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@@ -222,3 +223,20 @@ indent_sequence = 4 line_length = 80 preserve_quotes = false sequence_style = "block_style" + +[tool.vulture] +paths = ["tux", "tests"] +exclude = [ + "__pypackages__", + ".venv", + "build", + "dist", + "htmlcov", + "prisma", + "typings", +] +min_confidence = 80 +sort_by_size = true +verbose = false +ignore_decorators = ["@pytest.fixture"] +ignore_names = ["_*"] diff --git a/tux/cli/test.py b/tux/cli/test.py index aed41eb3a..e1ae48ac4 100644 --- a/tux/cli/test.py +++ b/tux/cli/test.py @@ -62,6 +62,238 @@ def test_benchmark() -> int: return run_command(["pytest", "--benchmark-only", "--benchmark-sort=mean"]) +# @command_registration_decorator(test_group, name="tasks") +# @click.option( +# "--format", +# "output_format", +# type=click.Choice(["table", "json", "summary"], case_sensitive=False), +# default="table", +# help="Output format for task analysis", +# ) +# @click.option( +# "--save", +# type=str, +# help="Save analysis to file (e.g., --save=task_analysis.json)", +# ) +# @click.option( +# "--category", +# type=str, +# help="Analyze specific task category (SCHEDULED, GATEWAY, SYSTEM, COMMAND, UNKNOWN)", +# ) +# @click.option( +# "--recommendations", +# is_flag=True, +# help="Show limit recommendations based on usage patterns", +# ) +# def analyze_tasks( +# output_format: str, +# save: str | None, +# category: str | None, +# recommendations: bool, +# ) -> int: +# """Analyze runtime task usage patterns and provide rate limiting insights. + +# This command analyzes task usage data collected from a running bot instance +# to help determine appropriate task monitoring thresholds and identify potential +# performance issues. + +# Examples: +# tux test tasks # Basic table view +# tux test tasks --format=json # JSON output +# tux test tasks --recommendations # Show limit recommendations +# tux test tasks --save=analysis.json # Save to file +# tux test tasks --category=SCHEDULED # Analyze specific category +# """ +# try: +# # Import here to avoid circular imports and allow CLI to work without bot running +# # from tux.utils.task_manager import TaskManager, TaskCategory + +# # Note: In a real implementation, you'd need to connect to a running bot instance +# # or read from a data file. For now, we'll show the structure and mock data. +# analysis_data = _get_task_analysis_data() + +# if not analysis_data: +# logger.error("No task analysis data available. Ensure the bot has been running to collect data.") +# return 1 + +# # Filter by category if specified +# if category: +# category = category.upper() +# if category not in analysis_data.get("categories", {}): +# logger.error( +# f"Category '{category}' not found. Available: {list(analysis_data.get('categories', {}).keys())}", +# ) +# return 1 +# analysis_data["categories"] = {category: analysis_data["categories"][category]} + +# # Display analysis +# if output_format == "json": +# _display_json_analysis(analysis_data) +# elif output_format == "summary": +# _display_summary_analysis(analysis_data, recommendations) +# else: +# _display_table_analysis(analysis_data, recommendations) + +# # Save to file if requested +# if save: +# _save_analysis_to_file(analysis_data, save) + +# except ImportError as e: +# logger.error(f"Failed to import task manager: {e}") +# logger.info("Task analysis requires the bot components to be available.") +# return 1 +# except Exception as e: +# logger.error(f"Task analysis failed: {e}") +# return 1 +# else: +# return 0 + + +# def _get_task_analysis_data() -> dict[str, Any]: +# """Get task analysis data from running bot or mock data for demo.""" +# # TODO: In a real implementation, this would: +# # 1. Connect to running bot via IPC/Redis/file +# # 2. Call task_manager.get_usage_analysis() +# # 3. Return real data + +# # For now, return mock data to demonstrate the structure +# return { +# "collection_period_hours": 24, +# "measurement_interval_minutes": 5, +# "categories": { +# "SCHEDULED": { +# "data_points": 288, +# "current_limit": 100, +# "peak_usage": 45, +# "average": 28.5, +# "median": 27, +# "p95": 42, +# "p99": 44, +# "recent_trend": "stable", +# "insights": ["āœ… Usage is stable"], +# }, +# "COMMAND": { +# "data_points": 288, +# "current_limit": 500, +# "peak_usage": 156, +# "average": 45.2, +# "median": 38, +# "p95": 120, +# "p99": 145, +# "recent_trend": "increasing", +# "insights": ["šŸ“ˆ Usage trending upward", "šŸ“Š High variability detected"], +# }, +# "SYSTEM": { +# "data_points": 288, +# "current_limit": 200, +# "peak_usage": 67, +# "average": 23.1, +# "median": 22, +# "p95": 45, +# "p99": 58, +# "recent_trend": "stable", +# "insights": ["āœ… Usage is stable"], +# }, +# }, +# "overall_recommendations": [ +# "šŸ” Monitor for at least 1 week before setting hard limits", +# "šŸ“Š Focus on P95/P99 values rather than peaks for limit setting", +# "āš” Set limits at 2-3x your P99 usage to handle legitimate spikes", +# "šŸ”„ Review and adjust limits monthly based on growth patterns", +# ], +# } + + +# def _display_table_analysis(analysis_data: dict[str, Any], show_recommendations: bool) -> None: +# """Display task analysis in table format.""" +# logger.info("šŸ“Š Task Usage Analysis") +# logger.info("=" * 70) +# logger.info(f"Collection Period: {analysis_data['collection_period_hours']} hours") +# logger.info(f"Measurement Interval: {analysis_data['measurement_interval_minutes']} minutes") +# logger.info("") + +# # Category analysis +# for category_name, stats in analysis_data["categories"].items(): +# logger.info(f"šŸ” {category_name}") +# logger.info("-" * 40) +# logger.info(f" Data Points: {stats['data_points']}") +# logger.info(f" Current Limit: {stats['current_limit']}") +# logger.info(f" Peak Usage: {stats['peak_usage']}") +# logger.info(f" Average: {stats['average']}") +# logger.info(f" P95/P99: {stats['p95']}/{stats['p99']}") +# logger.info(f" Recent Trend: {stats['recent_trend']}") + +# if stats.get("insights"): +# logger.info(" Insights:") +# for insight in stats["insights"]: +# logger.info(f" {insight}") + +# if show_recommendations: +# recommended_limit = int(stats["p99"] * 2.5) +# utilization = (stats["peak_usage"] / stats["current_limit"]) * 100 +# logger.info(f" Utilization: {utilization:.1f}%") +# logger.info(f" Recommended: {recommended_limit} (2.5x P99)") + +# logger.info("") + +# # Overall recommendations +# if show_recommendations: +# logger.info("šŸ’” General Recommendations:") +# for rec in analysis_data["overall_recommendations"]: +# logger.info(f" {rec}") + + +# def _display_summary_analysis(analysis_data: dict[str, Any], show_recommendations: bool) -> None: +# """Display condensed summary of task analysis.""" +# categories = analysis_data["categories"] +# total_categories = len(categories) +# stable_categories = sum(stats["recent_trend"] == "stable" for stats in categories.values()) + +# logger.info("šŸ“Š Task Analysis Summary") +# logger.info("=" * 40) +# logger.info(f"Categories Monitored: {total_categories}") +# logger.info(f"Stable Categories: {stable_categories}") +# logger.info(f"Data Quality: {analysis_data['collection_period_hours']}h collection") +# logger.info("") + +# # Quick stats +# for category_name, stats in categories.items(): +# trend_emoji = {"stable": "āœ…", "increasing": "šŸ“ˆ", "decreasing": "šŸ“‰"}.get(stats["recent_trend"], "ā“") +# utilization = (stats["peak_usage"] / stats["current_limit"]) * 100 +# logger.info( +# f"{trend_emoji} {category_name:10} | Peak: {stats['peak_usage']:3d} | P99: {stats['p99']:3d} | Util: {utilization:5.1f}%", +# ) + +# if show_recommendations: +# logger.info("\nšŸŽÆ Quick Recommendations:") +# if high_util_categories := [ +# name for name, stats in categories.items() if (stats["peak_usage"] / stats["current_limit"]) > 0.8 +# ]: +# logger.info(f" ā€¢ Consider increasing limits for: {', '.join(high_util_categories)}") +# else: +# logger.info(" ā€¢ Current limits appear adequate for observed usage") + + +# def _display_json_analysis(analysis_data: dict[str, Any]) -> None: +# """Display task analysis as formatted JSON.""" +# logger.debug(json.dumps(analysis_data, indent=2)) + + +# def _save_analysis_to_file(analysis_data: dict[str, Any], filename: str) -> None: +# """Save analysis data to a file.""" +# try: +# path = Path(filename) +# path.parent.mkdir(parents=True, exist_ok=True) + +# with path.open("w") as f: +# json.dump(analysis_data, f, indent=2) + +# except Exception as e: +# logger.error(f"Failed to save analysis: {e}") +# else: +# logger.info(f"šŸ“„ Analysis saved to: {path.resolve()}") + + @command_registration_decorator(test_group, name="coverage") @click.option( "--format", @@ -250,9 +482,9 @@ def _open_html_report() -> int: try: webbrowser.open(f"file://{html_report_path.resolve()}") - logger.info("Opening HTML coverage report in browser...") except Exception as e: logger.error(f"Failed to open HTML report: {e}") return 1 else: + logger.info("Opening HTML coverage report in browser...") return 0