This article explains the jstat command syntax, options, and detailed parameter descriptions for monitoring JVM class loading, memory usage, garbage collection, JIT compilation, and other runtime statistics, providing examples and visual tables to help developers analyze Java process performance.
This article describes Qunar's approach to quantifying, automatically detecting, and optimizing performance issues in React Native mobile apps by defining measurable metrics, real‑time data sampling, automated analysis rules, and visual feedback to enable proactive APM during development and testing.
This comprehensive guide explains how to monitor and tune service performance by examining CPU load, system and JVM memory usage, buffer/cache concepts, key performance metrics such as response time, throughput, QPS, and provides essential Linux tools and commands for effective operations management.
The article highlights Brendan Gregg’s updated Linux performance talk, presenting three concise visual guides that categorize the most reliable, lightweight tools for monitoring, testing, and optimizing various Linux subsystems, helping practitioners quickly select the right utilities for effective system performance management.
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.
The article describes the background, architecture, and implementation details of a non‑intrusive Android SDK that monitors UI‑thread stalls, collects performance data, aggregates it on the server, and automatically generates work orders to help developers pinpoint and resolve lag issues efficiently.
The article describes the evolution of Ctrip's wireless APM platform from the early UBT-based monitoring to a globally‑oriented, metric‑rich system that processes over 100 billion data points daily using Storm and Elasticsearch, detailing its design, key performance dimensions, data‑volume trade‑offs, and implementation choices.
When troubleshooting a Linux server, this guide shows the ten essential command‑line tools—uptime, dmesg, vmstat, mpstat, pidstat, iostat, free, sar, and top—to quickly assess CPU, memory, disk, and network health within the first sixty seconds, helping you identify saturation and bottlenecks.
This article expands on Elasticsearch performance monitoring by detailing host‑level system and network metrics, cluster health and node availability, resource saturation, thread‑pool behavior, cache usage, and common error indicators, offering practical guidance for maintaining a stable and efficient search cluster.
This article outlines fifteen key DevOps metrics—ranging from deployment frequency and lead time to MTTR and availability—that help organizations assess and improve their continuous delivery speed, quality, and overall system performance over time.
This article describes the design, challenges, and operational practices of a cloud‑native elastic computing platform that reuses idle resources from production servers to support massive image compression, video transcoding, AI inference, and log processing while ensuring online services maintain performance, latency, and reliability.
This article explains how 360 Mobile Guard, a massive Android security app built on the RePlugin framework, implements a comprehensive performance monitoring system—including offline code checks, Argus APM tools, online crash and metric collection, diagnostic plugins, and future AI-driven analysis—to identify and resolve performance issues and improve user experience.
The article shares practical insights on planning, designing, developing, testing, and rolling out a large‑scale backend system refactor, emphasizing resource limits, pain points of legacy code, unit‑test protection, layering, decoupling, monitoring, and staged deployment to ensure stability during high‑traffic events.
This article describes the design and implementation of a non‑intrusive Android SDK that monitors main‑thread UI lag by replacing the Looper printer, sampling stack traces, aggregating data on the server, and automatically generating work orders for precise performance optimization.
Suning’s 2017 O2O shopping festival achieved a “zero‑incident” goal by integrating real‑time browser performance monitoring, WEEX‑based WAP acceleration, comprehensive app data collection with cloud‑based analytics, precise DNS and HTTP2 optimizations, and a multi‑layer network and service monitoring system that enabled rapid fault detection and capacity planning.
This guide walks through key Linux commands—uptime, dmesg, vmstat, mpstat, pidstat, iostat, free, sar, and top—explaining their outputs and how to interpret them for effective system performance analysis and troubleshooting.
This article shares practical methods for frontline engineers to quickly understand, assess, and resolve online system problems by categorizing system layers, evaluating impact, using essential Linux monitoring tools, and applying systematic troubleshooting and design‑for‑failure strategies to minimize downtime.
This article outlines a systematic methodology for understanding, evaluating, and quickly resolving production system incidents by categorizing system layers, assessing impact, employing Linux diagnostic tools, and designing fault‑tolerant mechanisms to minimize downtime and maintain core functionality.
This article presents a collection of straightforward, developer‑friendly techniques for enhancing Java API security and performance, covering API key protection, TLS adoption, Spring Boot web service creation, application monitoring, and safeguarding sensitive configuration files.
This article presents a systematic methodology for diagnosing and resolving online system issues, covering system understanding, impact assessment, rapid recovery techniques, detailed troubleshooting steps with Linux and Java tools, and design principles to mitigate future failures.
This guide explains how to use Linux performance monitoring utilities such as top, vmstat, pidstat, iostat, sar, netstat, and tcpdump, describing each tool's output fields and showing how to interpret them for effective troubleshooting of CPU, memory, disk, and network issues.
This article describes LinkedIn's network‑level MySQL query analyzer, explaining why it was needed, its three‑component architecture (agent, central server, UI), fingerprinting and hash‑map techniques, performance benchmarks, metric collection, security considerations, and the benefits it brings to database operations.
This article explains the role of APM in automated operations, outlines the challenges of manual thread diagnostics, and presents a one‑click thread analysis solution with a five‑stage architecture—capture, aggregation, storage, query, and deep analysis—illustrated through multiple real‑world scenarios.
The article introduces Wang Yaobo’s presentation on Ctrip’s "real‑time handheld testing" technology, explaining how to monitor key performance metrics, capture crashes and stalls in authentic environments, and pinpoint problematic code lines to improve Android app quality.
This article provides a comprehensive guide to Linux performance monitoring, covering essential tools such as vmstat, iostat, dstat, iotop, pidstat, top/htop, netstat, ps, strace, perf, and advanced tracing utilities, with usage examples and interpretation of their output.
This article details the end‑to‑end process of designing and deploying an operations‑focused monitoring framework, covering semantic, log, and performance monitoring, protobuf integration, implementation challenges, actual effects, and future optimization directions for large‑scale online services.
This article details the design and implementation of an iOS performance‑monitoring SDK that captures fundamental metrics such as CPU usage, memory consumption, frame rate, cold and hot startup times, and power draw, explains the underlying Mach APIs, provides sample Objective‑C code, and discusses practical considerations for accurate measurement and troubleshooting.
The article examines the challenges of selecting hardware and monitoring tools, comparing Intel CPUs on performance versus price, and outlines criteria for evaluating bypass‑monitoring products, emphasizing cost, reliability, scalability, and data‑decoding capabilities to guide informed operational decisions.
This article provides a comprehensive guide to Linux performance analysis, covering background concepts, detailed explanations of tools such as vmstat, iostat, dstat, iotop, pidstat, top/htop, mpstat, netstat, ps, strace, lsof, perf, and sar, along with practical command examples and usage tips.
This article introduces core Linux performance monitoring utilities—top, vmstat, pidstat, iostat, netstat, sar, tcpdump, and others—explaining their output fields, typical usage scenarios, and how to interpret metrics to diagnose CPU, memory, disk, and network bottlenecks on servers.
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.
This article recaps a DBAplus community session where a seasoned DBA shares common operational scenarios, diagnostic techniques, lock‑handling strategies, and step‑by‑step SQL optimization methods, while unveiling the DPM platform that automates inspection, data collection, and performance analysis for Oracle databases.
As microservice architectures grow, service calls become increasingly complex, involving dozens of services and teams, making rapid fault localization and comprehensive data analysis critical; distributed tracing systems address these challenges by providing end‑to‑end visibility, low‑overhead instrumentation, and scalable monitoring across large‑scale applications.
The article introduces MTrace, Meituan‑Dianping’s large‑scale distributed session tracing system, explaining its call‑chain concept, architecture, data‑embedding SDK, trace and span identifiers, APIs for transparent data propagation, and how it enables bottleneck detection, performance optimization, and comprehensive monitoring across heterogeneous backend services.
This guide explains how to use MongoDB's built-in mongostat tool and key database functions like db.stats() and db.serverStatus(), detailing command options, output metrics, and how to interpret them for diagnosing performance issues and monitoring server health.
This comprehensive guide explains Oracle's Automatic Workload Repository (AWR), how snapshots are collected and retained, and provides step‑by‑step instructions for interpreting the most common AWR report sections, from cache sizes and load profiles to SQL statistics and RAC metrics, helping DBAs pinpoint bottlenecks and improve performance.
This guide explains how to use iostat, free, sar, and top commands to monitor disk I/O, memory usage, network throughput, and CPU load on a Linux server, interpreting key metrics to identify performance bottlenecks.
This article explains why Node.js applications can suffer memory leaks due to V8 garbage‑collection limits, describes V8 memory regions and GC types, and provides practical commands, monitoring tools, and a reproducible code example to identify and resolve leaks.
This article explains how to extract valuable performance and traffic insights from Nginx access logs using shell commands and awk, covering request volume, peak rates, bandwidth usage, slow‑query detection, URL normalization, and practical optimization recommendations for web operations.
This article introduces Baidu's SmartMonkey, an Android system‑level stability testing tool built on Robotium, outlines its key features such as accurate page interaction, configurable critical paths, performance data collection, and provides step‑by‑step instructions for project setup, configuration, test execution, and report analysis.
This article presents a curated list of 18 lesser‑known command‑line utilities for Linux/Unix administrators, explaining their purpose, typical usage scenarios, and how they help monitor system resources, network activity, and security events.
This article presents a curated list of twelve powerful command-line utilities—such as lsof, htop, iotop, IPTraf, Monit, netHogs, iftop, and Monitorix—that Linux system administrators can use to monitor, diagnose, and optimize system and network performance.
This article demonstrates how to employ Java's java.lang.instrument API together with Javassist to create a Java agent that instruments bytecode at runtime, enabling performance monitoring without modifying the original application code.
This guide provides Oracle SQL queries to list currently running statements, retrieve historical SQL, identify high‑CPU or high‑disk‑usage queries, find slow statements, and detect uncommitted transactions, enabling DBAs to quickly diagnose performance issues.
This guide walks through a rapid 10‑minute troubleshooting workflow for MySQL and Oracle databases, covering resource checks, log inspection, wait‑event analysis, SQL identification, and feedback best practices to quickly pinpoint performance problems without relying on external monitoring tools.
This guide explains what MySQL QPS and TPS metrics are, how to calculate them using show status or mysqladmin extended‑status, and provides a ready‑to‑run Bash/Awk script that records QPS, TPS, Threads_connected and Threads_running every second.
This article explains how the OraZ toolset streamlines Oracle database administration by automating routine tasks, providing intelligent hang analysis, offering comprehensive health checks, and delivering real‑time instance activity insights, ultimately reducing DBA workload and improving operational efficiency.
This article details Meituan's 2014 presentation on building a performance analysis framework and monitoring platform, describing the bottlenecks they faced, the metrics they collected, the insights gained, and the tangible improvements achieved across front‑end and back‑end processing.
The article presents a cloud‑plugin based method for mobile app testing that dynamically injects offline test capabilities into production devices, enabling comprehensive user‑interaction logging, low‑impact issue detection, and rapid debugging of performance problems such as frame‑rate drops.
This article introduces a customized Fiddler plugin that enhances mobile app testing by analyzing packet size, request latency, frequency, coverage, and simulating various network speeds, helping QA teams detect backend interface anomalies and improve test completeness.
The article compiles detailed notes from the 2015 Internet+ Big Data Summit, highlighting how data monetization reshapes advertising, drives financial analytics, improves operational efficiency, and strengthens security, while presenting real‑world case studies, models, and practical recommendations from industry experts.
The article recounts Ctrip’s three‑year transition from a traditional call‑center and web‑based OTA to a mobile‑first travel platform, detailing statistical growth, product consolidation, open API initiatives, system decoupling, performance monitoring, and continuous reliability improvements.
Senior DBA and architect Bai Shan answers community questions on advancing from DBA to architect, future career routes, testing environment setup, interpreting AWR storage metrics, using sar and iostat for disk I/O monitoring, cache sizing, and the growing importance of automation in cloud-era database operations.
This article presents a comprehensive overview of PostgreSQL, covering its history, architecture, core features, step‑by‑step lab exercises for database creation, CRUD operations, configuration tuning, performance monitoring, backup and recovery, Hot Standby replication, PGPOOL clustering, and a curated Q&A session addressing common DBA challenges.
These notes distill key insights from the book “Linux System and Performance Monitoring,” outlining how to pinpoint and remove system bottlenecks across CPU, memory, I/O, and network, classify application types, establish baselines, and leverage common monitoring tools for effective Linux performance tuning.
When system performance degrades, start by examining OS reports—CPU usage, memory consumption, disk and network I/O, and connection counts—using tools like Windows PerfMon, Linux vmstat, iostat, top, iftop, or Wireshark to pinpoint bottlenecks before diving into code.
This article explains the causes of UI stutters on iOS, outlines a thread‑stack‑dump monitoring approach, describes detection thresholds, classification methods, sampling strategies, and shares the practical results of deploying the solution in WeChat.
This guide explains how to use the Linux vmstat command to monitor system performance, detailing its output fields such as process states, memory, swap, I/O, system interrupts, and CPU usage, and shows how to interpret each metric for effective troubleshooting.
This article explains why high CPU usage on Linux servers isn’t always a problem, describes how to identify true CPU bottlenecks using tools like uptime, top, sar and vmstat, and offers practical tuning steps such as stopping unnecessary processes, adjusting priorities, using CPU affinity, and updating drivers.
This guide lists the key performance monitoring metrics for popular web servers, application servers, databases, and caching systems, detailing throughput, connection counts, cache hit rates, memory usage, and other vital statistics needed to keep services running efficiently.
