import datetime import time import random import logging import os class PCDROrchestrator: """ The PCDR (Probe, Comprehend, Document, Repeat) Orchestrator autonomously manages daily operational cycles. """ def __init__(self, log_dir="pcdr_logs", log_file_prefix="pcdr_report"): """ Initializes the PCDR Orchestrator. Args: log_dir (str): Directory to store documentation logs. log_file_prefix (str): Prefix for documentation log filenames. """ self.log_dir = log_dir self.log_file_prefix = log_file_prefix self._setup_logging() self.cycle_count = 0 self.known_targets = { "service_web_api": {"type": "API", "endpoint": "https://api.example.com"}, "database_main": {"type": "DB", "endpoint": "tcp://db.example.com:5432"}, "message_queue_prod": {"type": "Queue", "endpoint": "amqp://mq.example.com"}, } # Tracks all targets ever discovered to simulate growth self.discovered_targets_history = set(self.known_targets.keys()) self.logger.info("PCDR Orchestrator initialized.") def _setup_logging(self): """Sets up the logger for documentation.""" os.makedirs(self.log_dir, exist_ok=True) log_filename = f"{self.log_file_prefix}_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.log" log_filepath = os.path.join(self.log_dir, log_filename) self.logger = logging.getLogger("PCDROrchestrator") self.logger.setLevel(logging.INFO) # Prevent adding multiple handlers if called more than once if not self.logger.handlers: formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') # Console handler for real-time feedback ch = logging.StreamHandler() ch.setLevel(logging.INFO) ch.setFormatter(formatter) self.logger.addHandler(ch) # File handler for permanent documentation fh = logging.FileHandler(log_filepath) fh.setLevel(logging.INFO) fh.setFormatter(formatter) self.logger.addHandler(fh) self.logger.info(f"Documentation will be saved to: {log_filepath}") def _discover_targets(self): """ Simulates auto-discovery of targets. Periodically "discovers" new mock targets. """ current_targets = list(self.known_targets.keys()) # Simulate discovering a new target every 3 cycles if self.cycle_count % 3 == 0 and self.cycle_count > 0: new_target_name = f"service_new_discovery_{self.cycle_count // 3}" if new_target_name not in self.discovered_targets_history: new_target_data = { "type": random.choice(["API", "DB", "Cache", "Microservice"]), "endpoint": f"http://{new_target_name}.example.com:8080" } self.known_targets[new_target_name] = new_target_data self.discovered_targets_history.add(new_target_name) self.logger.info(f"Discovered new target: {new_target_name} ({new_target_data['type']})") current_targets.append(new_target_name) self.logger.debug(f"Targets discovered for probing: {current_targets}") return current_targets def _probe_target(self, target_name): """ Simulates probing a single target. Returns mock status and metrics. """ target_info = self.known_targets.get(target_name, {"type": "Unknown", "endpoint": "N/A"}) status_options = ["healthy", "degraded", "unreachable"] # Make 'unreachable' less common status = random.choices(status_options, weights=[0.8, 0.15, 0.05], k=1)[0] mock_data = { "latency_ms": random.randint(10, 500) if status != "unreachable" else None, "error_rate_percent": round(random.uniform(0, 5), 2) if status != "unreachable" else None, "cpu_util_percent": random.randint(10, 90) if target_info["type"] in ["API", "Microservice"] and status != "unreachable" else None, "db_connections": random.randint(5, 100) if target_info["type"] == "DB" and status != "unreachable" else None, } probe_result = { "target": target_name, "type": target_info["type"], "endpoint": target_info["endpoint"], "status": status, "timestamp": datetime.datetime.now().isoformat(), "metrics": mock_data } self.logger.debug(f"Probed {target_name}: {status}") return probe_result def _comprehend_findings(self, probe_results): """ Simulates comprehending the probe results to identify issues and insights. """ summary = { "total_targets_probed": len(probe_results), "healthy_count": 0, "degraded_count": 0, "unreachable_count": 0, "average_latency_ms": 0, "average_error_rate_percent": 0, } anomalies = [] recommendations = [] total_latency = 0 total_error_rate = 0 healthy_probes_count = 0 for result in probe_results: summary[f"{result['status']}_count"] += 1 if result["status"] == "healthy": healthy_probes_count += 1 if result["metrics"]["latency_ms"] is not None: total_latency += result["metrics"]["latency_ms"] if result["metrics"]["error_rate_percent"] is not None: total_error_rate += result["metrics"]["error_rate_percent"] elif result["status"] == "degraded": anomalies.append(f"Target '{result['target']}' is DEGRADED. Metrics: {result['metrics']}") recommendations.append(f"Investigate '{result['target']}' for performance issues.") elif result["status"] == "unreachable": anomalies.append(f"Target '{result['target']}' is UNREACHABLE. Immediate attention required.") recommendations.append(f"Urgent: Check network/service status for '{result['target']}'.") if result["metrics"]["error_rate_percent"] is not None and result["metrics"]["error_rate_percent"] > 2: anomalies.append(f"Target '{result['target']}' has high error rate: {result['metrics']['error_rate_percent']}%") recommendations.append(f"Review recent deployments or traffic patterns for '{result['target']}'.") if healthy_probes_count > 0: summary["average_latency_ms"] = round(total_latency / healthy_probes_count, 2) summary["average_error_rate_percent"] = round(total_error_rate / healthy_probes_count, 2) comprehension_output = { "cycle": self.cycle_count, "timestamp": datetime.datetime.now().isoformat(), "summary": summary, "anomalies_detected": anomalies if anomalies else ["No significant anomalies detected."], "recommendations": recommendations if recommendations else ["System operating normally. No specific recommendations."], "raw_probe_results_count": len(probe_results) # For brevity, not including all raw results in final doc } self.logger.debug(f"Comprehension complete for cycle {self.cycle_count}") return comprehension_output def _document_findings(self, comprehension_results): """ Documents the comprehension results using the configured logger. This fulfills the auto-documentation requirement. """ self.logger.info("\n--- PCDR Cycle Documentation ---") self.logger.info(f"Cycle: {comprehension_results['cycle']}") self.logger.info(f"Timestamp: {comprehension_results['timestamp']}") self.logger.info("Summary:") for key, value in comprehension_results["summary"].items(): self.logger.info(f" {key}: {value}") self.logger.info("Anomalies Detected:") for anomaly in comprehension_results["anomalies_detected"]: self.logger.info(f" - {anomaly}") self.logger.info("Recommendations:") for recommendation in comprehension_results["recommendations"]: self.logger.info(f" - {recommendation}") self.logger.info("--- End of Documentation ---\n") def run_cycle(self): """ Executes a single full PCDR cycle: Probe, Comprehend, Document. """ self.cycle_count += 1 self.logger.info(f"\n--- Starting PCDR Cycle {self.cycle_count} ---") # 1. Probe: Auto-discover targets and probe them targets_to_probe = self._discover_targets() all_probe_results = [] for target in targets_to_probe: try: result = self._probe_target(target) all_probe_results.append(result) except Exception as e: self.logger.error(f"Error probing target {target}: {e}") all_probe_results.append({"target": target, "status": "error", "error": str(e)}) # 2. Comprehend: Analyze the findings comprehension = self._comprehend_findings(all_probe_results) # 3. Document: Save the insights self._document_findings(comprehension) self.logger.info(f"--- PCDR Cycle {self.cycle_count} Completed ---\n") return comprehension # Return for potential external use/testing def start(self, num_cycles=5, interval_seconds=5): """ Starts the autonomous PCDR loop. Args: num_cycles (int): The number of cycles to run. interval_seconds (int): The delay between cycles in seconds. """ self.logger.info(f"PCDR Orchestrator starting autonomous loop for {num_cycles} cycles, with {interval_seconds}s interval.") for i in range(num_cycles): self.run_cycle() if i < num_cycles - 1: # Don't sleep after the last cycle self.logger.info(f"Waiting {interval_seconds} seconds before next cycle...") time.sleep(interval_seconds) self.logger.info("PCDR Orchestrator autonomous loop finished.") # Example of how to run this if __name__ == "__main__": # It's good practice to clear handlers if running multiple orchestrator instances # or re-running main in an interactive environment. # For a simple script execution, it's often not strictly necessary. # However, to avoid duplicate log entries if the script is run multiple times # without a full interpreter reset, this can be helpful. # for handler in logging.getLogger("PCDROrchestrator").handlers[:]: # logging.getLogger("PCDROrchestrator").removeHandler(handler) # handler.close() # Create a temporary directory for logs for demonstration temp_log_dir = "temp_pcdr_logs" if not os.path.exists(temp_log_dir): os.makedirs(temp_log_dir) orchestrator = PCDROrchestrator(log_dir=temp_log_dir) orchestrator.start(num_cycles=3, interval_seconds=2) # Run 3 cycles, 2 seconds apart # Optional: Clean up temporary log directory after demonstration # import shutil # print(f"\nReview logs in '{temp_log_dir}' before cleanup.") # # time.sleep(5) # Give time to review # shutil.rmtree(temp_log_dir) # print(f"Cleaned up '{temp_log_dir}'")