This article presents ten practical PromQL query examples covering CPU, memory, disk, network, database, Kubernetes, and business metrics, explains the underlying concepts, provides alert thresholds and best‑practice tips, and includes advanced optimization and alert‑rule design guidance for reliable monitoring.
This comprehensive guide walks you through the background, architecture, and technology selection for monitoring, then details step‑by‑step installation of Prometheus, configuring exporters for Linux, MySQL, and Java applications, introduces core PromQL concepts, and shows how to integrate and visualize data with Grafana.
This guide presents ten essential Grafana dashboards—covering SLO burn, user‑journey funnel, infrastructure USE metrics, queue lag, database health, cache hit‑rate, CDN latency, rollout guardrails, trace topology, and a command‑center view—each explained with its purpose, panel layout, and ready‑to‑use PromQL or LogQL queries.
This guide explains how to leverage Nightingale's table panels for efficient multi‑dimensional monitoring, covering why tables are essential, step‑by‑step setups for single and multiple PromQL queries, overrides, styling options, and advanced data transformations to boost operational insight.
This guide walks you through creating effective Nightingale monitoring dashboards by configuring three common chart types—Metric (Gauge), Pie, and Honeycomb—including step‑by‑step PromQL queries, legend settings, panel options, styling, and advanced configurations to turn raw data into actionable visual insights.
This comprehensive guide walks you through PromQL fundamentals, covering data types, gauge and counter metrics, time‑series concepts, query selectors, offsets, arithmetic and logical operators, vector matching, aggregation functions, and key Prometheus functions such as increase, rate, and histogram_quantile, with practical examples and visual illustrations.
This article presents ten practical PromQL query examples covering CPU, memory, disk, network, application, database, Kubernetes, and business metrics, along with key concepts, alerting thresholds, and best‑practice tips to help operations engineers build a comprehensive monitoring system in cloud‑native environments.
This guide walks you through installing Prometheus via binary download, configuring global scrape settings and job definitions, running the server with command‑line options, and using the web UI and PromQL to verify target health and query metrics, illustrated with screenshots and example queries.
This article explains Prometheus' origins, open‑source development, CNCF graduation, core components, time‑series data model, text‑based metric protocol, powerful PromQL queries, service discovery mechanisms, and alerting practices, providing a comprehensive guide for cloud‑native observability.
This article explains how the Grafana Table panel serves as a versatile, data‑driven Swiss‑army‑knife for deep troubleshooting, covering its advantages, typical use cases, step‑by‑step configuration, PromQL queries, JSON panel definition, and visual customization tips.
This guide explains why Grafana's Gauge panel creates a powerful visual metaphor for system pressure, walks through creating the gauge, configuring PromQL queries, setting panel options, thresholds, and JSON definitions, and shows how to produce clear, boss‑friendly monitoring dashboards.
Learn when to choose a Pie Chart in Grafana, explore common use cases like browser market share and HTTP status codes, and follow step‑by‑step instructions—including panel options, legend, tooltip, and JSON configuration—to create clear, proportion‑focused visualizations.
This guide explains why Grafana’s Time Series panel is essential for proactive monitoring, walks through browser selection, PromQL queries, panel options such as titles, tooltips, legends, axes, graph styles, and provides a ready‑to‑use JSON configuration to visualize trends and detect anomalies.
PromQL Copilot leverages Alibaba Cloud's observability platform and AI techniques to convert ambiguous natural‑language monitoring requests into accurate PromQL statements, addressing challenges of ambiguity, domain knowledge, and metric coverage while providing generation, explanation, diagnosis, and recommendation features for cloud‑native environments.
This comprehensive guide walks you through PromQL fundamentals, data types, query expressions, selectors, operators, aggregation, and essential functions, illustrating each concept with real‑world monitoring scenarios and code examples to help you effectively query and analyze time‑series data in Prometheus.
This article provides a comprehensive overview of Prometheus, covering its architecture, suitable and unsuitable use cases, data model with TSDB and WAL, metric types, PromQL query language, and practical examples for monitoring CPU, memory, and disk usage in Kubernetes environments.
This article details how Bilibili redesigned its monitoring system to overcome explosive metric growth by separating collection and storage, adopting VictoriaMetrics, implementing zone‑based scheduling, automating PromQL query replacement, and using Flink for efficient pre‑aggregation, resulting in dramatically lower latency and higher stability.
This comprehensive guide explains Prometheus' architecture, metric collection models, storage format, query language (PromQL), alerting workflow, configuration reload methods, metric types, custom exporters, and how to visualise data with Grafana, providing a complete end‑to‑end monitoring solution.
This guide walks through installing and configuring Prometheus, Grafana, and Node Exporter on a Linux server using Docker, shows how to set up monitoring dashboards, customize PromQL queries, and verify the monitoring system, providing complete steps and code snippets for a functional host monitoring solution.
This guide translates and condenses six frequent errors new Prometheus users make—high‑cardinality labels, losing valuable tags during aggregation, using bare selectors, omitting the for field, choosing too‑short rate windows, and applying rate‑related functions to wrong metric types—offering practical fixes to improve monitoring reliability.
This article visually dissects Prometheus, explaining its architecture, core features, data collection methods, exporter role, PromQL query language, and alerting workflow, while contrasting it with ELK and highlighting practical configuration examples for real‑world monitoring.
This comprehensive guide introduces Prometheus as an open‑source monitoring solution, covering metric exposition, scraping, storage, PromQL queries, custom exporters in Go, dynamic configuration reloads, Grafana dashboards, and Alertmanager alerting with practical code examples.
To replace costly, duplicated Flink jobs, Didi built Observe‑Compute (OBC), a cloud‑native, PromQL‑driven real‑time metric engine with centralized policy management, scalable containerized workers, and zero‑downtime scaling, achieving million‑RMB annual savings while handling 10 M points per second.
The article examines performance bottlenecks in Prometheus‑Grafana monitoring dashboards caused by high metric cardinality, explains the internal query processing steps, demonstrates how to analyze and reduce cardinality with PromQL and recording rules, and shows concrete command‑line examples that dramatically improve query latency.
This comprehensive guide explains Prometheus fundamentals, its ecosystem, metric collection models, configuration, PromQL querying, custom exporters, Grafana visualization, and Alertmanager setup, providing step‑by‑step instructions and code examples for effective system monitoring and alerting.
This article explores the structure and evaluation of PromQL queries, covering its nested functional language nature, expression types, time handling with instant and range queries, and practical examples using the PromLens visualizer, helping readers grasp how Prometheus processes and types queries.
Prometheus is an open‑source monitoring solution that covers metric exposition, scraping, storage, querying, visualization, and alerting, and this guide walks through its architecture, configuration, custom exporters, PromQL queries, Grafana integration, and alert management, providing a comprehensive introduction for developers and ops engineers.
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 visually explains Prometheus by covering its architecture, key features, metric collection methods, exporter role, PromQL query language, and alerting mechanisms, helping readers understand how to monitor cloud‑native systems effectively in production.
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 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.
This article provides a thorough overview of Prometheus, covering its ecosystem, how metrics are exposed and scraped, storage and query mechanisms, metric types, PromQL usage, exporter implementation, dynamic configuration reload, Grafana visualization, and Alertmanager alerting, with practical code examples throughout.
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 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 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 guide walks through installing the Go‑based Node Exporter, configuring it as a systemd service or Docker container, exposing standard Prometheus metrics, and using PromQL to calculate CPU usage across multiple nodes.
This guide walks through the history, core features, installation methods, configuration, PromQL queries, exporter setup, Grafana integration, and alerting with Alertmanager for Prometheus, providing practical commands and examples for building a complete monitoring solution in cloud‑native environments.
Boussole is Shopee’s real‑time analytics engine that transforms each dimension into key‑value pairs stored primarily in HBase, pre‑aggregates selected dimension combos, hashes metrics and tags, executes distributed PromQL queries with a CockroachDB‑inspired executor, applies Delta‑of‑Delta compression and point‑capping, and continues to evolve with adaptive pre‑aggregation and new storage models to maintain millisecond latency for massive multi‑dimensional analysis.
This comprehensive guide walks you through Prometheus' origins, core features, installation methods, configuration files, PromQL basics, exporter setup, Grafana integration, alerting with Alertmanager, and advanced topics like service discovery, providing a complete roadmap for building a production‑grade monitoring system.
This guide explains Kubernetes capacity planning, showing how to detect idle CPU and memory, identify wasteful namespaces, use open‑source tools like kube‑state‑metrics and cAdvisor, and apply PromQL queries to optimize resource requests and measure the impact of your improvements.
This article provides a complete tutorial on Prometheus, covering its origins, core features, installation methods (binary and Docker), configuration file structure, PromQL basics, HTTP API usage, Grafana integration, various exporters for metrics collection, and alerting with Alertmanager, all within a cloud‑native monitoring context.
This article provides a detailed introduction to Prometheus, covering its history, core features, installation methods, configuration file structure, PromQL basics, various exporters, Grafana visualization, alerting with Alertmanager, service discovery, and best‑practice recommendations for building a production‑grade monitoring system.
This guide explains the most common Prometheus Operator metrics for Kubernetes, detailing each metric's purpose, the PromQL expression to monitor it, and the related underlying metrics, helping you fine‑tune alerts and avoid unnecessary noise in your cluster monitoring.
This article introduces Prometheus' query language PromQL, explains instant and range vector selectors, label matching, offset handling, storage design, common functions and aggregation operators, and provides practical advice for efficient querying and avoiding performance issues.
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 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.
