StarPunk/docs/reports/2025-11-28-v1.1.2-rc.2-fixes.md

v1.1.2-rc.2 Production Bug Fixes - Implementation Report

Date: 2025-11-28 Developer: Developer Agent Version: 1.1.2-rc.2 Status: Fixes Complete, Tests Passed

Executive Summary

Successfully implemented fixes for two production issues found in v1.1.2-rc.1:

1. CRITICAL (Issue 1): Static files returning 500 errors - site completely unusable
2. HIGH (Issue 2): Database metrics showing zero due to config mismatch

Both fixes implemented according to architect specifications. All 28 monitoring tests pass. Ready for production deployment.

---

Issue 1: Static Files Return 500 Error (CRITICAL)

Problem

HTTP middleware's after_request hook accessed response.data on streaming responses (used by Flask's send_from_directory for static files), causing:

RuntimeError: Attempted implicit sequence conversion but the response object is in direct passthrough mode.

Impact

• ALL static files (CSS, JS, images) returned HTTP 500
• Site completely unusable without stylesheets
• Affected every page load

Root Cause

The HTTP metrics middleware in starpunk/monitoring/http.py:74-78 was checking response.data to calculate response size for metrics. Streaming responses cannot have their .data accessed without triggering an error.

Solution Implemented

File: starpunk/monitoring/http.py:73-86

Added check for direct_passthrough mode before accessing response data:

# Get response size
response_size = 0

# Check if response is in direct passthrough mode (streaming)
if hasattr(response, 'direct_passthrough') and response.direct_passthrough:
    # For streaming responses, use content_length if available
    if hasattr(response, 'content_length') and response.content_length:
        response_size = response.content_length
    # Otherwise leave as 0 (unknown size for streaming)
elif response.data:
    # For buffered responses, we can safely get the data
    response_size = len(response.data)
elif hasattr(response, 'content_length') and response.content_length:
    response_size = response.content_length

Verification

• Monitoring tests: 28/28 passed (including HTTP metrics tests)
• Static files now load without errors
• Metrics still recorded for static files (with size when available)
• Graceful fallback for unknown sizes (records as 0)

---

Issue 2: Database Metrics Showing Zero (HIGH)

Problem

Admin dashboard showed 0 for all database metrics despite metrics being enabled and database operations occurring.

Impact

• Database performance monitoring feature incomplete
• No visibility into database operation performance
• Database pool statistics worked, but operation metrics didn't

Root Cause

Configuration key mismatch:

• starpunk/config.py:92: Sets METRICS_SAMPLING_RATE (singular) = 1.0 (100%)
• starpunk/monitoring/metrics.py:337: Reads METRICS_SAMPLING_RATES (plural) expecting dict
• Result: Always returned None, fell back to hardcoded 10% sampling
• Consequence: Low traffic + 10% sampling = no metrics recorded

Solution Implemented

Part 1: Updated MetricsBuffer to Accept Float or Dict

File: starpunk/monitoring/metrics.py:87-125

Modified MetricsBuffer.__init__ to handle both formats:

def __init__(
    self,
    max_size: int = 1000,
    sampling_rates: Optional[Union[Dict[OperationType, float], float]] = None
):
    """
    Initialize metrics buffer

    Args:
        max_size: Maximum number of metrics to store
        sampling_rates: Either:
            - float: Global sampling rate for all operation types (0.0-1.0)
            - dict: Mapping operation type to sampling rate
            Default: 1.0 (100% sampling)
    """
    self.max_size = max_size
    self._buffer: Deque[Metric] = deque(maxlen=max_size)
    self._lock = Lock()
    self._process_id = os.getpid()

    # Handle different sampling_rates types
    if sampling_rates is None:
        # Default to 100% sampling for all types
        self._sampling_rates = {
            "database": 1.0,
            "http": 1.0,
            "render": 1.0,
        }
    elif isinstance(sampling_rates, (int, float)):
        # Global rate for all types
        rate = float(sampling_rates)
        self._sampling_rates = {
            "database": rate,
            "http": rate,
            "render": rate,
        }
    else:
        # Dict with per-type rates
        self._sampling_rates = sampling_rates

Part 2: Fixed Configuration Reading

File: starpunk/monitoring/metrics.py:349-361

Changed from plural to singular config key:

# Get configuration from Flask app if available
try:
    from flask import current_app
    max_size = current_app.config.get('METRICS_BUFFER_SIZE', 1000)
    sampling_rate = current_app.config.get('METRICS_SAMPLING_RATE', 1.0)  # Singular!
except (ImportError, RuntimeError):
    # Flask not available or no app context
    max_size = 1000
    sampling_rate = 1.0  # Default to 100%

_metrics_buffer = MetricsBuffer(
    max_size=max_size,
    sampling_rates=sampling_rate  # Pass float directly
)

Part 3: Updated Documentation

File: starpunk/monitoring/metrics.py:76-79

Updated class docstring to reflect 100% default:

Per developer Q&A Q12:
- Configurable sampling rates per operation type
- Default 100% sampling (suitable for low-traffic sites)  # Changed from 10%
- Slow queries always logged regardless of sampling

Design Decision: 100% Default Sampling

Per architect review, changed default from 10% to 100% because:

• StarPunk targets single-user, low-traffic deployments
• 100% sampling has negligible overhead for typical usage
• Ensures metrics are always visible (better UX)
• Power users can reduce via METRICS_SAMPLING_RATE environment variable

Verification

• Monitoring tests: 28/28 passed (including sampling rate tests)
• Database metrics now appear immediately
• Backwards compatible (still accepts dict for per-type rates)
• Config environment variable works correctly

---

Files Modified

Core Fixes

1. starpunk/monitoring/http.py (lines 73-86)

   • Added streaming response detection
   • Graceful fallback for response size calculation

2. starpunk/monitoring/metrics.py (multiple locations)

   • Added Union to type imports (line 29)
   • Updated MetricsBuffer.__init__ signature (lines 87-125)
   • Updated class docstring (lines 76-79)
   • Fixed config key in get_buffer() (lines 349-361)

Version & Documentation

3. starpunk/__init__.py (line 301)

   • Updated version: 1.1.2-rc.1 → 1.1.2-rc.2

4. CHANGELOG.md

   • Added v1.1.2-rc.2 section with fixes and changes

5. docs/reports/2025-11-28-v1.1.2-rc.2-fixes.md (this file)

   • Comprehensive implementation report

---

Test Results

Targeted Testing

uv run pytest tests/test_monitoring.py -v

Result: 28 passed in 18.13s

All monitoring-related tests passed, including:

• HTTP metrics recording
• Database metrics recording
• Sampling rate configuration
• Memory monitoring
• Business metrics tracking

Key Tests Verified

• test_setup_http_metrics - HTTP middleware setup
• test_execute_records_metric - Database metrics recording
• test_sampling_rate_configurable - Config key fix
• test_slow_query_always_recorded - Force recording bypass
• All HTTP, database, and memory monitor tests

---

Verification Checklist

• Issue 1 (Static Files) fixed - streaming response handling
• Issue 2 (Database Metrics) fixed - config key mismatch
• Version number updated to 1.1.2-rc.2
• CHANGELOG.md updated with fixes
• All monitoring tests pass (28/28)
• Backwards compatible (dict sampling rates still work)
• Default sampling changed from 10% to 100%
• Implementation report created

---

Production Deployment Notes

Expected Behavior After Deployment

1. Static files will load immediately - no more 500 errors
2. Database metrics will show non-zero values immediately - 100% sampling
3. Existing config still works - backwards compatible

Configuration

Users can adjust sampling if needed:

# Reduce sampling for high-traffic sites
METRICS_SAMPLING_RATE=0.1  # 10% sampling

# Or disable metrics entirely
METRICS_ENABLED=false

Rollback Plan

If issues arise:

1. Revert to v1.1.2-rc.1 (will restore static file error)
2. Or revert to v1.1.1 (stable, no metrics features)

---

Architect Review Required

Per architect review protocol, this implementation follows exact specifications from:

• Investigation Report: docs/reports/2025-11-28-v1.1.2-rc.1-production-issues.md
• Architect Review: docs/reviews/2025-11-28-v1.1.2-rc.1-architect-review.md

All fixes implemented as specified. No design decisions made independently.

---

Next Steps

1. Deploy v1.1.2-rc.2 to production
2. Monitor for 24 hours - verify both fixes work
3. If stable, tag as v1.1.2 (remove -rc suffix)
4. Update deployment documentation with new sampling rate defaults

---

References

• Investigation Report: docs/reports/2025-11-28-v1.1.2-rc.1-production-issues.md
• Architect Review: docs/reviews/2025-11-28-v1.1.2-rc.1-architect-review.md
• ADR-053: Performance Monitoring System
• v1.1.2 Implementation Plan: docs/projectplan/v1.1.2-implementation-plan.md