document processing timed out; do not let a single task stall the entire batch

This paper uses synthetic samples to demonstrate the processing method. The code has been verified using examples in an isolated environment; this does not guarantee effectiveness for all real files or products. Before processing important files, please retain the original copies and verify them against the boundaries and acceptance criteria outlined in this document. A scanned PDF got stuck, and the subsequent ninety-nine tasks remained idle. Adding a timeout to the request seemed to resolve the issue, but in reality, the processing process might still be consuming memory in the background. A timeout involves at least three actions: stopping the wait, halting execution, and marking the output as undeliverable. wait timeout does not equal work has been terminated subprocess.run(timeout=...) provides timeout handling for direct child processes, but this cannot be generalized to “any process tree has been cleaned up.” The outcome is influenced by the operating system, the method of process initiation, and the behavior of grandchild processes. Production implementations require isolation mechanisms such as process groups, containers, or job objects. First, clearly define the task status contract only simulates state determination; it does not initiate external commands nor deliberately suspend processes. Its primary demonstration is that, once the deadline has been reached, even if a work unit submits a result late, the timed-out task cannot be directly marked as successful. from dataclasses import dataclass @dataclass(frozen=True) class Decision: state: str deliver: bool terminate_requested: bool def decide(now, deadline, worker_done, artifact_valid): if now >= deadline: return Decision("timed_out", False, not worker_done) if not worker_done: return Decision("processing", False, False) if not artifact_valid: return Decision("invalid_artifact", False, False) return Decision("verified", True, False) assert decide(9, 10, False, False).state == "processing" assert decide(10, 10, False, False).terminate_requested assert not decide(11, 10, True, True).deliver assert decide(9, 10, True, True).deliver assert decide(9, 10, True, False).state == "invalid_artifact" This protocol treats a timeout as occurring when the time equals the deadline. You may also adopt an alternative boundary, but it should be documented in the tests. Real-time comparisons must use a monotonic clock appropriate for the computation interval; system time zones or wall-clock adjustments must not alter the task budget. What evidence remains after the timeout? needs to know purpose Note timeout phase distinguishes between downloading, parsing, or translating does not record just one timeout termination confirmation Prevents background residual tasks request to terminate does not equal having exited temporary product identity avoids mistakenly handing over semi-finished products does not share state with the formal result retry count controls the total budget cannot each time regain unlimited time retry is not the automatic next step If the same input consistently exhausts resources at the same step, the sample should be isolated and complexity issues investigated. Retrying is only meaningful when the failure cause is recoverable and the invocation possesses appropriate idempotency. Users should also be able to distinguish between “service busy, retrying” and “input unsupported, requires handling.” ’s external translation task retains its own cutoff strategy accesses OmniDoc (Product Entry) At that time, the client’s waiting time, the platform’s processing status, and the file download status should be displayed separately. The page stopping its wait does not mean that the remote task does not exist; before clicking the button again, a query should first be performed for known tasks to avoid redundant consumption or the generation of multiple results that are difficult to correlate. Reference: Python subprocess timeout behavior 。 - 1 -