微服务,最早由Martin Fowler在他的官博中提出,原文和翻译如下:

Microservices

a definition of this new architectural term

The term "Microservice Architecture" has sprung up over the last few years to describe a particular way of designing software applications as suites of independently deployable services. While there is no precise definition of this architectural style, there are certain common characteristics around organization around business capability, automated deployment, intelligence in the endpoints, and decentralized control of languages and data.

25 March 2014

微服务,一种新的系统架构

一个新的名词“微服务”在过去的几年间迅速崛起,微服务描述的是用一种特别的方式,即设计若干个可独立实施的服务组装起整个软件应用。即一个完整的软件系统由多个可独立部署的套件组成。

虽然这种架构风格目前还没有明确的定义,然而他们有如下的共同点:组织结构,业务能力,自动部署,端点职能,对编程语言和数据流进行去中心化的控制。

"Microservices" - yet another new term on the crowded streets of software architecture. Although our natural inclination is to pass such things by with a contemptuous glance, this bit of terminology describes a style of software systems that we are finding more and more appealing. We've seen many projects use this style in the last few years, and results so far have been positive, so much so that for many of our colleagues this is becoming the default style for building enterprise applications. Sadly, however, there's not much information that outlines what the microservice style is and how to do it.

微服务是目前众多软件架构中的一种新概念,我们会用轻蔑的眼神对之不屑一顾。微服务,这简单的术语描述了软件系统的一种风格,且这种风格有着越来越多的追随者。我们已经看到近些年来的很多项目都在使用这种风格,使用效果来看是积极的,如此对于我们很多同事来说,这已经成为一种默认的企业应用架构啦。不幸的是,目前并没有太多的信息指明什么是微服务风格,要如何来使用它。

In short, the microservice architectural style [1] is an approach to developing a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms, often an HTTP resource API. These services are built around business capabilities and independently deployable by fully automated deployment machinery. There is a bare minimum of centralized management of these services, which may be written in different programming languages and use different data storage technologies.

简而言之,微服务架构风格【1】是一种开发一个单独的应用,这种应用可以认为是很小的服务的一个套间。每一个套间在他自己的进程中运行,并且轻量的方式来彼此通信,诸如常见的用HTTP协议的API接口来进行通信。这些小的服务套间是围绕业务功能进行构建,可通过完全自动化的部署机制独立部署。

To start explaining the microservice style it's useful to compare it to the monolithic style: a monolithic application built as a single unit. Enterprise Applications are often built in three main parts: a client-side user interface (consisting of HTML pages and javascript running in a browser on the user's machine) a database (consisting of many tables inserted into a common, and usually relational, database management system), and a server-side application. The server-side application will handle HTTP requests, execute domain logic, retrieve and update data from the database, and select and populate HTML views to be sent to the browser. This server-side application is a monolith - a single logical executable[2]. Any changes to the system involve building and deploying a new version of the server-side application.

在开始介绍微服务风格之前,将其与单块(monolithic)风格进行对比还是很有用的:一个单块应用系统是以一个单个单元的方式来构建的。企业应用系统经常包含三个主要部分:客户端用户界面(包含用户端的浏览器中的HTML页面,javascipt)、数据库(包括一系列的表插入数据到类似的,通常是关系型数据库中)和服务端应用系统。服务端通常用来处理HTTP请求,执行逻辑处理,检索和更新数据到数据库中,并且弹出HTML页面到浏览器中。服务端的应用是一个单块应用——单个可执行的逻辑程序【2】。对于系统的任何改变,都会涉及构建和部署一个新版本。

Such a monolithic server is a natural way to approach building such a system. All your logic for handling a request runs in a single process, allowing you to use the basic features of your language to divide up the application into classes, functions, and namespaces. With some care, you can run and test the application on a developer's laptop, and use a deployment pipeline to ensure that changes are properly tested and deployed into production. You can horizontally scale the monolith by running many instances behind a load-balancer.

上述的单块服务是一种常见的建立一个系统的方法。所有的处理逻辑都在一个单一的进程中运行。只允许你使用你编程语言的简单风格来分析你的应用系统到类,功能和命名空间。

Monolithic applications can be successful, but increasingly people are feeling frustrations with them - especially as more applications are being deployed to the cloud . Change cycles are tied together - a change made to a small part of the application, requires the entire monolith to be rebuilt and deployed. Over time it's often hard to keep a good modular structure, making it harder to keep changes that ought to only affect one module within that module. Scaling requires scaling of the entire application rather than parts of it that require greater resource.

单块应用系统可以成功,但是人们对它越发地感到挫败,尤其是当越来越多的应用系统被部署到云端。变更被绑定到一起。一个很小部分的一处变更,需要将整个单块应用系统进行重新构建和部署。随着时间的推移,单块应用通常难以保持一个良好的模块化结构,这使得它变得越来越难以将一个模块的变更的影响控制在该模块内。扩展需要扩展整个应用程序,而不是一个小部分,使得要求更多资源的。【就像滚雪球一样,越滚越大,之后难以掌控,牵一发动全身】

 

什么是微服务 Martin Fowler的microservices_应用程序

Figure 1: Monoliths and Microservices

图片1: 单片应用和微服务【图中内容自己翻译】

These frustrations have led to the microservice architectural style: building applications as suites of services. As well as the fact that services are independently deployable and scalable, each service also provides a firm module boundary, even allowing for different services to be written in different programming languages. They can also be managed by different teams .

这些挫折导致了微服务架构风格的诞生:建立服务套间的应用。服务之间是独立部署和扩展的,每一个服务同样提供稳固的模式,甚至允许不同的服务之间可用不同的语言进行编程。这也能让不同的团队进行管理。

We do not claim that the microservice style is novel or innovative, its roots go back at least to the design principles of Unix. But we do think that not enough people consider a microservice architecture and that many software developments would be better off if they used it.

我们并不认为微服务风格是新颖或创新的,它的根源至少可以追溯到Unix的设计原则。但是我们认为没有足够多的人考虑微服务体系结构,如果他们使用它,许多软件开发将会更好。

 

Characteristics of a Microservice Architecture

微服务架构的特征

We cannot say there is a formal definition of the microservices architectural style, but we can attempt to describe what we see as common characteristics for architectures that fit the label. As with any definition that outlines common characteristics, not all microservice architectures have all the characteristics, but we do expect that most microservice architectures exhibit most characteristics. While we authors have been active members of this rather loose community, our intention is to attempt a description of what we see in our own work and in similar efforts by teams we know of. In particular we are not laying down some definition to conform to.

我们并不能说微服务架构风格有个明确的定义,但是我们可以尝试着来描述这种架构的相似的特征。与概述公共特性的任何定义一样,并不是所有微服务体系结构都具有所有的特性,但是我们确实希望大多数微服务体系结构都具有最多的特性。虽然我们作者一直是这个相当松散的社区的活跃成员,但我们的意图是尝试描述我们在自己的工作中和我们所知的团队的类似工作中所看到的内容。特别地,我们并没有给出一些需要遵守的定义。

Componentization via Services

特征一:组件化服务

For as long as we've been involved in the software industry, there's been a desire to build systems by plugging together components, much in the way we see things are made in the physical world. During the last couple of decades we've seen considerable progress with large compendiums of common libraries that are part of most language platforms.

When talking about components we run into the difficult definition of what makes a component. Our definition is that a component is a unit of software that is independently replaceable and upgradeable.

Microservice architectures will use libraries, but their primary way of componentizing their own software is by breaking down into services. We define libraries as components that are linked into a program and called using in-memory function calls, while services are out-of-process components who communicate with a mechanism such as a web service request, or remote procedure call. (This is a different concept to that of a service object in many OO programs [3].)

One main reason for using services as components (rather than libraries) is that services are independently deployable. If you have an application [4] that consists of a multiple libraries in a single process, a change to any single component results in having to redeploy the entire application. But if that application is decomposed into multiple services, you can expect many single service changes to only require that service to be redeployed. That's not an absolute, some changes will change service interfaces resulting in some coordination, but the aim of a good microservice architecture is to minimize these through cohesive service boundaries and evolution mechanisms in the service contracts.

自从我们涉足软件行业以来,就有一种通过将组件连接在一起来构建系统的愿望,就像我们在现实世界中看到的那样。在过去的几十年里,我们已经看到了作为大多数语言平台一部分的公共库的大型概要的巨大进展。

 

在讨论组件时,我们遇到了组件组成的困难定义。我们的定义是,组件是软件的一个单元,可以独立地替换和升级。

 

微服务体系结构将使用库,但它们将自己的软件组件化的主要方法是将其分解为服务。我们将库定义为连接到程序并使用内存内函数调用调用的组件,而服务则是与机制(如web服务请求或远程过程调用)通信的进程外组件。(这与许多面向对象程序【3】中的服务对象概念不同。)

 

将服务用作组件(而不是库)的一个主要原因是服务是可独立部署的。如果您的应用程序【4】在一个流程中包含多个库,对任何单个组件的更改都会导致必须重新部署整个应用程序。但是,如果将应用程序分解为多个服务,则可以预期许多单个服务更改只需要重新部署该服务。这并不是绝对的,一些更改会更改服务接口,从而导致一些协调,但是好的微服务体系结构的目标是通过服务契约中的内聚服务边界和演化机制将这些更改最小化。

 

Another consequence of using services as components is a more explicit component interface. Most languages do not have a good mechanism for defining an explicit Published Interface. Often it's only documentation and discipline that prevents clients breaking a component's encapsulation, leading to overly-tight coupling between components. Services make it easier to avoid this by using explicit remote call mechanisms.

Using services like this does have downsides. Remote calls are more expensive than in-process calls, and thus remote APIs need to be coarser-grained, which is often more awkward to use. If you need to change the allocation of responsibilities between components, such movements of behavior are harder to do when you're crossing process boundaries.

At a first approximation, we can observe that services map to runtime processes, but that is only a first approximation. A service may consist of multiple processes that will always be developed and deployed together, such as an application process and a database that's only used by that service.

 

使用服务作为组件的另一个结果是更显式的组件接口。大多数语言都没有定义显式发布接口的良好机制。通常,只有文档和规程才能防止客户机破坏组件的封装,从而导致组件之间过于紧密的耦合。通过使用显式远程调用机制,服务可以更容易地避免这种情况。

 

使用这样的服务确实有缺点。远程调用比进程内调用更昂贵,因此远程api需要粗粒度的,而使用粗粒度的api通常更困难。如果您需要更改组件之间的职责分配,那么当您跨越流程边界时,这种行为的移动将更加困难。

 

在第一个近似案例中,我们可以观察到服务映射到运行的进程中,但这只是第一个近似的案例。一个服务可能由多个进程组成,这些流程总是一起开发和部署,比如一个应用程序流程和一个只由该服务使用的数据库。

 

Organized around Business Capabilities

特征二:围绕业务功能的组织架构

When looking to split a large application into parts, often management focuses on the technology layer, leading to UI teams, server-side logic teams, and database teams. When teams are separated along these lines, even simple changes can lead to a cross-team project taking time and budgetary approval. A smart team will optimise around this and plump for the lesser of two evils - just force the logic into whichever application they have access to. Logic everywhere in other words. This is an example of Conway's Law[5] in action.

Any organization that designs a system (defined broadly) will produce a design whose structure is a copy of the organization's communication structure.

-- Melvyn Conway, 1967

 

当考虑将大型应用程序分割成多个部分时,管理层通常关注于技术层,从而导致(新创建)UI团队、服务器端逻辑团队和数据库团队。当团队围绕这些进行拆分时,即使是简单的变更也会导致跨团队的项目花费时间和预算批准。一个聪明的团队将围绕这一点进行优化,并选择两害相权取其轻——只要将逻辑强制应用到他们能够访问的任何应用程序中即可。换句话说,逻辑无处不在。这是康威定律【5】的一个例子。

 

 任何设计系统(广义定义)的组织都会产生一个结构是组织通信结构的副本的设计。

 

 —梅尔文·康韦,1967年

 

什么是微服务 Martin Fowler的microservices_sed_02

Figure 2: Conway's Law in action

The microservice approach to division is different, splitting up into services organized around business capability. Such services take a broad-stack implementation of software for that business area, including user-interface, persistant storage, and any external collaborations. Consequently the teams are cross-functional, including the full range of skills required for the development: user-experience, database, and project management.

什么是微服务 Martin Fowler的microservices_体系结构_03

Figure 3: Service boundaries reinforced by team boundaries

How big is a microservice?

Although “microservice” has become a popular name for this architectural style, its name does lead to an unfortunate focus on the size of service, and arguments about what constitutes “micro”. In our conversations with microservice practitioners, we see a range of sizes of services. The largest sizes reported follow Amazon's notion of the Two Pizza Team (i.e. the whole team can be fed by two pizzas), meaning no more than a dozen people. On the smaller size scale we've seen setups where a team of half-a-dozen would support half-a-dozen services.

This leads to the question of whether there are sufficiently large differences within this size range that the service-per-dozen-people and service-per-person sizes shouldn't be lumped under one microservices label. At the moment we think it's better to group them together, but it's certainly possible that we'll change our mind as we explore this style further.

One company organised in this way is www.comparethemarket.com. Cross functional teams are responsible for building and operating each product and each product is split out into a number of individual services communicating via a message bus.

Large monolithic applications can always be modularized around business capabilities too, although that's not the common case. Certainly we would urge a large team building a monolithic application to divide itself along business lines. The main issue we have seen here, is that they tend to be organised around too many contexts. If the monolith spans many of these modular boundaries it can be difficult for individual members of a team to fit them into their short-term memory. Additionally we see that the modular lines require a great deal of discipline to enforce. The necessarily more explicit separation required by service components makes it easier to keep the team boundaries clear.

 

微服务有多大?

尽管“微服务”已经成为这种体系结构风格的一个流行名称,但它的名称确实导致了对服务大小的不幸关注,以及关于什么构成“微”的争论。在与微服务从业人员的对话中,我们看到了一系列大小不一的服务。报告中最大的披萨尺寸遵循了亚马逊的概念,即两个披萨团队(即整个团队可以吃两个披萨),即不超过12个人。在规模较小的情况下,我们已经看到了由6人组成的团队支持6人服务的设置。

这就引出了一个问题:在这个大小范围内是否存在足够大的差异,以至于不应该将每12个人的服务大小和每个人的服务大小集中在一个微服务标签下。目前我们认为最好将它们组合在一起,但随着我们进一步探索这种风格,我们肯定会改变主意。

这样组织的一个公司是www.comparethemarket.com。跨功能团队负责构建和操作每个产品,每个产品被划分为许多通过消息总线通信的单个服务。

大型单片应用程序也可以围绕业务功能模块化,尽管这不是常见的情况。当然,我们会敦促构建统一应用程序的大型团队按照业务线进行划分。我们在这里看到的主要问题是,它们往往围绕着太多的上下文进行组织。如果一个整体跨越了这些模块的边界,那么团队中的个体成员就很难将它们放入他们的短期记忆中。此外,我们还看到,模块线需要大量的规程来执行。服务组件必需的更显式的分离使保持团队边界清晰变得更容易。

 

Products not Projects

特征三:产品而不是项目

Most application development efforts that we see use a project model: where the aim is to deliver some piece of software which is then considered to be completed. On completion the software is handed over to a maintenance organization and the project team that built it is disbanded.

Microservice proponents tend to avoid this model, preferring instead the notion that a team should own a product over its full lifetime. A common inspiration for this is Amazon's notion of "you build, you run it" where a development team takes full responsibility for the software in production. This brings developers into day-to-day contact with how their software behaves in production and increases contact with their users, as they have to take on at least some of the support burden.

The product mentality, ties in with the linkage to business capabilities. Rather than looking at the software as a set of functionality to be completed, there is an on-going relationship where the question is how can software assist its users to enhance the business capability.

There's no reason why this same approach can't be taken with monolithic applications, but the smaller granularity of services can make it easier to create the personal relationships between service developers and their users.

 

我们看到的大多数应用程序开发工作都使用项目模型:其中的目标是交付一些软件,然后认为这些软件已经完成。完成后,软件被移交给维护组织,构建软件的项目团队被解散。

微服务的支持者倾向于避免这种模型,而倾向于认为团队应该在整个生命周期内拥有一个产品。一个常见的灵感来自Amazon的“构建,运行”概念,在这个概念中,开发团队对生产中的软件承担全部责任。这将使开发人员与他们的软件在生产中的行为进行日常接触,并增加与用户的联系,因为他们必须承担至少一部分支持负担。

产品心态与业务能力的联系紧密相连。与其将软件视为一组要完成的功能,还不如将其视为一种持续的关系,问题是软件如何帮助用户增强业务能力。

没有理由不能在单一应用程序中采用相同的方法,但是较小粒度的服务可以更容易地在服务开发人员和用户之间创建个人关系。

 

Smart endpoints and dumb pipes

特征四:智能终端而不是傻瓜式管道

When building communication structures between different processes, we've seen many products and approaches that stress putting significant smarts into the communication mechanism itself. A good example of this is the Enterprise Service Bus (ESB), where ESB products often include sophisticated facilities for message routing, choreography, transformation, and applying business rules.

The microservice community favours an alternative approach: smart endpoints and dumb pipes. Applications built from microservices aim to be as decoupled and as cohesive as possible - they own their own domain logic and act more as filters in the classical Unix sense - receiving a request, applying logic as appropriate and producing a response. These are choreographed using simple RESTish protocols rather than complex protocols such as WS-Choreography or BPEL or orchestration by a central tool.

The two protocols used most commonly are HTTP request-response with resource API's and lightweight messaging[8]. The best expression of the first is

Be of the web, not behind the web

-- Ian Robinson

Microservices and SOA

When we've talked about microservices a common question is whether this is just Service Oriented Architecture (SOA) that we saw a decade ago. There is merit to this point, because the microservice style is very similar to what some advocates of SOA have been in favor of. The problem, however, is that SOA means too many different things, and that most of the time that we come across something called "SOA" it's significantly different to the style we're describing here, usually due to a focus on ESBs used to integrate monolithic applications.

In particular we have seen so many botched implementations of service orientation - from the tendency to hide complexity away in ESB's [6], to failed multi-year initiatives that cost millions and deliver no value, to centralised governance models that actively inhibit change, that it is sometimes difficult to see past these problems.

Certainly, many of the techniques in use in the microservice community have grown from the experiences of developers integrating services in large organisations. The Tolerant Reader pattern is an example of this. Efforts to use the web have contributed, using simple protocols is another approach derived from these experiences - a reaction away from central standards that have reached a complexity that is, frankly, breathtaking. (Any time you need an ontology to manage your ontologies you know you are in deep trouble.)

This common manifestation of SOA has led some microservice advocates to reject the SOA label entirely, although others consider microservices to be one form of SOA [7], perhaps service orientation done right. Either way, the fact that SOA means such different things means it's valuable to have a term that more crisply defines this architectural style.

 

Microservice teams use the principles and protocols that the world wide web (and to a large extent, Unix) is built on. Often used resources can be cached with very little effort on the part of developers or operations folk.

The second approach in common use is messaging over a lightweight message bus. The infrastructure chosen is typically dumb (dumb as in acts as a message router only) - simple implementations such as RabbitMQ or ZeroMQ don't do much more than provide a reliable asynchronous fabric - the smarts still live in the end points that are producing and consuming messages; in the services.

In a monolith, the components are executing in-process and communication between them is via either method invocation or function call. The biggest issue in changing a monolith into microservices lies in changing the communication pattern. A naive conversion from in-memory method calls to RPC leads to chatty communications which don't perform well. Instead you need to replace the fine-grained communication with a coarser -grained approach.

Decentralized Governance

特征五:去中心化治理,分散治理

One of the consequences of centralised governance is the tendency to standardise on single technology platforms. Experience shows that this approach is constricting - not every problem is a nail and not every solution a hammer. We prefer using the right tool for the job and while monolithic applications can take advantage of different languages to a certain extent, it isn't that common.

Splitting the monolith's components out into services we have a choice when building each of them. You want to use Node.js to standup a simple reports page? Go for it. C++ for a particularly gnarly near-real-time component? Fine. You want to swap in a different flavour of database that better suits the read behaviour of one component? We have the technology to rebuild him.

Of course, just because you can do something, doesn't mean you should - but partitioning your system in this way means you have the option.

Teams building microservices prefer a different approach to standards too. Rather than use a set of defined standards written down somewhere on paper they prefer the idea of producing useful tools that other developers can use to solve similar problems to the ones they are facing. These tools are usually harvested from implementations and shared with a wider group, sometimes, but not exclusively using an internal open source model. Now that git and github have become the de facto version control system of choice, open source practices are becoming more and more common in-house .

Netflix is a good example of an organisation that follows this philosophy. Sharing useful and, above all, battle-tested code as libraries encourages other developers to solve similar problems in similar ways yet leaves the door open to picking a different approach if required. Shared libraries tend to be focused on common problems of data storage, inter-process communication and as we discuss further below, infrastructure automation.

For the microservice community, overheads are particularly unattractive. That isn't to say that the community doesn't value service contracts. Quite the opposite, since there tend to be many more of them. It's just that they are looking at different ways of managing those contracts. Patterns like Tolerant Reader and Consumer-Driven Contracts are often applied to microservices. These aid service contracts in evolving independently. Executing consumer driven contracts as part of your build increases confidence and provides fast feedback on whether your services are functioning. Indeed we know of a team in Australia who drive the build of new services with consumer driven contracts. They use simple tools that allow them to define the contract for a service. This becomes part of the automated build before code for the new service is even written. The service is then built out only to the point where it satisfies the contract - an elegant approach to avoid the 'YAGNI'[9] dilemma when building new software. These techniques and the tooling growing up around them, limit the need for central contract management by decreasing the temporal coupling between services.

Perhaps the apogee of decentralised governance is the build it / run it ethos popularised by Amazon. Teams are responsible for all aspects of the software they build including operating the software 24/7. Devolution of this level of responsibility is definitely not the norm but we do see more and more companies pushing responsibility to the development teams. Netflix is another organisation that has adopted this ethos[11]. Being woken up at 3am every night by your pager is certainly a powerful incentive to focus on quality when writing your code. These ideas are about as far away from the traditional centralized governance model as it is possible to be.

Decentralized Data Management

特征六:分散的数据管理

Decentralization of data management presents in a number of different ways. At the most abstract level, it means that the conceptual model of the world will differ between systems. This is a common issue when integrating across a large enterprise, the sales view of a customer will differ from the support view. Some things that are called customers in the sales view may not appear at all in the support view. Those that do may have different attributes and (worse) common attributes with subtly different semantics.

Many languages, many options

The growth of JVM as a platform is just the latest example of mixing languages within a common platform. It's been common practice to shell-out to a higher level language to take advantage of higher level abstractions for decades. As is dropping down to the metal and writing performance sensitive code in a lower level one. However, many monoliths don't need this level of performance optimisation nor are DSL's and higher level abstractions that common (to our dismay). Instead monoliths are usually single language and the tendency is to limit the number of technologies in use [10].

This issue is common between applications, but can also occur within applications, particular when that application is divided into separate components. A useful way of thinking about this is the Domain-Driven Design notion of Bounded Context. DDD divides a complex domain up into multiple bounded contexts and maps out the relationships between them. This process is useful for both monolithic and microservice architectures, but there is a natural correlation between service and context boundaries that helps clarify, and as we describe in the section on business capabilities, reinforce the separations.

As well as decentralizing decisions about conceptual models, microservices also decentralize data storage decisions. While monolithic applications prefer a single logical database for persistant data, enterprises often prefer a single database across a range of applications - many of these decisions driven through vendor's commercial models around licensing. Microservices prefer letting each service manage its own database, either different instances of the same database technology, or entirely different database systems - an approach called Polyglot Persistence. You can use polyglot persistence in a monolith, but it appears more frequently with microservices.

Battle-tested standards and enforced standards

It's a bit of a dichotomy that microservice teams tend to eschew the kind of rigid enforced standards laid down by enterprise architecture groups but will happily use and even evangelise the use of open standards such as HTTP, ATOM and other microformats.

The key difference is how the standards are developed and how they are enforced. Standards managed by groups such as the IETF only become standards when there are several live implementations of them in the wider world and which often grow from successful open-source projects.

These standards are a world apart from many in a corporate world, which are often developed by groups that have little recent programming experience or overly influenced by vendors.

 什么是微服务 Martin Fowler的microservices_ide_04

Decentralizing responsibility for data across microservices has implications for managing updates. The common approach to dealing with updates has been to use transactions to guarantee consistency when updating multiple resources. This approach is often used within monoliths.

Using transactions like this helps with consistency, but imposes significant temporal coupling, which is problematic across multiple services. Distributed transactions are notoriously difficult to implement and as a consequence microservice architectures emphasize transactionless coordination between services, with explicit recognition that consistency may only be eventual consistency and problems are dealt with by compensating operations.

Choosing to manage inconsistencies in this way is a new challenge for many development teams, but it is one that often matches business practice. Often businesses handle a degree of inconsistency in order to respond quickly to demand, while having some kind of reversal process to deal with mistakes. The trade-off is worth it as long as the cost of fixing mistakes is less than the cost of lost business under greater consistency.

Infrastructure Automation

特征七:基础设施高度自动化

Infrastructure automation techniques have evolved enormously over the last few years - the evolution of the cloud and AWS in particular has reduced the operational complexity of building, deploying and operating microservices.

Many of the products or systems being build with microservices are being built by teams with extensive experience of Continuous Delivery and it's precursor, Continuous Integration. Teams building software this way make extensive use of infrastructure automation techniques. This is illustrated in the build pipeline shown below.

什么是微服务 Martin Fowler的microservices_应用程序_05

 

Figure 5: basic build pipeline

 

Since this isn't an article on Continuous Delivery we will call attention to just a couple of key features here. We want as much confidence as possible that our software is working, so we run lots of automated tests. Promotion of working software 'up' the pipeline means we automate deployment to each new environment.

 

Make it easy to do the right thing

One side effect we have found of increased automation as a consequence of continuous delivery and deployment is the creation of useful tools to help developers and operations folk. Tooling for creating artefacts, managing codebases, standing up simple services or for adding standard monitoring and logging are pretty common now. The best example on the web is probably Netflix's set of open source tools, but there are others including Dropwizard which we have used extensively.

 

A monolithic application will be built, tested and pushed through these environments quite happlily. It turns out that once you have invested in automating the path to production for a monolith, then deploying more applications doesn't seem so scary any more. Remember, one of the aims of CD is to make deployment boring, so whether its one or three applications, as long as its still boring it doesn't matter[12].

 

Another area where we see teams using extensive infrastructure automation is when managing microservices in production. In contrast to our assertion above that as long as deployment is boring there isn't that much difference between monoliths and microservices, the operational landscape for each can be strikingly different.

什么是微服务 Martin Fowler的microservices_应用程序_06

Figure 6: Module deployment often differs

Design for failure

特征八:容错设计

A consequence of using services as components, is that applications need to be designed so that they can tolerate the failure of services. Any service call could fail due to unavailability of the supplier, the client has to respond to this as gracefully as possible. This is a disadvantage compared to a monolithic design as it introduces additional complexity to handle it. The consequence is that microservice teams constantly reflect on how service failures affect the user experience. Netflix's Simian Army induces failures of services and even datacenters during the working day to test both the application's resilience and monitoring.

The circuit breaker and production ready code

Circuit Breaker appears in Release It!alongside other patterns such as Bulkhead and Timeout. Implemented together, these patterns are crucially important when building communicating applications. This Netflix blog entry does a great job of explaining their application of them.

This kind of automated testing in production would be enough to give most operation groups the kind of shivers usually preceding a week off work. This isn't to say that monolithic architectural styles aren't capable of sophisticated monitoring setups - it's just less common in our experience.

Since services can fail at any time, it's important to be able to detect the failures quickly and, if possible, automatically restore service. Microservice applications put a lot of emphasis on real-time monitoring of the application, checking both architectural elements (how many requests per second is the database getting) and business relevant metrics (such as how many orders per minute are received). Semantic monitoring can provide an early warning system of something going wrong that triggers development teams to follow up and investigate.

This is particularly important to a microservices architecture because the microservice preference towards choreography and event collaboration leads to emergent behavior. While many pundits praise the value of serendipitous emergence, the truth is that emergent behavior can sometimes be a bad thing. Monitoring is vital to spot bad emergent behavior quickly so it can be fixed.

Synchronous calls considered harmful

Any time you have a number of synchronous calls between services you will encounter the multiplicative effect of downtime. Simply, this is when the downtime of your system becomes the product of the downtimes of the individual components. You face a choice, making your calls asynchronous or managing the downtime. At www.guardian.co.uk they have implemented a simple rule on the new platform - one synchronous call per user request while at Netflix, their platform API redesign has built asynchronicity into the API fabric.

Monoliths can be built to be as transparent as a microservice - in fact, they should be. The difference is that you absolutely need to know when services running in different processes are disconnected. With libraries within the same process this kind of transparency is less likely to be useful.

Microservice teams would expect to see sophisticated monitoring and logging setups for each individual service such as dashboards showing up/down status and a variety of operational and business relevant metrics. Details on circuit breaker status, current throughput and latency are other examples we often encounter in the wild.

Evolutionary Design

特征九:进化型设计

Microservice practitioners, usually have come from an evolutionary design background and see service decomposition as a further tool to enable application developers to control changes in their application without slowing down change. Change control doesn't necessarily mean change reduction - with the right attitudes and tools you can make frequent, fast, and well-controlled changes to software.

Whenever you try to break a software system into components, you're faced with the decision of how to divide up the pieces - what are the principles on which we decide to slice up our application? The key property of a component is the notion of independent replacement and upgradeability[13] - which implies we look for points where we can imagine rewriting a component without affecting its collaborators. Indeed many microservice groups take this further by explicitly expecting many services to be scrapped rather than evolved in the longer term.

The Guardian website is a good example of an application that was designed and built as a monolith, but has been evolving in a microservice direction. The monolith still is the core of the website, but they prefer to add new features by building microservices that use the monolith's API. This approach is particularly handy for features that are inherently temporary, such as specialized pages to handle a sporting event. Such a part of the website can quickly be put together using rapid development languages, and removed once the event is over. We've seen similar approaches at a financial institution where new services are added for a market opportunity and discarded after a few months or even weeks.

This emphasis on replaceability is a special case of a more general principle of modular design, which is to drive modularity through the pattern of change [14]. You want to keep things that change at the same time in the same module. Parts of a system that change rarely should be in different services to those that are currently undergoing lots of churn. If you find yourself repeatedly changing two services together, that's a sign that they should be merged.

Putting components into services adds an opportunity for more granular release planning. With a monolith any changes require a full build and deployment of the entire application. With microservices, however, you only need to redeploy the service(s) you modified. This can simplify and speed up the release process. The downside is that you have to worry about changes to one service breaking its consumers. The traditional integration approach is to try to deal with this problem using versioning, but the preference in the microservice world is to only use versioning as a last resort. We can avoid a lot of versioning by designing services to be as tolerant as possible to changes in their suppliers.

Are Microservices the Future?

Our main aim in writing this article is to explain the major ideas and principles of microservices. By taking the time to do this we clearly think that the microservices architectural style is an important idea - one worth serious consideration for enterprise applications. We have recently built several systems using the style and know of others who have used and favor this approach.

Those we know about who are in some way pioneering the architectural style include Amazon, Netflix, The Guardian, the UK Government Digital Service, realestate.com.au, Forward and comparethemarket.com. The conference circuit in 2013 was full of examples of companies that are moving to something that would class as microservices - including Travis CI. In addition there are plenty of organizations that have long been doing what we would class as microservices, but without ever using the name. (Often this is labelled as SOA - although, as we've said, SOA comes in many contradictory forms. [15])

Despite these positive experiences, however, we aren't arguing that we are certain that microservices are the future direction for software architectures. While our experiences so far are positive compared to monolithic applications, we're conscious of the fact that not enough time has passed for us to make a full judgement.

 

Our colleague Sam Newman spent most of 2014 working on a book that captures our experiences with building microservices. This should be your next step if you want a deeper dive into the topic.

Often the true consequences of your architectural decisions are only evident several years after you made them. We have seen projects where a good team, with a strong desire for modularity, has built a monolithic architecture that has decayed over the years. Many people believe that such decay is less likely with microservices, since the service boundaries are explicit and hard to patch around. Yet until we see enough systems with enough age, we can't truly assess how microservice architectures mature.

There are certainly reasons why one might expect microservices to mature poorly. In any effort at componentization, success depends on how well the software fits into components. It's hard to figure out exactly where the component boundaries should lie. Evolutionary design recognizes the difficulties of getting boundaries right and thus the importance of it being easy to refactor them. But when your components are services with remote communications, then refactoring is much harder than with in-process libraries. Moving code is difficult across service boundaries, any interface changes need to be coordinated between participants, layers of backwards compatibility need to be added, and testing is made more complicated.

Another issue is If the components do not compose cleanly, then all you are doing is shifting complexity from inside a component to the connections between components. Not just does this just move complexity around, it moves it to a place that's less explicit and harder to control. It's easy to think things are better when you are looking at the inside of a small, simple component, while missing messy connections between services.

Finally, there is the factor of team skill. New techniques tend to be adopted by more skillful teams. But a technique that is more effective for a more skillful team isn't necessarily going to work for less skillful teams. We've seen plenty of cases of less skillful teams building messy monolithic architectures, but it takes time to see what happens when this kind of mess occurs with microservices. A poor team will always create a poor system - it's very hard to tell if microservices reduce the mess in this case or make it worse.

One reasonable argument we've heard is that you shouldn't start with a microservices architecture. Instead begin with a monolith, keep it modular, and split it into microservices once the monolith becomes a problem. (Although this advice isn't ideal, since a good in-process interface is usually not a good service interface.)

So we write this with cautious optimism. So far, we've seen enough about the microservice style to feel that it can be a worthwhile road to tread. We can't say for sure where we'll end up, but one of the challenges of software development is that you can only make decisions based on the imperfect information that you currently have to hand.

 


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