能概括运维本质的词, 没有比“可视化”更好的这惟独一个词了然而, “可视化”须得分成两部分, 一部分是可视化的服务交付, 另一部分是可视化的服务度量。第一部分可视化的服务交付在早期的时候, 是源自于ITIL开启运维的前奏, 那时众人都弄不太明晰运维究竟为何方事物, 还好寻觅到一项IT服务最佳实践, 即ITIL。由此展开了互联网运维的探索迈进路径, 从CMDB、服务台、事件管理、变更管理、可用性管理、容量管理等等方面逐一去深入知悉, 并且同步着手搭建相对应的管理平台。但是, 随后我们迅速察觉到, 倘若碰到大规模运维情形, 那么这一完备的流程框架无法予以应对, 究其缘由, 是由于过度聚焦于流程以及规范, 我们感悟出难以提升运维敏捷度与精细性, 况且我们依旧不清楚一个完整的 IT 服务边界处于何处, 又该如何去将其实现?然而在ITIL的实践进程当中, 实际上引出了一个相当不错的概念那便是IT服务。就运维而言, 给出一种具备高效性、一致性、透明化以及面向用户特质的服务乃运维的价值的归属之处, 如此一来便要求运维将其给出的服务背后的全部实现细节予以屏蔽。对于运维具体事务或者活动而言, 从这个角度出发, 怎样对它进行一次乃至多次的组合封装呢, 要把它们转变为一个完整的 IT 运维服务, 这是此刻运维自动化的关键重点方向。如果繁杂的运维事务不进一步实施封装, 那么对于个人或者团队来讲, 都意味着存在很高的学习成本与事务执行成本。在传统的 IT 运维组织当中, 我们能够察觉到彼此事务之间的断裂极为显著, 就好像网络、机房、服务器、应用部署等等, 皆是在不同的团队完成, 彼此的工作独立开展。受到敏捷以及精益运维的驱动, 必然需要有一个集成平台, 来将这些事务流进行调度, 若不然, 就没办法提升事务执行的效率与质量, 进而难以切实把运维交付功能转变为交付服务的模式。就怎样去进行这些事务或者活动的封装而言, 从所倡导的“自动化一切”Auto当中能够找寻到一些答案, 其关键的自动化主线乃是面向用户的敏捷持续交付。我将持续交付划分成了两类场景, 其一为持续交付基础设施, 其二是持续应用交付持续构建、持续测试、持续部署、持续反馈, 它们有着类似IAAS和PAAS的那种关系。在公有云IAAS平台里, 持续交付基础设施被很好解决, 借助软件定义计算技术、利用存储及网络等技术, 达成对上层应用所需资源方面, 进行快速交付。于私有IT环境中, 当下有大量客户, 采用虚拟机方案或者私有云方案方式, 来解决交付难以及交付慢的问题。最新的轻量级虚拟化技术更是成为热点, 根本原因在于, 把应用交付行为放置在镜像级别完成, 进而使应用交付变得愈发快速。从代码生成之时起, 软件的持续交付便开始管理, 历经编译, 又历经测试, 再历经灰度环境验收, 而后到正式环境部署, 且期望这条主线达至完全自动化。针对程序包的持续集成甚为简单, 当下 име很多开源解決方案用以达成, 诸如、Go等, 然而存在一种情况需格外留意, 那就是程序包的配置管理问题, 此亦常常是对部署产生影响的关键因素。那么, 在不少情形下, 我们运用开源平台, 仅仅是为了搭建程序包, 后续的包以及里面的配置管理, 还有实例化部署, 尤其是大规模集群部署, 都是靠单独的持续部署平台去处理, 并非以往的持续集成工具虽说它们也能支持发布, 然而, 持续部署平台得具备和持续集成平台无缝对接的能力。解决基于软件包的交付后, 我们期望交付粒度更大, 怎样达成全应用从应用前端接入直至后端存储的交付, 这时便出现了PAAS平台与基于应用架构的可视化部署服务这两种方案。这两种实现思路差异极大我们清楚完整的PAAS平台具备对底层公共服务的向上API统一抽象, 像数据库服务、存储服务、Cache服务。PAAS平台最典型的实现当属Cloud, 国内众多PAAS平台大多参照CF来实现。阿里UC也有一个类似的PAAS平台示意图如下。那么在实际的情形中, 极少有公司具备将MySQL、MC, 封装成公共服务以供上层应用直接调用的能力, 这表明对于研发程序是存在一定要求的, 那是否存在一种更为轻量的无约束自动化方式呢? 我们能够对运维的全应用部署转换下思路, 在这个时候, 把应用架构里的各个部分拆分成对象组件此项包含属性以及状态, 例如机房、OS、应用包等, 全应用部署即为这些对象的编排, 这类似于可视化IDE编程环境。总括来说, 运维自动化最终得达成可视化, 复杂的运维工作流程得借由可视化去呈现, 自动化经可视化后才能使所有人于理解方面保持一致、在执行方面达到一致、在结果方面实现一致。第二部分可视化服务度量有着这样一条运维人员必须恪守的信条, 那便是“除了上帝, 一切人都必须用数据说话”。我曾写过一篇文章, 一篇名为《关于数据驱动运维的几点认识》的完整的数据驱动还未运维的文章。在这篇文章里, 系统地介绍了好多方面的内容, 其中包括数据化运维的目的, 如何构建数据体系, 还有数据的来源, 等等。最近也正在开展一项数据实践, 此实践是要构筑面向应用的端到端数据分析体系, 该体系会对数据予以标准化的分层归类, 先是基础设施类, 接着是上层组件类, 然后是应用服务类, 再之后是接口类, 最后到用户侧类, 依据应用的拓扑架构, 去收集各种各样的指标, 将这些指标统一归集到一个分析平台里用以展现, 情况如下列图示。凭借这套呈现分层特性的数据体系标准所在, 对于我们而言, 也会有与之形成对应关系的系统实现情况存在, 就如同下面所展示的图形那样。当构建起标准的数据采集体系, 再构建起标准的数据分析体系, 接着构建起标准的展现体系之后, 能够朝着其他应用持续不断地去复制这套方案, 所有人仅仅需要遵循一套数据标准就行, 最终数据的采集是标准化达成, 数据的分析是标准化达成, 数据的展现是标准化达成, 数据的告警也是标准化达成。这套数据体系建设完毕之后, 能够在运维的故障定位方面找到应用场景, 能够在运维的服务优化方面找到应用场景, 能够在运维的架构改进方面找到应用场景, 能够在运维的运维规划等各方面找到应用场景。此刻, 有人会心生疑问, 怎样面向应用将这些数据整合关联起来呢? 我们当下是依据配置文件的静态视图以及基于接口调用所生成的动态视图开展集成的。动态调用视图的生成会相对复杂一些, 能够让线上的接口调用统统由名字服务中心接手调度, 抽样针对接口调用进行染色, 进而生成动态的访问关系。以上视图能够迅即发觉以及定位规模方面的故障, 然而在针对单个用户的故障指标这一情况上却是应对起来显得乏力。就在这个时候分布式 Trace 服务所具备的作用便呈现出来了, 是有着能够借鉴的实现思路的。当下, 我们依据自身业务架构所具有的特点, 达成了一个统一的服务调度框架以及名字服务中心, 在业务代码不存在侵入现象的情形下, 能够将业务调度链的染色数据予以上报并且进行关联, 达成对于单个问题的快速定位。数据具备的可视化能力相当关键, 在朝着整体方向及针对某个业务流方面都得达成实现。它首要呈现出你眼中的运维理解状态怎样, 借由可视化能够瞧见最为直观的运维经验其次依托可视化之上的数据分享, 促使众人在数据理解方面获得一致最后借助达成一致的可视化数据施展运维的驱动效能, 驱动, 数据的关键价值即在这儿。所以, 可视化的本事就意味着运维的本事, 可视化的度数越高, 运维的本事越高。那么, 你如今究竟已将哪些运维服务予以可视化, 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