Forty years ago, there was no cloud computing, there were no global-scale require‐ ments, and there was no need to deploy a system every 11.7 seconds. But engineers still had to tame systems’ complexity. Even though the tools in those days were differ‐ ent, the challenges—and more importantly, the solution—are relevant nowadays and can be applied to the design of microservices-based systems.

In his book, Composite/Structured Design, Glenford J. Myers discusses how to struc‐ ture procedural code to reduce its complexity. On the first page of the book, he writes:

There is much more to the subject of complexity than simply attempting to minimize the local complexity of each part of a program. A much more important type of com‐ plexity is global complexity: the complexity of the overall structure of a program or system (i.e., the degree of association or interdependence among the major pieces of a program).

In our context, local complexity is the complexity of each individual microservice, whereas global complexity is the complexity of the whole system. Local complexity depends on the implementation of a service; global complexity is defined by the inter‐ actions and dependencies between the services. Which of the complexities is more important to optimize when designing a microservices-based system? Let’s analyze both extremes.

It’s surprisingly easy to reduce global complexity to a minimum. All we have to do is eliminate any interactions between the system’s components—that is, implement all functionality in one monolithic service. As we’ve seen earlier, this strategy may work in certain scenarios. In others, it may lead to the dreaded big ball of mud: probably the highest possible level of local complexity.

On the other hand, we know what happens when we optimize only the local com‐ plexity but neglect the system’s global complexity—the even more dreaded distributed big ball of mud. This relationship is shown in Figure 14-5.

Figure 14-5. Service granularity and system complexities

To design a proper microservices-based system, we have to optimize both global and local complexities. Setting the design goal of optimizing either one individually is a local optima. The global optima balances both complexities. Let’s see how the notion of micro-public interfaces lends itself to balancing global and local complexities.