什麼是微服務 Martin Fowler的microservices

https://martinfowler.com/articles/microservices.html

https://martinfowler.com/microservices/

微服務，最先由Martin Fowler在他的官博中提出，原文和翻譯以下：

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

微服務，一種新的系統架構

一個新的名詞「微服務」在過去的幾年間迅速崛起，微服務描述的是用一種特別的方式，即設計若干個可獨立實施的服務組裝起整個軟件應用。即一個完整的軟件系統由多個可獨立部署的套件組成。

雖然這種架構風格目前尚未明確的定義，然而他們有以下的共同點：組織結構，業務能力，自動部署，端點職能，對編程語言和數據流進行去中心化的控制。

"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.

微服務是目前衆多軟件架構中的一種新概念，咱們會用輕蔑的眼神對之不屑一顧。微服務，這簡單的術語描述了軟件系統的一種風格，且這種風格有着愈來愈多的追隨者。咱們已經看到近些年來的不少項目都在使用這種風格，使用效果來看是積極的，如此對於咱們不少同事來講，這已經成爲一種默認的企業應用架構啦。不幸的是，目前並無太多的信息指明什麼是微服務風格，要如何來使用它。

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.

簡而言之，微服務架構風格【1】是一種開發一個單獨的應用，這種應用能夠認爲是很小的服務的一個套間。每個套間在他本身的進程中運行，而且輕量的方式來彼此通訊，諸如常見的用HTTP協議的API接口來進行通訊。這些小的服務套間是圍繞業務功能進行構建，可經過徹底自動化的部署機制獨立部署。

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.

在開始介紹微服務風格以前，將其與單塊（monolithic）風格進行對比仍是頗有用的：一個單塊應用系統是以一個單個單元的方式來構建的。企業應用系統常常包含三個主要部分：客戶端用戶界面(包含用戶端的瀏覽器中的HTML頁面，javascipt)、數據庫(包括一系列的表插入數據到相似的，一般是關係型數據庫中)和服務端應用系統。服務端一般用來處理HTTP請求，執行邏輯處理，檢索和更新數據到數據庫中，而且彈出HTML頁面到瀏覽器中。服務端的應用是一個單塊應用——單個可執行的邏輯程序【2】。對於系統的任何改變，都會涉及構建和部署一個新版本。

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.

上述的單塊服務是一種常見的創建一個系統的方法。全部的處理邏輯都在一個單一的進程中運行。只容許你使用你編程語言的簡單風格來分析你的應用系統到類，功能和命名空間。

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.

單塊應用系統能夠成功，可是人們對它愈加地感到挫敗，尤爲是當愈來愈多的應用系統被部署到雲端。變動被綁定到一塊兒。一個很小部分的一處變動，須要將整個單塊應用系統進行從新構建和部署。隨着時間的推移，單塊應用一般難以保持一個良好的模塊化結構，這使得它變得愈來愈難以將一個模塊的變動的影響控制在該模塊內。擴展須要擴展整個應用程序，而不是一個小部分，使得要求更多資源的。【就像滾雪球同樣，越滾越大，以後難以掌控，牽一髮動全身】

 

Figure 1: Monoliths and Microservices

圖片1： 單片應用和微服務【圖中內容本身翻譯】

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 .

這些挫折致使了微服務架構風格的誕生：創建服務套間的應用。服務之間是獨立部署和擴展的，每個服務一樣提供穩固的模式，甚至容許不一樣的服務之間可用不一樣的語言進行編程。這也能讓不一樣的團隊進行管理。

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.

咱們並不認爲微服務風格是新穎或創新的，它的根源至少能夠追溯到Unix的設計原則。可是咱們認爲沒有足夠多的人考慮微服務體系結構，若是他們使用它，許多軟件開發將會更好。

 

Characteristics of a Microservice Architecture

微服務架構的特徵

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.

咱們並不能說微服務架構風格有個明確的定義，可是咱們能夠嘗試着來描述這種架構的類似的特徵。與概述公共特性的任何定義同樣，並非全部微服務體系結構都具備全部的特性，可是咱們確實但願大多數微服務體系結構都具備最多的特性。雖然咱們做者一直是這個至關鬆散的社區的活躍成員，但咱們的意圖是嘗試描述咱們在本身的工做中和咱們所知的團隊的相似工做中所看到的內容。特別地，咱們並無給出一些須要遵照的定義。

Componentization via Services

特徵一：組件化服務
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.

自從咱們涉足軟件行業以來，就有一種經過將組件鏈接在一塊兒來構建系統的願望，就像咱們在現實世界中看到的那樣。在過去的幾十年裏，咱們已經看到了做爲大多數語言平臺一部分的公共庫的大型概要的巨大進展。

 

在討論組件時，咱們遇到了組件組成的困難定義。咱們的定義是，組件是軟件的一個單元，能夠獨立地替換和升級。

 

微服務體系結構將使用庫，但它們將本身的軟件組件化的主要方法是將其分解爲服務。咱們將庫定義爲鏈接到程序並使用內存內函數調用調用的組件，而服務則是與機制(如web服務請求或遠程過程調用)通訊的進程外組件。(這與許多面向對象程序【3】中的服務對象概念不一樣。)

 

將服務用做組件(而不是庫)的一個主要緣由是服務是可獨立部署的。若是您的應用程序【4】在一個流程中包含多個庫，對任何單個組件的更改都會致使必須從新部署整個應用程序。可是，若是將應用程序分解爲多個服務，則能夠預期許多單個服務更改只須要從新部署該服務。這並非絕對的，一些更改會更改服務接口，從而致使一些協調，可是好的微服務體系結構的目標是經過服務契約中的內聚服務邊界和演化機制將這些更改最小化。

 

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.

 

使用服務做爲組件的另外一個結果是更顯式的組件接口。大多數語言都沒有定義顯式發佈接口的良好機制。一般，只有文檔和規程才能防止客戶機破壞組件的封裝，從而致使組件之間過於緊密的耦合。經過使用顯式遠程調用機制，服務能夠更容易地避免這種狀況。

 

使用這樣的服務確實有缺點。遠程調用比進程內調用更昂貴，所以遠程api須要粗粒度的，而使用粗粒度的api一般更困難。若是您須要更改組件之間的職責分配，那麼當您跨越流程邊界時，這種行爲的移動將更加困難。

 

在第一個近似案例中，咱們能夠觀察到服務映射到運行的進程中，但這只是第一個近似的案例。一個服務可能由多個進程組成，這些流程老是一塊兒開發和部署，好比一個應用程序流程和一個只由該服務使用的數據庫。

 

Organized around Business Capabilities

特徵二：圍繞業務功能的組織架構
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

 

當考慮將大型應用程序分割成多個部分時，管理層一般關注於技術層，從而致使(新建立)UI團隊、服務器端邏輯團隊和數據庫團隊。當團隊圍繞這些進行拆分時，即便是簡單的變動也會致使跨團隊的項目花費時間和預算批准。一個聰明的團隊將圍繞這一點進行優化，並選擇兩害相權取其輕——只要將邏輯強制應用到他們可以訪問的任何應用程序中便可。換句話說，邏輯無處不在。這是康威定律【5】的一個例子。

 

 任何設計系統(廣義定義)的組織都會產生一個結構是組織通訊結構的副本的設計。

 

 —梅爾文·康韋，1967年

 

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.

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.

 

微服務有多大?
儘管「微服務」已經成爲這種體系結構風格的一個流行名稱，但它的名稱確實致使了對服務大小的不幸關注，以及關於什麼構成「微」的爭論。在與微服務從業人員的對話中，咱們看到了一系列大小不一的服務。報告中最大的披薩尺寸遵循了亞馬遜的概念，即兩個披薩團隊(即整個團隊能夠吃兩個披薩)，即不超過12我的。在規模較小的狀況下，咱們已經看到了由6人組成的團隊支持6人服務的設置。
這就引出了一個問題:在這個大小範圍內是否存在足夠大的差別，以致於不該該將每12我的的服務大小和每一個人的服務大小集中在一個微服務標籤下。目前咱們認爲最好將它們組合在一塊兒，但隨着咱們進一步探索這種風格，咱們確定會改變主意。
這樣組織的一個公司是www.comparethemarket.com。跨功能團隊負責構建和操做每一個產品，每一個產品被劃分爲許多經過消息總線通訊的單個服務。
大型單片應用程序也能夠圍繞業務功能模塊化，儘管這不是常見的狀況。固然，咱們會敦促構建統一應用程序的大型團隊按照業務線進行劃分。咱們在這裏看到的主要問題是，它們每每圍繞着太多的上下文進行組織。若是一個總體跨越了這些模塊的邊界，那麼團隊中的個體成員就很難將它們放入他們的短時間記憶中。此外，咱們還看到，模塊線須要大量的規程來執行。服務組件必需的更顯式的分離使保持團隊邊界清晰變得更容易。

 

Products not Projects

特徵三：產品而不是項目
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.

 

咱們看到的大多數應用程序開發工做都使用項目模型:其中的目標是交付一些軟件，而後認爲這些軟件已經完成。完成後，軟件被移交給維護組織，構建軟件的項目團隊被解散。
微服務的支持者傾向於避免這種模型，而傾向於認爲團隊應該在整個生命週期內擁有一個產品。一個常見的靈感來自Amazon的「構建，運行」概念，在這個概念中，開發團隊對生產中的軟件承擔所有責任。這將使開發人員與他們的軟件在生產中的行爲進行平常接觸，並增長與用戶的聯繫，由於他們必須承擔至少一部分支持負擔。
產品心態與業務能力的聯繫緊密相連。與其將軟件視爲一組要完成的功能，還不如將其視爲一種持續的關係，問題是軟件如何幫助用戶加強業務能力。
沒有理由不能在單一應用程序中採用相同的方法，可是較小粒度的服務能夠更容易地在服務開發人員和用戶之間建立我的關係。

 

Smart endpoints and dumb pipes

特徵四：智能終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

特徵五：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

特徵六：分散的數據管理
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.

 

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

特徵七：基礎設施高度自動化
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.

 

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.

Figure 6: Module deployment often differs

Design for failure

特徵八：容錯設計
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

特徵九：進化型設計

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.