Engineering Logs for Troubleshooting & Debugging
Problem The LangChain service is deployed as a stateless Deployment behind an API gateway in a Kubernetes cluster. An HorizontalPodAutoscaler (HPA) is configured to scale the Deployment based on CPU utilization (target 70%) and a custom latency metric exported to Prometheus. Under load the request latency spikes and the CPU usage reported by the metrics-server … Read more
Problem When a 48 kHz .wav file is uploaded to the Qwen transcription service running on a multi‑tenant Kubernetes cluster, the API either returns an empty transcription string or raises a ProcessingException. The issue appears only under load when the autoscaler creates new pods; lower‑sample‑rate files (16 kHz, 22.05 kHz) are processed correctly. Root Cause The Qwen model … Read more
Problem: Gemini RAG pipeline cannot connect to the vector store after autoscaling The Gemini Retrieval‑Augmented Generation (RAG) pipeline is deployed on a managed cloud service that scales compute instances automatically. After a scale‑out event, the pipeline consistently fails to retrieve documents from the configured managed vector store. The failure manifests as connection timeouts and, intermittently, … Read more
Problem – TGI Node Out‑of‑Memory (OOM) During Large Batch Inference with Autoscaling Disabled In a production Kubernetes deployment of Text Generation Inference (TGI), a batch job that aggregates thousands of text‑generation requests into a single inference call caused the pod to be killed with an OOM error. Autoscaling was deliberately disabled, so the node could … Read more
Problem In a development sandbox (a local kind cluster) the haproxy deployment is configured with a Horizontal Pod Autoscaler (HPA) that should react to both CPU utilization and a custom request‑latency metric. During load testing of an AI model serving pipeline the following symptoms appear: Average request latency climbs from ~30 ms to >500 ms. CPU usage … Read more
Problem Description When querying a LlamaIndex microservice deployed in a Kubernetes cluster, the service returns a FilterExpressionParseError for any query that contains complex boolean or range filters. Typical error messages observed in the pod logs are: FilterExpressionParseError: Unexpected token ‘AND’ at position 12 ValueError: Could not parse filter expression: syntax error near ‘>=’, expected ‘:’ … Read more
Problem – Shard Rebalancing Timeout After Autoscaling Nodes In an inference‑serving deployment of Weaviate, automatic scaling of worker nodes frequently triggers a cascade of errors: Search requests return 504 Gateway Timeout or stale vectors. Cluster logs contain repeated messages such as: rebalance_timeout: shard “shard-7” could not be moved within the configured timeout Metrics show CPU … Read more
Problem – Stale Inference Results from NVIDIA GPU EndpointSlice after Autoscaling In a hybrid‑cloud deployment that mixes on‑premises NVIDIA GPU nodes with cloud‑based orchestration, engineers observed that after a Horizontal Pod Autoscaler (HPA) scale‑up or scale‑down event the Triton Inference Server began returning identical outputs for distinct inputs. The symptom manifested as: Log entry from … Read more
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