540151290b
A one-shot Maintenance action to remove the blurred locked-preview images the ingester downloaded from tier-gated Patreon posts before #874. current_user_can_view was never persisted, so the cleanup re-walks each enabled Patreon source (read-only) to re-derive which posts are gated now and the blurred filehashes Patreon serves for them, then matches by CONTENT HASH against stored source_filehash. Because the hash is content-addressed, a real file downloaded when access existed has a different hash and can never match — regained-then-lost-access content is provably spared (operator's hard requirement). NULL source_filehash => unverifiable, kept + reported. On apply: delete matched ImageRecords + files (provenance cascades), clear seen/dead-letter ledger rows for those hashes so the real media re-ingests if access returns, and delete gated posts left bare. Shares one match predicate between preview and apply (rule 93). - cleanup_service: collect_gated_previews + purge_gated_previews - tasks.admin: purge_gated_previews_task (async re-walk bridge, timeboxed) - api.admin: POST /maintenance/purge-gated-previews - GatedPurgeCard.vue in Settings > Maintenance (preview -> confirm -> apply) - tests: collect predicate, hash-match delete/spare/unverifiable, ledger clear, bare-post removal, no-op Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
412 lines
18 KiB
Python
412 lines
18 KiB
Python
"""FC-3k: admin destructive Celery tasks.
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Two long-running ops on the maintenance queue. task_run lifecycle is
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captured automatically by FC-3i signals — these tasks just return
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their summary dict so it lands in task_run.metadata (via Celery's
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result backend) for the dashboard to surface.
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Soft/hard time limits inherit the FC-3i recovery sweep: a runaway
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task gets killed and flipped to status='timeout' by
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recover_stalled_task_runs.
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"""
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from __future__ import annotations
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import logging
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from pathlib import Path
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from sqlalchemy import delete, select
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from sqlalchemy.exc import DBAPIError, OperationalError
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from ..celery_app import celery
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from ..models import ImageRecord
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from ..services import cleanup_service
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from ._sync_engine import sync_session_factory as _sync_session_factory
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log = logging.getLogger(__name__)
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IMAGES_ROOT = Path("/images")
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@celery.task(
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name="backend.app.tasks.admin.delete_artist_cascade_task",
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bind=True,
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autoretry_for=(OperationalError, DBAPIError),
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retry_backoff=15, retry_backoff_max=180, max_retries=1,
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soft_time_limit=1800, time_limit=2400, # 30 min / 40 min
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)
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def delete_artist_cascade_task(self, *, artist_id: int) -> dict:
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"""Wraps cleanup_service.delete_artist_cascade. Returns the
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service's summary dict for FC-3i task_run.metadata capture."""
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SessionLocal = _sync_session_factory()
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with SessionLocal() as session:
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return cleanup_service.delete_artist_cascade(
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session, artist_id=artist_id, images_root=IMAGES_ROOT,
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)
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# Orphan repair (#859). Safety guard: an NFS/filesystem stall makes EVERY file
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# look missing — never delete records en masse on that basis. If a non-trivial
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# sample comes back mostly missing, ABORT without deleting (assume the FS is
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# unhealthy, not that the library evaporated). Operator-triggered ONLY — NOT a
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# periodic sweep, precisely to avoid an unattended run firing during an NFS blip.
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_ORPHAN_MIN_SAMPLE = 50
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_ORPHAN_MAX_MISSING_FRAC = 0.10
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@celery.task(
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name="backend.app.tasks.admin.prune_missing_file_records_task",
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bind=True,
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autoretry_for=(OperationalError, DBAPIError),
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retry_backoff=15, retry_backoff_max=180, max_retries=1,
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soft_time_limit=1800, time_limit=2400, # 30 min / 40 min
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)
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def prune_missing_file_records_task(self) -> dict:
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"""Delete ImageRecords whose backing file is gone from disk (orphans — e.g.
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the external-attach unlink bug #859). Every FK to image_record is CASCADE /
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SET NULL, so a Core DELETE cleans provenance, series pages, predictions and
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tag links; leftover thumbnails are unlinked best-effort. Returns a summary.
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Aborts WITHOUT deleting if a non-trivial sample is mostly missing (a
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filesystem/NFS stall, not real orphans) — see the guard constants above.
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"""
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SessionLocal = _sync_session_factory()
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checked = 0
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missing_ids: list[int] = []
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thumbs: list[str] = []
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last_id = 0
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with SessionLocal() as session:
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while True:
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rows = session.execute(
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select(ImageRecord.id, ImageRecord.path, ImageRecord.thumbnail_path)
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.where(ImageRecord.id > last_id)
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.order_by(ImageRecord.id)
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.limit(1000)
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).all()
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if not rows:
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break
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for rid, path, thumb in rows:
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last_id = rid
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checked += 1
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if not (IMAGES_ROOT / path).exists():
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missing_ids.append(rid)
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if thumb:
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thumbs.append(thumb)
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if not missing_ids:
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return {"checked": checked, "missing": 0, "deleted": 0}
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frac = len(missing_ids) / checked if checked else 0.0
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if checked >= _ORPHAN_MIN_SAMPLE and frac > _ORPHAN_MAX_MISSING_FRAC:
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log.warning(
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"orphan-repair ABORTED: %d/%d (%.0f%%) records missing on disk — "
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"likely a filesystem/NFS problem, not real orphans. No deletions.",
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len(missing_ids), checked, frac * 100,
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)
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return {
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"checked": checked, "missing": len(missing_ids), "deleted": 0,
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"aborted": "too many missing — filesystem problem suspected",
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}
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deleted = 0
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for i in range(0, len(missing_ids), 500): # keep well under psycopg's param ceiling
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chunk = missing_ids[i:i + 500]
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session.execute(delete(ImageRecord).where(ImageRecord.id.in_(chunk)))
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deleted += len(chunk)
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session.commit()
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for t in thumbs: # cosmetic — outside the txn, never fail the repair on these
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try:
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(IMAGES_ROOT / t).unlink(missing_ok=True)
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except OSError:
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pass
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log.info("orphan-repair: checked=%d missing=%d deleted=%d", checked, len(missing_ids), deleted)
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return {"checked": checked, "missing": len(missing_ids), "deleted": deleted}
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@celery.task(
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name="backend.app.tasks.admin.dedup_videos_task",
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bind=True,
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autoretry_for=(OperationalError, DBAPIError),
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retry_backoff=15, retry_backoff_max=180, max_retries=1,
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soft_time_limit=1800, time_limit=2400, # 30 min / 40 min
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)
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def dedup_videos_task(self, dry_run: bool = False) -> dict:
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"""Tier-1 video dedup (#871): re-probe NULL-duration videos, cluster by
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artist + duration + aspect, keep the highest-res copy per cluster. dry_run
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returns the projection (groups/redundant/reclaimable bytes) WITHOUT deleting;
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apply re-points each loser's post links to the keeper then deletes the
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redundant records + files. Operator-triggered; the summary lands in
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task_run.metadata (FC-3i) for the Maintenance card to surface."""
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SessionLocal = _sync_session_factory()
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with SessionLocal() as session:
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return cleanup_service.dedup_videos(
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session, images_root=IMAGES_ROOT, dry_run=dry_run,
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)
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# Wall-clock budget for the gated-preview re-walk: stop walking new sources past
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# this and report partial (operator re-runs to finish). Sits under the soft limit
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# so the task returns its summary cleanly instead of being SIGKILLed mid-walk.
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_GATED_PURGE_BUDGET_SECONDS = 1500
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@celery.task(
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name="backend.app.tasks.admin.purge_gated_previews_task",
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bind=True,
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autoretry_for=(OperationalError, DBAPIError),
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retry_backoff=15, retry_backoff_max=180, max_retries=1,
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soft_time_limit=1800, time_limit=2400, # 30 min / 40 min
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)
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def purge_gated_previews_task(self, dry_run: bool = True) -> dict:
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"""Cleanup (#874 follow-up): purge blurred locked-preview images grabbed from
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tier-gated Patreon posts before the ingester fix. Re-walks every enabled
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Patreon source (read-only, no downloads) to re-derive which posts are gated NOW
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and the blurred filehashes Patreon serves for them, then matches by content
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hash and (unless dry_run) deletes only those exact files — real content
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downloaded when access existed has a different hash and is provably spared.
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dry_run=True returns the projection (gated posts / matched images / reclaimable
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bytes / unverifiable kept) WITHOUT deleting; dry_run=false applies it. The
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summary lands in task_run.metadata (FC-3i) for the Maintenance card. Time-boxed
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across sources: a `partial` result means re-run to finish (idempotent — a
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re-walk re-derives the same matches and already-deleted files stay deleted)."""
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import asyncio
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import time as _time
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from sqlalchemy import select as _select
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from ..models import Source
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from ..services.credential_crypto import CredentialCrypto
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from ..services.credential_service import CredentialService
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from ..services.patreon_client import PatreonClient
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from ..services.patreon_resolver import resolve_campaign_id_for_source
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from ._async_session import async_session_factory
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key_path = IMAGES_ROOT / "secrets" / "credential_key.b64"
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async def _run() -> dict:
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async_factory, async_engine = async_session_factory()
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SessionLocal = _sync_session_factory()
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try:
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async with async_factory() as async_session:
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cred = CredentialService(async_session, CredentialCrypto(key_path))
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cookies_obj = await cred.get_cookies_path("patreon")
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cookies_path = str(cookies_obj) if cookies_obj else None
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with SessionLocal() as session:
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sources = session.execute(
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_select(Source.id, Source.url, Source.config_overrides)
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.where(Source.platform == "patreon", Source.enabled.is_(True))
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).all()
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gated_map: dict[int, dict] = {}
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scanned = 0
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partial = False
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loop = asyncio.get_running_loop()
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start = _time.monotonic()
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for sid, url, overrides in sources:
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if _time.monotonic() - start >= _GATED_PURGE_BUDGET_SECONDS:
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partial = True
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break
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campaign_id, _resolved = await resolve_campaign_id_for_source(
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url, cookies_path, overrides or {}
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)
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if not campaign_id:
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log.warning(
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"gated-purge: couldn't resolve campaign for source %s (%s) "
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"— skipping", sid, url,
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)
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continue
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client = PatreonClient(cookies_path)
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try:
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gated = await loop.run_in_executor(
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None, cleanup_service.collect_gated_previews, client, campaign_id
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)
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except Exception as exc: # one bad feed must not strand the sweep
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log.warning("gated-purge walk failed for source %s: %s", sid, exc)
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continue
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scanned += 1
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if gated:
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gated_map[int(sid)] = gated
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with SessionLocal() as session:
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summary = cleanup_service.purge_gated_previews(
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session, gated_map=gated_map, images_root=IMAGES_ROOT,
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dry_run=dry_run,
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)
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summary["sources_scanned"] = scanned
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summary["sources_total"] = len(sources)
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summary["partial"] = partial
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return summary
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finally:
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await async_engine.dispose()
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return asyncio.run(_run())
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@celery.task(
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name="backend.app.tasks.admin.bulk_delete_images_task",
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bind=True,
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autoretry_for=(OperationalError, DBAPIError),
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retry_backoff=15, retry_backoff_max=180, max_retries=1,
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soft_time_limit=900, time_limit=1200, # 15 min / 20 min
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)
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def bulk_delete_images_task(self, *, image_ids: list[int]) -> dict:
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"""Wraps cleanup_service.delete_images."""
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SessionLocal = _sync_session_factory()
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with SessionLocal() as session:
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return cleanup_service.delete_images(
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session, image_ids=image_ids, images_root=IMAGES_ROOT,
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)
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# Time-box one chunk well under the soft limit so a large archive back-catalog
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# can't run the task into the Celery time limit (or hog the maintenance_long
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# lane). The task re-enqueues itself with the resume cursor until the scan is
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# exhausted — mirrors normalize_tags_task (operator-asked 2026-06-07: reasonable
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# timeout, then re-queue so other work keeps flowing).
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_REEXTRACT_CHUNK_SECONDS = 600
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@celery.task(
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name="backend.app.tasks.admin.reextract_archive_attachments_task",
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bind=True,
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autoretry_for=(OperationalError, DBAPIError),
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retry_backoff=15, retry_backoff_max=180, max_retries=1,
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soft_time_limit=1800, time_limit=2400, # 30 min / 40 min
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)
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def reextract_archive_attachments_task(self, after_id: int = 0) -> dict:
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"""Wraps cleanup_service.reextract_archive_attachments (#713 part 2):
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re-extract PostAttachments that are actually archives but were filed
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opaquely before the magic-byte gate, and link their members to the post.
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Time-boxed + self-resuming: scans attachments after ``after_id`` and, on a
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chunk cut, re-enqueues from where it stopped so a big backlog finishes across
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chunks instead of dying at the soft limit."""
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SessionLocal = _sync_session_factory()
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with SessionLocal() as session:
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summary = cleanup_service.reextract_archive_attachments(
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session, images_root=IMAGES_ROOT,
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time_budget_seconds=_REEXTRACT_CHUNK_SECONDS, after_id=after_id,
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)
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# More attachments past this chunk's cursor — continue in the next.
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if summary.get("partial") and summary.get("resume_after_id", 0) > after_id:
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log.info(
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"reextract chunk done (%d scanned, %d archives, resume after id %s) "
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"— re-enqueuing to continue",
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summary.get("scanned", 0), summary.get("archives", 0),
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summary["resume_after_id"],
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)
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reextract_archive_attachments_task.delay(summary["resume_after_id"])
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return summary
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# Time-box one chunk well under the soft limit so a large back-catalog (the
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# first run recases the whole booru vocabulary) can't run the task into the
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# Celery time limit — it timed out at 40 min, operator-flagged 2026-06-07. The
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# task re-enqueues itself until nothing remains (idempotent — already-canonical
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# groups are skipped). 600s keeps each chunk short enough that the recovery
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# sweep and other maintenance tasks interleave on the concurrency-1 queue.
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_NORMALIZE_CHUNK_SECONDS = 600
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@celery.task(
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name="backend.app.tasks.admin.normalize_tags_task",
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bind=True,
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autoretry_for=(OperationalError, DBAPIError),
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retry_backoff=15, retry_backoff_max=180, max_retries=1,
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soft_time_limit=1800, time_limit=2400, # 30 min / 40 min
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)
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def normalize_tags_task(self) -> dict:
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"""Wraps tag_service.normalize_existing_tags (#714): Title-Case the
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back-catalog and merge case/whitespace-variant duplicate tags via the
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tested async merge path. Time-boxed + self-resuming so a huge first run
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finishes across chunks instead of timing out. Runs under its own asyncio
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loop + per-task async engine (NullPool), mirroring download_source."""
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import asyncio
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from ..services.tag_service import normalize_existing_tags
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from ._async_session import async_session_factory
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async def _run() -> dict:
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# lock_timeout=30s: a per-group merge repoints FKs across image_tag and
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# series_page; if a statement blocks on a lock (e.g. behind a schema
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# migration holding ACCESS EXCLUSIVE on series_page — the exact wedge that
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# made this task run to the 40-min hard limit with no progress,
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# operator-flagged 2026-06-07), it now fails fast. The per-group handler
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# catches it (rollback + error++) and the loop continues, so one blocked
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# group can't strand the whole chunk.
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async_factory, async_engine = async_session_factory(
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server_settings={"lock_timeout": "30s"}
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)
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try:
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async with async_factory() as session:
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# normalize_existing_tags commits per group internally.
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return await normalize_existing_tags(
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session, dry_run=False,
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time_budget_seconds=_NORMALIZE_CHUNK_SECONDS,
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)
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finally:
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await async_engine.dispose()
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summary = asyncio.run(_run())
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# More groups to canonicalize than fit this chunk — continue in the next.
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if summary.get("partial") and summary.get("remaining", 0) > 0:
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log.info(
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"normalize_tags_task chunk done (%d processed, %d remaining) — "
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"re-enqueuing to continue",
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summary.get("groups_processed", 0), summary["remaining"],
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)
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normalize_tags_task.delay()
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return summary
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# Time-box one rescan chunk well under the soft limit and re-enqueue from the
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# cursor — scoring every post against its artist's series is O(posts) and grows
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# with the library (FC-6.3). Mirrors normalize_tags_task.
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_SERIES_RESCAN_CHUNK_SECONDS = 600
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@celery.task(
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name="backend.app.tasks.admin.rescan_series_suggestions_task",
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bind=True,
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autoretry_for=(OperationalError, DBAPIError),
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retry_backoff=15, retry_backoff_max=180, max_retries=1,
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soft_time_limit=1800, time_limit=2400, # 30 min / 40 min
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)
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def rescan_series_suggestions_task(self, after_post_id: int = 0) -> dict:
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"""Score posts against their artist's series and write pending suggestions
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(FC-6.3). Settings-gated; time-boxed + self-resuming from a post-id cursor.
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Per-task async engine (NullPool) under its own asyncio loop, like normalize."""
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import asyncio
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from ..models import ImportSettings
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from ..services.series_match_service import SeriesMatchService
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from ._async_session import async_session_factory
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async def _run() -> dict:
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async_factory, async_engine = async_session_factory()
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try:
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async with async_factory() as session:
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settings = await ImportSettings.load(session)
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if not settings.series_suggest_enabled:
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return {"skipped": "series suggestions disabled"}
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threshold = settings.series_suggest_threshold
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return await SeriesMatchService(session).rescan(
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threshold=threshold,
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time_budget_seconds=_SERIES_RESCAN_CHUNK_SECONDS,
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after_post_id=after_post_id,
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)
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finally:
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await async_engine.dispose()
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summary = asyncio.run(_run())
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if summary.get("partial") and summary.get("resume_after_id", 0) > after_post_id:
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log.info(
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"rescan_series_suggestions chunk done (%d scanned, %d suggested, "
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"resume after %s) — re-enqueuing",
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summary.get("scanned", 0), summary.get("suggested", 0),
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summary["resume_after_id"],
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)
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rescan_series_suggestions_task.delay(summary["resume_after_id"])
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return summary
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