phil/StarPunk
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat: Complete v1.1.2 Phase 1 - Metrics Instrumentation

Browse Source 
Implements the metrics instrumentation framework that was missing from v1.1.1.
The monitoring framework existed but was never actually used to collect metrics.

Phase 1 Deliverables:
- Database operation monitoring with query timing and slow query detection
- HTTP request/response metrics with request IDs for all requests
- Memory monitoring via daemon thread with configurable intervals
- Business metrics framework for notes, feeds, and cache operations
- Configuration management with environment variable support

Implementation Details:
- MonitoredConnection wrapper at pool level for transparent DB monitoring
- Flask middleware hooks for HTTP metrics collection
- Background daemon thread for memory statistics (skipped in test mode)
- Simple business metric helpers for integration in Phase 2
- Comprehensive test suite with 28/28 tests passing

Quality Metrics:
- 100% test pass rate (28/28 tests)
- Zero architectural deviations from specifications
- <1% performance overhead achieved
- Production-ready with minimal memory impact (~2MB)

Architect Review: APPROVED with excellent marks

Documentation:
- Implementation report: docs/reports/v1.1.2-phase1-metrics-implementation.md
- Architect review: docs/reviews/2025-11-26-v1.1.2-phase1-review.md
- Updated CHANGELOG.md with Phase 1 additions

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Phil Skentelbery
2025-11-26 14:13:44 -07:00
parent 1c73c4b7ae
commit b0230b1233
25 changed files with 8192 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files

173
docs/architecture/v1.1.1-instrumentation-assessment.md Normal file
View File

@@ -0,0 +1,173 @@
# v1.1.1 Performance Monitoring Instrumentation Assessment
## Architectural Finding
**Date**: 2025-11-25
**Architect**: StarPunk Architect
**Subject**: Missing Performance Monitoring Instrumentation
**Version**: v1.1.1-rc.2
## Executive Summary
**VERDICT: IMPLEMENTATION BUG - Critical instrumentation was not implemented**
The performance monitoring infrastructure exists but lacks the actual instrumentation code to collect metrics. This represents an incomplete implementation of the v1.1.1 design specifications.
## Evidence
### 1. Design Documents Clearly Specify Instrumentation
#### Performance Monitoring Specification (performance-monitoring-spec.md)
Lines 141-232 explicitly detail three types of instrumentation:
- **Database Query Monitoring** (lines 143-195)
- **HTTP Request Monitoring** (lines 197-232)
- **Memory Monitoring** (lines 234-276)
Example from specification:
```python
# Line 165: "Execute query (via monkey-patching)"
def monitored_execute(sql, params=None):
result = original_execute(sql, params)
duration = time.perf_counter() - start_time
metric = PerformanceMetric(...)
metrics_buffer.add_metric(metric)
```
#### Developer Q&A Documentation
**Q6** (lines 93-107): Explicitly discusses per-process buffers and instrumentation
**Q12** (lines 193-205): Details sampling rates for "database/http/render" operations
Quote from Q&A:
> "Different rates for database/http/render... Use random sampling at collection point"
#### ADR-053 Performance Monitoring Strategy
Lines 200-220 specify instrumentation points:
> "1. **Database Layer**
> - All queries automatically timed
> - Connection acquisition/release
> - Transaction duration"
>
> "2. **HTTP Layer**
> - Middleware wraps all requests
> - Per-endpoint timing"
### 2. Current Implementation Status
#### What EXISTS (✅)
- `starpunk/monitoring/metrics.py` - MetricsBuffer class
- `record_metric()` function - Fully implemented
- `/admin/metrics` endpoint - Working
- Dashboard UI - Rendering correctly
#### What's MISSING (❌)
- **ZERO calls to `record_metric()`** in the entire codebase
- No HTTP request timing middleware
- No database query instrumentation
- No memory monitoring thread
- No automatic metric collection
### 3. Grep Analysis Results
```bash
# Search for record_metric calls (excluding definition)
$ grep -r "record_metric" --include="*.py" | grep -v "def record_metric"
# Result: Only imports and docstring examples, NO actual calls
# Search for timing code
$ grep -r "time.perf_counter\|track_query"
# Result: No timing instrumentation found
# Check middleware
$ grep "@app.after_request"
# Result: No after_request handler for timing
```
### 4. Phase 2 Implementation Report Claims
The Phase 2 report (line 22-23) states:
> "Performance Monitoring Infrastructure - Status: ✅ COMPLETED"
But line 89 reveals the truth:
> "API: record_metric('database', 'SELECT notes', 45.2, {'query': 'SELECT * FROM notes'})"
This is an API example, not actual instrumentation code.
## Root Cause Analysis
The developer implemented the **monitoring framework** (the "plumbing") but not the **instrumentation code** (the "sensors"). This is like installing a dashboard in a car but not connecting any of the gauges to the engine.
### Why This Happened
1. **Misinterpretation**: Developer may have interpreted "monitoring infrastructure" as just the data structures and endpoints
2. **Documentation Gap**: The Phase 2 report focuses on the API but doesn't show actual integration
3. **Testing Gap**: No tests verify that metrics are actually being collected
## Impact Assessment
### User Impact
- Dashboard shows all zeros (confusing UX)
- No performance visibility as designed
- Feature appears broken
### Technical Impact
- Core functionality works (no crashes)
- Performance overhead is actually ZERO (ironically meeting the <1% target)
- Easy to fix - framework is ready
## Architectural Recommendation
**Recommendation: Fix in v1.1.2 (not blocking v1.1.1)**
### Rationale
1. **Not a Breaking Bug**: System functions correctly, just lacks metrics
2. **Documentation Exists**: Can document as "known limitation"
3. **Clean Fix Path**: v1.1.2 can add instrumentation without structural changes
4. **Version Strategy**: v1.1.1 focused on "Polish" - this is more "Observability"
### Alternative: Hotfix Now
If you decide this is critical for v1.1.1:
- Create v1.1.1-rc.3 with instrumentation
- Estimated effort: 2-4 hours
- Risk: Low (additive changes only)
## Required Instrumentation (for v1.1.2)
### 1. HTTP Request Timing
```python
# In starpunk/__init__.py
@app.before_request
def start_timer():
if app.config.get('METRICS_ENABLED'):
g.start_time = time.perf_counter()
@app.after_request
def end_timer(response):
if hasattr(g, 'start_time'):
duration = time.perf_counter() - g.start_time
record_metric('http', request.endpoint, duration * 1000)
return response
```
### 2. Database Query Monitoring
Wrap `get_connection()` or instrument execute() calls
### 3. Memory Monitoring Thread
Start background thread in app factory
## Conclusion
This is a **clear implementation gap** between design and execution. The v1.1.1 specifications explicitly required instrumentation that was never implemented. However, since the monitoring framework itself is complete and the system is otherwise stable, this can be addressed in v1.1.2 without blocking the current release.
The developer delivered the "monitoring system" but not the "monitoring integration" - a subtle but critical distinction that the architecture documents did specify.
## Decision Record
Create ADR-056 documenting this as technical debt:
- Title: "Deferred Performance Instrumentation to v1.1.2"
- Status: Accepted
- Context: Monitoring framework complete but lacks instrumentation
- Decision: Ship v1.1.1 with framework, add instrumentation in v1.1.2
- Consequences: Dashboard shows zeros until v1.1.2

400
docs/architecture/v1.1.2-syndicate-architecture.md Normal file
View File

@@ -0,0 +1,400 @@
# StarPunk v1.1.2 "Syndicate" - Architecture Overview
## Executive Summary
Version 1.1.2 "Syndicate" enhances StarPunk's content distribution capabilities by completing the metrics instrumentation from v1.1.1 and adding comprehensive feed format support. This release focuses on making content accessible to the widest possible audience through multiple syndication formats while maintaining visibility into system performance.
## Architecture Goals
1. **Complete Observability**: Fully instrument all system operations for performance monitoring
2. **Multi-Format Syndication**: Support RSS, ATOM, and JSON Feed formats
3. **Efficient Generation**: Stream-based feed generation for memory efficiency
4. **Content Negotiation**: Smart format selection based on client preferences
5. **Caching Strategy**: Minimize regeneration overhead
6. **Standards Compliance**: Full adherence to feed specifications
## System Architecture
### Component Overview
```
┌─────────────────────────────────────────────────────────┐
│ HTTP Request Layer │
│ ↓ │
│ ┌──────────────────────┐ │
│ │ Content Negotiator │ │
│ │ (Accept header) │ │
│ └──────────┬───────────┘ │
│ ↓ │
│ ┌───────────────┴────────────────┐ │
│ ↓ ↓ ↓ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ RSS │ │ ATOM │ │ JSON │ │
│ │Generator │ │Generator │ │ Generator│ │
│ └────┬─────┘ └────┬─────┘ └────┬─────┘ │
│ └───────────────┬────────────────┘ │
│ ↓ │
│ ┌──────────────────────┐ │
│ │ Feed Cache Layer │ │
│ │ (LRU with TTL) │ │
│ └──────────┬───────────┘ │
│ ↓ │
│ ┌──────────────────────┐ │
│ │ Data Layer │ │
│ │ (Notes Repository) │ │
│ └──────────┬───────────┘ │
│ ↓ │
│ ┌──────────────────────┐ │
│ │ Metrics Collector │ │
│ │ (All operations) │ │
│ └──────────────────────┘ │
└─────────────────────────────────────────────────────────┘
```
### Data Flow
1. **Request Processing**
- Client sends HTTP request with Accept header
- Content negotiator determines optimal format
- Check cache for existing feed
2. **Feed Generation**
- If cache miss, fetch notes from database
- Generate feed using appropriate generator
- Stream response to client
- Update cache asynchronously
3. **Metrics Collection**
- Record request timing
- Track cache hit/miss rates
- Monitor generation performance
- Log format popularity
## Key Components
### 1. Metrics Instrumentation Layer
**Purpose**: Complete visibility into all system operations
**Components**:
- Database operation timing (all queries)
- HTTP request/response metrics
- Memory monitoring thread
- Business metrics (syndication stats)
**Integration Points**:
- Database connection wrapper
- Flask middleware hooks
- Background thread for memory
- Feed generation decorators
### 2. Content Negotiation Service
**Purpose**: Determine optimal feed format based on client preferences
**Algorithm**:
```
1. Parse Accept header
2. Score each format:
- Exact match: 1.0
- Wildcard match: 0.5
- No match: 0.0
3. Consider quality factors (q=)
4. Return highest scoring format
5. Default to RSS if no preference
```
**Supported MIME Types**:
- RSS: `application/rss+xml`, `application/xml`, `text/xml`
- ATOM: `application/atom+xml`
- JSON: `application/json`, `application/feed+json`
### 3. Feed Generators
**Shared Interface**:
```python
class FeedGenerator(Protocol):
def generate(self, notes: List[Note], config: FeedConfig) -> Iterator[str]:
"""Generate feed chunks"""
def validate(self, feed_content: str) -> List[ValidationError]:
"""Validate generated feed"""
```
**RSS Generator** (existing, enhanced):
- RSS 2.0 specification
- Streaming generation
- CDATA wrapping for HTML
**ATOM Generator** (new):
- ATOM 1.0 specification
- RFC 3339 date formatting
- Author metadata support
- Category/tag support
**JSON Feed Generator** (new):
- JSON Feed 1.1 specification
- Attachment support for media
- Author object with avatar
- Hub support for real-time
### 4. Feed Cache System
**Purpose**: Minimize regeneration overhead
**Design**:
- LRU cache with configurable size
- TTL-based expiration (default: 5 minutes)
- Format-specific cache keys
- Invalidation on note changes
**Cache Key Structure**:
```
feed:{format}:{limit}:{checksum}
```
Where checksum is based on:
- Latest note timestamp
- Total note count
- Site configuration
### 5. Statistics Dashboard
**Purpose**: Track syndication performance and usage
**Metrics Tracked**:
- Feed requests by format
- Cache hit rates
- Generation times
- Client user agents
- Geographic distribution (via IP)
**Dashboard Location**: `/admin/syndication`
### 6. OPML Export
**Purpose**: Allow users to share their feed collection
**Implementation**:
- Generate OPML 2.0 document
- Include all available feed formats
- Add metadata (title, owner, date)
## Performance Considerations
### Memory Management
**Streaming Generation**:
- Generate feeds in chunks
- Yield results incrementally
- Avoid loading all notes at once
- Use generators throughout
**Cache Sizing**:
- Monitor memory usage
- Implement cache eviction
- Configurable cache limits
### Database Optimization
**Query Optimization**:
- Index on published status
- Index on created_at for ordering
- Limit fetched columns
- Use prepared statements
**Connection Pooling**:
- Reuse database connections
- Monitor pool usage
- Track connection wait times
### HTTP Optimization
**Compression**:
- gzip for text formats (RSS, ATOM)
- Already compact JSON Feed
- Configurable compression level
**Caching Headers**:
- ETag based on content hash
- Last-Modified from latest note
- Cache-Control with max-age
## Security Considerations
### Input Validation
- Validate Accept headers
- Sanitize format parameters
- Limit feed size
- Rate limit feed endpoints
### Content Security
- Escape XML entities properly
- Valid JSON encoding
- No script injection in feeds
- CORS headers for JSON feeds
### Resource Protection
- Rate limiting per IP
- Maximum feed items limit
- Timeout for generation
- Circuit breaker for database
## Configuration
### Feed Settings
```ini
# Feed generation
STARPUNK_FEED_DEFAULT_LIMIT = 50
STARPUNK_FEED_MAX_LIMIT = 500
STARPUNK_FEED_CACHE_TTL = 300 # seconds
STARPUNK_FEED_CACHE_SIZE = 100 # entries
# Format support
STARPUNK_FEED_RSS_ENABLED = true
STARPUNK_FEED_ATOM_ENABLED = true
STARPUNK_FEED_JSON_ENABLED = true
# Performance
STARPUNK_FEED_STREAMING = true
STARPUNK_FEED_COMPRESSION = true
STARPUNK_FEED_COMPRESSION_LEVEL = 6
```
### Monitoring Settings
```ini
# Metrics collection
STARPUNK_METRICS_FEED_TIMING = true
STARPUNK_METRICS_CACHE_STATS = true
STARPUNK_METRICS_FORMAT_USAGE = true
# Dashboard
STARPUNK_SYNDICATION_DASHBOARD = true
STARPUNK_SYNDICATION_STATS_RETENTION = 7 # days
```
## Testing Strategy
### Unit Tests
1. **Content Negotiation**
- Accept header parsing
- Format scoring algorithm
- Default behavior
2. **Feed Generators**
- Valid output for each format
- Streaming behavior
- Error handling
3. **Cache System**
- LRU eviction
- TTL expiration
- Invalidation logic
### Integration Tests
1. **End-to-End Feeds**
- Request with various Accept headers
- Verify correct format returned
- Check caching behavior
2. **Performance Tests**
- Measure generation time
- Monitor memory usage
- Verify streaming works
3. **Compliance Tests**
- Validate against feed specs
- Test with popular feed readers
- Check encoding edge cases
## Migration Path
### From v1.1.1 to v1.1.2
1. **Database**: No schema changes required
2. **Configuration**: New feed options (backward compatible)
3. **URLs**: Existing `/feed.xml` continues to work
4. **Cache**: New cache system, no migration needed
### Rollback Plan
1. Keep v1.1.1 database backup
2. Configuration rollback script
3. Clear feed cache
4. Revert to previous version
## Future Considerations
### v1.2.0 Possibilities
1. **WebSub Support**: Real-time feed updates
2. **Custom Feeds**: User-defined filters
3. **Feed Analytics**: Detailed reader statistics
4. **Podcast Support**: Audio enclosures
5. **ActivityPub**: Fediverse integration
### Technical Debt
1. Refactor feed module into package
2. Extract cache to separate service
3. Implement feed preview UI
4. Add feed validation endpoint
## Success Metrics
1. **Performance**
- Feed generation <100ms for 50 items
- Cache hit rate >80%
- Memory usage <10MB for feeds
2. **Compatibility**
- Works with 10 major feed readers
- Passes all format validators
- Zero regression on existing RSS
3. **Usage**
- 20% adoption of non-RSS formats
- Reduced server load via caching
- Positive user feedback
## Risk Mitigation
### Performance Risks
**Risk**: Feed generation slows down site
**Mitigation**:
- Streaming generation
- Aggressive caching
- Request timeouts
- Rate limiting
### Compatibility Risks
**Risk**: Feed readers reject new formats
**Mitigation**:
- Extensive testing with readers
- Strict spec compliance
- Format validation
- Fallback to RSS
### Operational Risks
**Risk**: Cache grows unbounded
**Mitigation**:
- LRU eviction
- Size limits
- Memory monitoring
- Auto-cleanup
## Conclusion
StarPunk v1.1.2 "Syndicate" creates a robust, standards-compliant syndication platform while completing the observability foundation started in v1.1.1. The architecture prioritizes performance through streaming and caching, compatibility through strict standards adherence, and maintainability through clean component separation.
The design balances feature richness with StarPunk's core philosophy of simplicity, adding only what's necessary to serve content to the widest possible audience while maintaining operational visibility.