This article explains how massive online game user data can be turned into insightful visualizations by distinguishing in‑game and out‑of‑game contexts, focusing on core metrics such as activity, retention, and payment, and recommending suitable chart types and web‑based visualization tools.
The article explains the importance of measuring software architecture, outlines the granularity of metrics from code to infrastructure, and provides concrete measurement indicators for code implementation, component design, architecture design, and runtime infrastructure to guide effective architecture governance.
The team implements a sustainable “discover‑analyze‑validate” loop that uses a performance blacklist, user‑centric metrics such as interactive time and second‑open rate, automated SDK data collection, and continuous trend monitoring to pinpoint front‑end bottlenecks, apply targeted fixes, and verify measurable load‑time improvements.
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.
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 explains the core H5 page performance metrics such as FCP, LCP, CLS, TTI, and TBT, describes how to measure them with Lighthouse, and provides comprehensive network‑ and rendering‑layer optimization techniques along with real‑world case studies from the PLUS team to improve user experience.
This article provides a thorough overview of server performance testing, covering fundamental concepts, the influence on users and revenue, key metrics, various test types, a step‑by‑step testing workflow, and a comparative analysis of popular performance testing tools.
This comprehensive guide defines growth, distinguishes it from development, explains how to assess and sustain growth, outlines systematic methodologies, experimentation, growth models, organizational capabilities, and strategic leverage to achieve lasting, scalable product success.
This article describes the design and implementation of an automated FlinkSQL tuning system that monitors metrics, evaluates task health with rule‑based logic, calculates optimal resource adjustments, and performs fast scaling to reduce cluster waste, lower operational costs, and maintain SLA compliance.
After a three‑day, ten‑person investigation into a mini‑program image‑upload failure, we designed and implemented an end‑to‑end observability platform using MDD and SRE principles, defining SLI/SLO, instrumenting client, network, gateway and backend layers, and visualizing metrics with Grafana, ClickHouse and Prometheus.
This article provides a step‑by‑step tutorial on installing and configuring Spring Boot Admin, including Maven dependencies, server and client setup, YML properties, security, Nacos registration, email notifications, custom health indicators, and Micrometer metrics to monitor Spring Boot services.
Architects should treat system capacity like a pre‑flight checklist, using this comprehensive guide to monitor resource usage across services, databases, and queues, and to define business metrics and state‑machine indicators that reveal bottlenecks and guide scaling decisions.
This article explores the factors influencing video quality, explains why continuous improvement is essential, examines the importance and challenges of both subjective and objective quality assessments, and introduces Lingjing—a comprehensive, standards‑based video quality evaluation platform that addresses confidence issues and supports diverse testing scenarios.
After a year of cross‑BU experimentation, the Tianji precision testing platform was built to centralize coverage data, automate incremental analysis, and provide actionable insights, resulting in higher test coverage, faster release gating, and measurable productivity gains for both developers and QA teams.
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.
Bilibili’s SRE team, confronting rapid growth and complex systems, built a systematic stability operation that includes emergency response, incident handling, on‑call scheduling, and an Event Operations Center platform, using metrics like MTTR, MTTI and AI‑assisted automation to reduce downtime and improve reliability.
The Meituan Accommodation data governance team shares how they evolved from ad‑hoc, single‑point fixes to a systematic, automated governance framework—covering management, standards, capability, execution, evaluation, and vision—using standardization, digitization, and systematization to achieve measurable quality, cost and efficiency gains across thousands of data assets.
The article explains how technical leaders can apply quantitative analysis and systematic measurement to optimize both software system performance and organizational workflows, using a picture‑recognition service example and a real‑world DevOps incident to illustrate the need for end‑to‑end process mapping, bottleneck identification, and continuous improvement.
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.
Bilibili’s SRE team combines stability theory, detailed fault‑stage and operational metrics, and a unified emergency‑response platform—including on‑call scheduling, fault‑command incident commanders, automated fault portraits, and rapid post‑mortems—to transform frequent incidents into data‑driven, collaborative recoveries and lay groundwork for AI‑assisted self‑healing.
In this interview series, three industry experts explain how monitoring differs from observability, the shifts required for ops, developers, and architects, the core methodologies and technologies behind metrics, traces, and logs, and practical guidance for selecting and integrating observability tools in cloud‑native environments.
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 outlines the end‑to‑end workflow for front‑end monitoring in an APM platform, covering data collection, reporting, cleaning, storage, and consumption, and dives deep into environment info, exception handling, performance metrics, and efficient data upload strategies.
The document outlines the background, vision, current status, technical research, value, product and technical architecture, and functional design of a cloud‑native monitoring platform that integrates SkyWalking and Prometheus to provide comprehensive APM, resource utilization, alerting, and rapid fault localization for business and technical middle‑platform services.
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 the challenges of long request chains in micro‑service architectures, reviews Google’s Dapper tracing requirements, introduces OpenTracing and OpenCensus standards, compares their strengths, and details how OpenTelemetry unifies tracing, metrics and logs with practical integration steps and best‑practice guidance.
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 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 article explains how Kubernetes Horizontal Pod Autoscaler (HPA) balances resource demand and workload by automatically scaling pod replicas, describes the different metric types it supports, walks through the core controller code (Run, worker, reconcile, and replica calculation), highlights current limitations, and shares practical observations from real‑world usage.
This article outlines nine key DevOps metrics and KPIs—including the four DORA indicators and five additional measures—explaining how they help teams monitor performance, improve delivery speed, ensure quality, and maintain reliable, high‑availability applications.
The article outlines a three‑level indicator system that quantifies engineering quality across efficiency, quality, stability, and resource dimensions, using high‑level result metrics, detailed level‑2 breakdowns, and actionable level‑3 measures to enable drill‑down analysis, risk‑warning, and continuous, data‑driven improvement.
The article introduces a new R&D efficiency metric—throughput standard deviation—explains its statistical basis, shows how it was derived from annual reports, illustrates its application across multiple teams, and discusses practical insights and limitations for software development operations.
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.
This article explains the most important DevOps metrics—such as deployment frequency, lead time, automated test pass rate, change failure rate, MTTR, and others—and how tracking them helps teams improve software delivery speed, quality, and operational efficiency.
To raise front‑end quality, the team implements gray‑release monitoring that triggers log analysis at a 5 % rollout, automatically generates reports within ten minutes, and uses dynamic thresholds and noise‑reduction tactics to detect errors early, enabling rapid rollback or expansion and markedly improving stability and release efficiency.
Even when individual algorithm modules show higher accuracy or revenue, the overall platform can decline due to factors like competitor encroachment, macro‑economic shifts, concept drift, overlapping marginal returns, attribution errors, and coupled A/B experiments, all of which require careful analysis and mitigation.
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 guide explains what vmagent is, its key features, and provides step‑by‑step instructions to install, configure, and verify vmagent on a Kubernetes cluster, including namespace and RBAC setup, custom scrape configs, monitoring endpoints, and troubleshooting tips.
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.
The article explains why and how to use the GSM (Goal‑Signal‑Metric) model in product design, detailing its definition, applicable scenarios, step‑by‑step implementation, and a real‑world case study that demonstrates its value for aligning business, user, and design objectives.
This article compares Kubernetes pod monitoring metrics with traditional KVM/VM metrics across CPU, memory, disk, and network, explaining the underlying reasons for the differences and offering guidance on interpreting the data for effective performance troubleshooting.
This article explains three frequent mistakes in churn analysis—misinterpreting churn rates, falling into Simpson's paradox, and incorrectly inferring causality—illustrated with game‑related examples and emphasizes the need to combine multiple metrics for accurate conclusions.
This article introduces the essential concepts of data quality management, covering the six key quality dimensions, detailed rule and metric templates, data profiling techniques, a systematic quality assurance workflow, and practical data cleaning methods to improve overall data governance.
The article explains how adopting self‑organization in agile teams changes the responsibilities of technical managers, makes work visible, introduces servant‑leadership and technical‑expert roles, and uses metrics and coaching to strengthen team accountability while preventing loss of control.
This article details the background, research, architecture, performance testing, and deployment of a comprehensive monitoring system that leverages Prometheus, Grafana, and M3DB to provide flexible metric collection, automatic dashboard generation, and a custom alerting service for large‑scale business services.
This article explains what an APM system is, explores the three core components—Logs, Traces, and Metrics—and details the design and implementation of the Dog APM platform, covering client data models, automatic instrumentation, server processing pipelines, and storage strategies.
This article presents a comprehensive evaluation framework for OPPO's XiaoBu AI assistant, covering evaluation concepts, objectives, five key elements, sampling methods, dimension selection, annotation scoring, report generation, and a detailed Q&A that illustrates practical metrics and processes for voice and search services.
This article presents a comprehensive evaluation framework for OPPO's XiaoBu AI assistant, covering the concept and purpose of evaluation, the five key evaluation elements, data sampling strategies, dimension and rule selection, annotation scoring, reporting guidelines, and detailed procedures for assessing wake‑up, ASR, NLU, and TTS performance.
This article introduces VictoriaMetrics, a fast and cost‑effective time‑series monitoring solution that can replace Prometheus, covering its definition, key features, core components, and essential installation considerations for both binary and Kubernetes deployments.
This article describes Beike's real‑time computing team's end‑to‑end practices for collecting and storing Flink metrics, building visual monitoring dashboards, implementing multi‑level alerting, analyzing logs, estimating CPU and memory resources, and deploying Flink on Kubernetes with containerization and storage separation to improve stability, resource utilization, and operational efficiency.
This article details Douyin's systematic approach to improving creation experience by defining measurable goals, building a comprehensive metric system, performing relevance analysis, and implementing concrete Android and iOS performance optimizations—including album loading, component architecture, and small‑screen video quality—while outlining monitoring, tooling, and internal platform support that together deliver significant user‑facing gains.
The article explains how managers' love of metrics can unintentionally promote harmful behaviors in software teams, illustrates the problem with real-world stories, and provides practical guidelines—linking metrics to goals, tracking trends, using shorter cycles, and adapting metrics—to ensure they support, rather than hinder, the delivery of valuable software.
This article details Qunar's implementation of PostgreSQL high‑availability using Patroni, covering solution selection, custom DCS and failover mechanisms, operational impact, comprehensive testing procedures, performance metrics, and future directions for cross‑region HA deployment.
This guide outlines the key Redis monitoring metrics—including performance, memory, activity, persistence, and error indicators—provides detailed descriptions, command examples, and practical tips for using tools like redis-cli, redis-benchmark, and info commands to effectively track and troubleshoot your Redis instances.
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.
Spring Boot 2.6 introduces Cookie SameSite support, reactive session timeout, custom data‑masking rules, automatic Redis pool configuration, richer runtime Java metrics, build‑info personalization, new startup and disk metrics, enhanced Docker image building, and many deprecated properties removed or renamed, improving security and performance.
This article outlines six practical metrics—unified backlog, user‑centric requirement description, consistent requirement flow, short lead time, high iteration completion rate, and stable iteration velocity—to help teams distinguish superficial agile ceremonies from truly effective agile execution.
This guide outlines the key Redis monitoring metrics—including performance, memory, basic activity, persistence, and error indicators—explains their meanings, shows how to retrieve them with Redis commands, and provides practical tips for effective performance and health tracking.
This article explains step‑by‑step how to collect JMeter test metrics via Backend Listener, store them in InfluxDB, and display real‑time performance charts—including TPS, response time, and error rates—in Grafana, while also covering node_exporter integration with Prometheus for system‑level monitoring.
AB testing often fails because practitioners skip power analysis, peek at interim results, set unrealistic null hypotheses, randomize at inappropriate units, ignore sample‑ratio mismatches, choose misleading metrics, and fall prey to segmentation errors like Simpson’s paradox, any of which can invalidate conclusions.
This guide explains how to define, name, and organize monitoring metrics—covering Google’s four golden indicators, system‑specific measurement objects, vector selection, label conventions, bucket design, and practical Grafana tips—for reliable observability of diverse services.
This article shares a practical three‑step method for turning KPI from a hindrance into a catalyst during agile and DevOps transformation, covering urgency creation, pilot projects, and performance‑assessment redesign, while illustrating how integrated tools and feedback loops boost delivery efficiency and business value.
This article explains how frontend engineers can measure and improve system stability through clear availability metrics, incident grading, and comprehensive team norms covering code standards, branch management, code review, and a structured release process.
This article explains how to monitor Redis by categorizing essential performance, memory, activity, persistence, and error metrics, provides detailed tables of metric names and descriptions, lists common monitoring tools, shows configuration snippets, and demonstrates useful Redis CLI commands for real‑time insight.
The article examines why measurement systems often backfire by recounting historical tax‑related mis‑metrics, modern corporate examples like Haidilao, and a series of fundamental mistakes in software R&D efficiency metrics, urging a shift from metric‑driven thinking to purpose‑driven measurement.
This article presents a comprehensive MySQL slow‑query risk‑scoring model that quantifies each slow query's impact using metrics such as query count, execution time, lock wait, bytes sent and rows examined, assigns weighted scores up to 100, and demonstrates how the model enables proactive, business‑aligned remediation.
This article explains the common pain points of locating anomalies in Kubernetes environments and presents a multi‑layer monitoring framework—trace, metrics, events, and alerts—along with best‑practice scenarios such as network performance, DNS issues, full‑link stress testing, external MySQL access, and multi‑tenant architectures.
This article explains what MySQL slow queries are, why they cause system failures, proposes multi‑dimensional metrics to assess their severity, outlines concrete guidelines and change standards, and shares real‑world optimization cases and daily operational practices for eliminating slow SQL.
This article explains how Prometheus relabeling works, covering the purpose of relabeling, hidden and meta labels, the various actions such as replace, keep, drop, labelmap, labelkeep, labeldrop, and hashmod, and provides concrete configuration examples for common monitoring scenarios.
This article examines the challenges faced by documentation engineers, proposes a documentation‑centric workflow, outlines key elements such as edit‑publish separation, automation, formalization, open collaboration, and versioning, and suggests metrics and practices to measure and improve the overall developer experience.
This article outlines how improving R&D productivity hinges on three essential factors—people, process, and tools—detailing practical insights on cultivating engineering mindset, optimizing workflows, selecting effective tools, establishing robust metrics, and implementing fair assessment methods.
This article explains how to monitor Flink jobs running on YARN by leveraging Flink metrics, configuring reporters, defining custom metrics, and visualizing the data in real time with Prometheus, Grafana, and Graphite‑exporter, complete with deployment diagrams and code examples.
This article explores the fundamental difficulties of quantifying software development efficiency, outlines common measurement pitfalls and anti‑patterns, and offers practical guidance for building a systematic, data‑driven R&D performance framework that truly drives improvement.
This article introduces the Micrometer metrics library for JVM applications, explains its core Meter types and registries, demonstrates how to instrument Spring Boot services with counters, timers, gauges, and other meters, and shows end‑to‑end integration with Prometheus and Grafana for monitoring.
China Everbright Bank’s interview reveals how adopting DevOps standards and a continuous delivery pipeline boosted development speed, quality, and efficiency, with measurable improvements such as 90% build success, 60 daily integrations, and faster release cycles, illustrating the strategic value of standardized, tool‑enabled software delivery.
The article examines why many automation testing initiatives fail to deliver value, introduces a set of practical metrics such as test case count, execution frequency, success rate, coverage, EMTE, ROI, and bug‑detection efficiency, and explains how to combine them to assess and improve automation effectiveness within software development processes.
This article details 哈啰's front‑end performance monitoring architecture, covering the background of rapid growth, a three‑step optimization workflow, data collection, cleaning, aggregation, visualization, and practical techniques like pre‑rendering and offline packages to dramatically improve page load metrics.
iQIYI’s mobile backend employs dogfooding‑driven gray releases with cloud‑controlled traffic, gray‑tag propagation, comprehensive front‑end and back‑end metrics, device white‑lists, and downstream service integration, allowing internal users to quickly verify code and configuration changes and catch issues before full production rollout.
This tutorial walks through setting up a DTLE 3.21.07.0 monitoring environment by configuring DTLE and Nomad metrics, deploying Prometheus and Grafana via Docker, and creating common monitoring panels such as CPU, memory, bandwidth, latency, and TPS using PromQL.
This article explains ByteDance's journey from basic fault‑injection testing to a production‑grade, scenario‑driven proactive chaos engineering platform that automates experiments, defines stability metrics, controls blast radius, and continuously validates service dependencies to improve system resilience.
This article walks through setting up Spring Boot Actuator for microservice monitoring, explains the most important endpoints such as /health, /metrics, /loggers, shows how to expose and secure them, and demonstrates dynamic log level changes, custom health indicators, and integration with Spring Security.
The article argues that diversity in recommendation systems should be treated as a means rather than an ultimate goal, explains why it is hard to quantify, suggests using real performance metrics such as click‑through rate and dwell time, and offers practical strategies to improve listwise ranking.
This article outlines a systematic approach for designers to identify key priorities during rapid product iterations, covering business analysis, user segmentation, strategy formulation, implementation tactics, and data‑driven validation, using the real‑world case of the Ku Dashi (Cool Master) platform.
This article examines how Baidu Search achieves five‑nine‑plus availability by analyzing stability challenges, introducing the Kepler 1.0 observability stack, evolving to Kepler 2.0 with full‑trace collection, custom compression, and practical use‑cases that dramatically improve fault diagnosis and capacity management in a massive micro‑service environment.
This article provides a comprehensive guide to Prometheus' PQL language, covering instant and range vectors, metric types, label selectors, offsets, arithmetic and logical operators, as well as a wide range of built‑in functions with practical code examples for effective monitoring.
This article explains what constitutes a MySQL slow query, why they cause failures, defines quantitative metrics such as micro‑average and macro‑average to assess severity, outlines target goals, presents concrete optimization examples, and shares operational practices for ongoing slow‑SQL governance.
This article identifies the 16 most critical Kubernetes metrics—ranging from crash loops and CPU utilization to etcd leader status and application metrics—that you should monitor and alert on to maintain a healthy, performant cluster.
This article explains what an APM system is, compares logs, traces and metrics, reviews popular tools, and then details the design and implementation of the in‑house Dog APM platform—including client data models, Kafka pipelines, processing pipelines, storage in ClickHouse/Cassandra, and UI visualizations.
This article provides a comprehensive overview of Redis monitoring metrics—including performance, memory, basic activity, persistence, and error indicators—along with recommended monitoring tools, configuration settings, and command-line examples for gathering and interpreting these metrics in production environments.
This article details NetEase Cloud Commerce's journey of building a comprehensive SaaS quality assurance system—covering business challenges, goal setting, methodology, automation, visualization, and measurable improvements—to sustain fast iteration and high‑availability service delivery.
This guide walks you through configuring Flink metrics, installing and linking Pushgateway, Node_exporter, Prometheus, and Grafana, and finally visualizing and alerting on Flink metrics, providing a complete end‑to‑end monitoring solution for Flink clusters.
This article walks through Flink's metric system by explaining the core interfaces such as MetricReporter and MetricRegistry, showing how metrics are added, registered, and queried during TaskManager startup, and detailing both REST and Prometheus approaches for retrieving metric values.
This article dissects Baidu Search's ultra‑large micro‑service architecture, detailing the challenges of maintaining five‑nine‑plus availability, the diverse failure modes, and the step‑by‑step evolution of its observability stack—from early log‑only analysis to the kepler1.0/kepler2.0 tracing, full‑log indexing, custom span‑id generation, and compression techniques that together enable rapid root‑cause diagnosis at massive scale.
This article clarifies core web‑service metrics—QPS, TPS, PV, UV, DAU, MAU—explains their differences, shows how concurrency and throughput relate, and outlines key performance‑testing concepts and evaluation methods for modern system capacity planning.
By adapting growth‑hacking concepts such as north‑star metrics, conversion funnels, and A/B testing to the software development lifecycle, this article proposes a data‑driven “efficiency hacker” model that visualizes demand delivery paths, classifies tasks with the RIW framework, and guides teams toward faster, more transparent project outcomes.
This article explores how growth‑hacking concepts can be adapted to software R&D, proposing a north‑star metric, visual demand‑delivery pathways, an RIW model for prioritization, and timeline‑based A/B testing to achieve more objective and transparent development efficiency.
This article investigates why Sentinel’s QPS threshold of 20 is triggered at 15, analyzing metric logs, the underlying MetricNode and MetricTimerListener code, and revealing a timing discrepancy between second‑level and minute‑level counters that can cause premature flow‑control blocking.
This article provides an extensive overview of data warehouse fundamentals, including its purpose, core characteristics, layered architecture, modeling methods such as dimensional and normalization, as well as detailed discussions on data governance, metric systems, security standards, and practical implementation strategies for enterprise data management.
