This view of microservices shares much in common with object-oriented programming: encapsulated data access and mutable state change are both achieved via synchronous calls, the web of such calls among services forming a graph of dependencies. Programmers can and should enjoy a lively debate about OO’s merits and drawbacks for organizing code within a single memory and process space. However, when the object-oriented analogy is extended to distributed systems, many problems arise: latency which grows with the depth of the dependency graph, temporal liveness coupling, cascading failures, complex and inconsistent read-time orchestration, data storage proliferation and fragmentation, and extreme difficulty in reasoning about the state of the system at any point in time.
Luckily, another programming style analogy better fits the distributed case: functional programming. Functional programming describes behavior not in terms of in-place mutation of objects, but in terms of the immutable input and output values of pure functions. Such functions may be organized to create a dataflow graph such that when the computation pipeline receives a new input value, all downstream intermediate and final values are reactively computed. The introduction of such input values into this reactive dataflow pipeline forms a logical clock that we can use to reason consistently about the state of the system as of a particular input event, especially if the sequence of input, intermediate, and output values is stored on a durable, immutable log.
It’s an interesting analogy.