Layer 3 — prevent the hard worker crash rather than just recovering from
it. The realistic process-crash vectors (operator's observed slow/heavy
tasks) are video decode and archive extraction; images decode in-process
and Pillow raises-and-skips cleanly, and a subprocess per image would
wreck deep-scan throughput, so images are intentionally not probed.
New backend/app/utils/safe_probe.py (leaf module, lazy heavy imports so
the spawned child stays light):
- probe_video(path): validates the container + first video stream via
ffprobe (a separate binary — a decoder crash kills only ffprobe, not
the worker). Returns width/height, which the importer didn't capture
for videos before. crashed=True only on ffprobe timeout.
- probe_archive(path): an uncompressed-size bomb guard
(MAX_ARCHIVE_UNCOMPRESSED_BYTES = 4 GiB) plus the format integrity
test (zipfile.testzip / rarfile.testrar / py7zr.test) run in a
spawned child process. A decompression-bomb OOM or native-lib
segfault on a malformed archive shows up as a non-zero child exit
code → crashed=True, never a dead worker.
ProbeResult.crashed distinguishes a HARD failure (subprocess killed /
timed out — the poison-pill signature → caller returns terminal
'failed') from a CLEAN rejection (corrupt-but-handled, bomb cap,
integrity mismatch → caller's choice of skipped/attached).
Wired:
- importer._import_media video branch: probe_video before the pipeline;
crash → failed, clean reject → invalid_image skip, ok → capture dims.
- importer._import_archive: probe_archive before extract_archive; crash
→ failed, clean reject → still preserve the archive as a
PostAttachment (matches extract_archive's fail-soft contract).
- ml.tag_and_embed video branch: probe_video before sampling 10 frames,
so a corrupt video is rejected (status='bad_video') instead of
crashing the ml-worker on frame decode.
Tests (test_safe_probe.py): valid/corrupt zip via probe_archive, direct
_inspect_archive size+integrity, in-process _archive_probe_target bomb
guard (monkeypatch can't reach a spawned child, so the target is called
directly), and a non-video → ok=False that's robust to ffprobe presence
in CI.
Operator-flagged 2026-05-28: tag_and_embed on image 6288 (an mp4) was
marked failed by recover_stalled_task_runs at the 5-min sweep tick
while still legitimately running. The error_type='RecoverySweep' /
"no completion signal received within 5 min" message was misleading
— the worker was busy, not stuck.
Root cause is two interacting limits, both undersized for video work:
tag_and_embed: soft_time_limit=300, time_limit=420
(sized for the image branch, ≈2 GPU ops)
recovery sweep: STUCK_THRESHOLD_MINUTES = 5 across all queues
The video branch samples 10 frames via ffmpeg, then runs tagger +
embedder on EACH frame — ~20 GPU ops vs 2 for an image. A loaded
ml-worker can take 5-10 min on a long video, which trips both
limits well before the task naturally finishes.
**Two-part fix**
1. `tag_and_embed` time limits bumped to soft=900 (15 min) / time=1200
(20 min). Sized for the video path's worst case; image runs return
in seconds and don't care.
2. New `QUEUE_STUCK_THRESHOLD_MINUTES` override dict in maintenance.py.
Queues with legitimately-long-running tasks (currently just `ml` at
25 min — 5-min buffer past the new hard kill) get their own
threshold; queues not in the dict use the default 5 min. The sweep
now issues one UPDATE per distinct threshold value, with
`queue.notin_(override_queues)` on the default pass so each row is
touched at most once.
Tests:
- _make_task_run helper accepts `queue=` (defaults to "default") so
existing tests use the default-threshold path.
- New test `test_recover_stalled_task_runs_ml_queue_uses_longer_threshold`
pins both directions: a 10-min-old ml row survives (fresh by 25-min
override), a 30-min-old ml row gets flagged.
After deploy, operator's mp4 ML jobs run to completion without
spurious RecoverySweep failures.
apply_allowlist_tags: 4 modes (tag-only / image-only / both / full sweep),
matches a tag to a prediction either by direct name or via alias
(name, category) resolution, gates on per-tag min_confidence, skips
applied/rejected, applies source='ml_auto'. recompute_centroid /
recompute_centroids: async-bridged calls into CentroidService, delta-gated.
Beat: daily backfill, daily centroid recompute, daily allowlist sweep.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
tag_and_embed: Camie + SigLIP on one image (video → 10-frame sample,
max-pool tags, mean-pool embeddings), stores predictions/embedding with
model versions, then enqueues per-image allowlist apply. backfill:
keyset-paginated discovery of images missing predictions/embeddings for
the current model versions (restart-safe). apply_allowlist_tags stub
included so .delay() resolves between commits (filled in Task 9).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
bvandeusen