This tutorial walks through building a Spring Boot application, integrating Micrometer for metric collection, deploying Prometheus and Grafana via Docker, configuring dynamic service discovery, and creating custom request‑count metrics with AOP, providing a complete end‑to‑end monitoring solution.
This guide explains how to install and configure the Prometheus plugin for Jenkins, set up monitoring endpoints, adjust ConfigMap settings, and connect Grafana to display Jenkins CI metrics, providing a complete end‑to‑end DevOps monitoring solution.
This guide walks through deploying node‑exporter, Prometheus, and Grafana on a Kubernetes cluster with YAML manifests, configuring services, RBAC, and Grafana dashboards to monitor cluster metrics, and includes verification steps and code examples.
This article provides a comprehensive overview of using Prometheus and Grafana for metric collection, storage, querying with PromQL, visualization, and alerting, including exporter integration, metric types, high‑availability setups, and practical examples for modern microservice architectures.
This article introduces Prometheus and Grafana as a powerful monitoring stack, explains their architecture, metric collection, storage options, query language, integration with Grafana for dashboards and alerts, and shares practical deployment patterns and high‑availability solutions.
This guide explains how to monitor Alibaba Cloud RDS instances by deploying Prometheus and Grafana, using the official mysqld_exporter, a custom aliyun-exporter, rebuilding Docker images, configuring supervisor and Prometheus service discovery, and automating the entire workflow while noting limitations.
This guide explains the background, key features, architecture, and step‑by‑step deployment of Thanos—including Sidecar, Store, Query, Compact, Bucket, Rule, and Check components—to provide a unified, high‑availability Prometheus monitoring view with unlimited historical data storage using object storage.
Jiangsu Mobile’s IT operations team partnered with Newland to build a high‑availability, real‑time performance management platform using Prometheus, achieving billion‑level call‑record monitoring, low‑latency queries, data compression, and advanced forecasting, dramatically improving system health visibility and operational efficiency.
The article explains how Thanos combined with Prometheus provides a cloud‑native, highly available solution for long‑term metric storage and fast querying to address the exponential growth of monitoring data in large OpenStack and Ceph deployments.
This article explains how the Thanos and Prometheus combination provides long‑term, highly available monitoring for massive OpenStack and Ceph clusters, detailing its features, architecture, key components, practical deployment issues, and the operational problems it resolves.
This article explains how Java memory leaks can occur despite automatic garbage collection, describes JVM reachability analysis, shows how to monitor and detect leaks using Spring Boot Actuator, Prometheus, and Grafana, and provides step‑by‑step instructions for heap dump analysis and code fixes.
This article explains how Alibaba Cloud Container Service (ACK) designs a unit‑based, tiered management system, capacity planning model, global observability architecture, and pluggable components to reliably operate more than ten thousand diverse Kubernetes clusters during the massive Double‑11 shopping event.
This article introduces Prometheus, an open‑source monitoring system, explains its core components such as server, exporters, and Alertmanager, provides step‑by‑step installation and configuration instructions, demonstrates alert rule setup, and shows integration with tools like Grafana, Telegraf, Spring Boot and Canal.
This article explains the fundamentals of monitoring and logging in large‑scale Kubernetes clusters, classifies monitoring types, traces the evolution from Heapster to the lightweight metrics‑server, outlines the three Kubernetes monitoring APIs, reviews Prometheus as the de‑facto standard, and describes Alibaba Cloud’s enhanced monitoring and logging solutions.
This roundup highlights the latest cloud‑native announcements, including Helm 3’s stable release, the GitHub Octoverse language trends, upcoming KubeCon North America, CNCF’s Prometheus report, Kubernetes 1.17 code freeze, key upstream feature improvements, and a curated list of open‑source projects and reading recommendations.
This article shares practical Prometheus best‑practice tips, covering the accuracy‑reliability trade‑off, self‑monitoring setups, avoiding NFS storage, pruning high‑cardinality metrics, handling rate‑function traps, alert‑graph mismatches, group_interval effects, and the overarching goal of stable, cost‑effective observability.
This article explains the purpose, core functions, classification, layered architecture, and popular implementations of IT monitoring systems, covering log‑based, trace‑based, and metric‑based approaches as well as a comparison of Zabbix and Prometheus.
This guide walks through the architecture of Prometheus, the key Kubernetes monitoring metrics, and step‑by‑step instructions to deploy Prometheus, Grafana, and Alertmanager on a K8s cluster, configure RBAC, set up ConfigMaps, expose services, import dashboards, and test alert notifications via email.
This article explains the essential functions of IT monitoring systems, classifies them into log, trace, and metric types, describes a five‑layer monitoring architecture, and compares two popular open‑source solutions—Zabbix and Prometheus—helping practitioners choose the right tool for their environment.
This article explains how to migrate a Mesos+Marathon environment to OpenShift and configure Prometheus Operator ServiceMonitor resources, including service creation, ServiceMonitor definition, and verification steps, with full YAML examples and screenshots of the monitoring UI.
This article introduces Prometheus, compares it with Zabbix, Open‑Falcon and Nagios, explains its architecture, data model, exporters, storage options, query language, alerting and federation, and shares practical deployment experiences and common Q&A for cloud‑native environments.
This article introduces Prometheus, an open‑source cloud‑native monitoring and alerting system, covering its main characteristics, core components, architecture diagram, typical use cases, query language syntax, built‑in functions, time‑series types, and practical tips for reliable operation.
This article explains the three main monitoring approaches—log collection, distributed tracing, and metric gathering—in microservice architectures, outlines the layered monitoring model, lists key system, application, and user metrics, and reviews popular open‑source time‑series monitoring tools such as Prometheus, OpenTSDB, and InfluxDB.
This tutorial explains how to install the Contour Ingress controller backed by Envoy on Kubernetes, configure IngressRoute resources, examine Envoy's static and dynamic configuration, and integrate Prometheus and Grafana monitoring with proper RBAC and ServiceMonitor setup.
This guide walks through downloading, installing, configuring, and verifying Prometheus, Alertmanager, Node Exporter, and Grafana on a Linux server, including service setup, YAML configuration files, and a simple test to trigger and receive an alert via email.
Prometheus, an open‑source monitoring and alerting toolkit originally developed by SoundCloud and now a CNCF project, offers multidimensional data models, flexible queries, pull‑based data collection, various metric types (counter, gauge, summary, histogram), local and remote storage, service discovery, and integrates with Grafana for visualization.
This article explains the fundamentals of Prometheus histogram metrics, illustrates why they are cumulative, shows how to drop unwanted buckets with relabeling, and demonstrates quantile calculations using the histogram_quantile function, providing practical examples and code snippets for effective monitoring.
This article explains the four core Prometheus metric types—Counter, Gauge, Histogram, and Summary—detailing their characteristics, appropriate use cases, PromQL functions, and how they differ, while providing language-specific client library references and visual examples.
This article explains why comprehensive, multi‑layer monitoring is essential for distributed systems, outlines environment, program, and business metrics, recommends practical tools such as Zabbix, open‑falcon, Prometheus and Grafana, and provides a step‑by‑step evolution plan and alerting strategy.
This week’s roundup covers Kubernetes 1.16 API deprecations, new managed Prometheus services from Azure and Alibaba Cloud, upcoming KEP enhancements, Knative serving updates, the Kopf operator framework, curated reading links, and a CNCF‑Alibaba cloud‑native course on networking and policies.
This article details Xiaomi's Ocean elastic scheduling platform's challenges in monitoring massive Kubernetes clusters, the transition from Open‑Falcon to a Prometheus‑based solution with remote storage, partitioned deployment strategies, performance testing, and future plans for automated scaling and data analytics.
This article compares three container alerting frameworks—Alertmanager, Kapacitor, and Bosun—explains why Bosun was chosen for its flexible HTTP API rule deployment and low learning curve, and provides step‑by‑step configuration, rule definition, notification, and templating examples for integrating Bosun with Prometheus.
This article examines the limitations of Prometheus's original V2 time‑series storage, proposes a block‑oriented V3 architecture that tackles series churn, write amplification, and indexing inefficiencies, and validates the new design with extensive benchmarks showing dramatic reductions in memory, CPU, and disk usage.
This article details Xiaomi's end‑to‑end, highly available Kubernetes monitoring solution that combines Prometheus, OpenTSDB, and Falcon to handle massive dynamic metrics, ensure persistent storage, and support seamless scaling across multiple clusters.
This article provides a comprehensive overview of Prometheus, an open‑source monitoring and alerting system, covering its origins, key features, architecture, core components, metric types, data collection methods, service discovery, storage options, and query capabilities.
This article details the design and evolution of a highly available, persistent, and dynamically adjustable Kubernetes monitoring solution at Xiaomi, covering initial Falcon‑based approaches, the transition to Prometheus with remote storage via OpenTSDB, federation‑based partitioning, deployment strategies, performance testing, and future enhancements.
This guide walks through downloading, extracting, and setting up Node Exporter, Alertmanager, Prometheus, and Grafana on a Linux server, configuring their systemd services, customizing alert rules, and verifying the monitoring and alerting pipeline with screenshots of each verification step.
This article explains Prometheus metric naming conventions, label usage, metric types such as Counter, Gauge, Summary, and Histogram, and describes the structure of data samples, providing examples and best‑practice guidelines for defining and classifying metrics in monitoring systems.
This article shares a small team’s step‑by‑step journey from basic monitoring to fully automated CI/CD pipelines, detailing why Prometheus was chosen, how Ansible and GitLab integrate with Jenkins, and tips for configuration versioning, alerting, and scaling the setup.
This article reviews common open‑source monitoring tools, shares the evolution of China Unicom's big‑data platform monitoring, and provides practical guidance on selecting collectors, databases, and visualization components, with detailed configurations for Prometheus, Alertmanager, Grafana, and automation recovery techniques.
This article describes the challenges of scaling the 58 Cloud Search service, explains why Prometheus was selected as the monitoring stack, and details the architecture, data collection, storage, alerting, visualization, and future enhancements of the resulting cloud‑native monitoring system.
This article reviews common open‑source monitoring combinations, compares their strengths and weaknesses, and shares practical guidance on selecting collectors, storage back‑ends, and visualization tools such as Telegraf, InfluxDB, Prometheus, Grafana, and alertmanager for large‑scale data platform operations.
This article analyses why a custom Prometheus exporter for Percona MySQL 5.7.23 caused new‑connection failures and query timeouts, reveals a mutex deadlock involving LOCK_global_system_variables, LOCK_log and LOCK_status, and shows that upgrading to MySQL 5.7.25‑28 resolves the issue.
This article explains how to configure Spring Boot Actuator to export metrics to InfluxDB and Prometheus, covering Docker setup, Micrometer dependencies, application properties, sample controller code, test data generation, and visualisation with Grafana.
The article introduces Nacos 0.8.0, highlighting its three major production features—user login, Prometheus metrics, and namespace isolation—while providing step‑by‑step download links, startup commands for Linux and Windows, and instructions to access the default login console.
The article describes how 360 built a Kubernetes‑based container platform monitoring system using Prometheus, ELK, Grafana and custom components, detailing its architecture, monitoring dimensions, log collection, alerting, selection rationale, high‑availability design, and future evolution for scalable cloud‑native operations.
This article details the architectural design and engineering practices behind building a comprehensive Application Performance Management dashboard for Android applications, covering real-time monitoring, version comparison, development cycle tracking, automated data collection, and integrated test coverage analysis to ensure sustainable software quality and delivery efficiency.
This article shares a step‑by‑step experience of a small team that built a low‑cost automated operations pipeline using Prometheus for monitoring, Ansible for configuration management, and Jenkins for CI/CD, covering monitoring, alerting, versioned configs, and scalable deployment practices.
This article walks through a small team’s step‑by‑step journey to low‑cost automated operations, covering monitoring with Prometheus, configuration versioning via Ansible, CI/CD pipelines in Jenkins, and scalable script generation using Cookiecutter.
This guide walks small teams through step‑by‑step implementation of low‑cost automated operations, covering basic monitoring with Prometheus, configuration versioning via Ansible, CI/CD pipelines using Jenkins, and scaling practices, enabling gradual evolution toward enterprise‑grade DevOps architectures.
This article explains how to configure Kubernetes Horizontal Pod Autoscaler (HPA) to scale workloads based on custom business metrics collected by Prometheus, covering installation of Metrics Server, deployment of a demo app, setup of the Prometheus adapter, and practical load‑testing steps.
This article shares practical Go microservice building experiences, covering gRPC-based communication, TLS security, request tracing, and comprehensive monitoring with Prometheus, including metric selection, alerting, and log management using Logrus and Graylog, to help reduce coupling and improve system observability.
This article outlines a large‑scale monitoring solution for distributed microservice environments, comparing traditional ELK logging with a custom GPE stack (Grafana, Prometheus, Exporter, Consul), detailing architecture, components, workflows, and practical considerations for reliable observability.
This guide explains how to choose between push and pull monitoring models, introduces Prometheus architecture and metric syntax, shows Node.js client integration with code examples, and covers Alertmanager features and Grafana visualization for effective application monitoring.
This article shares practical experiences of using Prometheus for monitoring complex services, covering metric types, PromQL query techniques, naming conventions, service discovery with file‑based configs, high‑availability sharding, alerting via Alertmanager, and visualisation with Grafana, providing actionable guidance for reliable observability.
This article examines the unique challenges of monitoring containers, outlines three categories of metrics to collect, compares host‑centric and layered monitoring architectures, provides detailed methods for gathering CPU, memory, I/O and network data via cgroup files and Docker commands, and shares practical insights, tooling recommendations, and a Q&A session for effective container observability.
From early manual deployments to a sophisticated, multi-layered monitoring stack—including ELK, Zabbix, Statsd, Grafana, and Prometheus—Bilibili’s ops team shares the evolution, challenges, and lessons learned in building scalable, automated infrastructure for massive internet traffic.
This article examines the daunting performance‑optimization challenges of a complex PaaS architecture, breaks the system into control, data, and monitoring subsystems, defines concrete metrics, demonstrates testing with Prometheus and other tools, and shares practical automation techniques to accelerate iterative improvements.
This article translates and expands a comparative analysis of Prometheus against Graphite, InfluxDB, OpenTSDB, Nagios, and Sensu, covering their applicability, data models, storage mechanisms, architecture, and strengths to help readers choose the most suitable time‑series database for monitoring needs.
This article explains why monitoring is essential for system reliability, outlines the key components of a comprehensive monitoring framework, compares data collection methods, and presents practical container monitoring solutions—from Docker stats to cAdvisor with InfluxDB and Grafana, as well as Kubernetes and Mesos integrations.
This article explains why Docker services need monitoring, distinguishes black‑box and white‑box approaches, compares self‑hosted and SaaS monitoring stacks, and reviews key components and popular tools such as Prometheus, InfluxDB, Grafana, Datadog, and Sysdig.
