网易CW-运维研发工程师
社招全职3-5年网易数智地点:杭州状态:招聘
任职要求
熟悉云平台主流的产品技术栈 1、掌握kubernates集群管理,熟练部署和运维常见的问题,熟悉K8s网络,vlan和calico 2、熟悉 ArgoCD,掌握Helm Chart 3、熟悉ES,Redis、MySQL等中间件的常规部署和运…
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工作职责
1、负责参与智能开发平台的组件发版,效能和规范平台建设 2、负责参与智能开发平台的组件和中间件稳定性治理 3、负责参与智能开发平台的日常环境运维及问题排查
包括英文材料
Kubernetes+
https://kubernetes.io/docs/tutorials/kubernetes-basics/
This tutorial provides a walkthrough of the basics of the Kubernetes cluster orchestration system.
https://kubernetes.io/zh-cn/docs/tutorials/kubernetes-basics/
本教程介绍 Kubernetes 集群编排系统的基础知识。每个模块包含关于 Kubernetes 主要特性和概念的一些背景信息,还包括一个在线教程供你学习。
https://www.youtube.com/watch?v=s_o8dwzRlu4
Hands-On Kubernetes Tutorial | Learn Kubernetes in 1 Hour - Kubernetes Course for Beginners
https://www.youtube.com/watch?v=X48VuDVv0do
Full Kubernetes Tutorial | Kubernetes Course | Hands-on course with a lot of demos
Argo+
https://argo-cd.readthedocs.io/en/stable/understand_the_basics/
Before effectively using Argo CD, it is necessary to understand the underlying technology that the platform is built on.
https://www.youtube.com/watch?v=MeU5_k9ssrs
The ArgoCD chapter includes building a pipeline of dynamically updating & building a new application version using GitLab downstream pipeline feature.
Helm+
[英文] Introduction to Helm
https://helm.sh/docs/intro/
Are you new to Helm? This is the place to start!
https://www.baeldung.com/ops/kubernetes-helm
In this tutorial, we’ll understand the basics of Helm and how they form a powerful tool for working with Kubernetes resources.
ElasticSearch+
https://www.youtube.com/watch?v=a4HBKEda_F8
Learn about Elasticsearch with this comprehensive course designed for beginners, featuring both theoretical concepts and hands-on applications using Python (though applicable to any programming language). The course is structured in two parts: first covering essential Elasticsearch fundamentals including index management, document storage, text analysis, pipeline creation, search functionality, and advanced features like semantic search and embeddings; followed by a practical section where you'll build a real-world website using Elasticsearch as a search engine, working with the Astronomy Picture of the Day (APOD) dataset to implement features such as data cleaning pipelines, tokenization, pagination, and aggregations.
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