a message on a channel
(@pxref{Channels}).  Operations can also be combined and transformed
using the @code{choice-operation} and @code{wrap-operation} from this module:

@example
(use-modules (fibers operations))
@end example

@defun wrap-operation op f
Given the operation @var{op}, return a new operation that, if and when
it succeeds, will apply @var{f} to the values yielded by performing
@var{op}, and yield the result as the values of the wrapped operation.
@end defun

@defun choice-operation . ops
Given the operations @var{ops}, return a new operation that if it
succeeds, will succeed with one and only one of the sub-operations
@var{ops}.
@end defun

Finally, once you have an operation, you can perform it using
@code{perform-operation}.

@defun perform-operation op
Perform the operation @var{op} and return the resulting values.  If the
operation cannot complete directly, block until it can complete.
@end defun

@xref{Introduction}, for more on the ``Concurrent ML'' system that
introduced the concept of the operation abstraction.  In the context
of Fibers, ``blocking'' means to suspend the current fiber, or to
suspend the current kernel thread if the operation is performed
outside of a fiber.

There is also a low-level constructor for other modules that implement
primitive operation types:

@defun make-base-operation wrap-fn try-fn block-fn
Make a fresh base operation.
@end defun

This is a low-level constructor, though; if you ever feel the need to
call @code{make-base-operation}, make sure you're familiar with the
Concurrent ML literature.  Godspeed!

@node Channels
@section Channels

Channels are the way to communicate between fibers.  To use them, load
the channels module:

@example
(use-modules (fibers channels))
@end example

@defun make-channel
Make a fresh channel.
@end defun

@defun channel? obj
Return @code{#t} if @var{obj} is a channel, or @code{#f} otherwise.
@end defun

@defun put-operation channel message
Make an operation that if and when it completes will rendezvous with a
receiving operation to send @var{message} over @var{channel}.
@end defun

@defun get-operation channel
Make an operation that if and when it completes will rendezvous with a
sending operation to receive one value from @var{channel}.
@end defun

@defun put-message channel message
Send @var{message} on @var{channel}, and return zero values.  If there
is already a receiver waiting to receive a message on this channel,
give it our message and continue.  Otherwise, block until a receiver
becomes available.

Equivalent to:
@example
(perform-operation (put-operation channel message))
@end example
@end defun

@defun get-message channel
Receive a message from @var{channel} and return it.  If there is
already a sender waiting to send a message on this channel, take its
message directly.  Otherwise, block until a sender becomes available.

Equivalent to:
@example
(perform-operation (get-operation channel))
@end example
@end defun

Channels are thread-safe; you can use them to send and receive values
between fibers on different kernel threads.

@node Timers
@section Timers

Timers are a kind of operation that, you guessed it, let you sleep
until a certain time.

@example
(use-modules (fibers timers))
@end example

@defun sleep-operation seconds
Make an operation that will succeed with no values when @var{seconds}
have elapsed.
@end defun

@defun timer-operation expiry
Make an operation that will succeed when the current time is greater
than or equal to @var{expiry}, expressed in internal time units.  The
operation will succeed with no values.
@end defun

@defun sleep seconds
Block the calling fiber or kernel thread until @var{seconds} have
elapsed.
@end defun

@node Conditions
@section Conditions

Condition variables are a simple one-bit form of concurrent
communication.  A condition variable has two states: it starts in the
@dfn{unsignalled} state and later may transition to the
@dfn{signalled} state.  When a condition becomes signalled, any
associated waiting operations complete.

@example
(use-modules (fibers conditions))
@end example

@defun make-condition
Make a new condition variable.
@end defun

@defun condition? obj
Return @code{#t} if @var{obj} is a condition variable, or @code{#f}
otherwise.
@end defun

@defun signal-condition! cvar
Signal @var{cvar}, notifying all waiting fibers and preventing
blocking of future fibers waiting on this condition.
@end defun

@defun wait-operation cvar
Make an operation that will succeed with no values when @var{cvar}
becomes signalled.
@end defun

@defun wait cvar
Block the calling fiber or kernel thread until @var{cvar} becomes
signalled.  Equivalent to @code{(perform-operation (wait-operation
cvar))}.
@end defun

@node Port Readiness
@section Port Readiness

These two operations can be used on file ports to wait until
they are readable or writable.  Spurious wake-ups are possible.
This is complementary to Guile's suspendable ports.

@example
(use-modules (fibers io-wakeup))
@end example

@defun wait-until-port-readable-operation port
Make an operation that will succeed with no values when the input
port @var{port} becomes readable.  For passive sockets, this operation
succeeds when a connection becomes available.
@end defun

@defun wait-until-port-writable-operation port
Make an operation that will succeed with no values when the output
port @var{port} becomes writable.
@end defun

@node REPL Commands
@section REPL Commands

Fibers implements some basic extensions to the Guile command-line
interface (its Read-Eval-Print Loop, or the REPL).  Prefix these
commands with a comma (@code{,}) to run them at the REPL; see
@code{,help fibers} for full details, once you have loaded the
@code{(fibers)} module of course.

@deffn {REPL Command} scheds
Show a list of all schedulers.
@end deffn

@deffn {REPL Command} spawn-sched
Create a new scheduler for fibers, and run it on a new kernel thread.
@end deffn

@deffn {REPL Command} kill-sched name
Shut down the scheduler named @var{name}.  Use @code{,scheds} to list
scheduler names.
@end deffn

@deffn {REPL Command} spawn-fiber exp [sched]
Spawn a new fiber that runs @var{exp}.  If @var{sched} is given, the
fiber will be spawned on the given scheduler.
@end deffn

@node Schedulers and Tasks
@section Schedulers and Tasks

Internally, fibers are built on top of schedulers and tasks.

A scheduler runs tasks.  A task is just a thunk (a function of no
arguments) whose return value is ignored.  A scheduler runs tasks in
batches, once per turn.  Each turn, a scheduler takes all tasks from
its ``next'' run-queue and adds them to its ``current'' run-queue, and
then runs the tasks on the current run-queue in order.  The scheduler
then goes to the next turn, unless its ``finished?'' function returns
true.

Both the ``next'' and the ``current'' run-queues are public atomic
data structures.  Scheduling a task adds it to the ``next'' run-queue.
Scheduling a task is a thread-safe operation; it can be done by any
thread.  Scheduling a task on a scheduler running on a remote thread
will ensure that the thread wakes up from any sleeping operation it
might be currently engaged in.

There is normally just one scheduler for each kernel thread that runs
fibers.  Several schedulers can be made aware of each other so that
they can one can spread out the load when spawning tasks that should
run in parallel.  Also, before moving to the next turn, a scheduler
will try to steal work from other schedulers that it knows about,
popping off an item from the remote scheduler's ``current'' run-queue.

There are two additional sources of tasks for a scheduler: file
descriptor events and timers.  When gathering tasks to schedule for
the next turn, a scheduler will call the native events system faciliy
implementation to be notified of activity on file descriptors of
interest.  If there are no pending tasks on the next run-queue, the
call to the events system facility will sleep until the scheduler is
woken up, or until a timer expires.

The capability that allows fibers to be built on schedulers is that
tasks can suspend.  Suspending a task calls a user-supplied
after-suspend handler that is passed the continuation of the task.
The user can then schedule that continuation at some later time.  In
this way a fiber starts as a single task run by a scheduler, but each
time it suspends and is resumed, a fresh task consisting of the
fiber's is scheduled.  The fibers layer also uses other Guile
mechanisms to isolate fibers from each other, such as dynamic states.

All interfaces in this module are thread-safe except where marked
otherwise.

@example
(use-modules (fibers scheduler))
@end example

@defun make-scheduler [#:parallelism=@code{#f}] @
       [#:prompt-tag=@code{(make-prompt-tag "fibers")}]
Make a new scheduler in which to run fibers.  If @var{parallelism} is
true, it should be an integer indicating the number of schedulers to
make.  The resulting schedulers will all share the same prompt tag and
will steal and share out work from among themselves.
@end defun

@defun run-scheduler sched finished?
Run @var{sched} until calling the supplied @var{finished?} thunk
returns true.  Return zero values.  Signal an error if @var{scheduler}
is already running in some other kernel thread.
@end defun

@defun current-scheduler
Return the current scheduler, or @code{#f} if no scheduler is current.
@end defun

@defun scheduler-kernel-thread sched
Return the kernel thread that @var{sched} is running on, or @code{#f}
if it is not currently running.
@end defun

@defun scheduler-runcount sched
Return the number of tasks that have been run on @var{sched}, modulo
@math{2^{32}}.  This interface is useful as a lightweight check to see if
a remote scheduler is making progress.
@end defun

@defun scheduler-remote-peers sched
Return a list of peer schedulers of @var{sched}, not including
@var{sched} itself.
@end defun

@defun scheduler-work-pending? sched
Return @code{#t} if @var{sched} has any work pending: any runnable
tasks or any pending timeouts.
@end defun

@defun choose-parallel-scheduler sched
Return a random scheduler from @var{sched}'s peer set.  Note that
@var{sched}'s peer set includes @var{sched} itself.
@end defun

@defun destroy-scheduler sched
Release any resources associated with @var{sched}.
@end defun

@defun schedule-task sched task
Arrange to run @var{task}, a procedure of no arguments, on the next
turn of @var{sched}.  If @var{sched} is remote and sleeping, it will
be woken up.
@end defun

@defun schedule-task-when-fd-readable sched fd task
Arrange to schedule @var{task} when the file descriptor @var{fd}
becomes readable.  @emph{Not thread-safe.}
@end defun

@defun schedule-task-when-fd-writable sched fd task
Arrange to schedule @var{task} on @var{sched} when the file descriptor
@var{fd} becomes writable.  @emph{Not thread-safe.}
@end defun

@defun schedule-task-at-time sched expiry task
Arrange to schedule @var{task} on @var{sched} when the absolute real
time is greater than or equal to @var{expiry}, expressed in internal
time units.  @emph{Not thread-safe.}
@end defun

@defun suspend-current-task after-suspend
Suspend the current task to the current scheduler.  After suspending,
call the @var{after-suspend} callback with two arguments: the current
scheduler, and the continuation of the current task.  The continuation
passed to the @var{after-suspend} handler is the continuation of the
@code{suspend-current-task} call.
@end defun

@defun yield-current-task
Yield control to the current scheduler.  Like calling
@code{(suspend-current-task schedule-task)} except that it avoids
suspending if the current continuation isn't suspendable.  Returns
@code{#t} if the yield succeeded, or @code{#f} otherwise.
@end defun

@node Pitfalls
@chapter Pitfalls

Running Guile code within a fiber mostly ``just works''.  There are a
few pitfalls to be aware of though.

@menu
* Blocking::            Avoid calling blocking operations.
* Barriers::            Avoid suspending inside continuation barriers.
* Mutation::            Avoid unstructured mutation of shared data.
* Mutexes::             Mutexes and fibers don't mix very well.
@end menu

@node Blocking
@section Blocking

When you run a program under fibers, the fibers library arranges to
make it so that port operations can suspend the fiber instead of
block.  This generally works, with some caveats.

@enumerate
@item
The port type has to either never block, or support non-blocking I/O.
Currently the only kind of port in Guile are file ports (including
sockets), and for them this condition is fulfilled.  However notably
non-blocking I/O is not supported for custom binary I/O ports, not yet
anyway.  If you need this, get it fixed in Guile :)
@item
You have to make sure that any file port you operate on is opened in
nonblocking mode.  @xref{Non-Blocking I/O,,,guile.info,Guile Reference
Manual}, for the obscure @code{fcntl} incantation to use on your
ports.
@item
You have to avoid any operation on ports that is not supported yet in
Guile for non-blocking I/O.  Since non-blocking I/O is new in Guile,
only some I/O operations are expressed in terms of the primitive
operations.  Notably, Scheme @code{read}, @code{display}, and
@code{write} are still implemented in C, which prevents any fiber that
uses them from suspending and resuming correctly.  What will happen
instead is that the call blocks instead of suspending.  If you find a
situation like this, talk to Guile developers to get it fixed :)
@item
You can enable non-blocking I/O for local files, but Linux at least
will always say that the local file is ready for I/O even if it has to
page in data from a spinning-metal device.  This is a well-known
limitation for which the solution is apparently to do local I/O via a
thread pool.  We could implement this in Fibers, or in Guile... not
sure what the right thing is!
@end enumerate

You also have to avoid any other library or system calls that would
block.  One common source of blocking is @code{getaddrinfo} and
related network address resolution library calls.  Again, apparently
the solution is thread pools?  Probably in Fibers we should implement
a thread-pooled address resolver.

The @code{(fibers)} module exports a @code{sleep} replacement.  Code
that sleeps should import the @code{(fibers)} module to be sure that
they aren't using Guile's @code{sleep} function.

Finally, a fiber itself has to avoid blocking other fibers; it must
reach a ``yield point'' some time.  A yield point includes a read or
write on a port or a channel that would block, or a @code{sleep}.
Other than that, nothing will pre-empt a fiber, at least not
currently.  If you need to yield to the scheduler, then at least do a
@code{(sleep 0)} or something.

@node Barriers
@section Barriers

When a fiber suspends, Fibers uses @code{abort-to-prompt} to save the
fiber's continuation, saving each pending computation in that fiber to
the heap.  When the fiber resumes, Fibers invokes the saved
continuation, effectively replaying these saved stack frames back onto
the current stack.  For this operation to succeed, the saved
continuation needs to be @dfn{suspendable}.  A suspendable
continuation should be able to be resumed after the call to
@code{abort-to-prompt}.

Most continuations in Guile are suspendable.  However, not all of them
are.  It's possible to explicitly instate a continuation barrier
(@pxref{Continuation Barriers,,,guile.info,Guile Reference Manual})
that will allow the continuation to be aborted but not reinstated:

@example
;; If put-message suspends, we will never resume!
(run-fibers
 (lambda ()
   (with-continuation-barrier
     (lambda () (put-message channel 42)))))
@end example

If the @code{put-message} call can't succeed directly, then the fiber
will suspend.  However when the fiber becomes runnable again, it can't
be rewound because of the barrier.  Because this is the case, when
Fibers goes to suspend a computation but realizes that the suspended
fiber could never be resumed, it throws an error instead.

@code{with-continuation-barrier} is the only function in Guile that
establishes a continuation barrier on purpose.  However there are
number of other functions that accidentally establish a continuation
barrier by recursing into C code and then back to Scheme.  (Guile can
only rewind the state of a saved computation if Guile created the
corresponding stack frame, and that's not the case for the
intermediate stack frame created by the C compiler.)

Accidental continuation barriers are bugs, and the Guile developers
have been working on removing them over the years.  By now, most of
the high-priority accidental barriers are gone.  Those that are left
include:

@itemize
@item The body thunk of @code{call-with-blocked-asyncs}
@item GOOPS methods attached to a primitive-generic like @code{+} or
@code{equal?}
@item Dynwind entry/exit handlers, but only when called due to nonlocal
entry or exit
@item R6RS custom binary port callbacks
@item Legacy ``soft port'' callbacks
@item R5RS ``delay'' callbacks
@item Many module system callbacks (module transformers, etc)
@item SRFI-13 string and character-set callbacks
@item Callbacks from some SRFI-1 functions
@item Callbacks from @code{sort}
@item Custom hash table assoc functions
@item Calls to @code{load-from-path} (though, oddly, not @code{load})
@item Object printers, e.g. custom record printers
@item @code{call-with-vm}
@item @code{array-map} and related array functions
@end itemize

This set will be reduced over time as more of @code{libguile} is
rewritten in Scheme.

Finally, for port operations, @xref{Non-Blocking
I/O,,,guile.info,Guile Reference Manual}.  When Guile tries to read
from a file descriptor and nothing is available, normally it would
call the current read waiter, which Fibers customizes to suspend the
fiber and run another one in the meantime.  However for procedures
that have not been rewritten in terms of the ``suspendable port
operations'', notably including @code{read}, @code{write}, and
@code{display}, the nothing-to-read condition is handled in C, not
Scheme, so Guile cannot create a resumable continuation.  In this
case, instead of erroring, Guile will wait until the file descriptor
is readable or writable (as appropriate) and then continue.  However
in the meantime, which may be forever, this blocks other fibers from
running.  Therefore Fibers users sometimes have to be aware of the
state of Guile's rewrite of port operations in terms of
suspendable-port primitives, and to help out if things aren't moving
fast enough :)

@node Mutation
@section Mutation

Although code run within fibers looks like normal straight-up Scheme,
it runs concurrently with other fibers.  This means that if you mutate
shared state and other fibers mutate the same shared state using
normal Scheme procedures like @code{set!}, @code{vector-set!}, or the
like, then probably you're going to have a bad time.  This is
especially the case considering that the default is to run as many
schedulers in parallel as your machine has cores, and also to preempt
fibers at any time.

Even if you explicitly choose a cooperative scheduling mode by
disabling interrupts and parallelism, multi-step transactions may be
suspended if your code reaches a yield point in the middle of
performing the transaction.

The best way around this problem is to avoid unstructured mutation,
and to instead share data by communicating over channels.  Using
channels to communicate data produces much more robust, safe systems.

If you need to mutate global data, the best way is to use an atomic
variable.  If that is not possible, then consider spawning a fiber to
manage the mutable data, and communicating with that fiber over
channels.  Mutexes are also an option but are difficult to use
correctly; see the considerations from the following section.

@node Mutexes
@section Mutexes

Mutexes are low-level synchronization primitives provided by Guile.
Used properly, they can be used to build concurrent systems that
concurrently access data without corrupting it.

@xref{Mutexes and Condition Variables,,,guile.info,Guile Reference
Manual}, for some reasons why mutexes aren't so great for Guile in
general.

Guile's mutexes are an even worse solution with a Fibers system.  It
is a bad idea for a fiber to grab a Guile mutex, because if the mutex
is not available, Guile will suspend not just the fiber that is
running but the entire kernel thread.  If the mutex is available, the
fiber obtains it, cool; but if it the fiber suspends while holding a
mutex, that's bad news.  Any fiber trying to acquire a mutex while a
suspended fiber from the same thread already has the mutex will result
in an error: as Guile thinks that the mutex has already been acquired
by the current thread, it detects recursion and bails out.

With condition variables, similar problems arise: waiting on a
condition variable will block indefinitely, if the condition can only
be signalled by another fiber in the current kernel thread.

The root of this problem is that Guile associates mutexes with kernel
threads, not fibers.  It would be possible however to make a
Fibers-appropriate implementation of mutexes, but we suggest that
users try atomic boxes or channels instead.  If you do use mutexes,
make sure you disable preemption (possibly by a local call to
@code{call-with-blocked-asyncs}), and take care to never suspend a
fiber while it owns any mutex.

@node Examples
@chapter Examples

Here are some small examples to get you started.

@menu
* Ping::                  An echo server and client.
* Memcached::             A simple memcached server and client.
* Web Server Backend::    A backend for Guile's web server.
* Concurrent Web Server:: A more concurrent web server.
@end menu

More examples would be great, especially demonstrating interesting
things that can be done with channels.

@node Ping
@section Ping

@subsection Server

This simple server listens on a TCP port, echoing lines back to any
user that connects.  This file can be found in
@code{examples/ping-server.scm}, and can be run from the build dir as
@code{./env guile examples/ping-server.scm}.

First, we use some standard Guile modules, and the fibers module.

@example
(use-modules (rnrs bytevectors)
             (fibers)
             (ice-9 textual-ports)
             (ice-9 rdelim)
             (ice-9 match))
@end example

We run the server within a @code{run-fibers} call.

@example
(define* (run-ping-server #:key
                          (host #f)
                          (family AF_INET)
                          (addr (if host
                                    (inet-pton family host)
                                    INADDR_LOOPBACK))
                          (port 11211)
                          (socket (make-default-socket family addr port)))
  (listen socket 1024)
  (sigaction SIGPIPE SIG_IGN)
  (socket-loop socket (make-hash-table)))

(run-fibers run-ping-server)
@end example

Up to here, all good.  Perhaps we should look at how to open a socket;
here's a couple helper that appears often in applications that use
suspendable ports.  @xref{Non-Blocking I/O,,,guile.info,Guile
Reference Manual}, for full details.

@example
(define (make-default-socket family addr port)
  (let ((sock (socket PF_INET SOCK_STREAM 0)))
    (setsockopt sock SOL_SOCKET SO_REUSEADDR 1)
    (fcntl sock F_SETFD FD_CLOEXEC)
    (fcntl sock F_SETFL (logior O_NONBLOCK (fcntl sock F_GETFL)))
    (bind sock family addr port)
    sock))
@end example

We hope to make this easier in the future; it's a bit too much
ceremony.  Now, the main dish is the server loop, that simply accepts
new connections, forking off a fiber for each connection:

@example
(define (socket-loop socket store)
  (let loop ()
    (match (accept socket SOCK_NONBLOCK)
      ((client . addr)
       (spawn-fiber (lambda () (client-loop client addr store)))
       (loop)))))
@end example

Finally, the loop to handle a single client:

@example
(define (client-loop port addr store)
  (setvbuf port 'block 1024)
  ;; Disable Nagle's algorithm.  We buffer ourselves.
  (setsockopt port IPPROTO_TCP TCP_NODELAY 1)
  (let loop ()
    ;; TODO: Restrict read-line to 512 chars.
    (let ((line (read-line port)))
      (cond
       ((eof-object? line)
        (close-port port))
       (else
        (put-string port line)
        (put-char port #\newline)
        (force-output port)
        (loop))))))
@end example

This ping server is fairly straightforward, and is only flawed in a
couple of ways: it doesn't limit the line size, and so is vulnerable
to memory exhaustion if the client gives it a very, very big line, and
additionally, it does not time out clients after inactivity, so the
poll set could get quite large.

In practice the limit for the number of connections is imposed by the
system in the form of a file descriptor limit.  Use @code{ulimit -n}
to increase this limit on the console, or @code{setrlimit} to increase
it from Guile, within the hard limits imposed by the system.

@subsection Client

The client is similar to the server; see
@code{examples/ping-client.scm} for full details.  It can be run as
@code{./env guile examples/ping-client.scm N M}, to make N concurrent
connections to the server and make a series of M requests on each
connection.  It spawns a fiber per connection, and then uses a normal
Guile loop to make the serial requests.

@example
(define (run-ping-test num-clients num-connections)
  ;; The getaddrinfo call blocks, unfortunately.  Call it once before
  ;; spawning clients.
  (let ((addrinfo (car (getaddrinfo "localhost" "11211"))))
    (let lp ((n 0))
      (when (< n num-clients)
        (spawn-fiber
         (lambda ()
           (client-loop addrinfo n num-connections)))
        (lp (1+ n))))))
@end example

Running this on a laptop from 2015 yields results more or less like
this:

@example
$ time ./env guile examples/ping-client.scm 1000 100

real	0m1.647s
user	0m2.176s
sys	0m0.816s
@end example

That's a throughput of somewhere around 60000 fiber switches per
second on each side, which is none too shabby.

@node Memcached
@section Memcached

Similarly to the echo server, Fibers includes an example memcached
server and client.  Run the server like this:

@example
$ ./env guile examples/memcached-server.scm
@end example

Run the client as with the ping client:

@example
$ time ./env guile examples/memcached-client.scm 1000 100

real	0m6.343s
user	0m9.868s
sys	0m1.808s
@end example

Here we see a throughput of around 16000 puts plus 16000 gets per
second on this 2-core, 2-thread-per-core machine; pretty OK for a
basic implementation.

@node Web Server Backend
@section Web Server Backend

Fibers includes a ``backend'' for Guile's built-in web server that
uses non-blocking fibers to read requests and write responses.  Fibers
also includes a standalone web server that uses Guile's HTTP
facilities, but not its web server framework.  @xref{Concurrent Web
Server}, for more on the standalone web server.

To run a web server that serves each client from
fibers, specify the @code{fibers} backend when running your web
server:

@example
(use-modules (web server))

(define (handler request body)
  (values '((content-type . (text/plain)))
          "Hello, World!"))

(run-server handler 'fibers)
@end example

Performance seems to be about 60% of the standard web server backend
implementation shipped with Guile, though it is not as
battle-hardened.

The fibers web server backend is an interesting case because it uses
channels to invert the inversion-of-control imposed on the backend by
the web server framework.  The web server wants the backend to provide
``read-request'' and ``write-response'' callbacks, whereas in fibers
we usually want to run dedicated REPL-like fibers over the client
sockets.  The fibers backend enables this by passing a callback to the
per-client loops:

@example
(define (have-request response-channel request body)
  (put-message request-channel
               (vector response-channel request body))
  (match (get-message response-channel)
    (#(response body)
     (values response body))))
(let loop ()
  (match (accept socket SOCK_NONBLOCK)
    ((client . sockaddr)
     ;; ...
     (spawn-fiber (lambda () (client-loop client have-request))
                  #:parallel? #t)
     (loop))))
@end example

From the perspective of the @code{client-loop} fiber,
@code{have-request} is a normal function that takes a request and
returns a response, and the @code{client-loop} fiber is in control.
But from the perspective of the web server, the @code{server-read} and
@code{server-write} callbacks are straightforward and idiomatic too:

@example
(define (server-read server)
  (match (get-message (server-request-channel server))
    (#(response-channel request body)
     (let ((client response-channel))
       (values client request body)))))

(define (server-write server client response body)
  (let ((response-channel client))
    (put-message response-channel (vector response body)))
  (values))
@end example

This takes advantage of the fact that we can use @code{get-message},
@code{put-message}, and other CML operations both inside and outside
of fibers, and it mostly just does the right thing.

Note also the @code{#:parallel? #t} on the @code{spawn-fiber}
invocation.  The natural unit of parallelism in a web server is the
request (or the client), so it's at this point that we introduce work
sharing to our system, allowing us to naturally take advantage of
multiple cores on our server.

@node Concurrent Web Server
@section Concurrent Web Server

Guile's web server framework single-threads all web request handling.
The handler procedure can be passed a number of additional ``state''
arguments, and is expected to return a corresponding number of
additional values to use as the next state.  This is sometimes what
you want, but it does limit concurrency; instead it would be nice to
be able to not only the input and output running concurrently, but
also handlers too.

For this reason, Fibers includes a simple standalone web server that
uses Guile's HTTP facilities, but not its web server framework.  To
run a standalone web server, use the @code{(fibers web server)}
module:

@example
(use-modules (fibers web server))

(define (handler request body)
  (values '((content-type . (text/plain)))
          "Hello, World!"))

(run-server handler)
@end example

Compared to the Fibers web server backend (@pxref{Web Server
Backend}), using the standalone fibers web server enables more
parallelism, as the handlers can run in parallel when you have
multiple cores.  Single-core performance of the standalone server is
slightly better than the web server backend, and unlike the backend it
scales with the number of cores available.

@node Status
@chapter Project status

Fibers is feature-complete and ready to go!  It's early days but
things are solid enough to say without embarrassment or misgiving that
Guile now has a solid concurrency story.  Use fibers, incorporate it
directly into your project, fork it, improve it: what happens now is
up to you.  Happy hacking and godspeed!

@c @node Concept Index
@c @unnumbered Concept Index
@c @printindex cp
@c @node Function Index
@c @unnumbered Function Index
@c @printindex fn

@bye