Production GKE Troubleshooting Toolkit

Overview

When supporting production-grade Google Kubernetes Engine (GKE) clusters across multiple time zones, incident triage consistency becomes a major challenge. Engineers often faced fragmented approaches to debugging, with varying levels of visibility into logs, resource metrics, and node health. To solve this, I led the design and development of a custom troubleshooting toolkit, built using Python and Bash, to accelerate root cause identification and ensure standardized diagnostics across teams.

🎯 Objectives

• Build reusable tools to automate first-level diagnostics for GKE clusters.

• Reduce the time and complexity involved in parsing logs and checking node status.

• Enable global support teams to follow a consistent triage workflow, regardless of timezone or experience level.

• Improve documentation of incident handling and RCA data collection.

🔧 What I Built

1. Log Parsing Script (Python):

   • Parses container and system logs from kubectl logs, journalctl, and GCP operations logging APIs.

   • Supports keyword-based error filtering, regex highlighting, and time-range scoping.

2. Node Health Checker (Bash):

   • Automates collection of kubectl describe node, disk I/O stats, kubelet status, and pod distribution checks.

   • Validates readiness and taints across nodes and flags underutilized or failing nodes.

3. Resource Usage Analyzer (Python/Bash Hybrid):

   • Gathers CPU/memory requests, limits, and actual usage across all namespaces.

   • Identifies overprovisioned pods and resource bottlenecks using data from kubectl top and GCP metrics.

4. Unified Diagnostic Report Generator:

   • Outputs results from all tools into a single JSON or HTML report.

   • Can be shared across Slack/Email during P1/P2 incident calls.

📈 Impact

• ✅ Reduced average incident triage time by ~40%, especially for after-hours/on-call escalations.

• ✅ Enabled faster onboarding of new support engineers by providing structured debug steps and tool outputs.

• ✅ Helped surface recurring issues, feeding directly into SRE-led problem management and platform improvements.

💡 Lessons Learned

• Automation tools for support are not just time-savers — they enable knowledge transfer and reduce cognitive load during high-pressure scenarios.

• Visibility into “what normal looks like” is critical; baseline outputs were included to help teams compare incident behavior to healthy states.

• Collaboration with SREs and Platform Engineers ensured the toolkit fit naturally into existing workflows (Cloud Logging, PagerDuty, etc.).

🚀 Next Steps

• Integrate toolkit into a central CLI plugin or internal web UI for broader adoption.

• Add support for Anthos clusters and hybrid/on-prem GKE monitoring.

• Explore integration with Stackdriver APIs and BigQuery logs for deeper analysis.