The article details how Shenwan Hongyuan Securities leveraged standardized DevOps practices and a continuous delivery pipeline to attain Level 3 maturity, improve security, accelerate delivery, and enhance overall competitiveness, illustrated through a Q&A session and key performance metrics.
This article explains performance monitoring concepts, lists essential metrics such as response time and CPU utilization, introduces popular monitoring tools like Prometheus and New Relic, and outlines a step‑by‑step process for selecting, configuring, visualizing, alerting, and continuously improving system performance.
The article details Taobao’s data‑driven approach to redesigning logistics information display and self‑service tickets—defining problems, preparing subjective and objective data, creating metrics, analyzing pain points, implementing timed soothing messages and proactive tickets, and showing through A/B tests reduced help volume and improved user satisfaction.
This article explains how HuoLaLa’s stability team tackled the challenge of proving their work’s value by designing and implementing a comprehensive stability metric system from scratch, detailing the motivations, principles, step‑by‑step construction, data platform, cultural adoption, measurable results, and future plans.
MyPerf4J is a Java‑agent based, low‑overhead performance monitoring library that provides real‑time method, memory, GC and class metrics for high‑concurrency, low‑latency applications, offering quick start, configurable properties, and detailed statistical reports for both development and production environments.
The author reflects on the shortcomings of current logging, metrics, and tracing practices, explains why they become costly and unscalable, and offers concrete recommendations—including log level discipline, structured logging, metric aggregation, and the use of tools like Prometheus, Cortex, and Thanos—to build a more efficient observability stack.
This article explains what an indicator system is, how to design and analyze metrics, methods for managing them, and the right mindset for using indicator systems to improve business decision‑making and operational efficiency.
This article examines the fairness of recommendation systems from both consumer and provider viewpoints, discussing sources of bias, definitions of equality and equity, measurement metrics such as CGF and MMF, causal embedding techniques, experimental results on MovieLens and Yelp, and future research directions.
This article explains how to design effective Prometheus metrics for various application types, covering golden metrics, label selection, naming conventions, bucket choices, and Grafana visualization tips to help engineers build reliable observability solutions.
ICBC‑RS Fund Management’s Transaction Management Platform passed the China Academy of Information and Communications Technology’s DevOps Continuous Delivery Level 3 assessment, showcasing significant improvements in build times, release cycles, and organizational efficiency while highlighting the cultural and tooling benefits of standardized DevOps practices.
This article details how Shanghai Financial Futures' technology subsidiary passed the Level 3 Continuous Delivery assessment of the national DevOps maturity model, showcasing the platform’s cloud‑native architecture, metric‑driven improvements, challenges faced, and future plans for scaling DevOps practices across the exchange.
MyPerf4J is a Java‑agent based, low‑overhead monitoring solution that provides real‑time method, memory, GC and class metrics, enabling developers to quickly locate performance bottlenecks and assess service capacity in both development and production environments.
This article provides a comprehensive overview of enterprise architecture (EA) governance, covering its definition, context, framework components, guiding principles, organizational structure, roles and responsibilities, processes, classification, metrics, and tool selection to help organizations align IT with business strategy and achieve cost savings and compliance.
The article presents Tencent Oula's comprehensive data‑asset platform that integrates data collection, integration, warehouse and metric modeling, unified services, and a governance engine to create trustworthy, low‑entropy data assets while addressing common data‑governance challenges and outlining future AI‑for‑BI possibilities.
This article explains why tag scoring is essential for data governance, outlines a five‑dimensional scoring model—including usage, attention, quality, continuous optimization, and security—and demonstrates how the scores can drive dashboards, alerts, and resource‑saving decisions.
This article details JD's end‑to‑end H5 observability solution, covering the challenges of hybrid app development, the design of a three‑stage UEM platform, deep active and passive monitoring, automated quality gates, and real‑world case studies that demonstrate cost savings and performance improvements.
This guide explains how to use mimirtool to extract the list of metrics used in Grafana dashboards and Prometheus rule files, compare them with all metrics in a Prometheus instance, and generate files of used and unused metrics to help optimize monitoring in a Kubernetes environment.
This article explains what metrics are, outlines the four essential elements of a good metric, evaluates metrics across effectiveness, credibility, sensitivity, and operability, and provides a practical guide for building and applying a comprehensive data metric system, while also promoting a related ebook.
This article details Bilibili's journey from rapid data growth to a structured big‑data governance framework, describing the challenges of fragmented ownership, the "Wanglou" project launch, asset metadata modeling, metric design, user engagement strategies, automation tools, and the shift from reactive to proactive, multi‑dimensional resource management.
This tutorial explains how to gather build data from Jenkins pipelines, store the metrics in InfluxDB deployed on Kubernetes, and visualize the information using Grafana dashboards, providing a complete end‑to‑end solution for continuous delivery measurement and feedback.
An experienced Tencent backend engineer outlines five practical methodologies—quantification of technical and business metrics, systematic comparison of performance data, thorough documentation and defined processes, consistent standardization across code and tools, and extensive automation via CI/CD and testing—to boost efficiency, quality, and reliability in backend development.
This article explains why observability is critical in cloud‑native architectures and describes how Baidu’s search middle‑platform handles hundred‑billion‑level traffic by implementing low‑cost real‑time metrics, distributed tracing, log querying and topology analysis, while tackling challenges of massive microservice scale, scenario‑level monitoring, and efficient resource usage.
This article provides a detailed analysis of the OMS community edition 3.3.1 architecture, explains each internal process, describes the migration workflow and monitoring metrics, and offers practical optimization tips to improve data migration performance.
This article explains how to measure and improve R&D efficiency by defining core factors, building data‑driven analysis models, presenting practical case studies on human productivity, code‑review processes and workflow bottlenecks, and describing the technical architecture of a digital platform that turns metrics into actionable decisions.
This article explains the essential front‑end performance metrics—FCP, Speed Index, LCP, TBT, and CLS—detailing their definitions, target values, and practical optimization techniques to improve page load speed and visual stability.
This article explains how to design and implement a comprehensive front‑end monitoring solution that captures errors, performance metrics, and client data, covering data collection, tracing, transmission, storage, and analysis to help developers quickly locate and resolve issues.
This article explains what logs are, when to record them, their value in large‑scale architectures, and how to build effective logging, metrics, and tracing platforms using tools such as ELK, Prometheus, and SkyWalking, while also presenting good and bad logging practices and sample batch‑log retrieval code.
This article explains what logs are, when and why to record them, their value in large‑scale systems, the challenges of log management in micro‑service architectures, and how to design observability platforms using metrics, logging, tracing, and tools such as ELK, Prometheus, OpenTracing, and SkyWalking.
This article explains the fundamentals of A/B testing, why it is essential for data‑driven product decisions, how to design and run experiments—including hypothesis formulation, metric selection, sample size calculation, traffic segmentation, and duration planning—and how to analyze results using T‑tests, P‑values, and structured decision processes.
The article reflects on the 2020‑2022 subsidy wars in community e‑commerce, exposing classic growth misconceptions, the limits of traffic‑centric strategies, the pitfalls of single‑metric thinking, and the need for holistic, value‑driven product management to achieve sustainable user growth.
This article explores Kubernetes horizontal pod autoscaling mechanisms, comparing HPA and KEDA, their implementation principles, limitations, and practical deployment scenarios for cloud-native applications.
This article reviews G Bank's multi‑year journey to standardize application monitoring, detailing the methodology, models, metrics, automation mechanisms, and quantitative evaluation that together improve visibility, early fault detection, and overall operations management for both traditional and distributed systems.
This article explores why logs are essential in software development, when to record them, their value for debugging, performance, security and business decisions, and how distributed architectures require robust log‑operation tools such as ELK, Prometheus, tracing systems to achieve effective observability.
This article explains why data product value assessment is essential, outlines common usage scenarios and a DBA evaluation framework, describes quantitative methods such as usage, business, and data‑driven metrics, and offers practical ways to enhance data product value through metric optimization, high‑value direction selection, and resource allocation.
This article details Cloud Music's year‑long data assetization journey, covering the background, practical achievements, governance methods, and future roadmap for turning massive data warehouses into high‑value, well‑governed assets that drive cost reduction and business insight.
This article clarifies the often‑confused concepts of system stability, high availability, reliability and availability, explains their metrics such as MTBF, MTTR and MTTF, and shows how they interrelate to guide engineers in building resilient services.
The article examines Go’s database/sql connection pool implementation, showing how reusing connections cuts latency, explains idle/in‑use/closed state transitions, details configuration parameters such as MaxIdleConns and MaxOpenConns, demonstrates metric collection via Gorm DBStats for monitoring, and provides a stress‑test illustrating the impact of proper tuning.
This article details Kuashou's metric middle platform, covering its role as a semantic layer, the unified architecture of OneMetric and OneService, metric standardization processes, automated modeling techniques, the Headless BI concept, and future directions for intelligent modeling and acceleration.
This article shares practical insights from a data product expert on the problems faced by content‑oriented data products, outlines a comprehensive governance methodology—including DAMA, Huawei, and Alibaba frameworks—and demonstrates how to operationalize these ideas through concrete examples such as event‑tracking and metric governance.
This article benchmarks Alibaba Cloud Prometheus against the open‑source Prometheus across multiple cluster sizes, churn rates, and query patterns, revealing that while the open‑source version remains stable under light load, its CPU and memory usage grow non‑linearly with high cardinality, whereas Alibaba's managed service delivers higher compatibility, better query performance, and more predictable scaling.
This article explains essential front‑end performance metrics such as FP, FCP, LCP, TTI, FID, TBT and CLS, and provides a comprehensive set of network, code, CSS, image, and build‑time optimization techniques—including gzip, HTTP/2, lazy loading, SSR, debouncing, and tree‑shaking—to dramatically shorten white‑screen time and improve user experience on H5 pages.
This guide explains essential Redis monitoring metrics, the tools and commands for collecting performance, memory, activity, persistence, and error data, and shows how to use INFO, slowlog, and redis-benchmark to assess and improve database operations.
This article presents the JD Retail search data‑science team's practical approaches to e‑commerce search, covering the scene’s unique data characteristics, order attribution methods, AB experiment design, causal‑inference frameworks, variance‑reduction techniques, quasi‑experimental evaluations, and metric design for traffic distribution, all illustrated with real‑world examples and visualizations.
The article examines common pitfalls when introducing performance metrics in software development teams, offering five key considerations—metric selection, implementation cost, proper usage, clear value, and team consensus—to ensure metrics drive improvement without adding unnecessary burden.
Facing the challenges of distributed microservice architectures, a financial services company implemented a unified observability platform that combines metrics, tracing, and logging via OpenTelemetry and custom agents, enabling real‑time visualization, anomaly detection, and performance analysis across seven core business middle‑platforms.
The article reviews traditional availability metrics such as Success‑Ratio, Error‑Budget, MTTR/MTTF, SLA/SLO, and highlights their limitations, then introduces Google’s User‑Uptime and Windowed User‑Uptime metrics, explains their definitions, challenges, experimental results, and why they provide a more user‑centric view of service reliability.
The platform centralizes message‑center management for e‑commerce by adding end‑to‑end tracing, real‑time metrics, and unified logging, enabling business users to query message links, view dashboards, automate retries and approvals, dramatically reducing manual monitoring, improving completion rates above 90%, and paving the way for cost‑optimized, data‑driven operations.
The article outlines JD's data‑driven business development strategy, describing the current challenges of its business data marketplace, the governance framework—including layered architecture, standardization, ClickHouse dictionary refresh, and optimization measures—and the resulting performance improvements and future outlook.
The paper presents a two‑stage e‑commerce search framework where the coarse‑ranking stage is redesigned with multi‑objective optimization, expanded negative sampling, and listwise distillation—guided by a new global transaction hitrate metric—enabling it to surpass fine‑ranking on large candidate sets and boost overall GMV by about one percent.
This article explains how to design effective Prometheus metrics, choose appropriate labels and buckets, and leverage Grafana visualizations to monitor online services, offline processing, and batch jobs, offering concrete examples and best‑practice recommendations for reliable application observability.
This article examines how fragmented metrics and local optimizations can harm overall software quality, discussing the bug‑vs‑feature debate, various measurement approaches, and the importance of viewing quality as a collective team capability rather than an individual or departmental responsibility.
This article summarizes four years of agile transformation in the JD Cloud Platform R&D department, detailing cultural building, continuous agile practices, practical guidelines, effectiveness measurement, and insight analysis across five key areas to improve organizational, technical, and project agility.
The article explains Kubernetes’ built‑in Horizontal Pod Autoscaler, then details the custom Water Pod Autoscaler (WPA) that extends HPA with dual‑signal (load and SOA registration) detection, dual‑threshold scaling, noise filtering, configurable cooldown, frequency limits, tolerance buffers, and integrated alerting for reliable elastic scaling.
This article explains how to design a high‑availability SMS system by identifying stability bottlenecks, defining reliability goals, implementing failover strategies for Redis, MySQL and external services, establishing a comprehensive observability framework, and measuring key quality metrics to ensure 99.99% uptime.
The article details ByteDance’s DevMind platform, describing its origins, the challenges of measuring software development efficiency, the collaborative and value‑driving “flywheel” concepts, the architectural design across data lifecycle and query engine layers, and the principles and future roadmap for scaling R&D performance.
This article explains how to design effective Prometheus metrics, choose appropriate vectors, labels, buckets, and naming conventions, and offers Grafana usage tricks to help engineers monitor online services, batch jobs, and offline processing systems with clear, actionable insights.
The article explains the concepts, classifications, construction methods, and practical usage of data tags and metrics in the financial sector, illustrating how to build indicator and label systems and how to apply them effectively for refined customer operations and business management.
Kubernetes 1.26, themed “Electrifying,” introduces 37 enhancements—including registry changes, storage upgrades, signed release artifacts, Windows high‑privilege containers, metric and scheduling improvements—while promoting 11 features to stable, deprecating 12 APIs, and emphasizing sustainability and carbon‑footprint awareness.
Kubernetes 1.26 is officially released, introducing 37 enhancements—including 11 stable and 10 beta features—while deprecating 12 APIs, updating the container image registry, removing CRI v1alpha2, advancing storage CSI migrations, enhancing metrics, and adding support for Windows privileged containers and dynamic resource allocation.
This article explores the three pillars of cloud‑native observability—metrics, logging, and tracing—detailing their definitions, relationships, and practical implementation with tools like Prometheus, ELK/EFK, and SkyWalking, while offering guidance on metric design, collection, visualization, and alerting.
Prometheus is an open‑source monitoring solution that covers metric exposition, scraping, storage, querying, visualization, and alerting, and this guide walks through its architecture, configuration, custom exporters, PromQL queries, Grafana integration, and alert management, providing a comprehensive introduction for developers and ops engineers.
This article explains how to use the MySQL innodb_metrics table to transform the unreadable output of the show engine innodb status command into clear, queryable metrics, including buffer pool statistics and CPU counters, and shows how to enable or disable specific counters as needed.
This guide explains how to collect essential Windows metrics with Prometheus, set up Grafana dashboards for CPU, memory, disk, network, and process monitoring, and configure alert rules, while also comparing self‑hosted and Alibaba Cloud Prometheus solutions for seamless Windows observability.
The article outlines a complete data‑quality‑management framework that first captures business expectations, then translates them into basic and personalized measurement rules, defines four assurance approaches for handling violations, and scales operation with indicators, tooling, and metrics to continuously improve data quality across the lifecycle.
This article explains how RocketMQ's message lifecycle can be fully observed using OpenTelemetry‑based metrics, covering producer, broker, and consumer stages, and shows practical monitoring, alerting, and troubleshooting practices for cloud‑native deployments.
This article explains how to use the Prometheus HTTP API to query instant and range metrics, interpret the JSON responses, and fetch data programmatically with Python, providing code examples and details on request parameters, error handling, and practical usage.
The article examines why engineers favor reuse, outlines the logical arguments that link reuse to productivity, exposes common misconceptions and hidden costs, and proposes autonomy and consistency metrics as practical criteria for deciding when and how to abstract shared components in software systems.
This guide explains what performance testing is, why it’s essential, the key metrics such as throughput, response time, and bandwidth, outlines a step‑by‑step testing process, compares load, stress, endurance and capacity testing types, and reviews popular tools like JMeter, LoadRunner and NeoLoad.
This article visually explains Prometheus’s architecture, core features, metric collection methods, exporters, PromQL query language, and alerting workflow, helping readers understand how to monitor cloud‑native systems effectively while noting its strengths and limitations.
This article explains how RocketMQ integrates with Prometheus and Grafana to provide comprehensive metrics, tracing, and logging, detailing the exporter architecture, deployment choices, span topology, dashboard examples, and ARMS‑based alerting for cloud‑native message‑queue observability.
This guide details key Redis monitoring metrics—including performance, memory, activity, persistence, and error indicators—along with practical commands, configuration settings, and code snippets to help operators efficiently track and troubleshoot Redis instances.
This article visually explains Prometheus by covering its architecture, key features, metric collection methods, exporter role, PromQL query language, and alerting mechanisms, helping readers understand how to monitor cloud‑native systems effectively in production.
This article explains how Spring Boot 3.0.0‑RC1 integrates Micrometer Observation API to provide unified metrics, logging, and distributed tracing, showing the observation lifecycle, configuration steps, sample server and client code, Docker‑compose setup, and notes on native image support for comprehensive application observability.
Programming productivity, also known as software or development productivity, examines how output relates to input, covering definitions, measurement models such as COCOMO II and Jones’s factors, function points, value‑based engineering, and human aspects, while discussing efficiency, effectiveness, profitability, and various factors influencing individual and team efficiency.
Learn how to use Prometheus's stable V1 API to query instant and range data, handle responses and errors, and fetch metrics programmatically with curl and Python, enabling data analysis, cost management, and advanced monitoring beyond basic alerts.
This article explains why measuring a design system is essential, outlines how Baidu's Light Design System built a PATS metric framework through research and four practical steps, and shares real-world results that improved usability, reliability, and overall workflow efficiency.
This article details the redesign of an internal monitoring system, explaining why the original zzmonitor fell short, how Prometheus and its ecosystem were selected, the architecture that integrates remote storage with M3DB, performance benchmarks, Grafana visualisation, and a custom alerting solution.
This article explains how to assess hardware resources, calculate required Elasticsearch cluster size based on data volume, and perform indexing and search benchmark tests to ensure stable performance and optimal throughput for log and metric workloads in production environments.
This article walks through adding complete observability to Spring Boot 3 applications using Micrometer's Observation API, covering metrics, tracing, log correlation, configuration, code examples for both server and client, and even native image support for production-ready monitoring.
This article explains how Spring Boot 3.0.0‑RC1 integrates Micrometer Observation to provide unified metrics, logging, and distributed tracing, showing the required dependencies, configuration, code examples for both server and client, and how to visualize data with Prometheus, Grafana, Loki, and Tempo.
This article outlines the essential components of operational monitoring, covering monitoring objectives, methods, core processes, key tools, metrics for hardware, system, application, network, and business layers, as well as alerting, handling, and best practices for building a comprehensive, reliable monitoring solution.
Ctrip’s internal Dashboard monitoring platform, originally built on HBase, was redesigned by migrating its core writer and storage components to a hybrid VictoriaMetrics‑ClickHouse solution, delivering faster queries, higher write stability, and full Prometheus compatibility while keeping the user experience unchanged.
This article introduces the Prometheus monitoring platform, explains Alertmanager's grouping, inhibition and silencing features, describes Grafana's visualization and alerting capabilities, and provides a detailed course syllabus covering installation, configuration, and advanced monitoring techniques across various environments.
This article explains how to monitor and analyze front‑end performance using the deprecated performance.timing API and the modern PerformanceObserver, detailing key web‑vital metrics such as TTFB, FCP, LCP, FID, and CLS, with code examples and practical interpretation guidelines.
This article outlines the DevMind system—a comprehensive, data‑driven framework that turns R&D efficiency measurement into an online, low‑threshold, scalable practice, covering four best‑practice pillars, technical architectures, product modules, and real‑world impact within large organizations.
This article presents DeepFlow, an open‑source, highly automated observability platform that uses eBPF to provide zero‑code AutoMetrics and AutoTracing, integrates with Prometheus, OpenTelemetry and SkyWalking, and enables SRE, DevOps and NewOps teams to build full‑stack metrics and blind‑spot‑free tracing for cloud‑native applications.
This article details the evolution of NetEase's self‑built time‑series database EyesTSDB into a cloud‑native, second‑level monitoring solution, covering its architecture, core features, integration with VictoriaMetrics, custom plugin workflow, CMDB linkage, real‑world use cases, and future challenges.
This article details the design, data‑collection pipeline, monitoring stack, visualization practices, and alert‑response workflow of a globally deployed Alibaba Cloud‑native system that uses ACK, Prometheus, Grafana, and ARMS to achieve end‑to‑end observability across metrics, tracing, and logs.
This article explains how to enable JMX for Kafka, retrieve and interpret key metrics such as UnderReplicatedPartitions, and troubleshoot common issues like broker failures, disk outages, and replica lag by examining metric values and related logs.
This article details Ctrip's internal Dashboard monitoring platform, explains why its HBase‑based TSDB became a bottleneck, and describes the step‑by‑step migration to a hybrid VictoriaMetrics‑ClickHouse solution with upgraded writers, unified query APIs, performance gains, and future roadmap.
This article explains the fundamental assumptions of linear regression, compares linear and nonlinear models, discusses multicollinearity, outliers, regularization, heteroscedasticity, VIF, stepwise regression, and reviews essential evaluation metrics such as MAE, MSE, RMSE, R² and Adjusted R².
This article details the redesign of Ctrip's internal Dashboard monitoring system, explaining why the original HBase‑based TSDB was replaced, the new architecture using VictoriaMetrics and ClickHouse, component upgrades, unified query layer, performance gains, and future roadmap for metrics handling.
This article explains why measuring is essential for continuous delivery, introduces four valuable metrics—deployable package count, cycle time, mean time between failures, and mean time to recovery—and offers practical tips to improve delivery speed and reliability.
This article explains how to collect comprehensive MySQL performance metrics—including connections, buffer cache, locks, statement counts, throughput, server variables, and slow‑query analysis—using only MySQL's native SHOW commands, providing a fast, low‑overhead monitoring solution.
The article outlines Bilibili’s refined SLO framework—categorizing services into four business tiers, selecting availability, latency, and freshness SLIs, setting concrete SLO targets, and employing multi‑window error‑budget and consumption‑rate alerting strategies to improve stability and provide comprehensive quality dashboards.
Learn how to set up Prometheus to scrape Nexus repository metrics, configure authentication, and create insightful Grafana dashboards that visualize component, Jetty, and JVM metrics, enabling proactive troubleshooting and resource optimization for Nexus services.
This article outlines Snowball's data warehouse background, compares major modeling approaches such as ER, dimensional, DataVault and Anchor models, describes the current challenges of their dimensional model, and details the OneData methodology—including OneModel, OneID, and OneService—along with its practical implementation, results, and future plans.
This tutorial explains how to install Aeraki Mesh sample applications, forward ports to Prometheus and Grafana, and use these tools to query and visualize L7 protocol metrics for Dubbo and Thrift services within an Istio service mesh.
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.
