Test 3: Standard Claude Response

Kubernetes etcd Failure Analysis: 30% Packet Loss Scenario

Executive Summary

When an etcd node becomes unresponsive with 30% packet loss, it triggers a cascading failure pattern throughout the Kubernetes cluster. This analysis examines the failure progression, component behaviors, and recovery mechanisms based on the Kubernetes codebase architecture.

Initial Impact Timeline

T+0 to T+2 seconds: etcd Client Detection

• First affected: API server storage layer (staging/src/k8s.io/apiserver/pkg/storage/etcd3/store.go:617-642)
• etcd client timeout set to 2 seconds (cmd/kubeadm/app/util/etcd/etcd.go:47)
• API server begins experiencing storage operation failures
• Watch connections start experiencing intermittent failures

T+2 to T+15 seconds: API Server Degradation

• API server storage operations become unreliable
• Watch cache becomes inconsistent due to failed etcd watch connections
• GET requests may succeed from cache, but PUT/POST/DELETE operations fail
• Health check endpoints begin reporting issues

Component-by-Component Analysis

1. API Server (kube-apiserver)

Immediate Effects:

• Storage interface (staging/src/k8s.io/apiserver/pkg/storage/etcd3/store.go) encounters timeouts
• etcd client retry mechanism activates with constants.EtcdAPICallRetryInterval
• Watch connections become unstable, affecting real-time event delivery
• Internal health checks begin failing

Behavioral Changes:

• Requests timeout after etcd client timeout (2 seconds default)
• Watch connections may break and require re-establishment
• Cache inconsistencies emerge between API server instances
• Some read operations may succeed from cache while writes fail

2. Leader Election Mechanisms

Impact on Control Plane Components:

Based on staging/src/k8s.io/client-go/tools/leaderelection/leaderelection.go, the leader election system experiences:

• Lease Renewal Failures: Current leaders cannot renew their leases in etcd
• Election Disruption: New leader elections cannot complete due to etcd unavailability
• Split-Brain Risk: Multiple components may claim leadership if etcd becomes partitioned

Affected Components:

• kube-controller-manager (cmd/kube-controller-manager/app/controllermanager.go:794)
• kube-scheduler
• Any custom controllers using leader election

Timeline:

• T+0-15s: Current leaders attempt lease renewal, experiencing failures
• T+15-30s: Leadership expires, triggering new elections
• T+30s+: New elections fail, causing control plane operational halt

3. In-Flight API Server Requests

Request Categories:

1. Read Operations (GET)

   • May succeed if served from watch cache
   • Fail if cache is stale or unavailable
   • Success rate depends on cache validity window

2. Write Operations (POST/PUT/DELETE/PATCH)

   • Immediately begin failing as they require etcd persistence
   • Return timeout errors after 2-second etcd timeout
   • No fallback mechanism exists

3. Watch Requests

   • Existing watches break when etcd connection fails
   • New watch requests cannot establish connections
   • Clients experience watch event delivery gaps

Workload Impact Analysis

StatefulSets

Criticality: HIGH

From pkg/controller/statefulset/stateful_set_control.go:

• Identity Management: Cannot update pod ordinal assignments
• Persistent Volume Claims: PVC operations fail, preventing storage attachment
• Rolling Updates: Halt mid-process, potentially leaving workloads in inconsistent state
• Scale Operations: Cannot safely scale up/down due to ordering requirements

Failure Pattern:

1. StatefulSet controller loses leader election
2. Pod creation/deletion operations freeze
3. PVC management fails
4. Workload becomes stuck in transitional state

Deployments

Criticality: MEDIUM

• Rolling Updates: Halt, leaving mixed pod versions
• Scaling: New replica count cannot be persisted
• Self-Healing: ReplicaSet controller cannot respond to pod failures
• Existing Pods: Continue running but cannot be managed

Degradation:

• Gradual degradation as pods naturally terminate without replacement
• No immediate impact on running workloads
• Recovery dependent on controller plane restoration

DaemonSets

Criticality: MEDIUM-LOW

• Node Addition: Cannot deploy to new nodes
• Pod Replacement: Failed pods not recreated
• Updates: Cannot progress configuration changes
• Existing Pods: Least affected, continue operating independently

Recovery Mechanisms

Automatic Recovery Features

1. etcd Client Retries (cmd/kubeadm/app/util/etcd/etcd.go:250-278)

   • Built-in retry logic with exponential backoff
   • Automatic endpoint synchronization
   • Connection re-establishment attempts

2. Leader Election Recovery

   • Automatic retry of leader election once etcd recovers
   • Lease renewal resumption
   • Controller restart and state reconciliation

3. Watch Cache Rebuilding

   • Automatic watch stream re-establishment
   • Cache warming from etcd state
   • Event replay for missed updates

Recovery Order

1. T+0: etcd node connectivity restored
2. T+0-5s: API server re-establishes etcd connections
3. T+5-15s: Leader elections complete, controllers resume
4. T+15-30s: Watch caches rebuild and synchronize
5. T+30-60s: Workload controllers reconcile desired state
6. T+60s+: Full cluster functionality restored

Network Partition vs Node Failure Comparison

Network Partition Scenario

Characteristics:

• etcd node remains running but isolated
• May cause split-brain if partition isolates minority of nodes
• Raft consensus breaks down across partition boundary
• More complex recovery requiring careful cluster healing

Additional Impacts:

• Split Leadership: Controllers may claim leadership on both sides
• Data Divergence: Separate etcd clusters may accept writes
• Complex Recovery: Manual intervention often required
• Longer Downtime: Cluster split resolution needed

Node Failure Scenario

Characteristics:

• Clean failure detection by remaining etcd members
• Raft cluster maintains consensus with remaining nodes
• Simpler recovery once node is restored or replaced
• Built-in etcd failure handling mechanisms activate

Recovery Advantages:

• Immediate Detection: Clear failure signal to remaining nodes
• Automatic Promotion: Raft automatically promotes new leader
• Clean State: No conflicting writes or split-brain scenarios
• Faster Recovery: Standard cluster healing procedures apply

Monitoring and Detection

Key Metrics to Watch

1. etcd Health: /healthz endpoint status
2. API Server Latency: Storage operation response times
3. Leader Election Status: Current leader and lease expiration times
4. Controller Metrics: Workqueue depths and processing rates
5. Event Gaps: Missing or delayed Kubernetes events

Early Warning Indicators

• Increased API server request latencies (>100ms for storage operations)
• Leader election lease renewal failures
• Watch connection drops across multiple API server instances
• Controller workqueue backup and processing delays

Conclusion

An etcd node failure with 30% packet loss creates a cascading failure pattern that progressively impacts the entire Kubernetes control plane. While the cluster has built-in resilience mechanisms, the dependency on etcd for all persistent state means that stateful workloads like StatefulSets are most severely affected. The distinction between network partition and node failure scenarios is critical for determining recovery complexity and time-to-restoration.

The analysis demonstrates Kubernetes’ graceful degradation capabilities while highlighting the critical importance of etcd cluster health monitoring and rapid recovery procedures.