Maintained by a like‑minded team, covering both operations and development. Topics span Linux ops, DevOps toolchain, Kubernetes containerization, monitoring, log collection, network security, and Python or Go development. Team members: Qiao Ke, wanger, Dong Ge, Su Xin, Hua Zai, Zheng Ge, Teacher Xia.
This article explores why influence matters for Site Reliability Engineers, outlines the challenges they face in gaining recognition, and provides practical strategies—enhancing technical expertise, improving communication, quantifying achievements, and sharing knowledge—to elevate their impact within organizations.
This article examines how the rapid rise of AI reshapes Site Reliability Engineering by enhancing monitoring, automating operations, improving fault diagnosis, and presenting new challenges such as data security and model explainability, ultimately driving more efficient and reliable system management.
This guide explains how to replace KubeVirt's local‑storage with OpenEBS‑LVM for dynamic PV allocation, configure CPU/memory overcommit ratios, perform hot‑plug upgrades, expand disks online, and set node affinity and fixed IPs, providing full YAML examples and reference links.
This article walks through how the author leveraged GPT, Cursor, and carefully crafted prompts to automatically generate a fully functional TikTok‑style front‑end, from initial design screenshots to deployment on Vercel, all without manually writing any code.
This article introduces Nightingale monitoring's final v7.7 release, outlines its new features and major v7 changes, provides step‑by‑step upgrade instructions, and walks through a Docker‑based installation, data‑source integration, dashboard import, and alert‑rule configuration with DingTalk notifications.
This guide walks through deploying VictoriaLogs in Kubernetes with Helm, configuring Promtail, exploring its LogsSQL query language, comparing resource usage to Grafana Loki, and integrating the VictoriaLogs datasource into Grafana for efficient, low‑overhead log monitoring.
This article describes the design and implementation of a lightweight cloud‑native alert platform, outlining its core features, future enhancements, system architecture, and demo screenshots, offering practical insights for SREs and operations teams handling growing monitoring workloads.
The article examines how native Kubernetes scheduling based solely on resource requests leads to waste and imbalance, compares the open‑source crane‑scheduler and koord‑scheduler architectures, explains practical configuration of Koordinator, and provides step‑by‑step testing procedures to achieve load‑aware scheduling.
Koordinator is a QoS‑based Kubernetes scheduler that boosts efficiency and reliability for latency‑sensitive services and batch jobs, offering fine‑grained resource coordination, flexible priority classes, load‑aware scheduling, and integrated monitoring tools to maximize cluster utilization.
This tutorial walks through setting up a local k3d cluster, installing Higress, and using its AI plugins—including AI Proxy, AI JSON formatter, AI Agent, and AI Statistics—to integrate and observe Alibaba Cloud's Qwen large language models across various use cases such as weather and flight queries.
