dennisthiessenandClaude Opus 5
22bee28ac7
Job outcomes lived only in scheduler._job_runtime, an in-memory dict. Every
deploy wiped it, so Admin -> Jobs could report "Active" with no indication a job
had ever run or how it ended -- which is the main thing that page is for.
New job_run_state table (migration 031): one row per job, upserted on job_name.
Deliberately not history -- system_events already grows unbounded with no
retention job, and a second append-only operational table would repeat that
debt. Adding history later is purely additive.
Written from two hooks, NOT from _runtime_finish. That looked cheapest (one
function, ~40 call sites) but unit tests invoke job coroutines directly, so it
would fire detached DB writes at the real session factory throughout the suite,
and there is no testing flag to guard on.
- An APScheduler EVENT_JOB_EXECUTED/ERROR listener covers everything the
scheduler fires, including manual triggers. Its detached task is held in a
module-level set (a bare create_task result can be collected mid-flight) and
drained in the app lifespan before engine.dispose().
- _run_pipeline persists directly, and must: pipeline steps are plain
coroutine calls that emit no scheduler events, so the listener cannot see
them. The step persist sits AFTER the except that swallows step errors --
inside it, exactly the failed runs worth seeing would be skipped. The
orchestrator persists in the finally, and the disabled early-return persists
too, or "skipped" is silently dropped.
_persist_job_run never raises: a persistence failure must not break an otherwise
successful pipeline.
The API reports this as last_run_* and leaves runtime_* meaning strictly live
in-memory state. Reusing runtime_status would have been a regression, not a
no-op: JobControls drives the status chip from it (a job that errored eight days
ago would read "Last run error" forever instead of "Active") and picks the
rate-limit banner from it (a week-old rate limit would pin the banner
permanently). Tests pin the split.
The table starts empty; each job fills its row the next time it finishes. No
backfill from system_events, which records only warning/error outcomes under a
different status vocabulary and would invent successes that never happened.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
38 lines
1.8 KiB
Python
38 lines
1.8 KiB
Python
from datetime import datetime
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from sqlalchemy import DateTime, Integer, String, Text
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from sqlalchemy.orm import Mapped, mapped_column
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from app.database import Base
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class JobRunState(Base):
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"""How each scheduled job last finished. One row per job, overwritten.
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The scheduler's ``_job_runtime`` dict is the live view and is deliberately
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in-memory, but it is also wiped by every process restart -- so after a deploy
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Admin → Jobs could only say "Active" with no indication of whether a job had
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ever run. This is the durable half.
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Deliberately not history: ``system_events`` already grows without a reaper,
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and a second append-only operational table would repeat that. Rows are
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upserted on ``job_name``; adding history later is purely additive.
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"""
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__tablename__ = "job_run_state"
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id: Mapped[int] = mapped_column(primary_key=True)
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job_name: Mapped[str] = mapped_column(String(64), unique=True, nullable=False)
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# Scheduler vocabulary: completed | skipped | error | rate_limited | deferred.
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# Distinct from data_import_runs' statuses, which is one reason this is its
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# own table rather than a widened column there.
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status: Mapped[str] = mapped_column(String(32), nullable=False)
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started_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
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finished_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
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processed: Mapped[int | None] = mapped_column(Integer, nullable=True)
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total: Mapped[int | None] = mapped_column(Integer, nullable=True)
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message: Mapped[str | None] = mapped_column(Text, nullable=True)
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updated_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), default=datetime.utcnow, onupdate=datetime.utcnow, nullable=False
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)
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