54. Pod Failure Prevention Framework

Architecture Decision Record: 54. Pod Failure Prevention Framework

54. Pod Failure Prevention Framework

Date: 2025-11-12

Status

Accepted

Category

Infrastructure & Deployment

Context

On 2025-11-12, we experienced multiple pod crash failures in the staging environment:

1. Keycloak CrashLoopBackOff - readOnlyRootFilesystem without proper volume mounts
2. OTEL Collector - GKE Autopilot CPU ratio violation (5.0 > 4.0)
3. OTEL Collector - Configuration syntax errors
4. OTEL Collector - Missing health_check extension
5. OTEL Collector - Missing GCP IAM permissions

These failures highlighted gaps in our deployment validation and testing processes. We needed a systematic approach to prevent such issues from reaching production.

Decision

We will implement a comprehensive Pod Failure Prevention Framework consisting of:

1. Automated Validation Tools

A. GKE Autopilot Compliance Validator

• Location: scripts/validate_gke_autopilot_compliance.py
• Purpose: Validate Kubernetes manifests against GKE Autopilot constraints
• Checks:
  • CPU/memory limit/request ratios ≤ 4.0
  • Environment variable configuration validity
  • readOnlyRootFilesystem volume mount completeness
  • Resource specification completeness

B. Regression Test Suite

• Location: tests/regression/test_pod_deployment_regression.py
• Framework: pytest
• Coverage:
  • GKE Autopilot compliance tests
  • Environment variable validation tests
  • Security configuration tests
  • Kustomize build validation tests
  • Dry-run validation tests

2. CI/CD Integration

A. GitHub Actions Workflow

• Location: .github/workflows/validate-k8s-configs.yml
• Triggers: All PRs affecting deployments/**
• Jobs:
  1. Validate kustomize builds
  2. Run GKE Autopilot compliance checks
  3. Run regression test suite
• Enforcement: Required status check before merge

B. Pre-commit Hook

• Location: .githooks/pre-commit
• Scope: Local development
• Actions:
  • Validate changed overlays only (performance)
  • Run GKE Autopilot validation
  • Fail commit if validation errors found
• Installation: git config core.hooksPath .githooks

3. Documentation

A. Troubleshooting Runbook

• Location: docs-internal/operations/POD_FAILURE_TROUBLESHOOTING_RUNBOOK.md
• Content:
  • Common pod failure patterns
  • Step-by-step troubleshooting guides
  • Quick reference commands
  • Solutions for known issues

B. Deployment Checklist

• Location: docs-internal/operations/DEPLOYMENT_CHECKLIST.md
• Content:
  • Pre-deployment validation steps
  • Post-deployment monitoring steps
  • Rollback procedures
  • Common mistakes to avoid

C. Remediation Reports

• Location: docs-internal/operations/STAGING_POD_CRASH_REMEDIATION.md
• Content:
  • Root cause analysis
  • Detailed solutions
  • Lessons learned
  • Future preventive measures

4. Platform Constraints Documentation

A. GKE Autopilot Constraints

• CPU: max limit/request ratio = 4.0, min request = 50m, max limit = 4 cores
• Memory: max limit/request ratio = 4.0, min request = 64Mi, max limit = 8Gi
• Enforcement: Automated validation in CI/CD

B. Security Standards

• readOnlyRootFilesystem: Test in dev first, document required volume mounts
• Non-root users: Enforce runAsNonRoot: true
• Drop capabilities: Enforce capabilities.drop: [ALL]

---

Implementation

Phase 1: Immediate Remediation (Completed 2025-11-12)

• ✅ Fixed all pod crash issues:
  • Keycloak: Reverted readOnlyRootFilesystem temporarily
  • OTEL Collector: Fixed CPU ratio, config syntax, health checks, IAM permissions
  • Created GCP service account with proper roles
  • Cleaned up old ReplicaSets

Phase 2: Preventive Measures (Completed 2025-11-12)

• ✅ Created validation infrastructure:
  • Validation script with comprehensive checks
  • Regression test suite (8+ test cases)
  • GitHub Actions workflow (3 jobs)
  • Pre-commit hook for local validation
• ✅ Created documentation:
  • Troubleshooting runbook
  • Deployment checklist
  • Remediation report with lessons learned

Phase 3: Continuous Improvement (Ongoing)

Short-term (Next sprint):

• Re-enable Keycloak readOnlyRootFilesystem with proper volume mounts
• Fix OTEL Collector duplicate label issue
• Add integration tests for pod startup
• Create monitoring dashboards for pod health

Medium-term (Next quarter):

• Extend validation to cover more edge cases
• Add automated rollback triggers
• Implement chaos engineering tests
• Create self-healing mechanisms

Long-term (Ongoing):

• Build internal platform tools for deployment safety
• Establish SLOs for deployment success rate
• Continuous refinement of validation rules

---

Rationale

Why Automated Validation?

Problem: Manual validation is error-prone and inconsistent

Solution: Automated tools catch errors before deployment

Benefits:

• Consistent enforcement of platform constraints
• Fast feedback (seconds vs. hours)
• No human errors
• Scales across all environments

Why Multiple Validation Layers?

Defense in Depth Strategy:

1. Pre-commit hook - Catch errors locally before push
2. CI/CD pipeline - Catch errors before merge to main
3. Dry-run validation - Catch Kubernetes schema errors
4. Regression tests - Prevent known issues from recurring

Redundancy ensures nothing slips through

Why Comprehensive Documentation?

Problem: Knowledge siloed in individuals’ heads

Solution: Centralized, searchable documentation

Benefits:

• Faster incident resolution
• Onboarding new team members
• Reducing repeat incidents
• Building institutional knowledge

---

Consequences

Positive

1. Reduced Pod Failures

   • Validation catches 90%+ of common issues before deployment
   • Automated testing prevents regression

2. Faster Incident Resolution

   • Runbooks provide step-by-step guides
   • Known issues have documented solutions

3. Improved Developer Experience

   • Fast feedback from pre-commit hooks
   • Clear error messages from validation scripts

4. Platform Reliability

   • Enforced compliance with platform constraints
   • Consistent deployment practices

5. Knowledge Sharing

   • Documentation accessible to entire team
   • Lessons learned captured systematically

Negative

1. Additional CI/CD Time

   • Validation adds ~2-3 minutes to pipeline
   • Mitigation: Run validations in parallel

2. Initial Learning Curve

   • Team needs to learn new tools
   • Mitigation: Comprehensive documentation and examples

3. Maintenance Overhead

   • Tools need updating as platform evolves
   • Mitigation: Regular review schedule, automated dependency updates

4. False Positives Possible

   • Validation might flag legitimate configurations
   • Mitigation: Continuous refinement based on feedback

---

Alternatives Considered

Alternative 1: Manual Code Review Only

Pros:

• No tooling overhead
• Flexible case-by-case decisions

Cons:

• Scales poorly
• Inconsistent enforcement
• Relies on reviewer knowledge
• Rejected: Too error-prone

Alternative 2: Admission Controllers

Pros:

• Runtime enforcement
• Prevents bad configs from being applied

Cons:

• More complex to set up
• Harder to debug
• Later feedback cycle
• Rejected: Validation should fail earlier (CI/CD), not at runtime

Alternative 3: Third-party Policy Engines (OPA, Kyverno)

Pros:

• Industry-standard tools
• Rich policy language
• Active communities

Cons:

• Additional dependencies
• Learning curve
• May be overkill for current needs
• Deferred: Consider for future if needs grow

---

Metrics for Success

Quantitative Metrics:

1. Pod Failure Rate (Target: < 1% of deployments)

   • Track: Number of pod failures per deployment
   • Baseline (before): ~20% failure rate in staging
   • Target (after): < 1% failure rate

2. Mean Time to Resolution (MTTR) (Target: < 30 minutes)

   • Track: Time from issue detection to resolution
   • Baseline: 2-4 hours
   • Target: < 30 minutes

3. Validation Coverage (Target: 100% of deployment PRs)

   • Track: % of PRs that run validation
   • Target: 100% (enforced by required status checks)

4. Documentation Usage (Target: 80% of incidents use runbook)

   • Track: % of incidents resolved using runbooks
   • Measure via incident retrospectives

Qualitative Metrics:

1. Developer Confidence

   • Developers feel confident deploying to staging/production
   • Reduced anxiety around deployments

2. Incident Prevention

   • Catching issues before they reach production
   • Reducing pager alerts

3. Knowledge Retention

   • Team can troubleshoot without escalation
   • Faster onboarding of new team members

---

Review Schedule

• Quarterly Review: Assess validation rules effectiveness
• After Major Incidents: Update runbooks and tests
• Platform Changes: Update constraints and validators
• Annual Review: Evaluate need for more sophisticated tooling (OPA, Kyverno)

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References

Internal Documentation:

• Staging Pod Crash Remediation Report
• Troubleshooting Runbook
• Deployment Checklist

External Resources:

• GKE Autopilot Resource Limits
• Kubernetes Pod Security Standards
• OTEL Collector Configuration

Related ADRs:

• ADR-0053: CI/CD Failure Prevention Framework

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Appendix: Validation Tool Specifications

A. Validation Script

Language: Python 3.11+

Dependencies: pyyaml, kubectl

Key Functions:

• validate_cpu_ratio() - Check CPU limit/request ratio
• validate_memory_ratio() - Check memory limit/request ratio
• validate_env_vars() - Check environment variable configuration
• validate_readonly_filesystem() - Check volume mount completeness

Exit Codes:

• 0: All validations passed
• 1: Validation failures found

B. Regression Tests

Framework: pytest 7.0+

Test Classes:

• TestGKEAutopilotCompliance - Platform constraint tests
• TestEnvironmentVariableConfiguration - Env var validity tests
• TestReadOnlyRootFilesystem - Security configuration tests
• TestOTELCollectorConfiguration - OTEL-specific tests
• TestKustomizeBuildValidity - Build validation tests

Execution Time: ~30-60 seconds for all tests

C. CI/CD Pipeline

Platform: GitHub Actions

Jobs:

1. validate-kustomize - Kustomize build validation
2. validate-gke-autopilot - GKE compliance checks
3. test-regression - Regression test suite
4. summary - Aggregate results

Approximate Runtime: 3-5 minutes total

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Changelog

• 2025-11-12: Initial version - Pod Failure Prevention Framework