Use monotonic clock for OpenAIBatchTrigger polling timeout

[YAshhh29]

OpenAIBatchTrigger measures its polling timeout with time.time() (wall
clock) in two places, and OpenAITriggerBatchOperator sets the deadline
with time.time() + self.timeout. Because wall-clock time can jump — NTP
corrections, DST, container clock skew, VM pause/resume — a deferred batch
task can either time out early or run far past its intended deadline.

This isn't tied to an existing issue — I spotted it while auditing
time.time()/time.monotonic() usage across the AI/ML providers, in the
same spirit as the dep-audit that produced #69408.

Airflow's own coding standards flag this exact pattern:

time.monotonic() for durations, not time.time().

— .github/instructions/code-review.instructions.md and AGENTS.md

Why the fix isn't a one-line swap

time.monotonic() values are only meaningful within a single process. The
operator (in the worker) and the trigger (in the Triggerer) run in
different processes, so the operator cannot pre-compute a monotonic
deadline and hand it to the trigger. The trigger must call
time.monotonic() itself.

To make that possible the trigger's preferred constructor argument
changed from end_time (absolute wall-clock deadline) to timeout (a
duration in seconds). The trigger now records time.monotonic() at the
start of run() and reports the elapsed monotonic duration on timeout.

Backward compatibility

OpenAIBatchTrigger.__init__ still accepts the legacy end_time
argument so that triggers serialized by the previous version of the
operator continue to run after an upgrade. When a legacy end_time is
present the trigger derives a best-effort remaining duration from the
wall clock once, then tracks the rest with the monotonic clock — so even
the legacy path is no longer fully at the mercy of wall-clock jumps
inside the polling loop. serialize() preserves whichever argument the
trigger was constructed with, so a rolling upgrade never rewrites an
in-flight trigger's schema.

What changes

• providers/openai/src/.../triggers/openai.py
  • Add timeout: float | None constructor arg and validation
    (exactly one of timeout/end_time must be given).
  • run() measures elapsed time via time.monotonic().
  • serialize() emits whichever field the trigger was constructed with.
  • Timeout error message now reports actual elapsed seconds (previously
    it reported time.time() - self.end_time, which is seconds past
    the deadline — misleading and sometimes negative).
• providers/openai/src/.../operators/openai.py
  • Passes timeout=self.timeout instead of computing end_time.
  • Removes the now-unused import time.
• providers/openai/tests/.../test_openai.py
  • Existing tests now use timeout=.
  • New test_serialization_with_legacy_end_time proves the
    backward-compat serialization path.
  • New test_timeout_uses_monotonic_not_wall_clock — the regression
    test — patches time.monotonic with an ever-increasing counter and
    asserts the timeout fires while time.time is never consulted. It
    fails against the pre-fix code, which decided the timeout from the
    wall clock. (time.monotonic is mocked with itertools.count
    rather than a fixed list because the asyncio event loop also calls
    time.monotonic internally and would exhaust a finite side_effect.)
  • Constructor validation tests for the "neither" and "both" error paths.
• providers/openai/docs/changelog.rst: bug-fix note explaining the
  behavior change and the end_time → timeout migration.

How to reproduce the original bug

1. Deploy a DAG with OpenAITriggerBatchOperator(deferrable=True, timeout=600).
2. While the batch is running and the trigger is deferred, force a
   backward wall-clock adjustment on the Triggerer host (e.g.
   sudo date -s '10 minutes ago', or a large NTP step).
3. Observe that the trigger keeps polling well past 600 seconds because
   self.end_time < time.time() is no longer true.

The same pattern also fires early on forward clock jumps.

Related

• Airflow's own code-review rule: .github/instructions/code-review.instructions.md
• Similar recent audit of ML-provider dep hygiene: Remove unused [datalib] extra from OpenAI provider base install #69408

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Was generative AI tooling used to co-author this PR?

• Yes — GitHub Copilot (Claude Opus 4.6)

Generated-by: GitHub Copilot (Claude Opus 4.6) following the guidelines

[YAshhh29]

Fixed the CI failures:

1. The trigger tests mocked time.monotonic with a fixed 2-element list, but the asyncio event loop calls time.monotonic internally — it exhausted the mock and raised StopIteration (which is why only the non-DB / lowest-deps / compat jobs failed; the trigger tests are deselected in the DB backends). Switched to an ever-increasing itertools.count side_effect that the loop can call freely; the trigger's two clock reads are consecutive so measured elapsed is deterministic.
2. Fixed British serialised → American serialized spellings that failed the docs spellcheck.

Verified the trigger under a real event loop locally.

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Drafted-by: GitHub Copilot (Claude Opus 4.6); reviewed by @YAshhh29 before posting