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This guide walks through a full Kubernetes monitoring solution using cAdvisor, node_exporter, Prometheus, and Grafana, covering architecture, data collection, service discovery, deployment steps with DaemonSets, and detailed YAML configurations for a production‑ready observability stack.
This tutorial shows how to build a lightweight request‑tracing solution in SpringBoot 3.2 using SLF4J, MDC, custom filters, logback configuration, and a global ResponseBodyAdvice to log and return trace IDs for improved debugging and observability.
After a series of change‑induced outages in early 2023, Bilibili instituted a comprehensive change‑management framework—including a preventive change platform, a central control system, quality and monitoring tools, strict gray‑release policies, observability checks, and rapid rollback mechanisms—to dramatically cut emergency incidents and embed a reliability‑first culture.
SpringBoot 3.2 and Spring Cloud 2023 are now supported by the PIG microservice, bringing JDK17/21 compatibility, virtual‑thread support, SSL hot‑reload, enhanced observability, and a host of dependency upgrades, while SpringBoot 2.x reaches end‑of‑maintenance.
Apache ShenYu is a high‑performance, reactive Java API gateway offering dynamic traffic control, hot‑plug plugins, multi‑protocol support, observability, and easy deployment, with detailed module descriptions, quick Docker‑based startup commands, selector and rule routing concepts, and data synchronization mechanisms.
This article explains how to use Prometheus to monitor business‑level metrics in a Kubernetes environment, covering observability fundamentals, metric definitions, metric types, exposing metrics via a /metrics endpoint, and practical Go code examples for defining, recording, and scraping custom metrics.
The article examines how rapid Kubernetes adoption accelerates development velocity but also introduces hidden frictions such as standardization limits, DevOps disruption, monitoring difficulties, and team isolation, emphasizing the need for collaborative platform engineering and contextual observability.
This article presents Kuaishou's comprehensive journey of adopting Flink on Kubernetes, covering its background, evolution, architecture, production migration, observability, testing, and future plans, and demonstrates how large‑scale streaming workloads are transformed to a cloud‑native environment.
This guide walks through installing OpenTelemetry Collector on a Kubernetes cluster using Helm, configuring DaemonSet and Deployment collectors, integrating Prometheus for metrics, and customizing receivers, processors, and exporters to achieve comprehensive observability of nodes, pods, containers, and cluster resources.
The article details a scalable live‑stream full‑stream data system that replaces a tightly‑coupled legacy architecture with a producer‑consumer model using a custom key‑value store, bucket sharding, gRPC server‑streaming, versioned caching, and comprehensive observability, achieving sub‑second queries, horizontal scalability, and reliable support for thousands of downstream services.