- tests/test_bq_cost_guardrail.py: assert fail-open warning is logged
(test previously only proved fail-open doesn't crash; review note:
warning is the only operator-visible signal of the silent failure).
- extractor._wrap_admin_sql_for_jobs_api: docstring no longer claims
DuckDB-flavor SQL is rejected — the function performs no inner-SQL
validation; the v24 migration + register-time validator are the
real enforcement points.
- extractor.materialize_query: safe_path uses _escape_sql_string_literal
instead of inline replace, for one-place-to-update consistency.
- extractor: import hashlib hoisted to module-level imports.
Pre-fix, materialize ran the admin source_query as 'COPY (sql) TO parquet'
through the DuckDB BQ extension session. The extension defaults to the
BQ Storage Read API for bq.<ds>.<tbl> references, which rejects views
('non-table entities cannot be read with the storage API'). The fix
always wraps admin SQL into bigquery_query('<billing>', '<inner>') so
COPY uses the BQ jobs API uniformly for tables and views.
Cost guardrail dry-run now operates on the inner SQL (BQ-native), so
the BQ Python client parses it and the cap engages — pre-fix the dry-run
hit 'Table-valued function not found: bigquery_query' and fail-opened.
Issue #160: da query --remote against query_mode='remote' BQ rows whose
underlying entity is a VIEW or MATERIALIZED_VIEW returned a DuckDB catalog
error because the extractor (with legacy_wrap_views=False default since
the v2 fetch primitives release) skipped master-view creation for those
entity types — but kept inserting the _meta row, leaving operators with a
registered name that resolves to nothing.
Always create a master view for entity types we have proven runtime support
for in this codebase:
BASE TABLE → bq."<dataset>"."<source_table>"
(Storage Read API path; predicate pushdown)
VIEW / MAT_VIEW → bigquery_query('<project>', 'SELECT * FROM `proj.ds.tbl`')
(jobs API path; no pushdown — the upcoming /api/query
cost guardrail bounds the scan; was the legacy
legacy_wrap_views=True branch SQL form, just always-on)
For other entity types (EXTERNAL, SNAPSHOT, CLONE, future), log a warning
and SKIP both the master view AND the _meta row. The registry row remains
intact so /api/v2/scan still works for `da fetch`; we just don't expose a
stale _meta entry that the orchestrator would later strand.
The legacy_wrap_views config knob is still readable in this commit (read
returns the value, which is then ignored). Removal across the rest of
the codebase happens in the follow-up REFACTOR commit.
tests/test_bigquery_extractor.py:
- Add 3 RED tests covering the new always-wrap behavior:
test_view_creates_wrap_view_with_default_config,
test_materialized_view_creates_wrap_view_with_default_config,
test_unsupported_entity_type_skips_meta_and_view.
- Fix pre-existing flakiness in test_main_exits_when_project_missing
by resetting app.instance_config cache before the no-project mock —
the prior test populates the cache with a project, and removing the
legacy_wrap_views get_value() call surfaced this latent ordering bug.
Issue #108 Milestone 1. Adds BigQuery table registration via /admin/tables UI and `da admin register-table` CLI without hand-editing table_registry. POST /api/admin/register-table/precheck for round-trip validation. --dry-run flag on CLI. Audit-log entries on register/update/unregister. PUT /api/admin/registry/{id} now preserves registered_at (closes#130).
Replaces the BigQuery wrap-view pattern with a discovery + scoped-fetch toolkit driven by the analyst's Claude session. Adds /api/v2/{catalog,schema,sample,scan,scan/estimate}, da catalog/schema/describe/fetch/snapshot/disk-info CLI commands, sqlglot-backed WHERE validator, process-local quota tracker, agent rails skill (cli/skills/agnes-data-querying.md). BREAKING: BQ wrap views off by default — set data_source.bigquery.legacy_wrap_views=true for one cycle. Backward-compat field_validator on primary_key. Catalog cache now matches documented 300s TTL with RBAC fresh per request. Cuts release v0.14.0.
Closes M15 from issue #81 — SQL injection via attacker-controlled
identifiers in connectors/keboola/extractor.py and
connectors/bigquery/extractor.py.
Lifted _validate_identifier from src/orchestrator.py into a new
src/identifier_validation.py shared module (single source of truth for
both layers). Two validator policies:
- validate_identifier (strict, ^[a-zA-Z_][a-zA-Z0-9_]{0,63}$) for
table_name — matches the orchestrator's rebuild-time check, so dashed
names fail fast at extraction rather than being silently dropped.
- validate_quoted_identifier (relaxed, accepts dashes/dots) for
bucket/dataset/source_table — Keboola in.c-foo and BigQuery
my-dataset are legitimate, just need to be safe inside `"..."`.
Both extractors skip-and-continue on unsafe rows (logged + counted in
failure stats); _extract_via_extension re-validates as defense-in-depth.
71/71 extractor + orchestrator tests pass.
Refs #81 Group D.
Write to extract.duckdb.tmp, then atomically swap into place with WAL cleanup.
Prevents lock conflicts with orchestrator holding read lock on existing database.
DuckDB has used WAL by default since v0.8, so this pragma is not
valid DuckDB syntax. Removed obsolete try-except block that attempted
to enable WAL on system database initialization.
BigQuery extension handles auth via GOOGLE_APPLICATION_CREDENTIALS env var,
so _remote_attach uses empty token_env. Orchestrator now supports both
token-based (Keboola) and env-based (BigQuery) authentication modes.
