Monitoring & Maintenance - Intelligent Operations

Design a monitoring strategy for: [APPLICATION_TYPE]

System characteristics:
- Architecture: [SYSTEM_ARCHITECTURE]
- Traffic patterns: [TRAFFIC_DESCRIPTION]
- Critical dependencies: [DEPENDENCY_LIST]
- SLA requirements: [SLA_TARGETS]

Create monitoring plan including:
1. Key Performance Indicators (KPIs) to track
2. Alert thresholds and escalation procedures
3. Dashboard design and visualization strategy
4. Log aggregation and analysis approach
5. Synthetic monitoring scenarios
6. Capacity planning metrics
7. Security monitoring requirements
8. Business impact tracking

Include specific tools, configurations, and implementation steps.

Create anomaly detection system for: [SYSTEM_COMPONENT]

Historical data patterns: [DATA_PATTERNS]
Normal operating parameters: [BASELINE_METRICS]
Business context: [BUSINESS_REQUIREMENTS]

Design detection system including:
1. Statistical anomaly detection algorithms
2. Machine learning model recommendations
3. Threshold-based alerting rules
4. Seasonal pattern recognition
5. Multi-dimensional correlation analysis
6. False positive reduction strategies
7. Alert prioritization and routing
8. Automated response triggers

Provide implementation using [MONITORING_PLATFORM] with configuration examples.

Create automated incident response for: [INCIDENT_TYPE]

System context: [SYSTEM_DESCRIPTION]
Impact scenarios: [POTENTIAL_IMPACTS]
Recovery procedures: [MANUAL_PROCEDURES]

Generate automation including:
1. Incident detection and classification
2. Automated diagnostic procedures
3. Self-healing remediation steps
4. Escalation workflows and notifications
5. Rollback and recovery mechanisms
6. Post-incident analysis automation
7. Documentation and reporting
8. Learning and improvement loops

Include runbooks, scripts, and monitoring configurations.

# Prometheus monitoring rules
groups:
  - name: application.rules
    rules:
      # High error rate alert
      - alert: HighErrorRate
        expr: rate(http_requests_total{status=~"5.."}[5m]) > 0.1
        for: 2m
        labels:
          severity: critical
        annotations:
          summary: "High error rate detected"
          description: "Error rate is {{ $value }} errors per second"

      # High response time alert
      - alert: HighResponseTime
        expr: histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m])) > 0.5
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High response time detected"
          description: "95th percentile response time is {{ $value }}s"

      # Memory usage alert
      - alert: HighMemoryUsage
        expr: (container_memory_usage_bytes / container_spec_memory_limit_bytes) > 0.8
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: "High memory usage"
          description: "Memory usage is {{ $value | humanizePercentage }}"

      # Database connection pool alert
      - alert: DatabaseConnectionPoolExhaustion
        expr: db_connection_pool_active / db_connection_pool_max > 0.9
        for: 2m
        labels:
          severity: critical
        annotations:
          summary: "Database connection pool near exhaustion"
          description: "{{ $value | humanizePercentage }} of connections in use"

# Fluentd configuration for log processing
<source>
  @type tail
  path /var/log/app/*.log
  pos_file /var/log/fluentd/app.log.pos
  tag app.logs
  format json
  time_key timestamp
  time_format %Y-%m-%dT%H:%M:%S.%L%z
</source>

<filter app.logs>
  @type parser
  key_name message
  reserve_data true
  <parse>
    @type grok
    grok_pattern %{TIMESTAMP_ISO8601:timestamp} %{LOGLEVEL:level} %{DATA:logger} - %{GREEDYDATA:message}
  </parse>
</filter>

<filter app.logs>
  @type grep
  <regexp>
    key level
    pattern ^(ERROR|FATAL)$
  </regexp>
</filter>

<match app.logs>
  @type elasticsearch
  host elasticsearch.monitoring.svc.cluster.local
  port 9200
  index_name app-logs
  type_name _doc

  <buffer>
    @type file
    path /var/log/fluentd/app-logs
    flush_mode interval
    flush_interval 10s
    chunk_limit_size 10MB
  </buffer>
</match>

Design predictive maintenance for: [SYSTEM_COMPONENT]

Historical performance data: [PERFORMANCE_HISTORY]
Failure patterns: [KNOWN_FAILURE_MODES]
Maintenance windows: [MAINTENANCE_CONSTRAINTS]

Create predictive system including:
1. Performance degradation detection models
2. Failure prediction algorithms
3. Maintenance scheduling optimization
4. Resource utilization forecasting
5. Capacity planning recommendations
6. Cost-benefit analysis for interventions
7. Automated maintenance task triggers
8. Performance trend analysis and reporting

Include machine learning model specifications and training data requirements.

Analyze system performance for optimization opportunities:

Current metrics: [PERFORMANCE_METRICS]
System architecture: [ARCHITECTURE_DETAILS]
Usage patterns: [USAGE_ANALYTICS]
Resource constraints: [RESOURCE_LIMITS]

Provide optimization recommendations for:
1. Database query performance
2. Caching strategy improvements
3. Resource allocation optimization
4. Code-level performance enhancements
5. Infrastructure scaling decisions
6. Network latency reduction
7. Memory usage optimization
8. CPU utilization improvements

Include specific implementation steps and expected impact measurements.

Create security monitoring for: [APPLICATION_TYPE]

Security requirements: [SECURITY_STANDARDS]
Threat landscape: [THREAT_ASSESSMENT]
Compliance needs: [COMPLIANCE_REQUIREMENTS]

Design security monitoring including:
1. Intrusion detection and prevention
2. Anomalous behavior identification
3. Authentication and authorization monitoring
4. Data access pattern analysis
5. Vulnerability scanning automation
6. Security incident correlation
7. Threat intelligence integration
8. Compliance reporting automation

Include SIEM integration and incident response workflows.

# AI-powered health check system
import asyncio
import logging
from typing import Dict, List, Any
from dataclasses import dataclass
from enum import Enum

class HealthStatus(Enum):
    HEALTHY = "healthy"
    WARNING = "warning"
    CRITICAL = "critical"
    UNKNOWN = "unknown"

@dataclass
class HealthCheck:
    name: str
    status: HealthStatus
    message: str
    metrics: Dict[str, Any]
    timestamp: float

class HealthMonitor:
    def __init__(self):
        self.checks = []
        self.thresholds = {}
        self.ai_analyzer = AIAnomalyDetector()

    async def run_health_checks(self) -> List[HealthCheck]:
        """Run all registered health checks"""
        results = []

        # Database connectivity
        db_health = await self.check_database_health()
        results.append(db_health)

        # API response times
        api_health = await self.check_api_performance()
        results.append(api_health)

        # Memory and CPU usage
        resource_health = await self.check_resource_usage()
        results.append(resource_health)

        # External dependencies
        deps_health = await self.check_dependencies()
        results.append(deps_health)

        # AI-powered anomaly detection
        anomaly_health = await self.ai_analyzer.detect_anomalies(results)
        results.append(anomaly_health)

        return results

    async def check_database_health(self) -> HealthCheck:
        """Check database connectivity and performance"""
        try:
            start_time = time.time()
            # Perform database health check
            connection_time = time.time() - start_time

            if connection_time > 1.0:
                return HealthCheck(
                    name="database",
                    status=HealthStatus.WARNING,
                    message=f"Slow database response: {connection_time:.2f}s",
                    metrics={"connection_time": connection_time},
                    timestamp=time.time()
                )

            return HealthCheck(
                name="database",
                status=HealthStatus.HEALTHY,
                message="Database connection healthy",
                metrics={"connection_time": connection_time},
                timestamp=time.time()
            )

        except Exception as e:
            return HealthCheck(
                name="database",
                status=HealthStatus.CRITICAL,
                message=f"Database connection failed: {str(e)}",
                metrics={},
                timestamp=time.time()
            )

class SelfHealingSystem:
    def __init__(self):
        self.healing_strategies = {
            "high_memory": self.restart_service,
            "database_timeout": self.reset_connection_pool,
            "high_error_rate": self.enable_circuit_breaker,
            "disk_space_low": self.cleanup_logs
        }

    async def analyze_and_heal(self, health_results: List[HealthCheck]):
        """Analyze health results and apply healing strategies"""
        for result in health_results:
            if result.status in [HealthStatus.WARNING, HealthStatus.CRITICAL]:
                await self.apply_healing_strategy(result)

    async def apply_healing_strategy(self, health_check: HealthCheck):
        """Apply appropriate healing strategy"""
        strategy_key = self.determine_strategy(health_check)

        if strategy_key in self.healing_strategies:
            logging.info(f"Applying healing strategy: {strategy_key}")
            await self.healing_strategies[strategy_key](health_check)

            # Verify healing effectiveness
            await self.verify_healing(health_check)

    async def restart_service(self, health_check: HealthCheck):
        """Restart service with high memory usage"""
        # Implementation for service restart
        pass

    async def reset_connection_pool(self, health_check: HealthCheck):
        """Reset database connection pool"""
        # Implementation for connection pool reset
        pass

{
  "dashboard": {
    "title": "Application Health Overview",
    "tags": ["monitoring", "health"],
    "panels": [
      {
        "title": "Request Rate",
        "type": "stat",
        "targets": [
          {
            "expr": "sum(rate(http_requests_total[5m]))",
            "legendFormat": "Requests/sec"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "thresholds": {
              "steps": [
                {"color": "green", "value": null},
                {"color": "yellow", "value": 100},
                {"color": "red", "value": 1000}
              ]
            }
          }
        }
      },
      {
        "title": "Error Rate",
        "type": "stat",
        "targets": [
          {
            "expr": "sum(rate(http_requests_total{status=~\"5..\"}[5m])) / sum(rate(http_requests_total[5m]))",
            "legendFormat": "Error Rate"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "unit": "percentunit",
            "thresholds": {
              "steps": [
                {"color": "green", "value": null},
                {"color": "yellow", "value": 0.01},
                {"color": "red", "value": 0.05}
              ]
            }
          }
        }
      },
      {
        "title": "Response Time Distribution",
        "type": "heatmap",
        "targets": [
          {
            "expr": "sum(rate(http_request_duration_seconds_bucket[5m])) by (le)",
            "format": "heatmap",
            "legendFormat": "{{le}}"
          }
        ]
      }
    ]
  }
}

#!/bin/bash
# AI-optimized backup strategy

BACKUP_DIR="/backups"
RETENTION_DAYS=30
DB_NAME="production_db"

# Intelligent backup scheduling based on usage patterns
get_optimal_backup_time() {
    # AI analysis of usage patterns to determine best backup time
    python3 /scripts/analyze_usage_patterns.py --output-time
}

# Perform backup with compression and encryption
perform_backup() {
    local timestamp=$(date +%Y%m%d_%H%M%S)
    local backup_file="${BACKUP_DIR}/${DB_NAME}_${timestamp}.sql.gz.enc"

    echo "Starting backup at $(date)"

    # Create backup with progress monitoring
    pg_dump $DB_NAME | \
    gzip | \
    openssl enc -aes-256-cbc -salt -in - -out "$backup_file" -k "$BACKUP_KEY"

    if [ $? -eq 0 ]; then
        echo "Backup completed successfully: $backup_file"

        # Verify backup integrity
        verify_backup "$backup_file"

        # Update backup metadata
        update_backup_metadata "$backup_file"

        # Clean old backups
        cleanup_old_backups
    else
        echo "Backup failed!" >&2
        send_alert "Backup failed for $DB_NAME"
        exit 1
    fi
}

# AI-powered backup verification
verify_backup() {
    local backup_file="$1"

    # Decrypt and test restore to temporary database
    openssl enc -aes-256-cbc -d -in "$backup_file" -k "$BACKUP_KEY" | \
    gunzip | \
    psql test_restore_db

    if [ $? -eq 0 ]; then
        echo "Backup verification successful"
        dropdb test_restore_db
    else
        echo "Backup verification failed!" >&2
        send_alert "Backup verification failed for $backup_file"
    fi
}