This article explains how to use the Prometheus HTTP API to query instant and range metrics, interpret the JSON responses, and fetch data programmatically with Python, providing code examples and details on request parameters, error handling, and practical usage.
This article details Qunar’s migration from a traditional Prometheus‑based monitoring stack to VictoriaMetrics, describing the challenges of large‑scale container metrics collection, the architectural redesign using VM‑Cluster, vmagent, and vmalert, and the performance improvements achieved after full replacement.
Monitoring Kubernetes clusters is essential for visibility and scalability, but selecting the right tools can be complex; this article outlines best‑practice approaches and compares popular open‑source solutions such as Prometheus, Grafana, Thanos, Elasticsearch, Logstash, and Kibana, helping you build an effective monitoring stack.
This article describes how to replace Zabbix with Prometheus for MySQL monitoring by configuring mysqld_exporter to collect metrics from multiple MySQL instances, including environment setup, user creation, exporter configuration, troubleshooting common errors, and Prometheus job adjustments, providing step‑by‑step commands and code examples.
This article visually explains Prometheus’s architecture, core features, metric collection methods, exporters, PromQL query language, and alerting workflow, helping readers understand how to monitor cloud‑native systems effectively while noting its strengths and limitations.
This article explains how RocketMQ integrates with Prometheus and Grafana to provide comprehensive metrics, tracing, and logging, detailing the exporter architecture, deployment choices, span topology, dashboard examples, and ARMS‑based alerting for cloud‑native message‑queue observability.
Tencent transformed its popular Happy Dou Dizhu game’s monitoring by migrating to Tencent Cloud Managed Prometheus and Grafana, unifying metric naming, consolidating ServiceMonitors, defining dashboards as code, and avoiding high‑cardinality labels, which cut labor costs by over 30% and greatly improved operational efficiency.
This article provides a systematic overview of monitoring fundamentals, compares three popular open‑source monitoring solutions—Zabbix, Open‑Falcon, and Prometheus—and offers practical guidance for selecting the most suitable system based on scale, features, and operational needs.
This article explains the current state of cloud‑native alerting, introduces Grafana Mimir as a horizontally scalable, multi‑tenant storage for Prometheus, details its architecture and components, and provides step‑by‑step guidance for installing, configuring, and operating Mimir in Kubernetes environments.
This guide walks you through creating a complete observability stack—exporting metrics, traces, and logs from a Go web service, collecting them with OpenTelemetry Collector, and storing them in Grafana Mimir, Loki, and Tempo, then visualizing everything on a unified Grafana dashboard.
This guide explains why native cloud monitoring falls short, how building custom Prometheus exporters adds overhead, and how Alibaba Cloud's fully managed Prometheus service—through enterprise cloud‑monitoring and self‑monitoring integration modes—provides ready‑to‑use exporters, agents, Grafana dashboards, and alert templates for dozens of cloud products.
Learn how to use Prometheus's stable V1 API to query instant and range data, handle responses and errors, and fetch metrics programmatically with curl and Python, enabling data analysis, cost management, and advanced monitoring beyond basic alerts.
This article details the redesign of an internal monitoring system, explaining why the original zzmonitor fell short, how Prometheus and its ecosystem were selected, the architecture that integrates remote storage with M3DB, performance benchmarks, Grafana visualisation, and a custom alerting solution.
This article explains how Grafana Mimir provides a scalable, highly‑available, multi‑tenant long‑term storage for Prometheus, details its architecture and core components such as compactor, distributor, ingester, querier, query‑frontend and store‑gateway, and shows step‑by‑step installation, status checking, and Alertmanager configuration for cloud‑native environments.
This article systematically introduces monitoring fundamentals, explains the architecture and key metrics of typical monitoring objects, compares three popular open‑source monitoring solutions—Zabbix, Open‑Falcon, and Prometheus—and provides practical guidance for selecting the most suitable system.
This article explains how to design and implement a Prometheus‑based monitoring system for big‑data components running on Kubernetes, covering metric exposure methods, scrape configurations, exporter deployment, and dynamic alert rule management with Alertmanager.
This guide explains how to use Alibaba Cloud's fully managed Prometheus service to collect and visualize metrics from ECS‑based applications across pure VPC, hybrid VPC‑IDC, and multi‑cloud scenarios, detailing the pain points of self‑built solutions and providing step‑by‑step configuration instructions.
This article introduces five popular open‑source network monitoring solutions—Cacti, Nagios Core, Icinga 2, Zabbix, and Prometheus—explaining their main features, data collection methods, platform support, and typical use cases to help administrators choose the right tool for reliable system oversight.
This article details the design and implementation of a three‑dimensional monitoring system that replaces an outdated custom solution with Prometheus, M3DB remote storage, and Grafana, covering data model choices, metric types, architecture, performance testing, automatic dashboard generation, and a custom alerting service.
This article explains how Grafana Mimir extends Prometheus and Alertmanager to provide a horizontally scalable, highly available, multi‑tenant monitoring solution for Kubernetes, covering its architecture, key components, compression mechanisms, deployment steps, and configuration of Alertmanager and multi‑tenant support.
This article explains how to achieve comprehensive service governance in a microservice architecture using SpringCloud Alibaba's Nacos and Dubbo, covering service registration and discovery, load balancing, rate limiting and circuit breaking with Sentinel, configuration management, and monitoring with Prometheus and SkyWalking.
This article explains how to prepare Linkerd for production use by covering high‑availability deployment, Helm chart installation, Prometheus metric handling, external Prometheus integration, multi‑cluster communication, and additional operational best‑practices such as resource tuning and security considerations.
This article explains how to build comprehensive system and application monitoring using the USE (Utilization, Saturation, Errors) method, outlines key performance metrics, and details the architecture of tools like Prometheus, Grafana, ELK, and distributed tracing to quickly detect and resolve bottlenecks.
Effective monitoring combines comprehensive system and application metrics—using the USE (Utilization, Saturation, Errors) method to pinpoint resource bottlenecks, and leveraging tools like Prometheus, Grafana, and ELK stacks for data collection, storage, querying, alerting, visualization, and full‑stack tracing across distributed services.
This article shares practical experiences and solutions for improving an Alertmanager‑based alert system, addressing problems such as noisy alerts, lack of escalation, missing recovery notifications, suppression limitations, and cumbersome silence management by redesigning architecture, adding custom scripts, and extending database support.
This guide walks through deploying a MySQL primary‑replica cluster on Kubernetes using Helm charts, configuring persistent volumes, exposing services, and adding Prometheus and Grafana monitoring, while also covering installation, testing, and clean‑up steps.
This tutorial explains how to deploy a primary‑replica MySQL cluster on Kubernetes using Helm, configuring persistent volumes, setting up Prometheus monitoring, and provides commands for installation, verification, and clean removal, all with detailed code snippets.
This article introduces essential Redis operational tools, covering real‑time monitoring with the INFO command and Prometheus‑exporter, data migration using Redis‑shake, consistency checking via Redis‑full‑check, and cluster management through CacheCloud, providing practical guidance for administrators.
This guide walks through designing a simple front‑end/back‑end microservices architecture, implementing it with Spring Boot and Eureka, deploying the services on a Kubernetes cluster using K8seasy, and adding high‑availability features such as multi‑instance registration, Prometheus‑Grafana monitoring, Zipkin tracing, and Sentinel flow‑control.
This guide walks through installing KubeSphere on Tencent Cloud's TKE, covering official documentation steps, common issues such as CBS disk size limits, uninstall complications, monitoring gaps on super‑nodes, and provides concrete YAML snippets and kubectl commands to resolve them.
This article presents a step‑by‑step solution for consolidating TiDB multi‑cluster monitoring by deploying Consul for service registration, configuring Prometheus to discover services via Consul, and optionally replacing Prometheus with VictoriaMetrics to achieve unified dashboards, scalable data collection, and easier health inspection across dozens or hundreds of instances.
The article describes the motivation, requirements, and architectural design of a highly available, scalable, low‑cost distributed time‑series database built on Mimir, detailing write and read paths, multi‑tenant isolation, compaction, and the performance and cost improvements achieved after deployment.
This article details the design, data‑collection pipeline, monitoring stack, visualization practices, and alert‑response workflow of a globally deployed Alibaba Cloud‑native system that uses ACK, Prometheus, Grafana, and ARMS to achieve end‑to‑end observability across metrics, tracing, and logs.
This article provides a step‑by‑step guide on extending the Kubernetes scheduler, covering configuration of scheduler profiles, implementing out‑of‑tree plugins, integrating Prometheus‑based network traffic scoring, and deploying the custom scheduler both inside and outside a cluster, complete with code samples and troubleshooting tips.
This article walks through setting up real‑time performance monitoring by sending JMeter metrics to InfluxDB via Backend Listener, visualizing them in Grafana, and extending the approach to system metrics with node_exporter, Prometheus, and Grafana, covering configuration steps, code snippets, and query examples.
This article explains the limitations of a standard Prometheus monitoring stack on Kubernetes and demonstrates how to migrate to a Thanos‑based solution for long‑term metric retention, reduced infrastructure cost, and scalable multi‑cluster observability using Terraform and cloud‑native components.
This article explains how to build a comprehensive monitoring system using the concise USE (Utilization, Saturation, Errors) method, outlines key system and application metrics, and demonstrates practical implementation with Prometheus, Grafana, full‑link tracing, and ELK for observability and performance troubleshooting.
Grafana 9.0 focuses on improving user experience for observability and data visualization, introducing visual Prometheus and Loki query builders, an Explore‑to‑dashboard workflow, a revamped heatmap panel, command palette, panel search, trace panels, navigation upgrades, and enhanced alerting, all aimed at making data discovery and investigation more intuitive and efficient.
Learn how to set up Prometheus to scrape Nexus repository metrics, configure authentication, and create insightful Grafana dashboards that visualize component, Jetty, and JVM metrics, enabling proactive troubleshooting and resource optimization for Nexus services.
This article introduces four CNCF‑graduated open‑source projects—Prometheus, Jaeger, OpenTelemetry, and Thanos—that together provide metrics, alerts, tracing, and long‑term storage to improve observability, reduce downtime, and streamline troubleshooting for workloads running on Kubernetes.
This article introduces five popular open‑source network monitoring solutions—Cacti, Nagios Core, Icinga 2, Zabbix, and Prometheus—explaining their main features, supported platforms, data collection methods, and where to obtain them, helping administrators choose the right tool for reliable system oversight.
This tutorial explains how to install Aeraki Mesh sample applications, forward ports to Prometheus and Grafana, and use these tools to query and visualize L7 protocol metrics for Dubbo and Thrift services within an Istio service mesh.
This article explains the key Prometheus metrics for Kubernetes container CPU usage, provides exact PromQL formulas for calculating per‑container CPU percentages, and details how Docker stats reports memory and CPU usage, including the necessary calculations and sample code.
This article compares Prometheus and Zabbix, outlining their histories, architectures, storage models, configuration complexity, community activity, and suitability for different environments, and concludes with recommendations on when to choose each monitoring system.
This article details a practical approach to constructing a semi‑automated monitoring solution for small‑scale services using Prometheus, covering active monitoring concepts, metric types, service‑framework integration, Grafana dashboards, Alertmanager routing, and deployment on Mesos.
Grafana 9.0 introduces a suite of usability enhancements—including a visual Prometheus query builder, a visual Loki LogQL generator, improved Explore‑to‑dashboard workflow, revamped heatmap panel, command palette, panel search, trace panel, navigation upgrades, and alerting refinements—aimed at simplifying observability, data visualization, and operational efficiency.
This comprehensive guide walks you through Prometheus fundamentals, including metric exposure, scraping, storage, querying with PromQL, custom exporter creation in Go, dynamic configuration reloading, and visualizing data with Grafana, while also covering alerting with Alertmanager and best practices for accurate histogram bucket design.
This article explains why downsampling is essential for handling massive time‑series data in Prometheus, describes the aggregation rules and intervals, compares ARMS Prometheus' implementation with other solutions, and shows performance and accuracy results that demonstrate significant query speed improvements.
This comprehensive guide walks you through designing a simple front‑end/back‑end microservice architecture, implementing it with Spring Boot, adding service discovery, monitoring, logging, tracing, and flow control, and finally deploying the entire system on a Kubernetes cluster with high availability and verification steps.
This article provides a detailed overview of Prometheus, covering its architecture, metric exposure, scraping models, storage format, metric types, custom exporter implementation in Go, PromQL query language, built‑in functions, Grafana integration, and alerting with Alertmanager, offering practical code examples throughout.
This article provides a detailed overview of Prometheus, covering its architecture, metric exposure and scraping models, data model, metric types, configuration reload, PromQL query language, custom exporters, Grafana integration, and Alertmanager alerting, with practical code examples and best‑practice tips.
Grafana 9.0 introduces a suite of usability enhancements—including visual Prometheus and Loki query builders, an Explore‑to‑dashboard workflow, a high‑performance heatmap panel, command‑palette navigation, and improved alerting—making data exploration, visualization, and monitoring more intuitive for developers and operators.
This article compares the open‑source monitoring systems Zabbix and Prometheus, detailing their architectures, component roles, strengths, and weaknesses, then describes how to integrate Zabbix data into Prometheus using a custom super_exporter and visualise the combined metrics with Grafana.
This article describes Vivo's practical experience building a cloud‑native monitoring system for large‑scale container clusters, covering the shortcomings of traditional monitoring, the Prometheus‑centric ecosystem, high‑availability architecture, challenges faced, and future directions such as automation and AI‑driven operations.
This article compares Prometheus and Zabbix, outlining their histories, architectures, data storage models, configuration complexity, scalability, and suitability for traditional versus cloud-native environments, helping readers decide which monitoring solution best fits their infrastructure needs.
Vivo’s cloud‑native monitoring solution combines high‑availability Prometheus clusters, VictoriaMetrics storage, Grafana visualization, and a custom leader‑election adapter to deduplicate data while forwarding metrics to Kafka and OLAP systems, addressing large‑scale performance, scalability, and integration challenges and paving the way for AI‑driven AIOps.
This article explains the limitations of a traditional Prometheus monitoring stack, demonstrates how Thanos provides unlimited long‑term storage and lower infrastructure costs, and walks through a complete multi‑cluster deployment on Kubernetes using Terraform and AWS.
Prometheus is an open‑source monitoring system that scrapes metrics from services or exporters, stores them in a time‑series database, lets users query with PromQL, visualizes data via its web UI or Grafana, and sends alerts through Alertmanager, supporting custom Go metrics, various discovery methods, and four metric types.
This article details a practical, semi‑automated monitoring solution for environments with fewer than 500 nodes, covering active monitoring concepts, Prometheus data modeling, service‑framework instrumentation, data scraping and visualization with Grafana, and alert handling via AlertManager.
This article explains the functional, nested nature of PromQL, its expression types, how queries are parsed and evaluated over time, and the differences between instant and range queries, providing code examples and visual insights for better monitoring with Prometheus.
This guide introduces Loki, the open‑source, horizontally scalable log aggregation system optimized for Prometheus and Kubernetes, covering its core concepts, architecture, components, deployment steps, Grafana integration, label‑based indexing, and best practices for handling dynamic and high‑cardinality tags.
This tutorial walks through the end‑to‑end setup of JMeter performance testing data collection using Backend Listener, sending metrics to InfluxDB, and visualizing real‑time TPS, response time, and error rates in Grafana, as well as monitoring OS metrics with node_exporter, Prometheus, and Grafana.
This article provides a step‑by‑step tutorial on setting up Prometheus and Grafana for 24/7 monitoring of Linux servers and MySQL databases, covering installation, configuration, data visualization, alerting with onealert, and common troubleshooting tips for reliable operations.
This article walks through the most common performance‑monitoring stack—JMeter, node_exporter, Prometheus, InfluxDB, and Grafana—explaining how to configure backend listeners, send metrics, store them, and build real‑time dashboards while highlighting code snippets and query examples.
This article provides a comprehensive guide to Micrometer, covering its core metric types, MeterRegistry usage, tagging conventions, and practical code examples, and shows how to integrate it with Spring Boot, Prometheus, and Grafana for end‑to‑end application monitoring.
This article explains how to use Prometheus to create a precise, end‑to‑end alerting system that shortens detection and diagnosis time, integrates logs and metrics, routes alerts to the right owners, and prevents overwhelming alert storms in production environments.
This article explains how Prometheus’s time‑series database handles massive monitoring data, from basic concepts and query examples to storage engine design, indexing strategies, and powerful data computation techniques such as recording rules.
This guide explains the essential observability concepts—metrics, logs, and traces—for performance testing, compares Zabbix and Prometheus, shows how to extend JMeter with a Prometheus exporter, and details step‑by‑step integration of Alibaba Cloud PTS and Grafana dashboards for comprehensive monitoring.
This article explains how Prometheus, a data‑driven monitoring system, leverages a time‑series database (TSDB) to handle massive metric volumes, perform efficient queries, and enable powerful calculations such as recording rules for pre‑computed results.
This article chronicles the evolution of a company's monitoring system from a Zipkin‑based tracing solution to a cloud‑native observability platform called Hera, detailing design goals, technology choices, challenges with MySQL storage, and the adoption of Prometheus‑compatible metrics, Jaeger tracing, and Kubernetes operators.
This article explains the monitoring challenges introduced by microservice and container evolution, why Prometheus is the preferred observability solution in the cloud‑native era, and presents a comprehensive, multi‑tenant, high‑availability architecture with practical techniques for data collection, storage, query optimization, security, and future trends.
This article traces the development of open‑source monitoring solutions from early tools like Nagios and Cacti through modern platforms such as Prometheus and Nightingale, comparing their strengths, weaknesses, and typical use cases while also looking ahead to emerging observability trends in cloud‑native environments.
This article explains how to use Spring Boot Actuator to expose Tomcat performance metrics, retrieve health and metric endpoints via HTTP, configure custom endpoints, and optionally log or export the data to Prometheus for continuous monitoring and analysis.
This comprehensive guide walks you through Prometheus' origins, core features, installation methods, configuration syntax, PromQL basics, exporter integrations, Grafana visualization, alerting with Alertmanager, and advanced topics like service discovery and Pushgateway, enabling you to build a robust monitoring system.
This article explains how Prometheus transforms raw monitoring data into actionable insights by using a time‑series database (TSDB) that efficiently stores massive metric streams, supports powerful queries, and enables pre‑computed calculations for fast dashboards and alerts.
Learn how to enable and use Spring Boot Actuator's monitoring features—including HTTP and JMX endpoints—configure built‑in endpoints, expose custom metrics, dynamically adjust log levels, and manually register JMX MBeans, with code examples and integration tips for Prometheus and Grafana.
This article explains how to build a comprehensive monitoring system for both infrastructure and applications, introducing the USE (Utilization‑Saturation‑Errors) method, key performance metrics, and practical components such as Prometheus, Grafana, full‑link tracing, and the ELK stack to detect and diagnose performance bottlenecks.
The article introduces Promoter, a Go‑based webhook that fetches Prometheus metrics, renders alert graphs with gonum/plot, stores the images in S3‑compatible object storage, and embeds them in DingTalk notifications, providing deployment instructions, template customization, and core implementation details.
This guide explains how to combine a PostgreSQL Operator with Pgpool-II on Kubernetes to create a PostgreSQL cluster that supports query read‑load balancing and connection pooling, covering prerequisites, deployment steps, environment‑variable and ConfigMap configuration, backend settings, failover handling, password registration, streaming replication checks, SSL setup, and Prometheus monitoring.
This article explains how to programmatically render Prometheus alert charts, upload them to object storage, and embed the images in DingTalk notifications using the Go‑based Promoter tool, including template customization, deployment steps, and core rendering logic.
This article describes the end‑to‑end design, metric hierarchy, data collection methods, visualization dashboards, and alerting mechanisms used to build a robust monitoring system for a large‑scale big‑data platform, covering physical hosts, Hadoop components, business services, and data layers with tools such as Telegraf, Prometheus, and Grafana.
This article provides a comprehensive guide to optimizing SpringBoot services, covering metric exposure with Prometheus, custom business monitoring, Java flame‑graph profiling, SkyWalking distributed tracing, HTTP and Tomcat tuning, layer‑wise code improvements, and practical code examples for real‑world performance gains.
This article explains how to design effective Prometheus metrics for various application types, choose appropriate vectors, labels, and buckets, and offers Grafana tricks for visualizing dimensions and linking tooltips, providing a comprehensive guide for robust observability.
This article outlines practical Prometheus monitoring techniques, covering how to choose metrics, define labels, select vectors and buckets, and use Grafana tips to build reliable observability for various application types.
This article examines the limitations of a standard Prometheus‑based monitoring stack on Kubernetes, explains how adopting Thanos improves metric retention and reduces infrastructure costs, and provides a detailed multi‑cluster deployment guide with Terraform, TLS configuration, and Grafana visualization.
This article outlines practical guidelines for designing Prometheus metrics, covering how to define monitoring targets, choose appropriate vectors and labels, name metrics and labels correctly, select histogram buckets, and leverage Grafana features to visualize and troubleshoot data effectively.
This article introduces essential Redis operational tools, covering the INFO command for monitoring, Prometheus‑based redis‑exporter visualization, the Redis‑shake data migration utility, Redis‑full‑check consistency verification, and the CacheCloud platform for comprehensive cluster management.
This article explains how to design and implement a terabyte‑level log monitoring platform using ELK Stack, FileBeat, Elastic APM, Kafka Streams, Prometheus, and Grafana, covering data collection, filtering, visualization, and resource‑efficient processing for large‑scale microservice environments.
This guide walks through installing Prometheus and Node Exporter on a Linux server, copying binaries to system paths, configuring Prometheus with scrape jobs for the local node and remote hosts, and running the exporters with specific collector options for system metrics.
This article explains how to build both small‑scale and large‑scale high‑availability Prometheus setups using local and remote storage, federation, keepalived, and PostgreSQL + TimescaleDB adapters to ensure reliable monitoring and alerting across growing infrastructures.
This article introduces essential Redis operational tools, covering real‑time monitoring with the INFO command and exporters, data migration using Redis‑shake, consistency checking via Redis‑full‑check, and cluster management through CacheCloud, while highlighting key metrics such as stat, commandstat, cpu, and memory.
This article traces the 30‑year evolution of system monitoring, explains the differences between monitoring, APM and observability, outlines key practices for building an observability platform, and provides a step‑by‑step guide to implementing Prometheus + Grafana in a cloud‑native environment.
Baidu’s Aifanfan product migrated its entire suite to Kubernetes through a two‑phase, 11‑step process that standardized CI/CD, containerization, and traffic routing, enabling deployment of 200 + modules in under an hour, 99.99 % stability, cost‑effective operations, and laying groundwork for multi‑cluster, service‑mesh expansion.
This article explains the design, benefits, and practical usage of Prometheus' new Agent mode and remote‑write capabilities, covering its pull‑model origins, global‑view challenges, federation alternatives, and how the lightweight Agent improves efficiency and scalability for cloud‑native monitoring.
This article explains how Prometheus’s local storage limits scalability and how Remote Storage, federation, and high‑availability setups—using dual instances, keepalived, and adapters with PostgreSQL + TimescaleDB—can overcome data persistence and performance challenges for both small‑scale and large‑scale monitoring environments.
This guide walks you through installing Prometheus, configuring node_exporter and mysqld_exporter for remote Linux and MySQL monitoring, visualizing metrics with Grafana, and setting up multi‑level alerts using Grafana integrated with OneAlert for a robust 24/7 operations monitoring solution.
This article shares practical experiences and best‑practice guidelines for deploying and operating Prometheus in Kubernetes, covering version selection, inherent limitations, exporter choices, metric design, multi‑cluster scraping, memory and storage planning, GPU monitoring, timezone handling, and alerting considerations.
This article explains how to design and deploy a robust monitoring solution using Prometheus, Thanos, Pushgateway, and Alertmanager on Kubernetes, covering metric collection, naming conventions, query language, high‑availability strategies, and practical YAML configurations for a production‑grade observability platform.
This guide explains how to collect TiDB metrics via its HTTP monitoring API, preprocess the data into JSON, create master and regular items in Zabbix, and configure triggers using Prometheus‑style expressions to achieve effective TiDB monitoring.
This article compares Prometheus and Zabbix, covering their histories, architectures, data storage models, configuration complexity, community activity, and container support, to help you decide which monitoring solution best matches your operational needs in cloud and on‑premise environments.
