feat(scheduler): every background run is a durable row
What a job last did lived in the API process's memory and was gone at every restart; the audit log kept a run only when it was manual, failed or changed something. Now each run is a job_runs row written at its start and its end in its own session: who started it, its outcome as data, what it spent of the YouTube quota (quota_events.job_run_id) and the lines it logged (log_events.run_id, filterable in the log console). - a run a stopped process left open is marked interrupted at startup - "run now" is a one-shot job on the scheduler, which now always starts (interval jobs only with SCHEDULER_ENABLED); the manual actor is reset after the run, since pool threads are reused - the dashboard's last run comes from job_runs, so it survives a deploy - runs leave the audit log: migration 0109 moves the old job_run rows over (and back on downgrade); the audit page's run folding goes - job_run_retention_days (default 30) prunes old runs in audit_gc
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@@ -16,7 +16,7 @@ from sqlalchemy.dialects.postgresql import insert as pg_insert
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from sqlalchemy.engine import CursorResult
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from sqlalchemy.orm import Session
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from app import sysconfig
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from app import logs, sysconfig
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from app.db import SessionLocal
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from app.models import ApiQuotaUsage, QuotaEvent
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@@ -204,6 +204,14 @@ def record_usage(units: int) -> None:
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)
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with SessionLocal() as qs:
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qs.execute(stmt)
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# Audit detail: who/what spent it (per-user attribution; NULL actor = system).
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qs.add(QuotaEvent(user_id=_actor_id.get(), action=_action.get(), units=units))
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# Audit detail: who/what spent it (per-user attribution; NULL actor = system), and the
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# background run it belongs to, so the run's row can say what it cost.
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qs.add(
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QuotaEvent(
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user_id=_actor_id.get(),
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action=_action.get(),
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units=units,
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job_run_id=logs.current_run_id(),
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)
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)
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qs.commit()
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