Source URL: https://metoro.io/blog/go-production-performance-gotcha-gomaxprocs
Source: Hacker News
Title: Golang and Containers Perf Gotcha – Gomaxprocs
Feedly Summary: Comments
AI Summary and Description: Yes
Summary: The text discusses a performance issue faced by Metoro, an observability platform, due to incorrect configuration of the GOMAXPROCS parameter in a Go application. This led to unexpected CPU usage on larger hosts. The resolution involved using Kubernetes’ downward API to properly set the GOMAXPROCS variable, resulting in normalized CPU performance.
Detailed Description: The text delves into a technical challenge encountered while deploying Metoro’s node agent on customer clusters. Below are the key insights and implications relevant to security, infrastructure, and software professionals:
– **Background Context**:
– Metoro utilizes a daemonset for observability in Kubernetes environments, where a node agent collects telemetry data.
– The node agent relies on eBPF for generating distributed traces and telemetry, affecting its CPU usage based on incoming request volumes.
– **Identified Problem**:
– During deployment on a high request-volume cluster, node agents exhibited CPU usage nearly double the expected amount.
– Observations showed that the Go runtime, particularly functions related to scheduling and garbage collection (`runtime.Schedule` and `runtime.gcBgMarkWorker`), consumed excessive CPU resources.
– **Investigation Process**:
– Comparative benchmarking on different host sizes revealed that larger host configurations disproportionately increased CPU usage, directly correlating with the default GOMAXPROCS setting.
– Defaulting GOMAXPROCS to the number of available CPU cores led to inefficient resource utilization in a containerized setting.
– **Understanding GOMAXPROCS**:
– It limits the threads executing user-level Go code simultaneously and can be misconfigured in environments where services are shared across multiple containers.
– **Resolution Approach**:
– Implemented corrective measures by programmatically setting the GOMAXPROCS variable using the Kubernetes downward API.
– By retrieving the CPU limit specified in Kubernetes, Metoro adjusted the GOMAXPROCS, bringing CPU usage back to expected levels.
– **Practical Implications**:
– Emphasizes the need for proper resource management and configuration in cloud-native applications to prevent performance degradation.
– Highlights the importance of understanding the operational environment’s constraints, especially in Kubernetes, where resource limits and quotas must be adhered to for optimal performance.
This case serves as a critical reminder for software engineers and DevOps professionals about the intricacies of runtime configurations and the significance of accurate resource allocation in cloud architectures.