作者：Sam Newman
出版社：O'Reilly Media
副标题：Designing Fine-Grained Systems
发行时间：February 20th 2015
来源：下载的 pdf 版本
Goodreads：4.16 (2130 Ratings)
豆瓣：7.8（82人评价）
Eric Evans’s book Domain-Driven Design (Addison-Wesley) helped us understand the importance of representing the real world in our code, and showed us better ways to model our systems. The concept of continuous delivery showed how we can more effectively and efficiently get our software into production, instilling in us the idea that we should treat every check-in as a release candidate. Our understanding of how the Web works has led us to develop better ways of having machines talk to other machines. Alistair Cockburn’s concept of hexagonal architecture guided us away from layered architectures where business logic could hide. Virtualization platforms allowed us to provision and resize our machines at will, with infrastructure automation giving us a way to handle these machines at scale. Some large, successful organizations like Amazon and Google espoused the view of small teams owning the full lifecycle of their services. And, more recently, Netflix has shared with us ways of building antifragile systems at a scale that would have been hard to comprehend just 10 years ago.
Domain-driven design. Continuous delivery. On-demand virtualization. Infrastructure automation. Small autonomous teams. Systems at scale. Microservices have emerged from this world. They weren’t invented or described before the fact; they emerged as a trend, or a pattern, from real-world use. But they exist only because of all that has gone before. Throughout this book, I will pull strands out of this prior work to help paint a picture of how to build, manage, and evolve microservices.
Within a monolithic system, we fight against these forces by trying to ensure our code is more cohesive, often by creating abstractions or modules. Cohesion — the drive to have related code grouped together — is an important concept when we think about microservices. This is reinforced by Robert C. Martin’s definition of the Single Responsibility Principle, which states “Gather together those things that change for the same reason, and separate those things that change for different reasons.”
Microservices take this same approach to independent services. We focus our service boundaries on business boundaries, making it obvious where code lives for a given piece of functionality. And by keeping this service focused on an explicit boundary, we avoid the temptation for it to grow too large, with all the associated difficulties that this can introduce.
The question I am often asked is how small is small? Giving a number for lines of code is problematic, as some languages are more expressive than others and can therefore do more in fewer lines of code. We must also consider the fact that we could be pulling in multiple dependencies, which themselves contain many lines of code. In addition, some part of your domain may be legitimately complex, requiring more code. Jon Eaves at RealEstate.com.au in Australia characterizes a microservice as something that could be rewritten in two weeks, a rule of thumb that makes sense for his particular context.
Gilt, an online fashion retailer, adopted microservices for this exact reason. Starting in 2007 with a monolithic Rails application, by 2009 Gilt’s system was unable to cope with the load being placed on it. By splitting out core parts of its system, Gilt was better able to deal with its traffic spikes, and today has over 450 microservices, each one running on multiple separate machines.
When embracing on-demand provisioning systems like those provided by Amazon Web Services, we can even apply this scaling on demand for those pieces that need it. This allows us to control our costs more effectively. It’s not often that an architectural approach can be so closely correlated to an almost immediate cost savings.
With microservices, think of us opening up seams in our system that are addressable by outside parties. As circumstances change, we can build things in different ways. With a monolithic application, I often have one coarse-grained seam that can be used from the outside. If I want to break that up to get something more useful, I’ll need a hammer! In Chapter 5, I’ll discuss ways for you to break apart existing monolithic systems, and hopefully change them into some reusable, re-composable microservices.
Service-oriented architecture (SOA) is a design approach where multiple services collaborate to provide some end set of capabilities. A service here typically means a completely separate operating system process. Communication between these services occurs via calls across a network rather than method calls within a process boundary.
SOA emerged as an approach to combat the challenges of the large monolithic applications. It is an approach that aims to promote the reusability of software; two or more end-user applications, for example, could both use the same services. It aims to make it easier to maintain or rewrite software, as theoretically we can replace one service with another without anyone knowing, as long as the semantics of the service don’t change too much.
SOA at its heart is a very sensible idea. However, despite many efforts, there is a lack of good consensus on how to do SOA well. In my opinion, much of the industry has failed to look holistically enough at the problem and present a compelling alternative to the narrative set out by various vendors in this space.
The microservice approach has emerged from real-world use, taking our better understanding of systems and architecture to do SOA well. So you should instead think of microservices as a specific approach for SOA in the same way that XP or Scrum are specific approaches for Agile software development.
Within each service, you may be OK with the team who owns that zone picking a different technology stack or data store. Other concerns may kick in here, of course. Your inclination to let teams pick the right tool for the job may be tempered by the fact that it becomes harder to hire people or move them between teams if you have 10 different technology stacks to support. Similarly, if each team picks a completely different data store, you may find yourself lacking enough experience to run any of them at scale. Netflix, for example, has mostly standardized on Cassandra as a data-store technology. Although it may not be the best fit for all of its cases, Netflix feels that the value gained by building tooling and expertise around Cassandra is more important than having to support and operate at scale multiple other platforms that may be a better fit for certain tasks. Netflix is an extreme example, where scale is likely the strongest overriding factor, but you get the idea.
Between services is where things can get messy, however. If one service decides to expose REST over HTTP, another makes use of protocol buffers, and a third uses Java RMI, then integration can become a nightmare as consuming services have to understand and support multiple styles of interchange. This is why I try to stick to the guideline that we should “be worried about what happens between the boxes, and be liberal in what happens inside.”
Rules are for the obedience of fools and the guidance of wise men.
- Generally attributed to Douglas Bader
In his book Antifragile (Random House), Nassim Taleb talks about things that actually benefit from failure and disorder. Ariel Tseitlin used this concept to coin the concept of the antifragile organization in regards to how Netflix operates.
The scale at which Netflix operates is well known, as is the fact that Netflix is based entirely on the AWS infrastructure. These two factors mean that it has to embrace failure well. Netflix goes beyond that by actually inciting failure to ensure that its systems are tolerant of it.
Some organizations would be happy with game days, where failure is simulated by systems being switched off and having the various teams react. During my time at Google, this was a fairly common occurrence for various systems, and I certainly think that many organizations could benefit from having these sorts of exercises regularly. Google goes beyond simple tests to mimic server failure, and as part of its annual DiRT (Disaster Recovery Test) exercises it has simulated large-scale disasters such as earthquakes. Netflix also takes a more aggressive approach, by writing programs that cause failure and running them in production on a daily basis.
The most famous of these programs is the Chaos Monkey, which during certain hours of the day will turn off random machines. Knowing that this can and will happen in production means that the developers who create the systems really have to be prepared for it. The Chaos Monkey is just one part of Netflix’s Simian Army of failure bots. The Chaos Gorilla is used to take out an entire availability center (the AWS equivalent of a data center), whereas the Latency Monkey simulates slow network connectivity between machines. Netflix has made these tools available under an open source license. For many, the ultimate test of whether your system really is robust might be unleashing your very own Simian Army on your production infrastructure.
Embracing and inciting failure through software, and building systems that can handle it, is only part of what Netflix does. It also understands the importance of learning from the failure when it occurs, and adopting a blameless culture when mistakes do happen. Developers are further empowered to be part of this learning and evolving process, as each developer is also responsible for managing his or her production services.
By causing failure to happen, and building for it, Netflix has ensured that the systems it has scale better, and better support the needs of its customers.
Not everyone needs to go to the sorts of extremes that Google or Netflix do, but it is important to understand the mindset shift that is required with distributed systems. Things will fail. The fact that your system is now spread across multiple machines (which can and will fail) across a network (which will be unreliable) can actually make your system more vulnerable, not less. So regardless of whether you’re trying to provide a service at the scale of Google or Netflix, preparing yourself for the sorts of failure that happen with more distributed architectures is pretty important. So what do we need to do to handle failure in our systems?
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