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Service-Disoriented Architecture

“You can have a second computer once you’ve shown you know how to use the first one.” -Paul Barham

The first rule of distributed systems is don’t distribute your system until you have an observable reason to. Teams break this rule on the regular. People have been talking about service-oriented architecture for a long time, but only recently have microservices been receiving the hype.

The problem, as Martin Fowler observes, is that teams are becoming too eager to adopt a microservice architecture without first understanding the inherent overheads. A contributing factor, I think, is you only hear the success stories from companies who did it right, like Netflix. However, what folks often fail to realize is that these companies—in almost all cases—didn’t start out that way. There was a long and winding path which led them to where they are today. The inverse of this, which some refer to as microservice envy, is causing teams to rush into microservice hell. I call this service-disoriented architecture (or sometimes disservice-oriented architecture when the architecture is DOA).

The term “monolith” has a very negative connotation—unscalable, unmaintainable, unresilient. These things are not intrinsically tied to each other, however, and there’s no reason a single system can’t be modular, maintainable, and fault tolerant at reasonable scale. It’s just less sexy. Refactoring modular code is much easier than refactoring architecture, and refactoring across service boundaries is equally difficult. Fowler describes this as monolith-first, and I think it’s the right approach (with some exceptions, of course).

Don’t even consider microservices unless you have a system that’s too complex to manage as a monolith. The majority of software systems should be built as a single monolithic application. Do pay attention to good modularity within that monolith, but don’t try to separate it into separate services.

Service-oriented architecture is about organizational complexity and system complexity. If you have both, you have a case to distribute. If you have one of the two, you might have a case (although if you have organizational complexity without system complexity, you’ve probably scaled your organization improperly). If you have neither, you do not have a case to distribute. State, specifically distributed state, is hell, and some pundits argue SOA is satan—perhaps a necessary evil.

There are a lot of motivations for microservices: anti-fragility, fault tolerance, independent deployment and scaling, architectural abstraction, and technology isolation. When services are loosely coupled, the system as a whole tends to be less fragile. When instances are disposable and stateless, services tend to be more fault tolerant because we can spin them up and down, balance traffic, and failover. When responsibility is divided across domain boundaries, services can be independently developed, deployed, and scaled while allowing the right tools to be used for each.

We also need to acknowledge the disadvantages. Adopting a microservice architecture does not automatically buy you anti-fragility. Distributed systems are incredibly precarious. We have to be aware of things like asynchrony, network partitions, node failures, and the trade-off between availability and data consistency. We have to think about resiliency but also the business and UX implications. We have to consider the boundaries of distributed systems like CAP and exactly-once delivery.

When distributing, the emphasis should be on resilience engineering and adopting loosely coupled, stateless components—not microservices for microservices’ sake. We need to view eventual consistency as a tool, not a side effect. The problem I see is that teams often end up with what is essentially a complex, distributed monolith. Now you have two problems. If you’re building a microservice which doesn’t make sense outside the context of another system or isn’t useful on its own, stop and re-evaluate. If you’re designing something to be fast and correct, realize that distributing it will frequently take away both.

Like anti-fragility, microservices do not automatically buy you better maintainability or even scalability. Adopting them requires the proper infrastructure and organization to be in place. Without these, you are bound to fail. In theory, they are intended to increase development velocity, but in many cases the microservice premium ends up slowing it down while creating organizational dependencies and bottlenecks.

There are some key things which must be in place in order for a microservice architecture to be successful: a proper continuous-delivery pipeline, competent DevOps and Ops teams, and prudent service boundaries, to name a few. Good monitoring is essential. It’s also important we have a thorough testing and integration story. This isn’t even considering the fundamental development complexities associated with SOA mentioned earlier.

The better strategy is a bottom-up approach. Start with a monolith or small set of coarse-grained services and work your way up. Make sure you have the data model right. Break out new, finer-grained services as you need to and as you become more confident in your ability to maintain and deploy discrete services. It’s largely about organizational momentum. A young company jumping straight to a microservice architecture is like a golf cart getting on the freeway.

Microservices offer a number of advantages, but for many companies they are a bit of a Holy Grail. Developers are always looking for a silver bullet, but there is always a cost. What we need to do is minimize this cost, and with microservices, this typically means easing our way into it rather than diving into the deep end. Team autonomy and rapid iteration are noble goals, but if we’re not careful, we can end up creating an impedance. Microservices require organization and system maturity. Otherwise, they end up being a premature architectural optimization with a lot of baggage. They end up creating a service-disoriented architecture.

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Distributed Systems Are a UX Problem

Distributed systems are not strictly an engineering problem. It’s far too easy to assume a “backend” development concern, but the reality is there are implications at every point in the stack. Often the trade-offs we make lower in the stack in order to buy responsiveness bubble up to the top—so much, in fact, that it rarely doesn’t impact the application in some way. Distributed systems affect the user. We need to shift the focus from system properties and guarantees to business rules and application behavior. We need to understand the limitations and trade-offs at each level in the stack and why they exist. We need to assume failure and plan for recovery. We need to start thinking of distributed systems as a UX problem.

The Truth is Prohibitively Expensive

Stop relying on strong consistency. Coordination and distributed transactions are slow and inhibit availability. The cost of knowing the “truth” is prohibitively expensive for many applications. For that matter, what you think is the truth is likely just a partial or outdated version of it.

Instead, choose availability over consistency by making local decisions with the knowledge at hand and design the UX accordingly. By making this trade-off, we can dramatically improve the user’s experience—most of the time.

Failure Is an Option

There are a lot of problems with simultaneity in distributed computing. As Justin Sheehy describes it, there is no “now” when it comes to distributed systems—that article, by the way, is a must-read for every engineer, regardless of where they work in the stack.

While some things about computers are “virtual,” they still must operate in the physical world and cannot ignore the challenges of that world.

Even though computers operate in the real world, they are disconnected from it. Imagine an inventory system. It may place orders to its artificial heart’s desire, but if the warehouse burns down, there’s no fulfilling them. Even if the system is perfect, its state may be impossible. But the system is typically not perfect because the truth is prohibitively expensive. And not only do warehouses catch fire or forklifts break down, as rare as this may be, but computers fail and networks partition—and that’s far less rare.

The point is, stop trying to build perfect systems because one of two things will happen:

1. You have a false sense of security because you think the system is perfect, and it’s not.

or

2. You will never ship because perfection is out of reach or exorbitantly expensive.

Either case can be catastrophic, depending on the situation. With systems, failure is not only an option, it’s an inevitability, so let’s plan for it as such. We have a lot to gain by embracing failure. Eric Brewer articulated this idea in a recent interview:

So the general answer is you allow things to be inconsistent and then you find ways to compensate for mistakes, versus trying to prevent mistakes altogether. In fact, the financial system is actually not based on consistency, it’s based on auditing and compensation. They didn’t know anything about the CAP theorem, that was just the decision they made in figuring out what they wanted, and that’s actually, I think, the right decision.

We can look to ATMs, and banks in general, as the canonical example for how this works. When you withdraw money, the bank could choose to first coordinate your account, calculating your available balance at that moment in time, before issuing the withdrawal. But what happens when the ATM is temporarily disconnected from the bank? The bank loses out on revenue.

Instead, they make a calculated risk. They choose availability and compensate the risk of overdraft with interest and charges. Likewise, banks use double-entry bookkeeping to provide an audit trail. Every credit has a corresponding debit. Mistakes happen—accounts are debited twice, an account is credited without another being debited—the failure modes are virtually endless. But we audit and compensate, detect and recover. Banks are loosely coupled systems. Accountants don’t use erasers. Why should programmers?

When you find yourself saying “this is important data or people’s money, it has to be correct,” consider how the problem was solved before computers. Building on Quicksand by Dave Campbell and Pat Helland is a great read on this topic:

Whenever the authors struggle with explaining how to implement loosely-coupled solutions, we look to how things were done before computers. In almost every case, we can find inspiration in paper forms, pneumatic tubes, and forms filed in triplicate.

Consider the lost request and its idempotent execution. In the past, a form would have multiple carbon copies with a printed serial number on top of them. When a purchase-order request was submitted, a copy was kept in the file of the submitter and placed in a folder with the expected date of the response. If the form and its work were not completed by the expected date, the submitter would initiate an inquiry and ask to locate the purchase-order form in question. Even if the work was lost, the purchase-order would be resubmitted without modification to ensure a lack of confusion in the processing of the work. You wouldn’t change the number of items being ordered as that may cause confusion. The unique serial number on the top would act as a mechanism to ensure the work was not performed twice.

Computers allow us to greatly improve the user experience, but many of the same fail-safes still exist, just slightly rethought.

The idea of compensation is actually a common theme within distributed systems. The Saga pattern is a great example of this. Large-scale systems often have to coordinate resources across disparate services.  Traditionally, we might solve this problem using distributed transactions like two-phase commit. The problem with this approach is it doesn’t scale very well, it’s slow, and it’s not particularly fault tolerant. With 2PC, we have deadlock problems and even 3PC is still susceptible to network partitions.

Sagas split a long-lived transaction into individual, interleaved sub-transactions. Each sub-transaction in the sequence has a corresponding compensating transaction which reverses its effects. The compensating transactions must be idempotent so they can be safely retried. In the event of a partial execution, the compensating transactions are run and the Saga is effectively rolled back.

The commonly used example for Sagas is booking a trip. We need to ensure flight, car rental, and hotel are all booked or none are booked. If booking the flight fails, we cancel the hotel and car, etc. Sagas trade off atomicity for availability while still allowing us to manage failure, a common occurrence in distributed systems.

Compensation has a lot of applications as a UX principle because it’s really the only way to build loosely coupled, highly available services.

Calculated Recovery

Pat Helland describes computing as nothing more than “memories, guesses, and apologies.” Computers always have partial knowledge. Either there is a disconnect with the real world (warehouse is on fire) or there is a disconnect between systems (System A sold a Foo Widget but, unbeknownst to it, System B just sold the last one in inventory—oops!). Systems don’t make decisions, they make guesses. The guess might be good or it might be bad, but rarely is there certainty. We can wait to collect as much information as possible before making a guess, but it means progress can’t be made until the system is confident enough to do so.

Computers have memory. This means they remember facts they have learned and guesses they have made. Memories help systems make better guesses in the future, and they can share those memories with other systems to help in their guesses. We can store more memories at the cost of more money, and we can survey other systems’ memories at the cost of more latency.

It is a business decision how much money, latency, and energy should be spent on reducing forgetfulness. To make this decision, the costs of the increased probability of remembering should be weighed against the costs of occasionally forgetting stuff.

Generally speaking, the more forgetfulness we can tolerate, the more responsive our systems will be, provided we know how to handle the situations where something is forgotten.

Sooner or later, a system guesses wrong. It sucks. It might mean we lose out on revenue; the business isn’t happy. It might mean the user loses out on what they want; the customer isn’t happy. But we calculate the impact of these wrong guesses, we determine when the trade-offs do and don’t make sense, we compensate, and—when shit hits the fan—we apologize.

Business realities force apologies.  To cope with these difficult realities, we need code and, frequently, we need human beings to apologize. It is essential that businesses have both code and people to manage these apologies.

Distributed systems are as much about failure modes and recovery as they are about being operationally correct. It’s critical that we can recover gracefully when something goes wrong, and often that affects the UX.

We could choose to spend extraordinary amounts of money and countless man-hours laboring over a system which provides the reliability we want. We could construct a data center. We could deploy big, expensive machines. We could install redundant fiber and switches. We could drudge over infallible code. Or we could stop, think for a moment, and realize maybe “sorry” is a more effective alternative. Knowing when to make that distinction can be the difference between a successful business and a failed one. The implications of distributed systems may be wider reaching than you thought.