This guide explains how to design and implement Prometheus metrics for application monitoring, covering the selection of monitoring targets, the four golden metrics, system‑specific metric groups, vector and label choices, naming conventions, histogram bucket design, and useful Grafana visualization tips.
Observability turns a black‑box application into a system by gathering logs, metrics, and traces, using alerts to spot anomalies, then linking trace IDs to logs; OpenTelemetry standardizes this with instrumented client agents, a Collector (receivers, processors, exporters), and backend storage, while Java agents, span propagation, exemplars, eBPF, and bundles like SigNoz or OpenObserve let teams choose between a custom OTel stack or a solution.
Airbnb’s Android Page Performance Score (PPS) framework instruments fragments to collect user‑centric metrics such as TTFL, TTIL, MTH, ALT and RCLT, using a standardized logging config, LoadableView interface, and visibility algorithms, enabling detailed performance analysis and automated alerts for mobile teams.
The article presents MyPerf4J, a Java‑agent based, low‑overhead performance monitoring library that provides real‑time metrics such as method latency, QPS, memory usage, GC statistics, and class loading, along with quick‑start instructions, configuration details, and open‑source links for Java backend services.
The article details Bilibili's evolution of its monitoring platform, describing the stability and performance challenges of a Prometheus‑Thanos stack, the redesign using VictoriaMetrics, collection‑storage separation, unit‑level disaster recovery, query‑tree auto‑replacement, Flink‑based pre‑aggregation, Grafana upgrades, and future roadmap for observability.
Airbnb’s iOS Page Performance Score implementation introduces a PPSStateMachine that ties a UIViewController’s lifecycle to metric collection—tracking first layout, initial load, scroll thread hangs, additional and rich content load times—using nanosecond timestamps, state‑machine protocols, and view‑responder discovery to emit standardized performance logs.
The new OpenTelemetry Java instrumentation adds client and server request‑size metrics to gRPC by injecting a tracing interceptor via Byte‑Buddy bytecode enhancement, extracting payload sizes from protobuf messages, recording them with custom attributes and histograms, and applying analogous handler‑based logic for Go.
This article provides a comprehensive overview of Prometheus, covering its architecture, suitable and unsuitable use cases, data model with TSDB and WAL, metric types, PromQL query language, and practical examples for monitoring CPU, memory, and disk usage in Kubernetes environments.
The new Monitoring 2.0 architecture separates collection, compute and storage, adopts VictoriaMetrics for compact time‑series storage and a zone‑based scheduler, introduces push‑based ingestion, uses Flink for real‑time pre‑aggregation and automatic PromQL rewrite, delivering ten‑fold query speedups, sub‑300 ms p90 latency, and dramatically higher write and query throughput.
This article provides a comprehensive, step‑by‑step guide to designing, prototyping, and continuously improving an AI‑assisted software engineering (AI4SE) framework, covering goal definition, pain‑point identification, technology selection, cross‑disciplinary team building, metric evaluation, and real‑world examples for teams of all sizes.
This guide outlines key operational metrics—availability, failure rate, MTTR, MTBF, response time, throughput, error rate, capacity utilization, latency, data integrity, and more—explaining their calculations, typical benchmark values, and practical application areas to help organizations monitor and improve IT performance.
This article presents five key R&D efficiency dashboards—covering requirements, project progress, incidents, risks, and defects—designed for CTOs to monitor software development quality, along with a curated list of further reading on metrics and best practices.
This guide walks through Spring Cloud Gateway features—including marking exchanges as routed, enabling route metrics, configuring metadata, accessing Reactor Netty logs, troubleshooting with debug logging, disabling automatic route refresh, ordering global filters, using Actuator sub‑paths, and rewriting request parameters—complete with code snippets and configuration examples.
This article explains the challenges of deploying large language model (LLM) applications, outlines the need for end‑to‑end observability, and details Alibaba Cloud ARMS' LLM‑specific tracing, metrics, and Python agent solutions for monitoring, debugging, and performance optimization.
The article explains the four core DORA metrics—deployment frequency, change failure rate, mean time to recovery, and lead time for changes—highlighting their focus on speed and stability, their limited business relevance, and proposes using leading indicators and broader measures to align engineering performance with business outcomes.
Airbnb tracks five real‑user performance metrics—Time To First Contentful Paint, Time To First Meaningful Paint, First Input Delay, Total Blocking Time, and Cumulative Layout Shift—using a mix of browser APIs and custom polyfills, combines them into a weighted Page Performance Score, and leverages that score to guide trade‑off decisions and detect regressions.
This article explains what OpenTelemetry is, its core components, key terminology, benefits, usage steps, and detailed architecture—including APIs, SDK pipelines, and the collector—providing a comprehensive overview for developers and operators seeking vendor‑neutral observability solutions.
Airbnb created the Page Performance Score (PPS), a unified 0‑100 metric that aggregates platform‑specific initial‑load and post‑load user‑centric measurements for Web, iOS, and Android, using weighted curves to enable cross‑page, cross‑team comparisons, track organizational weighted averages, and evolve with new metrics while preserving a stable scale.
This article explains the concept of observability, details Prometheus metric definitions and types, and provides Go code examples for exposing, defining, generating, and scraping business‑level metrics in a Kubernetes‑based cloud‑native environment.
This article presents JD Retail’s metric middle‑platform practice, describing the background problems of legacy metric systems, the four‑step solution framework, the overall architecture, semantic‑layer construction with the 4W1H method, configurable metric production, acceleration techniques, governance mechanisms, achieved results and future plans.
This article explains the background of metrics in cloud‑native systems, shows how to expose custom Prometheus metrics using OpenTelemetry's MeterProvider, compares different exporters, and provides a complete Pulsar client example with code snippets and configuration for end‑to‑end observability.
This article explains essential DevOps metrics—including deployment frequency, lead time for changes, change failure rate, mean time to recovery, and customer satisfaction—detailing why they matter, how to measure them, and practical practices to improve each metric for more efficient and reliable software delivery.
This article explains why interface analysis is essential, how to collect request data from web, mini‑programs, and Flutter using hooks and overrides, and how to process, store, and visualize metrics with Redis, InfluxDB, MySQL, and Elasticsearch while managing concurrency and flow control.
This guide introduces new Alibaba Cloud users to the fundamentals of cloud observability, explaining the metric‑trace‑log stack, the layered monitoring pyramid, and step‑by‑step how to set up out‑of‑the‑box resource monitoring, dashboards, alerts, and advanced integration options.
The article explains event tracking, its data‑quality challenges, and presents a logging platform that enforces quality standards, an end‑to‑end online workflow, and specialized design, testing, and validation tools—including extended field types—to govern, monitor, and improve tracking point compliance across applications.
This article details Huya's transition from fragmented, non‑standard monitoring solutions to a unified OpenTelemetry‑based platform, covering project background, pain points, design decisions, SDK architecture, data pipeline, storage, alerting, root‑cause analysis, and future plans, highlighting the benefits of standardization and zero‑cost service integration.
This article outlines the challenges of front‑end performance troubleshooting for a 2D design tool, proposes systematic approaches for identifying issues, describes monitoring metrics such as load time and frame rate, and presents real‑world case studies demonstrating effective optimization and baseline management.
This article explains how to build a robust observability system for cloud‑native microservice architectures, detailing the three core signals—metrics, traces, and logs—common challenges such as complexity and data silos, and presents Yanhuang Data’s integrated platform with unified data collection, storage, analysis, and visualization solutions.
The article analyzes why Prometheus sometimes returns inaccurate metric values, revealing the design trade‑offs that favor efficiency over precision, and walks through common pitfalls in rate/increase calculations, histogram P99 estimation, and practical recommendations for choosing scrape intervals and query windows.
The article explains why pinpointing core variables is crucial, outlines domain‑knowledge and technical methods such as sensitivity analysis and data mining to discover them, and describes practical ways to turn those variables into quantitative indicators like scoring systems, composite indices, and real‑world examples.
Full‑link load testing evaluates the performance of an entire application stack—from user interface to databases—by simulating real‑world traffic, isolating test data, verifying security and SLA thresholds, measuring key metrics such as throughput and response time, and comparing tools like tcpcopy and goreplay to ensure system stability and scalability.
This article explores the complete lifecycle of an indicator system—from defining metrics and addressing common ambiguities, through designing concept consensus, semantic layers, mechanisms, and governance, to productizing platforms, optimizing development, and envisioning future AI‑driven enhancements.
This tutorial walks through configuring Spring Cloud OpenFeign with Spring Boot, covering basic setup, advanced capabilities like Micrometer metrics, Redis caching, @SpringQueryMap, @MatrixVariable, and @CollectionFormat, and shows how to view metrics via Prometheus or Actuator.
The article walks through building a custom OpenTelemetry Java‑agent extension for Pulsar client metrics—migrating from SkyWalking, setting up a Gradle project, using ByteBuddy to instrument methods with advice, registering gauge metrics, packaging the jar, handling common class‑loader pitfalls, and configuring deployment via the OpenTelemetry operator.
By introducing the unified tRPC scaffolding tool trpcx and embedding OpenTelemetry‑generated observability configurations, the Tencent Docs backend team streamlined service creation, standardized directory structures, migrated metrics and logs to ClickHouse for cost‑effective performance, and established best‑practice workflows that dramatically improve development speed and fault‑diagnosis efficiency.
The article details how DeWu’s technology organization scaled from a few hundred to over a thousand engineers by adopting the custom Type‑P framework—emphasizing value‑orientation, small‑step rapid iteration, bi‑weekly sprints, unified domain‑level agile processes, metric‑driven governance, and an evolving PMO that shifted from throughput‑first to value‑first objectives.
This article examines ByteDance's DataOps journey, detailing the data‑engineering challenges faced, the concrete solutions and productization through the DataLeap platform, the metrics and best‑practice framework adopted, and the future directions involving AI‑assisted development and open‑source collaboration.
This tutorial explains how to install the MySQL Exporter, configure Prometheus to scrape MySQL metrics, set up Grafana dashboards for visualization, and define alerting rules for common MySQL performance indicators, providing a complete end‑to‑end monitoring solution.
This article explores the concept of R&D efficiency, outlines its goals, debunks common misconceptions, and presents a practical framework—including practice, platform, and measurement components—supported by visual models to help technology organizations improve delivery speed, quality, reliability, and sustainability.
This article examines a real‑world case where a B2B product team prioritized video‑quality metrics over user experience, discusses the resulting trade‑offs, and offers a step‑by‑step framework for aligning business goals with optimal UX design.
This article explains how to leverage Go's built‑in expvar package to expose application state and custom metrics via a JSON endpoint, compare it with pprof profiling, and visualize the data using tools like expvarmon for effective backend monitoring.
The article explains Flink’s metrics architecture—core concepts, reporter interfaces, built‑in and custom metric types, elastic plugin design, and scheduled reporting—illustrated with a consumption‑latency example, and shows how Didi uses these metrics for real‑time UI curves, alerts, and intelligent task diagnosis.
This guide helps test managers understand and control performance testing by outlining its purpose, timing, process, types, analysis methods, key metrics, essential skills, common terminology, and a step‑by‑step workflow, ensuring high‑quality, reliable software performance assessments.
This comprehensive guide explains Prometheus' architecture, metric collection models, storage format, query language (PromQL), alerting workflow, configuration reload methods, metric types, custom exporters, and how to visualise data with Grafana, providing a complete end‑to‑end monitoring solution.
Developers often struggle with vague OKR metrics, so this article outlines a comprehensive framework—grounded in first principles—to define, measure, and achieve developer OKRs by focusing on efficiency, quality, sustainability, and clear, objective metrics that align with business goals.
This article explains how to design and implement Prometheus metrics for application monitoring, covering the selection of monitoring targets, golden metrics, label conventions, naming rules, histogram bucket choices, and Grafana visualization tricks to help engineers build reliable observability pipelines.
This talk by Ant Group’s senior technical expert Wang Gaohang details the definition, design, mechanism, productization, and future outlook of the company’s metric system, covering concept consensus, semantic layers, workflow, AI assistance, performance optimization, and practical case studies.
Observability in distributed systems combines event logging, aggregated metrics, and request tracing—each offering distinct trade‑offs in detail, storage, and overhead—and while the ELK stack dominates log and metric handling, tracing solutions such as EagleEye and SkyWalking differ by protocol and language, prompting many teams to adopt unified, cloud‑native platforms like Alibaba Cloud’s Log Service for lower cost, real‑time analysis and simplified management.
This article explains monitoring fundamentals, distinguishes black‑box and white‑box approaches, outlines key metrics and their aggregation, and provides a comprehensive guide to Prometheus architecture, data model, query language, and practical examples for CPU, memory, and disk usage monitoring.
This article presents Xiaomi's sales data warehouse practice, detailing its evolution, positioning, dimensional modeling, layered architecture, Lambda design, Iceberg integration, capability building, security governance, and future directions toward data value and real‑time metrics.
This article explains how YHP enables cloud‑native systems to achieve comprehensive observability by defining the three core signals—metrics, traces, and logs—addressing common enterprise pain points, and presenting an integrated platform that unifies data collection, storage, analysis, and visualization for efficient fault diagnosis and performance monitoring.
This article explores the design, implementation, and iterative improvement of management dashboards in fast‑moving e‑commerce data operations, covering metric system construction, product interaction, data accuracy, user experience, and common challenges with actionable solutions.
Facing exploding metric volumes, high resource consumption, and fragile operations, Xiaohongshu's observability team completely rebuilt its metrics pipeline using Victoriametrics, achieving ten‑fold performance gains, minute‑level scaling, high‑availability, cost reduction, and robust multi‑cloud active‑active deployment while preserving data safety and query speed.
This article details the complete redesign of our internal APM system, covering the motivations, architecture choices, metric collection pipeline, integration of VictoriaMetrics and VMAlert, metric and alert design principles, implementation steps, visualizations, performance gains, and future plans for scaling and SaaS‑ification.
The article defines R&D efficiency as the effective use of human and time resources to deliver high‑quality software quickly, and outlines a systematic, multi‑dimensional approach—including culture, structure, architecture, process design, engineering systems, and measurement—to enhance it.
This article explains Douyin’s growth‑analysis methodology, how to construct a comprehensive growth‑metric system with North‑Star indicators and hierarchical metric layers, the end‑to‑end analysis loop, new scenario‑driven metric applications, and a detailed case study on improving video‑submission rates.
This article explains why metrics are essential, defines what a metric is, describes the four essential elements of metric design, outlines the three‑step design process, discusses measurement scales and time characteristics, and provides criteria for evaluating good metrics.
Xiaohongshu’s observability team rebuilt its Prometheus‑based metrics platform using vmagent, dual‑active HA clusters, query push‑down, high‑cardinality governance and multi‑cloud active‑active design, delivering ten‑fold collection speed, up to 70× query capacity, massive CPU‑memory‑storage savings and fully automated scaling.
While A/B testing is widely used for data-driven decisions, short-term experimental results often diverge from long-term impacts, leading to misguided strategies; this article examines why such inconsistencies arise and reviews major methods—including extended experiments, holdout groups, post‑analysis, CCD, and surrogate‑metric modeling—to reliably estimate long‑term effects.
This article outlines the essential steps for embracing DevOps culture, emphasizing tailored strategies, deep understanding of CI/CD, clear role assignments, extensive automation, key performance metrics, and the critical role of quality assurance to achieve faster, reliable software delivery.
This article explains how to build a data‑driven metric system—from defining end‑to‑start metrics and combining business and data drivers, to applying statistical analysis, machine‑learning, causal inference, and practical case studies for alerting, diagnosing, and strategizing product performance.
This study analyzes JD e‑commerce operational data from February to April 2018, employing Python and SQL to compute key metrics such as PV, UV, conversion rates, attrition, purchase frequency, time‑based behavior, funnel analysis, retention, product sales, and RFM segmentation, and provides actionable recommendations for improving user engagement and sales performance.
This article outlines typical interview questions about handling a growing number of business metrics, structuring and managing metric definitions, providing unified services, and performing anomaly detection and attribution analysis, offering practical guidance for data‑focused roles.
This article explains the INP (Interaction to Next Paint) performance metric, its calculation method, satisfaction thresholds, differences from FID, and provides practical guidance on measuring INP using Chrome's CrUX, PageSpeed Insights, the web‑vitals library, Chrome extensions, and custom console scripts.
This article details the design and implementation of the Pylon APM monitoring platform for NetEase Cloud Music, covering background challenges, the choice of Pinpoint, extensions to trace models, tail‑based exception sampling, Prometheus integration, automated JStack collection, and the resulting APM product features.
Dongguan Securities' "Zhangzhengbao" service platform passed the China Academy of Information and Communications Technology's DevOps Continuous Delivery Level 3 assessment, demonstrating how standardized processes, automation, and metric‑driven improvements can boost efficiency, reduce costs, and enhance competitiveness in the financial sector.
This article explains what metrics are, categorizes them, describes how to design effective metrics for R&D teams, outlines criteria for judging their quality, and provides practical guidance for tracking, reviewing, and continuously improving them.
This article explores why logs are essential in software development, outlines when to record them, discusses the value of logging in large-scale distributed systems, and examines the capabilities required of log‑operation tools such as APM, metrics, tracing, ELK, Prometheus, and custom batch querying solutions.
This article explains how to use Prometheus to monitor business‑level metrics in a Kubernetes environment, covering observability fundamentals, metric definitions, metric types, exposing metrics via a /metrics endpoint, and practical Go code examples for defining, recording, and scraping custom metrics.
This article explains how Kuaishou's e‑commerce team built a unified OLAP‑based data platform—covering data ingestion, consistent dimensional and fact layers, metric management, and real‑time services—to address rapid growth, metric inconsistency, and operational inefficiencies across multiple business scenarios.
This article visually dissects Prometheus, explaining its architecture, core features, data collection methods, exporter role, PromQL query language, and alerting workflow, while contrasting it with ELK and highlighting practical configuration examples for real‑world monitoring.
This article explains why web performance matters, introduces key user‑centric metrics such as First Contentful Paint, Largest Contentful Paint and Cumulative Layout Shift, describes how to measure them with tools like Chrome DevTools, Lighthouse and ftwo, and provides practical optimization techniques including gzip, HTTP/2, CDN, image handling, code splitting and Vue router lazy‑loading.
This article explains how to combine OKR and agile activities to set measurable business goals, avoid common pitfalls, and create a continuous loop of planning, execution, review, and optimization that aligns strategic objectives with day‑to‑day value delivery in R&D projects.
The article explains how misleading metrics like “watermelon‑type” KPIs can mask poor system availability, introduces the OKRa framework that adds activities to traditional OKRs, and promotes a nationally certified DevOps engineering program to improve end‑to‑end R&D efficiency.
This guide explains how to enable OpenTelemetry in Apache Airflow, configure an OTel collector, use Prometheus as a metrics backend, set up Grafana dashboards, and visualize sample DAG metrics, providing a complete observability stack for Airflow pipelines.
This article explains how SaaS companies can quantify and deliver customer value by defining, measuring, and optimizing key success metrics such as ROI, adoption rates, time‑to‑value, and lifecycle duration, while emphasizing continuous communication and expectation management throughout the customer journey.
The article explains what metrics are, why they matter, how they can be classified, the structured process for creating effective R&D metrics, criteria for judging good metrics, and practical methods for tracking, reporting, and periodically reviewing them to ensure continuous improvement.
DevOps testing integrates continuous, automated testing throughout the software development lifecycle, shifting left from traditional isolated testing, emphasizing automation, appropriate tool selection, metric tracking, documentation, and dedicated test automation engineers to ensure high‑quality, rapid software delivery.
This article explains the fundamentals of metrics, introduces dimensional metrics, compares Prometheus, OpenMetrics, and OpenTelemetry standards, and provides detailed guidance on the four Prometheus metric types—Counters, Gauges, Histograms, and Summaries—including their use‑cases, PromQL queries, and Python client examples.
The presentation details Hello’s end‑to‑end R&D efficiency platform, covering its digital resource‑strategy system, custom iteration model, integrated MRD/PRD workflow, metric dashboards, and concrete savings of up to 400 man‑days in project management and 1,400 man‑days in development operations.
Didi’s observability platform leverages non‑intrusive eBPF probes to automatically capture and validate service‑to‑service call tuples, supplement missing SDK data, achieve roughly 80 % core‑path coverage, and address verification challenges while planning future user‑space VM hooks and deeper MTL integration.
This article introduces the fundamentals of metrics in IT monitoring, explains the structure of metric data points, explores dimensional metrics, and provides an in‑depth guide to Prometheus metric types—Counters, Gauges, Histograms, and Summaries—along with practical code examples and usage considerations in cloud‑native environments.
This article explains how metrics are used to monitor software performance, introduces basic metric components and dimensional metrics, compares Prometheus, OpenMetrics and OpenTelemetry standards, and provides detailed guidance on Prometheus metric types—Counter, Gauge, Histogram, and Summary—with code examples and query patterns.
Learn step‑by‑step how to set up Prometheus and Grafana with a Spring Boot 2.4.12 application, configure dependencies, expose metrics via Actuator, customize meters, and monitor database connection pools, providing a complete observability solution for Java backend services.
The article explains how Google measures and improves software quality before release by separating development and operations responsibilities, using monorepo and trunk‑based development, daily release candidates, automated testing, performance benchmarks, and a comprehensive Project Health (pH) metric system that balances speed, reliability, and quality.
This article compares six open‑source log management platforms—OpenObserve, Grafana Loki, SigNoz, Graylog, Syslog‑ng, and Highlight.io—detailing their features, deployment options, advantages, and drawbacks to help you choose the most suitable solution for effective observability and system performance.
To give the business team a global, application‑level view of Pulsar performance, the team built a SkyWalking Java‑Agent plugin that automatically collects producer and consumer metrics from the Pulsar client, exposing latency, backlog and failure counts via Prometheus without modifying the client code.
This guide explains how to design effective Prometheus metrics, choose appropriate monitoring objects, labels, and buckets, and leverage Grafana visualizations to gain deep insight into application performance across online services, offline processing, and batch jobs.
This article explains how to define, measure, and improve microservice reliability using availability metrics, the four golden signals, RED and USE methods, and practical SLI/SLO and MTTR practices, offering concrete guidance for effective service governance.
This article explains why measuring software development productivity is challenging, introduces the concept of DevOps metrics, details the four DORA indicators and their performance levels, and discusses additional metrics such as cycle time, quality, customer and employee satisfaction, CI/CD health, uptime, and service level indicators to help teams monitor progress and identify problems.
The article explains why relying solely on SQL optimization often fails to resolve deep database performance problems, illustrating with real Oracle and domestic PostgreSQL‑compatible cases where I/O configuration, shared buffers, and system‑level metrics were essential to achieve stability.
This article examines Dewu Technology’s SQC (Supplier‑Quality‑Customer) quality management model, detailing its AI‑driven inspection and authentication, the comprehensive quality assurance system built on mechanisms, processes, methods and tools, the “Quality Month” initiative, the iteration quality score model, automation testing and traffic‑replay platforms, and the measurable improvements in software quality and production reliability.
This tutorial explains how to use Docker to deploy DBLE, Prometheus, and Grafana, configure JMX Exporter for JVM metrics, and create a monitoring dashboard that visualizes CPU, memory pool, GC, and thread statistics of DBLE instances.
The article analyses why many organizations stall during DevOps adoption, explains the underlying causes, discusses metric design and second‑order changes, and proposes organizational and technical strategies to cross the gap and achieve sustainable continuous delivery.
The article outlines a practical framework for business‑level monitoring in banking systems, describing three evolution stages, key metrics such as transaction success rates and volume spikes, concrete alert rules, and the technical challenges of data collection, standardization, and massive parameter management.
This article explains how Dubbo3’s new observability starter provides visual cluster metrics, full‑link tracing, multi‑dimensional monitoring, Prometheus/Grafana integration, and log management, offering practical steps and configurations for building a robust cloud‑native microservice observability platform.
This article describes Anjuke's R&D efficiency measurement framework, detailing its quality and efficiency metrics across project phases, the data collection and processing architecture, visualization dashboards, and analysis methods used to monitor and improve development productivity, reliability, and continuous delivery.
This comprehensive guide explains Prometheus fundamentals, its ecosystem, metric collection models, configuration, PromQL querying, custom exporters, Grafana visualization, and Alertmanager setup, providing step‑by‑step instructions and code examples for effective system monitoring and alerting.
This article shares practical experience building a payment data analysis system from scratch, explaining why data analysis matters, outlining a five‑stage framework, detailing metric design, and presenting common analytical methods such as funnel, multi‑dimensional, trend, comparison, Pareto, and cross analysis to drive product decisions.
This article summarizes a technical talk on the challenges of cloud‑native transformation, the design of an application‑centric observability platform using CMDB, Prometheus, Thanos and VictoriaMetrics, practical solutions for high‑cardinality metrics and alerting, and future directions such as eBPF and AI‑driven fault detection.
