45 lines
2.0 KiB
Python
45 lines
2.0 KiB
Python
from datetime import date, datetime
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from sqlalchemy import Date, DateTime, Index, String, Text
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from sqlalchemy.orm import Mapped, mapped_column
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from app.database import Base
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class DataImportRun(Base):
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"""One row per bulk-import attempt (SEC facts / Dolt earnings / Dolt stocks).
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Lean audit record for the batch import framework: every attempt is logged,
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whether it promoted, was a ``no_op`` (unchanged revision), was ``deferred``
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for an expected retry, or ``failed``.
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``row_counts`` and ``validation`` hold JSON strings (repo convention — see
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``fundamental_data.unavailable_fields_json``), not JSONB; the validation
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blob carries reconciliation/discrepancy summaries so no separate conflicts
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table is needed. One run per source at a time is enforced at write time by a
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Postgres advisory lock keyed by ``source``.
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"""
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__tablename__ = "data_import_runs"
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__table_args__ = (
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Index("ix_data_import_runs_source_started", "source", "started_at"),
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)
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id: Mapped[int] = mapped_column(primary_key=True)
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# sec_facts | dolt_earnings | dolt_stocks
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source: Mapped[str] = mapped_column(String(32), nullable=False)
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# Dolt commit hash, or SEC archive SHA-256. Null until known.
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revision: Mapped[str | None] = mapped_column(String(64), nullable=True)
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# running | validated | promoted | no_op | deferred | failed
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status: Mapped[str] = mapped_column(String(16), nullable=False)
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source_max_date: Mapped[date | None] = mapped_column(Date, nullable=True)
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row_counts_json: Mapped[str | None] = mapped_column(Text, nullable=True)
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validation_json: Mapped[str | None] = mapped_column(Text, nullable=True)
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started_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), default=datetime.utcnow, nullable=False
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)
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completed_at: Mapped[datetime | None] = mapped_column(
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DateTime(timezone=True), nullable=True
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)
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# Failure detail, or the non-error reason when status is deferred.
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error_details: Mapped[str | None] = mapped_column(Text, nullable=True)
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