Performance Metrics Measure

Performance testing is only as effective as the metrics you measure and act on. In distributed systems, it’s not just about response time — it’s about end-to-end system behavior under load, resource utilization, and failure thresholds.

Here’s how I typically categorize and collect key performance testing metrics, based on my real-world experience with high-scale platforms.

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✅ 1. Core Performance Metrics

Metric	Why It Matters
Throughput (TPS/QPS)	Measures system capacity — are we handling the expected load?
Latency (P50, P95, P99)	Helps detect tail latencies and slow paths. P99 is critical for user experience.
Error Rate (%)	Any spike under load suggests bottlenecks or instability.
Concurrency	Helps test thread safety and async processing under pressure.
Time to First Byte / Full Response	Important for APIs and UI performance perception.

✅ 2. Resource Utilization Metrics

Resource	Metric	Purpose
CPU	% Usage, context switches	Detect CPU-bound operations
Memory	Heap/Non-heap usage, GC pause time	Tune for memory leaks, OOM risk
Disk I/O	Read/write IOPS, latency	Ensure storage doesn’t become a bottleneck
Network	Throughput, packet loss, RTT	Catch bandwidth saturation, dropped packets
Thread Pools	Active threads, queue size	Avoid thread starvation under load

Tools used: Prometheus, Grafana, New Relic, top, vmstat, iostat, jstat, jmap, async-profiler

✅ 3. Application-Specific Metrics

Component	Metrics to Monitor
Kafka	Consumer lag, messages/sec, ISR count
DB/Cache (e.g., Redis, Postgres)	Query latency, cache hit/miss, slow query logs
Elasticsearch	Query throughput, indexing rate, segment merges, node GC
Spark Jobs	Task duration, shuffle read/write, executor memory spill
API Layer	Response codes breakdown (2xx, 4xx, 5xx), rate-limited requests

✅ 4. Infrastructure & Cluster Health

Service	Key Indicators
Kubernetes	Pod restarts, node CPU/mem pressure, eviction count
Disk Space	Free space per node, inode usage
GC Behavior	GC frequency, full GC %, pause durations
Auto-scaling Logs	Scale-up/down events, throttle rates

✅ 5. Stability & Reliability Metrics

Category	Why It Matters
Test Flakiness Rate	Detects inconsistent behavior under load
Success % under chaos	How gracefully does the system degrade?
Retry Count / Circuit Breaker Trips	Signals downstream failures under load
Service Uptime %	Validates HA/resilience against failures

🔧 How I Collect & Analyze Metrics

• Test Harness Integration: I integrate metrics collection directly into test frameworks (e.g., expose custom Prometheus counters in Java test harness).

• Dashboards: Build tailored Grafana dashboards for real-time observability of test runs.

• Thresholds & SLOs: Define thresholds for acceptable P95 latency, error rate, and resource usage — any breach flags a performance regression.

• Baseline Comparison: Run nightly jobs to compare metrics vs. last known good release and flag deltas.

🚀 Mastering Kubernetes: 20+ Daily kubectl Commands Every Engineer Should Know

Whether you’re debugging a pod or scaling deployments, having the right commands at your fingertips can save hours of troubleshooting.

Here’s your quick-reference guide to essential kubectl commands used by DevOps, SREs, and Cloud Engineers every single day:

In below examples, assume "services" is the namespace given for your micro-services

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🔹 Context & Cluster Navigation

• kubectl config get-contexts – List all available contexts

• kubectx <env-name> – Switch between environments

🔹 Nodes & Namespaces

• kubectl get nodes – View all cluster nodes

• kubectl get namespaces – List all namespaces

🔹 Pods & Deployments

• kubectl -n services get pod <pod-name> – Get specific pod details

• kubectl -n services get pods – List all pods in a namespace

• kubectl -n services delete pod <pod-name> – Delete a specific pod

• kubectl -n services get pods -o wide | grep ½ – Filter for unhealthy pods

🔹 Detailed Views

• -o wide – Add node-level details

• -o yaml – See full YAML output

• kubectl describe pod <pod-name> -n services – Inspect pod specs

🔹 Inside the Pod

• kubectl exec -it <pod-name> -n services -- /bin/bash – SSH into a pod

• kubectl logs <pod-name> -c install -f -n services – View Init container logs

🔹 Scaling & Rollouts

• kubectl scale deploy <pod-name> --replicas=3 -n services – Manual scaling

• kubectl rollout restart deployment <pod-name> -n services – Restart a service

🔹 HPA & Canary Checks

• kubectl get hpa <pod-name> -n services – Horizontal Pod Autoscaler status

• kubectl describe canary <pod-name> -n services – Inspect a canary deployment

• kubectl get canary -n services – List all canary-enabled pods

🔹 Logs & Troubleshooting (with Stern)

• stern -n services <pod-name> -c <app-name> -t --since 1m – Tail recent logs

• stern -n services geolayers-api-primary -c geolayers-api -t --since 1m | grep '<text>' – Filter logs with grep

🔹 Consul & Vault Checks

• kubectl get pods -n configuration – View Consul/Vault status

• stern -n configuration <name> – Stream logs from Vault

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📘 Bonus: Official kubectl Cheat Sheet

💬 Which of these commands saved you recently — or is there a favorite one missing from the list?

Drop it in the comments and let’s build the ultimate K8s cheat sheet together. 👇

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