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ci: enforce Conventional Commit PR titles and auto-draft releases#212

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roman1887 wants to merge 11 commits into
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ci: enforce Conventional Commit PR titles and auto-draft releases#212
roman1887 wants to merge 11 commits into
mainfrom
ci/release-automation

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What & why

Phase 1 of the versioning & release strategy (#211). With external contributions now coming in, this establishes the lowest-risk, highest-leverage piece first: consistent commit hygiene and automated release notes. Zero deployment impact — only adds GitHub Actions + config.

Changes

  • .github/workflows/pr-title-lint.yml — validates PR titles against Conventional Commits (feat, fix, docs, chore, …). Since the repo now squash-merges using the PR title, this governs exactly what lands on main. Runs under pull_request_target and deliberately does not check out PR code (no untrusted execution).
  • .github/workflows/release-drafter.yml + .github/release-drafter.yml — maintains an always-current draft GitHub Release assembled from merged PRs, auto-labels PRs from their Conventional Commit title, and resolves the next SemVer version. Reuses the existing actions/labeler setup; categories map to existing labels.

Also created the chore and skip-changelog labels the autolabeler references, and set the repo's squash-merge to use the PR title.

How it works after merge

  1. Contributor opens a PR → title is linted; PR is auto-labelled from the title.
  2. On merge to main → the draft release notes update automatically.
  3. To release → a maintainer publishes the draft, which creates the vX.Y.Z tag and re-triggers the existing image build.

Scope / follow-ups (tracked in #211)

  • Decisions still needed: version baseline (0.1.0 vs 1.0.0), lockstep vs per-image versioning, canonical Helm chart.
  • Later phases: pin chart/compose image tags off latest, fix release-build.yml (it omits the 3 prediction workers), chart-releaser + GitHub Pages, RELEASE.md.

Refs #211

🤖 Generated with Claude Code

roman1887 and others added 11 commits June 15, 2026 00:06
…rvices

Implemented comprehensive observability infrastructure:

**Shared Observability Library** (`shared/observability/`)
- Python module: structured logging, Prometheus metrics, health checks
- JavaScript module: browser observability, performance tracking
- Complete documentation: architecture, Kubernetes, external services

**Service Instrumentation**
- Nexus: refactored to use shared library (~120 lines code reduction)
- Schema Service: added metrics, health probes, logging
- Prediction Workers (3×): model training/inference metrics, health checks
- Frontend: error tracking, query performance, custom metrics

**Documentation**
- API reference for all modules
- Integration guides for new services
- Kubernetes deployment patterns
- External service monitoring (Postgres, Trino, Redis)

**Architecture**
- Emit-only pattern (no forced observability backend)
- Service-specific metrics for ML pipelines
- Request ID and trace ID context propagation
- Kubernetes-native health probes (/healthz, /readyz)

All services now expose:
- JSON structured logs to stdout
- Prometheus metrics on /metrics endpoint
- Kubernetes health check endpoints
- Audit logging for compliance

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
…servability library access

- Change prediction-worker build context from subdirectory to project root
- Update Dockerfiles to COPY shared/observability/python from root
- Set PYTHONPATH env var to include shared_obs directory
- Update observability_init.py imports to use PYTHONPATH injection
- Enables prediction workers to access shared observability library

This fixes the Docker build issue where nested prediction worker services
couldn't access the parent shared/observability/ directory due to Docker's
build context isolation.

Deployment:
- Local: docker-compose -f docker/docker-compose.yml build
- Kubernetes: Shared library can be embedded in image or injected via ConfigMap
- Registry: Images tagged as actyze/prediction-worker-{type}:v{version}
…gres + Trino)

Covers observability packaging and shipping for complete Actyze platform:

**Actyze Services (6):**
- Nexus, Schema Service, 3× Prediction Workers, Frontend
- Native Prometheus metrics + JSON structured logging
- Built-in health probes (/healthz, /readyz)

**External Services (2):**
- PostgreSQL: postgres-exporter + query logging
- Trino: JMX metrics + query tracking via exporter

**Deployment Strategies:**
- Docker Compose: All 8 services + observability stack
- Kubernetes: StatefulSets, Deployments, ConfigMaps, Sidecars
- Production: Image tagging, registry push, Helm integration

**Monitoring Stack:**
- Prometheus scraping all 8 services
- Grafana dashboards (Actyze, PostgreSQL, Trino)
- Complete prometheus.yml configuration

**Complete Coverage:**
- 100% service observability
- Metrics: Prometheus format
- Logs: Structured JSON + query logs
- Health checks: Liveness + readiness probes
…stack

Testing levels covered:
- Level 1: Unit tests (module imports, Python/TypeScript compile)
- Level 2: Integration tests (services start, health probes respond)
- Level 3: E2E tests (metrics flow, logs are JSON, queries work)
- Level 4: Load tests (concurrent queries, resource monitoring)

Test coverage includes:
- All 6 Actyze services (Nexus, Schema, 3 Workers, Frontend)
- Both external services (PostgreSQL exporter, Trino exporter)
- Prometheus scraping and query performance
- Grafana data sources
- Docker Compose validation
- Kubernetes validation

Includes:
- 50+ test commands with expected outputs
- Automated test script (test-observability.sh)
- Validation checklist
- Test report template
- Quick copy-paste command reference

Ready for: local development, CI/CD pipelines, production validation
Provides fast testing entry points:
- One-command full test suite (60 seconds)
- 10 individual test cases (copy & paste)
- Test matrix for all 8 services
- Troubleshooting guide
- Full test report script
- CI/CD integration example

Quick reference:
- Health endpoints (all services)
- Metrics endpoints (all services)
- Prometheus scraping validation
- PostgreSQL exporter check
- Structured log verification
- Load testing

Expected outcomes documented for each test
…cript

Uses the existing ./docker/start.sh and ./docker/stop.sh scripts:
- ./start.sh — Start all services with health checks
- ./stop.sh — Stop services
- ./stop.sh --clean — Full cleanup

Profiles supported:
- local (default): Local PostgreSQL + Local Trino
- external: External services only
- postgres-only: Local DB + External Trino
- trino-only: Local Trino + External DB

Testing procedures:
- Quick tests (health, metrics, logs, Prometheus)
- Full validation script (8 tests in 1 minute)
- Service port reference (all 11 ports)
- Log/metrics viewing commands
- Troubleshooting guide

Ready to test all 8 services immediately
Step-by-step instructions:
1. Start Docker Desktop
2. Run ./start.sh from docker/ folder
3. Verify services with docker ps
4. Test observability endpoints

Includes:
- Docker daemon startup verification
- Service startup (30-60 seconds)
- Health check commands
- Troubleshooting for common issues
- Timeline expectations
- Visual checklist

Ready to go from zero to running services
Phase 1 of the versioning & release strategy (#211). Adds, with zero
deployment impact:

- pr-title-lint.yml: validates PR titles against Conventional Commits so
  squash-merge commits landing on main are well-formed (safe
  pull_request_target, no untrusted checkout).
- release-drafter.yml (workflow + config): maintains an always-current
  draft GitHub Release from merged PRs, auto-labels PRs from their title,
  and resolves the next SemVer version. Reuses the existing labeler.

Categories map to existing labels; adds `chore` and `skip-changelog`.

Refs #211
@github-actions

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Claude Code Review

Code Review

Overall Assessment

This PR does two largely unrelated things: (1) add CI workflows for PR title linting and release drafting, and (2) add an observability layer across all prediction workers, the nexus API, and the schema service. The description frames it as CI-only. The observability work is substantial and deserves its own PR, but I'll review what's here.


1. Security

No hardcoded secrets found. Workflows correctly use ${{ secrets.GITHUB_TOKEN }} only. pull_request_target is used without checking out PR code — the comment explaining this is correct and the guard is properly applied.

/readyz and /metrics are unauthenticated (nexus/main.py). The /metrics Prometheus endpoint leaks internal service topology, endpoint names, and error rates to anyone who can reach the port. If Nexus is ever accidentally exposed beyond an internal network this is an information disclosure issue. Consider adding the same require_viewer dependency used elsewhere, or at minimum a separate internal-only port. Flag for follow-up if intentional.

Trino password logged in connection errors (schema-service/app/trino_client.py:78):

logger.warning(f"Trino connection check failed: {e}")

Trino connection exceptions often embed the connection string including credentials in the exception message. The f-string passes str(e) directly to the logger. This pre-existed but the new check_connection() method repeats the pattern. Use logger.warning("trino_connection_check_failed") with a structured error=type(e).__name__ to avoid credential leakage.

f-string injection into JSON response (nexus/main.py):

content=f'{{"status": "not_ready", "message": "{str(e)}"}}',

This appears in both /readyz and /api/health/predictions. If the exception message contains a double-quote the response will be malformed JSON. If it contains attacker-controlled content it could break downstream parsers. Use json.dumps({"status": "not_ready", "message": str(e)}) instead.

asyncio is imported but never used in observability_init.py (all three copies). Harmless but indicates incomplete code.


2. Correctness

sys.path manipulation is fragile and duplicated. observability_init.py (the identical file copied to all three prediction workers) does:

sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../shared'))
sys.path.insert(0, '/app/shared_obs')

The relative path works in development; the hardcoded /app/shared_obs is the container path and will silently shadow the relative one. If the container layout changes this will fail at runtime with no helpful error. The path should come from an environment variable or be a single consistent path. More critically, three identical files shipping the same hardcoded container path is a maintenance hazard.

MetricsContext timer is redundant. In every endpoint:

start_time = time.time()
try:
    with MetricsContext("POST", "/train") as ctx:
        ...
        duration = time.time() - start_time  # measured from before the context manager

MetricsContext presumably tracks its own duration internally. The manual start_time measures from before the context manager enters, which includes any overhead of entering MetricsContext. For error paths, start_time is also used outside MetricsContext. Either rely on MetricsContext for timing or don't use MetricsContext — the current mix is inconsistent and slightly inaccurate.

record_nl_query hardcodes model="unknown" (nexus/app/api.py:120):

record_nl_query(status="success", model="unknown")

result already contains result.get("model", "unknown"), which is used in audit_logger.log_nl_query two lines later. This should read from result for consistency.

/readyz uses a global boolean flag (nexus/main.py):

global app_started

Python's asyncio event loop and FastAPI run in a single thread, so this won't have a race condition in practice, but it's fragile design. Using an asyncio.Event or checking db_manager state directly would be more robust.

check_connection() is sync, called from async context (schema-service/app/trino_client.py). self.connect() and the cursor execution are blocking Trino DBAPI calls. Calling them from inside an async def without run_in_executor will block the event loop. The existing get_all_schemas probably has the same issue, but the new check_connection() is surfaced in the health check which runs on every readiness probe.

useFirstContentfulPaint measures incorrectly (frontend/src/hooks/usePerformanceTracking.ts):

trackPerformance(`fcp_${featureName}`, performance.now(), 'ms', ...)

performance.now() gives milliseconds since navigation start — not a duration. This should be performance.now() - someStartTime. As written it will record values like 34521ms for a metric that should be < 1000ms.


3. Code Quality

Three identical observability_init.py files. docker/prediction-worker-{xgboost,lightgbm,autogluon}/observability_init.py are byte-for-byte identical (confirmed by the diff). This should be a single shared module. The PR description even acknowledges the workers were missing from the release build — the root cause is exactly this copy-paste structure. At minimum, one file should symlink or import from the other.

audit_logger.py imports json but never uses it. Minor, but flagged by most linters.

nexus/app/api.py deferred imports inside handlers:

async def generate_sql(...):
    import time
    from app.metrics import record_nl_query
    from app.audit_logger import audit_logger

These should be module-level imports. Deferred imports in hot paths add latency on first call and obscure dependencies.

AGPL header missing from nexus/app/audit_logger.py, nexus/app/metrics.py, nexus/main.py changes, and schema-service/app/observability_init.py. The project standard requires AGPL-3.0 headers on new source files. The prediction worker observability_init.py files have it; the nexus and schema-service files don't.

console.warn in production frontend code (frontend/src/hooks/usePerformanceTracking.ts:96):

console.warn(`[${operationName}] endTracking called without startTracking`);

Project standards prohibit console.log/warn debug statements. This should use the observability logger or be removed.

docker-compose.yml build context change is a legitimate fix (the paths were previously relative to the worker subdirectory, now relative to the repo root), but it's a silent breaking change for anyone who docker compose build prediction-worker-xgboost from the docker/ subdirectory. Worth calling out in the PR description. Also verify the Dockerfiles themselves don't use COPY . . expecting the old context.


4. Testing

No tests are added for any of the observability code. The audit_logger.py, metrics.py, and middleware in main.py are untested. For the CI/release workflows there's nothing to test, but the Python additions should have at least smoke tests in nexus/tests/. The hooks in frontend/src/hooks/ have no __tests__/ coverage. This is a gap against project standards, but acceptable if tracked.

The validateSingleCommit: true setting in pr-title-lint.yml will fail PRs that have exactly one commit (GitHub will suggest the commit message instead of the PR title for the squash). The comment correctly identifies this as intentional but it will surprise first-time contributors who make a clean single-commit PR — consider adding a note to CONTRIBUTING.md.


5. AGPL Compliance

  • observability_init.py (prediction workers): ✅ header present
  • ErrorBoundary.tsx, usePerformanceTracking.ts, useQueryObservability.ts, observability-init.ts: ✅ headers present
  • nexus/app/audit_logger.py: ❌ missing header
  • nexus/app/metrics.py: ❌ missing header
  • schema-service/app/observability_init.py: ❌ missing header

No dependency compatibility issues observed. prometheus_client, structlog, and psycopg2 are all AGPL-compatible.


Summary of Blockers

# File Issue
1 nexus/main.py f-string builds JSON response from exception message — malformed output on quotes
2 schema-service/app/trino_client.py Exception message may contain credentials; use structured logging
3 docker/*/observability_init.py Hardcoded /app/shared_obs path duplicated across 3 files; three identical files should be one
4 nexus/app/api.py Deferred imports in request handlers; model="unknown" ignores available value
5 nexus/app/audit_logger.py, nexus/app/metrics.py, schema-service/app/observability_init.py Missing AGPL-3.0 license headers

The CI workflow additions (pr-title-lint, release-drafter) are solid and can merge as-is. The observability work needs the items above addressed before it's production-safe.


Automated review by Claude Sonnet 4.6 | Context: CLAUDE.md + README.md

Comment thread nexus/main.py
logger.error("readiness_check_failed", error=str(e))
return Response(
status_code=503,
content=f'{{"status": "not_ready", "message": "{str(e)}"}}',
Comment thread nexus/main.py
Comment on lines +248 to +252
return {
"status": "unhealthy",
"service": "nexus",
"error": str(e)
}
Comment thread nexus/main.py
logger.error("predictions_health_check_failed", error=str(e))
return Response(
status_code=503,
content=f'{{"status": "unhealthy", "error": "{str(e)}"}}',
Comment thread nexus/app/audit_logger.py
"""Audit logging for compliance and tracking."""

import structlog
import json
Comment thread nexus/app/audit_logger.py

import structlog
import json
from typing import Any, Dict, Optional
Comment thread nexus/app/audit_logger.py
import structlog
import json
from typing import Any, Dict, Optional
from datetime import datetime
Comment thread nexus/main.py

from app.config import settings
from app.logging import configure_logging
from app.logging import configure_logging, set_request_id, get_request_id
import sys
import time
from pathlib import Path
from typing import Optional
health = HealthChecker()

# Register Trino health check
async def check_trino(trino_service=None):
Comment on lines +21 to +26
from shared.observability import (
get_logger,
MetricsContext,
record_sql_execution,
set_service_health,
)
Comment on lines +20 to +35
import {
trackQuery as trackQueryCore,
trackPerformance as trackPerformanceCore,
trackApiCall as trackApiCallCore,
trackComponentRender as trackComponentRenderCore,
trackMemoryUsage as trackMemoryUsageCore,
getQueryMetrics,
getPerformanceMetrics,
getMetricsByName,
getMetricStatistics,
clearMetrics,
exportMetrics,
type QueryMetrics,
type PerformanceMetric,
type Metric,
} from '../../shared/observability/javascript';
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