Identifying Domain-Based Cyclic Dependencies in Microservice APIs Using Source Code Detectors

Abstract

Isolation, autonomy, and loose coupling are critical success factors of microservice architectures. Unfortunately, systems tend to become strongly coupled over time, sometimes even exhibiting cyclic communication chains, making the individual deployment of services challenging. Such chains are highly problematic when strongly coupled communication e.g. based on synchronous invocations is used, but also create complexity and maintenance issues in more loosely coupled asynchronous or event-based communication. Here, cycles only manifest on a conceptual or domain level, making them hard to track for algorithms that rely solely on static analysis. Accordingly, previous attempts to detect cycles either focused on synchronous communication or had to collect additional runtime data, which can be costly and time-consuming. We suggest a novel approach for identifying and evaluating domain-based cyclic dependencies in microservice systems based on modular, reusable source code detectors. Based on the architecture model reconstructed by the detectors, we derived a set of architectural metrics for detecting and classifying domain-based cyclical dependencies. By conducting two case studies on open-source microservice architectures, we validated the feasibility and applicability of our approach.

Avoiding Excessive Data Exposure Through Microservice APIs

“…The communication model we extract from the underlying microservice artifacts is derived from our previous work [13]. We express inter-service calls within the system as a directed graph G = (V, E, DA, F ), where V represents the set of architectural elements essential for the communication flow -like APIs (V AP I ∈ V ) -and E ⊆ V × V is the set of invocations (each represented by a tuple), both synchronous and asynchronous.…”

“…Instead, we used a different mining approach adapted from our previous works [13,23]. for mining code artifacts: To identify significant architectural hotspots within a system, it is often sufficient to look only for specific patterns in code that describe a particular communication model or API technology and ignore all other parts that are not relevant in this context.…”

“…Our work provides a novel approach for tracking the data transferred between microservice APIs and aims to identify any excessive data exposure that happens as part of this transfer 1 . To achieve this, we use our source code detector approach from our previous research [13] to derive a communication and data flow model from the underlying system's code artifacts. Based on this model, we define a set of architectural metrics for guiding architectural design decisions targeting the reduction of excessive data exposure.…”