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LINE株式会社は、2023年10月1日にLINEヤフー株式会社になりました。LINEヤフー株式会社の新しいブログはこちらです。 LINEヤフー Tech Blog LINE株式会社OA SREチームのhasebeです。 先日、私の担当するプロダクトにてPyroscopeというツールを導入しました。このブログではなぜPyroscopeを導入したのか、導入した結果どういった利点があったのかなどについてご紹介したいと思います。 Pyroscopeとは Pyroscopeとは、Continuous Profilingを実施することができるOSSのツールです。 Profilingについては特に説明は不要でしょう。ざっくりいうと、CPUやメモリ等のリソースをプログラム中のどこが多く消費しているのか(= ボトルネック)を突き止める手法のことを意味します。 一般的には、なにか問題が起きたときに手動でPr
【Go】profefeでContinuous Profilingをやっていく話 - Mirrativ Tech Blog
こんにちは、サーバーエンジニアの牧野です。 今回はGoで開発しているアプリケーションでContinuous Profilingを実践するために導入した profefe を紹介したいと思います。 Continuous Profilingとは Continuous Profilingとは、ざっくり言うと本番環境で継続的にプロファイリングすることを指します。Continuous Profilingができると、本番環境でのみ発生するパフォーマンスの問題を捉えることができたり、継続的にプロファイリングすることで問題が発生する前後の状態を比較することができます。 Goには pprof というプロファイリングのための標準パッケージがあり、プロファイリング自体は容易に行うことができますが、Continuous Profilingを実現するとなると、以下のような課題と向き合う必要があります。 本番環境でオー
GitHub - evilsocket/uroboros: A GNU/Linux monitoring and profiling tool focused on single processes.
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Python未導入環境においてPandasGUIとpandas-profilingを使用可能なEDAツール『Pandas Anywhere』を作ってみた - Qiita
Python未導入環境においてPandasGUIとpandas-profilingを使用可能なEDAツール『Pandas Anywhere』を作ってみたPythonpandasデータ分析TkinterPyInstaller はじめに この度、PythonライブラリであるPandasGUIとpandas-profilingを、Pythonをインストールしていない環境においても使用できるEDAツール**『Pandas Anywhere』**を作成したので公開します。本ツールを使用することで、誰でもどこでも簡易にビッグデータ※の分析が可能となります。 ※本記事でいうビッグデータとはMicrosoft ExcelやAccessで扱うのが困難な大容量データを指します。 作成の動機 前回書いた記事「Python初学者のためのPandas100本ノック」では、知り合いにPython・機械学習を始める人が
Nic Jansma (@nicj) is a software developer at Akamai building high-performance websites, apps and open-source tools. Table of Contents The JS Self-Profiling API What is Sampled Profiling? Downsides to Sampled Profiling API Document Policy API Shape Sample Interval Buffer Who to Profile When to Profile Specific Operations User Interactions Page Load Overhead Anatomy of a Profile Beaconing Size Comp
Datadog Profilerで継続的なProfilingを実施できるようにした話 - unless’s blog
おまえだれ? 株式会社Kyash でサーバサイドエンジニアをしている @uncke__ko です Fundsチームに所属していて主にお金の入出金部分を担当しています これは何? Kyashではシステムの監視やモニタリングにDatadogを使用しています www.datadoghq.com Datadogにある Continuous Profilerという機能を使い、継続的にProfilingできる環境を整えたのでその話になります docs.datadoghq.com Profilerとは プロファイラとはアプリケーションの性能を解析するためのツールです ソフトウェア開発をしていると下記のようなことが度々起こった経験が1度はあると思います OOMが発動するようになる latencyが遅くなる アプリケーションの動作が徐々に重くなる Profilerがあると、このようなときにアプリケーションの
tl;dr In our continued efforts to expand and improve your profiling experience, we are excited to announce new additions to our language support: Ruby and Python. All the features that are described in this blog post have recently been released as part v0.26.0 of Parca Agent. While currently these language supports are in beta, they can be enabled using the --enable-ruby-unwinding and --enable-pyt
April 8, 2020 Today, we'll look at measuring React component render performance with the React Profiler API, measuring interactions with React's new experimental Interaction Tracing API and measuring custom metrics using the User Timing API. For demonstration purposes, we'll be using a Movie queueing app. The React Profiler API The React Profiler API measures renders and the cost of rendering to h
Rubyのパフォーマンスプロファイリングの改善 / Enhancing performance profiling for Ruby
Ruby Association Activity Report https://www.ruby.or.jp/ja/news/20240520_2 https://osyoyu.com/blog/2024/07/18/213247
A graphical Python profiler that provides a clear view of your application's execution while being both low-overhead and easy to use. Install Live Demo Learn More Quickly see everything your application is doing, without modifying any of your code FunctionTrace can be called on existing Python applications with absolutely no modifications, and provides a complete and precise view of your applicati
Knowing how to profile a React application to improve real-world performance is a good tool in any front-end developer’s toolkit. The Profiler API allows us to do just that with insights on why and how long our components are rendering for. We can use the profiling data to find unnecessary and expensive renders that may be impacting performance negatively. Thankfully, it’s not that complicated. Le
GitHub - yahoo/kubectl-flame: Kubectl plugin for effortless profiling on kubernetes
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RubyKaigi 2024 - The depths of profiling Ruby (Day1) - Techouse Developers Blog
こんにちは、株式会社Techouse バックエンドエンジニアの @nodematerial です。 今回は、RubyKaigi 2024 1日目のセッション「The depths of profiling Ruby」を聴講したので、その内容をブログにまとめさせていただきます。 プロファイラとは プロファイラはコードのパフォーマンスを調べるためのツール全般を指します。 プロファイラには、コードの中で最も遅いところ(ホットスポット)を特定するという大きな役割があります。 Pf2 について スピーカーの osyoyu さんは Pf2 というプロファイラを作成されており、発表ではPf2の実装を中心に解説が行われました。 Pf2 に特徴的な機能 マルチスレッドのプロファイリング GCの所要時間や、GVL によって有効活用できなかった時間を表示することができ、スレッド毎の実行状況の統計を取ることができ
Sentry Profiling now supports Browser Javascript, React Native, and Ruby
Sentry Profiling now supports Browser Javascript, React Native, and Ruby Profiling is an essential component of a developer’s toolkit for identifying and addressing the thorniest performance bottlenecks. Whether you’re a backend developer looking to cut down cloud infrastructure costs, a frontend developer trying to speed up page load times, or a mobile app developer working to ensure smooth scrol
2024/08/30 Optimizing UE5: Advanced Rendering, Graphics Performance, and Memory Management | Unreal Fest 2024 2024/08/30 Performance Capture for Games, Movies, and Transmedia | Unreal Fest 2024 2024/08/28 Dev Iteration: How to Improve Developer Efficiency in Unreal 5.4 | Unreal Fest 2024 ZenServer 2024/08/28 Budgeting in a Post-Polycount World | Unreal Fest 2024 2024/08/27 How Small Open Doors Can
sort Linux kernel tracing Capture high frequency ftrace data: scheduling activity, task switching latency, CPU frequency and much more nfc Userspace profilers and extra probes Native heap profiling, Java heap profiling, pollers for /proc stat files
package main import "github.com/pyroscope-io/client/pyroscope" func main() { pyroscope.Start(pyroscope.Config{ ApplicationName: "simple.golang.app", ServerAddress: "http://pyroscope-server:4040", Logger: pyroscope.StandardLogger, ProfileTypes: []pyroscope.ProfileType{ // these profile types are enabled by default: pyroscope.ProfileCPU, pyroscope.ProfileAllocObjects, pyroscope.ProfileAllocSpace, py
November 1, 2020 It's historically been difficult for web developers to understand how the JavaScript in their apps performs in a wide range of cirumstances on real user devices. They've had to instrument code with poorly performing profiling hooks that could slow down page execution, without having the ability to collect stack samples efficiently. This is where the proposed native JavaScript self
Parca - Open Source infrastructure-wide continuous profiling
by method name, class name, and line number. Without complex overhead, in any language or framework. IO31%Address:0x4553e01415161718192021222324252627282930313233343536 < n> ( std::array<std::array<, n>, n> &board, &row, &col) { i = , j = ; (i = ; i < col; i++) { (board[row][i]) { ; } } // Check upper diagonal on left side (i = row, j = col; i >= && j >= ; i--, j--) { (board[i][j]) { ; } } (i = ro
GitHub - grafana/pyroscope: Continuous Profiling Platform. Debug performance issues down to a single line of code
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In this post, we are going to use a brand-new (at the time of writing) and still somewhat experimental profiling method in GHC to show how to identify a memory leak and the code causing it. This new profiling method, implemented by Matthew, allows us to map heap closures to source locations. A key feature of this new profiling mode is that it does not require a profiled build (i.e. building with -
Profiling template metaprograms with C++ Build Insights - C++ Team Blog
The use of templates in C++ programs can sometimes lead to longer builds. C++ Build Insights puts tools at your disposal for analyzing template usage patterns and their associated impact on build time. In this article, we show you how to use the vcperf analysis tool and the C++ Build Insights SDK to understand and fix problematic template patterns. We demonstrate the practical use of these tools t
Guide to Profile-Guided Optimization: inlining, devirtualizing, and profiling
Guide to Profile-Guided Optimization: inlining, devirtualizing, and profiling
What is continuous profiling?
Community post by Uchechukwu Obasi Coming from a background working as a frontend developer at Grafana I’m no stranger to open source performance monitoring. I was part of a team that was responsible for the overall user experience of Grafana and performance was one of the key considerations. Along the line, I learned about a debugging technique known as profiling for monitoring application perfor
GitHub - hschne/rails-mini-profiler: Performance profiling for Rails, made simple 🦔
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Project announcement by OpenTelemetry maintainers In 2023, OpenTelemetry announced that it achieved stability for logs, metrics, and traces. While this was our initial goal at the formation of the project, fulfilling our vision of enabling built-in observability for cloud native applications requires us to continue evolving with the community. This year, we’re proud to announce that exactly two ye
Subscribe to my newsletter, support me on Patreon or by PayPal donation. I would love to hear your feedback! I wrote this blog series for the second edition of my book titled “Performance Analysis and Tuning on Modern CPUs”. It is open-sourced on Github: perf-book. The book primarily targets mainstream C and C++ developers who want to learn low-level performance engineering, but devs in other lang
Hive Distributed Profiling System in Treasure Data - Japanese version #tdtechtalk
『Hive Distributed Profiling System in Treasure Data』の日本語版スライド。 English version -> https://speakerdeck.com/okumin/hive-distributed-profiling-system-in…
In 2023, OpenTelemetry announced that it achieved stability for logs, metrics, and traces. While this was our initial goal at the formation of the project, fulfilling our vision of enabling built-in observability for cloud native applications requires us to continue evolving with the community. This year, we’re proud to announce that exactly two years after the Profiling SIG was created at KubeCon
探索的データ分析の第一歩に便利なpandas-profilingの導入と概要 - Qiita
はじめに 機械学習のデータの前処理工程において、pandas等を用いて、いろいろとデータを眺めながら探索的データ分析をすると思いますが、データの可視化を1コマンドで簡単にやってくれるツール｢pandas-profiling｣というものがあるので、それを紹介します。
GitHub - city-mobil/insecticide: A tool suite for Redis configuration profiling
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Tutorial: Profiling Rust applications in Docker with perf | Blog | Guillaume Endignoux
With Moore’s law coming to an end, optimizing code to avoid performance pitfalls is becoming more and more useful. To this end, programming languages like Rust are designed to produce fast and memory-efficient programs out-of-the-box. When that is not sufficient, profilers like perf are useful to measure where the code is slow and therefore which algorithms and data structures should be optimized.
It’s been a while since ScyllaDB Rust Driver was born during ScyllaDB’s internal developer hackathon. Since then, its development and adoption accelerated a lot. We’ve added many new features and published a couple of releases on crates.io. Along the way, we also stumbled upon a few interesting performance bottlenecks to investigate and overcome — read on for more details. First Issue Arises A few
GitHub - reloadware/reloadium: Hot Reloading and Profiling for Python
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GitHub - k0kubun/perf-profile: Profiling C code with Linux perf made easy
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Identify bottlenecks, improve resource utilization, and reduce ML training costs with the deep profiling feature in Amazon SageMaker Debugger | Amazon Web Services
AWS Machine Learning Blog Identify bottlenecks, improve resource utilization, and reduce ML training costs with the deep profiling feature in Amazon SageMaker Debugger Machine learning (ML) has shown great promise across domains such as predictive analysis, speech processing, image recognition, recommendation systems, bioinformatics, and more. Training ML models is a time- and compute-intensive pr
Announcing Grafana Phlare, the open source database for continuous profiling at massive scale | Grafana Labs
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GitHub - DataDog/go-profiler-notes: felixge's notes on the various go profiling methods that are available.
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Focused Session: Performance profiling for Web Applications with Sam Saccone
Sam Saccone presents an overview of how to use Chrome DevTools to understand a Web application's performance bottlenecks. In this presentation, Sam goes over a few different workflows that help us to answer the question "Why is this slow and how can I fix it". Slides: https://t.co/pumumktAEG
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Pyroscopeを使ったContinuous Profilingの活用事例
JS Self-Profiling API In Practice
Profiling Python and Ruby using eBPF
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The definitive guide to profiling React applications
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OpenTelemetry announces support for profiling
Async Rust in Practice: Performance, Pitfalls, Profiling
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