Unix Parallelism and Concurrency: Processes & Signalling

In this era of threads and asynchronous abstractions, applications and processes have become almost synonymous. A process is widely seen as the operating system’s underlying representation of a whole running application. However, by limiting ourselves to this model we cut outselves off from an elegant set of tools for parallelism and concurrency.

In case you thought this blog’s design looked prehistoric enough, it’s starting with a post about following concurrency patterns rooted in the era in which the mouse was a keen invention.

The key construct behind process-based Unix concurrency is the fork system call. It’s practically a paradox: one program calls it yet two programs finish calling it to move on. This appears as quite the oddity to programmers of contemporary environments like Java and the JavaScript; how can a mere function call violate the fundamental laws of how a written program executes?

Most environments, despite having a set of rules its programmers can depend on, have occasional strange artefacts on the surface that violate such rules and are exposed by the underlying system.

A Java program holding a reference to an object ensures that object remains alive, but the system exposes a different type of reference tracking with the java.lang.ref.WeakReference type insofar as allowing an object to be deleted while the programmer is still holding onto it. Likewise, storing a string literal into a object will never block execution in JavaScript, but there’s an exception of course that exposes the underlying browser environment: window.location. Assigning a string to that cancels the current flow of execution by redirecting the page, halting the current JavaScript environment and throwing it away.

In C, or even other higher level language like Python or Ruby, the environment is not a language-specific world unto itself like Java or a web browser; it is instead our underlying operating system. Python and Ruby build their own abstractions on top, but they are not as aggressive about hiding the underlying operating systems as programming environments like Java and in-browser JavaScript.

Apart from your operating system, languages like Python and Ruby provide little more than extra gizmos provided by the language runtime like garbage collection and some introspection capabilities. System calls from these languages, like the previous examples, provide capabilities that can step outside of the normal rules of the language you are using. Unix doesn’t care about the finally blocks that your language runtime makes “guarantees” about running; if you exec, the whole process’ runtime image is being swapped out and execution is jumping. Those finally blocks will be deep sixed.

Invoking the fork system call in Python 3 is just:

import os

if os.fork() == 0:
    print('running the child process')
    print('running the parent process')

print('finished a process')

This program demonstrates that oxymoron of one program entering fork() and two leaving it; how else could both branches be run in a single if? This allows for a form of concurrency and parallelism:

$ python3 program.py
running the parent process
finished a process
running the child process
finished a process

Running operations side-by-side is hardly an elusive trick. Anyone spinning up a thread in Java or interleaving two setTimeouts in JavaScript could replicate something similar. Unlike the latter, however, the application can still continue while one of the tasks infinitely loops:

import os
import time

if os.fork() == 0:
    while True:

These clearly aren’t events being triggered, otherwise the infinite loop would block the event loop, stopping the second event print('finished') from running. Single events blocking the entire event loop and grinding large applications to a halt is not a theoretical problem.

Threads sidestep this problem by utilising the operating system’s scheduler which slices up time on the computer between competing tasks to avoid any one of them starving the whole system of resources, but they come with their own arsenel of footguns in many shapes and sizes:

import os
import time
import threading

class UserCounter:

    def __init__(self):
        self.count = 0

    def increment(self):
        count = self.count
        print(f'User {count} visited')
        self.count = count + 1

user_counter = UserCounter()

def handle_requests():
    while True:

        # Pretend to serve a user on the network.


for _ in range(os.cpu_count()):
    thread = threading.Thread(target=handle_requests)

An expensive operation is divided and conquered, handling user requests across as many threads as we have processor cores. Unfortunately user_counter is shared across all threads, meaning the loading and incrementing is interleaved with other concurrent threads, causing the counter to be wrong most of the time.

$ python3 program.py
User 0 visited
User 0 visited
User 1 visited
User 1 visited
User 1 visited

We must remember quite a few rules to avoid shooting ourselves in the foot in multithreaded systems. Just a few.

Between dealing with data races, deadlocks, stale reads, and other threading esoteria, programming with threads is playing Russian Roulette with a fully loaded uzi. An uzi that jams a lot too, as adding too many critical regions to synchronise threaded code bottlenecks your otherwise concurrent program into single-threaded hotspots that can end up throttling your application’s performance.

Have you managed to get the locking fine-grained enough to get good performance while avoiding those pitfalls? Well, hopefully you’re not building any more abstractions on top of it, as locks do not compose.

Operating system processes are bulky and cannot be spun up as fast as threads, but are more isolated from one another. They have their own memory space, meaning one buggy process can’t corrupt the in-memory data of another. Unlike threads, misbehaving processes can actually be killed without causing strange, hard to trace bugs in the underlying system.

Processes also encourage communication via message-passing mechanisms like signalling, domain sockets, and networking connections. It turns out that these solutions are easier to scale across multiple physical machines than shared memory communication, as others also discovered quite a long time ago.

Signalling is the easiest way to get your feet wet with Unix IPC, Inter-Process Communication:

from os import _exit, kill, getppid, fork,
from signal import sigwait, SIGCONT
from time import sleep

def expensive_operation_1():
    kill(getppid(), SIGCONT)
    sigwait((SIGCONT, ))
    print('Very expensive operation 1 complete.')

def expensive_operation_2():
    kill(getppid(), SIGCONT)
    sigwait((SIGCONT, ))
    print('Slightly expensive operation 2 complete.')

def start_children():
    pid_1 = fork()
    if pid_1 == 0:

    pid_2 = fork()
    if pid_2 == 0:

    return pid_1, pid_2

def wait_for_all(pids):
    for _ in pids:
        sigwait((SIGCONT, ))

def display_in_order(pids):
    for pid in pids:
        kill(pid, SIGCONT)
        waitpid(pid, 0)

pids = start_children()
print('All children started.')
    'All children finished main tasks; asking them to display results in '

Running this yields:

$ python3 test.py
All children started.
All children finished main tasks; asking them to display results in order.
Very expensive operation 1 complete.
Slightly expensive operation 2 complete.

The slightly expensive operation, despite being quicker, displays its output after the longer running one. Both of them ran at the same time; it waited for 5 seconds, not 7. Combining fork with Unix process signalling, the following was organised:

The whole process not only parallelises the compution, but it linearises the results. Notice the lack of locks, shared queues, and polling.

Running man signal on a Unix device tells us what signals exist. Choosing SIGCONT was an arbritary decision, as most of the signals here could have been used. SIGCONT just so happens to best describe what it was doing: continuing after the tasks had finished waiting for something else.

 No    Name         Default Action       Description
 1     SIGHUP       terminate process    terminal line hangup
 2     SIGINT       terminate process    interrupt program
 3     SIGQUIT      create core image    quit program
 4     SIGILL       create core image    illegal instruction
 5     SIGTRAP      create core image    trace trap
 6     SIGABRT      create core image    abort program (formerly SIGIOT)
 7     SIGEMT       create core image    emulate instruction executed
 8     SIGFPE       create core image    floating-point exception
 9     SIGKILL      terminate process    kill program
 10    SIGBUS       create core image    bus error
 11    SIGSEGV      create core image    segmentation violation
 12    SIGSYS       create core image    non-existent system call invoked
 13    SIGPIPE      terminate process    write on a pipe with no reader
 14    SIGALRM      terminate process    real-time timer expired
 15    SIGTERM      terminate process    software termination signal
 16    SIGURG       discard signal       urgent condition present on socket
 17    SIGSTOP      stop process         stop (cannot be caught or ignored)
 18    SIGTSTP      stop process         stop signal generated from keyboard
 19    SIGCONT      discard signal       continue after stop
 20    SIGCHLD      discard signal       child status has changed
 21    SIGTTIN      stop process         background read attempted from control terminal
 22    SIGTTOU      stop process         background write attempted to control terminal
 23    SIGIO        discard signal       I/O is possible on a descriptor (see fcntl(2))
 24    SIGXCPU      terminate process    cpu time limit exceeded (see setrlimit(2))
 25    SIGXFSZ      terminate process    file size limit exceeded (see setrlimit(2))
 26    SIGVTALRM    terminate process    virtual time alarm (see setitimer(2))
 27    SIGPROF      terminate process    profiling timer alarm (see setitimer(2))
 28    SIGWINCH     discard signal       Window size change
 29    SIGINFO      discard signal       status request from keyboard
 30    SIGUSR1      terminate process    User defined signal 1
 31    SIGUSR2      terminate process    User defined signal 2

Some of those signals have special behaviour, like being impossible to handle such as SIGKILL, or being handled by some language runtimes for us, like Python translating SIGINT into KeyboardInterrupt exceptions. SIGUSR1 and SIGUSR2 are good for non-standard signals for application-specific events.

Some notes about the previous code before moving on:

We might want to wait for a signal, but not if it takes too long:

from os import fork, _exit, kill
from signal import signal, SIGALRM, SIGCHLD, SIGKILL, alarm, sigwait
from time import sleep

signal(SIGCHLD, lambda signal_number, stack_frame: None)

def set_five_second_alarm():

def remove_alarm():

def run_slow_operation():
    print('finished slow operation')

def wait_for(pid):
        if sigwait((SIGCHLD, SIGALRM)) == SIGALRM:
            print('too late; killing child process')
            kill(pid, SIGKILL)
            print('child process finished')

pid = fork()
if pid == 0:

Alarms allow timers to be interleaved with events. Three things to note in that code: firstly, 0 is interpreted by alarm as ‘disable all active alarms’; secondly, SIGCHLD is a signal for when any child processes stop running; finally, some signals like SIGCHLD do nothing by default. Even waiting for them with sigwait does nothing unless the system knows you want to handle them, which the above example does by hooking an otherwise useless lambda expression to the SIGCHLD signal. We don’t care about the lambda, just that the system knows we want to listen to that signal in general.

If we wanted, we could put all of our event handling logic directly in signal handlers like that rather than waiting with sigwait. In fact, most programs that use signals do just that.

Be careful of default signal behaviour. If your program is being run by a parent process, it can pass down non-default ‘default’ signal handlers. If a signal must be picked up by your program, even for just sigwait, add a dummy signal hook like above to be sure.

Signalling is one of the simplest forms of Unix IPC. It’s enough to coordinate processes, but does not allow sending messages with payloads. Domain sockets, networking connections, and other IPC systems allow a programmer to go a lot further.

If Unix IPC is such a powerful and battle-tested standard for parallelisation and concurrency, why isn’t it the primary port of call for solving such problems today? Well, for starters it’s slow and bulky even with modern optimisations like copy-on-write for copying processes’ memory spaces to forked children. Although shared memory has problems, it is sometimes the best way of solving certain problems and it’s easier with threads. Event-driven systems avoid many of the problems with threads and handle the majority of concurrency use cases in modern webservices, so managing processes manually becomes unnecessary.

Many applications written in the likes of Node.js use processes just to utilize as many processor cores as possible, but hide process management behind modules like cluster. Processes are used to parallelise, but the concurrency is handled by abstractions built atop an event loop. Using a process per request would destroy performance as they are too coarse for that level of fine-grained concurrency. In fact, that’s how web applications were handled many years ago in CGI scripts. There’s a reason it isn’t done that way anymore.

Despite these problems, Unix IPC mechanisms are sometimes still the best way of tackling certain concurrency and parallelism problems, so it’s worth keeping those dusty old ‘70s techniques in the toolbox.