Stoppable task hierarchies and semantic context (the throughline) for Python concurrency.
Project description
gentletask
Stoppable task hierarchies and a semantic-context "throughline" for Python concurrency.
Python's concurrency primitives are capable but cumbersome. concurrent.futures
gives you threads and results; it does not give you a way to stop a hierarchy of
running tasks, propagate that stop signal inward, or know — when something goes
wrong three threads deep — what your program was actually trying to do when it
failed. gentletask fills those gaps without adding complexity that isn't
earned. A gentle task doesn't spin up a thread it doesn't need; when you ask it
to stop, it stops, asks its children to stop too, and waits for them. And it
carries a narrative of meaning across every boundary it crosses, so your logs
tell a story instead of a list of disconnected events.
Two problems, two primitives:
- Stopability. A
Taskcan be stopped cooperatively. Stop signals propagate down through hierarchies. Replacement blocking primitives (sleep,Queue,Event,poll) respect the stop signal, so you don't have to instrument every wait site by hand. - Semantic context. A
SemanticStackcarries labeled context — what your program is doing, not just where it is — across thread (and, when values are pickleable, process) boundaries. The module singleton is calledthroughline: a continuous thread of meaning running through all your concurrent execution.
Installation
pip install gentletask
gentletask is pure Python, requires Python >= 3.10, and has no runtime
dependencies.
Core concepts
Task hierarchies and cooperative stopping
A Task is anything that satisfies the Task protocol — a unit of work that
can report whether it is is_done / is_stopped, be wait()-ed on, and be
asked to stop(). The built-in implementations are ThreadTask (runs a
callable in its own daemon thread), WorkTask (one job queued to a long-lived
WorkerThread), and Promise (no thread or body — completed externally by an
already-existing producer).
Tasks form a hierarchy automatically: when a task is created or waited on from
inside another running task, it registers as a child of that task. Calling
stop() on a parent cascades the stop to every child, and when a task finishes
(normally or by exception) it stops any still-running children — unless they
were explicitly detach()-ed.
Stopping is cooperative: a task is never interrupted mid-statement. Instead,
the stop-aware blocking primitives (sleep, check_stop, Queue.get,
Event.wait, poll, and Task.wait) notice the stop at their wait site and
raise Stopped, which unwinds normally so your finally blocks run.
stop() takes an optional reason string for diagnostics. The first stop on a
task records its reason (a later stop is a no-op and does not overwrite it),
which then travels into the Stopped exception raised at the wait site — so
str(exc) is the reason — and cascades along with the stop into children. A
plain stop() with no reason behaves exactly as before, yielding a reason-less
Stopped(). Read the recorded reason back via the stop_reason property.
Stop propagation is poll-free: stop() pushes a notification rather than
relying on a polling loop. Before a primitive parks, it registers a zero-arg
callback via add_stop_callback; stop() fires those callbacks (exactly once,
on the first stop) to wake the waiter the instant it is requested. An idle wait
consumes no CPU, and stop latency is independent of any polling interval.
The throughline / semantic stack
A SemanticStack is a context-local stack of frames, where each frame is an
ordered collection of key/value pairs. It is backed by a single ContextVar,
and each frame is stored as an immutable tuple[tuple[str, Any], ...], so no
defensive copying is needed and returned dicts are always fresh.
The library keeps two SemanticStack singletons, each with one declared job:
throughline— the public, human-readable narrative. It requires anameon every frame and, by convention, carries nothing else.task_chain()andThroughlineNameFilterread this, and it is what shows up in your logs._task_stack— private to the module. It requires ataskon every frame and exists only to track whichTaskis running;current_task()reads it. Keeping the task object off the throughline keeps the narrative clean and name-only.
Task code enters both stacks at once through the task_context(task, name)
helper. Context travels across thread boundaries automatically: ThreadTask
copies the calling context with contextvars.copy_context(), while WorkTask
snapshots both stacks at submit() time and restores them when the worker
picks up the job — so a job inherits the context of the code that caused it to
be submitted, not the worker's.
asynch and synch
These are inverses that let a single call site choose how a function runs.
asynch(fn, ...)returns a launcher: calling the launcher startsfnin a newThreadTaskand returns the task immediately.synch(fn)returns a synchronous version that always yields a concrete value. It flattens two layers of asynchrony: iffnwas produced byasynch(), it is de-wrapped to the original callable so the work runs inline (no extra thread); and if the call returns something implementing theTaskprotocol,synchwaits for that task and returns its result instead of the task. A plain function returning a plain value is simply called — sosynchis safe to apply either way.
Quick start
import time
from gentletask import ThreadTask, sleep, Stopped
def counter(progress):
n = 0
while True:
n += 1
progress.append(n)
sleep(0.02) # stop-aware: raises Stopped when the task is stopped
progress = []
task = ThreadTask(counter, args=(progress,), name="counter")
time.sleep(0.1) # let it tick a few times
task.stop() # request a cooperative stop
try:
task.wait()
except Stopped:
print("counter stopped after", len(progress), "ticks")
print("is_stopped:", task.is_stopped)
Guide
SemanticStack basics
Each with block pushes a frame; leaving the block pops it. get(key) returns
the innermost value, collect(key) returns all values outermost-first
(skipping frames without the key), and frames() hands back fresh dicts.
from gentletask import SemanticStack
ctx = SemanticStack()
with ctx(operation="abc123", user="alice"):
with ctx(operation="resize"):
print(ctx.get("operation")) # "resize" (innermost)
print(ctx.get("user")) # "alice" (from the outer frame)
print(ctx.collect("operation")) # ('abc123', 'resize')
for f in ctx.frames():
print(f)
# {'operation': 'abc123', 'user': 'alice'}
# {'operation': 'resize'}
A stack can require certain keys on every frame. required is a floor, not a
schema — extra keys are always welcome.
labeled = SemanticStack(required=("name",))
with labeled(name="ok", extra="also fine"): # extra keys welcome
...
labeled(extra="but no name") # raises ValueError: missing "name"
Snapshots
snapshot() captures the current frames as a pure-data SemanticSnapshot (just
the frames, no stack reference); stack.restore(snapshot) is a context manager
that reinstalls them onto that stack for a block. This is the machinery that
carries context onto worker threads.
from gentletask import throughline
with throughline(name="request_handler", request_id="r-42"):
snap = throughline.snapshot()
print(throughline.collect("name")) # () — back outside, the stack is empty
with throughline.restore(snap):
print(throughline.collect("name"), throughline.get("request_id"))
# ('request_handler',) r-42
Because a snapshot is plain data, it is picklable (when its values are) and can
cross both thread and process boundaries. restore() also accepts a raw
iterable of frame dicts — e.g. snap.frames() after a serialization round-trip
returns a list of dicts — and replays already-validated frames, so it bypasses
the required-key check that __call__ enforces:
import pickle
payload = pickle.dumps(snap) # send to another process...
revived = pickle.loads(payload) # ...and restore into the same singleton
with throughline.restore(revived):
print(throughline.get("request_id")) # r-42
# Or restore a serialized frames() payload (a list of dicts) directly:
with throughline.restore([{"name": "request_handler", "request_id": "r-42"}]):
...
ThreadTask
ThreadTask runs a callable in a new daemon thread. wait() blocks and returns
the result (or re-raises the worker's exception); result is shorthand for
wait().
from gentletask import ThreadTask
t = ThreadTask(lambda: 6 * 7)
print(t.wait()) # 42
print(t.is_done) # True
# Exceptions surface through wait()
def boom():
raise ValueError("kaboom")
failed = ThreadTask(boom)
try:
failed.wait()
except ValueError as e:
print("caught:", e)
It accepts positional/keyword args, an explicit name, and an on_finish
callback invoked with (result, exception) when the task finishes.
log_lines = []
t = ThreadTask(
lambda x, y: x + y,
args=(3,),
kwargs={"y": 4},
name="adder",
on_finish=lambda result, exc: log_lines.append(f"adder finished -> {result}"),
)
print(t.wait()) # 7
print(log_lines) # ['adder finished -> 7']
Pass start=False to create the thread without launching it, then call
.start() once you have wired up callbacks. This lets you attach finish/stop
callbacks (or connect signals) before any work runs, race-free. .start() is
idempotent — a second call, or calling it on a task already started by the
default start=True, is a safe no-op. Context and parent registration are
still captured at construction time, so a not-yet-started child is stopped if
its parent stops.
t = ThreadTask(work, start=False)
t.add_finish_callback(on_done) # registered before any work runs
t.start()
asynch and synch
from gentletask import asynch, synch, ThreadTask
add = asynch(lambda x, y: x + y, name="async-add")
task = add(10, 20) # starts a ThreadTask
print(task.wait()) # 30
def process(x):
return x * 10
job = asynch(process) # job(...) -> ThreadTask
print(synch(job)(5)) # 50 — de-wrapped, runs inline, no thread
print(synch(process)(5)) # 50 — plain function passes through
def schedule(x): # a function that hands back a task
return ThreadTask(lambda: process(x), name="scheduled")
print(synch(schedule)(5)) # 50 — synch waits for the returned task
Cooperative stop and stop-aware primitives
sleep, check_stop, poll, Queue.get, and Event.wait all check
current_task() at their wait site and raise Stopped when a stop has been
requested. Called outside any task, they behave like their stdlib equivalents.
from gentletask import sleep, check_stop, Queue, Event, poll
def worker(q: Queue, done: Event):
while True:
check_stop() # surrender at a known-safe point (like sleep(0))
item = q.get() # raises Stopped if stopped while waiting
process(item)
if done.wait(0.01): # raises Stopped if stopped while waiting
break
# poll() samples a predicate on `interval`, but the inter-sample wait is
# poll-free with respect to stop:
poll(lambda: connection.ready, interval=0.05, timeout=5.0)
Stop cascades to children
Stopping a parent propagates to every child it started; when a task finishes it also stops any still-running children.
import time
from gentletask import ThreadTask, sleep, Stopped
child_ticks = []
def child():
while True:
child_ticks.append(1)
sleep(0.02)
def parent():
c = ThreadTask(child, name="child")
c.wait() # parent blocks on the child
p = ThreadTask(parent, name="parent")
time.sleep(0.1)
p.stop() # cascades into the child
try:
p.wait()
except Stopped:
pass
print("child ticks frozen at", len(child_ticks))
detach() opts a child out of the cascade, so it keeps running after the parent
stops:
def parent_detaching():
c = ThreadTask(long_child, name="detached-child")
c.detach() # parent.stop() will NOT reach this child
sleep(10)
WorkerThread and WorkTask — serialized jobs with inherited context
A WorkerThread is a long-lived thread that runs submitted jobs one at a time.
submit() returns a WorkTask immediately and snapshots both stacks at submit
time, so the job inherits the context of the code that caused it — not the
worker's.
from gentletask import WorkerThread, throughline, task_chain
worker = WorkerThread(name="io-worker")
captured = {}
def job(label):
captured[label] = task_chain()
return label.upper()
# Submitted inside a frame -> the job inherits it
with throughline(name="request_handler"):
a = worker.submit(job, ("a",), name="job-a")
a.wait()
# Submitted outside any frame -> the job sees only its own name
b = worker.submit(job, ("b",), name="job-b")
b.wait()
print(captured["a"]) # ('request_handler', 'job-a')
print(captured["b"]) # ('job-b',)
print(a.result) # 'A'
worker.stop() # already-queued jobs drain, then the thread shuts down
After stop(), the worker drains any jobs already queued and then exits;
further submit() calls raise RuntimeError.
Promise — an externally-completed task
ThreadTask and WorkTask are body-driven: a callable runs and its return
value (or exception) finishes the task. But many real results are externally
completed — finished by a producer that already exists (a hardware monitor
thread, a socket-reply reader, a GUI callback, a lock loop) rather than by a
body of their own. Wrapping those in a ThreadTask would burn a useless parking
thread per result. A Promise is the missing primitive: a Task with no
thread and no body, completed externally via resolve() / fail(), that
otherwise participates fully in the Task protocol and the stop hierarchy.
from gentletask import Promise, ThreadTask
# A producer hands out a Promise and finishes it later from wherever it runs.
target_reached = Promise(name="target-reached")
def monitor(): # some already-running producer
... # watch the hardware
target_reached.resolve(123) # complete it from outside — no new thread
ThreadTask(monitor, name="hw-monitor")
print(target_reached.wait()) # blocks poll-free until resolve() -> 123
resolve(value) completes it successfully; fail(exc) completes it with an
exception that wait() re-raises. Both are idempotent — the first completion
wins and later calls are no-ops. A Promise created inside a running task
registers as that task's child, so a parent stop() cascades to it; because a
stopped promise has no body to raise Stopped, stop() fires its stop
callbacks (letting the external producer abort its side-effects) and then
completes the promise with Stopped so its waiters never hang. stop(reason)
carries the reason into that injected Stopped.
MultiTask — aggregate several running tasks into one
Sometimes you have several tasks already running and want to treat them as a
single waitable unit: block until they have all finished, collect their
results, surface their errors together, and stop them as a group. MultiTask is
that aggregator. Like Promise it is bodyless and threadless — it spawns no
thread, and is driven entirely by its children's finish callbacks.
from gentletask import MultiTask, ThreadTask
a = ThreadTask(lambda: 1, name="a")
b = ThreadTask(lambda: 2, name="b")
c = ThreadTask(lambda: 3, name="c")
both = MultiTask([a, b, c], name="gather")
print(both.wait()) # blocks until all three finish -> [1, 2, 3] (task order)
wait() returns the list of child results in task order. Errors aggregate
by count:
- all children succeed →
wait()returns the list of results; - exactly one child fails →
wait()re-raises that child's exception directly (no wrapping); - two or more fail →
wait()raisesMultiException, whose.exceptionsholds the failing children's exceptions in task order and whose message combines them.
stop(reason) stops every child and then this task, completing with Stopped
(carrying the reason) so waiters never hang even if a child does not complete on
stop. A MultiTask created inside a running task registers as that task's child,
so a parent stop() cascades through the MultiTask to all of its children.
Because add_finish_callback fires immediately for an already-finished child, a
MultiTask constructed over a mix of already-done and still-pending children
counts them all correctly and only completes once the last pending child
finishes.
Logging integration
ThroughlineNameFilter injects throughline.collect("name") onto every log
record as record.throughline, giving each line its full task ancestry — across
thread boundaries, with no manual plumbing.
import logging, sys
from gentletask import ThreadTask, ThroughlineNameFilter
logger = logging.getLogger("gentletask.demo")
logger.setLevel(logging.INFO)
handler = logging.StreamHandler(sys.stdout)
handler.setFormatter(logging.Formatter("%(throughline)s | %(message)s"))
handler.addFilter(ThroughlineNameFilter())
logger.addHandler(handler)
logger.propagate = False
def calibrate():
logger.info("measuring offset")
def acquire():
logger.info("starting acquisition")
ThreadTask(calibrate, name="calibrate").wait()
logger.info("acquisition complete")
ThreadTask(acquire, name="acquisition").wait()
# ('acquisition',) | starting acquisition
# ('acquisition', 'calibrate') | measuring offset
# ('acquisition',) | acquisition complete
A custom Task
Task is a runtime_checkable structural Protocol — any object with the right
shape qualifies, no base class required. Wrap your work in task_context(self, name) so current_task() and the log chain line up without touching either
stack directly. For poll-free stop, implement add_stop_callback /
remove_stop_callback and fire the registered callbacks exactly once from
stop(): those are the hooks the blocking primitives use to wake without
polling.
import threading
from gentletask import task_context, sleep, Task
class CountdownTask:
"""A minimal hand-rolled Task that counts down on a background thread."""
def __init__(self, n):
self._n = n
self.is_stopped = False
self.is_done = False
self._result = None
# Internal plumbing uses plain threading primitives, NOT gentletask's.
# These coordinate the task's own lifecycle and are read from outside
# the task (e.g. wait() is usually called by a parent), so they must
# not raise Stopped based on the caller's current_task().
self._done = threading.Event() # completion signal
self._lock = threading.Lock() # guards the stop-callback list
self._stop_callbacks = []
self._thread = threading.Thread(target=self._run, daemon=True)
self._thread.start()
def _run(self):
with task_context(self, "countdown"):
try:
while self._n > 0 and not self.is_stopped:
self._n -= 1
# The actual work waits with gentletask.sleep so a stop
# aborts it cooperatively (poll-free, raises Stopped).
sleep(0.01)
self._result = "liftoff" if not self.is_stopped else "aborted"
finally:
self.is_done = True
self._done.set()
def wait(self, timeout=None):
self._done.wait(timeout)
return self._result
@property
def result(self):
return self.wait()
def stop(self):
with self._lock:
if self.is_stopped:
return
self.is_stopped = True
callbacks, self._stop_callbacks = list(self._stop_callbacks), []
for cb in callbacks:
cb()
def add_finish_callback(self, fn):
self.wait()
fn(self._result, None)
def add_stop_callback(self, fn):
with self._lock:
if not self.is_stopped:
self._stop_callbacks.append(fn)
return
fn()
def remove_stop_callback(self, fn):
with self._lock:
if fn in self._stop_callbacks:
self._stop_callbacks.remove(fn)
def detach(self):
pass
cd = CountdownTask(5)
print(isinstance(cd, Task)) # True
print(cd.wait()) # 'liftoff'
Note.
runtime_checkableonly verifies attribute presence, not full signatures — so an object can passisinstance(obj, Task)while still missing the behavior the primitives rely on. Implement all of the protocol members, especially the stop-callback hooks, for a task that participates fully in poll-free stopping.
threading or gentletask? When to use which
The example above deliberately mixes the two, and the choice is not arbitrary. The rule of thumb:
- Use the
gentletaskprimitives (sleep,Event,Queue,poll,check_stop) for the work a task performs — the wait points that should abort withStoppedwhen that task is stopped. These are stop-aware: they consultcurrent_task()and raiseStoppedso a cooperative stop unwinds the work cleanly. Reach for these whenever you are blocking inside a task and want the block to be interruptible. - Use the plain
threadingprimitives (threading.Event,Lock,Thread,queue.Queue) for a task's own lifecycle machinery — completion signals, internal locks, the worker thread itself. This plumbing is read and driven from outside the running task (a parent callswait(),stop()may fire from any thread), so it must not be stop-aware: agentletask.Eventused forself._donewould raiseStoppedifwait()happened to be called from some unrelated stopped task, corrupting the task's own bookkeeping.
In short: gentletask primitives for interruptible work; threading
primitives for the scaffolding that makes a task a task. The built-in
ThreadTask and WorkTask follow exactly this split internally — their
done/stop signaling is threading-based, while the work you hand them is free
to use the stop-aware primitives.
API reference
Exceptions
| Name | Description |
|---|---|
Stopped |
Raised inside a task when stop() has been requested. Carries the optional reason passed to stop() as its message (str(Stopped("foo")) == "foo"; a reason-less Stopped() is empty). Unwinds normally; finally blocks run. |
MultiException(message, exceptions) |
Aggregate raised by MultiTask.wait() when more than one child failed. .exceptions holds the child exceptions in task order; the message combines message with each child's string form. |
Tasks
| Name | Description |
|---|---|
Task |
runtime_checkable structural Protocol for a stoppable, waitable unit of work. |
ThreadTask(fn, args=(), kwargs=None, *, name=None, detach=False, on_finish=None, start=True) |
Runs fn in a new daemon thread; implements Task. With start=False, call .start() to launch (idempotent). |
WorkTask(fn, args, kwargs, name=None) |
One job queued to a WorkerThread; implements Task. Usually created via WorkerThread.submit. |
Promise(name=None, *, on_finish=None) |
A Task with no thread or body, completed externally via resolve(value) / fail(exc); implements Task. Idempotent completion; stop(reason=None) completes it with Stopped carrying the reason. |
MultiTask(tasks, name=None, *, on_finish=None) |
A bodyless, threadless Task that completes when ALL its child tasks complete; implements Task. wait() returns the list of child results in task order, re-raises a lone child failure, or raises MultiException for two or more. stop(reason=None) stops every child then completes with Stopped. tasks exposes the children. |
WorkerThread(name=None) |
Long-lived worker thread that serializes submitted jobs. submit(...) returns a WorkTask; stop() drains queued jobs and shuts down. |
asynch(fn, name=None, detach=False, on_finish=None) |
Returns a launcher that starts fn in a new ThreadTask when called. |
synch(fn) |
Returns a synchronous version of fn that de-wraps asynch and awaits returned tasks, yielding a concrete value. |
Task protocol members
| Member | Description |
|---|---|
is_done: bool |
Whether the task has finished. |
is_stopped: bool |
Whether a stop has been requested. |
stop_reason |
Property; the reason passed to the first stop(), or None for a reason-less stop. |
result |
Property; shorthand for wait(). |
wait(timeout=None) |
Block until done, re-raising any worker exception. A stop on the calling parent propagates here and raises Stopped carrying the parent's reason. |
stop(reason=None) |
Request cooperative stop; record reason (first stop only); fire stop callbacks once; cascade to children (passing the reason along). |
add_finish_callback(fn) |
Call fn(result, exception) when the task finishes (immediately if already finished). |
add_stop_callback(fn) |
Call zero-arg fn() once when the task is stopped (immediately if already stopped). |
remove_stop_callback(fn) |
Unregister a stop callback; no-op if absent. |
detach() |
Remove this task from its parent's stop propagation. Parent-only: the caller must be the task whose children include this one (a task cannot detach itself); otherwise raises RuntimeError. |
Context / throughline
| Name | Description |
|---|---|
SemanticStack(name="semantic_stack", *, required=()) |
Context-local stack of labeled frames backed by a ContextVar. |
SemanticStack.__call__(**kwargs) |
Context manager that pushes a frame for the block (raises ValueError if a required key is missing). |
SemanticStack.get(key, default=None) |
Value of key from the innermost frame that has it. |
SemanticStack.collect(key) |
Tuple of all values for key, outermost-first. |
SemanticStack.walk(fn) |
Apply fn to each frame dict, outermost-first; return a tuple of results. |
SemanticStack.frames() |
Full stack as fresh dicts, outermost-first. |
SemanticStack.snapshot() |
Capture current state as a pure-data SemanticSnapshot (picklable). |
SemanticStack.restore(snapshot_or_frames) |
Context manager that installs a snapshot (or an iterable of frame dicts) onto this stack for the block; replays without required-key validation. |
SemanticSnapshot.frames() |
Captured frames as fresh dicts, outermost-first (for serialization). |
throughline |
Module singleton SemanticStack("throughline", required=("name",)) — the human-readable narrative. |
task_context(task, name) |
Context manager that enters name on the throughline and task on the private task stack. |
current_task() |
The innermost running Task, or None outside any task. |
task_chain() |
Tuple of task names, outermost-first (throughline.collect("name")). |
ThroughlineNameFilter |
logging.Filter that injects task_chain() onto each record as record.throughline. |
Stop-aware blocking primitives
| Name | Description |
|---|---|
sleep(seconds) |
Drop-in for time.sleep; raises Stopped if the current task is stopped (poll-free). |
check_stop() |
Raise Stopped if the current task is stopped; like sleep(0). |
poll(fn, *, interval=0.05, timeout=None) |
Sample fn until truthy; stop-aware inter-sample wait. Returns fn's truthy value, or the last falsy value on timeout. |
Queue(maxsize=0) |
Drop-in for queue.Queue; get() raises Stopped if the current task is stopped. |
Event() |
Drop-in for threading.Event; wait() raises Stopped if the current task is stopped. |
Every Stopped these raise carries the stopped task's stop_reason as its
message, so callers can log why the task unwound.
Testing
The test suite runs under pytest:
pip install -e ".[dev]"
pytest
Contributing
Contributions are welcome. Please open an issue to discuss substantial changes first. Keep changes small and focused, match the surrounding code style, and add tests covering new behavior. The library is intentionally dependency-free and pure Python — please keep it that way.
License
MIT License. See the LICENSE file for details.
Authors
- Martin Chase
- Luke Campagnola
Copyright © Martin Chase and Luke Campagnola.
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File details
Details for the file gentletask-0.2.0-py3-none-any.whl.
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- Upload date:
- Size: 27.0 kB
- Tags: Python 3
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- Uploaded via: twine/6.1.0 CPython/3.13.12
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Provenance
The following attestation bundles were made for gentletask-0.2.0-py3-none-any.whl:
Publisher:
deploy.yml on acq4/gentletask
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Statement:
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https://in-toto.io/Statement/v1 -
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Permalink:
acq4/gentletask@faaf4f21e57c482b68d5b4f18f48531b1c5ba5ad -
Branch / Tag:
refs/tags/v0.2.0 - Owner: https://github.com/acq4
-
Access:
public
-
Token Issuer:
https://token.actions.githubusercontent.com -
Runner Environment:
github-hosted -
Publication workflow:
deploy.yml@faaf4f21e57c482b68d5b4f18f48531b1c5ba5ad -
Trigger Event:
push
-
Statement type: