Production Troubleshooting

Production Troubleshooting

Overview

Diagnose performance issues and errors in production/test environments using systematic investigation workflows with Sentry, kubectl, and Helm configuration analysis.

Prerequisites

Verify access to Sentry and Kubernetes tooling before troubleshooting.

• Prefer k8s-tool when available for environment-aware commands.
• Fall back to raw kubectl commands when k8s-tool is not installed or not configured in the current environment.
• Confirm namespace and target environment (test or prod) before running commands.

When to Use This Skill

Apply this skill when:

• Investigating incidents in test/production (not localhost)
• Troubleshooting slow endpoints, slow queries, or elevated latency
• Debugging pod crashes, restart loops, OOMKilled, or potential throttling
• Analyzing Sentry traces for failures or degraded transactions
• Validating Kubernetes resource limits and related Helm values

Investigation Workflow

Follow this symptom-driven workflow and confirm evidence before making changes.

Step 1: Triage by Primary Symptom

Choose the first investigation path based on the reported symptom.

• For pod crash/restart symptoms (CrashLoopBackOff, OOMKilled, frequent restarts): check pod status and logs first.
• For latency/slow endpoint symptoms: inspect traces first, then correlate with logs and pod state.

Step 2A: Inspect Pod Status and Logs (Crash/Restart Path)

Check pod health state before trace analysis when the incident is pod-centric.

Using k8s-tool (preferred):

k8s-tool describe --resource pod --name <pod-name> --env <env>
k8s-tool logs --pod <pod-name> --env <env> --tail 200

Fallback using kubectl:

kubectl describe pod <pod-name> -n <namespace>
kubectl logs <pod-name> -n <namespace> --tail 200

Look for restart reasons, termination messages, probe failures, and repeated startup errors.

Step 2B: Inspect Sentry Traces (Latency/Error Path)

Use Sentry to identify slow database calls, external latency, and transaction-level failures.

Using Sentry MCP:

• Search for traces related to the reported issue
• Look for slow database queries (for this project, >500ms is a useful baseline heuristic, not a universal threshold)
• Check external API call latency
• Identify error patterns and stack traces

What to look for:

• Database query times exceeding expected baseline (commonly ~500ms in this project)
• External API calls with high latency
• Repeated error patterns
• Performance degradation trends

Step 3: Review Application Logs

Examine kubectl logs for timing information and error patterns.

Using k8s-tool:

k8s-tool logs --pod <pod-name> --env <env> --tail 200

Key log patterns to search for:

• [Server] - Server startup and initialization timing
• [SSR] - Server-side rendering timing
• [tRPC] - TRPC query execution timing
• [DB Pool] - Database connection pool status
• ERROR or WARN - Application errors and warnings

Common issues:

• Sequential API calls instead of parallel (Promise.all)
• Long DB connection acquisition times
• Slow SSR rendering

Step 4: Check Pod Resource Usage

Verify CPU and memory usage to detect throttling.

Using k8s-tool:

k8s-tool top --env <env>

Warning signs:

• CPU usage >70% may indicate potential throttling
• Memory usage >80% may indicate elevated OOM risk
• Consistent high utilization suggests under-provisioning

Step 5: Review Pod Configuration

Check resource limits and Helm values to identify misconfigurations.

Using kubectl:

kubectl get pod <pod-name> -n <namespace> -o yaml

Key sections to check:

• resources.limits.cpu and resources.limits.memory
• resources.requests.cpu and resources.requests.memory
• Environment variables configuration
• Image version and tags

Helm values locations:

• web-app: /kubernetes/helm/web-app/values.{test,prod}.yaml

Reference references/helm-values-locations.md for detailed Helm configuration structure.

Step 6: Confirm Evidence Before Changing Configuration

Confirm that proposed fixes map to observed evidence before editing Helm values or code.

• Link each change to concrete evidence from traces, logs, pod events, or resource metrics.
• Prefer the smallest reversible change first.
• Re-check traces/logs after deployment to verify impact.

Common Causes & Solutions

CPU/Memory Throttling

• Symptom: Sustained high CPU/memory usage with degraded response times or restarts
• Confirm with evidence: Correlate resource metrics with throttling signals, restart events, and latency spikes
• Solution: Adjust resource requests/limits in Helm values only after confirmation

Network Latency

• Symptom: Slow external API calls, DNS resolution delays
• Confirm with evidence: Validate slow spans and timed log entries for network-bound operations
• Solution: Check network policies, verify DNS configuration, and tune retry behavior where appropriate

Database Connection Pool Issues

• Symptom: [DB Pool] errors, slow connection acquisition
• Confirm with evidence: Match pool warnings with trace timing and connection wait patterns
• Solution: Review idleTimeoutMillis and pool size configuration

Sequential API Calls

• Symptom: Multiple API calls taking cumulative time
• Confirm with evidence: Verify sequential span ordering in traces or timestamped log sequence
• Solution: Refactor to use Promise.all() for parallel execution

Resources

kubectl commands

Use these common operations with k8s-tool when available, or run equivalent raw kubectl commands as fallback:

• k8s-tool logs --pod <pod> --env <env> --tail 200 - Extract and filter pod logs
• k8s-tool top --env <env> - Show CPU/memory usage for pods
• k8s-tool describe --resource pod --name <pod> --env <env> - Check resource limits and pod configuration
• k8s-tool kubectl --env <env> --cmd "get pods" - Raw kubectl for anything else

references/

• helm-values-locations.md - Detailed guide to Helm values file structure and locations
• common-issues.md - Catalog of common production issues and solutions