pytest-failure-instrumentation
Most test failures explain themselves. A pytest_runtest_makereport gives you
an assertion, a traceback and a node id, and there is nothing left to
investigate.
The failures that happen outside the call phase explain nothing. A worker is killed, a run wedges, workers disagree about which tests exist, pytest raises inside its own machinery — and what reaches your reporting is a placeholder string, a line in a log, or nothing at all. This plugin records what those failures cannot say for themselves, works out whose code is responsible, and hands you one structured incident per problem.
Worker gw1 crashed with SIGSEGV (segmentation fault in native code) while running test_crashes.py::test_crashes (call), in native_call (engine.py:6) [worker_death NATIVE_CRASH, product, critical]
Exit status -11 read via waitid (pid 805).
The worker wrote a stack as it died.
Look at: test_crashes.py::test_crashes.
Measured: 1 test started and 0 finished on this worker.
The problem
When a pytest-xdist worker dies, this is the whole report:
[gw7] node down: Not properly terminated
An OOM kill, a segfault in a C extension and a stray os._exit(1) are
indistinguishable at that point. Not because nobody bothered to print the
difference — because by the time anything can ask, the difference is gone.
There are three independent reasons, and all three have to be worked around
separately.
1. The cause never leaves the process. SIGSEGV and SIGKILL end a process
without running any Python. No finally, no atexit, no __del__, no
pytest_sessionfinish. Whatever the worker knew about what it was doing, it
knew only in memory, and that memory is gone. Anything you want to know
afterwards has to have been written down before — while the run was healthy
and the cost of writing it lands on every passing test.
2. The exit status is read and thrown away. The kernel keeps one number
that separates all these cases, and the parent process is the only thing
allowed to read it. execnet does read it — Group.terminate calls
gw._io.wait() in execnet/multi.py — and discards the return value. Nothing
in xdist asks for it either.
3. There is no field to put it in. In xdist/workermanage.py,
process_from_remote handles the channel closing: it asks execnet for a remote
error, and when there is none — because the remote never got to send anything —
substitutes a literal string:
err = "Not properly terminated" # lost connection?
That string is then passed to pytest_testnodedown(node, error) as the error.
The hook is not withholding the cause. By the time it fires, the placeholder is
genuinely all that exists.
Why you never see a MemoryError
The most common way a worker dies is also the one Python is least able to
report. On Linux, malloc returning successfully is not a promise that the
memory exists — overcommit hands out address space and resolves it on first
touch. There is no allocation failure for CPython to raise MemoryError from.
The process is killed later, from outside, with SIGKILL, which cannot be
caught, blocked or handled.
And the exit status is -9 for all of it: the kernel OOM killer, a cgroup
limit, a cancelled CI job, a kill -9 from a stray script. There is no
distinct code for "out of memory". A cgroup counter increase shows that some
process in the group was OOM-killed; it does not identify this worker.
OOM_KILLED requires a kernel record naming the victim in the death window.
Without that evidence, the incident retains SIGKILLED and reports the counter
as context. Direct signal evidence takes precedence over that correlation.
The failures that reach no hook at all
A worker's death at least fires a hook. Four others do not.
A run that stalls. pytest_testnodedown needs a dead process; a wedged
one is alive. And the controller hears from a worker only when a phase
completes, so from outside, a twenty-minute test and a deadlock are the same
event: nothing. The run does not fail — it never ends, and CI kills the job an
hour later with no artifact naming a test.
Without xdist there is no outside at all, which sounds like the harder case and
is the easier one: a main thread blocked on a lock or a socket does not stop
the other threads in its process, so the run can be asked what it is doing by a
thread of its own. A plain pytest that deadlocks now names the test, prints
the stack of the thread that is stuck, and says so while it is still stuck.
Workers that collected different tests. xdist notices, writes a unified diff per differing worker into its own log, and aborts. Nothing structured reaches a hook. With sixty workers and one odd node that is fifty-nine complete diffs, every one of them naming the majority as the deviation.
A run that never came back. Every failure above is reported by a process
that survived to report it. A run that was itself killed has no survivor: a
plain pytest that segfaults, a controller reclaimed along with its workers, a
CI job cancelled mid-suite. Nothing fires, nothing is written, and the only
trace is a job that stopped. What that run recorded is on disk and complete —
so it is reported by the next run over the same directory, which was already
walking it to clear it out.
An internal error. pytest sets ExitCode.INTERNAL_ERROR, which is not in
summary_exit_codes in _pytest/terminal.py — so pytest_terminal_summary
never fires for it. Under xdist it is worse: a worker's internal error is
relayed to the controller as a flat string and re-raised there, so the
INTERNALERROR> block you read names xdist's frame, not the failure.
Who this is for
You ship a library into other people's test suites. Their run dies and the
bug report names your package. Nothing in the output can confirm or refute it,
and "cannot reproduce" is not an answer anyone accepts. owner is the field
that settles it — product, third-party, customer-code or runtime — and
it comes from a stack, not from a guess.
You own CI for a large suite. Runs fail with no test named. Was that the
OOM killer, or the runner getting reclaimed mid-job? -9 is identical either
way, and the answer decides whether you buy more memory or file a ticket with
your CI vendor.
Your suite hangs sometimes. Nothing fails, the job times out, and there is
no evidence at all because the process that would produce it is the one that is
stuck. worker_stall names the test, says whether the thread is blocked or the
whole process is frozen, and prints the stack of the thread actually stuck.
You run enough workers that they disagree. A conftest keyed on an environment variable, a machine-dependent skip, a plugin that collects conditionally. The run aborts and the reason is buried in N−1 diffs.
You are collecting failures across machines you do not control.
fingerprint groups recurrences so one defect on twelve workers is one row
with a count, and capabilities records what each machine could measure — so a
missing memory figure on a customer's Windows box reads as "unmeasurable here"
rather than "fine".
A worked example: xdist #1362
A worker dies in the window after it has sent its collection but before
scheduling begins, while a second worker is still collecting. The stale entry
in registered_collections is never cleaned up, and the run dies with a
KeyError naming an object rather than a problem
(pytest-dev/pytest-xdist#1362).
This is what the plugin reports for it — two incidents, because two things went wrong:
Worker gw1 was killed with SIGKILL before running any test [worker_death SIGKILLED, unknown, needs-triage]
Exit status -9 read via waitid (pid 21780).
No cgroup OOM kill was counted during this run. SIGKILL cannot be caught, and is sent by a host OOM killer, a container or CI cancellation, or a kill command.
Measured: 31 MB resident at the last heartbeat. 0 tests started and 0 finished on this worker.
pytest hit an internal error: KeyError: <WorkerController gw1>, in _assign_work_unit (loadscope.py:275) [internal_error INTERNAL_ERROR, runtime, high, run-ending]
Raised on the controller and captured first-hand.
pytest ends the session on an internal error.
Look at: the full traceback, kept in the incident's detail field.
Severity high rather than informational: a defect in the framework ended the run, and no test is at fault, so nothing else reports it.
The runtime owner in the tag is the load-bearing part. No test is at fault
and no worker is at fault, so nothing else in the run will ever surface this —
which is exactly why it is the one case where a framework defect is raised above
informational.
Install
pip install pytest-failure-instrumentation
Installed is not switched on. The package registers a pytest11 entry point
so that its hooks and its failure_* options exist on every run, and then does
nothing until a run asks for it:
pytest --failure-instrumentation
Put the switch in addopts to have every run ask, and tell it which packages
are yours, so a failing frame in your code can be told from one in a dependency
or in the customer's own tests:
[pytest]
addopts = --failure-instrumentation
failure_packages = yourcore, yourcore_ext
failure_product_version = 4.2.0
Implement one hook to receive what it finds:
# conftest.py, or your own plugin
def pytest_failure_incident(incident):
database.save(incident.model_dump())
alerts.send(str(incident))
Without the hook it still writes its evidence to .pytest-failures/.
Naming the live view switches it on as well: --callstack-port or
--callstack-host is a request for a server that cannot run without the
plugin under it, and an option accepted and then ignored for want of a second
one is worse than either behaviour on its own. The other way to switch it on is
to call install from your own code, below. -p no:failure_instrumentation
removes the entry point altogether, options and hookspecs included.
Installing it from your own framework
If you ship a test framework rather than consume one, ini is the wrong place for these settings. Your packages, your artifact directory and your build id are things your framework already knows, and an ini block every consuming team has to copy — and keep in step with you — is a migration that never finishes.
Call install from your plugin instead:
# yourframework/pytest_plugin.py
from pytest_failure_instrumentation import install
def pytest_configure(config):
install(config,
packages=deployment.owned_packages,
directory=deployment.artifact_dir,
product_version=deployment.version,
run_id=ci.build_id)
Keyword arguments layer on top of whatever ini said, so a team that has set
failure_stall_seconds keeps it. Passing a whole Settings instead replaces
ini entirely, for when your framework owns the configuration outright:
from pytest_failure_instrumentation import Settings, install
install(config, Settings(packages=("yourcore",), stall_seconds=600))
Settings has a default for every field, coerces a list of packages and a
string path to what it needs, and enforces its own invariants — so a
hand-built one cannot skip a floor that a resolved one obeys.
Three things this handles for you:
Load order. A conftest's pytest_configure runs before this plugin's, and
a plugin loaded as an entry point runs after it. Registration here is
trylast and only claims what nobody has installed, so your settings win
either way.
Workers. A worker is a separate process. Settings you computed in Python do
not exist there and your framework's code may not even be loaded — so whatever
is in force is pushed down through workerinput, and a worker prefers it over
anything it could read itself. run_id travels with it, which is what lets a
build id group a whole run's incidents.
Turning the entry point off. -p no:failure_instrumentation skips the
entry point entirely; install puts back the hookspecs so
pytest_failure_incident, pytest_failure_worker_sample and
pytest_failure_server_ready all still reach their implementers. Note that it also skips pytest_addoption, so
failure_* ini keys become unknown config options — which is the point if your
framework owns the settings, and a reason to leave the entry point enabled and
just call install if it does not. Either way install is itself the switch:
a framework that calls it has asked, and nobody has to pass
--failure-instrumentation as well.
install is idempotent and returns the settings in force. A second call keeps
the first one's and warns rather than silently losing to it;
installed_settings(config) reads them back. Like everything else here it
warns instead of raising — the one exception is a misspelled setting name,
which is your bug and is worth an error rather than a run that quietly
attributes nothing.
What you get
incident is a pydantic model, one class per kind, discriminated on
incident.kind. A segfault's resident memory and a run summary's exit code
have nothing to say to each other, so they are not fields of the same object:
kind |
Model | Raised on |
|---|---|---|
worker_death |
WorkerDeathIncident |
any run |
worker_stall |
WorkerStallIncident |
any run |
collection_mismatch |
CollectionMismatchIncident |
needs xdist |
internal_error |
InternalErrorIncident |
any run |
run_summary |
RunSummaryIncident |
any run |
Only the two that are about workers need workers. Everything else is raised whether or not you run under xdist, because the process that records is whichever one runs the tests — under xdist a worker, and without it the session itself.
They share verdict, confidence, severity, owner, fingerprint,
run_id, worker and evidence. str(incident) is the alert text — every
block quoted in this README is what it prints. A stored row comes back as the
model it was written from, and the union is a schema you can migrate a table
against:
from pytest_failure_instrumentation.incidents import registry
incident = registry.parse(json.loads(row)) # -> WorkerDeathIncident, ...
registry.json_schema()
Upgrading incident consumers from 0.11 or earlier. Upgrade readers to 0.12 or later before upgrading the pytest producers. Older readers reject new payload fields, including the new hash fields even when their value is null. The permissive models introduced in 0.12 protect readers on 0.12 or later against added fields; they do not change already-installed 0.11 readers. If readers cannot be upgraded first, keep producers pinned until they can. Unknown incident kinds still require a reader upgrade.
Every node id in the payload is paired with its hash. Wherever an incident
names a test there are two fields: the id as text, for a person to read, and
the sha256 of the whole id, for you to store and join on —
test_in_flight / test_in_flight_hash, last_test / last_test_hash,
nodeid / nodeid_hash, and the lists tests / test_hashes,
missing / missing_hashes, extra / extra_hashes,
unstable_tests / unstable_test_hashes. Node ids have no length bound —
a parametrize over file contents runs to kilobytes — so the text may have been
elided on its way here and the hash never is: it is taken from the whole id at
the moment the id was known, is 64 hex characters whatever the id was, and
hashing the text yourself will not reproduce it when the text was cut. List
hashes are positional, index for index. A null id has a null hash.
Hashes use UTF-8 with Python's surrogatepass error handler, preserving lone
surrogates (including undecodable filename bytes) without changing hashes of
ordinary Unicode IDs. Consumers should use the exported hash_of helper.
from pytest_failure_instrumentation import hash_of
incidents.find(test_in_flight_hash=hash_of("tests/e2e/test_replay.py::test_settles"))
The stack is in the payload but out of str(incident), because forty
frames turn a readable incident into a wall and whether they belong in an alert
is your call. incident.raw_stack() returns them as lines whatever the kind is;
top_frame and blamed_frame are the two already parsed, each with file,
line, function, module and owner.
What you get is the deepest frames of one thread from the most recent dump —
the other threads in a worker are this plugin's own heartbeat and execnet's
receiver, and reporting those blames the instrumentation. It is capped (40
frames for a death, 14 for a stall, 4000 characters for an internal error) and
a cut stack ends with ... and N more frames rather than pretending to be
whole. The complete dump stays in <worker>.crash on the runner.
def pytest_failure_incident(incident):
body = str(incident)
frames = incident.raw_stack()
if frames:
body += "\n\n" + "\n".join(frames)
alerts.send(body)
owner is the field that settles arguments — product, third-party,
customer-code, runtime, or unknown. It comes from walking outward past
runtime frames to the first one that belongs to somebody: the deepest frame is
usually ctypes.string_at, which tells nobody anything. A stack with no owned
frame at all is not unknown — it is a positive finding that the framework
itself failed.
severity follows from ownership rather than from how loud the failure
was, so a customer's segfaulting test does not page you. The exception is a
framework defect that ends the run, above.
fingerprint is stable across runs and excludes worker id, pid and
timings, so one defect on twelve workers is one incident with a count.
capabilities says what the machine could measure, so an absent figure is
never read as a healthy one. On Windows, its platform description records the
kernel version and product type (for example, Windows-Server-10.0.26100),
without a WMI query for a marketing release name during each worker's startup.
suspect_owner is kept apart from owner on purpose. When no stack names
anybody, the test that was in flight is a lead worth recording — but a guess
must never sit in the column a reader takes for a finding.
How an incident reads
str(incident) is the alert text, and every kind renders to one shape. It is
a convention rather than a style, and tests/test_message_convention.py
holds every kind to it:
- The first line says what happened, in words, specific to this instance:
which worker, which signal, which test, which function. It ends with a tag
[kind VERDICT, owner, severity]to grep for and route on, withrun-endingappended when the session died with it. Arun_summaryhas no owner slot, because nothing failed. - If no stack named anybody, one line says where the owner came from:
No stack was captured; the owner is taken from the test that was running, test_pool.py (customer-code).A lead, marked as one. - Every other line is exactly one of three things. A measurement: what
was observed, and where the figure came from when that matters (
Exit status -11 read via waitid). What that measurement means by construction: what follows from how it was taken, or from a fact about the OS, the runtime or xdist (SIGKILL cannot be caught,xdist addresses tests by position). Or a place to look:Look at:a file and line, a test, a setting, a flag. Several numbers share oneMeasured:line at the end, labelled.
What is deliberately absent: any guess at a cause in your code, and any fix to it. The analysis is arithmetic over evidence; it knows that a thread other than the test's used the CPU, not that the thread is a poller you forgot about. A line that named the poller would be right often enough to be trusted and wrong often enough to matter. Sentences are short, start with a capital and end with a full stop; nothing is a field dump, nothing argues for the finding, and nothing is said twice.
Handing one to an agent
An incident is written to be read without context, which is most of what an LLM
triaging a CI failure lacks. .claude/skills/reading-failure-incidents/SKILL.md
is that context in one file: the anatomy of the alert text, what each shared
field licenses a reader to conclude, the verdicts per kind, and the handful of
places where the text is a summary and the payload is the number — so an agent
reports what the incident found rather than what a -9 looks like.
Verdicts
A worker died
| Verdict | Told apart by |
|---|---|
POSSIBLE_TIMEOUT |
a self-exit or SIGALRM that reached its effective terminating deadline. Medium-confidence correlation, not proof |
OOM_KILLED |
-9 and the kernel log names this pid as the OOM killer's victim, within the death window |
KILLED_BY_PROCESS |
-9, and the kernel's signal tracepoint saw a process outside the run send it — the sender is named |
KILLED_BY_RUN |
-9 sent by another process of this run: the controller (execnet terminating a worker that would not exit) or a sibling worker |
SELF_KILLED |
-9 the worker sent to itself |
KILLED_BY_KERNEL |
-9 with si_code SI_KERNEL — the kernel's own kill, and no readable log to say which |
KILLED_AFTER_SIGTERM |
-9, and the controller had been sent SIGTERM shortly before — the run was being stopped, and the sender is named |
SIGKILLED |
-9 and no witness answered; the incident says which witnesses this machine withheld, and why |
NATIVE_CRASH |
SIGSEGV/SIGABRT/SIGBUS/SIGILL/SIGFPE, or a Windows NTSTATUS |
SIGNAL_<n> |
SIGTERM/SIGINT/SIGHUP — a request to stop, not a defect |
SELF_EXIT |
a non-signal POSIX exit code, 0 included; an unwitnessed Windows exit remains UNKNOWN because an external caller can choose the same code |
PROBABLY_SIGNALLED |
exit code 128–191, a wrapper ate the signal |
RUN_STOPPED |
a run found dead afterwards whose controller had been sent SIGTERM before this process's last heartbeat |
UNKNOWN |
no status obtainable (remote gateway) |
See Who killed it for where each of those witnesses comes from and what it needs.
A worker stalled
Silence proves nothing on its own: the controller hears from a worker only when a phase completes, so a twenty-minute test and a deadlock look identical from outside. What separates them is the worker's own heartbeat, which carries CPU time.
| Verdict | Heartbeat | CPU | Means |
|---|---|---|---|
STALLED_BLOCKED |
alive | none | the test thread is waiting on something that is not coming |
STALLED_FROZEN |
stopped | — | native code is holding the GIL, or the process is stopped |
STALLED_SILENT |
never ran | — | the watchdog is off, so there is no passive evidence either way |
| (not reported) | alive | burning | slow, not stuck |
The verdict is reached from beats already on disk. A stack is asked for afterwards, once the decision is made, because asking a wedged process a question can change its answer — see below.
In a run with no workers the assessment is the same assessment, from the same files, made by a thread inside the process it is about. The stack is read the way every live process here is read — py-spy, from outside — even though this one's frames are also directly to hand: a second reader for the single case that could avoid the first is a second set of failure modes and a second source to explain. It reads memory rather than asking the process to run anything, so no signal is sent that could return a blocked syscall early and dissolve the stall, and Windows — where no process can be asked for a stack — gets a current one like everywhere else. Where py-spy cannot read the process — macOS without root — the verdict is unchanged, since it comes from beats rather than frames, and the stack is whatever the watchdog last wrote.
The exception is STALLED_FROZEN, which is precisely the case where no Python
runs: the watcher thread cannot run either, so a frozen lone run reports
nothing until somebody reads what its fallback timer left behind.
Workers collected different tests
| Verdict | Means |
|---|---|
COLLECTION_MEMBERSHIP_DIFFERS |
a test exists on one machine and not another |
COLLECTION_ORDER_DIFFERS |
same tests, different sequence — fatal too, since xdist addresses tests by position |
COLLECTION_PARAMETERS_UNSTABLE |
same tests, different parameter values — a parametrize that is not deterministic |
Sixty workers never produce sixty collections. They produce two or three variants, so this reports one row per variant, measured against the largest:
Workers collected different tests: 2 workers produced 2 different collections [collection_mismatch COLLECTION_MEMBERSHIP_DIFFERS, unknown, needs-triage, run-ending]
No stack was captured; the owner is taken from a module the workers disagreed about, test_collect.py (customer-code).
Baseline: 1 worker collected 3 tests; the rows below are measured against that list.
1 worker is missing 1 test, in test_collect.py (gw1).
- test_collect.py::test_two
xdist addresses tests by position rather than by id, so any difference between the lists stops it: a reordering as much as a missing test.
The initial collections disagreed, so xdist aborted the run.
Look at: the full id lists in .pytest-failures/run-3f9a1c2d.
At sixty workers it stays the same shape, because the row count follows the number of variants rather than the number of workers:
Workers collected different tests: 58 workers produced 3 different collections [collection_mismatch COLLECTION_MEMBERSHIP_DIFFERS, unknown, needs-triage, run-ending]
Baseline: 55 workers collected 300 tests; the rows below are measured against that list.
2 workers are missing 1 test, in test_payments.py (gw41, gw58).
- test_payments.py::test_case_017
1 worker has 6 extra tests, in test_legacy.py (gw17).
+ test_legacy.py::test_extra_0
+ test_legacy.py::test_extra_1
+ test_legacy.py::test_extra_2
and 3 more
Read it as: how many distinct opinions existed, which workers held each, and how the minority differs from the majority. Magnitude leads each line and identity follows, samples use diff notation, and a truncated sample always says how much it withheld — a sample that looks like the whole story is worse than no sample at all.
The whole difference travels in the payload, not just the three ids the
text prints. missing and extra carry every differing node id, up to 500 per
side, with missing_count and extra_count as the true totals so you can see
whether that cap was reached. The distinction matters: a collection is
unbounded — sixty workers times fifty thousand node ids is hundreds of
megabytes — but a difference is almost always one test or one module's worth.
Only the digest is held per worker, and the whole collections are written to
collection-<digest>.txt for whoever still has the machine. That file is on a
runner which may be gone by the time anyone reads the alert, which is exactly
why the difference itself does not live there.
An order difference instead reports where the two lists first disagree, which is the one fact a unified diff of a reordered list destroys.
A parametrize whose values are drawn at collection time — random, a
timestamp, an unordered set — gives every worker a different id for the same
test, and reported as membership that reads as thousands of tests appearing and
disappearing. It is caught by asking a second question: are these the same
tests once the parameters are stripped from the ids? When they are, the report
names the parametrized tests responsible, drops the per-variant rows — which
would otherwise be one near-identical block per worker — and prints what each
of a few workers actually collected:
Workers collected the same tests with different parameter values: 6 workers produced 6 different collections [collection_mismatch COLLECTION_PARAMETERS_UNSTABLE, unknown, needs-triage, run-ending]
The same tests exist on every worker; only the parameter values in their ids differ.
test_billing.py::test_invoice
gw0 collected acct-1791, acct-3471, acct-6305, acct-7468
gw1 collected acct-2186, acct-2542, acct-6991, acct-9779
gw2 collected acct-1614, acct-1950, acct-4517, acct-9313
Ids that differ per worker were computed at collection time from something that differs per process, and xdist requires the ids to match.
Look at: the parametrize arguments of those tests.
xdist addresses tests by position rather than by id, so any difference between the lists stops it: a reordering as much as a missing test.
Stripping the parameters from the ids makes every worker's collection identical.
The initial collections disagreed, so xdist aborted the run.
The values are the diagnosis. Naming the test says where to look; three rows of disjoint account ids say a fetch is running at collection time, and three rows of floating-point noise say a random number is. Neither is apparent from one worker's list, which is the only thing xdist ever shows you.
That case is also why full id lists are held for only the first few variants.
"A handful of variants" is the assumption the whole design rests on, and
unstable ids turn it into one variant per worker. Past that limit a variant is
reported as not compared rather than diffed against a list nobody kept —
comparing two absent lists reports "the same tests in a different order", which
is a finding invented out of missing data.
A mismatch is run-ending usually, not always: xdist aborts when the initial
collections disagree, but silently drops a worker that registers a differing
collection after scheduling has begun. The run then continues one worker short,
and run_ending reflects which of the two happened.
How it knows
Whichever process runs the tests is the one that records. Everything below
is written from inside that process, because a process that is about to be
killed gets no warning and nothing it knew only in memory survives. Under
xdist that process is a worker and the controller reads what it left. Without
xdist there is one process and it does both jobs — so a plain pytest writes
the same state slot, the same heartbeat and the same stacks a worker does,
under the name main, and the live view, the sampler and the stall watcher
read them the same way.
The one thing it does not take, unless asked, is the fatal dump.
faulthandler keeps exactly one destination for a fatal signal and pytest's
own plugin has already pointed it at stderr; a worker claims it and loses
nothing, because that stderr is shared with fifteen others and a dump written
into it belongs to nobody. A run with no workers would be taking the crash out
of a terminal somebody is watching, so it leaves it there — see
failure_crash_stack, which is that trade written down.
A fixed-size state file. Which test and phase is open right now is written
to a fixed-size slot with os.pwrite — one syscall, no append, no growth, and a
file that is the same size after a million tests as after one. That is what
separates "died in teardown" from "died mid-call": pytest's own logfinish
fires only after the whole protocol, so it cannot tell them apart. The slot is
5 KiB. It carries the node id twice — the test in flight and the last test, see
below — plus a hash of each, so an id of around 2350 characters is kept whole:
past any real one by an order of magnitude, since a path, a class, a test name
and a couple of content hashes together use a fiftieth of it. The size is close to free: one
write of one buffer costs the same syscall from 256 bytes to 8 KiB, and 5 KiB
per worker is 320 KiB across a 64-way run. An id longer than that gives up its
middle, never the record: truncating the encoded record leaves it
unparseable, which costs the reader
the phase and the counters as well and reports a worker that died mid-call as
one that died before running anything. The middle goes rather than the tail
because both ends carry something the other does not — the head is the module
attribution reads, and the tail is where a parametrize puts the value saying
which case this was.
The slot carries two node ids, and the difference is why it is read at all.
test_in_flight is the test currently running and is cleared when teardown
returns; last_test is the most recent test whether or not it finished. One
field cannot be both, and being both is how a worker that died in the gap
between two tests came to be reported as having died in the one that had
already passed — attributed to whoever owns it, with an owner and a severity on
it. Where nothing is in flight the incident says so, and the lead it offers
names itself as the last test rather than the running one.
And beside each id, the sha256 of the whole one. Elision buys the record
its fixed size and costs it an identity: two parametrized cases that differ
only in the part that was dropped store as the same text, and nothing reading
the slot can tell either from a short id that happens to look like it. So each
of the two ids is written twice — test_in_flight and last_test for a person
to read, test_in_flight_hash and last_test_hash for a consumer to join on.
The hash is taken on the worker before anything is cut, is sha256 of the
whole id in hex, and is 64 characters however long the id was, which is why it
costs the slot the same at any length. Node ids have no bound at all — a
parametrize over file contents or a dozen-dimension matrix runs to kilobytes —
so match on the hash and show the text. The same pair travels everywhere a
node id is recorded: the worker rows /workers serves (nodeid_hash), the
sample hook, the resource inventory, the profiler's own records and every
profiling finding (nodeid_hash, test_hashes), and the ids a collection
mismatch reports (missing_hashes, extra_hashes). A null id has a null hash
rather than the hash of the empty string, so an idle worker never joins to a
test.
It carries the run id too. The evidence directory outlives a run and clearing it is best-effort — on Windows a file another process still has open cannot be unlinked at all — so a record stamped with a different run is refused rather than read as this one's. That check is load-bearing beyond the report: the pid in a stale record is a pid this run's stack probe would otherwise signal.
The exit status, taken from the OS. Where a Popen object survives, its
return code. Otherwise waitid(P_PID, pid, WEXITED | WNOWAIT | WNOHANG) —
WNOWAIT reads the status without consuming it, so execnet's own reaping
still works afterwards and nothing is broken by looking. Only a parent may do
this, which is why it happens on the controller, and why a remote gateway
honestly reports UNKNOWN rather than guessing. macOS does not expose
os.waitid at all and falls back to the Popen object. capabilities.exit_status
records which mechanism this machine has; exit_status_source on the incident
records which one actually answered — so a figure is never read as having come
from a mechanism that was never used. On Windows the code is normalised to its
unsigned form first: an NTSTATUS is above 2³¹, so 0xC000013A arrives signed
or unsigned depending on who answered — and a negative status means "killed by
signal N" to everything downstream.
faulthandler, pointed at a per-worker file. pytest's own faulthandler
plugin enables at configure time with trylast, aimed at shared stderr where
every worker's output interleaves. This claims the handler back afterwards, in
pytest_sessionstart. Its C handler is async-signal-safe and writes while the
GIL is held — which is the case that matters, since native code holding the
GIL is exactly what a frozen worker looks like.
Separate dump files. A fatal dump goes to <worker>.crash; the slow-test
watchdog's goes to <worker>.slow, and the frozen-interpreter fallback's to
<worker>.frozen. They are the same shape and only
the banner separates them — Fatal Python error against Timeout (…) — and a
watchdog dump is written by tests that go on to pass. Sharing one file made
"a stack exists" ambiguous, and on the Windows path where the dump is the only
thing distinguishing abort() from os._exit(3), ambiguous means a slow test
that passed can be reported as the crash that killed the worker, blamed on
whatever that stack happened to be doing.
Choosing the right dump, and the right thread inside it. Two things have to be picked here, and getting either wrong blames code that was not running.
A file holds as many dumps as were written to it, and the crash file accumulates: an on-demand stack taken while a worker was merely stalled precedes the fatal dump that ends it. The dump that describes the present is the last one. The watchdog's file holds only ever one, because each dump is written beside it and renamed into place — a reader that caught it mid-write would get the threads faulthandler had reached and not the one running the test.
Within a dump, all_threads=True means every thread is present, and the first
printed in a pytest worker is this plugin's own heartbeat thread; the second is
execnet's receiver. Reporting the first section would blame the instrumentation
for the failure it came to explain. The section reported is the one the fault
or signal reached (Current thread), else the one carrying pytest's runtest
protocol, else anything that is not ours. Current thread is skipped when it
is ours: the watchdog's dump is taken by the heartbeat thread, so
faulthandler labels that one current, and believing the label reported the
heartbeat as the frozen test.
Saying how old a stack is. A stack is evidence about a moment, and the
frames look the same whether they were taken just now or left behind four
minutes ago. A stall that could be probed reports a current stack; one that
falls back to the watchdog's file says stack written 47s ago by the slow-test watchdog, not taken just now, and carries stack_age_seconds. A death reports
crash_stack_age_seconds alongside, so a dump that predates the death reads as
the context it is rather than as the crash.
A watchdog on a cadence, written by the heartbeat thread. A test still
running after failure_slow_test_seconds has its stack written for it, and
keeps having it rewritten every interval, so whatever is on disk is at most one
interval old. That bound is the point: on Windows nothing can ask a live
process for a stack, so this is the only one a stalled worker will ever have.
The clock starts at setup and stops at the end of teardown, so a fixture
blocking on a container and a finalizer blocking on a connection are covered as
well as the test body — those are the commonest real hangs there are, and the
state slot has always told them apart. Once for the whole test rather than per
phase, or a test that spent most of the interval in setup and the rest in the
call would never reach it. The default is 20 seconds, and the file is dropped
when the test ends, so only the running test's stack is ever on disk (about
5 KB) and a healthy suite leaves nothing.
The obvious design instead has the controller signal a stalled worker and let
faulthandler answer. It has two flaws: Windows has no SIGUSR1 and os.kill
there cannot deliver one, and on POSIX the signal perturbs the subject —
PEP 475 makes Python retry on EINTR, but a C extension blocked in a raw
syscall need not, so it returns early and the stall being measured disappears.
The signal path remains as an extra, for asking an already-diagnosed worker for
a fresher stack.
The next design, and what this was until it was measured, is
faulthandler.dump_traceback_later(repeat=True). It needs no signal and works
on Windows, and it dumps even while native code holds the GIL — but it dumps
from a C thread that does not hold the GIL, walking every other thread's
frames while those threads push and pop them. A dump landing while the
interpreter is executing rather than blocked reads a frame being torn down, and
the worker segfaults. Over a suite whose tests were four times the cadence
long, that killed the worker in 10 runs out of 10, against 0 with the repeat
turned off; it left the dump ending mid-frame with a nonsense line number, and
the crash file empty because the fault was inside the dumper. Instrumentation
that crashes what it is watching is worse than no instrumentation, so the
cadence is driven from the heartbeat thread instead, in Python, holding the
GIL — nothing else can be mutating what is being walked.
A fallback for the one stack a Python thread cannot take. When native code
holds the GIL, no Python thread runs and the watchdog above writes nothing.
That is the case the C timer exists for, and also the case that makes it
dangerous — so it is armed such that it can only fire when it is safe. Every
heartbeat pushes its deadline out by three intervals, so while Python runs at
all the deadline is always in the future and the timer never fires; when three
beats in a row are missed it fires once, and by then nothing is executing for
it to trip over. Missing beats for that long has one realistic cause: a thread
holding the GIL and running C. A main thread running Python releases the GIL
every few milliseconds, and a machine loaded badly enough to starve a daemon
thread for three intervals would starve the timer's own thread with it. The
dump goes to <worker>.frozen, because it means something the watchdog's does
not — not "this test is slow" but "this process stopped responding" — and the
incident says which file its stack came out of in stack_source.
It stands down where pytest is using that timer. There is exactly one
faulthandler.dump_traceback_later timer per process, and arming it cancels
whatever was armed before. pytest's own faulthandler plugin arms it at the
start of every test when faulthandler_timeout is set, and this fallback
re-arms every second — so the fallback always won, and a configured timeout
silently never fired, faulthandler_exit_on_timeout and all. Where that ini is
set the fallback is not armed at all, and the worker records
frozen_fallback_stood_down in its event log saying why. It costs a stalled
worker its frozen-fallback stack, which is a worse report; the alternative is a
run that hangs past a timeout somebody configured, which is a worse run.
Nothing is signalled that cannot be confirmed. SIGUSR1's default
disposition is to terminate, and the pid the on-demand probe would signal was
read back out of a file the worker wrote. A worker that has since exited leaves
its number to be handed on by the kernel, so signalling it does not produce a
bad report — it kills an unrelated process. The pid is signalled only once
something says it is still ours: the controller can see that process running
under this worker's gateway, or the machine can be asked and answers that it is
a child of this one. A machine that cannot be asked at all is not taken as a
yes, and the incident says which of those it was instead of showing a stack it
never had.
A heartbeat carrying CPU time. One line every five seconds per worker,
bounded by wall-clock rather than by how many tests run. time.process_time()
in each beat is what turns silence into a verdict: alive with no CPU is
blocked, stopped is frozen, alive and burning is a slow test that must be
reported as nothing at all.
Evidence written before it is needed. Every mechanism above puts its output on disk during the healthy part of the run, because a process that is about to be killed gets no warning. The controller reads files, never the corpse.
And a run that never came back is read by the next one. The corpse being a whole run rather than one worker changes only who does the reading. A starting run already walks the evidence directory to clear out the runs that are over; the same walk now asks a second question of the same marker, and two answers separate a run to report from a run to delete:
- Is it over? The owner's pid is in the marker, so a run still going is recognisable however long it has been going — which matters because several run at once, and deleting a live run's evidence is how a cleanup once broke the very reports it existed to produce.
- Did it report for itself? A run that reaches session finish stamps the marker on the way out. It raised its own incidents; re-raising them a day later against whichever run happened to notice is worse than never raising them.
A run that is over and never stamped that mark is exactly a run that could not report for itself, and each process in it that started a session and never finished one is a death:
Worker main of run run-8f21c0b4e5d7 died while running test_pool.py::test_writes (call); its exit status could not be read [worker_death UNKNOWN, unknown, needs-triage]
No stack was captured; the owner is taken from the test that was running, test_pool.py (customer-code).
Found in the evidence of run run-8f21c0b4e5d7, which ended without reaching session finish; it was last seen alive at 2026-09-04 09:12:41. The death is somewhere after that.
Exit status unavailable (pid 21780): only the parent process could read it, and the parent was the run that died. An OOM kill, a segfault and an os._exit cannot be told apart without it.
Look at: test_pool.py::test_writes.
No stack was kept: this run had no workers, so its fatal dump went to the terminal that pytest's faulthandler plugin writes to.
Look at: failure_crash_stack, which keeps a copy in the evidence directory.
Measured: 412 MB resident at the last heartbeat. 12 tests started and 11 finished on this worker.
recovered_from_run leads the block, because a reader who takes this for the
current run's crash goes looking for a failure that is not there. run_id is
the dead run's — that is the key anything joins on, and the run that merely
found it had no part in what happened. raised_at is now.
The exit status is the one thing genuinely lost. Only a parent may read it, the
parent was the run that died, and the process is long gone — so -9, -11 and
os._exit(1) cannot be told apart afterwards, and the incident says that
rather than guessing. Everything else survives: which test was in flight and in
which phase, how many had run, the resident memory at the last beat, and — if
the run kept one — the fatal stack, which is enough to reach NATIVE_CRASH
with a blamed frame and no status at all. That is the same position Windows is
in for a watched worker, and it is why the dump is evidence in its own right.
A worker that timed out
The worker saves each test's effective timeout settings, including marker
overrides, disabled timeouts and call-only scope. Call-only deadlines use the
call clock; other deadlines use the test clock. A plain faulthandler_timeout
only dumps stacks and is excluded unless pytest also enables
faulthandler_exit_on_timeout.
A self-exit or SIGALRM after an effective deadline is POSSIBLE_TIMEOUT with
medium confidence. Timing is consistent with an enforcer, but does not prove
that it caused the exit. An explicit exit can occur after the same deadline.
matched_timeout, timeout_source, test_seconds and phase_seconds expose
the comparison. Marker values replace the global limit, including timeout(0);
a test with a raised limit is not judged against the smaller global value.
Older evidence without effective settings is left unclassified as a timeout.
What the worker last said
The line that explains a native death is usually on stderr and nowhere a stack
can reach it: OpenBLAS blas_thread_init: pthread_create failed when a hundred
workers each start a thread pool at once, a malloc(): corrupted top size, a
library's own abort message. pytest already captures it — its fd-level capture
catches even the writes C code makes straight to file descriptor 2 — but it
keeps it in a temporary file that the kill throws away, and hands it to the
report only for a phase that completed.
With failure_capture_output on, fd 2 is pointed at a real file for the length
of each phase, and the last few kilobytes reach the incident as recent_output,
with the final stderr line surfaced on the alert:
· segmentation fault in native code
· last stderr: OpenBLAS blas_thread_init: pthread_create failed
A real file rather than a pipe on purpose: a write followed immediately by
abort() is a synchronous write(2) the kernel has persisted before the abort
runs, where a pipe drained by a thread of the same dying process would lose the
race. So the message printed in the very phase that crashes is kept — the case
pytest's own capture never reports, because it hands a phase's output to a
report only once the phase has completed.
It coexists with pytest rather than fighting it. pytest owns fd 2 by pointing
it at its own file and re-points it there at the start of every phase and every
import; this takes it over just after, at each, and hands the phase's bytes
back to pytest's file at the phase's end, before pytest reads it — so pytest's
captured-output-on-failure is unchanged. It is the one facility here that takes
over a process-wide descriptor, which is why it is opt-in and guarded at every
step: an fd operation that fails leaves fd 2 as it was and records that output
was not captured, rather than raising. It stands down for a test that captures
its own fd output — one that asks for the capfd or capfdbinary fixture —
because taking fd 2 out from under such a fixture would make its readouterr()
miss what the test wrote, and changing a passing test is the one thing this may
never do; capsys and capsysbinary are sys-level and untouched. Tested
alongside -s, --capture=sys and pytest-cov, each of which keeps its own
output and its coverage while a crash is still caught. POSIX only. An absent
tail reads as "not captured", never as silence: the alert says which.
Who killed it
A wait status of -9 is the one number designed to say nothing about who sent
the signal, and for a long time this plugin stopped there: SIGKILLED, with a
list of what it might have been. Everything that actually ends a process keeps
a record somewhere else, so it now goes and asks each of them — and where a
machine refuses, the incident says which witness was withheld and by what,
rather than leaving an absence to be read as "nothing to know".
The kernel's signal tracepoint names the sender of every SIGKILL.
signal:signal_generate fires in the sender's context when a signal is
queued, so its line carries the sender's comm and pid in front and the target
behind, with si_code — 0 for a kill(2) from a process, 128 for the
kernel's own, the OOM killer included:
python-1771 [000] d..1. 401.375501: signal_generate: sig=9 errno=0 code=0 comm=sleep pid=1772 grp=1 res=0
That is the difference between "SIGKILL, could be anything" and:
[worker_death] KILLED_BY_PROCESS severity=informational owner=unknown
worker=gw1 in flight test_sleep.py::test_sleeps phase=call started=1 finished=0
· died while running test_sleep.py::test_sleeps (call)
· exit status -9 - SIGKILL: uncatchable kill (OOM killer or external kill) (pid 2011, via waitid)
· SIGKILL was sent by gitlab-runner (pid 2003, uid 998), outside this run - `gitlab-runner run`: something outside this run stopped it - a job cancellation, a timeout enforcer, an orchestrator, or a hand on the keyboard
No test is suspected, because no test did it; the severity is informational,
because a cancellation is not a defect; and the sender's command line is on
the incident for whoever wants to take it up with them. A worker that sent
the signal to itself is SELF_KILLED; one killed by the controller — execnet
terminating a worker that did not exit in time — or by a sibling worker is
KILLED_BY_RUN, and those two do point at a test.
Reading tracepoints needs root, so a sidecar does it: a second
interpreter running a stdlib-only script, started directly where the run is
already root and through sudo -n where it is not and failure_elevate
allows it (-n: a sudo that would prompt fails rather than hangs). It makes a
tracefs instance of its own under /sys/kernel/tracing/instances/, enables
one event in it filtered to SIGKILL and SIGTERM, writes one JSON line per event
into signals.log in the run's directory — stamped with the wall clock as it
reads the pipe, and with the sender's command line read out of /proc in the
same instant, because the sender of a kill -9 is usually gone a moment later
— and removes the instance on the way out. Nobody's perf or trace-cmd on
the same machine is touched.
The kernel log names the OOM killer's victim, and prints the whole fleet.
The cgroup counter says that something in the cgroup was OOM-killed; the log
says what. One Out of memory: Killed process 4242 (python3) ... anon-rss:...
line per kill, an oom-kill:constraint=CONSTRAINT_MEMCG,...,task_memcg=/docker/...,pid=4242
summary saying whether it was a cgroup's limit or the machine's, and — with
vm.oom_dump_tasks on, which is the default — a table of every task the
killer weighed with its RSS. For a run of a hundred workers that table is the
fleet at the instant the decision was made, and the incident does the
arithmetic:
· the kernel log (kmsg) records the OOM killer choosing pid 4242 (python3) at 1680 MB anonymous resident, having hit the machine's own memory; matched by pid
· it weighed 214 tasks holding 31650 MB; 100 of them were this run's, holding 29800 MB together; the victim was the 3rd largest
· largest: python3 pid 4240 [gw17] 1720 MB, python3 pid 4241 [gw3] 1700 MB, python3 pid 4242 [gw52] 1680 MB
· fleet pressure: the victim was an ordinary member of the run (median 290 MB), so the run's 100 processes exceeded the limit together - fewer workers or more memory, not one test
fleet pressure against its own weight is the question a hundred-worker run
needs answered: the killer takes whichever process is marginally the largest at
that instant, so the victim's own size explains nothing on its own, and the
in-flight test it happened to be running is the wrong suspect. Where the
tracepoint was also watching, the record says in whose context the kernel made
the kill — the process whose allocation hit the limit, which is often not the
victim.
Reading the log is the per-distro part, and every rung is tried in order with
the one that answered recorded on the incident: /dev/kmsg, open to everyone
where kernel.dmesg_restrict is 0 and only to CAP_SYSLOG where it is 1
(Ubuntu since 20.04, Fedora); journalctl -k, for members of adm or
systemd-journal; dmesg; and with failure_elevate, sudo -n dmesg.
Inside a pid namespace, host PIDs may not match the worker's PID. A shared
cgroup and nearby timestamp cannot identify a victim, so that case remains
unknown unless a direct witness identifies it. Undelivered signal-generation
events are ignored, and a catchable SIGTERM by itself does not prove death.
Signal delivery is preserved. The plugin does not block SIGTERM, replace
handlers or alter inherited masks in either controllers or workers. Where
permitted, tracefs records signal senders without intercepting delivery.
Without kernel tracing, an unprivileged controller death can be recovered as
UNKNOWN, with the unavailable evidence stated explicitly. Historical
controller records remain readable.
Live worker deaths share cached trace parsing and kernel-log snapshots. Slow kernel-log reads run off the controller thread, with a single short wait per refresh across a death cascade. Snapshot age is reported; an empty snapshot does not prove that no OOM kill occurred.
Reporting a killed run from the sidecar
On POSIX, a SIGTERM or SIGINT received by the sidecar starts a 15-second
observation grace period instead of ending it immediately. This covers a
supervisor signaling both controller and sidecar: it continues reading the
controller pipe, reports unexpected EOF, and exits promptly after the normal
stop/EOF handshake. Repeated signals do not extend the deadline. A signal to
the sidecar alone is never treated as proof that the controller was killed.
The grace period bounds waiting for controller exit; delivery still uses the
existing reporter timeout. SIGKILL, Windows forcible termination, or destruction
of the whole container/host can still prevent reporting.
Every incident above is raised by a process that survived to raise it, and a run whose controller is killed has none. The next run over the same evidence directory recovers it — but on a runner with a fresh workspace per job there is no next run, and a cancelled or OOM-killed job was a job about which nothing was ever said.
A sidecar can survive the controller. On POSIX it starts in a separate session,
but it can still be killed with the container, cgroup or host. It reads the
controller's pipe; a controller that reaches session finish writes stop first.
An independent process-owned POSIX record lock, or a retained Windows process
handle, detects controller death even if a child retains the pipe's write end.
The controller reports the reporter as armed only after the sidecar acknowledges
its payload. The acknowledgement contains a random token, never the payload.
Owner and worker records include process creation time where available, so a
reused PID does not hide a dead run. Missing or unreadable worker state falls
back to the event-log PID rather than treating missing state as death.
Legacy records and inaccessible identity
information retain conservative PID-based checks.
EOF without it is a death, and the sidecar then starts a reporter: a child
that builds the same incidents the next run would have recovered, the
controller's own death above all, and calls the callable you configured with
each one, the way the hook would be:
# ci/alerts.py
def report_killed_run(channel, incident):
from ourcorp.alerting import Client # loaded in the reporter, not at pickle time
Client.from_env().post(channel, str(incident), payload=incident.model_dump())
# conftest.py
import functools
from ci.alerts import report_killed_run
from pytest_failure_instrumentation import install
def pytest_configure(config):
install(config, on_run_death=functools.partial(report_killed_run, "#ci-deaths"))
Or, from ini, point to a module-level wrapper that accepts just the incident:
# ci/alerts.py (alongside report_killed_run above)
def report_run_death(incident):
report_killed_run("#ci-deaths", incident)
[pytest]
failure_on_run_death = ci.alerts:report_run_death
These function names are examples, not built-in APIs or new incident types.
pytest_failure_incident remains the normal delivery hook. The separate
on_run_death callback receives the same incident model from the surviving
helper when the controller has died and can no longer invoke that hook.
The callable travels as a pickle, and that sets the rules. A module-level
function, or a functools.partial of one with picklable bound arguments;
lambdas, closures and anything holding the pytest config will not pickle, and
the run says so at session start and proceeds without a reporter. What pickle
records is the function's module and name plus the bound arguments, so
anything the function imports inside its body — your SDK — is loaded only in
the reporter, which runs with the same interpreter, the run's import path and
working directory, and the run's environment, which is where an alerting token
lives. The environment travels down the two pipes and never touches disk.
Nothing of yours runs as root. The Linux sidecar may be root. It unpickles
nothing; it starts the reporter as the user that started the run, with that
user's groups and environment. Its output goes to reporter.log in the run's
directory, and nothing it does can reach a run that is already over. Once
reported, the run's marker is stamped so a later recovery skips it. Reporters,
recovery and pruning share an OS lock. Each successful callback is checkpointed;
a failed callback is retried up to three times with 250 ms between attempts,
without replaying checkpointed successes. Exhausting that budget leaves the
remaining evidence for later recovery. A confirmed controller death is delivered
even if a worker survives the 20-second worker grace period. Its evidence remains
available until those workers finish; delivery of the controller is checkpointed
independently. Delivery
is at least once: consumers should deduplicate by run ID and fingerprint
because a kill between callback success and its checkpoint can replay it.
The reporter's five-minute deadline includes input delivery; an overdue child
is killed and reaped. A successful callback means it returned without raising,
not that an external service durably stored the incident.
Incident volume. Live reporting emits each distinct fingerprint once per run, with recurrence counts in the existing summary. A completed stall probe cannot emit a new stall for a worker already reported as failed by xdist. Clean completion does not erase a previously confirmed stall. Delivered worker deaths are checkpointed so recovery does not announce them again.
Recovery groups equivalent fingerprints. When the controller died, unresolved
worker losses are attached to that interrupted-run incident in the optional
related_deaths field, preserving each worker's full record. This describes
unresolved losses, not proof that they share a cause. Independently diagnosed
failures remain separate. A grouped report retains the highest severity of its
members. A 20-worker interruption therefore need not create 21 alerts, while a
separate diagnosed crash remains visible.
The existing run_summary hook record stays informational; ordinary assertion
failures do not become additional instrumentation incidents. Resource samples,
gaps and unavailable counters are live data, not incident sources. Consumers
should route by kind and severity rather than page on every hook invocation.
Watch-only reporting needs no privilege. Where tracing is unavailable, the sidecar still starts when a reporter is configured. It reports durable state and explicitly leaves the sender unknown. If the sidecar also dies, recovery requires a later run over the retained evidence directory. Kernel sources may be unavailable even with elevated privileges; the incident names the sources that answered and those that did not.
Windows keeps the same record through ETW. There are no signals; a kill
is one process calling TerminateProcess on another, with whatever exit code
the caller chose — 1 from taskkill /F and from the GitLab runner, -1
from .NET's Process.Kill, 15 from Python's os.kill. The kernel logs the
call through the Microsoft-Windows-Kernel-Audit-API-Calls provider: event 2
carries the target's PID and API return status, and the event header carries the PID of
the process it was written in, which is the caller. Only successful calls
are attributed. killer.api_status preserves the API result; killer.exit_code
comes from the parent's observed process status and is unavailable during
recovery. It is never inferred from ETW's ReturnCode. Live attribution requests
an ETW buffer flush before its bounded wait. If no witness arrives, an ordinary
Windows exit code remains UNKNOWN: TerminateProcess can use the same code as
an intentional exit. Known fault codes and fatal dumps retain their diagnoses.
A rejected call or stale
PID match does not end the wait for a valid termination record. A sidecar of the same shape
as the Linux one consumes a real-time session on that provider, sweeps the
sessions a killed sidecar would have left (a machine holds at most 64), and
writes the same JSON lines; the verdict is KILLED_BY_PROCESS with the
caller's executable, or SELF_KILLED and KILLED_BY_RUN as on Linux. An ETW
session needs administrator rights or membership of Performance Log Users, and
there is no sudo to elevate with — failure_elevate does nothing there. Two
things differ from Linux: there is no OOM killer, so the memory case never
produces a kill (allocation fails inside the process as a MemoryError, which
is reported normally); and there is no warning shot, GitLab's own docs say the
kill is simply sent twice, so the unprivileged controller witness has nothing
to catch and ETW is the only source. A taskkill /T takes the sidecar with
the tree, so the kill of the controller itself may go unrecorded there; the
workers' kills before it are on disk.
macOS is the one platform still without a witness: it can be asked whether a
process died of memory pressure through kqueue's process filter, which is
not read yet.
Live stacks over HTTP
Everything above is for reading afterwards. This is the other direction: a UI watching a run, asking what a test is doing while it is still doing it.
$ curl localhost:8080/stack?pid=48213
{"pid": 48213, "source": "py-spy", "captured_at": 1756142887.31,
"options": {"native": false, "locals": false, "nonblocking": false},
"threads": [{"thread_id": 8632442880, "thread_name": "MainThread",
"os_thread_id": 48213, "owns_gil": true, "active": true,
"frames": [{"function": "_wait_for_lease", "file": "/app/pool.py", "line": 91,
"module": null, "native": false, "locals": null},
{"function": "checkout", "file": "/app/pool.py", "line": 44,
"module": null, "native": false, "locals": null},
{"function": "test_concurrent_writes", "file": "/tests/test_pool.py",
"line": 210, "module": null, "native": false, "locals": null}]}]}
Naming the process. ?pid= is what /workers reports and what a UI already
holds. ?worker=gw3 is what a person holds — somebody looking at a stalled
worker is asking about that worker, not about the test it happens to be on, and
resolving the name at the moment of the read closes the window where xdist
replaces it between two requests. The name is compared against a directory
listing and never joined onto one, and a worker whose process has exited
resolves to nothing rather than to its last pid — pids are reused, and reading
one afterwards means reading whatever the machine has since given that number
to. Name it one way or the other; both at once is refused, because they can
disagree and there is no right one to prefer.
Asking for more than the frames. Three options, each one py-spy flag, all
off unless switched on. A bare ?locals is on: only an explicit ?locals=0 is
a no.
| option | what it adds | what it costs |
|---|---|---|
?native |
frames from C, C++ and Cython extensions | needs the process paused, and a py-spy that can unwind |
?locals |
each frame's variables, rendered as text | the largest payload here, and the data a test is holding |
?nonblocking |
reads without pausing the target at all | accuracy, and owns_gil/active, which become null |
$ curl 'localhost:8080/stack?worker=gw3&locals'
{"pid": 48219, "worker": "gw3", "source": "py-spy", "captured_at": 1756142889.02,
"options": {"native": false, "locals": true, "nonblocking": false},
"threads": [{"thread_name": "MainThread", "frames":
[{"function": "_wait_for_lease", "file": "/app/pool.py", "line": 91,
"module": null, "native": false,
"locals": [{"name": "timeout", "repr": "30.0", "argument": true},
{"name": "waited", "repr": "27.4", "argument": false}]}]}]}
options on the way back is what was applied, not what was asked for, and
any difference is a sentence in notes. --native and --nonblocking are
refused as a pair by py-spy, so asking for both drops native — --nonblocking
is a promise about the target, and honouring native instead would pause a
process somebody asked not to have paused. A py-spy that cannot unwind is
likewise a reason to return the Python frames plus a note rather than an error.
A caller that displayed its own request back to a user would be captioning
frames with a setting that did not produce them, so read the toggles back from
here.
$ curl 'localhost:8080/stack?pid=48219&native&nonblocking'
{"options": {"native": false, "locals": false, "nonblocking": true},
"notes": ["native frames need the process paused, and --nonblocking was asked
for as well; py-spy refuses that pair, ..."], ...}
locals is null when they were not asked for and [] when the frame holds
none — a native frame has no Python variables, and answering null there would
read as "you did not ask". The variables are rendered by py-spy inside its own
process while the target is stopped, so what crosses the wire is text and no
__repr__ of yours is executed to produce it. They are nonetheless the one
thing this server discloses that is the data a test is working on rather than
the shape of the code — a fixture's credentials, a customer record, a decrypted
payload — so a deployment that cannot have that leave the process turns them off
and still gets the frames:
[pytest]
failure_stack_server_locals = false
Nothing is redacted selectively. This package cannot tell a password from a lease id, and a filter that pretended to would be worse than the honest switch.
Off by default — a plugin installed for crash reporting should not start opening listening sockets on everybody who upgrades it:
[pytest]
failure_stack_server = true
$ pytest --callstack-port 8080 # also switches it on
$ pytest --callstack-host 0.0.0.0 # so does this - and needs a token
$ PYTEST_CALLSTACK_TOKEN=$(openssl rand -hex 16) pytest --callstack-host 0.0.0.0
A token does not switch it on: "authenticate the server I am already
running" and "start a server" are different requests, and an exported
PYTEST_CALLSTACK_TOKEN in a shell profile must not open a socket on every
pytest run in that shell.
Two modes, and the port number picks between them
Drawn — the default, when no port is named. The session binds whatever the kernel hands it and writes the address into the evidence directory. Nothing is shared, so nothing is contended and nothing can be lost to another session.
Named — --callstack-port 8080, or the ini equivalent. The session claims
that exact port and shares it with every other session on the machine, since a
fixed port cannot be bound twice. First to start serves; the rest wait, and take
over within five seconds of the holder exiting.
Name a port when something outside has to be told the address once and for all — a firewall rule, a UI with it compiled in, a published container port. Otherwise let one be drawn: a UI that can read the evidence directory needs no agreement about numbers, and it has to read that directory anyway to know which pid is running which test.
What is running where
GET /workers answers the whole question in one request, assembled from files
the run was writing anyway — no ptrace, no per-test cost, nothing written:
{"served_by": {"service": "…", "pid": 17155}, "observed_at": 1787688175.421,
"runs": [{"session": "run-19d52c2ff8e2", "run_id": "757f3cc51790…",
"controller": {"pid": 17155, "alive": true},
"schedule": {"dist": "load", "collected": 812, "unassigned": 240, "settled": false,
"rerunning": 1},
"workers": [
{"worker": "gw0", "pid": 21615, "nodeid": "test_slow.py::test_alpha",
"nodeid_hash": "c8f2a10d4b…", "phase": "call", "status": "blocked", "attempt": 2,
"why": "heartbeat 0.5s old but no CPU progress: the test thread is waiting on something",
"process_exists": true, "heartbeat_age_s": 0.5, "cpu_rate": 0.001, "rss_mb": 32,
"rerunning": true,
"tests_finished": 51, "tests_running": 1, "tests_queued": 12, "tests_assigned": 64},
{"worker": "gw1", "pid": 21618, "nodeid": "test_slow.py::test_beta",
"nodeid_hash": "70b31ce9af…", "phase": "call", "status": "gone",
"why": "process 21618 no longer exists; last seen in call of test_slow.py::test_beta",
"process_exists": false,
"tests_finished": 12, "tests_running": 1, "tests_queued": 7, "tests_assigned": 20},
{"worker": "gw2", "pid": 21621, "nodeid": "test_slow.py::test_gamma",
"nodeid_hash": "1d04e7b562…", "phase": "call", "status": "working",
"why": "heartbeat 0.3s old, burning 1.00 cores", "cpu_rate": 1.0,
"tests_finished": 48, "tests_running": 1, "tests_queued": 11, "tests_assigned": 60}]}]}
?worker= narrows it to particular workers, which on a sixty-four-way run is
the difference between reading one state file and reading all of them. Both
spellings and both shapes work, and they mix:
$ curl 'localhost:8080/workers?worker=gw1'
$ curl 'localhost:8080/workers?worker=gw0,gw3'
$ curl 'localhost:8080/workers?worker=gw0&worker=gw2'
Runs left with no matching worker drop out, and names that matched nothing
anywhere come back under filter.unmatched — otherwise a caller cannot tell
"not running" from "misspelt". An empty ?worker= is treated as no filter,
because that is what a UI sends when its filter box is empty. The names are
compared against a directory listing and never joined onto one, so a value that
looks like a path is just a name that matches nothing.
A run with no workers is described the same way, under the name main. It is
the case where the two halves of the view coincide: the process serving is the
process running the tests, so controller.pid and the worker's pid are the same
number — and /stack for it is read exactly as any other pid is.
$ curl localhost:8080/workers
{"runs": [{"session": "run-8f21c0b4e5d7", "controller": {"pid": 4212, "alive": true},
"workers": [{"worker": "main", "pid": 4212, "nodeid": "test_pool.py::test_writes",
"nodeid_hash": "5a9e0c31f7…", "phase": "call", "status": "blocked",
"why": "heartbeat 0.4s old but no CPU progress: the test thread is waiting on something"}]}]}
$ curl 'localhost:8080/stack?pid=4212'
{"pid": 4212, "source": "py-spy", ...}
The status vocabulary is analysis/stall.py's truth table, as
a live status rather than a post-hoc verdict:
| status | heartbeat | CPU | process |
|---|---|---|---|
working |
fresh | above 0.05 cores | exists |
blocked |
fresh | below that | exists |
frozen |
stale | — | exists |
gone |
— | — | absent |
unmeasured |
never any | — | — |
finished |
stopped with the session | — | idle until the run ends |
The last row is the one that is not a finding, and it exists because of what a
worker does when it runs out of work: it does not exit. xdist sends it
shutdown once the queue is empty, which ends its test loop and nothing else.
The worker runs its own session finish, reports workerfinished, and its
process then sits inside execnet — main thread parked in
integrate_as_primary_thread on an Event.wait — until the controller's
session finish tears every gateway down at once. Under --dist load that is
however long the slowest worker's remaining tests take. Its heartbeat stopped
with its session, so from the beats alone it is a live process that has not
beaten for a while, which is the frozen row and its wording about native code
holding the GIL. The worker writes worker_finish into its event log on the
way out, and that record outranks the beats — and outranks gone too, since
being closed at the end of the run is how a finished worker's process ends, not
a death.
Three files answer three different questions, and keeping them apart is what
makes this cheap and correct. .state says what a worker is doing and is
written before each phase runs, so it is ahead of anything the controller
knows — but a twenty-minute test writes nothing for twenty minutes, so a stale
record says nothing about liveness. .events carries a heartbeat every few
seconds whatever the test is doing, and the CPU time on each beat is the only
thing separating a worker that is working from one that is stuck. The pid
answers the narrowest question of the three, about a number that can be reused.
Two details that are easy to get wrong and are handled here:
- A killed worker is a zombie until its parent reaps it, and the kernel
accepts signals for it the whole time — so a
kill(pid, 0)check reports a worker killed a moment ago as alive, which is the opposite of what a crash view is for. Linux answers from procfs, which is cheaper than building a psutil object per worker per request; everywhere else psutil answers. - Liveness is a different mechanism per platform. Signal 0 is a POSIX question; on Windows it is not a question at all (see below), so the platform picks the mechanism before anything else happens.
cpu_rate: nullis not zero. "It burned nothing" and "we could not measure" are different findings, and a worker at full tilt whose beats collide produces the second.
Nothing here signals a worker or asks it anything: every verdict comes from beats already on disk, because asking a wedged process a question can dissolve the stall you were measuring.
nodeid and phase are null between tests, and a very long nodeid is
trimmed from both ends with nodeid_elided: true saying so. nodeid_hash is
the sha256 of the id the worker had, whole, taken before the slot trimmed
anything — so it is what to match a row against a collection on, and the text
beside it is what to show.
How many tests each worker has
tests_started and tests_finished come from the worker's own state slot, and
on their own they are a numerator with no denominator: the one question anybody
watching a run actually has is is it nearly done, and nothing a worker writes
can answer it. No worker knows. It collects the whole suite and is then fed
indices a chunk at a time, so an empty queue and a pause look the same from
inside. The controller's scheduler holds what is outstanding per worker and
throws an index away the moment that test completes, so it cannot say how many
have been through either. The total exists only as the sum of the two, which is
why the controller works it out and writes it into the run's directory as
schedule.json — where /workers, the sample hook and anything else reading
the evidence pick it up like every other fact here.
| field | on | meaning |
|---|---|---|
tests_assigned |
worker | tests handed to this worker so far |
tests_finished |
worker | of those, how many it has run |
tests_running |
worker | the test in flight — 1, or 0 between tests |
tests_queued |
worker | the ones it has not begun |
attempt |
worker | which attempt of nodeid is running: 1, or 2 upwards under a rerun plugin. null between tests |
rerunning |
worker | whether the controller believes this worker is inside a rerun. attempt is the one to believe |
collected |
run | tests in the run's whole collection |
unassigned |
run | tests that are nobody's yet — what every total can still grow by |
settled |
run | whether any worker's total can still change |
rerunning |
run | how many workers are inside a rerun right now |
The three worker counts partition the total: finished + running + queued == assigned, always, with every test in exactly one of them. That shape is
deliberate. Reporting what was left instead read more naturally and was a
trap — "not finished" includes the test in flight, and so does tests_started,
so the two obvious numbers to add were the two that overlapped, and a row
saying started 2, pending 2, assigned 3 looked broken while being correct.
It is a running total, not a plan, and settled is how you tell. Under
--dist load and its relatives the scheduler keeps most of the suite in a queue
nobody is assigned yet and hands it out in chunks, so a worker's total grows for
as long as unassigned is above zero — a percentage drawn without it is a bar
whose end moves. Under --dist each it is settled from the first moment,
because every worker is given the whole collection at once. Under
--dist worksteal an empty queue is still not settled while a steal can
happen: that mode moves work between workers, so a total that has stopped
growing can still shrink. It takes both halves of xdist's own condition —
somebody idle to give the work to, and somebody holding more than the floor of
two to be worth taking it from — so one worker running the tail of a run is
not settled, while two workers holding two tests each are.
The three numbers in a row always agree, and that took arranging, because
they do not come from one place: the total is the controller's, written into
one file for the whole run, and tests_finished is the worker's own, written
into its own slot. Pairing a live read of one with a stale read of the other
produced rows saying a worker had finished nineteen of the fifteen tests it had
been given — not a lag a reader can interpret, but a row that cannot be true.
Two things stop it. The controller's record is rewritten whenever a test
starts rather than on a timer, so it is never more than one test behind; and
only the total comes from it — the split is measured from the worker's own
counts, and the total is floored at tests_started, because a worker cannot
begin a test it was never given either. A stale total can then only understate
the queue, never contradict the line above it.
What can still move is tests_assigned, by one, for an instant. A worker tells
the controller a test is finished before it tells the scheduler, so in
between it is counted as run and still outstanding both. Writing from the start
of a test rather than the end of one keeps that worker out of its own window;
what is left is the rarer case of another worker's two messages straddling this
one's, and it lasts until the next test starts somewhere.
A rerun is the same test, not another one. pytest-rerunfailures, flaky and every plugin written the same way run a failed test's phases again inside the same protocol, so setup and teardown happen once per attempt while the test, its node id and its slot in the scheduler's queue are one. Both counters count the test: the worker counts a start at the first setup of a protocol and not at a second, and takes back the finish it counted at the end of the attempt that turned out not to be the last, so a rerun in flight reads as one running; the controller does the same, and takes its own count back when a worker starts the test it has just finished. Before that, a run of 368 tests with six rerun once was reported as 374 — every attempt a test, and the total floored at the worker's count so that an attempt became a test nobody was given.
The take-back is what a rerun costs the controller, and it is why the count
moves at a start. A worker's total is what it has finished plus what it still
owes, and while a rerun runs the test is in both: the attempt that failed sent
its teardown, and the scheduler is told only when the protocol ends. Counting
the attempt and then ignoring the second teardown made the total right again
once the rerun was over and left it one high for the length of the attempt —
long enough to be written into every record taken in it, so a run of six tests
with one rerun read as seven of six with unassigned: 0 and settled: true
beside it. The finish is taken back instead, at the next attempt's start, which
is before the record is written rather than after. xdist relays that start
without the worker it came from, so the id names the worker — which it can do
under every mode but --dist each, where the same test runs on all of them and
a first run and a rerun read alike. There a rerun still reads one high, until
the attempt's own reports arrive and name their worker.
And a rerun is the one thing the counts cannot show, so it is reported
separately. Every number above is a number of tests, which is what makes
them add up — so a worker three test-lengths into the same node id moves none
of them, and a run full of reruns looks like a run that has stopped. attempt
on a worker's row says which attempt of the test in flight is running: 1
ordinarily, 2 upwards while a rerun plugin repeats it, null between tests. It
comes from the worker, which sees the protocol boundary and so is the one to
believe. rerunning beside it is the controller's own reading, from the
take-back above, and the run's rerunning is how many workers are inside one.
All three are null from a run older than they are, which is not zero: no
rerun anywhere and nobody counting them are different findings, and a product
polling across an upgrade reads both.
A crashed worker keeps its row, so the rows can add up to more than the run.
xdist drops a dead worker's queue back into the global one and starts a
replacement under a new id, and the test it died in is reported failed rather
than reassigned. The dead worker's row stays as it was — tests_assigned: 5, tests_finished: 3, tests_running: 1, tests_queued: 1 is "it was given five,
ran three, died inside the fourth and never started the fifth", which is the
line a death is triaged with, and the replacement gets a row of its own rather
than overwriting it. What that costs is that the tests it was given and
somebody else then ran are counted in both rows. So collected is the run's
size and summing tests_assigned is not; status: gone is what marks a row as
a record of a process rather than a report on one.
Nothing is written for a run with no scheduler to ask — a single-process run,
or a distributed one whose workers have not collected yet. Those fields come
back null, which is not zero: zero pending is a worker about to finish, and
not knowing is not.
When there is no live view
Switching the server on and getting no server raises a
stack_server_unavailable incident through the same hook as everything else.
Without it the run continues perfectly well and your UI shows nothing forever
with no error anywhere — because from the outside "no server" and "no tests
running" look identical, which is the exact misreading this package exists to
prevent:
No live stack view this run: 127.0.0.1 could not serve on port 8080 [stack_server_unavailable PORT_TAKEN, runtime, informational]
Port 8080 is held by something that is not a stack server (Address already in use); pass --callstack-port with an unused port, or leave it off entirely and let one be drawn.
The run itself is unaffected; only the live view is missing.
Two verdicts, because they have different remedies. PORT_TAKEN is a stranger
on the port — name another one. BIND_REFUSED is an address that is not an
interface on this machine, or a sandbox that forbids listening — naming another
port does not help.
Neither is raised when another of your own sessions holds the port: that is the shared mode working as designed, and alerting on the ordinary case is how a kind gets filtered out entirely. It is reported once per address per run, not once per retry, though a named port held by a stranger is re-probed for the life of the run.
Owner runtime, severity informational: no test is at fault and nothing is
broken. What is lost is a diagnostic, and somebody has to decide whether to
reconfigure it.
Who may ask
Four things, and the token is only one of them.
The bind. Loopback by default, and anything else refuses to open without a token — see below.
The Host header. A request naming a host this server never bound is
refused with 403. That is not about the network, which the bind already
settles; it is about a browser. A page you visit can re-resolve its own
hostname to 127.0.0.1, at which point its origin is this server's and the
same-origin policy stops protecting you. Checking Host costs nothing and
closes that. A bind that is not loopback is exempt, because the address a
legitimate client outside a container uses is one this process never learns —
there the token is what stands in for the check.
Which pids /stack will answer for. This run's: the serving process, plus
the worker pids read out of the evidence directory. Anything else is 403. The
server reads any process it has permission to read, so without this a caller
who got past the bind could walk pids and collect the stack of every process
you own — and each read pauses its target.
A token, if you supplied one. On loopback you usually will not:
$ curl localhost:8080/workers
With a token, which is what any bind but loopback requires:
$ export PYTEST_CALLSTACK_TOKEN=$(openssl rand -hex 16)
$ pytest -n8 --callstack-host 0.0.0.0 &
$ curl -H "Authorization: Bearer $PYTEST_CALLSTACK_TOKEN" host:8080/workers
$ curl "host:8080/workers?token=$PYTEST_CALLSTACK_TOKEN" # for a hurry
The token is supplied, never minted, and never written to disk. That is the whole design, and it comes from the two halves of the problem being opposites.
The port has to be published. A port drawn at random is unguessable by construction — that is the point of drawing it — so the run must write it down for anything outside to find it.
The token does not. It is the one value both ends can agree on in advance,
because whoever starts the run picks it. Minting one here made it discoverable
instead, which meant writing it into the address file — and that turned every
question about where a run may write its evidence into a question about where a
secret may live. POSIX answers that with an 0o600. Windows does not answer
it at all: a mode there is not an ACL, so the file inherits the evidence
directory's and the promise quietly stops holding on a supported platform.
So the address file is ordinary data — a host, a port and a pid, the address of
a server anyone who can reach it may query anyway. Put failure_directory
wherever evidence goes, on any platform. And the secret arrives the way secrets
already reach a container, a CI job and a shell:
PYTEST_CALLSTACK_TOKEN |
a shell, a CI job, docker run -e — prefer this |
--callstack-token SECRET |
one run, at the cost below |
| (nothing) | no authentication — the default, and right on loopback |
There is no ini setting, deliberately: ini files live in the repository.
The two are not equally private. --callstack-token puts the secret in the
controller's command line, and a command line is public on a shared machine:
/proc/<pid>/cmdline is world-readable on Linux, so any other account can take
the token out of ps -eww for as long as the run lasts — on exactly the
machine a token is worth having. Shell history and an echoed CI command keep it
after the run has ended, too. PYTEST_CALLSTACK_TOKEN reaches the same setting
by the same path and has none of that: /proc/<pid>/environ is 0400, the
owner alone. The flag still works — runs use it, and it is unobjectionable on a
machine with one user on it — and a run that uses it warns once, saying this.
No token is the default and the right one on loopback, where the bind already bounds the reachable set to processes on this machine. On a box you share with people you would not hand a debugger to, supply one or leave the server off — "only local" and "only you" are different statements, and without a token only the first is being made.
Off loopback without a token is refused, before the socket is opened, and
reported as a stack_server_unavailable incident. Serving every local
process's stack to whatever can route to the host is not something anybody
configures on purpose, and a warning is the wrong instrument for it: by the
time one is read the port has been open for the length of the run.
/identity stays open even with a token set: it is what one session asks
another before standing down from a contested port, and two sessions that
minted nothing have no way to share a credential. It answers with a service
name, a version and a pid.
Finding the server
The run tells you, on a hook, the moment it is serving:
def pytest_failure_server_ready(server):
registry.upsert(
session=server.session_id, # names this run's evidence directory
url=server.url, # already bracketed if the host is IPv6
port=server.port, # what got bound, never the 0 you asked for
token=server.token, # what you supplied, or "" if you did not
pid=server.pid, # the controller, not any worker
)
That is the whole address, and for a drawn port it is the only way to learn it
before the run is over — nobody can configure a number that did not exist a
moment ago. server is a LiveStackServer; server.headers() gives you the
Authorization header the endpoints want — {} when this run supplied no
token, so the same client code works either way — and server.endpoint("/workers")
joins the URL, so neither the scheme nor the slash is yours to get right.
The hook fires on a thread of its own once the server is already accepting, so it is free to call straight back into the server it was just handed. It does not fire at all when the server was never switched on, nor when this session stood down because another of ours already holds a named port — that session announced itself, and one server should not be stored twice.
No run id in the payload. At the moment the server binds, xdist has usually
not built its node manager, so this run's real id does not exist yet; stamping
the placeholder onto a row you will join against later is a key that silently
matches nothing. session_id is stable from the first moment, and /workers
reports the run id per directory as soon as a worker beats.
If you would rather poll the filesystem than implement a hook, the address is
also on disk. A drawn port is written to callstack-<pid>.json in this run's
evidence directory (see the layout below), one file per serving session, and
removed when that session ends. Files left by a
session that was killed are swept by whoever publishes next — by checking the pid
in the filename, so a live session's address is never deleted.
for address in Path(".pytest-failures").glob("*/callstack-*.json"):
run = address.parent # one directory per run
server = json.loads(address.read_text())["url"]
for state in run.glob("*.state"):
record = json.loads(state.read_bytes().rstrip(b"\x00").strip())
stack = requests.get(f"{server}/stack?pid={record['pid']}").json()
print(run.name, record["nodeid"], record["phase"], stack["threads"][0]["frames"][0])
test_pool.py::test_concurrent_writes call {'function': '_wait_for_lease', ...}
Pushing samples instead of polling for them
/workers and /stack are a pull: something outside asks, when it wants to
know. Where a dashboard can reach the run, that is the better route — it reports
more per worker than a sample does, at whatever cadence it chooses, and costs
nothing at all while nobody is watching.
What it needs is a listening socket, and there are runs that cannot have one: a
CI job forbidden to open a port, a container with nothing routed into it, a run
too short-lived for anything to discover and poll before it is over.
failure_sample_seconds turns the same information around and pushes it out of
the process instead, with no port and nothing to discover:
def pytest_failure_worker_sample(sample):
# collected / unassigned / settled ride on the sample too: a worker's
# total is what it has been given so far, so a bar drawn without them
# has a moving end — and this is the path for runs that cannot open a
# port, where there is no /workers to ask instead.
for worker in sample.workers:
rows.insert(session=sample.session_id, at=sample.observed_at,
worker=worker.worker, nodeid=worker.nodeid,
# The id the worker had, whole, as sha256 — a node id has
# no length bound and this column does not need one.
nodeid_hash=worker.nodeid_hash,
phase=worker.phase, status=worker.status, why=worker.why,
rss_mb=worker.rss_mb, cpu_rate=worker.cpu_rate,
assigned=worker.tests_assigned, done=worker.tests_finished,
queued=worker.tests_queued)
A run with no workers pushes one row per pass, for main, from the same files.
Off by default. It is the only hook here that fires when nothing is wrong, so it
is the only one with a running cost — and that cost is a directory walk: every
field above comes from the .state and .events files the run was writing
anyway, and nothing is asked of a worker itself. No ptrace, no subprocess, no
pause. A sample of sixty-four workers is a few kilobytes of statuses.
No frames, deliberately. Reading a stack per stuck worker per pass was tried
here and taken out again: blocked is the status of any worker under 0.05
cores, so on an I/O-bound suite every healthy worker waiting on a database
qualified, and each pass paid a subprocess and a pause for each of them. Frames
are worth that when a human is asking about one worker — /stack?pid=, on
demand — rather than for every stuck worker on a timer. session_id and the
worker's pid are what join a sample to a stack fetched that way.
Containers
--callstack-host 0.0.0.0 is what a container needs: its UI is outside, and
127.0.0.1 inside a container is unreachable from there. That bind requires a
token and is refused without one — see Who may ask — which
suits a container better than the alternative did:
$ docker run -e PYTEST_CALLSTACK_TOKEN -p 8080:8080 yourimage \
pytest -n8 --callstack-host 0.0.0.0 --callstack-port 8080
The UI outside reads the same value from the same place. Nothing has to be mounted out for it to find a secret in a file, which is what a minted token would have required.
Two things about containers make this easier than it looks:
- Each container has its own network namespace, so
8080inside one pod is not8080inside another. The port contention that the named mode exists to resolve does not arise between pods at all — it is a bare-metal and laptop problem. Name a port in a container, publish it, and every pod can use the same number. - Each container has its own PID namespace, so a server in one pod could not read another pod's workers even with every permission granted. Sharing is neither possible nor needed there.
Reading a process needs ptrace, which modern Docker permits under its default seccomp profile. Where it is refused, the endpoint says so and names the fix rather than returning nothing:
{"pid": 48213, "source": "py-spy", "error": "Operation not permitted (os error 1)
- ptrace is not permitted: check /proc/sys/kernel/yama/ptrace_scope (0 or 1 allows
this; 1 requires the target to be a descendant of the reader, which xdist workers
are), and add --cap-add=SYS_PTRACE if this is a container"}
ptrace_scope is a host-wide sysctl and is not namespaced, so a container
inherits the node's value and cannot change it. At ptrace_scope=1 the
tracer must be an ancestor of what it reads — and the tracer is not the
controller but py-spy, which the controller spawns. py-spy and a worker are
both children of the controller, so they are siblings, and a sibling is not an
ancestor.
A worker therefore nominates its parent as a permitted tracer at startup, via
prctl(PR_SET_PTRACER, <controller pid>) — the exception Yama provides for
exactly this. worker_start records whether it was granted, so a refused read
has an answer beside it rather than only a message.
Yama admits the nominated pid and every descendant of it, so this is wider than "the controller's py-spy may read this worker". The controller's descendants are the whole process tree of the run: every other worker, and any subprocess a test spawns while the declaration stands. The reader it exists for is one of them and is not the only one.
So the declaration is only made where something is going to read a worker's
stack — the live stack server, or the sampler (failure_sample_seconds) —
and is off on every run where neither is on, which is most runs. The
controller resolves that, being the only process that can see either, and hands
each worker the answer; a worker never judges it for itself. failure_tracer
says which declaration such a run makes, not that one is made.
A named port shared across sessions still reads only the workers of the
session hosting it: another session's workers nominated their controller, not
this one. failure_tracer = any is what lifts that — it drops the relationship
requirement entirely, so any reader on the machine that could already ptrace is
permitted. That is the setting a shared server needs and the one a private run
does not, which is why it is not the default.
Reading a live process is py-spy's job
py-spy is installed with the package. There is no way to walk another process's frames from Python, so a live stack is read from outside the target: py-spy reads its memory rather than asking it to run anything, and stops it before reading, so it never walks a frame that is being torn down. That is what makes it work on a worker whose GIL is held by native code, and it is why nothing here has to be signalled.
Every pid is read that way, including the process doing the reading. Its
own frames are also directly to hand — sys._current_frames(), no subprocess
and no permission — and answering from them was a second reader with a second
source for the one process that could avoid the first. A caller that has to
know which mechanism answered has been handed two APIs, and a UI is where that
ends up encoded. One reader, one shape, one thing to keep working.
Reading yourself is a child tracing its parent, which is the wrong direction
for Yama. The declaration that admits it names this process — so it permits
this run's own descendants and nothing else, narrower than the parent policy
above — and it is made at the moment of the read rather than at startup, so
only the runs that read a stack make it.
Where py-spy cannot read the target — macOS without root, a sibling under Yama — the endpoint still answers, with the reason instead of a stack. A UI that is told why it has no stack can tell a dead process from a missing permission; one that gets an empty response cannot. The same is true of a stall: the verdict comes from heartbeats rather than frames, so it is reached either way, and the stack falls back to whatever the watchdog last wrote with its age attached.
On Windows there is no ptrace and no equivalent restriction: any process can
read another running as the same user at the same integrity level, so the
descendant rule above simply does not apply. Reading an elevated process from
an unelevated one needs SeDebugPrivilege.
Live resource history
Resource history is an opt-in, active-run feature. It extends the existing
live server without changing /workers, /stack, incidents, or worker-sample
hooks. It collects even when no browser is connected. It requires neither
profiling nor an installed host service.
[pytest]
addopts = --failure-instrumentation
failure_stack_server = true
failure_resources_seconds = 5
failure_resources_max_mb = 256
# Optional: only these trees are inventoried. Never scan an entire drive.
failure_resources_roots =
test-output
downloads
failure_resources_scan_seconds = 60
failure_resources_max_files = 50000
The equivalent programmatic settings are resources_seconds,
resources_max_mb, resources_roots, resources_scan_seconds, and
resources_max_files. They belong to the controller and are not handed to
xdist workers. The default interval is 0 (disabled), including when the
callstack server is enabled. Positive intervals have a one-second minimum.
Installing the plugin still requires the normal enable switch or install().
What is collected
| Scope | Measurements | Timing |
|---|---|---|
| Visible OS | CPU, available/total RAM, swap, paging; supported native counters below | Resource interval, normally 5 seconds |
| Controller, workers, observed descendants | CPU time/rate, RSS, Linux PSS/USS, supported private commit/footprint, I/O, threads, handles/FDs | Resource interval |
| Surrounding processes | Up to ten largest RSS consumers and ten CPU consumers; names, identities and parent PIDs | 15 seconds |
| Linux cgroup | Resolved membership, memory limits/usage, OOM events, CPU quota/throttling, supported pressure | Resource interval |
| Disks | Supported throughput, operation and timing counters; derived read/write latency when available | Resource interval |
| Relevant volumes | Free/used/total space, deduplicated by device | 30 seconds, in the helper |
| Configured directories | Logical file sizes/counts, new remaining files, growth, removed baseline paths and largest growth | Initial incremental baseline; repeat 60 seconds after a scan completes |
| Events | Observed process arrival/disappearance and links to existing deduplicated incidents | Included in the next resource batch |
Windows adds system commit/headroom, kernel pools, handle/thread/process counts, and PDH counters for paging, disk queues/latency and processor queue. Counter names are added through the English PDH API, independent of the OS UI language. Linux reads procfs PSI and VM counters, and discovers cgroup v2 or v1 through membership and mount information. macOS adds Mach VM paging/compression and libproc process footprint/I/O. No shell command is launched per measurement and no process is suspended.
These are different quantities: private_commit_bytes (Windows),
physical_footprint_bytes (macOS), and rss_bytes are not interchangeable.
RSS totals can double-count shared pages. On Linux, each enabled resource sample
also attempts one smaps_rollup read for each tracked process: pss_bytes,
pss_anonymous_bytes, pss_file_bytes, pss_shared_bytes, swap_pss_bytes,
private_clean_bytes, private_dirty_bytes, and uss_bytes (private clean
plus private dirty). USS excludes shared resident pages; PSS apportions them
among all processes mapping them, including processes outside this run.
These resident figures exclude explicit hugetlb allocations, which Linux
accounts separately. Swap PSS is separate from resident PSS.
For a run's proportional resident share, sum PSS over unique process identities (controller, workers and observed descendants), not RSS. Report coverage: if any process lacks PSS, the sum is partial. Never replace missing PSS with RSS or zero. Samples are sequential, not an atomic machine snapshot, and summed PSS is not cgroup usage (which includes other charged memory).
Absent, denied or malformed rollup fields appear in unavailable; other
process counters remain available. There is no full smaps fallback.
This is default-on only when resource sampling is enabled. Although the file
is compact, the kernel still walks page tables and collection cost grows with
the mappings; inspect sampling duration and lag on representative workloads.
Process I/O accounting follows the OS API: it is
not necessarily physical-disk traffic. Disk latency is the counter interval's
average, not a percentile. Paging does not imply every fault required swap.
Disk *_time_ms fields remain cumulative counters; separate
*_time_per_second_ms fields describe their rate of increase. RAM and swap
capacity are gauges and are never treated as traffic counters.
Unsupported counters appear in unavailable; an unlimited cgroup limit is
null without an unavailable reason. A first rate sample or reset is null.
The scope is the visible OS/process namespace, not an inaccessible outer
Windows/macOS host when pytest runs inside a VM/container. On Linux with an
ancestor's procfs mount, pid_scope=procfs identifies the counter PID namespace
and pytest_pid separately names the collector in pytest's namespace.
Ownership is associated while a parent relationship is observable and retained
through reparenting. Five-second sampling can miss short spikes; the slower
process inventory can miss short-lived descendants. Process disappearance is
an observation, not an invented exit code. Existing kill-attribution incidents
remain the source of termination verdicts. This feature does not add Docker
API access, OS log subscriptions, stack reads, allocation tracing, automatic
file deletion, or networking diagnosis.
Reading from the current live server
Use the session returned by /workers; it is mandatory because multiple
runs on a shared server can each have a worker named gw0.
GET /resources?session=run-abc&latest=true
GET /resources?session=run-abc&after=0&limit=120
GET /resources?session=run-abc&worker=gw0&from=1788600000&to=1788600300
Authentication and host checks are identical to /workers. Responses have
schema_version=1, run/platform metadata, retained sequence bounds and
batches. Each batch contains sequence, observed_at, elapsed_s, host,
cgroup, disks, processes, consumers, files, events, and collector.
Measurements carry metrics and unavailable maps; units are in metric names.
CPU rates use cores (1.0 is one fully occupied core); host cpu_percent
normalizes against logical CPUs. Cgroup CPU quota remains a separate limit.
Descendants include worker_exited when their observed worker is no longer
present. This does not automatically diagnose a leaked process. A worker's
process row carries the test it was in as nodeid, capped at a kilobyte, and
nodeid_hash — the sha256 of the whole id, which is what to join these rows
to the worker rows and the incidents on.
after is an exclusive sequence cursor. Continue with next_after while
has_more is true, or pass it on the next poll. limit is 1–500 batches;
responses also have a roughly 2 MiB payload budget. Time bounds are Unix
seconds, inclusive. worker filters process/event rows while preserving shared
host conditions; it never changes collection. latest=true returns the newest
published batch. There is no lossy downsampling: the client can fetch bounded
pages without losing sampled peaks. Distinguish sampling resolution from a
promise to catch every instantaneous peak.
history_truncated says the start of the run was rotated out. cursor_expired
says a nonzero cursor predates retained history. Missing intervals remain
missing. Disabled, finished or unknown sessions return 404; invalid ranges
return 400; unavailable/busy readers return 503. At most two resource queries
are processed concurrently. Reads never start probes or file scans.
from pytest_failure_instrumentation.client import FailureServerClient
async with FailureServerClient(url=server_url, token=token) as client:
page = await client.resources(session, worker="gw0", latest=True)
for sample in page.batches:
print(sample.host.metrics, sample.processes)
For multiple servers, use the LiveStackServer payloads received through
pytest_failure_server_ready; each supplies its own token and session_id:
from pytest_failure_instrumentation.client import read_resources_fleet
cursors = {} # retain between polls, keyed by (server URL, session)
fleet = await read_resources_fleet(servers, after=cursors, limit=120, concurrency=16)
cursors.update(fleet.cursors) # failed members keep their previous cursor
for member in fleet.answered:
print(member.url, member.session, member.history.batches)
for member in fleet.silent:
print(member.url, member.session, member.status, member.error)
This reads one page per advertised session, with bounded concurrency (16 by
default). It preserves pagination, gaps and availability rather than draining
history automatically. start, end, worker, latest, and timeout have
the same meanings as the single-server call. A shared httpx.AsyncClient can
be passed as client; ownership stays with the caller. Cancellation propagates.
Different pytest sessions can observe the same host, so the fleet retains
host/session identity and does not sum their machine measurements. Existing
read_fleet() continues to gather worker snapshots with its original contract.
File tracking and bounded cost
A single session-owned helper handles filesystem work so a slow directory or volume cannot block cheap resource samples or test hooks. It starts with the collector and is terminated at run end; it is not a service. With no configured roots it only queries relevant-volume space. Never enable remote/unreliable roots unless their measurement is needed.
Directory walks yield after at most 500 entries or approximately 50 ms of
work, then pause for 50 ms. A syscall itself can exceed that duration, which
is why the helper has a bounded shutdown. Symlinks, Windows reparse points,
and the plugin's evidence tree are excluded. Scans have an entry budget,
counting directories as well as files; at most eight roots are accepted.
Each disposable SQLite inventory has a database cap of the larger of 32 MiB
and 2,048 bytes per configured entry (about 98 MiB at 50,000 entries), plus a
1 MiB cache. The entry and byte limits are independent: exceptionally long
paths or a full volume report inventory_over_budget / database_or_disk_full.
Rollback journals can temporarily require additional disk space.
Its temporary rollback journal can use approximately another database's worth
of disk space during a transaction. Failed/full transactions roll back, so a
later successful scan does not compare against a damaged partial inventory.
Inventories compare logical bytes by path, not allocated disk blocks or
hard-link-deduplicated storage. There is no per-test recursive scan.
The initial baseline records its start/end timestamps. Tests keep running;
this is not an atomic snapshot before the first test. Partial/error scans
report observed counts, coverage and age, and do not infer deletions or exact
baseline deltas. A scan does not overlap its next scan. Live updates include
scanning, complete, partial, failed or excluded status; each completed
scan includes the top 20 growing paths (path display capped at 1,024 characters).
Paths remain associated with a shared directory, not confidently blamed
on whichever parallel test happened to be running. Expected fixture/session
retention must be considered before calling remaining files a leak.
Numeric history uses rotating JSONL segments on local disk, not an in-memory list growing with run length. The history budget is separate from optional file inventories and small manifests. At most 512 processes are tracked and 8,192 are considered during an inventory; truncation is explicit. Collection duration, errors, missed intervals and dropped event counts are recorded. There is one controller sampling thread and one filesystem helper per enabled session, rather than a sampler in each xdist worker. Multiple controllers keep separate histories; their host counters must not be summed.
Normal shutdown deletes only resource history/inventories, preserving existing incident evidence. A run-held OS file lock is released before cleanup: readers reject a completed run even if locked files prevent their deletion and the Python process remains alive. Readers check the lease before and after reading and stay within the byte ranges in their published manifest snapshot. Short disk writes are truncated to the last complete batch before retrying. Cleanup retries transient sharing violations; persistent failures are reported on stderr and retried by pytest cleanup and subsequent-run pruning. Pytest cleanup is an idempotent fallback. After an abrupt process/host death, a subsequent run removes abandoned live-resource files when the existing owner checks establish that the owner is dead, even if unreported incident evidence must remain. There is no archive, upload, post-run endpoint or promise that a stopped process can delete its own files. Python buffers are flushed per batch; this is not an fsync durability guarantee.
All collection adds some work when enabled. Benchmark on representative workloads before selecting intervals or making test-duration/RAM guarantees. The default off path does not import or start the resource collector. A single-process pytest run can still stop its sampling thread by holding the GIL; this feature does not change the existing native-GIL limitation.
Profiling
Everything above waits for something to go wrong. --failure-profile waits for
nothing: it samples the process running the tests for the whole run and, at
the end, names the functions that burnt the CPU and the tests that kept the
memory — as incidents, through the same hook, because "your image comparison
is 38% of the run" is a finding you want flagged the way a segfault is.
pytest --failure-profile
or failure_profile = true in ini for every run. Off by default: it is the
one thing here with a running cost on every healthy test, rather than only on
a run that went wrong. What that cost is, is not a claim — benchmarks/profile_gate.py
measures it on the same fixed-CPU workload with and without the sampler and
fails the build past 1.10x serially and 1.12x on four xdist workers, and CI
runs it on every push. See "Cost".
What it measures
A thread of its own wakes fifty times a second on each worker, reads every
thread's stack, and charges the CPU each thread burnt since the last wake to
the stack it is in now. Weighted by CPU, not by wall time. A thread asleep
in recv weighs nothing and vanishes; a thread spinning on a poll becomes the
widest thing in the profile. That is the difference between "where did the
time go" and "where did the cores go", and only the second answers why a
worker sits at 30%.
The per-thread counters come from clock_gettime on each thread's CPU clock,
which reads in a few hundred nanoseconds and — this is the part that matters —
without releasing the GIL. A sampler that opens a file per thread per sample
releases the GIL on every read and then waits a whole switch interval to get it
back from a busy test; measured, that ran a fifty-hertz sampler at eight.
A function is named the way it is written — Poller.run, not run, and a
comprehension under the function it is written in rather than as a <listcomp>
nobody wrote. 3.11 and later carry that name on the code object; on 3.9 and
3.10, which this package supports, it is worked out once per function from the
module that defines it, at the end of a test rather than on the sampling path.
Each stack is charged to the first frame, walking outward from the innermost,
that belongs to somebody: your packages or the customer's tests first, a
dependency failing that, the runtime failing everything. So two million calls
into a C pixel accessor are charged to your is_images_different, and a
second spent in json/encoder.py is charged to your render_report, below
the json encoder. Threads Python has no stack for — a thread pool a C extension
started — are counted by their kernel names, so CPU outside the interpreter is
reported rather than quietly dropped.
Per test it also records resident memory at every phase boundary, the peak between them, Python's live-object count, and — on glibc — how much of the heap is actually in use, so memory a test freed that the allocator kept mapped is told from memory that is still alive.
And it keeps a timeline: every tenth of a second, the CPU the process burnt in that window and how busy the whole machine was. A share of the run's CPU is the wrong question for a suite that waits on I/O ninety-nine seconds in a hundred — the hundredth that pins a core is invisible in it — and the timeline is where that hundredth shows as a step with a start, a length and a height.
A tenth of a second of wall time, and a window never spans less than one sample — so where the sampler cannot tick that often, because the machine is loaded or the platform's per-thread read is expensive, the timeline is as fine as the sample rate allows and no coarser. It used to be five samples, which is a tenth of a second only while the sampler is getting the CPU it asked for: at ticks 150 ms apart a burst of 0.6 s became one window, one window is below the two a burst needs, and the finding was not raised at all rather than raised less precisely.
What it raises
Three kinds, all severity=informational whoever owns them, because nothing
failed.
cpu_hotspot — one per function over failure_profile_cpu_share percent
of the run's CPU (and at least half a second of it). The verdict says what kind
of cost it is:
| Verdict | Means |
|---|---|
PYTHON_CODE |
the function's own lines are hot: a Python loop, or C calls made from them that leave no frame |
LIBRARY_CALL |
the cost is under a library or runtime call it makes, which is named |
BACKGROUND_THREAD |
the CPU is on a thread that is not running the test — a poller, a watcher — and is paid whatever test is in flight |
GC_PRESSURE |
the collector took more than a tenth of the run, and here are the tests that drove it |
NATIVE_THREADS |
CPU in threads Python has no stack for, by kernel thread name |
cpu_burst — from the timeline. A window is busy when the process burnt
failure_profile_burst_cores cores' worth of CPU over it, and a burst is a
run of busy windows. Each finding carries the stack that was there for most of
the burst, blamed like a hotspot:
| Verdict | Means |
|---|---|
LONG_BURST |
one test held a core for failure_profile_burst_seconds or longer, with when it started, which phase, and how much of the test's CPU is in that one stretch |
RECURRING_BURST |
the same function burst in five or more tests, whatever the length of each — a fixture, or a helper doing per call what it could do once. One finding, with the total |
BACKGROUND_BURST |
a thread that is not running the test held a core — between tests or under one. What a worker at a steady percentage with nothing to blame is doing |
CONTENDED |
the machine was over 90% busy for most of the run and the workers got slices of a core while it was. Twenty workers on four cores; nothing on any stack explains it and the machine's own figure does |
memory_profile — per test, against failure_profile_retained_mb:
| Verdict | Means |
|---|---|
RETAINED_AFTER_TEST |
the worker was left holding more than it started with, still in use, with the phase it arrived in — setup is a fixture |
HEAP_NOT_RETURNED |
it was left holding more, but none of it is in use: the allocator kept freed pages mapped. Fragmentation, not a leak |
TRANSIENT_PEAK |
the test climbed and came back down: what decides how many workers fit on the machine |
STEADY_GROWTH |
the run's tests added memory between them, in use, and no one of them kept enough to be raised alone — the leak no per-test check sees. The typical test's step decides which bar applies and what the finding says: a cost that recurs is unbounded and is raised at failure_profile_growth_mb (5 MB) while it is still small, a cost paid once is bounded and is raised only at failure_profile_step_mb (20 MB) in one test, and a run with both is told both. The typical step and not the average, so a module imported once by one test can neither pass for a leak nor hide one. Under xdist the same rule runs again over the workers that did not reach it alone, pooled: what a test leaves behind is the test's property, not the worker count's, and ten megabytes a test is two hundred on one worker and fifty on each of four. That finding names the run rather than a worker, and worker_rss carries each one's share |
WORKER_IMBALANCE |
one worker peaked at twice its siblings, with the test after which it stood clear |
PEAK_OVER_CEILING |
a test climbed to failure_profile_peak_mb or past it, whatever it started from — the size is the finding, and it is raised even when the memory came back |
ALLOCATOR_RETENTION |
the worker grew by the threshold over its run and nothing is using the growth: memory the allocator was handed back and kept mapped. One finding for the run, saying which of the two causes it is — thread arenas each keeping what they freed, which MALLOC_ARENA_MAX=2 fixes, or one main heap fragmented by small survivors, which malloc_trim fixes and the arena variable does not |
A memory finding about one test also carries the code that was running while the memory climbed, blamed and attributed the way a crash is. The sampler charges every rise it sees in resident memory to the test thread's stack at that moment — or to the stack it was in a tick earlier, when the code running at the reading is the runtime's own, which is what a test body that allocated and returned within one reading looks like from outside. A climb with nobody's code on either stack is reported as unplaced rather than blamed on pytest. So a test that reads a file whole instead of streaming it comes back as:
Memory over the ceiling: tests/test_loading.py::test_loads_the_export reached 1532 MB, ceiling is 1200 MB [memory_profile PEAK_OVER_CEILING, product, informational]
Memory rose by 1611 MB, summed over every reading that found it higher; all of that increase happened while load_everything (loader.py:15) was running, called from test_loads_the_export (test_loading.py:11).
The memory was released before the test ended.
Look at: loader.py:15
Measured: process 377 MB before, 1532 MB peak, 395 MB after. Ceiling from failure_profile_peak_mb.
What a test kept is measured as the larger of the resident step and the live-heap step. Resident memory understates it whenever the test fills pages an earlier test freed and the allocator held on to, and the heap figure is not fooled by that.
The live heap is glibc's mallinfo2 and nothing else (see the platform table).
Where there is none — macOS, Windows, musl — resident memory that stayed up is
all there is to go on, so a test that freed what it allocated but left the
pages mapped reads as RETAINED_AFTER_TEST rather than HEAP_NOT_RETURNED or
TRANSIENT_PEAK. The size and the phase are right either way; what cannot be
had there is the sentence that separates a leak from fragmentation, and
ALLOCATOR_RETENTION is not raised at all. Rerunning with
--failure-profile-allocations is the answer on those platforms: tracemalloc
measures Python's own live allocations everywhere.
Memory that adds up across a run is the other case no per-test rule can
name. failure_profile_retained_mb is what one test may keep, and a run
held to the same number says nothing until a hundred megabytes have piled up
— which is a fact about how long the run was rather than about the code. Two
and a half megabytes a test is fifty megabytes over twenty tests and five
hundred over two hundred; it is one leak, and the run that was short enough
never heard about it.
So a run has a bar of its own, failure_profile_growth_mb, and it is low:
5 MB. What makes that safe is that the steps are read in kilobytes. A
test that keeps 300 KB used to read as a step of 0 MB, and two hundred of
them read as nothing two hundred times over rather than as the 60 MB the
process actually grew, so anything under a megabyte a test was invisible
however long it ran. Measured on a suite that leaks nothing, the typical
step is about 3 KB and the total over two hundred tests is under a
megabyte — so a 5 MB bar still stands clear of the floor by several times,
where the whole-megabyte reading could not have told the two apart at all.
A recurrence and a cost paid once are not judged the same way, because
they are not alike in what they cost. A recurrence is unbounded: 5 MB over
the tests that have run is 5 MB more over the next lot and 500 MB on a suite
a hundred times the size, so it is worth raising while it is still small —
which is what the low bar is for. A cost paid once is bounded: a module a
test imported, and then never imported again. It cannot grow with the suite,
so it is judged on its size instead, against failure_profile_step_mb —
20 MB, because five megabytes of import is nobody's problem and twenty is
a machine to size for. Both are settings; lower the first to hear about
smaller leaks, raise the second to hear about fewer imports.
How the memory arrived does not decide whether there is a finding. It decides what the finding says. The two shapes are fixed in different places, so the finding names which it is, using the typical step — the median, which a module imported once by one test moves not at all:
Memory growing across tests: the run kept 48 MB in use across 4 workers over 20 tests, about 2.4 MB per test
Memory added over the run: the run kept 96 MB in use across 4 workers over 200 tests, in steps that did not repeat
Memory added over the run: the run kept 258 MB in use across 4 workers over 30 tests, 29 MB of it recurring at 1 MB per test
The first is a leak, and it is the number that will be bigger next quarter. The second is a cost paid once — a module a test imported — and it is a size to plan for. The third is both, which is the common case and the one a single number describes worst: a reader told only "258 MB" goes looking for the import, and a reader told only "1 MB per test" under-orders the machine.
Using the median rather than the average is also what stops the one hiding the other. A sixty-megabyte import beside fifty-nine tests that each kept a megabyte is most of the window, and a rule that asked whether the biggest step was half the total threw the whole window away — the import, and the fifty-nine tests that were the actual leak, together. The bigger the import, the better it hid what was next to it. A median cannot be moved that way.
A run that wants to hear only about what repeats sets
failure_profile_growth_per_test_mb, and the typical test must then have
paid that much as well. It is 0 by default, which asks nothing of the shape.
Below the reading's own floor — 20 KB a test — no rate is claimed at all,
which is what keeps a very long run from reporting its own bookkeeping: ten
thousand tests at three kilobytes is thirty megabytes, past any bar for a run
and nobody's leak.
It was divided by the worker count. The rule is over one process, so
under xdist a leak reaches it already divided: ten megabytes a test over
twenty tests is 200 MB on one worker and 50 MB on each of four — the same
suite, leaking the same way, reported only when it is run with few enough
workers, and at -n 12 with fewer tests per worker than the rule's minimum
there is nothing for it to be over at all. What a test leaves behind is the
test's property, not the worker count's, so the rule runs a second time over
the workers that did not reach it alone, pooled. A worker that kept a whole
test's worth is reported as itself — that is the process that gets
OOM-killed — and workers merely over the rate are the suite leaking, which
is said once for the run rather than once per slice of the same number:
Memory growing across tests: the run kept 220 MB in use across 4 workers over 20 tests, about 11 MB per test [memory_profile STEADY_GROWTH, customer-code, informational]
No single worker kept enough to be reported on its own, and no single test did either. 20 of the 20 tests they ran between them each ended with more in use than they started with.
Kept per worker: gw0 55 MB over 5 tests, gw1 55 MB over 5 tests, gw2 55 MB over 5 tests, gw3 55 MB over 5 tests.
xdist divides the tests between the workers, so a leak in the tests is divided with them: this is what the run as a whole kept, whatever each process shows.
All of them are cases of tests/test_leak.py::test_leaks_a_little.
Look at: rerun those tests with --failure-profile-allocations to see which lines hold the memory.
Measured: the workers held 136 MB in total before their first of these tests and 356 MB after their last, summed over 4 processes, which counts the pages they share once each. Biggest single step 11 MB. +81 Python objects per test.
Every guard is the per-worker one applied over the pool: the tests reached
the bar between them, and the shape decides which bar. A worker already
raised on its own is left out of the pool and named in the finding rather
than counted twice, two of the remaining workers must have kept something so
that a finding about a run means the run, and failure_profile_growth_tests
is the minimum number of tests either pass needs — lower it for a suite whose
workers each run a handful. A leak confined to one worker keeps that worker's
own finding: the pooled rule dilutes it with its siblings' clean tests and
declines, and the per-worker one it would have replaced stands.
The worker that "freed everything and still sits at four gigabytes" is the
one case none of the per-test rules can name, because no test did it: a few
megabytes of freed-but-mapped memory per test, over a long worker, is under
every threshold and in use by nothing. ALLOCATOR_RETENTION is the rule over
the whole worker. Every record carries glibc's own account of its arenas —
how many, how much free memory sits in each, how much of that a
malloc_trim would return — read through malloc_info at test boundaries,
and when resident memory has grown by the threshold more than the live heap
did and the allocator's free figure accounts for the gap, the finding says
where the free memory is:
Memory held by the allocator: worker main has 289 MB that tests freed and the C allocator has not returned to the OS [memory_profile ALLOCATOR_RETENTION, runtime, informational]
No Python object holds this memory. It is inside glibc's heaps, mapped and unused.
144 MB of the 289 MB is in the main heap, 145 MB in thread arenas. 11 arenas existed for up to 10 threads on 4 cores.
Biggest single steps: tests/test_arenas.py::test_ingest_batch[0] (140 MB on main).
glibc keeps freed memory mapped inside each arena, and gives every thread that allocates an arena of its own, up to eight per core. MALLOC_ARENA_MAX limits how many thread arenas exist.
Measured: process 42 MB at the start, 881 MB at the end, up 839 MB over 73 tests with 466 MB of that in use.
A test that left a threshold's worth of it in one step would have been
HEAP_NOT_RETURNED on its own; under the worker's finding it is one of the
steps rather than a row of its own, since it is the same memory and the same
fix.
Free memory mostly in the main arena is the other cause — one heap
fragmented by small survivors between the big allocations — and the finding
says so instead, with what malloc_trim(0) would hand back right now, because
MALLOC_ARENA_MAX does nothing for that one. glibc only, like the live-heap
reading; elsewhere the finding is never raised.
Allocation tracing
The sampler sees the stack that was running while memory climbed, which is usually the answer and sometimes is not: a loader that hands its result to a cache is running when the memory arrives and is not what holds it. For that rerun the tests the plain profile named with
pytest --failure-profile-allocations tests/test_loading.py
which switches tracemalloc on for the run — every platform, no attaching, no
debugger — and adds to each finding the lines holding the memory at the peak
and the lines holding what the test kept, and to a STEADY_GROWTH finding
the lines holding what the worker accumulated over the whole session. The
memory figures then come from tracemalloc itself (Python allocations only,
the tracer's own tables left out), because the tracer churns the allocator
enough to leave resident memory up after a test freed everything. Every test
that climbed also gets a memory flame graph: its live allocations at the
peak, by traceback, weighted in bytes, beside its CPU one.
The growth finding above, rerun that way over the two modules it names:
Memory growing across tests: worker main kept 289 MB in use over 48 tests, about 6 MB per test [memory_profile STEADY_GROWTH, customer-code, informational]
No single test kept enough to be reported on its own. 48 of the 48 tests each ended with more in use than they started with.
Most of it during: tests/test_memory.py::test_leaks_a_little (167 MB over 7 tests), tests/test_drift.py::test_response_is_cached (122 MB over 40 tests).
Held at the end of the worker: 190.7 MB allocated at test_memory.py:52, called from python.py:167, _callers.py:121, _manager.py:120.
Held at the end of the worker: 145.9 MB allocated at test_memory.py:28, called from python.py:167, _callers.py:121, _manager.py:120.
Held at the end of the worker: 122.1 MB allocated at test_drift.py:13, called from python.py:167, _callers.py:121, _manager.py:120.
Measured: traced memory 290 MB before the first of these tests, 460 MB after the last. Biggest single step 24 MB. +20,264 Python objects per test.
Three lines, each an append to something module-level. A tracemalloc
frame knows the file and line and not the function, which is why those lines
carry no function name.
Nothing else may be tracing. tracemalloc has one set of tables per
process, and a second owner of them gets the first one's frames at the first
one's depth: both profilers then report numbers that are neither's. So a run
asked for --failure-profile-allocations while a tracer is already going —
another allocation profiler, PYTHONTRACEMALLOC, -X tracemalloc, a
tracemalloc.start() in a conftest.py — stops with a usage error naming the
depth it found, rather than starting. It is the one thing in this package that
ends a run instead of warning and carrying on, and it is deliberate: everything
else here reports a failure that has already happened, where being quietly
degraded is better than being absent, and this one is the measurement, where
being quietly wrong is worse than not running. The plain --failure-profile
never touches the tracer and never raises this; neither does
failure_tracemalloc_depth, which attaches to whatever is already tracing.
Tracing costs three to six times on allocation-heavy code and far more on a
tight loop of small objects, which is why it is a rerun and not the nightly —
and why a traced run raises no CPU findings: its CPU figures are the
tracer's as much as the tests', and the summary says so. Memory is what it is
for. failure_profile_allocation_depth is how many frames each allocation
keeps.
This is what the example suite under
examples/profiling prints, trimmed:
CPU hotspot: load_everything (loader.py:15) used 11% of this run's CPU, 1.9 s [cpu_hotspot PYTHON_CODE, product, informational]
The time is in this function's own lines, not in calls it makes. Mostly line 15 (68%), line 16 (32%).
Seen in 1 test: tests/test_loading.py::test_loads_the_export.
Look at: loader.py:15
CPU on a background thread: 'status-poller' used 11% of this run's CPU, 1.9 s, in Poller._run (poller.py:30) [cpu_hotspot BACKGROUND_THREAD, product, informational]
This thread is not the one running tests, so it uses this CPU whichever test is executing.
Seen in 13 tests: tests/test_polling.py::test_with_the_poller_running, tests/test_polling.py::test_another_with_the_poller_running, tests/test_sessions.py::test_request_answers[1] and 10 more, and between tests.
Look at: poller.py:30
Memory kept after test: tests/test_memory.py::test_big_fixture ended with 143 MB more in use than it started with [memory_profile RETAINED_AFTER_TEST, customer-code, informational]
Memory rose by 166 MB, summed over every reading that found it higher; all of that increase happened while big_fixture (test_memory.py:34) was running, called from call_fixture_func (fixtures.py:1005).
The increase happened during setup, so a fixture allocated it, and it was still in use after teardown.
Look at: test_memory.py:34 and what holds its result after the test.
Measured: process 512 MB before, 655 MB after. Live heap +143 MB. +429 Python objects.
Memory growing across tests: worker main kept 295 MB in use over 71 tests, about 4.2 MB per test [memory_profile STEADY_GROWTH, unknown, informational]
No stack was captured; the owner is taken from the test's file, tests/test_allocation.py (customer-code).
No single test kept enough to be reported on its own. 52 of the 71 tests each ended with more in use than they started with.
Most of it during: tests/test_memory.py::test_leaks_a_little (167 MB over 7 tests), tests/test_drift.py::test_response_is_cached (122 MB over 40 tests), tests/test_arenas.py::test_ingest_batch (6 MB over 4 tests).
Look at: rerun those tests with --failure-profile-allocations to see which lines hold the memory.
Measured: process 43 MB before the first of these tests, 882 MB after the last. Biggest single step 24 MB. 295 MB of the 576 MB increase is in use; the rest was freed and kept by the allocator. +501 Python objects per test.
And the I/O-bound suite under examples/profiling/tests/test_polling.py
and its neighbours, where the timeline is what finds the fixture — no single
one of its six bursts is worth a look, and a share of the CPU says nothing
about when it was spent:
Repeated CPU burst: Session.__init__ (session.py:30) ran at full CPU for about 0.4 s in each of 6 tests, during setup [cpu_burst RECURRING_BURST, product, informational]
2.5 s of CPU in total across the 6 bursts. Called from session (test_sessions.py:13).
Tests: tests/test_sessions.py::test_request_answers[5], tests/test_sessions.py::test_request_answers[3], tests/test_sessions.py::test_request_answers[4] and 3 more.
Machine load during these bursts: 26%.
Look at: session.py:30. It ran during setup of each of those tests.
CPU burst: tests/test_index.py::test_index_is_complete ran at 1.0 cores for 2.7 s, starting 1.1 s into the test, during call [cpu_burst LONG_BURST, product, informational]
Running build_index (reports.py:42), called from test_index_is_complete (test_index.py:13).
This burst is 87% of the test's 3.4 s of CPU. The other 1.5 s of the test's 4.8 s was waiting.
Machine load during the burst: 26%.
Look at: reports.py:42
The same run prints a summary at the end of the terminal output — the run's CPU against its wall time, what each worker peaked at and grew by, and the top functions:
Profile: 74 tests, 27 s of wall time, 18 s CPU (0.69 cores on average), 2.5 s of it in garbage collection
worker main: 74 tests, 18 s CPU, peak 1532 MB, 882 MB at the end, up 840 MB from 42 MB
Functions using the most CPU:
16.8% 2.98 s build_graph test_allocation.py [customer-code] in 2 tests
14.3% 2.53 s render_report reports.py [product] in 2 tests
13.6% 2.42 s build_index reports.py [product] in 1 test
13.4% 2.37 s is_images_different image_compare.py [product] in 3 tests
13.1% 2.33 s Session.__init__ session.py [product] in 6 tests
Every finding is printed the way every incident is (see "How an incident
reads"): a first line that says what was measured, in words, ending with a
[kind VERDICT, owner, severity] tag; then lines that are each a
measurement, what it means by how it was taken, or a place to look.
It also writes a speedscope flame graph for every
test a finding names, and for the gaps between tests, under
<run directory>/profiles/ (<test>-<hash>.speedscope.json for CPU, and
with allocation tracing on <test>-<hash>.memory.speedscope.json for the
allocations at its peak; the hash is of the full node id, so two names that
sanitise alike cannot overwrite each other). Repeated executions and separate
background windows receive -2, -3, and later suffixes after the hash,
keeping each window's CPU and memory profiles. The raw per-test records are in <worker>.profile.jsonl beside
the rest of the evidence, timeline included — each carrying the test's
nodeid and, beside it, nodeid_hash: the sha256 of the whole id, the column
these records join to the findings and the worker rows on. The findings
themselves carry the same pairing — nodeid_hash beside nodeid, and
test_hashes positionally beside tests.
What it cannot see
Reliable native-call attribution is outside the supported profiling contract. Sampling from inside the process needs the GIL. A native call that holds it can finish before the sampler observes its Python wrapper. CPU counters still measure the work on a surviving thread, but its cost may be assigned to a previous or subsequent sampled function. A Python function that mixes Python and native work has the same limitation during its native portions. GIL-releasing native calls are easier to sample, but exact native-call attribution is not guaranteed either. Use the profile to investigate Python hotspots and bursts; do not treat a missing native caller as evidence that it used no CPU.
Per-thread clocks use POSIX thread clocks on Linux, thread_info on macOS,
and GetThreadTimes on 64-bit Windows with psutil fallback. Windows counters
are coarse against the sampling interval. Where per-thread clocks are
unavailable, process CPU is charged to the test thread and the report says so.
Threads that start and exit between samples can go unseen.
Windows starts with CPU baselines for known Python threads and discovers native-only threads on the first sampling tick, then on the normal discovery schedule. Discovery does not block the test thread before sampling starts. When profiling and kill tracing are both enabled, the controller prepares its profile reporting models while the separate ETW process starts, then joins that preparation before startup returns.
The native qualification probe remains a visible, non-blocking diagnostic: its JSON preserves failed attribution measurements. Python detection, quiet workloads, overhead, resource budgets, and the full test suites remain release gates. This is an accepted limitation, not a claim that native attribution was repaired. The live-heap reading that tells freed-but-mapped memory from kept memory is glibc only; elsewhere what a test kept is its resident step alone.
Running under pytest-threadlanes
pytest-threadlanes runs
pytest-xdist's own schedulers on threads it calls lanes: --lanes 3 is one
process running three tests at once, and -n 2 --lanes 3 is two xdist workers
running three each. Everything above assumes one process is one worker with at
most one test in flight, and a process of lanes breaks that — read as a
process, /workers named whichever lane had written last, a hung lane was
hidden by its busy siblings, and a stall or a death was blamed on the wrong
test.
So a lane is recorded as the worker it is: a worker that is a thread of a named
process. Nothing needs configuring and nothing needs pytest-threadlanes
installed: the plugin never imports it, and it treats a run as one of lanes
only when that plugin is registered and --lanes asks for lanes. A run
without lanes writes what it wrote before — the suite checks its files, slot
bytes, captured stderr, rows, samples and payload schemas against 0.13.1's.
Each lane is a row. A lane gets a state slot of its own, named after it —
ln3.state in a run with no -n, gw0.ln3.state in one with — so /workers,
?worker= and /stack?worker= see it as a worker, with its own test, phase and
counts. What is per process stays on the process's files — the heartbeat, and
with it the resident memory, liveness and finish — and a lane's row reports its
process's. Three fields say which process and which of its threads a lane is:
{"worker": "gw0.ln1", "pid": 21615, "nodeid": "test_env.py::test_s[envB-3]",
"phase": "call", "status": "blocked", "cpu_rate": 0.0, "rss_mb": 212,
"tests_finished": 3, "tests_running": 1, "tests_queued": 4, "tests_assigned": 8,
"process": "gw0", "thread_name": "lane-gw0.ln1", "thread_id": 21631}
thread_id is the operating system's number for the thread, which a stack of
the pid reports as that thread's os_thread_id — the thread to open, since the
process's main thread is pytest-threadlanes' scheduler. The three fields are
only on a lane's row, and are on client.Worker and on a pushed sample's row
the same way. Once a process's first lane has started a test, its own row
leaves the list — its lanes are the rows. ?worker=gw0 asks for what that
process is running, and lists its lanes; /stack?worker=gw0 still reads the
whole process.
Each lane's progress is its own, in the fields every row has and with the
meaning they always had, for that lane. Under -n xdist's scheduler hands work
to lanes, so the controller's schedule has a row per lane — assigned, finished,
running, queued, and whether a rerun is under way — and a lane's row reads its
own. A run of lanes in one process has no xdist controller writing a schedule,
so there, as in any run with no workers, tests_assigned, tests_running and
tests_queued are null.
Each lane's CPU is its own. A process's CPU is every lane's summed, so one
busy lane would make every sibling read as working. With each beat the
heartbeat also writes each lane's thread CPU onto the lane's own record, and a
lane's row and its stall verdict are read from it — falling back to the
process's figure for a lane's first reading, and where the platform cannot
number threads: on macOS psutil numbers them by position rather than by native
id, so there every lane reads its process's CPU, and the event log says so
(lanes_adjusted, per_lane_cpu). The beat itself stays the line it always
was, however many lanes there are.
A stall is a lane's. Each lane is timed on its own — from its reports
(report.lane_id), and from its own record, where it writes the start of each
phase as it happens. The second is what catches a hang in a lane's very first
setup, before it has reported anything, and what keeps an exclusive test —
whose reports pytest-threadlanes holds until it ends — from reading as one
silence the length of all its phases. A lane that hangs while its siblings keep
going is named, with its own test and a stack of its own thread, and the
incident says which thread of which process that is. A lane with no test in
flight — never given one, out of work at the end of an uneven run, or waiting —
is idle and never reported. The process holding the lanes is judged only once
none of them has a test in flight, as any worker with no test running is.
A death names every lane it took. A process that dies takes each of its
lanes' tests with it, and its counts are all of theirs. With one lane in
flight, that lane's test is test_in_flight, as a worker's always was. With
several, a fatal dump written on one of their threads — a native fault is
delivered to the thread that faulted — blames that lane's test; otherwise none
is blamed, since nothing on disk says which caused it. Either way every lane in
flight is listed in lanes_in_flight, {lane, nodeid, nodeid_hash, phase}
each, which is absent from the payload of a death without lanes. An internal
error is named the same way: after the lane whose test it interrupted, and
listing them all where several were mid-test.
Resources say how many lanes are running. The worker process of a run of
lanes carries lanes_running in /resources — how many of its lanes had a test
in flight when the sample was taken — since it names no test of its own. It is
absent for every other process.
What is one per process is adjusted, and says so. Each of these was built
for one test at a time, and each adjustment is written to the process's event
log as lanes_adjusted:
| Mechanism | Under lanes |
|---|---|
Profiler (failure_profile) |
Off. It charges every sample to the one test in flight, and a process of lanes has one per lane |
Stderr tee (failure_capture_output) |
fd 2 is taken once for the session rather than per phase — lanes' phases overlap, and pytest-threadlanes does not swap fd 2 per test either. What arrives is passed on to stderr within a second. The disk under what was passed on is given back as it goes: on Linux by punching holes in the file, which keeps the one file every writer of fd 2 shares — a test's child processes included — so no byte is lost; on a Linux filesystem that cannot punch holes, by rotating it, keeping the last tail as <name>.output.prev, where a child that outlives a rotation loses what it writes afterwards. On macOS and other platforms the file keeps the whole session's stderr: rotating means swapping fd 2 under the lanes writing it, and only Linux does that in one step (on macOS a lane's write to stderr can fail with EBADF mid-swap) |
| Slow-test watchdog | One clock per lane. The dump is of every thread, so it holds whichever lane is overdue |
| Heartbeat | No test on the process's beat; its memory is not attributed to a test. Each lane's CPU is on its own record |
The files of a run with lanes, then:
.pytest-failures/
run-70a514cc7a93/
gw0.state <- "lanes": true once a lane has started
gw0.events <- the heartbeat, as it always was
gw0.ln0.state <- "process": "gw0", "thread_name", "thread_id", and
gw0.ln1.state "cpu": the lane's own CPU readings
gw0.ln2.state
Python 3.12 or later is pytest-threadlanes' own requirement; before 3.14 it
runs with -p no:warnings. The design, and what was measured before and after
it, is in docs/pytest-lanes-support.md.
One directory per run
.pytest-failures/
.gitignore <- so the directory never reaches a commit
run-70a514cc7a93/ <- this pytest process's own name for itself
owner.json <- the controller's pid, and the only thing that makes
gw0.state this directory ours to delete
gw0.events <- every line carries the *reported* run id
gw1.state
schedule.json <- how many tests each worker has been given, which is
callstack-4213.json the one fact no worker can write about itself
A run with no workers writes the same files under main, since it is the
process running the tests:
.pytest-failures/
run-8f21c0b4e5d7/
owner.json
main.state
main.events
Runs used to share a flat directory and name their files after the worker,
which works exactly until two runs happen at once — and on a laptop or a
bare-metal runner that is the ordinary case. Every worker is gw0, so the
second run's gw0.state is the first run's gw0.state: one run reads the
other's evidence, believes it, and attributes a stall to a test a different run
is running. The old start-of-run cleanup made it worse rather than better,
because it deleted the files of a run still using them.
A directory per run removes the class of bug rather than a symptom. Nothing inside is named for the run, because the directory already is, so every path a reader builds is unchanged.
Why the directory is not named after the run id you see on incidents. The
obvious name is xdist's own run id, and it cannot be used: it does not exist
until xdist has built its node manager, and there is no hook order that
reliably puts that first. trylast does not do it, because xdist's own session
start is also trylast — so which of the two runs first comes down to which
plugin registered first, and that differs between installing from the entry
point and installing from a framework's pytest_configure. The directory is
therefore named by something this process fixes for itself and nothing can
reorder. The reported run id still prefers xdist's, so incidents still line up
with xdist's logs, and every .events line inside carries it — which is how a
directory is matched back to a run.
PYTEST_RUN_ID names the directory if you set it, which is also the way to
make two runs deliberately share one. The value has to be a name rather
than a path: 1–128 characters of letters, digits, ., - and _, and
neither . nor ... Anything else is refused with a warning and the run
names itself — so a slugified branch works and a raw feature/x does not,
because a separator in there would put this run's evidence somewhere other
than failure_directory.
What gets cleaned up, and what is read first. Whole directories of runs
that are over — not old — and never before they have been asked whether they
have anything left to report.
The controller's pid is in owner.json, so a run still going is recognisable
as such however long it has been going, which matters precisely because several
run at once. A directory without that marker is not ours and is left alone
whatever it looks like, which includes the flat files an older version of this
plugin left behind: they cannot be mistaken for a current run's evidence,
because a current run does not look there for any.
The directory keeps itself out of git
The default directory is inside somebody's checkout, and what lands in it is
one run's scratch that a later run deletes — so the first run to make it writes
a .gitignore of * at the top, and nothing here ever turns up in a git status or in an unlucky git add -A. Nothing has to be added to the
repository's own .gitignore, which means the second checkout, the CI image
and the colleague who just installed the plugin all behave the same.
It is written only into a directory holding nothing but run directories of
ours, and an existing .gitignore is never rewritten. failure_directory is a
natural thing to point at an artifacts directory somebody else also writes to,
and * dropped in there would quietly stop git from seeing their files —
a change to their repository that this plugin has no business making. The same
rule is what makes failure_directory = . harmless: the checkout is full of
files that are not ours, so it gets no ignore file rather than one that hides
the whole repository.
Cost
A passing test must cost as close to nothing as possible, because that is the overwhelming majority of what runs.
- Per test: six fixed-size writes to a file that never grows — two per phase,
one as it opens and one as it closes, which is what separates "died in
teardown" from "died mid-call" — plus two clock reads, and one
sha256of the node id for the hash the record carries beside it. Each of those is taken once per test rather than once per write: the id does not change within a test, and the writes are what the slot exists for. No append log, no/procread, no allocation tracking. - Per test that outlives
failure_slow_test_seconds(measured setup through teardown): one ~5 KB stack dump every interval, written and renamed by the heartbeat thread, and an unlink when the test ends. Nothing accumulates across tests, and nothing is written for a test that finishes in time. - Per 5 seconds, per worker: one heartbeat carrying CPU time and resident memory. Per second, per worker: two deadline comparisons and a timer rearm, which is why the first stack of a wedged test does not wait for a beat.
- Per test, on the controller only:
schedule.jsonrewritten, which is a length read off the scheduler per worker and one small write at a fixed offset — 6µs at eight workers, 32µs at sixty-four. It was written by rename at first, at 54µs a time, and throttled to twice a second to pay for that; the throttle is what let a worker's row contradict itself, so the write got cheap instead. Nothing here scales with the number of tests: diffing a queue against its last reading would, and so would reading the loadscope family's per-test done flags — that one was measured at 1.7ms a write on a sixty-thousand-test--dist loadfilerun, near two minutes of controller time over the run, and is now a length per scope instead (133µs, 8s). - Off by default, and the only thing here that costs a passing test
anything: the profiler. One thread per worker, waking fifty times a second,
reading each thread's CPU clock with a syscall that keeps the GIL and
walking its stack — and every tenth of a second, the machine's load;
resident memory and the live heap every other tick. Measured against plain
pytest on the same fixed-CPU workload by
benchmarks/profile_gate.py, which CI runs on every push and which fails past 1.05x for instrumentation alone, 1.10x for serial profiling and 1.12x for profiling under four xdist workers. Allocation tracing is outside those budgets on purpose: it is a rerun over the tests a plain profile named, and its cost is the workload's (see "Allocation tracing"). - Per profiled test, on disk: one JSON line of about 3 KB appended to
<worker>.profile.jsonlas the test ends. It is the one thing this package writes that grows with the number of tests, and the controller reads all of it back at session finish — so a 20,000-test run folds 62 MB of log, which is where the profiler's memory goes and when: at the end of a long run, which is when a container is least likely to have it spare. The log is read a line at a time rather than whole, and the strings every record repeats — its frame table, which is the same paths on every test, and its keys — are shared across the run rather than copied per record. Measured on 20,000 records: 346 MB to 145 MB, and slightly faster. - Off by default:
tracemalloc(needed to attribute an OOM kill to a source line) and the live-object census — walking the heap on a worker near its ceiling is exactly the instrumentation that makes things worse. - The live stack server, when switched on: one thread per session, which either serves or retries the claim every five seconds. Nothing is sampled and nothing is written unless something asks.
- pydantic is imported on the controller, and only when xdist is active. A worker never loads it, so nothing about the per-test path changed when the payload became typed.
- Nothing in the reporting path may raise. A failure while gathering an
incident degrades it to what survived, because an exception in a reporting
hook becomes an
INTERNALERRORthat ends the customer's run.
Settings
All of these are read only once a run has switched the plugin on —
--failure-instrumentation on the command line or in addopts, a
--callstack-* option, or a call to install. Registered without it, they are
accepted and inert.
| Setting | Default | Purpose |
|---|---|---|
failure_packages |
— | Your top-level packages, for attribution |
failure_directory |
.pytest-failures |
Where evidence is written; each run gets a subdirectory under it |
failure_product_version |
— | Version recorded on every incident, for telling which build a failure came from |
failure_watchdog |
true |
Memory and liveness sampling |
failure_heartbeat_interval |
5.0 |
Seconds between liveness beats (floor 1.0) |
failure_tracemalloc_depth |
0 |
1 names the allocating line for OOM attribution |
failure_object_census |
false |
Count live objects at a high-water mark |
failure_high_water_mb |
auto | Memory mark for a snapshot; defaults to a share of the discovered limit |
failure_memory_limit_mb |
0 |
Soft cap (POSIX) turning an OOM kill into a MemoryError |
failure_slow_test_seconds |
20 |
How often a running test refreshes its stack (setup through teardown; needs failure_watchdog) |
failure_stall_seconds |
300 |
Silence before a stall is assessed |
failure_stack_probe |
true |
Ask a diagnosed stalled worker for a fresh stack (POSIX) |
failure_crash_stack |
false |
Keep the fatal stack of a run that has no workers, instead of leaving it on the stderr pytest points it at. A worker keeps its own either way |
failure_kill_trace |
true |
Observe kills using Linux tracefs or Windows ETW where permitted, while preserving signal masks and handlers. See Who killed it |
failure_elevate |
false |
Allow sudo -n for the witnesses that need root: dmesg where /dev/kmsg and the journal are closed, and the tracepoint above |
failure_capture_output |
false |
Keep the last few KB of each worker's captured stderr, so a native crash message survives the kill that swallows it. See What the worker last said |
failure_on_run_death |
— | A dotted path package.module:attribute to a callable the sidecar calls with each incident of a run whose controller was killed, so a killed run is reported at once. From Python, install(config, on_run_death=functools.partial(...)). See Reporting a killed run from the sidecar |
failure_tracer |
parent |
Who may read a worker on Linux under Yama: parent, any, off. Declared only when the stack server is on — a run with no reader declares nothing whatever this says |
failure_resources_seconds |
0 |
Live resource sample interval; off by default, minimum 1 second when enabled |
failure_resources_max_mb |
256 |
Numeric live-history disk budget in MiB, clamped to 8–1024 |
failure_resources_roots |
empty | Explicit directory inventory roots, at most eight |
failure_resources_scan_seconds |
60 |
Delay after each directory scan, minimum 10 seconds |
failure_resources_max_files |
50000 |
Entry budget per directory scan, clamped to 100–100000 |
failure_sample_seconds |
0 |
Push a worker sample this often while the run is going. 0 is off |
failure_stack_server |
false |
Serve live stacks over HTTP |
failure_stack_server_port |
0 |
0 draws a free port and writes it down; any other is claimed and shared (--callstack-port) |
failure_stack_server_host |
127.0.0.1 |
What it binds; 0.0.0.0 for a container (--callstack-host) |
failure_stack_server_locals |
true |
Whether /stack?locals answers with each frame's variables |
failure_profile |
false |
Sample every thread's stack and CPU for the whole run and raise what crosses the thresholds below (--failure-profile for one run) |
failure_profile_interval |
0.02 |
Seconds between profile samples (floor 0.002) |
failure_profile_cpu_share |
5 |
Percent of the run's CPU one function must hold to be raised |
failure_profile_cpu_floor_seconds |
0.5 |
Seconds of CPU one function must have used before its share counts, so that a short run does not raise the first thing it sampled |
failure_profile_retained_mb |
100 |
Megabytes a test may keep, or climb by, before it is raised |
failure_profile_peak_mb |
0 |
Resident megabytes no test may reach, whatever it started from; 0 is off |
failure_profile_growth_mb |
5 |
Megabytes a run's tests may add between them, in use, before the run is raised as growing. Low, because what it is really the bar for is a cost that recurs, and a recurrence is paid again for every test the suite ever gains. failure_profile_retained_mb is what one test may keep and is a separate question |
failure_profile_step_mb |
20 |
Megabytes one test must have added on its own for memory that did not recur to be worth raising. A cost paid once is bounded — a module a test imported — so it is judged on its size, where a recurrence is judged on the fact that it repeats |
failure_profile_growth_per_test_mb |
0 |
Megabytes the typical test must have left behind as well, for a run that only wants to hear about a leak that repeats. 0 asks nothing of the shape |
failure_profile_growth_tests |
4 |
Tests that must each leave something behind before the drift between them is raised as steady growth — per worker, and again over the workers that did not reach the rule alone |
failure_profile_imbalance_ratio |
2 |
Times the median sibling's peak a worker must hold to be raised as imbalanced |
failure_profile_allocations |
false |
Trace allocations with tracemalloc as well, naming the lines that hold the memory and writing memory flame graphs; tens of times slower on allocation-heavy pure-Python code, so for a rerun of the tests an untraced run named (--failure-profile-allocations for one run, which implies --failure-profile) |
failure_profile_allocation_depth |
12 |
Frames kept per allocation when tracing |
failure_profile_burst_cores |
0.7 |
Cores' worth of CPU a tenth-of-a-second window must hold to be part of a burst |
failure_profile_burst_seconds |
2 |
Seconds a burst must last to be raised as one test's own; the same function bursting in five tests is raised whatever their length |
There is deliberately no ini setting for the token. It comes from
PYTEST_CALLSTACK_TOKEN or --callstack-token and nowhere else: an ini file
lives in the repository, and a credential in the repository is the thing this
design exists to avoid. Prefer the environment variable — a token on the
command line is readable by every other user of the machine. See
Who may ask.
failure_slow_test_seconds and failure_stall_seconds are not independent.
The stack a stalled worker is reported with is whatever the watchdog last
wrote, so the cadence has to have fired before the stall is assessed — a stall
judged sooner is judged with no stack at all, and on Windows that is every
stall. Neither is clamped, but an inverted pair warns.
failure_crash_stack is a trade rather than a switch, and it is off because of
which way the trade goes by default. faulthandler keeps exactly one
destination for a fatal signal, and pytest's own plugin has already pointed it
at stderr. A worker takes it and loses nothing — that stderr is shared with
fifteen other workers and a dump written into it belongs to nobody. A run with
no workers would be taking the crash out of a terminal somebody is watching, in
exchange for one that cannot be reported until a later run reads the file.
There is no having both: faulthandler.register(SIGSEGV, chain=True) is
refused by CPython itself.
What is lost by leaving it off is the stack, not the report — see the
recovered incident above, which still names the test, the phase, the counters
and the memory, and still offers a suspect_owner. What it cannot carry is a
blamed frame. Turn it on where the incident is the artefact that gets read and
the terminal is not, which is most of CI.
failure_memory_limit_mb is worth a note: an RLIMIT_AS cap makes the
allocation fail inside the process, so you get a MemoryError with a
traceback and a node id instead of an uncatchable kill with neither. It costs
you a hard ceiling per worker, which is why it is opt-in.
failure_directory is safe to share between runs going at once, and it has to
be: worker ids start at gw0 in every run, so a flat directory would have two
sessions writing the same file names. Each run gets a subdirectory of its own
named for its session, holding an owner marker with the pid that made it.
Cleanup prunes whole directories whose owner is no longer running rather than
a list of file suffixes, so a live run's evidence is never what a starting run
deletes, and a coverage report that happens to live there is never touched at
all.
A directory the plugin has to itself also carries a .gitignore, so the
evidence never reaches a commit; one it shares with somebody else does not, for
the same reason the cleanup leaves their files alone.
Two things back that up rather than repeating it. Every record carries the run that wrote it, and a reader refuses one naming a different run — so even a directory that somehow got crossed yields missing evidence rather than another run's attributed to yours. And a run that finds a live session already owning its directory says so.
Platform coverage
| Capability | Linux | macOS | Windows |
|---|---|---|---|
| Test in flight, phase, exit status | yes | yes | yes |
| Crash stack | yes | yes | yes |
| Stack from a slow or hung test | yes | yes | yes |
| Current memory | procfs | psutil | psapi |
| Container limit, OOM counter | yes | n/a | n/a — no OOM killer |
| On-demand stack from a stalled worker | yes | yes | no |
| Live stack of a running process (py-spy) | yes | root only | yes |
| Stack from a worker that stopped running Python | yes | yes | yes |
| Who sent the SIGKILL (signal tracepoint, root or sudo) | yes | no | — |
Who called TerminateProcess (ETW audit event, administrator) |
— | — | yes |
| The OOM killer's own record of the victim and the fleet (kernel log) | yes | n/a | n/a — no OOM killer |
| SIGTERM sender without kernel tracing | unknown | unknown | n/a |
| Profiler: CPU per thread | thread clocks | mach thread_info |
psutil (16 ms ticks) |
| Profiler: live heap, to tell freed-but-mapped from kept | glibc mallinfo2 |
no | no |
Profiler: arenas, to tell MALLOC_ARENA_MAX from fragmentation |
glibc malloc_info |
no | no |
| Profiler: bursts, drift, allocation tracing, memory flame graphs | yes | yes | yes |
The last row is the frozen-interpreter fallback, and it is the one capability a
setting takes away on every platform rather than a platform taking away: where
faulthandler_timeout is set, pytest owns the single
dump_traceback_later timer it would arm, and it stands down rather than
cancel a timeout somebody configured. On Windows that leaves a worker frozen in
native code with no stack at all, since the on-demand probe is not available
there either — which is why the worker records frozen_fallback_stood_down in
its event log rather than leaving the absence to be guessed at.
Two Windows differences are worth knowing about, because they change what you will see rather than how it is reported.
ctypes wraps every foreign function call in structured exception handling, so
an access violation raised through ctypes comes back as an OSError and the
worker survives it. A fault inside a real C extension still ends the process —
but the reproduction that segfaults a worker on Linux may simply fail a test on
Windows.
And a Windows process that dies from a fault reports an NTSTATUS as its exit
code rather than a signal, while abort() reports plain 3 — the same code a
deliberate os._exit(3) gives. What separates a crash from a clean exit there
is whether a dump was written, not the exit status, which is why the crash
stack is evidence in its own right rather than a decoration on the verdict.
psutil is a dependency, imported like any other. It is the only
cross-platform way to ask whether a process is still there, and the POSIX way
is actively dangerous on Windows: os.kill(pid, 0) sends a console event only
for CTRL_C_EVENT and CTRL_BREAK_EVENT, and calls TerminateProcess for
every other value — including zero. A liveness check written the obvious way
would kill each worker it inspected, and the live view inspects every worker on
every request. psutil also carries the memory figures on macOS and Windows,
which procfs cannot.
Tests
pip install -e ".[test]"
pytest
The integration tests run a real pytest in a subprocess through pytester,
crash or wedge a worker for real, and read back what the plugin raised — so
they exercise the mechanism rather than a mock of it. Every one of them also
round-trips its incidents through registry.parse and asserts model_dump()
equals the stored row, which makes the payload contract a property of every
scenario rather than a test of its own.
CI runs the suite on Linux, macOS and Windows across Python 3.9–3.13 — every
platform path in the table above is executed on the platform it was written
for. The
probes are platform code — procfs, psapi, waitid, GetExitCodeProcess,
cgroup counters — and none of the Windows or macOS paths can be exercised on a
Linux runner, which is the whole reason the matrix exists. Two axes matter as
much as the operating system, so each gets its own job:
-
without
pytest-xdist, wherepytest_testnodedownhas no hookspec at all and an unspecced hookimpl is a registration error — the failure mode that once made a plainpytestrun report nothing. -
against the declared minimums,
pytest==7.0.1on Python 3.9. Every other job installs whatever is newest, so a hook signature or an ini type that arrived later would pass all of them and fail on a user's pinned pytest. -
with
pytest-threadlanes, where it is installed:tests/test_lanes.pyruns lanes for real in all three of its modes — a live/workers, a hung lane, idle lanes beside a busy one, a hybrid worker's death — and skips that half where it is not, since it needs Python 3.12. The other half needs nothing: the readers are fed evidence laid out as lanes write it, and a run without lanes is compared, file by file, with what 0.13.1 wrote for the same suite (tests/evidence_without_lanes.json, recorded from 0.13.1 itself bytests/without_lanes.py). -
the profiler's budget,
benchmarks/profile_gate.py, which is a job of its own because it is the one claim in this README a reader cannot check by reading. It times the same fixed-CPU workload under plain pytest, under instrumentation and under the profiler, serially and on four xdist workers; it asks five times whether a known sustained hotspot is found, and five times whether a quiet run stays free of CPU findings. Every raw timing is in its JSON output. A budget moves only with the measurements that say why.
ruff and mypy run as their own job, and fail first because they are cheap.
The source carries # noqa and # type: ignore markers, which are only worth
writing if something reads them.
Two of the tests are about the plugin rather than about a failure: a run whose evidence directory cannot be created has to keep running, and a directory shared with somebody else's artifacts has to come out of a run with those artifacts still in it. A reporting tool that ends a run, or eats a file, has cost more than the failure it came to explain.
Releasing
Bump version in pyproject.toml (and __version__ in
src/pytest_failure_instrumentation/__init__.py) in a pull request, and merge
it. The merge releases itself: .github/workflows/release.yml runs on every
push to master, and when that version has no vX.Y.Z tag yet it builds the
sdist and wheel, installs the built wheel on Linux, macOS and Windows and
runs the whole suite against it, publishes to PyPI, then tags the merge commit
vX.Y.Z and creates the GitHub release with the artifacts attached. A merge
that does not change the version finds its tag already there and stops at the
first job, so nothing is published twice. Merging a version bump is releasing
it: run the pre-merge gate in AGENTS.md first.
Tagging by hand still works, for a commit that is not a merge:
git tag v0.2.0 && git push origin v0.2.0
The run then refuses to continue if the tag disagrees with the version in
pyproject.toml.
The wheel is tested rather than the checkout because this plugin is one entry
point. If packaging drops it the import still succeeds, the suite still passes,
and nothing is instrumented at all — the one failure mode a green test run
cannot rule out. So the release explicitly asserts the entry point exists and
that the package under test came from site-packages.
Credentials
There is no API token to create and no secret to add to the repository.
Publishing uses trusted publishing:
PyPI verifies this workflow's OIDC identity at upload time, so nothing
long-lived exists to leak or rotate. GITHUB_TOKEN is supplied by Actions
automatically.
What it does need is configuration, once, on each side.
On PyPI — Your account → Publishing. The project does not exist there yet, so this is an "Add a new pending publisher", not a setting on an existing project; a pending publisher is how a first upload is authorised for a name nobody has claimed. It becomes a normal publisher after that first release.
| Field | Value |
|---|---|
| PyPI project name | pytest-failure-instrumentation |
| Owner | Heknon |
| Repository name | pytest-failure-instrumentation |
| Workflow name | release.yml |
| Environment name | pypi |
On GitHub — Settings → Environments → New environment, named pypi.
Under it, tick Required reviewers and add yourself. That is the manual gate:
the run pauses before anything reaches PyPI, shows you the tag it is about to
publish, and waits. Nothing is uploaded until someone approves, and waiting does
not consume the job's timeout.
Worth setting at the same time, under Deployment branches and tags: restrict
the environment to the branch master and the tag pattern v*, so the only
things that can ever reach PyPI are a merged commit and a tagged one. The
branch has to be in the list: a merge publishes from master, and an
environment limited to v* alone refuses it.
TestPyPI is a separate site with a separate account, so rehearsing needs its
own pending publisher at test.pypi.org with the environment named testpypi.
Leave that environment without reviewers — the point of a rehearsal is that it
does not need one.
Licence
MIT — see LICENSE. Declared as an SPDX expression under PEP 639 rather than a classifier, since PyPI rejects a distribution carrying both.
Status
All nine kinds and every verdict in the tables above are covered, on all three platforms, with and without xdist — a run with no workers records, serves, watches and is recovered through the same code paths a distributed one uses, and the tests drive it the same way: a real run, wedged or killed for real, read back through the hook.
Most are produced for real: a worker is crashed, killed, signalled, wedged or
made to disagree about its collection, and the incident is read back from the
hook. Two cannot be, by anyone: OOM_KILLED needs a kernel that has just
killed something, and UNKNOWN needs a remote gateway with no local process to
query. Those branches are exercised against a constructed incident instead — as
are the Windows NTSTATUS decodes, which additionally run against a process that
really exits with one.
The opt-in paths are covered too: the memory ceiling turning an uncatchable
kill into a MemoryError that names the test, and the high-water snapshot
naming the line holding the memory.
The probes are also called directly, because in normal use they shadow each
other — psutil answers before psapi, and execnet's Popen before waitid — so
the fallbacks a customer's machine actually runs were never being executed.
That includes the claim WNOWAIT rests on: the status is read, and the process
is still reapable afterwards with the same answer.
The first cross-platform run paid for itself twice. It found that a Windows
\Lib\ in sysconfig and a \lib\ in a traceback made every stdlib frame
look like nobody's code, so a blocked test was blamed on threading.py and
then on the customer who called it — a runtime frame reported as customer code,
which is the one direction this must never fail in. Only the 3.9 cell caught
it. And it found that ctypes cannot raise an uncaught fault on Windows at all,
which is a fact about what users will see rather than about the plugin.
Resource review clarifications:
-
/resourcesis opt-in host context, including surrounding process names/PIDs and configured directory metadata. Loopback clients can read it without a token under the existing server defaults. ConfigurePYTEST_CALLSTACK_TOKENwhen other local users must not read these measurements. No unrelated stack, environment, command line or file content is collected by this endpoint. -
LiveStackServer.session_id, delivered topytest_failure_server_ready, is also a source of the session identifier; polling/workersis not necessary. -
Events are acknowledged after history publication. Failed publication may replay an event; the bounded event queue reports overflow instead of growing indefinitely. File snapshot errors distinguish oversized, unavailable and not-yet-published results.
-
The on-demand
Profile readinessworkflow now also runs resource qualification: two alternating pairs, 80 lightweight workers, and a separate eight-worker 256 MiB allocation workload with file scans. Gates are 20% elapsed/test-p99 overhead, 25% controller/worker RSS overhead, sample time at most the smaller of one second and 20% of the configured interval, and 30-second shutdown. RSS comparisons exclude the filesystem helper and double-count shared pages; they are synthetic budgets, not fleet-wide guarantees. -
probes.capabilities(resources=True)performs an explicit resource preflight; ordinary capability checks do not initialize these adapters. -
Repeated filesystem snapshots are stored once per segment. Every retained segment is self-contained, and HTTP responses expand the snapshots unchanged.
-
Five consecutive collection/write errors stop sampling. The manifest records the reason when writable, with a stderr diagnostic if the volume cannot accept it. Run access still ends at session shutdown.
-
Existing live-history directories are not overwritten: a collision fails collection setup visibly rather than deleting potentially active data. Volume queries remain isolated even without directory roots, since filesystem calls can block. Neither requires a permanent collector.
1 maintainer
Release files for pytest-failure-instrumentation 0.14.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
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Total release size: 1.3 MB
Release files / pytest_failure_instrumentation-0.14.0.tar.gz
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| Tags | Source |
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