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More Environments Will Not Make Things Easier

Microservices are hard. They require extreme discipline. They require a lot more upfront thinking. They introduce integration challenges and complexity that you otherwise wouldn’t have with a monolith, but service-oriented design is an important part of scaling organization structure. Hundreds of engineers all working on the same codebase will only lead to angst and the inability to be nimble.

This requires a pretty significant change in the way we think about things. We’re creatures of habit, so if we’re not careful, we’ll just keep on applying the same practices we used before we did services. And that will end in frustration.

How can we possibly build working software that comprises dozens of services owned by dozens of teams? Instinct tells us full-scale integration. That’s how we did things before, right? We ran integration tests. We run all of the services we depend on and develop our service against that. But it turns out, these dozen or so services I depend on also have their own dependencies! This problem is not linear.

Okay, so we can’t run everything on our laptop. Instead, let’s just have a development environment that is a facsimile of production with everything deployed. This way, teams can develop their products against real, deployed services. The trade-off is teams need to provide a high level of stability for these “development” services since other teams are relying on them for their own development. If nothing works, development is hamstrung. Personally, I think this is a pretty reasonable trade-off because if we’re disciplined enough, it shouldn’t be hard to provide stable APIs. In fact, if we’re disciplined, it should be a requirement. This is why upfront thinking is critical. Designing your APIs is the most important thing you do. Service-oriented architecture necessitates API-driven development. Literally nothing else matters but the APIs. It reminds me of the famous Jeff Bezos mandate:

  1. All teams will henceforth expose their data and functionality through service interfaces.

  2. Teams must communicate with each other through these interfaces.

  3. There will be no other form of interprocess communication allowed: no direct linking, no direct reads of another team’s data store, no shared-memory model, no back-doors whatsoever. The only communication allowed is via service interface calls over the network.

  4. It doesn’t matter what technology they use. HTTP, Corba, Pubsub, custom protocols – doesn’t matter. Bezos doesn’t care.

  5. All service interfaces, without exception, must be designed from the ground up to be externalizable. That is to say, the team must plan and design to be able to expose the interface to developers in the outside world. No exceptions.

  6. Anyone who doesn’t do this will be fired.

  7. Thank you; have a nice day!

If we’re not disciplined, maintaining stability in a development environment becomes too difficult. So naturally, the solution becomes doubling down—we just need more environments. If every team just gets its own full-scale environment to develop against, no more stability problems. We get to develop our distributed monolith happily in our own little world. That sound you hear is every CFO collectively losing their shit, but whatever, they’re nerds and we’ve gotta get this feature to production!

Besides the obvious cost implications to this approach, perhaps the more insidious problem is it will cause teams to develop in a vacuum. In and of itself, this is not an issue, but for the undisciplined team who is not practicing rigorous API-driven development, it will create moving goalposts. A team will spend months developing its product against static dependencies only to find a massive integration headache come production time. It’s pain deferral, plain and simple. That pain isn’t being avoided or managed, you’re just neglecting to deal with instability and integration to a point where it is even more difficult. It is the opposite of the “fail-fast” mindset. It’s failing slowly and drawn out.

“We need to run everything with this particular configuration to test this, and if anyone so much as sneezes my service becomes unstable.” Good luck with that. I’ve got a dirty little secret: if you’re not disciplined, no amount of environments will make things easier. If you can’t keep your service running in an integration environment, production isn’t going to be any easier.

Similarly, massive end-to-end integration tests spanning numerous services  are an anti-pattern. Another dirty little secret: integrated tests are a scam. With a big enough system, you cannot reasonably expect to write meaningful large-scale tests in any tractable way.

What are we to do then? With respect to development, get it out of your head that you can run a facsimile of production to build features against. If you need local development, the only sane and cost-effective option is to stub. Stub everything. If you have a consistent RPC layer—discipline—this shouldn’t be too difficult. You might even be able to generate portions of stubs.

We used Google App Engine heavily at Workiva, which is a PaaS encompassing numerous services—app server, datastore, task queues, memcache, blobstore, cron, mail—all managed by Google. We were doing serverless before serverless was even a thing. App Engine provides an SDK for developing applications locally on your machine. Numerous times I overheard someone who thought the SDK was just running a facsimile of App Engine on their laptop. In reality, it was running a bunch of stubs!

If you need a full-scale deployed environment, keep in mind that stability is the cost of entry. Otherwise, you’re just delaying problems. In either case, you need stable APIs.

With respect to integration testing, the only tractable solution that doesn’t lull you into a false sense of security is consumer-driven contract testing. We run our tests against a stub, but these tests are also included in a consumer-driven contract. An API provider runs consumer-driven contract tests against its service to ensure it’s not breaking any downstream services.

All of this aside, the broader issue is ensuring a highly disciplined engineering organization. Without this, the rest becomes much more difficult as pain-driven development takes hold. Discipline is a key part of doing service-oriented design and preventing things from getting out of control as a company scales. Moving to microservices means using the right tools and processes, not just applying the old ones in a new context.

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Thrift on Steroids: A Tale of Scale and Abstraction

Apache Thrift is an RPC framework developed at Facebook for building “scalable cross-language services.” It consists of an interface definition language (IDL), communication protocol, API libraries, and a code generator that allows you to build and evolve services independently and in a polyglot fashion across a wide range of languages. This is nothing new and has been around for over a decade now.

There are a number of notable users of Thrift aside from Facebook, including Twitter (mainly by way of Finagle), Foursquare, Pinterest, Uber (via TChannel), and Evernote, among others—and for good reason, Thrift is mature and battle-tested.

The white paper explains the motivation behind Thrift in greater detail, though I think the following paragraph taken from the introduction does a pretty good job of summarizing it:

As Facebook’s traffic and network structure have scaled, the resource demands of many operations on the site (i.e. search, ad selection and delivery, event logging) have presented technical requirements drastically outside the scope of the LAMP framework. In our implementation of these services, various programming languages have been selected to optimize for the right combination of performance, ease and speed of development, availability of existing libraries, etc. By and large, Facebook’s engineering culture has tended towards choosing the best tools and implementations available over standardizing on any one programming language and begrudgingly accepting its inherent limitations.

Basically, as Facebook scaled, they moved more and more away from PHP and the LAMP stack and became increasingly polyglot. I think this same evolution is seen at most startups as they grow into themselves. We saw a similar transition in my time at Workiva, moving from our monolothic Python application on Google App Engine to a polyglot service-oriented architecture in AWS. It was an exciting but awkward time as we went through our adolescence as an engineering culture and teams started to find their identities. Teams learned what it meant to build backward-compatible APIs and loosely coupled services, how to deprecate APIs, how to build resilient and highly available systems, how to properly instrument services and diagnose issues, how to run and manage the underlying infrastructure, and—most importantly—how to collaborate with each other. There was lots of stumbling and mistakes along the way, lots of postmortems, lots of stress, but with that comes the learning and growing. The payoff is big but the process is painful. I don’t think it ever isn’t.

With one or two services written in the same language and relatively few developers, it was easy to just stick with “REST” (in quotes because it’s always a bastardized version of what REST ought to be), sling some JSON around, and call it a day. As the number of tech stacks and integration points increase, it becomes apparent that some standards are important. And once things are highly polyglot with lots of developers and lots of services running with lots of versions, strict service contracts become essential.

Uber has a blog post on building microservices that explains this and why they settled on Thrift to solve this problem.

Since the number of service calls grows rapidly, it is necessary to maintain a well-defined interface for every call. We knew we wanted to use an IDL for managing this interface, and we ultimately decided on Thrift. Thrift forces service owners to publish strict interface definitions, which streamlines the process of integrating with services. Calls that do not abide by the interface are rejected at the Thrift level instead of leaking into a service and failing deeper within the code. This strategy of publicly declaring your interface emphasizes the importance of backwards compatibility, since multiple versions of a service’s Thrift interface could be in use at any given time. The service author must not make breaking changes, and instead must only make non-breaking additions to the interface definition until all consumers are ready for deprecation.

Early on, I was tasked with building a unified messaging solution that would help with our integration challenges. The advantages of a unified solution should be obvious: reusability (before this, everyone was solving the problem in their own way), focus (allow developers to focus on their problem space, not the glue), acceleration (if the tools are already available, there’s less work to do), and shared pain points (it’s a lot easier to prioritize your work when everyone is complaining about the same thing). Also, a longer term benefit is developing the knowledge of this shared solution into an organizational competency which has a sort of “economy of scale” to it. Our job was not just to ship a messaging platform but evangelize it and help other teams to be successful with it. We did this through countless blog posts, training sessions, workshops, talks, and even a podcast.

Before we set out on building a common messaging solution, there were a few key principles we used to guide ourselves. We wanted to provide a core set of tools, libraries, and infrastructure for service integration. We wanted a solution that was rigid yet flexible. We provide only a minimal set of messaging patterns to act as generic building blocks with strict, strongly typed APIs, and promote design best practices and a service-oriented mindset. This meant supporting service evolution and API iteration through versioning and backward compatibility, allowing for resiliency patterns like timeouts, retries, circuit breakers, etc., and generally advocating asynchronous, loosely coupled communication. Lastly, we had to keep in mind that, at the end of the day, developers are just trying to ship stuff, so we had to balance these concerns out with ergonomics and developer experience so they could build, integrate, and ship quickly.

As much as I think RPC is a bad abstraction, it’s what developers want. If you don’t provide them with an RPC solution, they will build their own, so we had to provide first-class support for it. We evaluated solutions in the RPC space. We looked at GRPC extensively, which is the new RPC hotness from Google, but it had a few key drawbacks, namely its “newness” (it was still in early beta at the time and has since been almost entirely rewritten), it’s coupled to HTTP/2 as a transport (which at the time had fairly limited support), and it lacks support for JavaScript (let alone Dart, which is what most of our client applications were being written in). Avro was another we looked at.

Ultimately, we settled on Thrift due to its maturity and wide use in production, its performance, its architecture (it separates out the transports, protocols, and RPC layer with the first two being pluggable), its rich feature set, and its wide range of language support (checking off all the languages we standardized on as a company including Go, Java, Python, JavaScript, and Dart). Thrift is not without its problems though—more on this in a bit.

In addition to RPC, we wanted to promote a more asynchronous, message-passing style of communication with pub/sub. This would allow for greater flexibility in messaging patterns like fan-out and fan-in, interest-based messaging, and reduced coupling and fragility of services. This enables things like the worker pattern where we can distribute work to a pool of workers and scale that pool independently, whereas RPC tends to promote more stateful types of services. In my experience, developers tend to bias towards stateful services since this is how we’ve built things for a long time, but as we’ve entered the cloud-native era, things are running in containers which are autoscaled, more ephemeral, and more distributed. We have to grapple with the complexity imposed by distributed systems. This is why asynchronous messaging is important and why we wanted to support it from the onset.

We selected NATS as a messaging backplane because of its simplicity, performance, scalability, and adoption of the cloud-native mentality. When it comes to service integration, you need an always-on dial tone and NATS provides just that. Because of Thrift’s pluggable transport layer, we could build a NATS RPC transport while also providing HTTP and TCP transports.

Unfortunately, Thrift doesn’t provide any kind of support for pub/sub, and we wanted the same guarantees for it that we had with RPC, like type safety and versioning with code-generated APIs and service contracts. Aside from this, Thrift has a number of other, more glaring problems:

  • Head-of-line blocking: a single, slow request will block any subsequent requests for a client.
  • Out-of-order responses: an out-of-order response puts a Thrift transport in a bad state, requiring it to be torn down and reestablished, e.g. if a slow request times out at the client, the client issues a subsequent request, and a response comes back for the first request, the client blows up.
  • Concurrency: a Thrift client cannot be shared between multiple threads of execution, requiring each thread to have its own client issuing requests sequentially. This, combined with head-of-line blocking, is a major performance killer. This problem is compounded when each transport has its own resources, such as a socket.
  • RPC timeouts: Thrift does not provide good facilities for per-request timeouts, instead opting for a global transport read timeout.
  • Request headers: Thrift does not provide support for request metadata, making it difficult to implement things like authentication/authorization and distributed tracing. Instead, you are required to bake these things into your IDL or in a wrapped transport. The problem with this is it puts the onus on service providers rather than allowing an API gateway or middleware to perform these functions in a centralized way.
  • Middleware: Thrift does not have any support for client or server middleware. This means clients must be wrapped to implement interceptor logic and middleware code must be duplicated within handler functions. This makes it impossible to implement AOP-style logic in a clean, DRY way.

Twitter’s Finagle addresses many of these issues but is solely for the JVM, so we decided to address Thrift’s shortcomings in a cross-platform way without completely reinventing the wheel. That is, we took Thrift and extended it. What we ended up with was Frugal, a superset of Thrift recently open sourced that aims to solve the problems described above while also providing support for asynchronous pub/sub APIs—a sort of Thrift on steroids as I’ve come to call it. Its key features include:

  • Request multiplexing: client requests are fully multiplexed, allowing them to be issued concurrently while simultaneously avoiding the head-of-line blocking and out-of-order response problems. This also lays some groundwork for asynchronous messaging patterns.
  • Thread-safety: clients can be safely shared between multiple threads in which requests can be made in parallel.
  • Pub/sub: IDL and code-generation extensions for defining pub/sub APIs in a type-safe way.
  • Request context: a first-class request context object is added to every operation which allows defining request/response headers and per-request timeouts. By making the context part of the Frugal protocol, headers can be introspected or even injected by external middleware. This context could be used to send OAuth2 tokens and user-context information, avoiding the need to include it everywhere in your IDL and handler logic. Correlation IDs for distributed tracing purposes are also built into the request context.
  • Middleware: client- and server- side middleware is supported for RPC and pub/sub APIs. This allows you to implement interceptor logic around handler functions, e.g. for authentication, logging, or retry policies. One can easily integrate OpenTracing as a middleware, for example.
  • Cross-language: support for Go, Java, Dart, and Python (2.7 and 3.5).

Frugal adds a second kind of transport alongside Thrift’s RPC transport for pub/sub. With this, we provide a NATS transport for both pub/sub and RPC (internally, Workiva also has an at-least-once delivery pub/sub transport built around Amazon SQS for mission-critical data). In addition to this, we built a SDK which developers use to connect to the messaging infrastructure (such as NATS) with minimal ceremony. The messaging SDK played a vital role not just in making it easy for developers to adopt and integrate, but providing us a shim where we could introduce sweeping changes across the organization in one place, such as adding instrumentation, tracing, and authentication. This enabled us to roll critical integration components out to every service by making a change in one place.

To support pub/sub, we extended the Thrift IDL with an additional top-level construct called a scope, which is effectively a pub/sub namespace (basically what a service is to RPC). We wrote the IDL using a parsing expression grammar which allows us to generate a parser. We then implemented a code generator for the various language targets. The Frugal compiler is written in Go and is, at least in my opinion, much more maintainable than Thrift’s C++ codebase. However, the language libraries make use of the existing Thrift APIs, such as protocols, transports, etc. This means we didn’t need to implement any of the low-level mechanics like serialization.

I’ve since left Workiva (and am now actually working on NATS), but as far as I know, Frugal helps power nearly every production service at the company. It was an interesting experience from which I learned a lot. I was happy to see some of that work open sourced so others could use it and learn from it.

Of course, if I were starting over today, things would probably look different. GRPC is much more mature and the notion of a “service mesh” has taken the container world by storm with things like Istio, Linkerd, and Envoy. What we built was Workiva’s service mesh, we just didn’t have a name for it, so we called it a “Messaging SDK.” The corollary to this is you don’t need to adopt bleeding-edge tech to be successful. The concepts are what’s important, and if enough people are working on the same types of problems in parallel, they will likely converge on solutions that look very similar to each other given enough time and enough people working on them.

I think there’s a delicate balance between providing solutions that are “easy” from a developer point of view but may provide longer term drawbacks when it comes to building complex systems the “right” way. I see RPC as an example of this. It’s an “easy” abstraction but it hides a lot of complexity. Service meshes might even be in this category, but they have obvious upsides when it comes to building software in a way that is scalable. Peter Alvaro’s Strange Loop talk “I See What You Mean” does a great job of articulating this dilemma, which I’ve also written about myself. In the end, we decided to optimize for shipping, but we took a principled approach: provide the tools developers need (or want) but help educate them to utilize those tools in a way that allows them to ship products that are reliable and maintainable. Throwing tools or code over the wall is not enough.

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Take It to the Limit: Considerations for Building Reliable Systems

Complex systems usually operate in failure mode. This is because a complex system typically consists of many discrete pieces, each of which can fail in isolation (or in concert). In a microservice architecture where a given function potentially comprises several independent service calls, high availability hinges on the ability to be partially available. This is a core tenet behind resilience engineering. If a function depends on three services, each with a reliability of 90%, 95%, and 99%, respectively, partial availability could be the difference between 99.995% reliability and 84% reliability (assuming failures are independent). Resilience engineering means designing with failure as the normal.

Anticipating failure is the first step to resilience zen, but the second is embracing it. Telling the client “no” and failing on purpose is better than failing in unpredictable or unexpected ways. Backpressure is another critical resilience engineering pattern. Fundamentally, it’s about enforcing limits. This comes in the form of queue lengths, bandwidth throttling, traffic shaping, message rate limits, max payload sizes, etc. Prescribing these restrictions makes the limits explicit when they would otherwise be implicit (eventually your server will exhaust its memory, but since the limit is implicit, it’s unclear exactly when or what the consequences might be). Relying on unbounded queues and other implicit limits is like someone saying they know when to stop drinking because they eventually pass out.

Rate limiting is important not just to prevent bad actors from DoSing your system, but also yourself. Queue limits and message size limits are especially interesting because they seem to confuse and frustrate developers who haven’t fully internalized the motivation behind them. But really, these are just another form of rate limiting or, more generally, backpressure. Let’s look at max message size as a case study.

Imagine we have a system of distributed actors. An actor can send messages to other actors who, in turn, process the messages and may choose to send messages themselves. Now, as any good software engineer knows, the eighth fallacy of distributed computing is “the network is homogenous.” This means not all actors are using the same hardware, software, or network configuration. We have servers with 128GB RAM running Ubuntu, laptops with 16GB RAM running macOS, mobile clients with 2GB RAM running Android, IoT edge devices with 512MB RAM, and everything in between, all running a hodgepodge of software and network interfaces.

When we choose not to put an upper bound on message sizes, we are making an implicit assumption (recall the discussion on implicit/explicit limits from earlier). Put another way, you and everyone you interact with (likely unknowingly) enters an unspoken contract of which neither party can opt out. This is because any actor may send a message of arbitrary size. This means any downstream consumers of this message, either directly or indirectly, must also support arbitrarily large messages.

How can we test something that is arbitrary? We can’t. We have two options: either we make the limit explicit or we keep this implicit, arbitrarily binding contract. The former allows us to define our operating boundaries and gives us something to test. The latter requires us to test at some undefined production-level scale. The second option is literally gambling reliability for convenience. The limit is still there, it’s just hidden. When we don’t make it explicit, we make it easy to DoS ourselves in production. Limits become even more important when dealing with cloud infrastructure due to their multitenant nature. They prevent a bad actor (or yourself) from bringing down services or dominating infrastructure and system resources.

In our heterogeneous actor system, we have messages bound for mobile devices and web browsers, which are often single-threaded or memory-constrained consumers. Without an explicit limit on message size, a client could easily doom itself by requesting too much data or simply receiving data outside of its control—this is why the contract is unspoken but binding.

Let’s look at this from a different kind of engineering perspective. Consider another type of system: the US National Highway System. The US Department of Transportation uses the Federal Bridge Gross Weight Formula as a means to prevent heavy vehicles from damaging roads and bridges. It’s really the same engineering problem, just a different discipline and a different type of infrastructure.

The August 2007 collapse of the Interstate 35W Mississippi River bridge in Minneapolis brought renewed attention to the issue of truck weights and their relation to bridge stress. In November 2008, the National Transportation Safety Board determined there had been several reasons for the bridge’s collapse, including (but not limited to): faulty gusset plates, inadequate inspections, and the extra weight of heavy construction equipment combined with the weight of rush hour traffic.

The DOT relies on weigh stations to ensure trucks comply with federal weight regulations, fining those that exceed restrictions without an overweight permit.

The federal maximum weight is set at 80,000 pounds. Trucks exceeding the federal weight limit can still operate on the country’s highways with an overweight permit, but such permits are only issued before the scheduled trip and expire at the end of the trip. Overweight permits are only issued for loads that cannot be broken down to smaller shipments that fall below the federal weight limit, and if there is no other alternative to moving the cargo by truck.

Weight limits need to be enforced so civil engineers have a defined operating range for the roads, bridges, and other infrastructure they build. Computers are no different. This is the reason many systems enforce these types of limits. For example, Amazon clearly publishes the limits for its Simple Queue Service—the max in-flight messages for standard queues is 120,000 messages and 20,000 messages for FIFO queues. Messages are limited to 256KB in size. Amazon Kinesis, Apache Kafka, NATS, and Google App Engine pull queues all limit messages to 1MB in size. These limits allow the system designers to optimize their infrastructure and ameliorate some of the risks of multitenancy—not to mention it makes capacity planning much easier.

Unbounded anything—whether its queues, message sizes, queries, or traffic—is a resilience engineering anti-pattern. Without explicit limits, things fail in unexpected and unpredictable ways. Remember, the limits exist, they’re just hidden. By making them explicit, we restrict the failure domain giving us more predictability, longer mean time between failures, and shorter mean time to recovery at the cost of more upfront work or slightly more complexity.

It’s better to be explicit and handle these limits upfront than to punt on the problem and allow systems to fail in unexpected ways. The latter might seem like less work at first but will lead to more problems long term. By requiring developers to deal with these limitations directly, they will think through their APIs and business logic more thoroughly and design better interactions with respect to stability, scalability, and performance.

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Designed to Fail

When it comes to reliability engineering, people often talk about things like fault injection, monitoring, and operations runbooks. These are all critical pieces for building systems which can withstand failure, but what’s less talked about is the need to design systems which deliberately fail.

Reliability design has a natural progression which closely follows that of architectural design. With monolithic systems, we care more about preventing failure from occurring. With service-oriented architectures, controlling failure becomes less manageable, so instead we learn to anticipate it. With highly distributed microservice architectures where failure is all but guaranteed, we embrace it.

What does it mean to embrace failure? Anticipating failure is understanding the behavior when things go wrong, building systems to be resilient to it, and having a game plan for when it happens, either manual or automated. Embracing failure means making a conscious decision to purposely fail, and it’s essential for building highly available large-scale systems.

A microservice architecture typically means a complex web of service dependencies. One of SOA’s goals is to isolate failure and allow for graceful degradation. The key to being highly available is learning to be partially available. Frequently, one of the requirements for partial availability is telling the client “no.” Outright rejecting service requests is often better than allowing them to back up because, when dealing with distributed services, the latter usually results in cascading failure across dependent systems.

While designing our distributed messaging service at Workiva, we made explicit decisions to drop messages on the floor if we detect the system is becoming overloaded. As queues become backed up, incoming messages are discarded, a statsd counter is incremented, and a backpressure notification is sent to the client. Upon receiving this notification, the client can respond accordingly by failing fast, exponentially backing off, or using some other flow-control strategy. By bounding resource utilization, we maintain predictable performance, predictable (and measurable) lossiness, and impede cascading failure.

Other techniques include building kill switches into service calls and routers. If an overloaded service is not essential to core business, we fail fast on calls to it to prevent availability or latency problems upstream. For example, a spam-detection service is not essential to an email system, so if it’s unavailable or overwhelmed, we can simply bypass it. Netflix’s Hystrix has a set of really nice patterns for handling this.

If we’re not careful, we can often be our own worst enemy. Many times, it’s our own internal services which cause the biggest DoS attacks on ourselves. By isolating and controlling it, we can prevent failure from becoming widespread and unpredictable. By building in backpressure mechanisms and other types of intentional “failure” modes, we can ensure better availability and reliability for our systems through graceful degradation. Sometimes it’s better to fight fire with fire and failure with failure.

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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.