bvandeusen
75eab188c8
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>
83 lines
3.2 KiB
Python
83 lines
3.2 KiB
Python
"""image_prediction table + backfill from image_record.tagger_predictions
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Normalizes the per-image tagger predictions out of the JSON blob into a
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queryable table (#768). Backfills from the existing JSON in one set-based
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INSERT…SELECT over json_each — fast because the #764 prune already shrank
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each row to its >=0.70 entries. The old image_record.tagger_predictions
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column is left in place here (vestigial) and dropped in a follow-up once the
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code cutover is verified — dropping it needs an ACCESS EXCLUSIVE lock on the
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hot image_record table (the 0044 lock class), so it's deferred to a
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quiesced-worker window.
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Revision ID: 0045
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Revises: 0044
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Create Date: 2026-06-10
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"""
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from typing import Sequence, Union
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import sqlalchemy as sa
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from alembic import op
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revision: str = "0045"
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down_revision: Union[str, None] = "0044"
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branch_labels: Union[str, Sequence[str], None] = None
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depends_on: Union[str, Sequence[str], None] = None
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def upgrade() -> None:
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op.create_table(
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"image_prediction",
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sa.Column("id", sa.Integer(), primary_key=True),
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sa.Column(
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"image_record_id", sa.Integer(),
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sa.ForeignKey("image_record.id", ondelete="CASCADE"),
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nullable=False,
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),
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sa.Column("raw_name", sa.String(length=255), nullable=False),
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sa.Column("category", sa.String(length=64), nullable=False),
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sa.Column("score", sa.Float(), nullable=False),
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sa.UniqueConstraint(
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"image_record_id", "raw_name", name="image_raw_name",
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),
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)
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op.create_index(
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"ix_image_prediction_image", "image_prediction", ["image_record_id"],
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)
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op.create_index(
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"ix_image_prediction_name_score", "image_prediction",
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["raw_name", "score"],
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)
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# Backfill from the JSON blob. json_each expands {name: {category,
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# confidence}} into one row per prediction. category defaults to 'general'
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# to mirror the suggestion read path; rows with no confidence are skipped.
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# Filter to >= the store floor (ml_settings.tagger_store_floor, default
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# 0.70) right here so this is self-sufficient — it does NOT depend on the
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# #764 prune having run, and extracting only the >=floor tail keeps
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# image_prediction small (~tens of rows/image) even from the full JSON.
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op.execute(
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"""
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INSERT INTO image_prediction (image_record_id, raw_name, category, score)
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SELECT ir.id,
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je.key,
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COALESCE(je.value ->> 'category', 'general'),
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(je.value ->> 'confidence')::double precision
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FROM image_record ir,
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json_each(ir.tagger_predictions) je
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WHERE ir.tagger_predictions IS NOT NULL
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AND je.value ->> 'confidence' IS NOT NULL
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AND (je.value ->> 'confidence')::double precision
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>= COALESCE(
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(SELECT tagger_store_floor FROM ml_settings WHERE id = 1),
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0.70
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)
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ON CONFLICT (image_record_id, raw_name) DO NOTHING
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"""
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)
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def downgrade() -> None:
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op.drop_index("ix_image_prediction_name_score", "image_prediction")
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op.drop_index("ix_image_prediction_image", "image_prediction")
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op.drop_table("image_prediction")
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