5 个仓库
Utilities that produce flamegraph visualizations from profiling data to identify CPU bottlenecks.
Distinct from CPU Profilers: Distinct from CPU Profilers: focuses on generating flamegraph visualizations from profiling data, not on the profiling instrumentation itself.
Explore 5 awesome GitHub repositories matching development tools & productivity · Flamegraph Generators. Refine with filters or upvote what's useful.
The 1BRC (One Billion Row Challenge) is a Java performance benchmarking exercise that processes one billion temperature records from a text file to compute the minimum, mean, and maximum temperature per weather station. At its core, it is a large-scale data aggregation challenge designed to test how efficiently a Java program can parse and aggregate structured data from a plain text file, serving as both a programming exercise and a benchmark for Java performance optimization. The project distinguishes itself through a collection of performance-oriented architectural patterns for high-through
A utility that generates flamegraph visualizations to identify performance bottlenecks in Java applications.
Pixie is an open-source observability platform for Kubernetes that uses eBPF to automatically capture telemetry data from clusters without requiring any manual instrumentation or code changes. It functions as an eBPF telemetry collector, a continuous application profiler, a network traffic analyzer, and a scriptable telemetry query engine, all within a single Kubernetes-native tool. The platform distinguishes itself through several integrated capabilities. It continuously samples stack traces from compiled-language code to identify CPU performance bottlenecks, visualizing the results as inter
Visualizes stack trace samples as flamegraphs to identify where application code spends the most time.
本项目是一个用 Rust 编写的分析工具,用于捕获、转换和可视化函数调用栈,以识别系统性能瓶颈。它作为一个采样分析器包装器,将原始分析数据转换为交互式火焰图,即资源消耗的层级映射。 该工具提供了与 Rust 构建系统的专门集成,以分析二进制文件和性能基准。它还允许自定义分析配置,使用户能够覆盖默认的系统分析工具或记录标志,以控制数据的收集方式。 该实用程序支持应用程序性能监控和二进制执行分析。它可以通过附加到活动进程 ID 来捕获性能数据,从而在无需重启的情况下分析正在运行的应用程序。
Converts sampling data into interactive flamegraph visualizations to pinpoint CPU bottlenecks.
该项目是一个教育资源,提供了一个全面的开发教程,用于在 Linux 内核中使用 C、Go 和 Rust 编写并加载 eBPF 程序。它作为一个技术指南,用于开发直接在内核中执行的自定义逻辑。 这些材料涵盖了专门的领域,包括内核可观测性和追踪、用于入侵检测的安全实现,以及用于包过滤和负载均衡的高性能网络工程。它还包括用于 Linux 内核追踪以及使用 kprobes、uprobes 和 tracepoints 的专用手册。 该项目涵盖了广泛的功能领域,如内核插桩、系统监控和可观测性、网络分析以及安全强制执行。它进一步扩展到 GPU 和驱动程序的硬件级调试,以及底层系统操作和资源管理。
Formats combined processor and graphics trace data into folded stack files for flamegraph visualization.
这是一个针对使用 Rack 接口的 Ruby 应用程序的性能分析工具。它监控请求执行时间和资源使用情况,作为 Web 应用程序的性能分析器来测量延迟并识别瓶颈。 该工具为数据库查询性能、内存分配和垃圾回收统计提供了专门的分析器。它生成调用栈火焰图 (Flamegraphs) 以可视化方法间的时间分布,并将速度徽章和计时指标直接渲染到 HTML 页面上。 该系统涵盖了更广泛的性能分析功能,包括自定义代码块跟踪、将分析数据导出到远程 URL 以及生产环境性能采样。它包含在不同存储后端之间持久化分析数据的机制,并实现了访问控制以限制谁可以查看敏感的性能指标。
Generates flamegraph visualizations from profiling data to identify the most time-consuming methods in a request.