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.
* chore(oss): isolate customer-specific deploy bits from scripts/grpn/ (#88)
Vendor-neutralization step before public release. The directory mixed
two concerns: (1) generic ops scripts referenced from mainline OSS
infrastructure (TLS rotation, auto-upgrade cron) and (2) one operator's
hackathon manual-deploy helper with hardcoded GCP project IDs, VM names,
and admin emails. Splitting them per concern.
Moved (still in OSS, just under a vendor-neutral name):
- scripts/grpn/agnes-tls-rotate.sh → scripts/ops/agnes-tls-rotate.sh
- scripts/grpn/agnes-auto-upgrade.sh → scripts/ops/agnes-auto-upgrade.sh
Removed (belongs in private consumer infra repos, not upstream OSS):
- scripts/grpn/Makefile (hardcoded prj-grp-foundryai-dev-7c37, foundryai-development VM name, e_zsrotyr@groupon.com bootstrap email)
- scripts/grpn/README.md (GRPN hackathon deploy walkthrough)
- docs/superpowers/plans/2026-04-22-grpn-deploy-learnings.md (org-specific deploy log)
Cross-refs updated in README.md, CLAUDE.md, docs/DEPLOYMENT.md,
docker-compose.yml. CHANGELOG entry flags BREAKING (ops) for any
consumer infra repo that installs these scripts via path-based systemd
timers.
This is the first wave of #88 — the remaining leaks (test data with
prj-grp-dataview-prod-1ff9, AIAgent.FoundryAI tags in OpenMetadata test
fixtures, docstrings in connectors/openmetadata/enricher.py) will be a
separate, smaller PR.
Refs #88.
* chore(oss): comprehensive vendor-neutralization (#88 wave 2 + review fixes)
PR #94 review found that the original wave-1 grep was scoped wrong and
many leaks survived. This commit closes wave 1 properly AND folds in all
wave-2 anonymization in a single pass — easier to review than two PRs.
Wave-1 review-fix corrections:
- Caddyfile: scripts/grpn/agnes-tls-rotate.sh → scripts/ops/ (the original
wave-1 grep filter excluded extensionless files like Caddyfile).
- CHANGELOG bullet rewritten — original wording implied an in-repo migration
for infra/modules/customer-instance/, which is wrong (the TF module embeds
the script inline via heredoc, never sourced from scripts/grpn/). Now
flags downstream consumer infra repos only.
- infra/modules/customer-instance/variables.tf: Czech docstring with `grpn`
example → English description with `acme, example` placeholders.
Wave-2 anonymization:
- Code docstrings (connectors/openmetadata/{client,transformer,enricher}.py,
src/catalog_export.py, scripts/duckdb_manager.py): prj-grp-… →
my-bq-project / prj-example-1234, AIAgent.FoundryAI → AIAgent.MyAgent,
FoundryAIDataModel → AnalyticsDataModel.
- Test fixtures (4 files): same set of replacements — 157 tests still pass.
- .github/workflows/keboola-deploy.yml: "Groupon-side dev VMs" comment →
generic "per-developer dev VMs".
- docs/auth-groups.md + scripts/debug/probe_google_groups.py:
kids-ai-data-analysis project name → acme-internal-prod placeholder.
- 5 planning/spec docs under docs/superpowers/{plans,specs}/2026-04-21-*:
hardcoded IPs (34.77.94.14, 34.77.102.61) → <dev-vm-ip>/<prod-vm-ip>;
GRPN/Groupon → Acme/another-customer; prj-grp-… → prj-example-….
- scripts/switch-dev-vm.sh deleted — hackathon-era helper hardcoded to a
specific shared dev VM. Per-developer dev VMs are the supported pattern.
Final grep `groupon|grpn|foundryai|prj-grp|groupondev|34\.77\.(94|102)\.…|kids-ai-data`
returns zero hits (excluding CHANGELOG.md historical entries).
CHANGELOG entry expanded to document both waves under one bullet, with
the BREAKING (ops) clarification about the TF module being unaffected.
Refs review of #94, closes#88.
* fix(oss): close remaining #94 review-2 findings (Czech, padak refs, CHANGELOG)
Reviewer of PR #94 round 2 caught 4 remaining items the wave-2 pass missed:
1. infra/modules/customer-instance/variables.tf had Czech descriptions on
8 more variables. Previous review only flagged line 19; this round
audited the rest. Translated lines 2, 28, 42-46 (heredoc), 60, 65, 71,
78, 84 to English. Same review concern: a Terraform module that is
the customer-facing API surface in Czech is unfit for OSS distribution.
2. infra/modules/customer-instance/outputs.tf had Czech descriptions on
four outputs. Same fix.
3. docs/padak-security.md referenced a private repo (padak/keboola_agent_cli#206)
in two places. Replaced with generic 'tracked upstream in the auth-CLI repo'
per CLAUDE.md vendor-agnostic rule (no cross-refs to private repos).
4. scripts/fetch-env-from-secrets.sh:41 had a Czech comment.
Translated.
5. CHANGELOG cosmetic: bullet said 'AIAgent.FoundryAI -> AIAgent.MyAgent'
but the actual code uses both MyAgent (in docstrings) and Example
(in test fixtures). Reworded to mention both targets.
Final grep across all shipping file types (.md, .py, .yml, .yaml, .sh,
Makefile, .json, .tf, .tpl, Caddyfile, .toml) for groupon|grpn|foundryai|
prj-grp|groupondev|34.77.94.14|34.77.102.61|kids-ai-data|padak/keboola_agent_cli
returns ZERO hits (excluding CHANGELOG.md). Czech-diacritic grep across
.tf/.toml/Caddyfile/Makefile/.yml returns ZERO hits.
157/157 OpenMetadata + DuckDB tests still pass.
* fix(oss): close#94 round-3 leaks (env.template, instance.yaml.example, padak typo)
Round-3 reviewer caught two MUST-FIX leaks the round-2 grep missed
(grep was scoped to extensions that did not include .template / .example
suffixes — the audit was right, the previous grep was not paranoid enough):
1. config/instance.yaml.example:114 — '(optional - Groupon-specific)' brand
leak in a shipping config example. Replaced with '(optional)'.
2. config/.env.template:68 — stale path 'scripts/grpn/agnes-tls-rotate.sh'
in operator-facing env-template comment. The script lives at
scripts/ops/ now (commit 16a85cc); this comment had been pointing
operators at a non-existent path.
3. docs/padak-security.md:188 — phrase duplication 'tracked in tracked
upstream' from a sloppy substitution in round-2. Trivial wording fix.
Final paranoid grep across .md/.py/.yml/.yaml/.sh/Makefile/.json/.tf/.tpl/
Caddyfile/.toml/.template/.example/.env* with the full token set
(groupon|grpn|foundryai|prj-grp|groupondev|34\.77\.94\.14|34\.77\.102\.61|
kids-ai-data|padak/keboola_agent_cli) returns ZERO hits, excluding
CHANGELOG.md historical entries.
* fix(oss): #94 round-4 — QUICKSTART.md + rename padak-security.md
Devin Review caught two findings on the latest round-3 commit:
1. docs/QUICKSTART.md:67 still pointed users at the deleted
scripts/switch-dev-vm.sh. A Quickstart user following step-by-step
would hit a missing-file error at the final step. Replaced with the
inline gcloud-ssh equivalent that the Removed bullet documents.
2. docs/padak-security.md filename retains the personal identifier
'padak'. The PR fixed the body content (replaced
padak/keboola_agent_cli#206 references with generic wording) but
missed the filename. Renamed to docs/security-audit-2026-04.md
(date-anchored, vendor-neutral). Updated the historical CHANGELOG
link to point at the new path with an inline note about the rename.
* fix(oss): redact remaining hardcoded IPs from planning docs + remove default email
Devin Review caught two more leaks:
1. scripts/fetch-env-from-secrets.sh line 16 had a hardcoded
personal-email default (zdenek.srotyr@keboola.com). Replaced with
':?' bash error so SEED_ADMIN_EMAIL must be explicitly set —
safer than carrying any specific identity.
2. Planning docs still had 35.195.96.98 and 34.62.223.189 (legacy
prod/dev IPs) that the round-1 IP-replace pattern missed (it only
targeted 34.77.x.x). Generic regex redaction across all five
planning docs replaces every public IP with <redacted-ip>,
preserving private/loopback/IAP ranges.
* fix(security): close Jira webhook fail-open + path traversal (#83)
Two related vulnerabilities:
1. Fail-open signature check: when JIRA_WEBHOOK_SECRET was unset,
_verify_signature returned True and any unauthenticated POST to
/webhooks/jira would run the full ingest pipeline. Now fail-closed —
the handler short-circuits with 503 (operator-misconfiguration signal,
distinct from 401 wrong-signature) when the secret is missing.
2. Path traversal via attacker-controlled issue_key: webhook payloads
carry issue.key, which flowed unsanitized into save_issue (issues_dir /
"{issue_key}.json"), download_attachment (attachments_dir / issue_key),
and incremental_transform (raw_dir / "issues" / "{issue_key}.json"). A
crafted webhook with issue.key="../../etc/passwd" could write outside
the Jira data dir.
Defense-in-depth: new connectors/jira/validation.py exposes
is_valid_issue_key (whitelist regex ^[A-Z][A-Z0-9_]{0,31}-\d{1,12}$) and
safe_join_under (Path.resolve() containment check). Both are enforced at
the webhook entry point AND at every filesystem boundary in the connector.
Tests:
- New tests/test_jira_validation.py — unit tests for both helpers
(parametrized invalid keys, traversal/symlink/absolute-path cases).
- Webhook tests: test_unconfigured_secret_returns_503,
test_path_traversal_in_issue_key_rejected (parametrized over 10 bad keys),
test_valid_issue_key_accepted.
CHANGELOG: two CRITICAL Fixed bullets under Unreleased.
Closes#83.
* fix(security): close remaining #83 review findings — webhookEvent traversal, _handle_deletion guard, regex tightening
Reviewer of PR #93 flagged four MUST-FIXes:
1. _log_webhook_event used the attacker-controlled `webhookEvent` field
as a filename component without sanitization. Payload with
`webhookEvent: "../../tmp/pwn"` could escape WEBHOOK_LOG_DIR. Now:
- non-`[A-Za-z0-9_-]` runs are replaced with `_` (dot excluded so
`..` cannot survive sanitization as a directory component)
- length capped at 64 chars
- final path routed through safe_join_under
New regression test `test_webhook_event_path_traversal_sanitized`.
2. _handle_deletion (connectors/jira/service.py:530) and
process_webhook_event (line 487) still used raw issue_key in path
builds. Even though the webhook handler validates upstream, the
"defense-in-depth at every filesystem boundary" claim required these
too. Both now run is_valid_issue_key and safe_join_under guards.
3. Regex `^[A-Z][A-Z0-9_]{0,31}-\d{1,12}$` permitted underscores in
project keys. Atlassian's project-key validator does not — `A_B-1`
is rejected by Jira itself. Tightened to `[A-Z0-9]` and updated
tests: `ABC_DEF-1` is now invalid, added Cyrillic А-1 (lookalike),
CRLF, and oversize cases to the bad-key parametrization.
4. Existing test test_deletion_of_nonexistent_issue_returns_true used
`PROJ-NOEXIST` which is not a real Jira key shape. Updated to
`PROJ-99999`. The test still exercises the same intent (deletion of
issue with no local file is idempotent).
73/73 jira tests pass locally (test_jira_webhooks + test_jira_validation
+ test_jira_service + test_jira_service_full + test_jira_incremental).
CHANGELOG updated to document the regex tightening and the new
webhookEvent sanitization.
Refs review of #93.
* fix(tests): test_journey_jira tests assumed fail-open before #83 fix
CI failure on PR #93 caught two journey tests that pinned the OLD
fail-open contract:
- test_webhook_with_no_secret_configured_accepted asserted 200 when
JIRA_WEBHOOK_SECRET was unset. After the #83 fix that's a 503
(operator misconfig). Renamed to _refused and flipped the assertion.
- test_webhook_empty_payload_rejected didn't set the secret, so the
503 short-circuit fired before the empty-payload 400 could. Set
JIRA_WEBHOOK_SECRET in the patched Config so the test exercises the
intended path.
56/56 jira journey + webhook + validation tests now pass.
* fix(security): #93 round-3 — webhook fallback format + save_issue early validation
Devin Review caught two real findings:
1. Webhook handler regression: the round-2 fix extracted issue_key only
from event_data['issue']['key'], but process_webhook_event has long
supported a fallback 'issue_key' top-level field for certain Jira
event formats (e.g. delete events historically). The handler now
blocks those events with 400 before they reach the service layer.
Fix: mirror process_webhook_event's fallback in the handler — try
issue.key first, fall through to event_data.get('issue_key') when
empty. is_valid_issue_key still validates whichever source provided
the key.
2. save_issue defense-in-depth was incomplete: is_valid_issue_key ran
AFTER fetch_remote_links and fetch_sla_fields had already used the
unvalidated issue_key in HTTP URL construction
({base_url}/issue/{issue_key}/remotelink etc.). A future internal
caller invoking save_issue directly with attacker-controlled input
could trigger outbound requests with a malicious path component
(limited SSRF / URL-path manipulation against the Jira API server).
Fix: move the is_valid_issue_key check to immediately after the
null guard, before any HTTP request or filesystem op. Webhook layer
still validates upstream, this is the second layer.
66 jira tests pass.
Refs Devin Review of #93.
* fix(changelog): #93 round-4 — add BREAKING marker to fail-closed bullet
Devin Review caught: the JIRA_WEBHOOK_SECRET fail-closed change is a
behavior change for operators (response code 503 vs old 200) that
existing alerting may treat differently. Per CLAUDE.md changelog
discipline rule, operators grep for **BREAKING** before bumping the
pin. Added the marker + a short note on what action operators need
to take (set the env var if they haven't).
Refs Devin Review of #93.
* fix: #93 round-5 — null-issue crash + comment drift
Devin Review caught two findings on the round-4 commit:
1. Pre-existing crash on null issue field: a webhook payload with
{"issue": null} (rather than omitting the key) caused
event_data.get("issue", {}) to return None, then issue.get("key")
raised AttributeError → unhandled 500. Pre-existing but reachable.
Fix: 'event_data.get("issue") or {}' normalises None to {}, then
the existing fallback / validation path returns 400 cleanly.
New regression test test_null_issue_field_does_not_crash.
2. Inline comment drift: the comment at line 77 documented the allowed
character class as [A-Za-z0-9._-] (with dot) but the regex at line 27
excludes dot deliberately (so '..' cannot survive sanitization).
Fixed the comment to match.
52 jira tests pass.
Refs Devin Review of #93 round 5.
* fix: #93 round-6 — process_webhook_event also normalises null issue field
Devin Review caught: the webhook handler at app/api/jira_webhooks.py
correctly handles {"issue": null} via 'event_data.get("issue") or {}',
but process_webhook_event at connectors/jira/service.py:509 still
used the bare 'event_data.get("issue", {})' which returns None on
explicit null. Internal callers (anything that invokes
process_webhook_event without going through the HTTP handler) would
hit the same AttributeError the round-5 fix closed at the handler
layer. Same one-line fix.
32 jira tests pass.
Refs Devin Review of #93 round 5.
* fix: #93 round-7 — issue-key regex uses [0-9] not \d
Devin Review caught: Python 3's \d matches any Unicode decimal digit
(Arabic-Indic ٣, Bengali ৩, Devanagari ३, …). A key like TEST-٣ would
pass the regex even though it's not a valid Jira input. Tightened to
[0-9] (ASCII only).
Added three Unicode-digit cases to the bad-key parametrization in
test_jira_validation.py to lock in the contract.
Refs Devin Review of #93 round 6.
* fix: #93 round-8 — use \\Z anchor not $ in issue-key regex
Devin Review caught: Python's $ anchor matches before a trailing \\n,
so re.match('…$', 'TEST-1\\n') returns a match. is_valid_issue_key
returned True for CRLF-injected keys. \\Z is hard end-of-string and
closes that bypass.
Manual verification:
is_valid_issue_key('TEST-1\\n') → False (was True before fix)
is_valid_issue_key('TEST-1\\r\\n') → False
is_valid_issue_key('TEST-1') → True
Refs Devin Review of #93 round 7.
* docs: #93 round-9 — CHANGELOG regex matches implementation
The Flask-based webhook endpoint at connectors/jira/webhook.py is no longer used.
FastAPI handles webhooks via app/api/jira_webhooks.py which is already integrated
into the application. This removes the redundant Flask code.
Write to extract.duckdb.tmp, then atomically swap into place with WAL cleanup.
Prevents lock conflicts with orchestrator holding read lock on existing database.
- In src/orchestrator.py _attach_remote_extensions: escape token with '' before passing to ATTACH
- In connectors/keboola/extractor.py _try_attach_extension: escape token with '' before passing to ATTACH
Prevents SQL injection if token contains single quotes.
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.
Add _atomic_swap_db helper that removes stale WAL files before and after
moving the temp DuckDB into place. Apply CHECKPOINT before close in both
orchestrator and Keboola extractor so DuckDB flushes WAL before the swap.
- Replace padak/tmp_oss → keboola/agnes-the-ai-analyst in all docs, infra, CLI
- Replace your-org/ai-data-analyst → keboola/agnes-the-ai-analyst in README, Jira docs
- Remove real GCP project ID from terraform.tfvars.example
- Delete internal draft documents (dev_docs/draft/)
- Update infra/main.tf to clone from main branch
- Legacy extractor now uses read_csv(all_varchar=true) to avoid type
inference errors (e.g. seniority column typed as DOUBLE with string values)
- DEPLOYMENT.md rewritten based on actual dev VM deployment experience:
deploy key setup, DuckDB write locking, env reload gotchas, bootstrap flow
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.
Extractors with remote tables now write a _remote_attach table into
extract.duckdb so the orchestrator can re-ATTACH external extensions
at query time. The mechanism is source-agnostic — any connector can use it.
- Keboola extractor writes _remote_attach + creates views on kbc.*
- Orchestrator reads _remote_attach, installs extension, reads token from env
- Graceful degradation: missing token → warning, local tables still work
Three-pronged fix for DuckDB lock conflicts:
1. WAL mode on system.duckdb — enables concurrent readers + writer
2. Sync trigger runs extractor as subprocess (not background task) —
separate process = separate DuckDB connections, no lock conflict
3. Both extractor and orchestrator write to .tmp then atomic rename —
avoids lock conflict with API reads on extract.duckdb/analytics.duckdb
Fixes#9 permanently.
- New extract_init.py: creates extract.duckdb with _meta + views for 6 entity types
- Update default paths to /data/extracts/jira/data/ and /data/extracts/jira/raw/
- After parquet writes, update _meta table in extract.duckdb
- Trigger SyncOrchestrator.rebuild_source("jira") after successful transform
Replace hardwired Anthropic API calls with a pluggable provider system.
Each deployment configures its AI provider in instance.yaml — switching
between Anthropic, LiteLLM, OpenRouter, or any OpenAI-compatible proxy
is a config change, not a code change.
New connectors/llm/ module:
- StructuredExtractor Protocol with extract_json() interface
- AnthropicExtractor: direct Anthropic SDK with retry + backoff
- OpenAICompatExtractor: any OpenAI-compatible proxy with three-layer
structured output fallback (json_schema -> json_object -> prompt)
- Configurable structured_output policy (strict/json/auto)
- Custom exception hierarchy (auth/rate_limit/timeout/format/refusal)
- Zero secrets in logs: no API keys, prompts, or responses logged
Reviewed by: Google Gemini, Claude Sonnet, OpenAI GPT-5.4.
Security audit passed with all critical findings resolved.
OpenMetadata search API ignores queryFilter for dataProducts field.
Use type-specific index + client-side filtering by dataProducts
membership instead. Correctly returns 16/32 metrics for FoundryAI.
- client.py: add search_by_data_product() for OpenMetadata search API
- catalog_export.py: prefer data product discovery over tag filtering
(finds all 16 metrics in FoundryAIDataModel vs 3 with tag filter)
- remove-analyst: fix GROUPS bash variable collision, improve messaging
When BQ returns empty results (e.g., data not yet refreshed), the
scheduler was marking sync as complete for the day. This meant the
next 15-min tick would skip it ("none are due") and data would stay
stale until the next day's scheduled run.
Now: if partitioned sync processes partitions but gets 0 new rows,
last_sync is NOT updated. The scheduler will retry on the next tick
(15 min later) when data may be available.
List view:
- Show display_name ("M1 + VFM Operational") instead of name ("M1PlusVFMOperational")
- Strip HTML and truncate description for clean list excerpts
Modal detail:
- Render original HTML from catalog instead of stripped plain text
- Add .om-description CSS class for structured HTML (bold labels, lists, code)
- Pass description_html alongside plain text description for backwards compat
Add filter_tag support to catalog_export and webapp so only metrics
with the required tag are exported to YAML and displayed in UI.
Previously all 19+ metrics were exported regardless of relevance.
- Add has_tag() helper to transformer module
- catalog_export.py: filter_tag parameter from instance.yaml openmetadata config
- webapp/app.py: filter metrics in _load_metrics_from_catalog()
- 7 new tests (has_tag, filter_tag export, stale cleanup)
OpenMetadata stores descriptions as rich HTML (<p>, <strong>, , etc.).
Add strip_html() to transformer that converts to clean plain text for YAML
files consumed by Claude Code agent. Applied to metric descriptions, table
descriptions, and column descriptions. Webapp display dict keeps raw HTML
since the modal renders it correctly.
- New `connectors/openmetadata/transformer.py` with shared parsing logic
for extracting categories, grain, dimensions, expressions from OM tags
- New `src/catalog_export.py` script (python -m src.catalog_export) that
fetches metrics/tables from OpenMetadata API and writes YAML files to
/data/docs/metrics/ and /data/docs/tables/ for agent consumption
- Refactor webapp/app.py to delegate to transformer (with inline fallback)
- Add `fields` parameter to client.get_metrics() and get_metric_by_fqn()
for fetching tags+owners in a single API call
- Fix pre-existing mock bug in test_openmetadata_enricher (base_url)
- 101 new tests (80 transformer + 21 export), all passing
OpenMetadata uses different field names than expected:
- metricExpression instead of expression
- metricType instead of type
- unitOfMeasurement instead of unit
- granularity instead of grain
Remove 'fields' query parameter from /api/v1/metrics - returns 400 Bad Request
when invalid field names are specified. Let API return full metric objects.
Update parsing to extract metadata from proper OpenMetadata fields instead
of relying on tags (tags are optional, fields are always present).
- Add get_metric_by_fqn() to OpenMetadataClient
- Add get_metrics() to CatalogEnricher with TTL caching
- Implement _parse_om_metric() to extract category/grain from OpenMetadata tags
- Implement _load_metrics_from_catalog() to fetch and categorize metrics
- Implement _build_om_metric_detail() to convert OpenMetadata format to MetricParser JSON
- Add /api/catalog/metrics/<fqn> endpoint for metric detail modal
- Update _load_metrics_data() to prefer catalog over YAML fallback
- Update metric_modal.js to route catalog:{fqn} to catalog API endpoint
- Delete 10 demo YAML files from docs/metrics/
- Replace metric tests with new unit tests for catalog parsing functions (19 tests)
Catalog metrics provide single source of truth vs maintaining demo YAML files.
UI remains unchanged - only data source changes from YAML to OpenMetadata catalog.
Add OpenMetadata REST API connector and enricher to merge table/column metadata
from OpenMetadata catalog at sync and query time.
Changes:
- connectors/openmetadata/client.py: HTTP client for OM API
- connectors/openmetadata/enricher.py: Data enrichment with TTL cache
- tests/test_openmetadata_*: Unit tests for client and enricher
- src/config.py: Add catalog_fqn field to TableConfig
- src/data_sync.py: Use enricher in _generate_schema_yaml (catalog > BQ API > data_description.md)
- webapp/app.py: Initialize enricher, enrich catalog data with tags/tier/owners/url
- config/instance.yaml.example: Document openmetadata section
Features:
- FQN auto-derivation: bigquery.{table.id}
- TTL cache (default 1h) to avoid repeated API calls
- Graceful degradation: disabled if token missing, silent on HTTP errors
- Column description priority: catalog > BQ API > (none)
- Table description priority: catalog > data_description.md
Without explicit bqstorage_client parameter, to_arrow_iterable() silently
falls back to REST API pagination (~5K rows/sec). With explicit client,
it uses parallel gRPC streams via BQ Storage API (~300K rows/sec).
No temp table materialization - BQ already writes query results to an
internal temp table automatically. We just tell the reader to use the
fast gRPC path instead of slow HTTP pagination.
QueryJob only has to_arrow(), not to_arrow_iterable().
Must call query_job.result() first to get RowIterator,
which has the streaming to_arrow_iterable() method.
