5 Commits

Include renames

Author	SHA1	Message	Date
bvandeusenandClaude Opus 4.8	65211a3f2f	fix(migration): make 0045 DDL-only; backfill image_prediction via batched task (#768) ... The inline INSERT…SELECT backfill in migration 0045 wrapped the table creation and a ~100 GB pass over image_record.tagger_predictions in one transaction: nothing committed until the end, it was unmonitorable, and an earlier MATERIALIZED-CTE form spilled the full 100 GB to temp on NFS. A deploy got stuck on it for ~2h with image_prediction never appearing. Split the concerns: - 0045 now creates ONLY the table + indexes (instant DDL → web boots). - New backend.app.tasks.admin.backfill_image_predictions_task copies the >= store-floor predictions from the JSON into image_prediction, batched by id window and committed per chunk: live progress, resumable (re-enqueues from the last committed id), idempotent (ON CONFLICT DO NOTHING). json_each stays in the DB executor streaming each window — no Python-side 100 GB load, no materialization. - POST /api/admin/maintenance/backfill-predictions + a Maintenance-tab card to trigger the one-time run after upgrading. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-11 09:18:25 -04:00
bvandeusenandClaude Opus 4.8	e6d5f67f11	perf(migration): 0045 streams json_each via inline CASE guard (no temp spill) ... The MATERIALIZED-CTE scalar guard forced Postgres to materialize all object rows with their full JSON (~100 GB) to temp before json_each — on NFS that's a huge spill and pathologically slow (risks disk-full). Replace with an inline CASE that feeds json_each an empty object for non-object rows: same scalar guard, but a single streaming pass with no materialization. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-10 20:58:47 -04:00
bvandeusenandClaude Opus 4.8	a712cef92d	fix(migration): 0045 backfill guards json_each against non-object rows ... Some image_record rows store tagger_predictions as a JSON scalar/null rather than an object; json_each throws 'cannot deconstruct a scalar' on those, rolling back the whole migration. Filter to json_typeof = 'object' in a MATERIALIZED CTE so the guard runs before json_each ever evaluates a scalar. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-10 20:29:42 -04:00
bvandeusenandClaude Opus 4.8	75eab188c8	fix(migration): 0045 backfill filters to >= store floor (supersedes #764 prune) ... The #764 in-place prune (rewrite tagger_predictions to >=0.70) is too slow on 100 GB of TOAST and fails at its soft limit (interrupts a query mid-flight -> 'another command is already in progress'). #768 supersedes it: extract only the >=floor predictions into image_prediction via this set-based backfill, then drop the column (step 3) — reading 100 GB once + writing ~840k small rows beats rewriting 100 GB in place. So this backfill no longer assumes the prune ran: it filters by ml_settings.tagger_store_floor (default 0.70) itself, handling the full or partially-pruned JSON identically. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-10 19:37:38 -04:00
bvandeusenandClaude Opus 4.8	79089b50b0	feat(ml): image_prediction table + backfill + dual-write (#768 step 1) ... Normalize tagger predictions out of the image_record.tagger_predictions JSON blob into a queryable per-prediction table. Step 1 of the cutover (expand): additive + low-risk — reads still use the JSON, this just adds the table and keeps it populated. - ImagePrediction(image_record_id, raw_name, category, score) — stores the RAW tagger vocab name (not tag_id) so read-time alias→canonical resolution is unchanged. Indexed for per-image reads + by (raw_name, score). - Migration 0045: create table + set-based backfill from the JSON via json_each (fast post-#764-prune). The old column stays (vestigial) and is dropped in a later follow-up — DROP needs an ACCESS EXCLUSIVE lock on the hot image_record table, so it waits for a quiesced-worker window. - tag_and_embed dual-writes the rows (delete-then-insert, idempotent); tagger_store_floor already applied in infer(). Next: switch suggestion + allowlist reads to the table, then drop the JSON write. Plan-task #768. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-10 15:55:32 -04:00