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.
Anxin Securities’ CIO discusses how the company’s Internet Customer Service Platform passed the DevOps Standard Continuous Delivery Level 3 assessment, detailing the motivations, implementation challenges, measurable improvements, and future plans for scaling DevOps across its technology stacks.
This guide explains why monitoring is essential throughout a product lifecycle, outlines monitoring modes and methods, compares health checks, logs, tracing and metric solutions, and provides a detailed Prometheus‑based monitoring architecture with concrete metric definitions, alerting rules, and incident‑response procedures.
This article shares how a large SaaS company tackled the difficulty of defining and adopting R&D efficiency metrics in agile squads by strengthening awareness, boosting participation, establishing clear standards, and using data‑driven feedback to achieve measurable improvements in delivery speed and quality.
This article explains the core concepts of observability—metrics, logging, and tracing—how they interrelate, and how to implement them effectively in Kubernetes environments using tools like Prometheus, Grafana, ELK, and distributed tracing solutions.
This article presents a detailed R&D performance measurement framework that outlines five design principles, three categories of metrics, and concrete calculations for indicators such as demand lead time, defect ratios, availability, throughput, and flow efficiency, helping organizations balance speed, quality, and sustainability.
Spring Boot 2.5 introduces secure info endpoints, graceful shutdown via GET, shutdown logging, a new Quartz actuator endpoint, layered WAR support, Buildpack image building, environment variable prefixes, enhanced metrics, HTTP/2 over TCP (h2c), and several datasource improvements, all aimed at smoother microservice development.
The 2020 China DevOps Status Survey reveals that while over 60% of companies have adopted DevOps, many still struggle to assess its success, highlighting key performance criteria, maturity levels, and ongoing challenges such as expertise gaps and metric definition.
This article explains how to extend a performance testing framework with new QPS and deviation fields, implement accurate QPS calculations for both fixed‑thread and fixed‑QPS models, and prepare the system for quantitative error analysis using detailed Java code examples.
OpenTelemetry, a CNCF observability project, standardizes telemetry data models, collection, processing, and export, offering vendor‑agnostic APIs, SDKs, and a configurable collector; the article explains its problem domain, solution components, history, future outlook, and provides a practical Go demo with code and configuration examples.
Suning’s data middle platform integrates an accelerated OLAP engine, a star‑schema metrics system, a standardized visualization tool, and a unified report portal to break data silos, enhance security, and transform traditional enterprises into technology‑driven businesses.
Tubi’s Popper platform combines a Scala‑based experiment engine, reproducible JSON‑stored configurations, a React UI, and data pipelines using Spark and Akka to enable fast, cross‑team A/B testing, automated analysis, health checks, and data‑driven decision making across mobile and OTT services.
Suning’s Data Middle Platform integrates an accelerated OLAP engine, a star‑schema metric system, a visualization tool built on standardized dimensions, and a unified report portal to solve data silos, improve security, and enable enterprises to evolve into technology‑driven organizations.
The article explains how Google measures and maintains software quality before release by dividing responsibilities between product teams and SRE, using monorepo, trunk‑based development, daily release candidates, automated testing, performance monitoring, and a Project Health (pH) metric system that tracks productivity, release velocity, reliability, and quality.
The article explains the concept of innovation networks, outlines four key roles—Inventor, Transformer, Financier, and Broker—describes the surrounding ecosystem, and provides practical guidance on aligning values, understanding organizational capabilities, and mapping metrics to improve network performance.
Obsuite, DiDi’s open‑source observability suite, tackles hybrid‑cloud monitoring challenges by combining metrics, logs, and traces, while the article analyzes market trends, private‑cloud demand, and the product’s architecture, open‑source components, and the OCE certification program for enterprise users.
This article presents a comprehensive guide to Redis monitoring and alerting, covering metric classification, threshold settings, client traffic collection, host resource usage, instance health checks, cluster failover diagnostics, and detailed explanations of Redis INFO sections with practical code examples.
This article explains how to design and implement a flexible, accurate data reporting platform using Elasticsearch aggregation, covering architecture, data synchronization, metric definition, SDK integration, and future scaling considerations for enterprise analytics.
This article explains how to diagnose and optimize Flink jobs by first examining metrics, then checking resource allocation, analyzing throughput and back‑pressure, and finally tuning JVM settings, while providing concrete configuration examples and practical tips for big‑data practitioners.
The article explains the importance of microservice monitoring and presents a five‑level monitoring hierarchy—from infrastructure to end‑user experience—along with five essential monitoring aspects and a typical architecture using agents, message queues, ELK, and time‑series databases to ensure reliable, observable services.
This article explains the layered architecture of microservice monitoring, detailing five monitoring levels—from infrastructure to end-user experience—along with essential monitoring points such as logs, metrics, tracing, alerts, and health checks, and presents a typical monitoring stack using agents, Kafka, ELK, and InfluxDB.
This guide walks through building a comprehensive MySQL monitoring platform using Prometheus and Grafana, covering exporter installation, configuration, key performance metrics such as replication health, query throughput, slow queries, connection limits, buffer pool usage, and provides ready‑made Grafana dashboards and alerting rules.
The article outlines essential DevOps metrics—such as deployment frequency, deployment time, automated test pass rate, code commit volume, defect escape rate, cost, failure rates, detection time, unplanned work, MTTF, application performance, MTTD, MTTR, delivery time, change quality, and customer feedback—to help teams monitor and improve software delivery speed, quality, and reliability.
The presentation outlines the AI engineering efficiency platform covering algorithm metric and evaluation, micro‑service performance testing, and dataset management architectures, detailing business pain points, platform‑wide improvements, technical designs, real‑world demos, and future directions to achieve accurate, fast, and stable AI services.
This guide explains the core concepts of logging, metrics, and tracing in micro‑service monitoring, introduces Prometheus, exporters, and Grafana, and provides step‑by‑step instructions for downloading, installing, configuring, and visualizing metrics using these tools.
In this interview, Microsoft Visual Studio Cloud Services Program Management Director Lori Lamkin shares the evolution of their DevOps journey, covering rhythm changes, team autonomy, continuous delivery, trunk‑based development, dogfooding, frequent deployments, testing, security, telemetry, metrics, and the cultural shift toward operational responsibility.
This article presents a detailed case study of how an international hotel division identified low pre‑sale service smoothness, defined quantitative metrics, analyzed caching and real‑time quote‑fetching trade‑offs, and redesigned the quote‑fetching scheduling mechanism to raise overall flow smoothness toward an 87% target.
Quantifying DevOps is challenging but possible, requiring baseline measurement of technical, financial, and cultural metrics, understanding bottlenecks, adopting appropriate tools, and continuously tracking progress to improve delivery speed, quality, and team satisfaction while avoiding blame culture.
The article details the 2020 upgrade of the Crystal Ball design‑asset sharing platform, outlining identified problems, clarified goals, quarterly action plans, key initiatives such as search improvements and a points system, and the resulting growth in material volume, usage rates, and overall design efficiency.
This article explains Google’s Core Web Vitals initiative, detailing the key performance metrics—Largest Contentful Paint, First Input Delay, and Cumulative Layout Shift—along with tools, APIs, and a JavaScript library to help developers measure and improve user experience.
This article outlines a request log analysis system that records core request fields, adds proxy‑related data, derives IP‑based ASN and geographic information, parses user‑agent details, and provides comprehensive metrics such as PV/QPS, UV, traffic, latency, status monitoring, and business‑specific insights, all visualized via an ELK‑Kafka architecture.
Monitoring is essential throughout an application's lifecycle, covering purpose, modes, methods, tool selection, and detailed Prometheus implementation for host, container, service, and third‑party monitoring, along with alerting strategies and fault‑handling processes to ensure stable, secure operations.
The article details how Xianyu improved long‑list scrolling smoothness on both native Android and Flutter by building a cross‑platform frame‑jank detection tool, applying async view‑caching and unbinding in Android, and using repaint boundaries, clipping tweaks, time‑slicing and custom scroll physics in Flutter, achieving up to 57 FPS and halving big‑jank counts on low‑end devices.
This article explains Google’s DevOps solution framework, focusing on the measurement pillar by detailing how to implement visual management boards, avoid typical mistakes, improve their effectiveness, and measure their impact against team goals, while referencing the DORA study that underpins the approach.
This article explores Prometheus as a cloud‑native monitoring solution, covering core principles, limitations, metric selection, exporter consolidation, Kubernetes deployment nuances, memory and storage planning, high‑availability designs, and advanced features like rate calculations, cardinality management, and predictive alerts.
This article provides an in‑depth analysis of Sentinel‑Go, covering its fundamental concepts such as Resource and Rule, the Entry and SlotChain mechanisms, flow‑control components like TrafficShapingController, metric storage structures including BucketWrap and LeapArray, and practical code examples illustrating the library’s internal workflow.
This article explains how Prometheus uses a time‑series database (TSDB) to handle massive monitoring data, detailing its concepts, query examples, storage engine design, indexing mechanisms, and the benefits of pre‑computing expressions for efficient real‑time analysis.
This article explains how to measure website performance using various metrics and introduces Core Web Vitals as key indicators for user experience.
Over six months, 58.com revamped the category pages for its rental and home services, introducing clear metrics like connection conversion rate and redesigning navigation to prioritize top services, which resulted in a three‑fold increase in order and contact conversions across both platforms.
This article enumerates the core Linux system metrics collected by the Open‑Falcon agent—including CPU, disk, memory, network, kernel, and process statistics—explaining how each metric is derived from /proc or other system tools and why it matters for reliable operations monitoring.
This article explains how to define appropriate software quality measurement goals, choose quantitative or qualitative methods, apply periodic and lifecycle‑wide metrics, and use visual dashboards to turn measurement data into actionable improvements throughout the development process.
This article explores how to classify, model, and store R&D efficiency metrics—distinguishing human‑oriented from process‑oriented measures, illustrating a meta‑model of value flow, advocating graph databases for flexible storage, and sharing expert insights for proactive risk control.
This guide enumerates the key Linux system metrics—covering CPU, memory, disk, I/O, network, kernel parameters, RAID, SMART, NTP, and process information—that open-falcon agents collect every minute to enable comprehensive operations monitoring and timely issue detection.
This article explains the concept of test coverage, describes various coverage techniques such as statement, branch, path, condition, and boundary-value coverage, outlines key metrics, provides a practical coverage matrix example, and offers actionable tips for improving overall software testing quality.
This article explains the definitions and differences of QPS and TPS, how they relate to concurrency and system throughput, and introduces related metrics such as PV, UV, DAU, response time, and practical considerations for performance testing and capacity planning.
This case study details JD.com's strategy to attract price‑sensitive users during the 618 promotion by redefining core metrics, enhancing visual content, implementing interactive front‑end features, and optimizing retention mechanisms, resulting in significant order growth and improved user perception of value.
The article explains how to evaluate and improve data warehouse design by measuring completeness, reusability and standardization, proposes concrete metrics such as cross‑layer reference rate and model reuse coefficient, and outlines a step‑by‑step process—from ODS control to domain division, dimension unification, fact‑table integration, model development and migration—while introducing the EasyDesign tool for systematic management.
Mica, originally derived from the internal lutool project, is a lightweight Java 8‑based microservice core that supports both traditional Servlet and Reactive WebFlux, auto‑generates Spring configuration, adds metrics, Redis utilities, RSA helpers, and integrates with Prometheus and Grafana, with its source available on GitHub and Gitee.
iQIYI’s tech product team designed a unified full‑link automated monitoring platform that integrates link, metric, and log collection with deep analysis, enhancing fault localization, performance insight, and scalability across microservices, while addressing limitations of existing tools like ELK, Prometheus, and Dapper.
This article explains the purpose and operation modes of Prometheus exporters, details the text-based metric exposition format including HELP, TYPE, and sample lines for counters, gauges, summaries, and histograms, and provides a complete Go example showing how to build, run, and expose a custom exporter with Prometheus client libraries.
This comprehensive guide explains what data analysis is, the types of data, its historical evolution, the relationship between data analysis, data science and business intelligence, essential skills, why analysis matters, and a step‑by‑step framework with models and metric design for effective decision‑making.
This article describes how our team designed and implemented a comprehensive online monitoring system for construction drawing generation, covering business background, technical architecture analysis, metric definition, monitoring methods, and the resulting dashboards that improve quality, stability, and rapid issue resolution.
Youzan created an internal A/B testing platform—combining Java/Node SDKs, a real‑time data pipeline, and a metadata‑driven workflow—to enable data‑driven product iteration, granular traffic allocation, automated logging, statistical analysis, and scalable growth insights across its merchant services, while planning further automation and integration.
This case study details how a five‑iteration, six‑release redesign of the 58.com second‑hand car publishing flow—covering entry consolidation, form simplification, field reduction, real‑car verification, and image quality enhancements—boosted listing success rates, user conversion, and overall system value.
The article examines the rapid growth of live‑streaming e‑commerce, explains the trust‑based dynamics of influencers, outlines standard recommendation‑system metrics such as accuracy, recall, diversity and explainability, and argues that these models fall short of long‑term user utility because they are driven by short‑term commercial goals, highlighting economic and neuroscientific perspectives on preference randomness.
An innovation network is a refined management approach that aligns four key roles—Inventor, Transformer, Financier, and Broker—within an ecosystem, emphasizing shared values, organizational capabilities, and metric mapping to improve network performance and sustainable value creation.
This article details the Agricultural Bank’s Distributed Application Interconnection (AIR) platform’s DevOps journey, covering small‑granularity development, comprehensive configuration management, end‑to‑end automated pipelines, advanced testing automation, metric‑driven improvements, and the successful third‑level continuous delivery assessment across multiple projects.
Facing fragmented credit product development, Agricultural Bank built a micro‑service‑based credit middle platform and implemented a comprehensive DevOps pipeline—featuring lightweight branching, visualized continuous deployment, end‑to‑end traceability, three‑layer quality gates, and metric‑driven automation—that dramatically improved delivery speed, quality, and operational efficiency.
This article explores how frontend engineers at WeDoctor adapt experience design and performance metrics—such as page load time, interactive readiness, and second‑open rate—to the specific needs of the medical industry, detailing analysis methods, integration approaches, and future plans for micro‑frontend governance.
This guide details Redis monitoring metrics across performance, memory, activity, persistence, and error categories, explains how to retrieve them with the INFO command, outlines useful monitoring tools, provides slowlog configuration examples, and includes command‑line snippets for performance testing and metric inspection.
iQIYI’s micro‑service monitoring combines low‑cost automatic instrumentation, declarative method metrics, and push‑gateway data into a unified multi‑dimensional schema, visualized centrally in Grafana and managed with standardized alert rules, demonstrating that simple integration, centralized dashboards, and early‑stage governance enable rapid anomaly detection and effective incident response.
iQiyi’s full‑link automated monitoring platform unifies tracing, metric and log collection with deep offline and real‑time analysis, delivering a DAG‑based call graph, near‑real‑time ingestion of tens of millions of logs, multi‑dimensional alerts and rapid root‑cause diagnosis that cut error‑lookup time by over 50 % and now serves as a core component of the company’s microservice reference architecture.
The article details Meituan Delivery’s A/B testing platform and evaluation system, explaining its closed‑loop design, multi‑strategy traffic allocation with AA grouping, comprehensive metric hierarchy, statistical rigor, data integration, and implementation architecture, and outlines future tools for traffic‑volume recommendation.
