Design and Implementation of a YAML-Driven Metrics Layer Framework for Standardized KPI Delivery in Microservices

Citation: Abhishek Anand, "Design and Implementation of a YAML-Driven Metrics Layer Framework for Standardized KPI Delivery in Microservices", Universal Library of Engineering Technology, Volume 02, Issue 04.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article reviews a declarative YAML layer framework implemented to standardize the delivery of key performance indicators within microservice architectures. Justification of work is laid down in the context of a highly rapid shift from monolithic systems to microservices, the widespread adoption of Kubernetes as an orchestrator, and increasing volumes of system telemetry, with fundamental challenges in unifying all these metrics and delivering them as understandable business KPIs. In the absence of a unified semantic vocabulary, the calculation of indicators across different services is performed using disparate filters, which prolongs incident-analysis processes and may result in financial losses reaching millions of dollars per hour. The objective of the study is to externalize the descriptions of source metrics and business formulas from microservice code into a declarative layer, formatted as YAML configurations, and to integrate this layer into a GitOps pipeline to ensure versioning, automatic propagation, and auditing of changes without requiring service restarts. The solution proposed here merges three logical entities: a light-sidecar adapter for Prometheus label normalization and data transfer over the Remote Write 2.0 specification; centralized YAML storage, registered and semantically version-controlled with pull requests; and a proxy processor that compiles declarative formulas into recording rules, performing calculation execution as well as aggregating series for publishing. Therefore, it is possible to say that a declarative framework reduces the time required to deploy a new metric to production from days to hours, minimizes the share of duplicate KPIs, improves SLA compliance regarding calculation latency and telemetry collection overhead, and reduces the need for code-based instrumentation. YAML specifications capture REQUEST (why/what) and encode ACCUT guardrails (what ‘good’ means) for KPI delivery. The scalable architecture, which separates hot and cold data-processing streams, employs semantic versioning and utilizes an isolated formula interpreter to ensure reliability, stack-storage independence, and compatibility with existing monitoring and tracing systems. This article will be valuable to distributed-systems architects, DevOps engineers, and observability researchers for designing manageable and scalable KPI-standardization platforms.

Keywords: YAML, Microservices, KPI, Declarative Framework, GitOps, Prometheus, Observability, Metrics, CI/CD, Semantic Versioning.

https://doi.org/10.70315/uloap.ulete.2025.0204002