Threads — Threads and Stackless

Stackless is a lightweight threading solution. It works by scheduling its tasklets within the CPU time allocated to the real thread that the Python® interpreter, and therefore the scheduler running within it, is on.

Stackless does not:

  • Magically move tasklets between threads to do some wonderful load balancing.
  • Magically remove the global interpreter lock.
  • Solve all your scalability needs out of the box.

But it does allow its functionality to be used flexibly, when you want to make use of more than one thread.

Tasklets and Threads

A tasklet usually has an associated thread. This thread is identified by the attribute tasklet.thread_id. A newly created tasklet is always associated with its creator thread.

The associated thread of a tasklet changes only as the result of a tasklet.bind_thread() call or if the associated thread terminates. In the later case the thread id becomes -1. Application code can bind the tasklet to another thread using bind_thread().

When a thread dies, only tasklets with a C-state are actively killed. Soft-switched tasklets simply stop. All tasklets bound to the thread will lose their thread-state, which means that their thread_id will report as -1. This also includes soft-switched tasklets, which share a C-state.

The reason Stackless kills tasklets with C-state is that not doing so can cause serious leaks when a C-state is not unwound. If Stackless runs in verbose mode (command line option -v or PYTHONVERBOSE), Stackless prints a warning message, if it deallocates a tasklet with a C-state. Stackless cannot kill soft-switched tasklets, because there is no central list of them. Stackless only knows about the hard-switched ones.

Threads that end really should make sure that they finish whatever worker tasklets they have going by manually killing (tasklet.kill()) or unbinding (tasklet.bind(None)) them, but that is up to application code.

During interpreter shutdown Stackless kills other daemon threads (non-daemon are already dead at this point), if they execute Python code or switch tasklets. This way Stackless tries to avoid access violations, that might happen later after clearing the thread state structures.

A scheduler per thread

The operating system thread that the Python® runtime is started in and runs on, is called the main thread. The typical use of Stackless, is to run the scheduler in this thread. But there is nothing that prevents a different scheduler, and therefore a different set of tasklets, from running in every Python® thread you care to start.

Example - scheduler per thread:

import threading
import stackless

def secondary_thread_func():
    print("THREAD(2): Has", stackless.runcount, "tasklets in its scheduler")

def main_thread_func():
    print("THREAD(1): Waiting for death of THREAD(2)")
    while thread.is_alive():
    print("THREAD(1): Death of THREAD(2) detected")

mainThreadTasklet = stackless.tasklet(main_thread_func)()

thread = threading.Thread(target=secondary_thread_func)
THREAD(2): Has 1 tasklets in its scheduler THREAD(1): Waiting for death of THREAD(2) THREAD(1): Death of THREAD(2) detected

This example demonstrates that there actually are two independent schedulers present, one in each participating Python® thread. We know that the main thread has one manually created tasklet running, in addition to its main tasklet which is running the scheduler. If the secondary thread is truly independent, then when it runs it should have a tasklet count of 1 representing its own main tasklet. And this is indeed what we see.

See also:

Channels are thread-safe

Whether or not you are running a scheduler on multiple threads, you can still communicate with a thread that is running a scheduler using a channel object.

Example - interthread channel usage:

import threading
import stackless

commandChannel =

def master_func():
    commandChannel.send("ECHO 1")
    commandChannel.send("ECHO 2")
    commandChannel.send("ECHO 3")

def slave_func():
    print("SLAVE STARTING")
    while 1:
        command = commandChannel.receive()
        print("SLAVE:", command)
        if command == "QUIT":
    print("SLAVE ENDING")

def scheduler_run(tasklet_func):
    t = stackless.tasklet(tasklet_func)()
    while t.alive:

thread = threading.Thread(target=scheduler_run, args=(master_func,))




This example runs slave_func as a tasklet on the main thread, and master_func as a tasklet on a secondary thread that is manually created. The idea is that the master thread tells the slave thread what to do, with a QUIT message meaning that it should exit.


The reason the scheduler is repeatedly run in a loop, is because when a scheduler has no remaining tasklets scheduled within it, it will exit. As there is only one tasklet in each thread, as each channel operation in the thread blocks the calling tasklet, the scheduler will exit. Linking how long the scheduler is driven to the lifetime of all tasklets that it handles, ensures correct behaviour.