This guide explains how to enable Spring Boot's default metrics via Actuator, view them through the /actuator/metrics endpoint, and enrich them with Micrometer MeterBinders for JVM, cache, Hibernate, and Tomcat, including code samples and logging output.
This article explains the fundamentals of Prometheus histogram metrics, illustrates why they are cumulative, shows how to drop unwanted buckets with relabeling, and demonstrates quantile calculations using the histogram_quantile function, providing practical examples and code snippets for effective monitoring.
This article explains why comprehensive, multi‑layer monitoring is essential for distributed systems, outlines environment, program, and business metrics, recommends practical tools such as Zabbix, open‑falcon, Prometheus and Grafana, and provides a step‑by‑step evolution plan and alerting strategy.
This article provides a comprehensive overview of Prometheus, an open‑source monitoring and alerting system, covering its origins, key features, architecture, core components, metric types, data collection methods, service discovery, storage options, and query capabilities.
This article explains how A/B testing serves as a vital measurement and optimization tool for internet products, covering metric definition, experiment management platforms, traffic splitting strategies, orthogonal and exclusive rules, and essential statistical concepts such as hypothesis testing, t‑test, z‑test, and p‑value analysis.
This article explains Prometheus metric naming conventions, label usage, metric types such as Counter, Gauge, Summary, and Histogram, and describes the structure of data samples, providing examples and best‑practice guidelines for defining and classifying metrics in monitoring systems.
The article records Google VP Michael Bachman's 2018 talk on engineering productivity, covering the origins of EP, Google’s rebranding of QA to an Engineering Productivity team, and a detailed Q&A on rollbacks, data models, metric visualisation, tool adoption, testing strategies, static scanning, and experimentation practices.
The article explains how Google introduced the Engineering Productivity (EP) team, its evolution from QA, the organizational model across product areas, and the various metrics used to assess developer productivity, providing insight into large‑scale software engineering management.
This case study details how a user product center team applied the APD agile methodology and stream development practices—such as visual value streams, explicit process rules, WIP limits, bug‑board visualization, workflow management, and an evolved TDD approach—to double throughput, cut delivery cycles by 60%, and reduce monthly bugs from 50 to 2, demonstrating measurable R&D efficiency gains.
Senior Director Wang Xiaoxiang of Qunar shares how a data‑centric, metrics‑driven DevOps approach transformed the company's engineering efficiency, detailing Qunar’s measurement model, indicator definitions, technology choices, results, and strategies for leveraging metrics to continuously improve the software delivery lifecycle.
This article explains how to build a comprehensive monitoring system for distributed applications by classifying monitoring functions, describing data quadrants, outlining core modules such as collection, processing, feature extraction, and visualization, and reviewing typical implementations for metrics, logs, tracing, alerting, and the key open‑source components involved.
This article walks through a complete data‑analysis workflow—scraping Zhihu dating‑preference answers, cleaning and filtering the data, deriving gender and activity metrics, designing a four‑step screening process, and finally ranking candidates with a custom like‑to‑comment index—to help a single programmer create a concise, high‑quality list of potential partners.
The article explains the importance of bug analysis for improving product quality, outlines the information that can be analyzed, describes common bug resolution categories, discusses bug severity levels, and shows how various stakeholders can use bug metrics to drive process optimization and predict future issues.
This article explains why most alerts are poorly designed, defines actionable alerts, outlines monitoring objectives, discusses metric selection, and presents simple yet powerful algorithms for anomaly detection to improve system reliability and operational efficiency.
This article presents nine practical logging best‑practice recommendations—from understanding human and machine audiences and standardizing log formats to leveraging metrics, proper alerting, severity levels, contextual information, and advanced framework features—helping operations teams improve system performance and troubleshooting efficiency.
This article presents a comprehensive approach to testing, automating, and monitoring AI prediction models in a high‑traffic environment, covering background, challenges, evaluation metrics, data sampling methods, automated test scripts, and online monitoring to ensure model accuracy, performance, and reliability.
To meet Xianyu’s rapid‑release targets, the team built a CI‑integrated automation suite that gathers Flutter FPS, CPU, memory and network data via extended Android tools, drives mixed Flutter/Native UI tests with Appium and integration‑test, and generates lane‑by‑lane performance reports that quickly flag regressions across dozens of feature branches.
This article explains how to configure Spring Boot Actuator to export metrics to InfluxDB and Prometheus, covering Docker setup, Micrometer dependencies, application properties, sample controller code, test data generation, and visualisation with Grafana.
This article defines R&D efficiency as the ability to continuously deliver value quickly, explains the problem of efficiency silos, introduces five comprehensive measurement groups, showcases a defect‑trend chart example, and presents the practical 2‑1‑1 target framework for improving development speed and quality.
This article explains how to approach monitoring for a newly introduced system by focusing on white‑box metric collection, distinguishing basic and business metrics, outlining common collection methods, and detailing Google SRE's four golden indicators—error, latency, traffic, and saturation—to guide effective observability.
This article explains the differences between white‑box and black‑box monitoring, outlines common data‑collection techniques for both basic and business metrics, and details Google SRE’s four golden indicators—error, latency, traffic, and saturation—to help engineers design effective monitoring solutions.
The article presents seven tactical principles for agile coaching, explains their system‑dynamic foundations, and offers practical guidance on building leadership groups, clarifying roles, aligning management, measuring improvement, strengthening execution, optimizing communication, and fostering deliberate practice to drive sustainable agile transformation.
This article details Meituan's systematic approach to reducing iOS app cold‑start time, covering the definition of startup phases, identification of performance bottlenecks, phased launch, self‑registration of startup tasks, code slimming, profiling tools, and continuous monitoring to achieve smoother user experience.
The article analyzes why many organizations stall on the DevOps adoption curve, identifies the root causes of the "gap", proposes two critical thinking areas—measurement and structural change—and outlines practical steps and future directions for sustainable DevOps transformation.
This article explains agile development’s core business goals of faster value delivery and effective learning, defines agile as early value delivery and adaptive response, and presents a comprehensive five‑group metric system—including release capability, demand response cycle, throughput, built‑in quality, and external delivery quality—to assess and improve continuous delivery performance.
This article outlines common challenges managers face with performance testing, explains when testing should be introduced, describes its process, types, metrics, and quality checks, and provides practical guidance for analyzing requirements and ensuring effective, repeatable performance test execution.
This talk explains how visualizing end‑to‑end value flow, focusing on demand bottlenecks, defining proper metrics, and aligning features with key business goals can dramatically speed up product innovation cycles and deliver higher‑value software.
This article outlines Baidu's comprehensive product evaluation framework, describing its multi‑layer assessment system, the combination of subjective and objective metrics, and a suite of common evaluation methods such as indicator analysis, AB testing, user feedback, behavior analysis, big‑data profiling, and competitor comparison.
This article outlines the challenges of algorithm testing in real‑world engineering, proposes a step‑by‑step testing framework—from understanding business context and verifying data exchanges to evaluating performance metrics and iterating improvements—while offering practical advice and examples.
This article introduces the recently open‑sourced SOFA middleware components—including SOFATracer, SOFALookout, SOFA‑Boot, SOFA‑RPC (Node and Java), Eggjs integration, and JarsLink—explaining their features, usage scenarios, and providing GitHub links for developers to adopt these financial‑grade cloud‑native solutions.
This article describes Qunar's approach to quantifying, automatically detecting, and optimizing performance issues in React Native mobile apps by defining measurable metrics, real‑time data sampling, automated analysis rules, and visual feedback to enable proactive APM during development and testing.
This article summarizes Wang Qing’s DevOps talk, outlining the current state of DevOps, showcasing concrete case studies from ING, CapitalOne and a major Chinese bank, and providing a step‑by‑step guide to building a practical DevOps toolchain for traditional enterprises.
By applying a PDCA‑based quality‑operation framework that aligns QA KPIs, drills defect data across dimensions, automates test‑gate checks, and drives continuous improvement actions, Meituan Dianping’s Intelligent Payment team reduced severe defect ratios, met defined metric targets, and boosted iteration efficiency while supporting rapid business growth.
This article explains how to assess customer acquisition by defining key metrics such as new users, retained users, ROI, and LTV, detailing their calculations, assumptions, and practical steps for product teams to measure and improve acquisition efficiency throughout a product’s lifecycle.
This guide explains how to collect and visualize Kubernetes performance metrics—CPU, memory, network, and filesystem—by deploying Heapster with a StatsD sink that forwards data to Splunk's Metrics Store, then using Splunk SPL commands and dashboards for real‑time monitoring.
This article presents a comprehensive 2018 frontend performance checklist that guides teams from establishing a performance culture and selecting metrics to optimizing build processes, static assets, HTTP/2, security, and monitoring, while offering quick‑win actions for immediate impact.
The author shares personal insights from a recent discussion with Microsoft Seattle on DevOps transformation, highlighting the lack of a standard definition, various performance metrics, the role of story points, and considerations for UI automation testing within agile teams.
The article outlines Baidu's comprehensive continuous integration (CI) capability model, detailing its guiding principles, standardized metrics, tiered evaluation system, and practical implementation steps to improve software quality and development efficiency across numerous product lines.
This article outlines the real‑world demand for MySQL performance management, critiques traditional monitoring approaches, and presents a product‑focused design that emphasizes lightweight data collection, incremental recording, metric‑driven drill‑down, and comprehensive analysis techniques such as AS/PS and QRTi.
This article explains how to anticipate and monitor Elasticsearch issues such as node unavailability, OOM errors, and long garbage‑collection pauses by tracking key performance metrics across query, indexing, memory, and system levels, helping prevent service disruptions.
Pandora.js, the Alibaba Midway team's first open-source project, consolidates years of production-grade Node.js operations by offering three core capabilities—manageable application and process control, comprehensive metrics, and Open-Tracing-based request tracing—exposed via RESTful APIs and logs for easy integration.
Metrics act as health checks for applications, enabling developers to monitor performance, track changes, and assess stability; this article explains Pandora.js’s metric naming conventions, types like Gauge, Counter, Histogram, and Meter, and provides practical Node.js code examples for implementing these measurements.
This article explains how to assess unsupervised clustering algorithms by describing realistic user‑watching scenarios, outlining common cluster and algorithm types, presenting five key evaluation criteria, and introducing practical metrics such as RMSSTD, R‑Square, and the improved Hubert‑Gamma statistic.
The USE Method provides a concise, structured framework for quickly locating resource bottlenecks and errors in complex systems by examining utilization, saturation, and error metrics across hardware, software, and cloud environments, enabling practitioners to prioritize and resolve performance issues efficiently.
This article explains Kafka end‑to‑end auditing, compares three products (Chaperone, Confluent Control Center, Kafka Monitor), describes timestamp and index embedding techniques, and outlines their architectures, metrics, and implementation details for detecting data loss, duplication, and latency.
This article explains essential iOS performance metrics, compares Xcode Instruments, third‑party SDKs, and custom instrumentation, and provides code examples for measuring startup time, memory, CPU, frame rate, network latency, UI blocking, and power consumption.
This article describes LinkedIn's network‑level MySQL query analyzer, explaining why it was needed, its three‑component architecture (agent, central server, UI), fingerprinting and hash‑map techniques, performance benchmarks, metric collection, security considerations, and the benefits it brings to database operations.
This guide explains how to choose between push and pull monitoring models, introduces Prometheus architecture and metric syntax, shows Node.js client integration with code examples, and covers Alertmanager features and Grafana visualization for effective application monitoring.
This article outlines the fundamentals, objectives, methods, core processes, tool selection, metrics, alerting, and interview tips for constructing a robust monitoring ecosystem, emphasizing Zabbix while comparing various open‑source solutions.
This article recounts how the Taobao Live team applied agile practices over three months, tracking capacity, lead time, quality, and predictability, and demonstrates measurable improvements in delivery speed, defect reduction, and business metric alignment through focused goals, rapid hypothesis testing, and continuous delivery.
The article explores how artificial intelligence can transform DevOps by introducing predictive metrics, machine‑learning models, and intelligent automation to improve code review, defect forecasting, and overall software lifecycle management, while discussing current research, challenges, and future directions.
This article explains the fundamentals of monitoring, distinguishes traditional OPS from SRE perspectives, defines monitoring objects and metrics, introduces quantitative thinking with SLI/SLO, and presents the USE method with a MySQL example to help engineers detect and prevent failures efficiently.
This article describes the background, architecture, encountered performance bottlenecks, and a three‑level caching strategy (Redis‑based) implemented in Python to accelerate a measurement platform that aggregates bug, issue, and code metrics for a rapidly growing development team.
This article presents a comprehensive server‑side monitoring solution covering functional and performance requirements, monitoring objects, design choices between self‑monitoring and centralized reporting, system architecture, API definitions, key challenges such as key collisions, data formats, storage options, and operational considerations.
Metrics‑Driven Development (MDD) extends test‑driven principles by embedding real‑time monitoring into design, enabling rapid, precise, and granular software iterations, improving early problem detection, decision support, and aligning development with DevOps culture.
This article explains Ctrip's flight ticket tracking framework (UBT), detailing client‑side and server‑side event collection methods, the purpose and trade‑offs of each tracking type, metric definitions, data association challenges, common pitfalls, and best practices for reliable data‑driven analysis.
This guide explains how to integrate Spring Boot Actuator into a traditional Spring application by manually adding required Maven dependencies, importing core Actuator configuration classes, and defining beans that expose the /health and /metrics monitoring endpoints.
This article outlines mobile performance testing methods—including startup time, page response time, basic metrics, Monkey testing, and image‑recognition techniques—reviews third‑party tools, and introduces the in‑house ETest solution with its features and future development plans.
This article presents a detailed overview of mobile app performance testing, covering the importance of performance metrics for video applications, the prioritized dimensions such as smoothness, crash, memory and power consumption, and practical methods and tools for acquiring Android and iOS system metrics.
This article explains what OpenStack is, outlines its core modules, and details the most important monitoring metrics for Nova, Neutron, Keystone, hypervisors, tenants, and RabbitMQ, helping engineers build a robust, scalable OpenStack monitoring solution.
This article explains how to build a comprehensive mobile app quality monitoring framework by defining quality standards, setting capability indicators, and leveraging data acquisition, analysis, and visualization to continuously improve product reliability and user experience across different development stages.
This article explains which external and internal performance metrics matter for backend services, how to interpret them, common bottlenecks, and provides a step‑by‑step example using JMeter to measure throughput, latency, CPU, memory, load, network and disk I/O.
The article details how Qunar’s operations team designed and implemented the Watcher monitoring platform—based on Graphite, Grafana, and Nagios—to achieve high availability, horizontal scalability, and rich visualization for over six million system metrics and two million business metrics.
The article introduces a four‑dimensional evaluation framework for retrieval systems—Fast, Accurate, Complete, and New—explaining how each metric is measured, why it matters to users, and how crowdsourced testing across devices and networks can provide objective quality assessments.
This article presents a systematic methodology for evaluating the full‑stack resource data of online appointment services, using Baidu Doctor as a case study, and details the three‑step process of data capture, parsing, and metric calculation together with tool selection, code examples, and strategy formulas.
Twitter’s internal monitoring team built a full‑stack observability platform that handles billions of metric writes per minute, supports distributed tracing, log aggregation, visual dashboards, and alerting across data centers and public clouds, and shares the architecture, components, and key lessons learned.
The article explains why operators should start from clear goals rather than events, defines meaningful metrics such as user retention and API call volume, shows how to break down and evaluate these metrics, and offers practical advice on data collection, benchmarking, and continuous improvement.
This article explains what user feedback is, why it should be analyzed, and provides a step‑by‑step methodology—including channel setup, data collection, coding, categorization, and statistical analysis—along with key performance indicators for monitoring feedback handling in product management.
This article explains how to build an operations evaluation model within a quality competitiveness framework, detailing a three‑step process for defining metrics, evaluation methods, and quantification, and highlighting essential evaluation points, attention areas, and data collection practices for product operations.
After a major release, this article explains the most important Java performance metrics—response time, throughput, average load, error rate, GC pauses, business KPIs, uptime and log volume—and recommends tools to monitor them without relying on raw log files.
This article presents a comprehensive overview of MySQL performance testing, covering testing methodology, reasons for testing, influencing factors across DB, OS, and storage layers, key metrics, popular open‑source tools, practical considerations, and suggestions for more automated testing solutions.
Based on the 2015 DevOps State Report, this article translates and analyzes the key qualities of high‑performing DevOps teams, highlighting metrics such as deployment frequency, speed, failure recovery, gender gaps, cross‑functional responsibilities, non‑blame culture, experimental mindset, useful metrics, and full commitment.
This guide explains how to monitor MySQL's Query Cache using the 'SHOW GLOBAL STATUS' command, interprets key metrics such as Qcache_hits, Qcache_inserts, and Qcache_free_memory, and shows how to assess cache fragmentation and calculate hit rates for performance tuning.
The article explores how focusing on IT performance through lean principles, precise throughput and latency metrics, continuous integration, trust between development and operations, visualization, and end‑to‑end monitoring can transform operations teams into high‑speed, value‑driven service providers.
This article examines website performance by breaking down the user request journey into three paths—client, network, and server—offering practical optimization techniques such as DNS prefetch, caching strategies, CDN deployment, and outlining essential metrics like response time, concurrency, and throughput.
Dropbox operations engineer Rajiv shares practical scalability design techniques, including load‑testing, app‑specific metrics, Bash analytics, log management, UTC usage, and the reliable technology stack that enables a service with 40 million users to run with a very small operations team.
This article introduces five key aspects of Java code quality—coding standards, duplicate code, test coverage, dependency analysis, and complexity analysis—and demonstrates how Eclipse plugins such as CheckStyle, PMD, EclEmma, JDepend, and Metrics can be configured and used to assess and improve each aspect.
This article explains why and how to quantify user experience using UX KPIs, detailing both quantitative and qualitative metrics such as task success rate, task time, search/navigation usage, error rate, and the System Usability Scale (SUS).
