The article explains cloud native concepts, detailing its key characteristics, four foundational pillars, organizational practices, architectural patterns, essential tooling, and operational responsibilities that enable agile, scalable, and highly available applications built on cloud infrastructure.
This article explains the essential functions of IT monitoring systems, classifies them into log, trace, and metric types, describes a five‑layer monitoring architecture, and compares two popular open‑source solutions—Zabbix and Prometheus—helping practitioners choose the right tool for their environment.
This article shares Alibaba's extensive experience building a Kubernetes‑based logging system, detailing the evolution from single‑machine to containerized environments, the critical role of observability, and the specific technical challenges such as dynamic log sources, integration complexity, and massive scale 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.
The article recounts JD Digits architect Zhang Liang’s experience representing the Apache ShardingSphere community at ApacheCon 2019 in Las Vegas, describing the conference atmosphere, community interactions, ShardingSphere’s observability talk and Shark Tank showcase, and the growing Chinese contribution to the Apache ecosystem.
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 guide surveys the most popular open‑source distributed tracing projects, classifying them by instrumentation, tracer, and analysis capabilities, and explains how they fit into modern microservice observability, helping newcomers understand each tool’s strengths, integrations, and the broader tracing ecosystem.
This article explains the fundamentals of Prometheus histogram metrics, illustrates why they are cumulative, shows how to drop unwanted buckets with relabeling, and demonstrates quantile calculations using the histogram_quantile function, providing practical examples and code snippets for effective monitoring.
This article explains why comprehensive, multi‑layer monitoring is essential for distributed systems, outlines environment, program, and business metrics, recommends practical tools such as Zabbix, open‑falcon, Prometheus and Grafana, and provides a step‑by‑step evolution plan and alerting strategy.
The article recounts Medium's transition from a monolithic Node.js application to a microservice architecture, explaining the motivations, core design principles, practical strategies, tooling choices, and lessons learned to avoid common pitfalls and improve development velocity and system reliability.
This article details the design, architecture, and AI‑driven features of 58’s intelligent monitoring platform, explaining how multi‑dimensional data collection, predictive analytics, and smart alarm merging ensure continuous, automated observability across network, server, application, and business layers.
This article explains why Jaeger is a popular cloud‑native tracing solution, describes its architecture, sampling options, and deployment strategies on Kubernetes—including DaemonSet and Sidecar modes—followed by a step‑by‑step Django integration example and guidance on monitoring, alerting, and resource cleanup.
This article explains why Medium transitioned from a monolithic Node.js application to a microservice architecture, outlines the three core design principles, shares practical strategies for service creation, decoupling, observability, deployment, and how to avoid common microservice pitfalls.
This article explains Prometheus metric naming conventions, label usage, metric types such as Counter, Gauge, Summary, and Histogram, and describes the structure of data samples, providing examples and best‑practice guidelines for defining and classifying metrics in monitoring systems.
This article introduces Istio as an open‑source service mesh, explains its data‑plane and control‑plane architecture, outlines its traffic management, security, and telemetry features, discusses performance considerations, and shows how Lingque Cloud ASM leverages Istio to solve typical microservice problems such as debugging, testing, release processes, and flexible network policies.
Apache SkyWalking, an open‑source observability platform that originated in 2015, has graduated to a top‑level Apache project, offering comprehensive APM features such as distributed tracing, metrics, service topology, root‑cause analysis, and flexible storage options for cloud‑native microservice environments.
The article explains why enterprise application monitoring systems are essential, outlines their core components such as Trace, Log, Metric, and Report, discusses distributed tracing techniques, and describes how these insights are designed to aid developers, testers, and operations engineers in performance tuning and fault diagnosis.
This article explains why systematic logging is essential for production debugging, introduces a practical log classification and field schema, describes trace‑ID propagation and performance instrumentation, and walks through building an ELK‑based log collection, storage, and real‑time visualization platform for reliable observability.
This article explains why most alerts are poorly designed, defines actionable alerts, outlines monitoring objectives, discusses metric selection, and presents simple yet powerful algorithms for anomaly detection to improve system reliability and operational efficiency.
This article presents nine practical logging best‑practice recommendations—from understanding human and machine audiences and standardizing log formats to leveraging metrics, proper alerting, severity levels, contextual information, and advanced framework features—helping operations teams improve system performance and troubleshooting efficiency.
This article explains how to approach monitoring for a newly introduced system by focusing on white‑box metric collection, distinguishing basic and business metrics, outlining common collection methods, and detailing Google SRE's four golden indicators—error, latency, traffic, and saturation—to guide effective observability.
This article introduces a series of microservice design patterns—including database isolation, observability, and cross‑cutting concerns—explaining the underlying problems each pattern solves and providing concrete solutions such as CQRS, Saga, log aggregation, health checks, and blue‑green deployments.
This presentation details how to correctly benchmark MySQL middleware performance, exposing common pitfalls, describing practical testing methodologies, emphasizing the need to observe both middleware and actual database pressure, and discussing distributed transaction considerations and metric selection for reliable results.
This article explains what APM (Application Performance Management) is, its core concepts, how Java agents collect performance data, the process of integrating APM into distributed systems, and how it helps locate slow interfaces and improve overall application reliability.
This tutorial explains how to use Byte Buddy to build a JavaAgent for SkyWalking, debug and continuously integrate the agent, and develop custom SkyWalking plugins such as the kob scheduling framework, providing step‑by‑step code examples and configuration details for observability in Java backend services.
The article summarizes the 58 Group technical salon where experts from Momo and 58 shared practical experiences on monitoring platform architectures, coverage, alarm configurations, convergence techniques, custom dimensions, multi‑view dashboards, and future directions for intelligent and automated monitoring across the company.
The article discusses common challenges with missing logs in production, proposes practical solutions such as adding machine identifiers via Nginx headers and embedding user information with Log4j's MDC, and outlines concise logging guidelines to improve traceability and performance analysis for Java backend systems.
Amid the rise of serverless Kubernetes, growing pod volumes, and real-time log demands, this article examines emerging log-handling challenges, evaluates traditional collection methods, and presents a comprehensive “Logtail + Log Service + Ecosystem” architecture that delivers high-throughput, reliable, and scalable logging for cloud-native environments.
Alibaba’s monitoring evolution—from fragmented early tools to the standardized Sunfire platform and now AI‑powered full‑link observability—addresses scaling challenges, introduces business‑centric metrics, automated traceability, and intelligent anomaly detection, illustrating how massive, multi‑tenant infrastructures achieve unified, proactive operations at scale.
The article explains why operating massive Kubernetes clusters is as challenging as building large systems, outlines three critical stability questions, shares real‑world data collection, visualization, and tooling practices, and provides concrete recommendations for high‑availability, monitoring, and performance optimization.
This article shares practical experience and best‑practice guidelines for operating large‑scale Kubernetes clusters, covering stability checks, component failure impact, recovery strategies, alerting mechanisms, data collection, visualization, and the suite of operational tools that help ensure reliable, high‑performance cloud‑native infrastructure.
This article explains how microservice architectures require a comprehensive monitoring system, covering data, resource, and code dimensions, and describes eight atomic monitoring scenarios such as URL, host, product, component, custom, resource, APM, and event monitoring to help engineers design effective observability solutions.
The article explains the concept, functions, design goals, data models, log collection, and deployment considerations of distributed tracing systems, and compares several open‑source and proprietary solutions such as Dapper, Zipkin, Pinpoint, Alibaba Eagle Eye, and JD Hydra to guide the selection of an appropriate tracing platform.
This article explains why microservice architectures need reliable communication, load balancing, fault tolerance, monitoring, tracing and circuit breaking, and shows how Istio—a cloud‑native service mesh built on Envoy—provides these capabilities, enabling blue‑green and canary deployments, traffic routing, retries, and observability within Kubernetes.
An in‑depth look at Bilibili’s multi‑layer monitoring overhaul, detailing the shift from a monolithic Zabbix setup to micro‑service‑based ELK, Dapper, Misaka, Traceon and Lancer systems, and how layered observability improves fault detection across business, application, and infrastructure levels.
This article explains Zipkin’s data model, how Spring Cloud Sleuth generates and sends Span and Annotation information, the message‑channel listener that converts Sleuth spans to Zipkin spans, debugging techniques to observe the collected data, and why the number of spans shown in Zipkin’s UI can differ from the raw count.
This article describes the background, design goals, architecture, implementation details, and lessons learned from building a low‑overhead, low‑intrusion distributed tracing system using Kafka, Elasticsearch, and OpenTracing to monitor microservice interactions and support performance analysis and DevOps decision‑making.
Pandora.js, the Alibaba Midway team's first open-source project, consolidates years of production-grade Node.js operations by offering three core capabilities—manageable application and process control, comprehensive metrics, and Open-Tracing-based request tracing—exposed via RESTful APIs and logs for easy integration.
Metrics act as health checks for applications, enabling developers to monitor performance, track changes, and assess stability; this article explains Pandora.js’s metric naming conventions, types like Gauge, Counter, Histogram, and Meter, and provides practical Node.js code examples for implementing these measurements.
This article describes how WiFi 万能钥匙 designed and implemented the Roma monitoring platform to handle billions of daily requests, covering background challenges, architectural principles, component design, data collection, transmission, storage, alerting, and future directions for large‑scale observability.
The article introduces the ELKB stack—a combination of Elasticsearch, Logstash, Kibana, and Beats—explaining its background, user needs, architecture, component functions, typical scenarios, and the team’s practical implementations for real‑time log and time‑series data processing.
Pinpoint is a full‑stack, non‑intrusive tracing platform that visualizes service topology, active threads, request latency, and application health, and this article explains its architecture, data model, and step‑by‑step process for creating custom plugins—including ServiceLoader configuration, TraceMetadataProvider, and ProfilerPlugin implementations with code examples.
Over seven years, Pinterest’s monitoring team built and refined a three‑pronged observability platform—time‑series metrics, log search, and distributed tracing—scaling from a single‑machine system to handling millions of data points per second across tens of thousands of AWS VMs, while addressing reliability, cost, and usability challenges.
This article examines how Meipai, a popular live‑streaming and short‑video platform with over 150 million monthly active users, engineered a comprehensive, three‑dimensional monitoring architecture that spans client to server, integrates unified dashboards, and leverages both private and public cloud resources to ensure reliable, scalable operations.
QTracer is Qunar’s internal distributed tracing system that generates a global TraceID for each request, records operations across services, and provides features such as execution chain visualization, log correlation, conditional search, service dependency analysis, database statistics, transparent data propagation, and low‑overhead instrumentation for debugging and performance monitoring.
This article provides a comprehensive technical overview of QTracer, Qunar’s internal distributed tracing platform, covering its architecture, core concepts, key features such as execution‑chain queries, log association, conditional searches, data storage, non‑intrusive instrumentation, bytecode injection, and the QTracer Debug tool for online breakpoint debugging.
This article explains how Lianjia designed and implemented LTrace, a zero‑intrusion, high‑performance distributed tracing system that captures full request chains across heterogeneous services, supports multi‑language environments, offers flexible sampling, and enables rapid fault isolation and performance optimization.
Uber’s engineering team chronicles the evolution of its distributed tracing system—from the early Merckx pull‑based solution and TChannel integration to the open‑source Jaeger platform—detailing architectural shifts, sampling strategies, multi‑language client libraries, and the move toward a fully cloud‑native, end‑to‑end observability stack.
This article explains why distributed systems need tracing, outlines design goals, compares major implementations like Zipkin, EagleEye, and Hydra, and details the data collection, storage, and analysis pipelines that enable end‑to‑end visibility and performance optimization in large‑scale services.
Metrics‑Driven Development (MDD) extends test‑driven principles by embedding real‑time monitoring into design, enabling rapid, precise, and granular software iterations, improving early problem detection, decision support, and aligning development with DevOps culture.
Effective Node.js monitoring boosts competitiveness, user experience, and cost efficiency, and this guide outlines eleven key recommendations—from tracking downtime and response thresholds to linking performance with business metrics and leveraging third‑party APM tools—ensuring robust, noise‑free alerts and secure, scalable applications.
Twitter’s internal monitoring team built a full‑stack observability platform that handles billions of metric writes per minute, supports distributed tracing, log aggregation, visual dashboards, and alerting across data centers and public clouds, and shares the architecture, components, and key lessons learned.
This article explains how focusing on non‑functional quality during external acceptance testing can drive DevOps teams to improve system monitorability, reduce technical debt, and establish concrete change‑control, acceptance, and performance verification processes for both operational and business‑level observability.
This article introduces CAT, a Java‑based open‑source distributed real‑time monitoring system, covering its origins, design goals, architecture, message processing pipeline, instrumentation model, and how it achieves high availability, scalability, and low‑latency analytics for large‑scale internet services.
