Performance Troubleshooting📜

This guide helps you diagnose and resolve performance issues in your Big Bang deployment. Performance problems can manifest in various ways including slow response times, high resource utilization, or application timeouts.

Identifying Performance Issues📜

1. Review Monitoring Dashboards📜

Start by examining your observability stack:

Grafana Dashboards: Check Big Bang’s built-in dashboards for system metrics
Cluster overview dashboards for CPU, memory, and network usage
Application-specific dashboards for service performance
Node-level metrics for infrastructure health
Prometheus Metrics: Query specific metrics to identify bottlenecks
Use the Prometheus UI to explore available metrics
Check the kubernetes-service-endpoints and kubernetes-pods targets for application metrics

2. Common Performance Indicators📜

Look for these warning signs:

High CPU or memory utilization (>80% sustained)
Increased response times in application logs
Pod restarts due to resource limits
Network latency or packet loss
Storage I/O bottlenecks

Resource Optimization📜

1. Adjusting Pod Resources📜

Edit deployment resource specifications:

resources:
  requests:
    cpu: 100m
    memory: 128Mi
  limits:
    cpu: 500m
    memory: 512Mi

Best Practices: - Set requests based on actual usage patterns - Use limits to prevent resource starvation - Monitor resource utilization over time before adjusting

2. Horizontal Pod Autoscaling (HPA)📜

Configure HPA to automatically scale pods based on metrics:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

3. Vertical Pod Autoscaling (VPA)📜

Use VPA for automatic resource recommendations:

apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: myapp-vpa
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp
  updatePolicy:
    updateMode: "Auto"

Infrastructure Optimization📜

1. Node Pool Management📜

Consider node pool adjustments:

Instance Types: Use compute-optimized instances for CPU-intensive workloads
Storage: Choose appropriate storage classes (SSD vs HDD)
Network: Ensure adequate network bandwidth between nodes

2. Pod Placement📜

Optimize pod scheduling:

# Node affinity for performance-critical workloads
affinity:
  nodeAffinity:
    requiredDuringSchedulingIgnoredDuringExecution:
      nodeSelectorTerms:
      - matchExpressions:
        - key: node-type
          operator: In
          values: ["high-performance"]

# Anti-affinity to spread pods across nodes
podAntiAffinity:
  preferredDuringSchedulingIgnoredDuringExecution:
  - weight: 100
    podAffinityTerm:
      labelSelector:
        matchLabels:
          app: myapp
      topologyKey: kubernetes.io/hostname

3. Resource Quotas and Limits📜

Set namespace-level resource management:

apiVersion: v1
kind: ResourceQuota
metadata:
  name: namespace-quota
spec:
  hard:
    requests.cpu: "4"
    requests.memory: 8Gi
    limits.cpu: "8"
    limits.memory: 16Gi

Application-Level Optimizations📜

1. Connection Pooling📜

Configure appropriate connection limits:

Database connection pools
HTTP client timeouts
Keep-alive settings

2. Caching Strategies📜

Implement caching where appropriate:

Redis for session storage
CDN for static content
Application-level caching

3. Database Performance📜

Optimize database interactions:

Index optimization
Query performance tuning
Connection pooling
Read replicas for read-heavy workloads

Network Performance📜

1. Service Mesh Optimization📜

If using Istio:

Configure appropriate circuit breakers
Optimize retry policies
Use traffic splitting for gradual deployments

2. Load Balancing📜

Ensure proper load distribution:

Configure service load balancing algorithms
Use ingress controllers efficiently
Consider geographic traffic routing

Monitoring and Alerting📜

1. Set Up Performance Alerts📜

Create alerts for key metrics:

# Example Prometheus alert rule
groups:
- name: performance.rules
  rules:
  - alert: HighCPUUsage
    expr: container_cpu_usage_seconds_total > 0.8
    for: 5m
    labels:
      severity: warning
    annotations:
      summary: "High CPU usage detected"

2. Custom Metrics📜

Implement application-specific monitoring as described in the monitoring guide:

Add Prometheus annotations to services
Enable global endpoint metrics if needed
Create custom dashboards for your applications

Performance Testing📜

1. Load Testing📜

Regular performance validation:

Use tools like k6, JMeter, or Artillery
Test under realistic load conditions
Establish performance baselines

2. Chaos Engineering📜

Implement fault injection testing:

Use tools like Chaos Monkey or Litmus
Test system resilience under stress
Validate auto-scaling behavior

Common Performance Bottlenecks📜

1. CPU Throttling📜

Symptoms: Slow response times despite low CPU usage Solutions: - Increase CPU limits - Optimize application code - Use CPU profiling tools

2. Memory Pressure📜

Symptoms: Pod restarts, OOM kills Solutions: - Increase memory limits - Optimize memory usage patterns - Implement garbage collection tuning

3. I/O Bottlenecks📜

Symptoms: High disk wait times Solutions: - Use faster storage classes - Optimize database queries - Implement read caching

4. Network Latency📜

Symptoms: Slow inter-service communication Solutions: - Optimize service mesh configuration - Use connection pooling - Consider service colocation

Next Steps📜

If performance issues persist:

Review monitoring documentation for advanced observability setup
Check networking troubleshooting for network-related issues
Consider engaging with the Big Bang community for complex performance challenges
Plan capacity upgrades if current infrastructure is insufficient

Remember to always test performance changes in a non-production environment first and monitor the impact of optimizations over time.