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.

If State Is Hell, SOA Is Satan

More and more companies are describing their success stories regarding the switch to a service-oriented architecture. As with any technological upswing, there’s a clear and palpable hype factor involved (Big Data™ or The Cloud™ anyone?), but obviously it’s not just puff.

While microservices and SOA have seen a staggering rate of adoption in recent years, the mindset of developers often seems to be stuck in the past. I think this is, at least in part, because we seek a mental model we can reason about. It’s why we build abstractions in the first place. In a sense, I would argue there’s a comparison to be made between the explosion of OOP in the early 90’s and today’s SOA trend. After all, SOA is as much about people scale as it is about workload scale, so it makes sense from an organizational perspective.

The Perils of Good Abstractions

While systems are becoming more and more distributed, abstractions are attempting to make them less and less complex. Mesosphere is a perfect example of this, attempting to provide the “datacenter operating system.” Apache Mesos allows you to “program against your datacenter like it’s a single pool of resources.” It’s an appealing proposition to say the least. PaaS like Google App Engine and Heroku offer similar abstractions—write your code without thinking about scale. The problem is you absolutely have to think about scale or you’re bound to run into problems down the road. And while these abstractions are nice, they can be dangerous just the same. Welcome to the perils of good abstractions.

I like to talk about App Engine because I have firsthand experience with it. It’s an easy sell for startups. It handles spinning up instances when you need them, turning them down when you don’t. It’s your app server, database, caching, job scheduler, task queue all in one, and it does it at scale. There’s vendor lock-in, sure, yet it means no ops, no sysadmins, no overhead. Push to deploy. But it’s a leaky abstraction. It has to be. App Engine scales because it’s distributed, but it allows—no, encourages—you to write your system as a monolith. The datastore, memcache, and task queue accesses are masked as RPCs. This is great for our developer mental model, but it will bite you if you’re not careful. App Engine imposes certain limitations to encourage good design; for instance, front-end requests and datastore calls are limited to 60 seconds (it used to be much less), but the leakiness goes beyond that.

RPC is consistently at odds with distributed systems. I would go so far as to say it’s an anti-pattern in many cases. RPC encourages writing synchronous code, but distributed systems are inherently asynchronous. The network is not reliable. The network is not fast. The network is not your friend. Developers who either don’t understand this or don’t realize what’s happening when they make an RPC will write code as if they were calling a function. It will sure as hell look like just calling a function. When we think synchronously, we end up with systems that are slow, fault intolerant, and generally not scalable. To be quite honest, however, this is perfectly acceptable for 90% of startups as they are getting off the ground because they don’t have workloads at meaningful scale.

There’s certainly some irony here. One of the selling points of App Engine is its ability to scale to large amounts of traffic, yet the vast majority of startups would be perfectly suited to scaling up rather than out, perhaps with some failover in place for good measure. Stack Overflow is the poster child of scale-up architecture. In truth, your architecture should be a function of your access patterns, not the other way around (and App Engine is very much tailored to a specific set of access patterns). Nonetheless, it shows that vertical scaling can work. I would bet a lot of startups could sufficiently run on a large, adequately specced machine or maybe a small handful of them.

The cruel irony is that once you hit a certain scale with App Engine, both in terms of your development organization and user base, you’ve reached a point where you have to migrate off it. And if your data model isn’t properly thought out, you will without a doubt hit scale problems. It’s to the point where you need someone with deep knowledge of how App Engine works in order to build quality systems on it. Good luck hiring a team of engineers who understand it. GAE is great at accelerating you to 100 mph, but you better have some nice airbags for the brick wall it launches you into. In fairness, this is a problem every org hits—Conway’s law is very much a reality and every startup has growing pains. To be clear, this isn’t a jab at GAE, which is actually very effective at accelerating a product using little capital and can sustain long-term success given the right use case. Instead, I use it to illustrate a point.

Peering Through the Abstraction

Eventually SOA makes sense, but our abstractions can cause problems if we don’t understand what’s going on behind the curtain (hence the leakiness). Partial failure is all but guaranteed, and latency, partitioning, and other network pressure happens all the time.

Ken Arnold is famed with once saying “state is hell” in reference to designing distributed systems. In the past, I’ve written how scaling shared data is hard, but with SOA it’s practically a requirement. Ken is right though—state is hell, and SOA is fundamentally competing with consistency. The FLP Impossibility result and the CAP theorem can prove it formally, but really this should be intuitively obvious if we accept the laws of physics.

On the other hand, if you store information that I can’t reconstruct, then a whole host of questions suddenly surface. One question is, “Are you now a single point of failure?” I have to talk to you now. I can’t talk to anyone else. So what happens if you go down?

To deal with that, you could be replicated. But now you have to worry about replication strategies. What if I talk to one replicant and modify some data, then I talk to another? Is that modification guaranteed to have already arrived there? What is the replication strategy? What kind of consistency do you need—tight or loose? What happens if the network gets partitioned and the replicants can’t talk to each other? Can anybody proceed?

Essentially, the more stateful your system is, the harder it’s going to be to scale it because distributing that state introduces a rich tapestry of problems. In practice, we often can’t eliminate state wholesale, but basically everything that can be stateless should be stateless.

Making servers disposable allows you a great deal of flexibility. Former Netflix Cloud Architect Adrian Cockcroft articulates this idea well:

You want to think of servers like cattle, not pets. If you have a machine in production that performs a specialized function, and you know it by name, and everyone gets sad when it goes down, it’s a pet. Instead you should think of your servers like a herd of cows. What you care about is how many gallons of milk you get. If one day you notice you’re getting less milk than usual, you find out which cows aren’t producing well and replace them.

This is effectively how App Engine achieves its scalability. With lightweight, stateless, and disposable instances, it can spin them up and down on the fly without worrying about being in an invalid state.

App Engine also relies on eventual consistency as the default model for datastore interactions. This makes queries fast and highly available, while snapshot isolation can be achieved using entity-group transactions if necessary. The latter, of course, can result in a lot of contention and latency. Yet, people seem to have a hard time grappling with the reality of eventual consistency in distributed systems. State is hell, but calling SOA “satan” is clearly a hyperbole. It is a tough problem nevertheless.

A State of Mind

In the situations where we need state, we have to reconcile with the realities of distributed systems. This means understanding the limitations and accepting the complexities, not papering over them. It doesn’t mean throwing away abstractions. Fortunately, distributed computing is the focus of a lot of great research, so there are primitives with which we can build: immutability, causal ordering, eventual consistency, CRDTs, and other ideas.

As long as we recognize the trade-offs, we can design around them. The crux is knowing they exist in the first place. We can’t have ACID semantics while remaining highly available, but we can use Highly Available Transactions to provide strong-enough guarantees. At the same time, not all operations require coordination or concurrency control. The sooner we view eventual consistency as a solution and not a consequence, the sooner we can let go of this existential crisis. Other interesting research includes BOOM, which seeks to provide a high-level, declarative approach to distributed programming.

State might be hell, but it’s a hell we have to live. I don’t advocate an all-out microservice architecture for a company just getting its start. The complications far outweigh any benefits to be gained, but it becomes a necessity at a certain point. The key is having an exit strategy. PaaS providers make this difficult due to vendor lock-in and architectural constraints. Weigh their advantages carefully.

Once you do transition to a SOA, make as many of those services, or the pieces backing them, as stateless as possible. For those which aren’t stateless, know that the problem typically isn’t novel. These problems have been solved or are continuing to be solved in new and interesting ways. Academic research is naturally at the bleeding edge with industry often lagging behind. OOP concepts date back to as early as the 60’s but didn’t gain widespread adoption until several decades later. Distributed computing is no different. SOA is just a state of mind.

Modularizing Infinitum: A Postmortem

In addition to getting the code migrated from Google Code to GitHub, one of my projects over the holidays was to modularize the Infinitum Android framework I’ve been working on for the past year.

Infinitum began as a SQLite ORM and quickly grew to include a REST ORM implementation,  REST client, logging wrapper, DI framework, AOP module, and, of course, all of the framework tools needed to support these various functionalities. It evolved as I added more and more features in a semi-haphazard way. In my defense, the code was organized. It was logical. It made sense. There was no method, but there also was no madness. Everything was in an appropriately named package. Everything was coded to an interface. There was no duplicated code. However, modularity — in terms of minimizing framework dependencies — wasn’t really in mind at the time, and the code was all in a single project.

The Wild, Wild West

The issue wasn’t how the code was organized, it was how the code was integrated. The project was cowboy coding at its finest. I was the only stakeholder, the only tester, the only developer — judge, jury, and executioner. I was building it for my own personal use after all. Consequently, there was no planning involved, unit testing was somewhere between minimal and non-existent, and what got done was at my complete discretion. Ultimately, what was completed any given day, more or less, came down to what I felt like working on.

What started as an ORM framework became a REST framework, which became a logging framework, which became an IOC framework, which became an AOP framework. All of these features, built from the ground up, were tied together through a context, which provided framework configuration data. More important, the Infinitum context stored the bean factory used for storing and retrieving bean definitions used by both the framework and the client. The different modules themselves were not tightly coupled, but they were connected to the context like feathers on a bird.

infinitum-arch

The framework began to grow large. It was only about 300KB of actual code (JARed without ProGuard compression), but it had a number of library dependencies, namely Dexmaker, Simple XML, and GSON, which is over 1MB combined in size. Since it’s an Android framework, I wanted to keep the footprint as small as possible. Additionally, it’s likely that someone wouldn’t be using all of the features in the framework. Maybe they just need the SQLite ORM, or just the REST client, or just dependency injection. The way the framework was structured, they had to take it all or none.

A Painter Looking for a Brush

I began to investigate ways to modularize it. As I illustrated, the central problem lay in the fact that the Infinitum context had knowledge of all of the different modules and was responsible for calling and configuring their APIs. If the ORM is an optional dependency, the context should not need to have knowledge of it. How can the modules be decoupled from the context?

Obviously, there is a core dependency, Infinitum Core, which consists of the framework essentials. These are things used throughout the framework in all of the modules — logging, DI1, exceptions, and miscellaneous utilities. The goal was to pull off ORM, REST, and AOP modules.

My initial approach was to try and use the decorator pattern to “decorate” the Infinitum context with additional functionality. The OrmContextDecorator would implement the ORM-specific methods, the AopContextDecorator would implement the AOP-specific methods, and so on. The problem with this was that it would still require the module-specific methods to be declared in the Infinitum context interface. Not only would they need to be stubbed out in the context implementation, a lot of module interfaces would need to be shuffled and placed in Infinitum Core  in order to satisfy the compiler. The problem remained; the context still had knowledge of all the modules.

I had another idea in mind. Maybe I could turn the Infinitum context from a single point of configuration to a hierarchical structure where each module has its own context as a “child” of the root context. The OrmContext interface could extend the InfinitumContext interface, providing ORM-specific functionality while still inheriting the core context methods. The implementation would then contain a reference to the parent context, so if it was unable to perform a certain piece of functionality, it could delegate to the parent. This could work. The Infinitum context has no notion of module X, Y, or Z, and, in effect, the control has been inverted. You could call it the Hollywood Principle — “Don’t call us, we’ll call you.”

infinitum-context-hierarchy

There’s still one remaining question: how do we identify the “child” contexts and subsequently initialize them? The solution is to maintain a module registry. This registry will keep track of the optional framework dependencies and is responsible for initializing them if they are available. We use a marker class from each module, a class we know exists if the dependency is included in the classpath, to check its availability.

Lastly, we use reflection to instantiate an instance of the module context. I used an enum to maintain a registry of Infinitum modules. I then extended the enum to add an initialize method which loads a context instance.

The modules get picked up during a post-processing step in the ContextFactory. It’s this step that also adds them as child contexts to the parent.

New modules can be added to the registry without any changes elsewhere. As long as the context has been implemented, they will be picked up and processed automatically.

Once this architecture was in place, separating the framework into different projects was simple. Now Infinitum Core can be used by itself if only dependency injection is needed, the ORM can be included if needed for SQLite, AOP included for aspect-oriented programming, and Web for the RESTful web service client and various HTTP utilities.

We Shape Our Buildings, and Afterwards, Our Buildings Shape Us

I think this solution has helped to minimize some of the complexity a bit. As with any modular design, not only is it more extensible, it’s more maintainable. Each module context is responsible for its own configuration, so this certainly helped to reduce complexity in the InfinitumContext implementation as before it was handling the initialization for the ORM, AOP, and REST pieces. It also worked out in that I made the switch to GitHub2 by setting up four discrete repositories, one for each module.

In retrospect, I would have made things a lot easier on myself if I had taken a more modular approach from the beginning. I ended up having to reengineer quite a bit, although once I had a viable solution, it actually wasn’t all that much work. I was fortunate in that I had things fairly well designed (perhaps not at a very high level, but in general) and extremely organized. It’s difficult to anticipate change, but chances are you’ll be kicking yourself if you don’t. I started the framework almost a year ago, and I never imagined it would grow to what it is today.

  1. I was originally hoping to pull out dependency injection as a separate module, but the framework relies heavily on it to wire up components. []
  2. Now that the code’s pushed to GitHub, I begin the laborious task of migrating the documentation over from Google Code. []