In the early days of SparkPost Chris McFadden, VP of Engineering at SparkPost, and his team had to solve a problem that every SaaS business has: they needed to provide basic services like authentication, account management, and billing.
The core problem, of course, wasn’t how to charge their users money. It was how to design their user account microservices to support everything that goes along with that problem domain: user accounts, API keys, authentication, business accounts, billing, etc.
To tackle this they created two microservices: a Users API and an Accounts API. The Users API would handle user accounts, API keys, authentication and the Accounts API would handle all of the billing related logic. A very logical separation, but before long, they spotted a problem.
“We had one service that was called the User API, and we had another one called the Account API. But the problem was that they were actually having several calls back and forth between them. So you would do something in accounts and have call and endpoint in users or vice versa,” he continued.
The two services were too tightly coupled.
"We realized that in most cases, you really don't want to have one service calling another service in a sometimes circular way. That's generally not a good idea,” Chris explained.
I think this scenario will sound familiar to any developer who has ever tackled this design problem. I’ve made this mistake numerous times in the past. The question is, how do we avoid these microservice design pitfalls and what patterns should we look for? Read on to find out.
The importance of microservice boundaries
When McFadden and I spoke further, he highlighted one of the major challenges when it comes to creating a new system with a microservice architecture. It came about when I mentioned that one of the core benefits of developing new systems with microservices is that the architecture allows developers to build and modify individual components independently — but problems can arise when it comes to minimizing the number of callbacks between each API. The solution according to McFadden, is to apply the appropriate service boundaries.
But in contrast to the sometimes difficult-to-grasp and abstract concept of domain driven design (DDD) — a framework for microservices — I’ll be as practical as I can in this chapter as I discuss the need for well defined microservice boundaries with some of our industry’s tops CTOs.
Avoid arbitrary rules
When designing and creating a microservice, don’t fall into the trap of using arbitrary rules. If you read enough advice, you’ll come across some of the rules below. While appealing, these are not proper ways to determine boundaries for microservices.
1. “A microservice Should Have X lines of code”
Let’s get one thing straight; there are no limitations on how many lines of code there are in a microservice. A microservice doesn’t suddenly become a monolith just because you write a few lines of extra code. The key is ensuring there is high cohesion for the code within a service (more on this later).
2. “Turn each function into a microservice”
If a function that computes something based on three input values, and returns a result, is that a good candidate for a microservice? Should it be a separately deployable application of its own? This really depends on what the function is and how it serves to the entire system.
Other arbitrary rules include those that don’t take into account your entire context such as the team’s experience, DevOps capacity, what the service is doing and availability needs of the data.
Characteristics of a well-designed service
If you’ve read about microservices, you’ve no doubt come across advice on what makes a well-designed service. Simply put: high cohesion and loose coupling. There are many articles on these concepts to review if you’re not familiar with them. And while sound advice, these concepts are quite abstract.
I’ve spoken with dozens of CTO’s on this topic to learn from them on how they’ve drawn their microservice boundaries, and I’ve distilled down some of the underlying characteristics for you below.
Characteristic #1: It doesn’t share database tables with another service
As we saw in Chris McFadden’s case mentioned earlier in this chapter, when it comes to designing a microservices if you have multiple services referencing the same table, that’s a red flag as it likely means your DB is a source of coupling.
“Each service should have its own tables [and] should never share database tables.” — Darby Frey, Co-founder of Lead Honestly
It is really about how the service relates to the data, which is exactly what Oleksiy Kovrin, Head of Swiftype SRE, Elastic, told me:
“One of the main foundational principles we use when developing new services is that they should not cross database boundaries. Each service should rely on its own set of underlying data stores. This allows us to centralize access controls, audit logging, caching logic, et cetera,” he said.
Kovyrin went on to explain that if a subset of your database tables, “have no or very little connections to the rest of the dataset, it is a strong signal that component could be isolated into a separate API or a separate service.”
Sam Newman illustrates this scenario well and provides a couple of solutions.
You can split the table apart (27:41) or you can reify a new service (26:00):
Darby Frey, Co-founder of Lead Honestly, echoed this sentiment by telling me that, “each service should have its own tables [and] should never share database tables.”
Characteristic #2: It has a minimal amount of database tables
As mentioned in Chapter 1, the ideal size of a microservice is to be small enough, but no smaller. And the same goes for the number of database tables per service.
Steven Czerwinski, Head of Engineering, Scaylr, explained to me during an interview that the sweet spot for Scaylr is, “one or two database tables for a service.”
“We have a suppression microservices, and it handles, keeps track of, millions and billions of entries around suppressions but it's all very focused just around suppression so there's really only one or two tables there. The same goes for other services like webhooks” explained Chris McFadden.
Characteristic #3: It’s thoughtfully stateful or stateless
When designing your microservice, you need to ask yourself whether it requires access to a database or is it going to be a stateless service processing terabytes of data like emails or logs.
“We define the boundaries of a service by defining its input and output. Sometimes a service is a network API but it can also be a process consuming files and producing records in a database (this is the case of our log processing service)” - Julien Lemoine
Be clear about this upfront and it will lead to a better designed service.
Characteristic #4: Its data availability needs are accounted for
When designing a microservice, you need to keep in mind what services will rely on this new service and what’s the system-wide impact if that data becomes unavailable. Taking that into account allows you properly design data backup and recovery systems for this service.
When speaking to Steven Czerwinski, he mentioned their critical customer row space mapping data is replicated and separated in different ways due to its importance.
“Whereas the per shard information, that's in its own little partition. It sucks if it goes down because that portion of the customer population is not going to have their logs available, but it's only impacting 5 percent of the customers rather than 100 percent of the customers,” Czerwinski explained.
Characteristic #5: It’s a single source of truth
The final characteristic to keep in mind is to design a service to be the single source of truth for something in your system.
To give you an example, when you order something from an eCommerce site, an order ID is generated. This order ID can be used by other services to query an Order service for complete information about the order. Using the pub/sub concept, the data that is passed around between services should either be the order ID, not the attributes/information of the order itself. Only the Order service has complete information and is the single source of truth for a given order.
Considerations for larger teams
For larger organizations, where entire teams can be dedicated to owning a service, organizational consideration comes into play when determining service boundaries. And there are two considerations to keep in mind: independent release schedule and different uptime importance.
“The most successful implementation of microservices we've seen is either based on a software design principle like domain-driven design for example, and service-oriented architecture or the ones that reflect an organizational approach,” said Khash Sajadi, CEO of Cloud66.
“So [for the] payments team,” Sajadi continued, “they have the payment service or credit card validation service and that's the service they provide to the outside world. So it's not necessarily anything about software. It's mostly about the business unit [that] provides one more service to the outside world.”
“[Jeff Bezos] came up with the 'two pizza' rule — a team shouldn't be larger than what two pizzas can feed.” — Travis Reeder, CTO of Iron.io
Amazon is a perfect example of a large organization with multiple teams. As mentioned in an article published in API Evangelist, Jeff Bezos issued a mandate to all employees informing them that every team within the company had to communicate via API. Anyone who didn’t would be fired.
This way, all the data and functionality was exposed through the interface. Bezos also managed to get every team to decouple, define what their resources are, and make them available through the API. Amazon was building a system from the ground up. This allows every team within the company to become a partner of one another.
I spoke to Travis Reeder, CTO of Iron.io, about Bezos’ internal initiative.
"Jeff Bezos mandated that all teams had to build API's to communicate with other teams. He's also the guy who came up with the 'two pizza' rule; a team shouldn't be larger than what two pizzas can feed,” he said.
“I think the same could apply here: whatever a small team can develop, manage and be productive with. If it starts to get unwieldy or starts to slow down, it's probably getting too big," Reeder told me.
How to tell if a service is too small, or not properly defined
During the testing and implementation phase of your microservice system, there are a number of indicators to keep in mind.
The first indicator to look out for is any over-reliance between services. If two services are constantly calling back to one another, then that’s a strong indication of coupling and a signal that they might be better off combined into one service.
Going back to the example Chris McFadden shared at the beginning of this chapter where he had two API services, accounts and users, that were constantly communicating with one another, McFadden came up an idea to merge the services and decided to call it the Accuser’s API. This turned out to be a fruitful strategy:
“What we started doing was eliminating these links [which were the] internal API calls between them. It's helped simplify the code.” McFadden informed me.
The second is if the overhead of setting up the service outweighs the benefit of having it be independent.
Darby Frey explained, “Every app needs to have its logs aggregated somewhere and needs to be monitored. You need to set up alerting for it. You need to have standard operating procedures and run books for when things break. You have to manage SSH access to that thing. There's a huge foundation of things that have to exist in order for an app to just run.”
Consider these characteristics
Designing microservices can often feel more like an art than a science. As an engineer, that doesn’t sit well with my left-brain. There’s lots of general advice out there but at times it can be a bit too abstract so let’s quickly recap:
- It doesn’t share database tables with another service
- It has a minimal amount of database tables
- It’s thoughtfully stateful or stateless
- Its data availability needs are accounted for
- It’s a single source of truth
These are 5 specific characteristics to look for when designing your next set of microservices. So the next time you’re tasked with drawing the boundaries for new services like the Users and Accounts example mentioned at the beginning, I hope referring back to these helps make that task much easier.
Thanks to Chris McFadden, Darby Frey, Steven Czwerinski, Julien Lemoine, Oleksiy Kovyrin, Travis Reeder, and Khash Sajadi for reviewing and contributing to this chapter.
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- How teams get microservices wrong from the start
- Should you always start with a monolith?
- Microservice Boundaries: 5 characteristics to guide your design
- Five microservice testing strategies for startups
- Should you break up your monolithic application?
- Breaking Up a Monolith: Case Study
- Designing a Successful Microservices Engineering Culture
- Should you build or buy microservices?