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.
