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.
This article outlines a systematic approach to evaluating test automation effectiveness by detailing twelve quantifiable metrics—from bug detection rates and execution stability to platform usage and ROI—while emphasizing the critical mindset of automation champions for successful adoption.
This article presents a ready‑to‑use ELK‑based solution for private‑deployment environments, detailing fast one‑click deployment, log and metric collection with Filebeat and Metricbeat, centralized viewing in Kibana, alerting via ElastAlert, and extensible monitoring dashboards.
This article explains the evolution, responsibilities, and evaluation methods of the product‑effect assessment team at Amap, covering offline testing, AB experiments, metric analysis, automated scoring models, and the tools that support a comprehensive product‑performance framework.
This guide explains why monitoring is essential for the entire operations lifecycle, outlines key monitoring objectives, describes practical methods and workflows, reviews a range of open‑source tools (including Zabbix, MRTG, Ganglia, Nagios, Smokeping, OpenTSDB), and details metric categories such as hardware, system, application, network, log, security, API, performance and business monitoring.
This article examines the frequent pitfalls of recommendation systems—misleading metrics, over‑optimizing precision, data leakage, feature inconsistencies, and distribution bias—that cause offline AUC improvements to translate into lower online CTR and CPM, and offers practical mitigation strategies.
This comprehensive 2020 front‑end performance checklist guides teams through planning, metric selection, environment setup, static asset optimization, build and transport strategies, testing, monitoring, and realistic goal setting, offering tools, budgets, and cultural practices to achieve faster, more responsive web experiences.
Designers often overlook data analysis, yet mastering it helps identify problems, set measurable goals, generate hypotheses, collect and interpret metrics, and draw actionable conclusions, ultimately guiding product decisions and improving conversion rates across various scenarios such as page performance, financial calculations, and insurance plan selection.
This article walks through constructing a comprehensive MySQL monitoring solution using Prometheus and Grafana, covering exporter installation, configuration, key performance metrics, alert rule definitions, and practical tips for visualizing and troubleshooting MySQL instances in production environments.
This article explores how large wealth‑management platforms can model product recommendation as a mapping between customers and financial products, defines various evaluation goals such as transaction volume, revenue and user satisfaction, and outlines a systematic A/B‑testing workflow for comparing and optimizing recommendation algorithms.
This article walks through setting up a production‑grade MySQL monitoring solution using Prometheus and Grafana, covering exporter installation, MySQL user configuration, systemd service setup, Prometheus job definition, key MySQL performance metrics, and basic alerting rules.
This tutorial walks through Micrometer's metric types, how to register them with MeterRegistry, apply tags and naming conventions, and integrate the framework into Spring Boot applications with Actuator, Prometheus scraping, and Grafana visualization for comprehensive backend monitoring.
This article introduces the core concepts and architecture of the open‑source Prometheus monitoring system, explains its data model and metric types, and provides a step‑by‑step guide to install a Prometheus server, collect host metrics with Node Exporter, and visualize them using Grafana.
This article explains how to choose and combine appropriate evaluation metrics for recommendation systems by considering the specific scenario, business model, offline versus online testing, ecosystem balance, and user behavior, providing practical methods and a concise summary of common metric types.
This article outlines a DevOps‑inspired metric system for front‑end projects, describing the five development stages—design, development, personal verification, version verification, and online verification—and proposing concrete, automatable indicators to help leaders assess and improve delivery quality and speed.
The article reveals that JMeter often reports inflated throughput because it includes client‑side processing time—such as regex extraction and post‑processors—in its calculations, leading to misleading performance metrics unless corrected with micro‑benchmark adjustments.
The article explains the nature of raw data, defines metrics as data combined with business scenarios, introduces five‑question criteria for good indicators, and compares two popular metric‑system frameworks—the Pirate Metrics (AARRR) model and the First‑Key‑Metric method, illustrating their application in business decision‑making.
This article provides a comprehensive overview of Prometheus, covering its open‑source monitoring architecture, multi‑dimensional data model, query language, storage mechanisms, service discovery, alerting workflow with Alertmanager, and visualization using Grafana, all illustrated with key diagrams and configuration examples.
This article introduces OpenTSDB, a highly scalable time‑series monitoring system built on HBase, explains its architecture, demonstrates how it solves common monitoring challenges, and shows practical usage examples including data modeling, collector integration, and real‑world deployment insights.
This tutorial walks you through using Spring Boot Actuator to monitor microservice applications, covering quick setup, essential endpoints such as health, metrics, loggers, and shutdown, customizing health indicators, dynamically changing log levels at runtime, and securing actuator endpoints with Spring Security.
This article explains how to gather build metrics from Jenkins pipelines, store them in InfluxDB deployed on Kubernetes, and display the data using Grafana dashboards, providing a practical DevOps solution for continuous delivery measurement and feedback.
This article provides a comprehensive overview of using Prometheus and Grafana for metric collection, storage, querying with PromQL, visualization, and alerting, including exporter integration, metric types, high‑availability setups, and practical examples for modern microservice architectures.
This article introduces Prometheus and Grafana as a powerful monitoring stack, explains their architecture, metric collection, storage options, query language, integration with Grafana for dashboards and alerts, and shares practical deployment patterns and high‑availability solutions.
This guide introduces the open‑source GitStats tool, explains how to install its Python and Gnuplot dependencies on Linux, demonstrates step‑by‑step commands to generate a comprehensive HTML report for a Git repository, and interprets the resulting statistics on commits, contributors, file types, and tags.
This article examines common mistakes in measuring R&D efficiency, outlines seven guiding principles, presents case studies from leading tech companies, and shares practical steps and lessons for implementing a comprehensive R&D metrics system.
This article explains why many system alerts are poorly designed, describes the true purpose of alerts as actionable notifications, distinguishes business rule monitoring from reliability monitoring, and presents practical metrics, strategies, and simple anomaly‑detection algorithms to create high‑quality, actionable alerts for reliable operations.
This article explains the background, source‑code analysis, and practical implementation of Flink's LatencyMarker feature for measuring end‑to‑end job latency, including metric exposure, configuration options, and code snippets illustrating how latency markers are emitted and processed within the streaming pipeline.
The talk explains how Elastic Stack can be used to achieve comprehensive observability for cloud‑native applications through a four‑step methodology—health checks, metrics, logging, and tracing—detailing the challenges, implementation details, and best practices for monitoring and debugging modern microservice systems.
This guide explains how to add Spring Boot Actuator to a project, configure security, enable and customize monitoring endpoints, and use the built‑in REST APIs such as health, info, beans, metrics, and shutdown for production‑grade application observability.
This article compares Meituan’s in‑house EMonitor with the open‑source CAT platform, outlines their core monitoring models, sampling pipelines, custom metrics and integration capabilities, and traces the evolution of monitoring stages from log‑based to intelligent root‑cause analysis.
Gaode’s continuous delivery journey transformed its multi‑stage release process into a standardized, automated pipeline that delivers backend services within an hour, cuts manual effort, boosts release efficiency by up to 90%, and embeds early quality checks and data‑driven insights for faster, higher‑quality business value.
The article details how Yu'ebao, a financial platform with over five hundred million users and assets exceeding one trillion yuan, designs and operates a comprehensive service‑governance framework—including metrics collection, bi‑weekly governance committee reviews, dynamic and static call‑chain analysis, and layered service architecture—to balance security, compliance, and rapid service delivery.
This article explains the importance of code review for improving software quality, outlines the benefits of using Gerrit as a CR tool, describes the detailed workflow and promotion strategies, and discusses future extensions such as CI integration, issue tracking, and metric‑driven improvements.
This article provides a comprehensive guide to Elasticsearch’s architecture, node roles, data organization, and the most important performance metrics—including search, indexing, memory, JVM garbage collection, host‑level system metrics, cluster health, and resource saturation—offering practical advice on monitoring and tuning the cluster for reliability and efficiency.
The article argues that diversity should be treated as a tool rather than a final objective in recommendation systems, explains why it is hard to quantify, discusses appropriate metrics such as user feedback and engagement, and presents practical strategies—including expert rules, richer recall pipelines, and list‑wise modeling—to improve diversity while optimizing true business goals.
On September 18, Kubernetes v1.16 was released with 31 enhancements—including GA for custom resources, a revamped metrics system, volume resizing, Windows workload improvements, kubeadm alpha support, CSI integration, and the new Endpoint Slices feature that dramatically improves scalability and network topology handling.
This article explains the three main monitoring approaches—log collection, distributed tracing, and metric gathering—in microservice architectures, outlines the layered monitoring model, lists key system, application, and user metrics, and reviews popular open‑source time‑series monitoring tools such as Prometheus, OpenTSDB, and InfluxDB.
Prometheus, an open‑source monitoring and alerting toolkit originally developed by SoundCloud and now a CNCF project, offers multidimensional data models, flexible queries, pull‑based data collection, various metric types (counter, gauge, summary, histogram), local and remote storage, service discovery, and integrates with Grafana for visualization.
This article reviews Ant Financial's cloud‑native observability journey, covering its origins, the three pillars of tracing, metrics and logging, community projects like OpenTelemetry, practical implementations, sampling strategies, and future directions for unified microservice, mesh, and serverless monitoring.
