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taylanbakircioglu
fbe223250b
The auth_pass / stats-auth / userlist / URI patterns added by the redaction
fixes on this branch run on the writer task, on every string value of every
captured body, so their cost is paid per row forever. Measured, they were:
writer-side redaction, before the redaction fixes 12.2 us/row
writer-side redaction, with them 359.1 us/row
A 29x regression, and none of it was spent matching anything - almost every
body contains none of these keywords. Profiling the four patterns on an 8 KB
config body, 300 iterations:
one combined alternation, IGNORECASE, \b-anchored 308.2 us
the same four run separately (sum) 219.7 us
text.lower() once + four substring pre-checks 5.3 us
`\b` and IGNORECASE each defeat the regex engine's literal-prefix scan, so
every alphanumeric position in 8 KB became a candidate start and the engine
walked the whole body four times to find nothing. Isolated, `auth_pass` costs
27.5 us with IGNORECASE and 2.4 us without.
Split the alternation and gate each pattern behind a substring test on one
lowercased copy. `str.lower()` and `in` are C-level scans; a pattern now runs
only when its keyword is actually present, and then on text that genuinely
contains it. Cost becomes O(total string bytes) instead of O(bytes x patterns).
The URI pattern also drops IGNORECASE and `\b` - its character class already
covers both cases, and `://` gives the engine a literal to scan for.
writer-side redaction, after 18.8 us/row
= 3.4s of CPU/day
at 180 000 rows/day
6.6 us/row over the pre-fix baseline, for four secret classes that were
previously written to the table in cleartext.
The pre-checks are on the lowercased copy, so the patterns stay IGNORECASE: the
marker may well have been `AUTH_PASS` in the original. Request-path cost is
unchanged at 27.7 us p50 - none of this ever ran there.
All 123 redaction and payload tests still pass, so the behaviour is identical;
only the path to it is cheaper.
HAProxy Management UI - Unit Tests
Overview
Comprehensive unit test suite for the HAProxy Management UI backend, covering critical business logic and ensuring reliability.
Test Structure
Backend Tests (backend/tests/)
test_soft_delete.py- Soft delete functionality and unique constraintstest_apply_process.py- Critical apply process that manages entity statestest_entity_sync.py- Entity-specific agent sync calculationstest_haproxy_config.py- HAProxy configuration generationtest_auth.py- Authentication and authorization
Frontend Tests (frontend/src/components/__tests__/)
EntitySyncStatus.test.js- Agent sync status componentApplyManagement.test.js- Apply management workflowSSLManagement.test.js- SSL certificate management
Running Tests
Backend Tests
# Install test dependencies
pip install -r backend/requirements-test.txt
# Run all tests
pytest
# Run specific test file
pytest backend/tests/test_apply_process.py
# Run with coverage
pytest --cov=backend --cov-report=html
# Run specific test
pytest backend/tests/test_soft_delete.py::TestSoftDeleteUniqueConstraints::test_backend_soft_delete_allows_name_reuse
Frontend Tests
# Run all frontend tests
npm test
# Run with coverage
npm run test:coverage
# Run in CI mode
npm run test:ci
Test Coverage Goals
- Backend: 70% minimum coverage
- Frontend: 70% minimum coverage
- Critical paths: 90%+ coverage (apply process, soft delete, entity sync)
Critical Test Areas
🔴 HIGH PRIORITY
- Apply Process - Prevents entity disappearance bugs
- Soft Delete Logic - Ensures proper unique constraint handling
- Entity Sync Calculations - Agent sync status accuracy
- Authentication/Authorization - Security validation
🟡 MEDIUM PRIORITY
- HAProxy Config Generation - Configuration correctness
- SSL Management - Certificate lifecycle
- Form Validations - Input validation
🟢 LOW PRIORITY
- UI Components - Visual behavior
- Utility Functions - Helper functions
Mock Strategy
Backend Mocking
- Database connections:
AsyncMockfor database operations - External APIs: Mock HTTP calls
- File operations: Mock file system access
Frontend Mocking
- API calls: Mock axios requests
- Ant Design components: Mock component behavior
- Context providers: Mock React contexts
Test Data
All tests use consistent mock data from conftest.py:
- Sample clusters, backends, frontends
- Mock users and authentication
- Config versions and SSL certificates
Debugging Tests
# Run with verbose output
pytest -v -s
# Run specific failing test
pytest backend/tests/test_apply_process.py::TestApplyProcess::test_apply_process_preserves_active_entities -v -s
# Drop into debugger on failure
pytest --pdb
Integration with CI/CD
Tests are designed to run in Azure DevOps pipeline:
# Example pipeline step
- script: |
pip install -r backend/requirements-test.txt
pytest --cov=backend --cov-report=xml
displayName: 'Run Backend Tests'
- script: |
npm ci
npm run test:ci
displayName: 'Run Frontend Tests'
Adding New Tests
- Follow naming convention:
test_*.pyfor backend,*.test.jsfor frontend - Use appropriate fixtures: Leverage existing mock data
- Test edge cases: Include error scenarios and boundary conditions
- Update coverage: Ensure new code maintains coverage thresholds
Common Issues
Backend
- Async tests: Use
@pytest.mark.asynciodecorator - Database mocking: Ensure proper mock setup for database operations
- Import paths: Use relative imports for testable modules
Frontend
- Component rendering: Wait for async operations with
waitFor - Event simulation: Use
fireEventfor user interactions - Mock cleanup: Clear mocks between tests with
jest.clearAllMocks()
Test Philosophy
These tests focus on:
- Business logic correctness over implementation details
- Critical path coverage over 100% coverage
- Regression prevention based on actual bugs encountered
- Maintainability with clear, readable test cases
The test suite is designed to catch the types of bugs we've actually encountered in production, particularly around the apply process and soft delete behavior.
Test deployment trigger - $(date)