c4df92720b
Long videos blocked /playback for the entire encode duration. The fix is to populate the cache before the user clicks, not when they click. Changes: - Extract ffprobe + ffmpeg helpers to services/video.py so the request handler and the background task share one sync implementation. The endpoint wraps calls in asyncio.to_thread; celery just calls them. - New tasks/video.py with pretranscode_video. Idempotent: skips when the cache is already current and skips passthrough-safe sources (h264 in mp4/m4v/webm). 30-min task time limit so the long-tail files (3GP archive, multi-minute 1080p clips) still complete. - scan_folder now dispatches pretranscode_video alongside generate_thumbnails / extract_metadata for any new video row. - POST /library/maintenance/backfill-video-cache enqueues every active video so the existing library catches up. - libx264 preset bumped from fast to veryfast. ~2x throughput on this CPU-only box, output a few % larger but well within disk budget. - /playback simplifies to: cache check, passthrough if h264 in web-safe container, else sync transcode (still there as fallback for races against the queued task). Once the backfill task drains, /playback should be near-instant for every video. Any video added afterwards is pre-transcoded at scan time, so the user keeps that property going forward.
99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
"""
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Celery configuration and app initialization
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"""
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import logging
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import os
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from celery import Celery
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from celery.signals import worker_process_init
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from app.config import settings
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logger = logging.getLogger(__name__)
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# Create Celery app
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celery_app = Celery(
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'mulita',
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broker=settings.celery_broker_url,
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backend=settings.celery_result_backend,
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include=[
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'app.tasks.scan',
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'app.tasks.thumbs',
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'app.tasks.video',
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'app.tasks.vision',
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'app.services.metadata', # extract_metadata lives here
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]
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)
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# Configure Celery
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celery_app.conf.update(
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task_serializer='json',
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accept_content=['json'],
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result_serializer='json',
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timezone='UTC',
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enable_utc=True,
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# Robust acknowledgment: keep message in broker until task succeeds.
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task_acks_late=True,
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task_reject_on_worker_lost=True,
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# Global time limits — individual tasks can override via decorator.
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task_soft_time_limit=300, # 5 min — raises SoftTimeLimitExceeded
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task_time_limit=600, # 10 min — SIGKILL
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# Explicit routes for every task name. Wildcard patterns don't match
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# short names produced by @shared_task(name='...').
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task_routes={
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# Vision queue — CPU-bound binary classification
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'classify_content': {'queue': 'vision'},
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'vision_fanout': {'queue': 'vision'},
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# High-priority queue — thumbnails & duplicates
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'generate_thumbnails': {'queue': 'high'},
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'regenerate_all_thumbnails': {'queue': 'high'},
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'backfill_phashes': {'queue': 'high'},
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'regroup_duplicates': {'queue': 'high'},
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'incremental_regroup_duplicates': {'queue': 'high'},
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# Low-priority queue — scans
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'scan_folder': {'queue': 'low'},
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'scan_all_source_roots': {'queue': 'low'},
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'backfill_gps': {'queue': 'low'},
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# Pre-transcode HEVC videos in the background so /playback is a
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# cache hit on first user click. CPU-heavy but tolerant of the
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# low-priority queue (it doesn't block any user-facing flow).
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'pretranscode_video': {'queue': 'low'},
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# `watch_folders` is retired (file events come from NC webhooks)
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# but the task definition still exists as a no-op shim for any
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# in-flight apply_async. Route it to the default queue so the
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# remaining worker actually drains it.
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'watch_folders': {'queue': 'default'},
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'discard_missing_photos_beat': {'queue': 'low'},
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},
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task_default_queue='default',
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task_default_exchange='default',
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task_default_exchange_type='direct',
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task_default_routing_key='default',
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broker_connection_retry_on_startup=True,
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# Periodic catch-up so external file deletions in Nextcloud get
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# reflected even when the real-time watcher missed the event
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# (worker restart window, mount transient, etc).
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beat_schedule={
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'discard-missing-photos-every-30min': {
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'task': 'discard_missing_photos_beat',
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'schedule': 30 * 60,
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},
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},
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)
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@worker_process_init.connect
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def _warmup_vision_models(**kwargs):
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"""Pre-load vision models in the worker process so the first task
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doesn't pay cold-start latency. Only runs on the vision queue."""
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# The worker name contains the queue — only warm up vision workers.
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worker_queues = os.environ.get("CELERY_QUEUES", "")
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if "vision" not in worker_queues:
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# Heuristic: check the celery command line for -Q vision
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import sys
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if "vision" not in " ".join(sys.argv):
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return
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try:
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from app.services.vision.registry import registry
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registry.warmup()
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except Exception:
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logger.exception("Vision model warmup failed") |