## Summary
- Catalog enrichment for `query_mode='remote'` rows: `rows`, `size_bytes`, `partition_by`, `clustered_by` per table (BQ + Keboola providers).
- `/api/v2/schema/{id}` cache miss: 2 BQ jobs → 1 (-50%) via shared `fetch_bq_columns_full`.
- All four catalog/schema/sample/metadata caches flush on registry change; single-row re-warm scheduled.
- Automatic cache warmup at server startup (bounded concurrency, opt-out via `AGNES_SKIP_CACHE_WARMUP=1`).
- SSE-driven freshness toolbar on `/admin/tables` with progress bar, log, and per-row badge.
- New admin doc `docs/admin/query-modes.md` — single source of truth on `local` / `remote` / `materialized` choice.
Closes #155.
Closes #156.
## Test plan
- [x] 65+ targeted tests pass across 11 new test modules + 3 modified ones.
- [x] No DB migration; no wire-break; `MIN_COMPAT_CLI_VERSION` unchanged.
- [ ] Reviewer: register a remote BQ table via `/admin/tables`, observe the toolbar populates within ~2 s and the per-row badge transitions warming → fresh.
- [ ] Reviewer: trigger `Re-warm all`, verify SSE log scrolls and `cacheWarmupBar` progresses.
- [ ] Reviewer: edit a registered row's bucket, verify `agnes schema <id>` returns updated columns immediately (no 1-hour staleness).
- [ ] Reviewer: confirm `agnes admin register-table --query-mode remote` prints the new IAM-smoke-check hint.
## Notable design decisions
- BigQuery `INFORMATION_SCHEMA.TABLE_STORAGE` is the only valid scope for size+rows (verified live 2026-05-07; dataset-scoped doesn't exist). Region resolved from `instance.yaml.data_source.bigquery.location` → `bq.client().get_dataset(...)` → fall back to legacy `__TABLES__`.
- VIEW handling: TABLE_STORAGE returns no rows for views, fall through to `__TABLES__` (also empty) → `TableMetadata(rows=None, size_bytes=None, partition_by=..., clustered_by=...)`. Null size signals analyst Claude to apply existing CLAUDE.md guidance.
- `size_bytes` is `active_logical_bytes + long_term_logical_bytes` — full BQ scan reads both; reporting only active undercounts aged partitioned tables.
- Source-agnostic provider seam: per-source `connectors/<source>/metadata.py:fetch(MetadataRequest)`; dispatcher in `app/api/v2_catalog.py:_metadata_provider_for` lazily imports per source_type so a Keboola-only deployment doesn't pay the BQ-extension import cost.
- Warmup non-blocking: FastAPI `lifespan` schedules `asyncio.create_task(_warm_catalog_caches_bg)` before `yield`. Per-row failures isolated.
## Out of scope
- Profile / column histograms / dimension cardinality for remote tables (separate issue).
- Onboarding nudge ("you have 0 remote tables, consider registering some BQ ones") — separate UX call.
- Provider plug-in registration via entry-points (the dispatch table is a hardcoded if-tree today; one line per future source).
## Release
Bumps `pyproject.toml` 0.46.1 → 0.47.0 (main shipped 0.46.0 + 0.46.1 during this PR — see commit `d98976ec`). New CHANGELOG section under `## [0.47.0] — 2026-05-07`.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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207 lines
7.1 KiB
Python
207 lines
7.1 KiB
Python
"""GET /api/v2/schema/{table_id} — table column metadata (spec §3.2)."""
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from __future__ import annotations
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import logging
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from fastapi import APIRouter, Depends, HTTPException
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import duckdb
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from app.auth.dependencies import get_current_user, _get_db
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from src.rbac import can_access_table
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from src.repositories.table_registry import TableRegistryRepository
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from app.api.v2_cache import TTLCache
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from connectors.bigquery.access import BqAccess, BqAccessError, get_bq_access
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/v2", tags=["v2"])
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_schema_cache = TTLCache(maxsize=512, ttl_seconds=3600)
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class NotFound(Exception):
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pass
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_BQ_DIALECT_HINTS = {
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"date_literal": "DATE '2026-01-01'",
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"timestamp_literal": "TIMESTAMP '2026-01-01 00:00:00 UTC'",
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"interval_subtract": "DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)",
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"regex": "REGEXP_CONTAINS(field, r'pattern')",
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"cast": "CAST(x AS INT64)",
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}
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def _fetch_bq_schema(bq, dataset: str, table: str) -> list[dict]:
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"""Fetch column list via the shared ``_fetch_bq_columns_full_impl`` helper.
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Pre-#155 this had its own INFORMATION_SCHEMA.COLUMNS query; consolidating
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with ``_fetch_bq_table_options`` (now also delegating to the same shared
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SQL) halves the BQ job count on cache miss. Returns the schema-endpoint
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column shape: name / type / nullable / description.
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Calls the raising variant so BQ exceptions reach ``translate_bq_error``
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with their original type (Forbidden → 502, BadRequest → 400, etc.).
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"""
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from connectors.bigquery.access import _fetch_bq_columns_full_impl, translate_bq_error, BqAccessError
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try:
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rows = _fetch_bq_columns_full_impl(bq, dataset, table)
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except (ValueError, BqAccessError):
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# ValueError ("unsafe identifier") and BqAccessError propagate
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# unchanged — the endpoint's existing handlers expect those types.
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raise
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except Exception as e:
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# Any other BQ-side exception goes through translate_bq_error so
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# the response status is classified correctly.
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raise translate_bq_error(e, bq.projects, bad_request_status="upstream_error")
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return [
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{
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"name": r["name"],
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"type": r["type"],
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"nullable": r["nullable"],
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"description": "",
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}
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for r in rows
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]
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def _fetch_bq_table_options(bq, dataset: str, table: str) -> dict:
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"""Best-effort fetch of partition/cluster info via the shared
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`fetch_bq_columns_full` helper.
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Returns ``{}`` on ANY failure (best-effort). Same load-bearing
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contract as before: the /schema endpoint must keep returning 200
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with empty partition info when this fails.
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"""
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from connectors.bigquery.access import fetch_bq_columns_full
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rows = fetch_bq_columns_full(bq, dataset, table)
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if not rows:
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return {}
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partition_by = next(
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(r["name"] for r in rows if r["is_partitioning_column"]),
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None,
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)
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clustered_rows = [r for r in rows if r["clustering_ordinal_position"] is not None]
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clustered_rows.sort(key=lambda r: r["clustering_ordinal_position"])
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clustered_by = [r["name"] for r in clustered_rows]
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return {"partition_by": partition_by, "clustered_by": clustered_by}
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def build_schema(
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conn: duckdb.DuckDBPyConnection,
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user: dict,
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table_id: str,
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*,
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bq: BqAccess,
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) -> dict:
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# RBAC + existence check MUST run before cache lookup — otherwise an
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# unauthorized user can read cached schema fetched by an authorized one.
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repo = TableRegistryRepository(conn)
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row = repo.get(table_id)
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if not row:
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raise NotFound(table_id)
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if not can_access_table(user, table_id, conn):
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raise PermissionError(table_id)
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cached = _schema_cache.get(table_id)
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if cached is not None:
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return cached
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return build_schema_uncached(conn, table_id, bq=bq, row=row)
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def build_schema_uncached(
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conn: duckdb.DuckDBPyConnection,
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table_id: str,
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*,
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bq: BqAccess,
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row: dict | None = None,
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) -> dict:
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"""Build the schema response and populate `_schema_cache`. **Skips
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RBAC and cache-hit short-circuit** — call only from contexts where
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those are unnecessary (warmup) or already enforced upstream
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(`build_schema`).
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Pass `row` from the upstream caller's `repo.get(table_id)` to avoid
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a redundant DB round-trip; if not provided, `build_schema_uncached`
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fetches it itself (the warmup-direct call site).
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"""
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if row is None:
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repo = TableRegistryRepository(conn)
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row = repo.get(table_id)
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if not row:
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raise NotFound(table_id)
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source_type = row.get("source_type") or ""
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if source_type == "bigquery":
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dataset = row.get("bucket") or ""
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source_table = row.get("source_table") or table_id
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columns = _fetch_bq_schema(bq, dataset, source_table)
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opts = _fetch_bq_table_options(bq, dataset, source_table)
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payload = {
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"table_id": table_id,
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"source_type": source_type,
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"sql_flavor": "bigquery",
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"columns": columns,
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"partition_by": opts.get("partition_by"),
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"clustered_by": opts.get("clustered_by", []),
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"where_dialect_hints": _BQ_DIALECT_HINTS,
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}
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else:
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# Local source — read schema from the parquet via DuckDB
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from app.utils import get_data_dir
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parquet = (
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get_data_dir() / "extracts" / source_type / "data" / f"{table_id}.parquet"
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)
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local_conn = duckdb.connect(":memory:")
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try:
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cols = local_conn.execute(
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"DESCRIBE SELECT * FROM read_parquet(?)", [str(parquet)]
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).fetchall()
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finally:
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local_conn.close()
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payload = {
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"table_id": table_id,
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"source_type": source_type,
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"sql_flavor": "duckdb",
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"columns": [
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{"name": c[0], "type": c[1], "nullable": c[2] == "YES", "description": ""}
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for c in cols
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],
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"partition_by": None,
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"clustered_by": [],
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"where_dialect_hints": {},
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}
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_schema_cache.set(table_id, payload)
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return payload
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@router.get("/schema/{table_id}")
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def schema(
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table_id: str,
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user: dict = Depends(get_current_user),
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conn: duckdb.DuckDBPyConnection = Depends(_get_db),
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bq: BqAccess = Depends(get_bq_access),
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):
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# Plain ``def`` — opens a `bq.duckdb_session()` and runs sync metadata
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# queries through the BQ extension. See PR #188 Tier 1 entry.
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try:
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return build_schema(conn, user, table_id, bq=bq)
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except NotFound:
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raise HTTPException(status_code=404, detail=f"table {table_id!r} not found")
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except PermissionError:
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raise HTTPException(status_code=403, detail="not authorized for this table")
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except ValueError as e:
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raise HTTPException(
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status_code=400,
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detail={"error": "unsafe_identifier", "message": str(e), "details": {}},
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)
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except BqAccessError as e:
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raise HTTPException(
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status_code=BqAccessError.HTTP_STATUS.get(e.kind, 500),
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detail={"error": e.kind, "message": e.message, "details": e.details},
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)
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