Projects sharing features with Pyinstrument

This project is a performance analysis suite for Python applications, providing tools for both application-wide profiling and granular code benchmarking. It enables developers to identify execution bottlenecks and measure function call frequency through a combination of deterministic tracing and statistical sampling methods. The tool distinguishes itself by offering a terminal-based interactive interface that allows for real-time navigation and filtering of complex call stacks. It supports non-intrusive data collection through signal-based process interception, enabling performance monitoring

BCC is an eBPF development toolkit and tracing framework used for monitoring and analyzing the Linux kernel. It functions as a performance analysis tool and debugging utility to capture system events, measure kernel latency, and provide network observability. The project distinguishes itself by providing a build system that integrates with LLVM to compile C-like code into BPF bytecode at runtime. It utilizes BPF Type Format data for relocations to maintain cross-kernel compatibility and extracts kernel headers to ensure the generated programs match the specific kernel version. The toolkit co

gperftools is a collection of specialized tools for profiling CPU usage, detecting memory errors, and providing high-performance memory allocation. It provides a memory profiling toolkit for C++ applications, including a sampling CPU profiler and a heap profiler for analyzing consumption patterns. The project includes a high-performance memory allocator designed as a multi-threaded replacement for standard allocation to reduce contention and improve execution speed. It further provides a memory debugger to identify illegal memory access and double frees. The toolkit covers broad diagnostic c

snacks.nvim is a comprehensive collection of quality-of-life plugins and utilities designed to extend the core functionality of Neovim. It serves as a multi-purpose toolkit providing a UI framework, navigation enhancements, and integrations with external services. The project distinguishes itself by combining a wide array of specialized tools into a single suite, including a picker-based file explorer, a deep GitHub integration for managing issues and pull requests, and a set of development utilities for profiling Lua performance and inspecting code execution. Its broader capability surface

This project is a learning guide and collection of study notes designed to teach Node.js backend development. It provides a comprehensive core API reference and practical demonstrations for implementing server-side logic, network programming, and system APIs. The guide specifically covers advanced technical domains including process management for scaling applications via clusters and child processes, as well as network programming for building TCP, UDP, and HTTP services. It also includes detailed instructional material on security implementation, focusing on cryptographic hashing and encryp

Django Silk is a profiling and inspection toolset for Django applications designed to capture SQL queries, HTTP request data, and execution timing for diagnostics. It functions as a performance profiler and debugging middleware that records runtime execution data to provide a comprehensive overview of application behavior. The system includes a database profiler for identifying slow operations through detailed timing data and an HTTP request inspector for reviewing headers, bodies, and network traffic via a web interface. It allows for the reproduction of specific server requests through gene

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

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

Orbit is a set of specialized tools for C and C++ performance profiling, binary symbol mapping, and remote process and thread analysis. It provides a system for analyzing execution time and resource usage, utilizing a call graph visualizer to map function entries and exits into hierarchical execution flows for individual threads. The project distinguishes itself with a remote process profiler capable of capturing performance data from applications running on remote hosts. It also includes a thread scheduling analyzer that tracks context switches and processor core utilization to visualize thr

Memray is a memory profiler for Python that tracks heap allocations in both Python code and native C or C++ extensions. It captures memory events by hooking into the language runtime and traversing call stacks, providing a comprehensive view of how an application consumes memory. The tool is designed to minimize performance impact on the target application by using thread-local buffering and streaming data to an external process or file. The project distinguishes itself through its ability to monitor complex, multi-threaded systems and child processes in real-time. It provides diagnostic util

Walk is a comprehensive framework for building native Windows desktop applications. It functions as a GUI library and Windows API wrapper, providing a toolkit of native widgets and a declarative layout system for developing high-performance user interfaces. The project is distinguished by its data-binding framework, which uses reflection and string-based property paths to synchronize data sources with interface widgets. It also provides specialized support for high-DPI interface scaling and an optimized native message loop to reduce runtime overhead. The toolkit covers a wide range of capabi

Warp is a Python framework that JIT-compiles Python functions into CUDA kernels for GPU-accelerated parallel computation, with built-in automatic differentiation and multi-framework array interoperability. At its core, it provides a GPU kernel compilation system that enables writing and executing custom GPU kernels directly from Python, while supporting automatic gradient computation through those kernels for integration with machine learning pipelines. The framework also includes tile-based cooperative computing, where thread blocks partition into tiles for shared-memory and tensor-core opera

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

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

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

bpftrace is a high-level eBPF tracing tool and kernel instrumentation framework for Linux. It provides a tracing language to instrument kernel and user-space events without recompiling the system, functioning as a dynamic system profiler and event aggregator. The project enables dynamic system tracing and Linux kernel observability by capturing tracepoints and dynamic probes in real time. It allows for kernel data inspection and runtime process debugging by accessing internal data structures and filtering specific process events. Its capability surface covers system performance analysis, inc

gprof2dot is a performance graph generator and visualizer that converts gprof GNU profiler execution profiles into Graphviz DOT files. It transforms raw profiler data into a directed graph to map function call hierarchies and identify software bottlenecks. The tool employs heuristic-based color mapping to highlight performance hotspots by assigning colors to nodes and edges based on execution time percentages. It also supports differential profile analysis, allowing for the comparison of two distinct execution graphs to identify changes in timing and call counts between runs. To improve visu

Visualize call graph of a Go program using Graphviz

php-timer is a set of utilities for measuring, tracking, and formatting the execution duration and memory consumption of PHP code segments. It functions as an execution timer and performance profiling utility to analyze resource consumption. The project provides capabilities to track the duration between start and stop triggers in seconds, milliseconds, or nanoseconds. It also includes a resource usage tracker that converts raw execution timestamps and memory bytes into human-readable text strings for reporting. The tool covers performance profiling, resource monitoring, and request duration

This project is a vanilla JavaScript reference guide and implementation collection designed to replace legacy libraries with native browser patterns. It provides a set of native JavaScript patterns for selecting, modifying, and navigating HTML elements, alongside a web API implementation guide for handling events and styles. The project serves as a reference for implementing asynchronous JavaScript patterns using native promises and fetch for remote data and background tasks. It also includes a client-side utility collection for performing data transformations, type validation, and element me

Async-profiler is a suite of performance tools designed for sampling Java runtimes, tracking memory allocations, and monitoring hardware counters. It functions as a low-overhead sampling profiler for Java applications, collecting stack traces and memory allocation data without safepoint bias. The project provides specialized utilities for generating interactive flame graphs to visualize execution hotspots in a web browser. It includes a hardware performance counter monitor to track low-level system events such as cache misses and page faults. The toolset covers several diagnostic domains, in

This project is a sample library and implementation guide for using RxJava to manage asynchronous data streams and concurrent tasks in Android applications. It provides a collection of reference implementations for reactive programming, focusing on functional operators to transform and combine asynchronous data flows. The library demonstrates specific Android architectural patterns, such as implementing decoupled event buses for component communication and coordinating parallel network requests. It includes concrete examples of mobile-specific patterns including search input debouncing, list

This repository serves as the programming language design repository for C#, containing the official language specification and the technical standards governing its grammar, type safety, and memory management. It functions as a collaborative space for the formal design and evolution of the language. The project manages a community-driven evolution process, utilizing a public proposal backlog to debate and adopt new features. This involves formal syntax prototyping and the engineering of the type system to refine the language's behavior and implementation. The scope of the specification cove

Bytehound is a Linux memory profiler that utilizes a custom global allocator to intercept memory requests and track allocations and deallocations. It records full call-stack traces for every memory operation to map allocations back to their originating source code. The project features a remote memory profiling system that streams capture data via network sockets to a separate machine, minimizing resource overhead on the target system. Analysis is supported by a specialized domain-specific query language used to automate the detection of memory patterns and anomalies. The tool covers heap al

This project is a Java runtime diagnostic tool and bytecode instrumentation framework. It provides a remote troubleshooting interface for inspecting live Java systems, analyzing execution traces, and monitoring method performance without requiring application restarts. The system distinguishes itself through its ability to modify Java classes at runtime to capture parameters and return values, combined with a JavaScript-based scripting engine for custom diagnostic logic. It further supports collaborative live debugging, allowing multiple users to connect to a single remote process simultaneou

pprof is a tool for visualizing and analyzing performance profiling data. It converts sampled call stacks into a directed graph rendered as an SVG, enabling visual identification of execution hotspots. The tool also parses Linux perf.data files, converting them into an internal profile representation for further analysis. Beyond visualization, pprof provides a command-line REPL for interactive exploration of profiling data, allowing users to filter, refine, and query performance information on the fly. It generates sorted text reports that highlight the most resource-intensive call stacks, an

Criterion is a statistics-driven microbenchmarking library and performance regression tool for Rust. It provides a framework for isolating and measuring small code segments, using statistical analysis to eliminate noise and ensure reliable, repeatable measurements of execution speed. The tool distinguishes itself through a performance visualization suite that generates HTML reports and graphs to track performance trends and throughput. It includes a system for comparing current execution times against stored baselines to identify and prevent performance drops. The library covers asynchronous