This guide explains why and how to use Go's pprof and trace tools to diagnose memory leaks, goroutine stalls, and CPU bottlenecks, providing step‑by‑step commands, example code, a cheat‑sheet of core commands, and a practical workflow for microservice tracing.
This article explains how to replace invasive manual timing code with Java Instrumentation, demonstrating both premain and agentmain approaches, building and attaching agents, using ASM and Bytekit for bytecode enhancement, and leveraging Arthas for runtime tracing and debugging.
This article outlines a comprehensive approach to identifying and resolving both error‑related and slow‑request problems in online systems by leveraging trace data, log correlation, method‑stack analysis, unified entity models, and large‑language‑model assistance to accelerate root‑cause diagnosis.
The article introduces the latest Cool Request IDEA plugin update, detailing its Trace capability for method execution timing, selective MyBatis function monitoring, customizable slow‑method highlighting, and scripting support for headers and JSON parsing, all illustrated with Java code examples.
The article teaches Go developers how to generate and analyze CPU, heap, allocation, and goroutine profiles with pprof and full‑runtime traces, interpret SVG flame‑graphs, top lists, and source views, and apply concrete optimizations—such as buffering channels and using sync.Pool—to dramatically speed up a Mandelbrot generator.
Cool Request is an IDEA plugin that enhances HTTP debugging by offering reflection calls, traceable method execution, MyBatis function monitoring, customizable timing highlights, and scriptable environment handling, enabling Java developers to efficiently analyze performance and automate request workflows without leaving the IDE.
The Cool Request plugin for IntelliJ IDEA now adds a Trace feature that automatically measures method execution time, visualizes call counts, highlights slow calls, supports Mybatis tracing, and lets you script environment variables, making HTTP debugging and performance analysis in Java projects far easier.
The latest update of the Cool Request IDEA plugin introduces a powerful Trace function that automatically records execution times for any method—including Spring @Scheduled and xxl‑job tasks—offers customizable latency coloring, MyBatis SQL timing, call‑count indicators, and scriptable environment variables for seamless token handling and JSON parsing.
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.
This article introduces the xgo tool for Go, explains its cross‑platform monkey‑patching capabilities, demonstrates how to write unit tests with mock.Patch, shows installation, usage of Trap, Trace, and incremental coverage features, and evaluates its advantages and drawbacks for backend development.
This comprehensive guide teaches Go developers how to generate CPU, memory, and goroutine profiles with pprof, interpret SVG, top, source, and peek visualizations, understand the runtime’s sampling and allocation internals, use the trace tool to analyze events, and apply these techniques to real‑world optimizations such as speeding up a Mandelbrot image generator.
This article explains how to locate, trace, and resolve a syntax error that occurs when a stored procedure migrated from Oracle to OceanBase fails, detailing the use of trace IDs, SQL reconstruction, cursor analysis, and the necessary adjustments to primary‑key handling.
This article walks you through using the open‑source Java diagnostic tool Arthas to locate and analyze performance problems in Java applications, covering basic and advanced tracing, filtering, and flame‑graph visualization without modifying source code.
This article explains how to use the open‑source Java diagnostic tool Arthas to attach to a running JVM, view real‑time metrics, and employ the trace command to pinpoint and analyze time‑consuming methods without inserting manual timing code.
This article describes Qunar's comprehensive root cause analysis platform, detailing its background, data-driven fault categorization, architecture—including trace, runtime, middleware, and event analysis modules—and demonstrates its high accuracy and practical impact on reducing incident resolution times across microservice services.
The article details the background, architecture, core features, alert governance, trace integration, and cloud‑native evolution of Watcher, Qunar's internally built, highly scalable monitoring platform that unifies application‑level metrics, alerting, and observability across thousands of services and containers.
This article walks through a real‑world incident where an update to a MySQL row was not visible to a subsequent read, explains how SkyWalking tracing revealed the exact timing of binlog writing, MQ delivery, and query execution, and offers practical mitigation strategies.
This article introduces matrix differential calculus, explains its relationship to scalar and vector differentials, outlines key properties such as addition, multiplication, transpose, trace and determinant rules, and demonstrates how to compute matrix‑vector derivatives using trace tricks.
This article presents a layered approach to Go performance optimization, covering reasonable code practices, deliberate algorithmic and structural improvements, risky low‑level tweaks, and practical profiling techniques such as pprof, flame graphs, and trace, while providing concrete examples and actionable guidance.
This article explains how Android's built‑in Trace infrastructure can be leveraged on the device itself—by modifying native tag variables and hooking write calls—to run systrace without a PC, automatically instrument methods, and export data for Perfetto analysis.
This comprehensive guide explores Go performance optimization techniques, covering pprof profiling, trace analysis, GODEBUG diagnostics, and escape analysis, while providing practical code examples and real‑world case studies to help developers identify and resolve CPU, memory, and goroutine bottlenecks in production services.
This article explains the common pain points of locating anomalies in Kubernetes environments and presents a multi‑layer monitoring framework—trace, metrics, events, and alerts—along with best‑practice scenarios such as network performance, DNS issues, full‑link stress testing, external MySQL access, and multi‑tenant architectures.
This article dissects Python’s standard pdb module, explaining its pure‑Python implementation on top of bdb and cmd, the event‑driven tracing mechanism, core debugging commands such as step, next, continue, and how MNN Workbench embeds these capabilities for mobile AI development.
The article outlines why high‑load Go services need performance tuning and presents a systematic workflow—preparation, analysis with Linux tools and Go’s pprof/trace, targeted optimizations such as goroutine pooling, Redis MSET, efficient JSON handling and slice resizing—demonstrating how these changes boost throughput, lower latency, and stabilize memory usage while offering broader Go‑specific best‑practice recommendations.
Tracing a large method with Arthas caused a Metaspace OOM because constant‑pool rewrites inflated the Non‑Class space, each rewrite copying a massive StackMapTable, but preserving the original constant‑pool layout in Arthas stops the growth, and exposing off‑heap metrics aids production monitoring.
This article examines a slow MySQL 5.7 query, uses the optimizer trace tool to reveal why the optimizer chose an inefficient plan, explains the role of fan‑out estimation on information_schema tables, and shows how hints can be applied to correct the plan.
This article explains how to reproduce a slow‑response SpringBoot service using JMeter, then quickly locate the bottleneck in production by leveraging Alibaba's open‑source Arthas tool and its trace command to analyze the full call chain.
The article describes Alibaba’s flow‑based comparative testing and introduces Youku’s hot‑link coverage technique, which instruments method chains to automatically identify high‑frequency request paths, recommend them for regression testing, achieve full business‑link coverage without manual effort, and outlines future machine‑learning enhancements.
This article provides a comprehensive guide to using Arthas for Java backend debugging, covering installation, core trace and watch commands, OGNL expression syntax, practical examples for inspecting method calls, accessing static fields, and integrating with Spring context via advanced command techniques.
This guide explains how to use Arthas's trace command to capture Java method call paths, measure execution time for each node, handle class and method patterns, and highlights its limitations with lambda expressions, providing practical code examples and output analysis.
This guide explains why the Go trace viewer shows a blank page in Chrome, due to a disabled local API, and provides a step‑by‑step solution involving registering an origin‑trial token, modifying the trace source, and rebuilding the tool.
This article explains why Spring Boot 2.1.x stopped printing HTTP request mappings, shows that the log level was changed from INFO to TRACE, and provides the exact configuration needed to re‑enable detailed endpoint logs during application startup.
This article demonstrates how to enable DBLE's trace function, view detailed execution timing with `show trace`, and analyze performance bottlenecks across client, middleware, and MySQL layers, including a real‑world case study and optimization tips.
This article outlines how the Weibo advertising team evolved its operations from hand‑crafted scripts to a fully automated, AI‑enhanced platform, covering service governance, multi‑datacenter deployment, a custom automation system (Kunkka), effective alerting, full‑link tracing, and a massive metric monitoring solution built on big‑data technologies.
In this detailed case study, the author discovers a pornographic website, uses WHOIS and email reverse‑lookup to uncover a network of 28 related sites, registers an account, follows payment and contact clues, obtains the operator’s IP, phone and address, and ultimately pressures the owner until the sites disappear.
This article details Didi's 2016 full‑link load‑testing initiative, covering data‑isolation strategies, virtual driver/passenger tooling, trace‑based traffic marking, staged deployment tactics, and the operational insights gained from stress‑testing a massive ride‑hailing platform.
This article details Alibaba's mobile app operational practices, covering the challenges of client-side maintenance, their high‑frequency release pipeline, gray‑release mechanisms, monitoring, trace systems, remote logging, and rapid issue resolution to ensure stability and performance at massive scale.
This article explains Oracle deadlock fundamentals, classifies row‑level and block‑level (ITL) deadlocks, shows how to read trace files in both single‑instance and RAC environments, and provides step‑by‑step simulations that reveal root causes and practical mitigation techniques.
This article explains two practical techniques for identifying Oracle hidden parameters—querying internal views like GV$PARAMETER and X$ tables, and using AUTOTRACE to inspect execution plans—so DBAs can reveal undocumented settings, their defaults, and current values.
