Engineering Logs for Troubleshooting & Debugging
Problem Description The Horizontal Pod Autoscaler (HPA) attached to a Deployment that serves Hugging Face Transformers inference never creates additional replicas, even when the pods report CPU utilization above the configured target (e.g., 80%). The symptom manifests as: CPU usage in kubectl top pod consistently >90%. Inference latency spikes and occasional request time‑outs. HPA status shows … Read more
Problem Description The Horizontal Pod Autoscaler (HPA) in a Kubernetes‑based CI/CD pipeline is expected to add or remove training pods based on GPU utilization. In practice the HPA never scales out: the replica count stays at 1 even when GPU usage spikes to 95 % during model training. The symptom manifests as a growing job queue, … Read more
Problem – Inconsistent Kafka HPA Scaling Due to Mis‑configured CPU Metrics In a Kubernetes‑based telemetry pipeline ingesting IoT sensor data, the Horizontal Pod Autoscaler (HPA) for Kafka consumer pods either adds ~30 % extra replicas during low‑traffic periods or fails to add enough pods during traffic spikes. The symptoms manifest as: Over‑provisioned pods at night, inflating … Read more
Problem – PostgreSQL HPA Not Scaling Under High CPU Usage The PostgreSQL deployment runs in a Kubernetes cluster that is part of an AI‑driven orchestration platform. During traffic spikes the API gateway generates thousands of concurrent requests per second, causing PostgreSQL CPU utilization to rise sharply. Expected behavior is that the Horizontal Pod Autoscaler (HPA) … Read more
Problem Description In a Kubernetes‑based real‑time data pipeline, Redis is used as an in‑memory cache. The Horizontal Pod Autoscaler (HPA) is configured to scale the downstream consumer pods based on the Prometheus metric redis_memory_used_bytes exposed by redis_exporter. Operators observed the following inconsistent behavior: During brief traffic spikes the HPA adds 3‑4 extra pods, but the … Read more
Problem Description The Claude inference service experiences a cascade of pending pods during traffic spikes. Typical symptoms observed in the kubectl get pods -n claude-prod output are: NAME READY STATUS RESTARTS AGE claude-infer-7f9c9d8c9f-abcde 0/1 Pending 0 2m30s claude-infer-7f9c9d8c9f-fghij 0/1 Pending 0 2m30s … Relevant scheduler events (extracted from kubectl describe pod <pod>) contain the following … Read more