Projects sharing features with Profiling

This project is a comprehensive performance programming guide and reference for the Go language, focusing on runtime efficiency and memory optimization. It provides a collection of patterns and techniques designed to increase execution speed by reducing garbage collection overhead and optimizing memory usage. The resource distinguishes itself through detailed reference implementations for memory optimization, such as escape analysis, object pooling, and structure memory alignment. It offers specific strategies for reducing binary size and improving CPU cache efficiency through structure memor

pyinstrument is a statistical sampling profiler for Python that records the call stack at regular intervals to identify performance bottlenecks with low overhead. It tracks wall-clock time, including I/O and external service calls, and provides specialized profiling for asynchronous programs by attributing time spent awaiting tasks to the calling function. The project converts captured execution data into interactive HTML reports, JSON, and flamecharts. It includes a call stack visualizer to simplify the analysis of execution paths and supports the profiling of individual cells within interac

This project is a comprehensive technical guide and diagnostic manual for analyzing memory, performance, and asynchronous behavior within Node.js applications. It provides detailed methods for asynchronous tracing, memory diagnostics, and performance analysis to resolve runtime errors and execution bottlenecks. The resource distinguishes itself by covering advanced diagnostic workflows, including the use of flame graphs for CPU profiling, the capture and comparison of heap snapshots for memory leak detection, and the mapping of asynchronous call stacks. It also provides technical guidance on

Speedscope is a web-based performance profiler that visualizes profiling data through interactive flamegraphs and timeline views. It ingests performance profiles from a wide range of sources, including Chrome, Firefox, Safari, Node.js, .NET Core, Instruments, Hermes, GHC, and Ruby, normalizing them into a common schema for unified analysis. The tool distinguishes itself with a canvas-based rendering engine that draws flamegraphs without DOM nodes for each frame, and a WebAssembly-based rendering pipeline for high-performance drawing. It offers left-heavy stack sorting to surface the most time

OpenCensus-go is an observability instrumentation library designed to capture and export telemetry data from distributed systems. It functions as a framework for application performance monitoring and distributed request tracing, allowing developers to track system health, latency, and the progression of requests across service boundaries. The project distinguishes itself through a modular architecture that decouples data collection from storage. By utilizing a pluggable exporter interface, it enables the transmission of metrics and trace data to a variety of external monitoring and analysis

Pinpoint is a distributed application performance monitoring and tracing system. It functions as an application performance monitor and topology visualizer designed to analyze the execution behavior of large-scale distributed applications. The system uses bytecode instrumentation to monitor applications without requiring changes to the original source code. It captures call stacks and request flows across interconnected services to visualize system dependencies and generate real-time architectural maps of communication patterns. The platform covers a broad range of observability capabilities

Bistoury is a production diagnostics tool for Java applications that provides a distributed debugging console, a performance profiler, and a runtime bytecode manipulator. It enables real-time application debugging and production diagnostics by analyzing running Java applications through dynamic instrumentation and state inspection. The system distinguishes itself through a remote agent manager that coordinates diagnostic connections and a runtime bytecode manipulator capable of redefining classes in memory without requiring process restarts. It features a web interface for capturing heap dump

This project is a set of hands-on labs for practicing cloud development, focusing on implementing web apps, functions, storage solutions, and containerized workloads. It provides a practical framework for developing solutions within the Azure ecosystem. The content covers a wide range of specialized cloud capabilities, including serverless development with HTTP and timer triggers, container orchestration using apps and instances, and API management for routing and transforming traffic. It also emphasizes identity and access management through OpenID Connect and managed identities. Additional

100 Go Mistakes is a reference book and code review companion that catalogues frequent Go programming anti-patterns and provides corrected implementations for each one. It covers a wide range of common pitfalls, from range loop variable capture and interface nil handling to error wrapping and map iteration randomization, helping developers recognize and avoid these issues in their own code. The project distinguishes itself by offering a structured, example-driven approach to learning idiomatic Go. It covers core design decisions such as when to use pointer versus value receivers, how to apply

Hotspot is a graphical user interface for analyzing and visualizing performance data captured by the Linux perf tool. It functions as a performance profiling visualizer and assembly-level profiler that maps performance costs to specific instructions synchronized with original source code. The project distinguishes itself through a remote symbol resolver that maps performance data from embedded targets to local host debug symbols and sysroots. It also includes a specialized off-CPU analysis tool designed to identify thread wait times and I/O blocks using kernel scheduler tracepoints. The tool

Snowflake is a high-concurrency RPC framework and distributed ID generation service. It provides the infrastructure to create unique, time-ordered identifiers across a network of servers and facilitates the development of network services designed to handle massive volumes of simultaneous requests. The system separates low-level transport logic from application behavior, allowing for the implementation of custom RPC protocols. It includes a distributed request tracing tool to visualize execution flow across network boundaries and a server lifecycle management interface to adjust logging level

btrace is a JVM dynamic tracing tool and performance profiler used for injecting safe instrumentation scripts into a running Java Virtual Machine without requiring a process restart. It functions as a Java agent framework and a Model Context Protocol server, exposing JVM diagnostic operations and tracing tools to large language models and AI assistants. The project distinguishes itself by enabling real-time code injection and bytecode-level instrumentation via a secure binary protocol. It ensures production stability through a static safety analysis engine that blocks unstable code patterns,

gdu is a command line disk usage analyzer and interactive disk profiler used to scan directories and visualize space consumption across file systems. It functions as a file system management tool that allows for the identification and removal of large files and folders to free up storage. The tool features a cursor-based interface for navigating directory structures and archives. It provides a storage cleanup workflow that enables the deletion of selected items directly from the analysis view, utilizing parallel execution to reduce I/O wait times. The application supports recursive directory

Devilbox is a containerized development environment that provides a reproducible suite of web servers, databases, and language runtimes managed through a unified configuration. It functions as a Docker-based local development stack for LAMP and MEAN software stacks and as a manager for switching between different versions of these services to match specific project requirements. The system distinguishes itself by automating local network orchestration. It includes a Docker-based virtual host manager that automatically maps local directories to custom domains and a local DNS and SSL orchestrat

Carp is a statically typed Lisp compiler that compiles Lisp-like syntax directly to C source code, enabling seamless integration with existing C libraries and low-level system programming. It manages memory deterministically at compile time using ownership tracking and linear types, eliminating garbage collection pauses and runtime overhead while ensuring type safety through an inferred static type system. The language distinguishes itself through compile-time macro expansion and metaprogramming capabilities, allowing code generation and transformation before final binary output. It enforces

Async-profiler is a sampling profiler for Java applications that tracks CPU time and stack traces across execution frames to identify performance bottlenecks. It is designed to capture profiling data without introducing timing bias. The project provides capabilities for JVM memory analysis to locate native and heap allocation hotspots and memory leaks. It also includes system contention analysis to identify resource bottlenecks through the tracking of contended locks and hardware performance counters. The tool converts raw profiling data into visual performance reports, including interactive

VizTracer is a Python runtime instrumentation system and execution profiler used to trace and visualize code execution. It functions as a multi-process performance analyzer and trace visualizer, providing an interactive timeline and flamegraph interface to identify performance bottlenecks and analyze call sequences. The project distinguishes itself by its ability to aggregate execution data from multiple threads, subprocesses, and asynchronous tasks into a single unified report. It also features live process instrumentation, allowing users to attach to and detach from running Python applicati

py-spy is a sampling profiler and process debugger for Python. It allows for the analysis of running processes to identify performance bottlenecks and diagnose hanging programs without requiring code changes or restarts. The tool operates by reading the memory of a running process from the outside, which enables non-invasive sampling and state collection without pausing execution. It can resolve binary symbols to capture performance data from native extensions written in compiled languages and generate visual flame graphs for both native extensions and subprocesses. The project provides capa

This project is a performance analysis tool for Ruby applications using the Rack interface. It monitors request execution times and resource usage, serving as a profiler for web applications to measure latency and identify bottlenecks. The tool provides specific analyzers for database query performance, memory allocations, and garbage collection statistics. It generates call stack flamegraphs to visualize time distribution across methods and renders speed badges and timing metrics directly onto HTML pages. The system covers broader performance profiling capabilities including custom code blo

This project is a diagnostic utility for monitoring and analyzing memory consumption in Python applications. It provides tools for tracking resource usage at the process level and performing detailed, line-by-line analysis to identify memory leaks and performance bottlenecks. The tool distinguishes itself through its ability to aggregate memory metrics across entire process trees, capturing the total resource impact of both parent and child processes. It supports time-series visualization of memory usage over the duration of a script, allowing for the identification of long-term consumption p

Samply is a cross-platform CPU sampling profiler and performance analysis utility. It consists of a command-line tool for recording process stack traces at regular intervals and a visual interface for analyzing the resulting execution data. The system includes a debug symbol resolver that maps raw memory addresses to human-readable function names using local or remote symbol information. It transforms recorded execution data into flame graphs and timelines to pinpoint function-level hotspots. The tool provides capabilities for CPU execution recording, stack unwinding, and symbol resolution a

vprof is a visual profiling tool for Python designed to identify execution bottlenecks and monitor memory consumption. It functions as a CPU and memory profiler that transforms performance data into interactive visualizations to analyze processor time and call stacks. The project distinguishes itself through a suite of visual diagnostics, including flame graphs for stack visualization and heatmaps that map execution frequency and duration directly onto source code. It also includes a remote performance monitor capable of capturing function-specific metrics from a running server and streaming

Scalene is a high-performance diagnostic utility designed to measure resource consumption during the execution of Python applications. It functions as a line-level monitor, providing granular insights that pinpoint the specific source code responsible for performance overhead. The tool distinguishes itself through statistical profiling that captures stack traces and resource usage without requiring manual instrumentation of the source code. It tracks CPU, GPU, and memory consumption by intercepting library-level calls and hardware driver commands, allowing for the analysis of both managed and

Laravel Debugbar is a diagnostic utility and development toolbar designed for the Laravel framework. It functions as an application profiler that monitors runtime performance, memory usage, and database queries to assist in identifying bottlenecks during the development process. The tool integrates directly into the browser, providing a visual interface that displays request data, application state, and performance metrics. By utilizing a collector-based architecture, it aggregates information from various internal framework events and middleware, allowing developers to inspect the applicatio

Tracy is a real-time performance profiling framework for C and C++ applications. It provides a software instrumentation library that captures high-resolution telemetry data, which is then visualized through a separate graphical interface to identify bottlenecks and resource allocation issues. The system utilizes a client-server architecture that enables remote profiling, allowing performance data to be captured on a target machine and analyzed on a workstation. It employs lock-free event logging and shared-memory ring buffers to minimize the overhead of data collection, ensuring that the main

React Scan is a diagnostic utility and performance auditor designed to monitor the rendering lifecycle of components within user interfaces. It functions as an automated analysis tool that tracks component re-render cycles and execution timing to identify performance bottlenecks in real time. The tool distinguishes itself by providing visual feedback through a persistent overlay injected directly into the application. By instrumenting the reconciliation process and observing component state and props, it highlights specific rendering patterns that contribute to performance degradation. This

Booster is an Android app build optimizer and bytecode manipulator designed to reduce binary size, fix system-level crashes, and improve application performance. It functions as an extensible build process plugin that modifies compiled class files and build artifacts to inject stability fixes and optimizations without altering the original source code. The project differentiates itself through low-level bytecode manipulation to patch OS-level bugs and manage thread pools during the compilation phase. It also provides a performance profiling toolkit to identify main-thread blocking operations