2021-2022 DevOps and Cloud 趋势报告

「2022 年什么会火？什么该学？本文正在参与“聊聊 2022 技术趋势”征文活动 」

本文属译文，原文来源如下。 - 作者：Matt Campbell、Steef-Jan Wiggers - 原文链接： DevOps and Cloud InfoQ Trends Report

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Key Takeaways

• Hybrid cloud options have evolved beyond the traditional definition, and have expanded to enable the functionality of cloud services to run outside of the cloud. Services such as Azure Arc and Google Anthos allow for a much more seamless "hybrid" experience for developers and operators.
• 混合云选项已经超越了传统的定义，并且已经扩展到允许云服务的功能在云之外运行。Azure Arc 和 googleanthos 等服务为开发者和运营商提供了更加无缝的“混合”体验。
• In the emerging "no-copy data-sharing" approach it is not necessary to move or replicate the data to be able to access it from different services. We believe that the recently announced "Delta Sharing" open standard will contribute to the upward trajectory of no-copy data sharing.
• 在新出现的”无副本数据共享”方法中，没有必要移动或复制数据以便能够从不同的服务访问数据。我们相信，最近宣布的“ Delta 共享”开放标准将有助于无副本数据共享的上升轨迹。
• We believe that there has been limited progress on undoing the confusion between continuous integration (CI) and continuous delivery / (CD) tooling and practices. Both GitOps and site reliability engineering (SRE) practices are increasingly being adopted.
• 我们相信在消除持续集成(CI)和持续交付/(CD)工具和实践之间的混淆方面已经取得了有限的进展。GitOps 和现场可靠度(SRE)的做法正越来越多地被采用。
• Observability practices and tooling continue to mature. The logging and metrics domains of the three pillars of observability are relatively well-adopted, but the tracing pillar remains less so. There are a number of encouraging advancements in this space, especially with the more widespread adoption of OpenTelemetry.
• 可观察性实践和工具继续成熟。可观测性三大支柱的日志记录和度量领域得到了相对较好的采用，但跟踪支柱仍然较少。在这个领域有一些令人鼓舞的进步，特别是随着 opentelementtry 的更广泛的应用。
• We are following the innovative developments with FinOps, real-time information flow for cost analysis, aimed at the finance teams with public cloud vendors like Microsoft and AWS at the frontline.
• 我们正在追踪 FinOps 的创新发展，即成本分析的实时信息流，目标客户是微软和 AWS 等公共云供应商的财务团队。
• Increasingly popular practices, such as "Policy as Code", as promoted by Open Policy Agent (OPA), and remote access management tooling, e.g. HashiCorp's Boundary, are pushing forward identity as code and privacy as code.
• 越来越流行的做法，如开放策略代理(Open Policy Agent，OPA)推广的“策略即代码”(Policy as Code) ，以及远程访问管理工具(remote access management tool，如 HashiCorp 的 Boundary) ，正在推动身份即代码，隐私即代码。
• We have seen the Team Topologies book become the de facto reference for arranging teams within an organization to enable effective software delivery. There is also increasing focus on post-incident "blameless postmortems" in becoming more akin to "healthy retrospectives", from which the entire organisation can learn from.
• 我们已经看到，团队拓扑学书籍已经成为在组织中安排团队以实现有效软件交付的事实参考。此外，人们越来越关注事件后的“无可指摘的事后回顾”，以使其更接近于“健康回顾”，整个组织可以从中学习。

This article summarizes how we currently see the "cloud computing and DevOps" space, which focuses on fundamental infrastructure and operational patterns, the realization of patterns in technology frameworks, and the design processes and skills that a software architect or engineer must cultivate.

本文总结了我们目前如何看待“云计算和 DevOps”空间，这个空间侧重于基础架构和操作模式，技术框架中模式的实现，以及软件架构师或工程师必须培养的设计过程和技能。

Both InfoQ and the QCon conference series focus on topics that we believe fall into the "innovator, early adopter, and early majority stages" of the diffusion of technology, as defined in Geoffrey Moore’s book "Crossing the Chasm." What we try to do is identify ideas that fit into what Moore referred to as the early market, where "the customer base is made up of technology enthusiasts and visionaries who are looking to get ahead of either an opportunity or a looming problem." We are also looking for ideas that are likely to "cross the chasm" to broader adoption. It is perhaps worth saying, in this context, that a technology's exact position on the adoption curve can vary. For example, microservices are widely adopted amongst Bay Area companies but maybe less widely adopted and perhaps less appropriate elsewhere.

InfoQ 和 QCon 系列会议都聚焦于那些我们认为属于技术扩散的“创新者、早期接受者和早期主流阶段”的话题，正如 Geoffrey Moore 在《跨越鸿沟所定义的那样我们试图做的是找出符合摩尔所说的早期市场的想法，在这个市场中，“客户群是由技术爱好者和有远见的人组成的，他们希望抢在机会或迫在眉睫的问题之前。”我们也在寻找可能“跨越鸿沟”并得到更广泛采用的想法。在这种情况下，也许值得一提的是，一项技术在采用曲线上的确切位置可能会有所不同。例如，微型服务在旧金山湾区的公司中被广泛采用，但是在其他地方可能不那么被广泛采用，也不那么适合。

In this edition of the cloud computing and DevOps trend report, we believe that hybrid cloud approaches have evolved to become more "cloud native". In late 2019 all the three prominent public cloud vendors brought new hybrid cloud products to the market and over the last two years they have continued to invest heavily in them - Google with Anthos, Microsoft with the Azure Arc and Stack offerings, AWS with Outposts, and more recently, Amazon ECS Anywhere. For enterprises, for instance, it is about not only bringing workloads to the Cloud but also running them on-premise or both, or on multiple clouds. Thus, managing the infrastructure for the workloads centrally with a service like Arc or Anthos delivers value. Furthermore, these products allow enterprises to extend their platform.

在这一版的云计算和 DevOps 趋势报告中，我们认为混合云方法已经演变得更加“本地化”。2019年末，三家知名的公共云供应商都向市场推出了新的混合云产品，在过去两年中，他们继续大举投资这些产品——谷歌(Google)与安索斯(Anthos)合作，微软(Microsoft)与 Azure Arc 和 Stack 合作，AWS 与 Outposts 合作，以及最近的 Amazon ECS Anywhere。例如，对于企业来说，它不仅要将工作负载带到云中，而且还要在本地或者两者都运行，或者在多个云中运行。因此，通过 Arc 或者 Anthos 之类的服务集中管理工作负载的基础设施可以提供价值。此外，这些产品还允许企业扩展他们的平台。

There has been increasing adoption (and technological evolution) in the space of "edge cloud" and "edge computing", and so we believe this topic should move to the early adopter stage of our graph. There is a fair amount of traction here from specific vendor tools, such as Cloudflare Workers, Fastly’s Compute@Edge, and Amazon CloudFront’s Lambda@Edge.

“边缘云”和“边缘计算”的应用越来越广泛，技术也在不断发展，因此我们认为这个话题应该进入我们图形的早期应用阶段。这里有来自特定供应商工具的大量吸引力，比如 Cloudflare Workers、 Fastly 的 Compute@Edge 和 Amazon CloudFront 的 Lambda@Edge。

The participants of this report have also identified an emerging trend named "no copy data sharing." This can be seen in data management services such as Snowflake, which do not copy or move data, yet enable users to share data at its source. Another example is the Azure Synapse service, which supports no-copy data sharing from Azure Cosmos DB via Azure Synapse Link. The recently announced Delta Sharing open standard is also contributing to the upward trajectory of the no-copy data sharing tendency.

这份报告的参与者还发现了一种名为“无副本数据共享”的新兴趋势这可以在数据管理服务中看到，比如雪花，它不复制或移动数据，但允许用户在其源共享数据。另一个例子是 Azure Synapse 服务，它支持通过 Azure Synapse Link 从 Azure Cosmos DB 共享无拷贝数据。最近宣布的 Delta Sharing 开放标准也促进了无拷贝数据共享趋势的上升。

Observability continues to be a popular topic within DevOps and SRE. While most organizations have begun to implement some form of observability stack, as Holly Cummins notes, the term is overloaded and therefore should be broken down into its various components. Ideas such as centralized log aggregation are currently commonplace in most organizations, however, logs only make up one of the three pillars of observability.

可观察性仍然是 DevOps 和 SRE 中的一个热门话题。虽然大多数组织已经开始实现某种形式的可观察性堆栈，正如 Holly Cummins 指出的那样，这个术语已经超载，因此应该分解成各种组件。像集中式日志聚合这样的想法目前在大多数组织中都很常见，然而，日志只构成了可观察性的三大支柱之一。

The increasingly popular OpenTelemetry project provides a consistent framework for capturing not just logs, but also traces and metrics. The consistency provided by adopting a single framework helps with capturing data across hybrid and heterogeneous environments and also monitoring tooling. The use of service level objectives (SLOs) as a tool to communicate the desired outcome of monitoring and observability is also gaining popularity as seen with the first ever SLOConf earlier this year.

日益流行的 opentelementtry 项目提供了一个一致的框架，不仅可以捕获日志，还可以捕获跟踪和度量。通过采用单一框架提供的一致性有助于跨混合和异构环境捕获数据，并监视工具。正如今年早些时候的第一次 SLOConf 会议所显示的那样，使用服务级别目标(SLOs)作为传达监测和可观察性的预期结果的工具也越来越受欢迎。

"DevOps for Data" has seen increasing adoption over the past year with the rise of both MLOps and DataOps. MLOps focuses on using DevOps style practices (such as CI and CD) to implement continuous training for machine learning models. Open source tooling and commercial services exist to help in this area, such as KubeFlow for deploying ML models on Kubernetes, and Amazon SageMaker Model Monitor for automating monitoring of ML models. DataOps looks to shorten the cycle time of data analytics by applying similar concepts used by DevOps teams to reduce their own cycle times.

在过去的一年中，随着 MLOps 和 DataOps 的增加，“ DevOps for Data”被越来越多的人采用。MLOps 关注于使用 DevOps 风格的实践(如 CI 和 CD)来实现机器学习模型的持续培训。开源工具和商业服务可以在这个领域提供帮助，比如在 Kubernetes 上部署机器学习模型的 KubeFlow，以及自动监控机器学习模型的 Amazon SageMaker Model Monitor。DataOps 通过应用 DevOps 团队使用的类似概念来缩短数据分析的周期时间。

In the people and organisational space of DevOps we have seen the Team Topologies book, from Matthew Skelton and Manuel Pais, become the de facto reference for arranging teams within an organization to enable effective software delivery. Team Topologies describes four fundamental team types and three team interaction patterns, and dives into the responsibility boundaries of teams and how teams can communicate or interact with other teams.

在 DevOps 的人员和组织空间中，我们看到了 Matthew Skelton 和 Manuel Pais 写的 Team topology 一书，它成为组织内部安排团队以实现有效软件交付的事实参考。团队拓扑描述了四种基本的团队类型和三种团队交互模式，并深入探讨了团队的职责边界以及团队如何与其他团队进行沟通或交互。

There is also increasing focus on post-incident "blameless postmortems" in becoming more akin to "healthy retrospectives", from which the entire organisation can learn from. Key leaders in the computing domain of resilience engineering and the "Learning From Incidents" community have been influential in driving this discussion.

此外，人们越来越关注事件后的“blameless postmortems”，以使其更接近于“healthy retrospectives”，整个组织可以从中学习。在弹性工程计算领域和“从事件中学习”社区的主要领导人在推动这一讨论方面具有影响力。

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