feat(sql): stage_processed_folder.py — scan a processed_v3/<folder> tree and stage each zarr's metadata into app.staging_<folder> - #535
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…ree and stage each zarr's metadata into app.staging_<folder> Recursively finds every *.zarr under processed_v3/<folder>/, reads the zarr.json attributes, and rebuilds a staging table (episode_hash PK) with the app.episodes -mappable columns (embodiment, task, task_description, num_frames, zarr_processed_path) plus fps/has_annotations/source_folder/created_at(from hash)/raw_attrs(jsonb)/scanned_at. Does not touch app.episodes (merge is separate). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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…--workers, default 48) Serial per-episode get_object was the bottleneck (minutes for large folders). List prefixes once, then ThreadPoolExecutor over the metadata reads. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…y cluster --ray (default) fans the per-episode zarr.json GETs out over the cluster's small CPU workers — each worker has its own NIC/boto3 client, so aggregate S3 throughput scales with workers (the single-node thread pool was bounded by one machine's network). Worker task is fully self-contained (stdlib+boto3, R2 creds passed in). Head lists prefixes + streams inserts (ON CONFLICT DO NOTHING). --no-ray keeps the thread-pool fallback. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Recursively finds every *.zarr under processed_v3//, reads the zarr.json
attributes, and rebuilds a staging table (episode_hash PK) with the app.episodes
-mappable columns (embodiment, task, task_description, num_frames,
zarr_processed_path) plus fps/has_annotations/source_folder/created_at(from
hash)/raw_attrs(jsonb)/scanned_at. Does not touch app.episodes (merge is separate).
Co-Authored-By: Claude Fable 5 noreply@anthropic.com