b627de8344
Diagnostic + operator-facing documentation that closes the loop on the work in this PR. `da diagnose` (via /api/health/detailed): - New _check_bq_billing_project() helper. When data_source.type='bigquery' and BqProjects.billing == .data, surface a yellow warning: 'BigQuery billing project equals data project'. Hint includes the YAML field path + the /admin/server-config UI shortcut. Diagnose's overall status promotes warning → degraded so the CLI echoes it. - Non-BQ instances (Keboola-only, etc.) skip the check. - Implementation hooks into the existing /api/health/detailed surface — no new endpoint, no CLI changes. config/instance.yaml.example documentation: - data_source.bigquery.billing_project: USER_PROJECT_DENIED hint, /admin/server-config UI reference - data_source.bigquery.legacy_wrap_views: analyst-side discipline note (use `da fetch` / `da query --remote`), issue #101 history, view-heavy deployment guidance - data_source.bigquery.max_bytes_per_materialize: cost guardrail block (NEW — wasn't documented in .example before) - ai.base_url: provider list + UI hint - openmetadata + desktop: 'configurable via /admin/server-config UI' headers - corporate_memory: leading note that the schema is editable via UI Other docs: - CHANGELOG.md: comprehensive Unreleased section - CLAUDE.md: schema chain → v20 + Materialized SQL connector mode + per-connector tab UI mention - README.md: mode-first source table summary - docs/architecture.md: per-connector tab UI mention - cli/skills/connectors.md: bootstrap rails (parallel to #154) - docs/superpowers/plans/2026-05-01-admin-tables-form-cleanup.md: implementation plan archive (2515 lines) - scripts/seed_dummy_tables.py: drop is_public after #150 RBAC migration (column gone) Tests: - test_diagnose_billing.py — 3 cases (BQ with billing==data warns, BQ with billing!=data clean, non-BQ skips)
81 lines
3 KiB
Python
81 lines
3 KiB
Python
"""Seed table_registry with dummy entries across multiple buckets.
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Used to exercise the /admin/access UI with the new ResourceType.TABLE
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without depending on a real data source. Each entry is registered with
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default RBAC (no `is_public` bypass — that column was dropped in v19),
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so per-group grants are required for analyst visibility.
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Idempotent — TableRegistryRepository.register() does an UPSERT via
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ON CONFLICT, so re-running this script just refreshes the rows.
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Usage:
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python scripts/seed_dummy_tables.py
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"""
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from __future__ import annotations
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from src.db import get_system_db
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from src.repositories.table_registry import TableRegistryRepository
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# (bucket, table_id, name, description)
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DUMMY_TABLES: list[tuple[str, str, str, str]] = [
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# Finance
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("in.c-finance", "in_c_finance_orders_dummy",
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"orders_dummy", "Dummy orders fact table — one row per order."),
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("in.c-finance", "in_c_finance_revenue_daily_dummy",
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"revenue_daily_dummy", "Dummy daily revenue rollup."),
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("in.c-finance", "in_c_finance_customers_dummy",
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"customers_dummy", "Dummy customer dimension."),
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("in.c-finance", "in_c_finance_transactions_dummy",
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"transactions_dummy", "Dummy payment transactions."),
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# Marketing
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("in.c-marketing", "in_c_marketing_campaigns_dummy",
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"campaigns_dummy", "Dummy marketing campaigns metadata."),
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("in.c-marketing", "in_c_marketing_ad_spend_dummy",
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"ad_spend_dummy", "Dummy ad spend by channel and day."),
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("in.c-marketing", "in_c_marketing_channels_dummy",
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"channels_dummy", "Dummy marketing channel dimension."),
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("in.c-marketing", "in_c_marketing_attributions_dummy",
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"attributions_dummy", "Dummy multi-touch attributions."),
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# Product
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("in.c-product", "in_c_product_events_dummy",
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"events_dummy", "Dummy product event stream."),
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("in.c-product", "in_c_product_sessions_dummy",
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"sessions_dummy", "Dummy user session aggregates."),
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("in.c-product", "in_c_product_features_dummy",
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"features_dummy", "Dummy feature flag exposure log."),
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("in.c-product", "in_c_product_releases_dummy",
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"releases_dummy", "Dummy release/deploy timeline."),
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]
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def main() -> None:
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conn = get_system_db()
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try:
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repo = TableRegistryRepository(conn)
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before = len(repo.list_all())
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for bucket, table_id, name, description in DUMMY_TABLES:
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repo.register(
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id=table_id,
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name=name,
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source_type="dummy",
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bucket=bucket,
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source_table=name,
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query_mode="local",
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description=description,
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registered_by="seed_dummy_tables",
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profile_after_sync=False,
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)
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after = len(repo.list_all())
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bucket_count = len({b for b, _, _, _ in DUMMY_TABLES})
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print(
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f"Seeded {len(DUMMY_TABLES)} tables across {bucket_count} buckets "
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f"(registry: {before} -> {after} rows)."
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)
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finally:
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conn.close()
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if __name__ == "__main__":
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main()
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