Day 95 - DevOps Excellence: Best Practices for 2025#

1
apiVersion: platform.company.com/v1
2
kind: PlatformService
3
metadata:
4
  name: application-platform
5
spec:
6
  capabilities:
7
    - name: deployment
8
      description: "Automated application deployment"
9
      interfaces:
10
        - cli
11
        - api
12
        - ui
13
    - name: observability
14
      description: "Built-in monitoring and tracing"
15
      components:
16
        - prometheus
17
        - grafana
18
        - jaeger
19
    - name: security
20
      description: "Security scanning and compliance"
21
      features:
22
        - vulnerability-scanning
23
        - secret-management
24
        - policy-enforcement
25

26
  templates:
27
    - name: microservice
28
      description: "Standard microservice template"
29
      includes:
30
        - dockerfile
31
        - kubernetes-manifests
32
        - ci-pipeline
33
        - monitoring-dashboard
34

35
  golden-paths:
36
    - name: "Deploy to Production"
37
      steps:
38
        - id: code-quality
39
          tool: sonarqube
40
          required: true
41
        - id: security-scan
42
          tool: snyk
43
          required: true
44
        - id: build
45
          tool: github-actions
46
        - id: deploy
47
          tool: argocd
48
          environments: ["dev", "staging", "prod"]

1
export class PlatformAPI {
2
  async createApplication(spec: ApplicationSpec): Promise<Application> {
3
    // Validate application specification
4
    const validation = await this.validateSpec(spec);
5
    if (!validation.valid) {
6
      throw new ValidationError(validation.errors);
7
    }
8

9
    // Provision infrastructure
10
    const infrastructure = await this.provisionInfrastructure({
11
      name: spec.name,
12
      type: spec.type,
13
      resources: spec.resources,
14
      region: spec.region || "us-east-1",
15
    });
16

17
    // Setup CI/CD pipeline
18
    const pipeline = await this.createPipeline({
19
      repository: spec.repository,
20
      branch: spec.branch || "main",
21
      deploymentTargets: spec.environments,
22
      qualityGates: spec.qualityGates || this.getDefaultQualityGates(),
23
    });
24

25
    // Configure observability
26
    const observability = await this.setupObservability({
27
      applicationId: infrastructure.id,
28
      metrics: spec.metrics || this.getDefaultMetrics(),
29
      alerts: spec.alerts || this.getDefaultAlerts(),
30
      slos: spec.slos,
31
    });
32

33
    // Create developer documentation
34
    const docs = await this.generateDocumentation({
35
      application: spec,
36
      infrastructure: infrastructure,
37
      pipeline: pipeline,
38
      observability: observability,
39
    });
40

41
    return {
42
      id: infrastructure.id,
43
      name: spec.name,
44
      status: "provisioned",
45
      endpoints: infrastructure.endpoints,
46
      dashboards: observability.dashboards,
47
      documentation: docs.url,
48
    };
49
  }
50

51
  private getDefaultQualityGates(): QualityGate[] {
52
    return [
53
      {
54
        name: "code-coverage",
55
        threshold: 80,
56
        blocker: true,
57
      },
58
      {
59
        name: "security-vulnerabilities",
60
        threshold: 0,
61
        severity: "high",
62
        blocker: true,
63
      },
64
      {
65
        name: "performance-regression",
66
        threshold: 10, // 10% regression allowed
67
        blocker: false,
68
      },
69
    ];
70
  }
71
}

1
import openai
2
from prometheus_client import CollectorRegistry, Gauge
3
import pandas as pd
4
from sklearn.ensemble import IsolationForest
5
import numpy as np
6

7
class AIDevOpsAssistant:
8
    def __init__(self):
9
        self.openai_client = openai.Client()
10
        self.anomaly_detector = IsolationForest(contamination=0.1)
11
        self.metrics_history = []
12

13
    def analyze_deployment_failure(self, logs: str, metrics: dict) -> dict:
14
        """Use AI to analyze deployment failures and suggest fixes"""
15

16
        # Prepare context for AI
17
        context = f"""
18
        Deployment failed with the following logs:
19
        {logs[-2000:]}  # Last 2000 characters
20

21
        System metrics at failure time:
22
        - CPU Usage: {metrics.get('cpu_usage', 'N/A')}%
23
        - Memory Usage: {metrics.get('memory_usage', 'N/A')}%
24
        - Error Rate: {metrics.get('error_rate', 'N/A')}%
25
        - Response Time: {metrics.get('response_time', 'N/A')}ms
26

27
        Analyze the failure and provide:
28
        1. Root cause analysis
29
        2. Immediate fix suggestions
30
        3. Long-term prevention strategies
31
        """
32

33
        response = self.openai_client.chat.completions.create(
34
            model="gpt-4",
35
            messages=[
36
                {"role": "system", "content": "You are a DevOps expert analyzing deployment failures."},
37
                {"role": "user", "content": context}
38
            ],
39
            temperature=0.3
40
        )
41

42
        analysis = response.choices[0].message.content
43

44
        # Extract actionable items
45
        return {
46
            'analysis': analysis,
47
            'automated_fixes': self._extract_automated_fixes(analysis),
48
            'manual_actions': self._extract_manual_actions(analysis),
49
            'prevention_measures': self._extract_prevention_measures(analysis)
50
        }
51

52
    def predict_system_anomalies(self, metrics_df: pd.DataFrame) -> list:
53
        """Predict potential system anomalies using ML"""
54

55
        # Feature engineering
56
        features = self._engineer_features(metrics_df)
57

58
        # Detect anomalies
59
        anomalies = self.anomaly_detector.fit_predict(features)
60

61
        # Get anomalous points
62
        anomaly_indices = np.where(anomalies == -1)[0]
63

64
        predictions = []
65
        for idx in anomaly_indices:
66
            timestamp = metrics_df.iloc[idx]['timestamp']
67
            metrics = metrics_df.iloc[idx].to_dict()
68

69
            prediction = {
70
                'timestamp': timestamp,
71
                'metrics': metrics,
72
                'severity': self._calculate_severity(metrics),
73
                'recommended_action': self._recommend_action(metrics)
74
            }
75
            predictions.append(prediction)
76

77
        return predictions
78

79
    def optimize_resource_allocation(self, usage_patterns: dict) -> dict:
80
        """AI-driven resource optimization recommendations"""
81

82
        prompt = f"""
83
        Based on the following usage patterns, provide resource optimization recommendations:
84

85
        Current Configuration:
86
        - Instances: {usage_patterns['instances']}
87
        - CPU allocation: {usage_patterns['cpu_allocation']}
88
        - Memory allocation: {usage_patterns['memory_allocation']}
89

90
        Usage Patterns (last 30 days):
91
        - Average CPU usage: {usage_patterns['avg_cpu']}%
92
        - Peak CPU usage: {usage_patterns['peak_cpu']}%
93
        - Average Memory usage: {usage_patterns['avg_memory']}%
94
        - Peak Memory usage: {usage_patterns['peak_memory']}%
95
        - Traffic patterns: {usage_patterns['traffic_pattern']}
96

97
        Provide specific recommendations for:
98
        1. Right-sizing instances
99
        2. Auto-scaling policies
100
        3. Cost optimization
101
        4. Performance improvement
102
        """
103

104
        response = self.openai_client.chat.completions.create(
105
            model="gpt-4",
106
            messages=[
107
                {"role": "system", "content": "You are a cloud resource optimization expert."},
108
                {"role": "user", "content": prompt}
109
            ],
110
            temperature=0.5
111
        )
112

113
        recommendations = response.choices[0].message.content
114

115
        return {
116
            'recommendations': recommendations,
117
            'estimated_savings': self._calculate_savings(usage_patterns, recommendations),
118
            'implementation_plan': self._generate_implementation_plan(recommendations)
119
        }

1
class PredictiveIncidentManager:
2
    def __init__(self):
3
        self.time_series_model = Prophet()
4
        self.incident_classifier = RandomForestClassifier()
5

6
    def predict_incidents(self, historical_data: pd.DataFrame) -> list:
7
        """Predict potential incidents before they occur"""
8

9
        predictions = []
10

11
        # Analyze each metric
12
        for metric in ['cpu', 'memory', 'disk_io', 'network_latency']:
13
            # Prepare data for Prophet
14
            metric_data = historical_data[['timestamp', metric]].rename(
15
                columns={'timestamp': 'ds', metric: 'y'}
16
            )
17

18
            # Fit model
19
            self.time_series_model.fit(metric_data)
20

21
            # Make predictions
22
            future = self.time_series_model.make_future_dataframe(periods=24, freq='H')
23
            forecast = self.time_series_model.predict(future)
24

25
            # Check for anomalies in forecast
26
            anomalies = self._detect_forecast_anomalies(forecast)
27

28
            for anomaly in anomalies:
29
                incident_probability = self._calculate_incident_probability(
30
                    metric, anomaly, historical_data
31
                )
32

33
                if incident_probability > 0.7:
34
                    predictions.append({
35
                        'metric': metric,
36
                        'predicted_time': anomaly['ds'],
37
                        'predicted_value': anomaly['yhat'],
38
                        'incident_probability': incident_probability,
39
                        'recommended_action': self._get_preventive_action(metric, anomaly)
40
                    })
41

42
        return sorted(predictions, key=lambda x: x['incident_probability'], reverse=True)
43

44
    def auto_remediate(self, incident_prediction: dict) -> dict:
45
        """Automatically remediate predicted incidents"""
46

47
        remediation_actions = {
48
            'cpu': self._remediate_cpu_issue,
49
            'memory': self._remediate_memory_issue,
50
            'disk_io': self._remediate_disk_issue,
51
            'network_latency': self._remediate_network_issue
52
        }
53

54
        action_func = remediation_actions.get(incident_prediction['metric'])
55
        if action_func:
56
            result = action_func(incident_prediction)
57

58
            # Log remediation
59
            self._log_remediation(incident_prediction, result)
60

61
            # Update ML model with outcome
62
            self._update_model_with_outcome(incident_prediction, result)
63

64
            return result
65

66
        return {'status': 'no_action_available'}

1
apiVersion: flagger.app/v1beta1
2
kind: Canary
3
metadata:
4
  name: api-service
5
  namespace: production
6
spec:
7
  targetRef:
8
    apiVersion: apps/v1
9
    kind: Deployment
10
    name: api-service
11

12
  progressDeadlineSeconds: 300
13

14
  service:
15
    port: 80
16
    targetPort: 8080
17
    gateways:
18
      - public-gateway.istio-system.svc.cluster.local
19
    hosts:
20
      - api.company.com
21

22
  analysis:
23
    interval: 30s
24
    threshold: 5
25
    maxWeight: 50
26
    stepWeight: 10
27

28
    metrics:
29
      - name: request-success-rate
30
        thresholdRange:
31
          min: 99
32
        interval: 1m
33

34
      - name: request-duration
35
        thresholdRange:
36
          max: 500
37
        interval: 30s
38

39
      - name: custom-business-metric
40
        templateRef:
41
          name: business-metrics
42
          namespace: flagger-system
43
        thresholdRange:
44
          min: 95
45

46
    webhooks:
47
      - name: load-test
48
        url: http://loadtester.flagger/
49
        timeout: 5s
50
        metadata:
51
          cmd: "hey -z 1m -q 10 -c 2 http://api-canary.production:80/"
52

53
      - name: acceptance-test
54
        type: pre-rollout
55
        url: http://acceptance-test.production/
56
        timeout: 30s
57

58
      - name: notification
59
        type: post-rollout
60
        url: http://notification-service.production/
61
        metadata:
62
          severity: info

1
class GitOpsController:
2
    def __init__(self):
3
        self.git_client = GitClient()
4
        self.k8s_client = K8sClient()
5
        self.policy_engine = PolicyEngine()
6

7
    async def sync_environments(self):
8
        """Sync all environments with Git state"""
9
        environments = ['dev', 'staging', 'prod']
10

11
        for env in environments:
12
            # Get desired state from Git
13
            desired_state = await self.git_client.get_environment_state(env)
14

15
            # Get current state from cluster
16
            current_state = await self.k8s_client.get_cluster_state(env)
17

18
            # Calculate diff
19
            diff = self.calculate_diff(current_state, desired_state)
20

21
            # Apply policies
22
            approved_changes = await self.policy_engine.evaluate(diff, env)
23

24
            # Apply changes progressively
25
            if approved_changes:
26
                await self.apply_changes_progressively(env, approved_changes)
27

28
    async def apply_changes_progressively(self, env: str, changes: list):
29
        """Apply changes with progressive rollout"""
30

31
        for change in changes:
32
            # Create canary deployment
33
            canary = await self.create_canary_deployment(change)
34

35
            # Monitor canary
36
            metrics = await self.monitor_canary(canary, duration=300)
37

38
            if self.is_canary_healthy(metrics):
39
                # Gradually increase traffic
40
                for weight in [10, 25, 50, 75, 100]:
41
                    await self.update_traffic_split(canary, weight)
42
                    await asyncio.sleep(60)
43

44
                    if not await self.is_healthy(canary):
45
                        await self.rollback(canary)
46
                        raise Exception(f"Canary failed at {weight}% traffic")
47

48
                # Promote canary
49
                await self.promote_canary(canary)
50
            else:
51
                await self.rollback(canary)
52
                raise Exception("Canary failed health checks")
53

54
    def generate_drift_report(self) -> dict:
55
        """Generate comprehensive drift report"""
56
        report = {
57
            'timestamp': datetime.now().isoformat(),
58
            'environments': {}
59
        }
60

61
        for env in ['dev', 'staging', 'prod']:
62
            git_state = self.git_client.get_environment_state(env)
63
            cluster_state = self.k8s_client.get_cluster_state(env)
64

65
            drift = self.calculate_drift(git_state, cluster_state)
66

67
            report['environments'][env] = {
68
                'total_resources': len(cluster_state),
69
                'drifted_resources': len(drift),
70
                'drift_percentage': (len(drift) / len(cluster_state)) * 100,
71
                'details': drift
72
            }
73

74
        return report

1
package observability
2

3
import (
4
    "context"
5
    "github.com/opentracing/opentracing-go"
6
    "github.com/uber/jaeger-client-go"
7
)
8

9
type EnhancedTracer struct {
10
    tracer opentracing.Tracer
11
    aiAnalyzer *AIAnalyzer
12
}
13

14
func (et *EnhancedTracer) StartSpanWithContext(ctx context.Context, operationName string) (opentracing.Span, context.Context) {
15
    // Extract business context
16
    businessContext := extractBusinessContext(ctx)
17

18
    // Start span with enhanced tags
19
    span, ctx := opentracing.StartSpanFromContext(ctx, operationName)
20

21
    // Add standard tags
22
    span.SetTag("service.version", getServiceVersion())
23
    span.SetTag("deployment.id", getDeploymentID())
24
    span.SetTag("feature.flags", getActiveFeatureFlags())
25

26
    // Add business context
27
    span.SetTag("user.segment", businessContext.UserSegment)
28
    span.SetTag("transaction.value", businessContext.TransactionValue)
29
    span.SetTag("business.flow", businessContext.FlowType)
30

31
    // Add AI insights
32
    if prediction := et.aiAnalyzer.PredictSpanBehavior(operationName); prediction != nil {
33
        span.SetTag("ai.expected_duration_ms", prediction.ExpectedDuration)
34
        span.SetTag("ai.anomaly_probability", prediction.AnomalyProbability)
35
        span.SetTag("ai.suggested_optimization", prediction.Optimization)
36
    }
37

38
    return span, ctx
39
}
40

41
func (et *EnhancedTracer) AnalyzeTracePatterns() []TraceInsight {
42
    // Get recent traces
43
    traces := et.getRecentTraces(1000)
44

45
    insights := []TraceInsight{}
46

47
    // Analyze performance patterns
48
    perfPatterns := et.analyzePerformancePatterns(traces)
49
    insights = append(insights, perfPatterns...)
50

51
    // Detect anomalous traces
52
    anomalies := et.detectAnomalousTraces(traces)
53
    insights = append(insights, anomalies...)
54

55
    // Find optimization opportunities
56
    optimizations := et.findOptimizationOpportunities(traces)
57
    insights = append(insights, optimizations...)
58

59
    return insights
60
}
61

62
type TraceInsight struct {
63
    Type        string
64
    Severity    string
65
    Description string
66
    Impact      Impact
67
    Suggestion  string
68
    AutoFix     *AutoFix
69
}

1
class IntelligentLogAnalyzer:
2
    def __init__(self):
3
        self.pattern_matcher = PatternMatcher()
4
        self.anomaly_detector = AnomalyDetector()
5
        self.root_cause_analyzer = RootCauseAnalyzer()
6

7
    def analyze_logs_realtime(self, log_stream):
8
        """Real-time log analysis with ML"""
9

10
        buffer = []
11
        patterns_detected = defaultdict(int)
12

13
        for log_entry in log_stream:
14
            # Parse and enrich log
15
            enriched_log = self.enrich_log(log_entry)
16
            buffer.append(enriched_log)
17

18
            # Detect patterns
19
            patterns = self.pattern_matcher.match(enriched_log)
20
            for pattern in patterns:
21
                patterns_detected[pattern] += 1
22

23
                # Check if pattern indicates issue
24
                if self.is_critical_pattern(pattern):
25
                    self.handle_critical_pattern(pattern, enriched_log)
26

27
            # Anomaly detection on sliding window
28
            if len(buffer) >= 1000:
29
                anomalies = self.anomaly_detector.detect(buffer[-1000:])
30
                if anomalies:
31
                    self.investigate_anomalies(anomalies)
32

33
                # Clear old entries
34
                buffer = buffer[-1000:]
35

36
            # Correlation analysis
37
            if patterns_detected:
38
                correlations = self.find_correlations(patterns_detected)
39
                if correlations:
40
                    self.alert_on_correlations(correlations)
41

42
    def perform_root_cause_analysis(self, incident_id: str) -> dict:
43
        """AI-powered root cause analysis"""
44

45
        # Collect relevant logs
46
        logs = self.collect_incident_logs(incident_id)
47

48
        # Collect metrics
49
        metrics = self.collect_incident_metrics(incident_id)
50

51
        # Collect traces
52
        traces = self.collect_incident_traces(incident_id)
53

54
        # Perform analysis
55
        analysis = self.root_cause_analyzer.analyze(
56
            logs=logs,
57
            metrics=metrics,
58
            traces=traces
59
        )
60

61
        return {
62
            'incident_id': incident_id,
63
            'root_causes': analysis.root_causes,
64
            'contributing_factors': analysis.contributing_factors,
65
            'timeline': analysis.timeline,
66
            'recommendations': analysis.recommendations,
67
            'similar_incidents': self.find_similar_incidents(analysis)
68
        }

1
name: DevSecOps Pipeline
2

3
on:
4
  push:
5
    branches: [main, develop]
6
  pull_request:
7
    branches: [main]
8

9
jobs:
10
  security-scanning:
11
    runs-on: ubuntu-latest
12
    steps:
13
      - uses: actions/checkout@v3
14

15
      - name: Secret Scanning
16
        uses: trufflesecurity/trufflehog@main
17
        with:
18
          path: ./
19
          base: ${{ github.event.repository.default_branch }}
20
          head: HEAD
21

22
      - name: SAST Scan
23
        uses: github/super-linter@v4
24
        env:
25
          DEFAULT_BRANCH: main
26
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
27
          VALIDATE_ALL_CODEBASE: false
28

29
      - name: Dependency Scan
30
        run: |
31
          pip install safety
32
          safety check --json > safety-report.json
33

34
          npm audit --json > npm-audit.json
35

36
          go install github.com/sonatype-nexus-community/nancy@latest
37
          go list -json -m all | nancy sleuth
38

39
      - name: Container Scan
40
        uses: aquasecurity/trivy-action@master
41
        with:
42
          image-ref: ${{ env.IMAGE_NAME }}:${{ github.sha }}
43
          format: "sarif"
44
          output: "trivy-results.sarif"
45
          severity: "CRITICAL,HIGH"
46

47
      - name: IaC Scan
48
        uses: bridgecrewio/checkov-action@master
49
        with:
50
          directory: .
51
          framework: all
52
          output_format: sarif
53
          output_file_path: checkov.sarif
54

55
      - name: License Compliance
56
        run: |
57
          pip install licensecheck
58
          licensecheck --zero --report license-report.json
59

60
      - name: DAST Preparation
61
        if: github.ref == 'refs/heads/main'
62
        run: |
63
          echo "::set-output name=deploy_url::$(terraform output -raw app_url)"
64

65
  compliance-validation:
66
    needs: security-scanning
67
    runs-on: ubuntu-latest
68
    steps:
69
      - name: Policy Validation
70
        run: |
71
          opa test policies/
72
          conftest verify --policy policies/ .
73

74
      - name: Compliance Check
75
        run: |
76
          # SOC2 compliance
77
          python scripts/check_soc2_compliance.py
78

79
          # GDPR compliance
80
          python scripts/check_gdpr_compliance.py
81

82
          # Industry-specific compliance
83
          python scripts/check_industry_compliance.py

1
class RuntimeSecurityMonitor:
2
    def __init__(self):
3
        self.falco_client = FalcoClient()
4
        self.ebpf_monitor = eBPFMonitor()
5
        self.ml_detector = MLAnomalyDetector()
6

7
    async def monitor_runtime_security(self):
8
        """Continuous runtime security monitoring"""
9

10
        # Start eBPF monitoring
11
        self.ebpf_monitor.start_monitoring([
12
            'process_execution',
13
            'network_connections',
14
            'file_access',
15
            'system_calls'
16
        ])
17

18
        # Process security events
19
        async for event in self.get_security_events():
20
            # Enrich event with context
21
            enriched_event = await self.enrich_security_event(event)
22

23
            # ML-based anomaly detection
24
            anomaly_score = self.ml_detector.calculate_anomaly_score(enriched_event)
25

26
            if anomaly_score > 0.8:
27
                # High-risk event
28
                await self.handle_security_incident(enriched_event)
29
            elif anomaly_score > 0.6:
30
                # Medium-risk event
31
                await self.investigate_event(enriched_event)
32

33
            # Update ML model
34
            self.ml_detector.update_model(enriched_event)
35

36
    async def handle_security_incident(self, event: SecurityEvent):
37
        """Automated security incident response"""
38

39
        # Immediate containment
40
        if event.severity == 'CRITICAL':
41
            await self.contain_threat(event)
42

43
        # Collect forensics
44
        forensics = await self.collect_forensics(event)
45

46
        # Automated response
47
        response_plan = self.generate_response_plan(event, forensics)
48
        await self.execute_response_plan(response_plan)
49

50
        # Update security policies
51
        policy_updates = self.generate_policy_updates(event)
52
        await self.apply_policy_updates(policy_updates)
53

54
        # Alert security team
55
        await self.alert_security_team(event, forensics, response_plan)

1
class FinOpsAutomation:
2
    def __init__(self):
3
        self.cloud_providers = {
4
            'aws': AWSCostManager(),
5
            'azure': AzureCostManager(),
6
            'gcp': GCPCostManager()
7
        }
8
        self.optimizer = CostOptimizer()
9

10
    def analyze_and_optimize_costs(self) -> dict:
11
        """Comprehensive cost analysis and optimization"""
12

13
        total_savings = 0
14
        optimizations = []
15

16
        for cloud, manager in self.cloud_providers.items():
17
            # Get current costs
18
            current_costs = manager.get_current_costs()
19

20
            # Analyze usage patterns
21
            usage_analysis = manager.analyze_usage_patterns()
22

23
            # Find optimization opportunities
24
            opportunities = self.optimizer.find_opportunities(
25
                current_costs,
26
                usage_analysis
27
            )
28

29
            for opportunity in opportunities:
30
                if opportunity.confidence > 0.8:
31
                    # Auto-apply optimization
32
                    result = self.apply_optimization(cloud, opportunity)
33
                    total_savings += result.estimated_savings
34
                    optimizations.append(result)
35
                else:
36
                    # Queue for review
37
                    self.queue_for_review(opportunity)
38

39
        return {
40
            'total_monthly_savings': total_savings,
41
            'optimizations_applied': len(optimizations),
42
            'details': optimizations
43
        }
44

45
    def apply_optimization(self, cloud: str, opportunity: Optimization) -> OptimizationResult:
46
        """Apply cost optimization automatically"""
47

48
        manager = self.cloud_providers[cloud]
49

50
        if opportunity.type == 'rightsizing':
51
            return manager.rightsize_resources(opportunity.resources)
52
        elif opportunity.type == 'reserved_instances':
53
            return manager.purchase_reserved_instances(opportunity.recommendations)
54
        elif opportunity.type == 'spot_instances':
55
            return manager.migrate_to_spot(opportunity.workloads)
56
        elif opportunity.type == 'unused_resources':
57
            return manager.cleanup_unused_resources(opportunity.resources)
58
        elif opportunity.type == 'scheduling':
59
            return manager.implement_scheduling(opportunity.schedule)

1
export class DevelopmentEnvironmentService {
2
  async createEphemeralEnvironment(
3
    request: EnvironmentRequest
4
  ): Promise<Environment> {
5
    // Create isolated namespace
6
    const namespace = await this.k8sClient.createNamespace({
7
      name: `dev-${request.user}-${generateId()}`,
8
      labels: {
9
        owner: request.user,
10
        type: "ephemeral",
11
        expires: new Date(Date.now() + request.duration).toISOString(),
12
      },
13
    });
14

15
    // Deploy application stack
16
    const deployment = await this.deployApplicationStack({
17
      namespace: namespace.name,
18
      version: request.version || "latest",
19
      configuration: request.configuration,
20
      dependencies: await this.resolveDependencies(request.dependencies),
21
    });
22

23
    // Setup networking
24
    const ingress = await this.createIngress({
25
      namespace: namespace.name,
26
      host: `${request.user}-${generateId()}.dev.company.com`,
27
      tls: true,
28
    });
29

30
    // Seed test data
31
    if (request.seedData) {
32
      await this.seedTestData(namespace.name, request.seedData);
33
    }
34

35
    // Configure IDE integration
36
    const ideConfig = await this.configureIDEIntegration({
37
      environment: namespace.name,
38
      user: request.user,
39
      ide: request.ide || "vscode",
40
    });
41

42
    return {
43
      id: namespace.name,
44
      url: `https://${ingress.host}`,
45
      services: deployment.services,
46
      credentials: await this.generateCredentials(namespace.name),
47
      ideConfig: ideConfig,
48
      expiresAt: namespace.labels.expires,
49
    };
50
  }
51

52
  async setupDevContainer(spec: DevContainerSpec): Promise<DevContainer> {
53
    const config = {
54
      name: spec.name,
55
      image: spec.baseImage || "mcr.microsoft.com/devcontainers/universal:2",
56
      features: {
57
        "ghcr.io/devcontainers/features/common-utils:2": {},
58
        "ghcr.io/devcontainers/features/docker-in-docker:2": {},
59
        ...spec.additionalFeatures,
60
      },
61
      customizations: {
62
        vscode: {
63
          extensions: [
64
            "github.copilot",
65
            "ms-azuretools.vscode-docker",
66
            "hashicorp.terraform",
67
            ...spec.vscodeExtensions,
68
          ],
69
          settings: {
70
            "terminal.integrated.defaultProfile.linux": "zsh",
71
            "files.autoSave": "afterDelay",
72
            ...spec.vscodeSettings,
73
          },
74
        },
75
      },
76
      postCreateCommand: spec.postCreateCommand || 'echo "Environment ready!"',
77
      mounts: [
78
        "source=/var/run/docker.sock,target=/var/run/docker.sock,type=bind",
79
      ],
80
    };
81

82
    return await this.createDevContainer(config);
83
  }
84
}

1
class IntelligentChaosEngine:
2
    def __init__(self):
3
        self.ml_predictor = ChaosImpactPredictor()
4
        self.experiment_planner = ExperimentPlanner()
5
        self.safety_controller = SafetyController()
6

7
    def plan_chaos_experiments(self, system_state: SystemState) -> List[ChaosExperiment]:
8
        """AI-driven chaos experiment planning"""
9

10
        # Analyze system weaknesses
11
        weaknesses = self.analyze_system_weaknesses(system_state)
12

13
        # Generate experiment candidates
14
        candidates = []
15
        for weakness in weaknesses:
16
            experiment = self.experiment_planner.create_experiment(
17
                target=weakness.component,
18
                fault_type=weakness.fault_type,
19
                blast_radius=self.calculate_safe_blast_radius(weakness)
20
            )
21

22
            # Predict impact
23
            impact = self.ml_predictor.predict_impact(experiment, system_state)
24

25
            if self.is_safe_to_run(impact):
26
                candidates.append(experiment)
27

28
        # Prioritize experiments
29
        prioritized = self.prioritize_experiments(candidates)
30

31
        return prioritized[:5]  # Top 5 experiments
32

33
    async def run_adaptive_chaos(self, experiment: ChaosExperiment):
34
        """Run chaos experiment with adaptive controls"""
35

36
        # Start experiment
37
        experiment_id = await self.start_experiment(experiment)
38

39
        # Monitor in real-time
40
        while await self.is_experiment_running(experiment_id):
41
            # Get current metrics
42
            metrics = await self.get_system_metrics()
43

44
            # Check safety thresholds
45
            if self.safety_controller.is_unsafe(metrics):
46
                await self.abort_experiment(experiment_id)
47
                break
48

49
            # Adapt experiment based on impact
50
            if self.should_increase_chaos(metrics):
51
                await self.increase_chaos_intensity(experiment_id)
52
            elif self.should_decrease_chaos(metrics):
53
                await self.decrease_chaos_intensity(experiment_id)
54

55
            await asyncio.sleep(5)
56

57
        # Collect results
58
        results = await self.collect_experiment_results(experiment_id)
59

60
        # Update ML models
61
        self.ml_predictor.update_model(experiment, results)
62

63
        return results