fix: Resolve v1.1.2-rc.1 production issues - Static files and metrics

This release candidate fixes two critical production issues discovered in v1.1.2-rc.1:

1. CRITICAL: Static files returning 500 errors
   - HTTP monitoring middleware was accessing response.data on streaming responses
   - Fixed by checking direct_passthrough flag before accessing response data
   - Static files (CSS, JS, images) now load correctly
   - File: starpunk/monitoring/http.py

2. HIGH: Database metrics showing zero
   - Configuration key mismatch: config set METRICS_SAMPLING_RATE (singular),
     buffer read METRICS_SAMPLING_RATES (plural)
   - Fixed by standardizing on singular key name
   - Modified MetricsBuffer to accept both float and dict for flexibility
   - Changed default sampling from 10% to 100% for better visibility
   - Files: starpunk/monitoring/metrics.py, starpunk/config.py

Version: 1.1.2-rc.2

Documentation:
- 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
- Implementation report: docs/reports/2025-11-28-v1.1.2-rc.2-fixes.md

Testing: All monitoring tests pass (28/28)

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

Co-Authored-By: Claude <noreply@anthropic.com>

starpunk/monitoring/http.py

@@ -72,7 +72,15 @@ def setup_http_metrics(app: Flask) -> None:
 
         # Get response size
         response_size = 0
-        if response.data:
+
+        # 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

starpunk/monitoring/metrics.py

@@ -26,7 +26,7 @@ from collections import deque
 from dataclasses import dataclass, field, asdict
 from datetime import datetime
 from threading import Lock
-from typing import Any, Deque, Dict, List, Literal, Optional
+from typing import Any, Deque, Dict, List, Literal, Optional, Union
 
 # Operation types for categorizing metrics
 OperationType = Literal["database", "http", "render"]
@@ -75,7 +75,7 @@ class MetricsBuffer:
 
     Per developer Q&A Q12:
     - Configurable sampling rates per operation type
-    - Default 10% sampling
+    - Default 100% sampling (suitable for low-traffic sites)
     - Slow queries always logged regardless of sampling
 
     Example:
@@ -87,27 +87,42 @@ class MetricsBuffer:
     def __init__(
         self,
         max_size: int = 1000,
-        sampling_rates: Optional[Dict[OperationType, float]] = None
+        sampling_rates: Optional[Union[Dict[OperationType, float], float]] = None
     ):
         """
         Initialize metrics buffer
 
         Args:
             max_size: Maximum number of metrics to store
-            sampling_rates: Dict mapping operation type to sampling rate (0.0-1.0)
-                           Default: {'database': 0.1, 'http': 0.1, 'render': 0.1}
+            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()
 
-        # Default sampling rates (10% for all operation types)
-        self._sampling_rates = sampling_rates or {
-            "database": 0.1,
-            "http": 0.1,
-            "render": 0.1,
-        }
+        # 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
 
     def record(
         self,
@@ -334,15 +349,15 @@ def get_buffer() -> MetricsBuffer:
                 try:
                     from flask import current_app
                     max_size = current_app.config.get('METRICS_BUFFER_SIZE', 1000)
-                    sampling_rates = current_app.config.get('METRICS_SAMPLING_RATES', None)
+                    sampling_rate = current_app.config.get('METRICS_SAMPLING_RATE', 1.0)
                 except (ImportError, RuntimeError):
                     # Flask not available or no app context
                     max_size = 1000
-                    sampling_rates = None
+                    sampling_rate = 1.0  # Default to 100%
 
                 _metrics_buffer = MetricsBuffer(
                     max_size=max_size,
-                    sampling_rates=sampling_rates
+                    sampling_rates=sampling_rate
                 )
 
     return _metrics_buffer