mirror of
https://github.com/DRYTRIX/TimeTracker.git
synced 2026-01-04 10:40:23 -06:00
7dd39ef55a
Improved the Release Build workflow to clearly show that PostHog and Sentry credentials are being injected from the GitHub Secret Store, providing better transparency and auditability. Changes: - Enhanced workflow step name to explicitly mention "GitHub Secrets" - Added comprehensive logging with visual separators and clear sections - Added before/after file content display showing placeholder replacement - Added secret availability verification with format validation - Added detailed error messages with step-by-step fix instructions - Enhanced release summary to highlight successful credential injection - Updated build configuration documentation with cross-references Benefits: - Developers can immediately see credentials come from GitHub Secret Store - Security teams have clear audit trail of credential injection process - Better troubleshooting with detailed error messages - Secrets remain protected with proper redaction (first 8 + last 4 chars) - Multiple validation steps ensure correct injection The workflow now outputs 50+ lines of structured logging showing: - Secret store location (Settings → Secrets and variables → Actions) - Target file being modified (app/config/analytics_defaults.py) - Verification that secrets are available - Format validation (phc_* pattern for PostHog) - Confirmation of successful placeholder replacement - Summary with redacted credential previews Workflow: .github/workflows/cd-release.yml Documentation: docs/cicd/README_BUILD_CONFIGURATION.md Fully backward compatible - no breaking changes.
480 lines
13 KiB
Python
480 lines
13 KiB
Python
"""
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PostHog Monitoring Utilities
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Track errors, performance metrics, and application health through PostHog.
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"""
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from typing import Optional, Dict, Any
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import time
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import os
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from functools import wraps
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from contextlib import contextmanager
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def is_monitoring_enabled() -> bool:
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"""Check if PostHog monitoring is enabled."""
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return bool(os.getenv("POSTHOG_API_KEY", ""))
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# ============================================================================
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# Error Tracking
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# ============================================================================
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def track_error(
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user_id: Any,
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error_type: str,
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error_message: str,
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context: Optional[Dict] = None,
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severity: str = "error"
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) -> None:
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"""
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Track application errors in PostHog.
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Args:
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user_id: User ID (or 'anonymous' for unauthenticated)
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error_type: Type of error (e.g., 'validation', 'database', 'api', '404')
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error_message: Error message (sanitized, no PII)
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context: Additional context (page, action, etc.)
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severity: Error severity ('error', 'warning', 'critical')
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Example:
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try:
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generate_report()
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except ValueError as e:
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track_error(
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current_user.id,
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"validation",
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"Invalid date range for report",
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{"report_type": "summary"}
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)
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raise
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"""
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if not is_monitoring_enabled():
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return
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from app import track_event
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from flask import request
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error_properties = {
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"error_type": error_type,
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"error_message": error_message[:500], # Limit message length
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"severity": severity,
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"timestamp": time.time(),
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}
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# Add context
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if context:
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error_properties["error_context"] = context
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# Add request context if available
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try:
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if request:
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error_properties.update({
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"$current_url": request.url,
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"$pathname": request.path,
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"method": request.method,
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})
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except Exception:
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pass
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track_event(user_id, "error_occurred", error_properties)
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def track_http_error(
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user_id: Any,
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status_code: int,
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error_message: str,
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context: Optional[Dict] = None
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) -> None:
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"""
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Track HTTP errors (404, 500, etc.).
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Args:
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user_id: User ID
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status_code: HTTP status code
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error_message: Error message
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context: Additional context
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"""
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track_error(
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user_id,
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f"http_{status_code}",
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error_message,
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{
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"status_code": status_code,
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**(context or {})
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},
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severity="warning" if status_code < 500 else "error"
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)
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def track_validation_error(
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user_id: Any,
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field: str,
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error_message: str,
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context: Optional[Dict] = None
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) -> None:
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"""
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Track form validation errors.
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Args:
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user_id: User ID
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field: Field that failed validation
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error_message: Validation error message
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context: Additional context
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"""
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track_error(
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user_id,
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"validation",
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error_message,
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{
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"field": field,
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**(context or {})
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},
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severity="warning"
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)
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# ============================================================================
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# Performance Tracking
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# ============================================================================
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def track_performance(
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user_id: Any,
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metric_name: str,
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duration_ms: float,
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context: Optional[Dict] = None,
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threshold_ms: Optional[float] = None
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) -> None:
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"""
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Track performance metrics in PostHog.
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Args:
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user_id: User ID
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metric_name: Name of the metric (e.g., 'report_generation', 'export_csv')
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duration_ms: Duration in milliseconds
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context: Additional context
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threshold_ms: If provided, also track if duration exceeded threshold
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Example:
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start = time.time()
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generate_report()
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duration = (time.time() - start) * 1000
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track_performance(
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current_user.id,
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"report_generation",
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duration,
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{"report_type": "summary", "entries_count": 100}
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)
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"""
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if not is_monitoring_enabled():
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return
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from app import track_event
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performance_properties = {
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"metric_name": metric_name,
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"duration_ms": duration_ms,
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"duration_seconds": duration_ms / 1000,
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**(context or {})
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}
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# Check if threshold exceeded
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if threshold_ms is not None:
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performance_properties["threshold_exceeded"] = duration_ms > threshold_ms
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performance_properties["threshold_ms"] = threshold_ms
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track_event(user_id, "performance_metric", performance_properties)
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@contextmanager
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def measure_performance(
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user_id: Any,
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metric_name: str,
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context: Optional[Dict] = None,
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threshold_ms: Optional[float] = None
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):
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"""
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Context manager to measure performance of a code block.
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Usage:
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with measure_performance(current_user.id, "report_generation", {"type": "summary"}):
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generate_report()
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"""
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start = time.time()
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try:
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yield
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finally:
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duration_ms = (time.time() - start) * 1000
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track_performance(user_id, metric_name, duration_ms, context, threshold_ms)
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def performance_tracked(metric_name: str, threshold_ms: Optional[float] = None):
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"""
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Decorator to track performance of a function.
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Usage:
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@performance_tracked("report_generation", threshold_ms=5000)
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def generate_report():
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# ... generate report
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pass
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"""
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def decorator(f):
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@wraps(f)
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def wrapped(*args, **kwargs):
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from flask_login import current_user
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user_id = current_user.id if current_user.is_authenticated else "anonymous"
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start = time.time()
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try:
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result = f(*args, **kwargs)
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return result
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finally:
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duration_ms = (time.time() - start) * 1000
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track_performance(
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user_id,
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metric_name,
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duration_ms,
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{"function": f.__name__},
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threshold_ms
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)
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return wrapped
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return decorator
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# ============================================================================
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# Database Performance Tracking
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# ============================================================================
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def track_query_performance(
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user_id: Any,
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query_type: str,
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duration_ms: float,
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context: Optional[Dict] = None
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) -> None:
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"""
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Track database query performance.
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Args:
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user_id: User ID
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query_type: Type of query (e.g., 'select', 'insert', 'update', 'complex_join')
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duration_ms: Query duration in milliseconds
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context: Additional context (table, filters, etc.)
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"""
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track_performance(
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user_id,
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f"db_query.{query_type}",
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duration_ms,
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{
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"query_type": query_type,
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**(context or {})
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},
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threshold_ms=1000 # Warn if query takes > 1 second
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)
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# ============================================================================
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# API Performance Tracking
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# ============================================================================
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def track_api_call(
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user_id: Any,
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endpoint: str,
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method: str,
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status_code: int,
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duration_ms: float,
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context: Optional[Dict] = None
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) -> None:
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"""
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Track API call performance and status.
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Args:
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user_id: User ID
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endpoint: API endpoint
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method: HTTP method
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status_code: Response status code
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duration_ms: Request duration in milliseconds
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context: Additional context
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"""
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from app import track_event
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track_event(user_id, "api_call", {
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"endpoint": endpoint,
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"method": method,
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"status_code": status_code,
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"duration_ms": duration_ms,
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"success": 200 <= status_code < 400,
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**(context or {})
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})
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# ============================================================================
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# Page Load Tracking
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# ============================================================================
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def track_page_load(
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user_id: Any,
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page_name: str,
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duration_ms: float,
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context: Optional[Dict] = None
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) -> None:
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"""
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Track page load performance.
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Args:
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user_id: User ID
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page_name: Name of the page
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duration_ms: Load duration in milliseconds
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context: Additional context
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"""
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track_performance(
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user_id,
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f"page_load.{page_name}",
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duration_ms,
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{
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"page_name": page_name,
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**(context or {})
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},
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threshold_ms=3000 # Warn if page takes > 3 seconds
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)
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# ============================================================================
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# Export/Report Performance
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# ============================================================================
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def track_export_performance(
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user_id: Any,
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export_type: str,
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row_count: int,
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duration_ms: float,
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file_size_bytes: Optional[int] = None
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) -> None:
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"""
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Track export generation performance.
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Args:
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user_id: User ID
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export_type: Type of export (csv, excel, pdf)
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row_count: Number of rows exported
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duration_ms: Generation duration in milliseconds
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file_size_bytes: Generated file size in bytes
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"""
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context = {
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"export_type": export_type,
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"row_count": row_count,
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"rows_per_second": int(row_count / (duration_ms / 1000)) if duration_ms > 0 else 0,
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}
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if file_size_bytes:
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context["file_size_bytes"] = file_size_bytes
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context["file_size_kb"] = round(file_size_bytes / 1024, 2)
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track_performance(
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user_id,
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f"export.{export_type}",
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duration_ms,
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context,
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threshold_ms=10000 # Warn if export takes > 10 seconds
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)
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# ============================================================================
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# Health Monitoring
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# ============================================================================
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def track_health_check(
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status: str,
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checks: Dict[str, bool],
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response_time_ms: float
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) -> None:
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"""
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Track health check results.
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Args:
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status: Overall health status ('healthy', 'degraded', 'unhealthy')
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checks: Dict of individual health checks and their results
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response_time_ms: Health check response time
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"""
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if not is_monitoring_enabled():
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return
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from app import track_event
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track_event("system", "health_check", {
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"status": status,
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"checks": checks,
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"all_healthy": all(checks.values()),
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"response_time_ms": response_time_ms,
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"failed_checks": [k for k, v in checks.items() if not v],
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})
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# ============================================================================
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# Resource Usage Tracking
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# ============================================================================
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def track_resource_usage(
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user_id: Any,
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resource_type: str,
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usage_amount: float,
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unit: str,
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context: Optional[Dict] = None
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) -> None:
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"""
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Track resource usage (memory, CPU, disk, etc.).
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Args:
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user_id: User ID or 'system'
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resource_type: Type of resource (memory, cpu, disk, api_calls)
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usage_amount: Amount used
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unit: Unit of measurement (mb, percent, count)
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context: Additional context
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"""
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from app import track_event
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track_event(user_id, "resource_usage", {
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"resource_type": resource_type,
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"usage_amount": usage_amount,
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"unit": unit,
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**(context or {})
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})
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# ============================================================================
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# Slow Operation Detection
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# ============================================================================
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def track_slow_operation(
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user_id: Any,
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operation_name: str,
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expected_ms: float,
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actual_ms: float,
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context: Optional[Dict] = None
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) -> None:
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"""
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Track operations that exceed expected duration.
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Args:
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user_id: User ID
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operation_name: Name of the operation
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expected_ms: Expected duration in milliseconds
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actual_ms: Actual duration in milliseconds
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context: Additional context
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"""
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track_error(
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user_id,
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"slow_operation",
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f"{operation_name} took {actual_ms}ms (expected {expected_ms}ms)",
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{
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"operation": operation_name,
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"expected_ms": expected_ms,
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"actual_ms": actual_ms,
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"slowdown_factor": actual_ms / expected_ms if expected_ms > 0 else 0,
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**(context or {})
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},
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severity="warning"
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)
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