The Python team has launched the first beta of Python 3.15, introducing a stable free‑threaded ABI, lazy import syntax, the zero‑overhead Tachyon profiler, default UTF‑8 encoding and a faster JIT, while announcing that no further features will be added before the final 2026‑10‑01 release.
Python 3.15’s beta introduces lazy imports, a built‑in frozendict, a sentinel type, a low‑overhead statistical profiler, an upgraded JIT with 8‑13% speed gains, clearer error messages, type‑system tweaks, new list‑comprehension unpacking syntax, and a rollback to generational garbage collection.
The article introduces bpftime for GPU, a tool that extends eBPF's programmable, low‑overhead observation capabilities into GPU kernels, explains its implementation pipeline, compares its performance against Nsight and NVBit, and outlines future enhancements for GPU profiling.
A low‑traffic B2B service struggled to meet a 500 req/s demand, achieving only 50 req/s with high CPU usage; through systematic profiling, lock analysis, async refactoring, thread‑pool tuning, and eliminating costly Spring bean creation, the team dramatically improved response times and throughput, revealing deeper CPU‑usage mysteries.
Python’s reputation for slowness can be overcome by selecting the right tools—Numba, PyPy, CuPy, JAX, Ray, Joblib, async I/O, memory profilers, and big‑data frameworks—delivering speedups from 6× to over 50× with minimal or no code modifications.
The article walks through five concrete Python techniques—streaming file reads, generator expressions, __slots__, avoiding temporary objects in loops, and reusing buffers—showing code examples and measured memory reductions that together lowered overall RAM consumption by about 70%.
The article introduces nine lesser‑known Python packages—msgspec, glom, watchfiles, beartype, pyinstrument, duckdb, fakeredis, boltons, and returns—explaining how each tackles common prototyping pain points such as data serialization, file watching, runtime type safety, performance profiling, SQL querying without a server, mock Redis, utility functions, and explicit error handling, ultimately accelerating development by up to ten times.
This article dissects a non‑typical slow SQL on a massive e‑commerce order table, explains how index structures, B+Tree height, index‑pushdown, and filesort affect performance, and presents a step‑by‑step SOP for analyzing, optimizing, and safely deploying new indexes to eliminate the slowdown.
Python 3.15.0 alpha 3, the third of seven planned pre‑release versions, introduces a high‑frequency low‑overhead statistical profiler, makes UTF‑8 the default source encoding, adds a new C‑API for byte objects, and delivers clearer error messages, all while remaining a test‑only build.
Explore the comprehensive suite of built‑in and third‑party JVM tools—including jps, jstack, jmap, jcmd, JConsole, VisualVM, async‑profiler, Arthas, MAT, JProfiler, and APM solutions—detailing their core features, common commands, and practical use‑cases for monitoring, diagnosing, and optimizing Java applications in development and production.
In this detailed case study, the author walks through diagnosing and fixing severe throughput bottlenecks in a Java Spring‑based B2B service, covering lock contention, slow SQL, excessive logging, thread‑pool tuning, JVM memory adjustments, and the impact of bean creation on performance, ultimately achieving nearly a ten‑fold increase in requests per second.
When a Java application’s CPU spikes, this step‑by‑step guide walks you through a visual flowchart, essential Linux commands, thread‑dump analysis, common stack patterns, GC checks, flame‑graph profiling, and JVM tuning tips to quickly pinpoint and resolve the root cause.
This comprehensive guide walks you through Python performance optimization, covering profiling, algorithmic improvements, data‑structure choices, code‑level tricks, concurrency, memory management, third‑party libraries and a practical checklist to ensure your programs run faster and more efficiently.
Discover a comprehensive guide to dramatically improve Python program speed and memory usage through algorithmic refinements, profiling tools, concurrency models, and advanced techniques like generators, slots, and third‑party libraries, complete with real‑world code examples and performance benchmarks.
Python is praised for its clarity but often deemed slow; this article reveals twelve overlooked, sometimes unconventional techniques—from using enumerate instead of range loops to leveraging Numba, Polars, and mypyc—that can dramatically accelerate data pipelines, APIs, and scientific workloads without rewriting code in another language.
This article presents 20 production‑validated Go performance optimization tips, covering profiling, benchmarking, memory management, concurrency, and build‑time strategies, with clear principles and practical code examples to help engineers systematically improve Go application performance.
This article reveals ten often‑overlooked Python performance techniques—from using enumerate and array structures to leveraging Numba, Polars, and generators—showing how careful coding, profiling, and modern libraries can turn sluggish scripts into lightning‑fast production workloads.
This article explains Go's memory management mechanisms—including GC fundamentals, stop‑the‑world marking, tri‑color marking with write barriers, hybrid write barriers, and practical optimization techniques such as reducing heap allocations, using caches, concurrency patterns, and profiling tools—to help developers identify and eliminate performance bottlenecks.
This article walks through a PHP developer's systematic investigation and resolution of severe response‑time degradation, covering initial symptom detection, detailed profiling with microtime, Xdebug, XHProf, database query analysis, common PHP bottlenecks, and advanced optimizations such as OPcache, JIT, and preloading, ultimately achieving sub‑second latency and dramatically reduced server load.
After migrating thousands of Java cores to Rust, the team used jemalloc and pprof profiling to pinpoint why a few services only improved 10%, refactored the OSS client for reuse, and achieved up to 20% CPU reduction and significant memory savings, demonstrating the power of deep performance analysis in production Rust services.
This guide shows how to accelerate slow Python for‑loops by leveraging multi‑core parallelism, memory‑efficient generators, and a suite of profiling tools, providing step‑by‑step code examples and practical tips to identify and fix performance bottlenecks.
This article compiles ten practical Python data‑analysis tips, covering Pandas Profiling for quick exploratory reports, Cufflinks‑powered interactive visualisations, essential Jupyter magic commands, debugging shortcuts, and handy code snippets to boost productivity in data‑science workflows.
Profiling lets you pinpoint performance bottlenecks in Python code by measuring function execution times, call counts, and line‑by‑line costs, with tools like cProfile for quick overviews and line_profiler for detailed per‑line analysis, plus tips, visualizers, and complementary utilities to optimize scripts efficiently.
Learn how to write robust Go tests, leverage table‑driven and mock techniques, conduct precise benchmarks, profile with pprof, and apply advanced memory and concurrency optimizations—including sync.Pool and buffer reuse—to build high‑performance, maintainable Go applications.
Learn how hidden memory leaks, inefficient data structures, and poor import practices can slow Python scripts, and apply practical techniques—such as using weakref, generators, __slots__, lazy imports, and profiling tools—to dramatically reduce memory usage and speed up execution.
This article examines a MySQL case where importing a 4 GB tablespace takes 13 minutes, explains why SHOW PROFILE reports most of the time in the System lock stage, and dives into the internal implementation of SHOW PROFILE, data collection, calculation, and the tablespace import code path.
This article investigates the reasons behind Spring Boot's slow startup by examining the macro initialization flow, pinpointing time‑consuming stages such as environment preparation and bean refresh, and providing profiling code examples to help developers understand and optimize the boot process.
Optimizing Go services is essential for saving resources, improving stability, and delivering a responsive user experience, and this guide explains the underlying mechanisms, common pitfalls, and practical techniques—from GC tuning and concurrency control to I/O tricks and profiling tools—to help developers build high‑performance, production‑ready Go applications.
This article analyses why Spring Boot startup can take dozens of seconds, breaks down the most time‑consuming phases such as prepareEnvironment and refreshContext, shows profiling code and screenshots, compares a simple MVC demo with a real‑world project, and identifies external client connections and configuration loading as the biggest bottlenecks.
A ToB Java service that initially expected 500 requests per second fell to only 50 RPS under load, prompting a step‑by‑step investigation of CPU usage, lock contention, slow SQL, excessive logging, prototype‑scoped beans, and thread‑pool configuration, ultimately revealing how bean creation and async execution affect throughput.
This article explains how to measure, analyze, and improve CPU usage in software by examining key metrics, choosing efficient algorithms and data structures, leveraging compiler options, exploiting cache behavior and SIMD vectorization, and applying practical case studies with Linux profiling tools.
This article investigates the reasons behind Spring Boot's slow startup by tracing the execution timeline, examining the prepareEnvironment and refreshContext phases, profiling bean creation, configuration loading, and external client initialization, and provides sample Java code for measuring and visualizing each step.
The article recaps the 3rd eBPF Developer Conference in Xi'an, highlighting talks on BPF‑on‑MPTCP, system‑wide PGO, bperf, autonomous‑driving use cases, and AI‑driven observability, while sharing the author's insights on continuous profiling, SysOM, and future challenges of scaling eBPF with large models.
This article presents a comprehensive collection of practical PHP performance optimization methods—including code-level improvements, database query tuning, caching strategies, memory management, file operation reductions, profiling tools, and miscellaneous tips—to help developers build faster, more efficient web applications.
This article examines frequent performance optimization mistakes in Go programming—such as misunderstanding CPU cache, false sharing, data alignment, stack vs heap allocation, and inadequate use of profiling tools—provides concrete code examples, and offers practical guidelines to improve efficiency while maintaining code quality.
This article explains the Linux kernel’s perf utility, covering its architecture, key features such as lightweight event sampling, tracing, profiling and debugging, step‑by‑step installation, common commands with real code examples, and how to use perf and flame graphs to locate and optimise performance bottlenecks.
This article walks through concrete techniques to accelerate PyTorch training, covering mixed‑precision with torch.cuda.amp, profiling with torch.profiler, DataLoader tuning, torch.compile, distributed strategies like DataParallel and DDP, gradient accumulation, and advanced libraries such as Lightning, Apex, and DeepSpeed, plus model‑level optimizations and monitoring tips.
This article walks through a real-world investigation of memory leaks in a Rust-based frontend service, detailing common leak scenarios, profiling tools like tokio-console and jemalloc, load testing with k6, and the step-by-step analysis that uncovered regex misuse and cache bugs, ultimately stabilizing memory usage.
This tutorial showcases a comprehensive set of pandas techniques—including reading data from the clipboard, random sampling, multi‑condition filtering, handling missing values, string splitting, list expansion, multi‑function aggregation, slicing, descriptive statistics, categorical conversion, DataFrame styling, and profiling—to efficiently explore and transform DataFrames in Python.
This article presents ten practical Elasticsearch performance‑tuning techniques—including query DSL, deep pagination, mapping design, high‑cardinality aggregations, nested queries, script optimization, index templates, force‑merge, bulk writes, and profiling—each illustrated with concrete scenarios, code snippets, and step‑by‑step analysis to boost cluster speed and stability.
This article walks through ten practical techniques—including profiling with cProfile, using built‑in functions, list comprehensions, avoiding globals, leveraging NumPy, generators, multiprocessing, caching, selective imports, and upgrading Python—to dramatically accelerate Python scripts handling large data sets.
This article explores the One Billion Row Challenge, a Java benchmark that requires parsing a 13 GB file of one billion temperature records, and walks through baseline code, top‑ranked solutions, and a step‑by‑step performance tuning journey that reduces execution time from minutes to under two seconds.
This article explains how to use eBPF and perf events to trace NFS read/write system calls, capture kernel and user stack traces, resolve them to source locations via DWARF, and generate flame‑graph data that is pushed to Pyroscope for performance analysis.
This article walks through a systematic investigation of a slow‑starting Dubbo service built on Spring 3.2, using JProfile to pinpoint costly class‑loading checks, disabling unnecessary annotation processing, and resolving thread‑waiting issues caused by RabbitMQ and other components, ultimately reducing deployment time by about 40%.
This guide explains how to systematically diagnose Linux performance issues using tools such as top, vmstat, perf, and flame graphs, covering CPU, memory, disk I/O, network, and load analysis, and demonstrates a real-world nginx case study with step‑by‑step commands and visualizations.
This tutorial introduces core Python concepts—including dictionaries, sets, file and directory handling, regular expressions, logging, unit testing, performance profiling, and common third‑party libraries—providing clear explanations and ready‑to‑run code examples for each topic.
By deploying eBPF agents as DaemonSets that hook kernel network and performance events, the Xiaohongshu observability team extended cloud‑native monitoring from the application to the kernel, delivering real‑time traffic analysis and low‑overhead continuous profiling for C++ services, aggregating data into centralized collectors for dashboards, flame‑graphs, and rapid root‑cause diagnosis.
This article explains why Python may run slower than compiled languages, introduces methods for detecting performance bottlenecks, and presents six practical techniques—including using timeit, memory_profiler, line_profiler, built‑in functions, f‑strings, list comprehensions, and lru_cache—to significantly speed up Python programs.
This article explores why MySQL queries can become sluggish beyond just missing indexes, covering the full query execution flow, profiling tools, index pitfalls, connection limits, buffer pool sizing, and practical configuration tips to dramatically improve performance.
The article explains how zero‑intrusion eBPF provides full‑stack, high‑quality observability data that, when combined with large language models, enables AI‑driven agents to automate ticket handling, change impact analysis, and vulnerability triage, dramatically improving operational efficiency.
This article explains how containerization and Kubernetes gave rise to micro‑services, why observability (metrics, logging, tracing, profiling) became essential for managing their complexity, and how OpenTelemetry’s unified data model enables seamless integration of these signals across modern cloud‑native stacks.
This article provides a comprehensive guide to Go performance optimization, covering fundamental strategies such as proper Goroutine usage and memory leak avoidance, advanced techniques like concurrency pattern tuning and efficient use of interfaces, as well as essential profiling tools and practical case studies.
This article introduces the Perfetto web UI, explains its four main sections, walks through essential navigation shortcuts, and shares practical techniques such as viewing wake‑up sources, critical paths, pinning threads, inspecting CPU info, buffer usage, timeouts, and logs to help Android developers efficiently analyze performance traces.
This article explains how Go's runtime/trace package and the new APIs introduced in Go 1.20 and 1.21 reduce overhead, improve scalability, simplify trace control, and better link trace data to source code, making concurrency debugging more effective for Go developers.
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.
This article explains how to use the macOS Instruments Time Profiler to perform CPU‑time profiling of Rust programs, demonstrates a sample π‑calculation benchmark, shows the required Cargo configuration, walks through recording and inspecting trace files, and applies the method to diagnose performance regressions in the Rspack project.
The article details how a high‑traffic Helios scoring service, originally taking several seconds to return a day’s worth of data, was profiled with Arthas and iteratively optimized through four code revisions, ultimately reducing latency to around 60 ms while exposing key performance pitfalls such as object creation, date formatting, and trivial list operations.
This article introduces ten practical Python debugging tools and techniques—including breakpoints, print statements, logging, assertions, pdb, traceback, cProfile, timeit, memory_profiler, and pdbpp—providing code examples and explanations to help developers diagnose, trace, and optimize their code effectively.
The article shows how to write high‑performance Go code by spotting bottlenecks, using go‑test benchmarks and pprof profiling, and applying optimizations such as avoiding reflection, preferring strong‑type conversions, selecting appropriate map implementations, zero‑allocation string/slice tricks, efficient loops, generics, stack allocation, data alignment, pre‑allocation, and suitable lock primitives.
This article examines the causes of slow startup in large Spring applications, presents visual analysis techniques and a toolbox—including Arthas, JVM Sandbox, Async Profiler, and Spring Startup Analyzer—and provides concrete installation, configuration, and optimization procedures that can reduce startup time by 70‑80% while improving development efficiency and resource utilization.
This guide explains how to install and use Reli, a PHP‑written sampling profiler and VM state inspector, to detect bottlenecks, trace execution, locate memory issues, and generate flamegraphs for PHP processes without modifying the target program.
This article documents how the Helios scoring service, which originally took several seconds to retrieve a day's worth of data, was optimized from hundreds of milliseconds down to tens of milliseconds by analyzing Arthas trace data and applying multiple code and algorithm improvements.
This article explores the challenges of slow Spring application startup in large Java projects, presents detailed analysis techniques, visualizations, and a suite of tools—including Spring Startup Analyzer, Arthas, and Async Profiler—to identify bottlenecks, and provides step‑by‑step optimization methods such as bean lazy loading, jar slimming, and configuration tweaks that can reduce startup time by up to 80% while improving resource utilization and deployment efficiency.
The article details a step‑by‑step investigation of a Spring Boot service that failed to meet a 500 requests/second target, analyzes slow SQL, thread‑pool misconfiguration, excessive logging, and high CPU usage, and presents concrete optimizations that roughly doubled throughput.
This article presents several advanced Python debugging and performance optimization techniques—including using assert statements, interactive pdb debugging, cProfile profiling, generator expressions, dictionary/set lookups, appropriate data structures, and functools.lru_cache caching—to help developers write more efficient and reliable code.
This article introduces six practical techniques—including profiling with timeit, memory and line profilers, using built‑in functions, f‑strings, list comprehensions, and lru_cache—as well as algorithm and data‑structure choices to help Python developers significantly boost the execution speed of their code.
The article details a step‑by‑step investigation and optimization of a Java backend service that initially delivered only 50 requests per second under load, covering profiling, slow‑SQL fixes, thread‑pool tuning, JVM memory adjustments, and Spring bean creation overhead to approach the target 500 req/s.
Faced with a client demanding 500 requests per second, I discovered my Java backend only handled 50 /s due to slow SQL, excessive logging, thread‑pool misconfiguration, and costly Spring bean creation; by adding latency alerts, async execution, reducing thread counts, and fixing Redis bean scope, throughput nearly doubled.
A Java‑based ToB service struggled to meet a 500 requests‑per‑second target, revealing hidden bottlenecks such as slow SQL, excessive prototype bean creation, logging overhead, and misconfigured thread pools, which were systematically identified and resolved to boost throughput and reduce latency.
This article explains how to improve PHP application response speed and throughput through code, configuration, and server optimizations, covering caching strategies, precompilation, efficient data structures, loop reduction, high‑performance servers, and profiling tools such as XHProf and Blackfire.
A Java‑based B2B service struggled to meet a 500 requests‑per‑second target, revealing CPU saturation, slow SQL locks, excessive logging, prototype‑scoped beans, and suboptimal thread‑pool settings, which were systematically diagnosed and mitigated to roughly double the throughput.
This article documents a step‑by‑step investigation of a Java Spring backend that initially achieved only 50 requests per second under load, detailing how slow SQL, excessive logging, thread‑pool misconfiguration, bean‑creation overhead and CPU‑bound operations were identified and mitigated to roughly double the throughput while reducing response latency.
This article explores common reasons MySQL queries become sluggish—beyond missing indexes—including connection limits, insufficient InnoDB buffer pool size, and query cache issues, and provides practical steps such as profiling, using FORCE INDEX, adjusting max_connections, enlarging the buffer pool, and tuning connection pools to improve performance.
This article explains the principles of Profile‑Guided Optimization, outlines the three‑step workflow, demonstrates practical PGO implementations for Go and C++ with code examples and devirtualization techniques, and presents benchmark results showing significant latency reduction and throughput improvement.
The article details a step‑by‑step investigation of a Java Spring backend that initially achieved only 50 req/s under load, identifies bottlenecks such as slow SQL, excessive logging, thread‑pool misconfiguration and costly Spring bean creation, and demonstrates how targeted optimizations roughly doubled throughput while reducing response times.
This article documents how the Helios scoring service, which processes hundreds of thousands of data points per day, was progressively optimized from several seconds to tens of milliseconds by analyzing Arthas trace data, refactoring loops, reducing object creation, and improving date handling, ultimately revealing that database access becomes the remaining bottleneck.
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.
By patching ART’s internal debug flags to enable JVMTI on non‑debuggable apps, the authors created TBProfiler, a lightweight Android profiling tool that uses JVMTI’s event callbacks to trace method calls, thread and exception events, lock contention, and memory allocations, producing a compact mini‑hprof for production‑grade runtime monitoring and analysis.
The article walks through the complete eBPF injection workflow—opening a skeleton, loading and JIT‑compiling the program with bpf_object__load_skeleton, attaching it to a kernel kprobe via perf_event_open_probe and bpf_link_create, and finally triggering the probe so the JIT‑compiled eBPF code runs inside the kernel.
The article outlines practical GPU inference optimization for NetEase’s news recommendation, covering model analysis with Netron, multi‑GPU parallelism, memory‑copy reduction, batch sizing, TensorRT conversion and tuning, custom plugins, and the GRPS serving framework to achieve significant latency and utilization gains.
IntelliJ IDEA 2023.2 introduces a suite of enhancements—including a Run‑window profiler, inline return breakpoints, automatic test reruns for Gradle/Maven/JPS, WSL‑based Tomcat debugging, TLS‑enabled gRPC requests, Swagger and OpenAPI support, shared index generation, and numerous UI and code‑completion upgrades—streamlining Java development workflows.
This guide explains how to use the open‑source Java diagnostic tool Arthas to attach to a running Spring Boot application, view real‑time metrics, and trace method execution in order to pinpoint and optimize performance‑critical code sections without modifying the source.
This article explains how to quantify Android UI jank by calculating a jank rate, uses Jetpack's JankStats library to collect frame‑level metrics, and presents two practical methods—stack dumping and bytecode instrumentation—to pinpoint the slow functions causing stutter, complete with Kotlin code examples and performance considerations.
A high‑traffic Go service suffered intermittent availability drops below 99.9% due to timeouts, and after extensive profiling, tracing, and heap analysis the root cause was identified as a memory‑leak bug in the third‑party gcache LFU implementation, which was fixed by upgrading the library.
This article explains why traditional log‑based performance debugging is inefficient, introduces continuous profiling with the open‑source Pyroscope tool, and provides step‑by‑step instructions for installing Pyroscope on Kubernetes, integrating it with Python, .NET, and Go microservices, and visualizing low‑overhead flame graphs to optimize code and reduce cloud costs.
This guide explains how to use the open‑source Pyroscope continuous profiler on Kubernetes to collect, store, and visualize performance data from microservices written in Go, Python, .NET, and more, enabling developers to pinpoint bottlenecks, reduce CPU and memory usage, and lower cloud costs.
This article explains why SQL queries become slow, how to capture slow queries using MySQL's slow query log, and provides detailed step‑by‑step methods for analyzing execution plans, profiling, optimizer tracing, and I/O parameters to identify and resolve performance bottlenecks.
This article analyzes the challenges of stitching multi‑modal transport routes in Ctrip's backend, identifies performance bottlenecks through monitoring, profiling and benchmarking, and presents a series of optimizations—including code refactoring, indexing, multi‑way merge, multi‑level caching, preprocessing, multithreading, lazy computation, and JVM tuning—that collectively reduce latency and resource consumption.
This article explains how Android's JVMTI (ART Tooling Interface) works, details its monitoring capabilities, shows how to overcome debug‑only restrictions by modifying JDWP and Runtime flags, and describes the TBProfiler tool that leverages JVMTI for method, thread, exception, lock, and memory profiling in production apps.
This article walks through the evolution of a system’s performance concerns, defines speed and pressure dimensions, explains how to calculate RT, QPS and concurrency, compares QPS with TPS, and provides step‑by‑step methods using tools like Arthas, JMeter and JVM diagnostics to identify and fix CPU, memory and pressure issues before applying layered optimization strategies.
This article explains how to automate Elasticsearch slow‑query inspection by extracting DSL from slow‑log files, deduplicating queries, using the Profile API for detailed execution analysis, and applying rule‑based optimizations such as avoiding term‑long and range‑keyword queries to improve backend performance.
This comprehensive guide walks Go developers through performance tuning fundamentals, recommended profiling tools, code-level optimizations, and real-world case studies, offering actionable insights to measure, diagnose, and improve CPU, memory, and concurrency efficiency in high‑throughput Go services.
This article details a real‑world Kubernetes control‑plane outage where kube‑apiserver repeatedly OOM‑killed, examines cluster metrics, logs, heap and goroutine profiles, explores root‑cause hypotheses such as etcd latency and DeleteCollection memory leaks, and offers practical prevention steps.
This article details how to use NVIDIA profiling tools, mixed‑precision training, operator fusion, kernel optimizations, and INT8 quantization to identify and eliminate performance bottlenecks in Swin Transformer models, achieving up to 2.85× training speedup and up to 7.34× inference acceleration on modern GPUs.
This article introduces an open‑source MySQL Shell plugin collection, explains how to install it on Fedora, and demonstrates practical usage of its replication, locks, profiling, InnoDB fragmentation, and binlog‑check components with concrete command‑line examples and output.
This article explains system performance concepts such as throughput and latency, describes how to design and run performance tests, outlines methods for locating bottlenecks at the OS, code, network, and database levels, and presents practical optimization techniques ranging from algorithmic improvements to I/O and TCP tuning.
This guide explains how to enable and read MySQL's slow query log, use EXPLAIN, profiling, and optimizer trace to pinpoint inefficient SQL, and presents ten classic problem patterns—such as implicit conversion, left‑most prefix violations, deep pagination, large IN lists, and file‑sort order‑by—with concrete code examples and practical optimization steps.
This article outlines a comprehensive approach to system performance optimization, covering key metrics, profiling tools, common bottlenecks in CPU, memory, disk and network I/O, and practical guidelines for continuous monitoring and improvement in production environments.
This article explains why MySQL queries can become sluggish, covering the full query execution flow, profiling techniques, index pitfalls, connection‑pool limits, buffer‑pool sizing, and additional performance tricks, while providing concrete commands and code examples for each optimization step.
This article explains why traditional pprof sampling often fails in production, introduces Go's built‑in profiling tools and runtime.pprof, defines practical rules for triggering automatic sampling based on resource spikes, and demonstrates using the open‑source Holmes library and Docker demo to collect self‑diagnostic profiles.
