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项目能做什么

基于 BPF 的 Linux 工具集,用于 I/O 分析、网络排查和系统性能监控等。
来源:基于仓库元数据 · 内容以仓库实际 README 为准

使用说明 查看原始 README 原仓库文档

中文使用说明暂未生成。下面保留原仓库 README 摘录,命令与配置均以原文为准。
原始 README 摘录(英文原文,节选前 3000 字符)
![BCC Logo](images/logo2.png)
# BPF Compiler Collection (BCC)

BCC is a toolkit for creating efficient kernel tracing and manipulation
programs, and includes several useful tools and examples. It makes use of
extended BPF (Berkeley Packet Filters), formally known as eBPF, a new feature
that was first added to Linux 3.15. Much of what BCC uses requires Linux 4.1
and above.

eBPF was [described by](https://lkml.org/lkml/2015/4/14/232) Ingo Molnár as:

> One of the more interesting features in this cycle is the ability to attach eBPF programs (user-defined, sandboxed bytecode executed by the kernel) to kprobes. This allows user-defined instrumentation on a live kernel image that can never crash, hang or interfere with the kernel negatively.

BCC makes BPF programs easier to write, with kernel instrumentation in C
(and includes a C wrapper around LLVM), and front-ends in Python and lua.
It is suited for many tasks, including performance analysis and network
traffic control.

## Screenshot

This example traces a disk I/O kernel function, and populates an in-kernel
power-of-2 histogram of the I/O size. For efficiency, only the histogram
summary is returned to user-level.

```Shell
# ./bitehist.py
Tracing... Hit Ctrl-C to end.
^C
     kbytes          : count     distribution
       0 -> 1        : 3        |                                      |
       2 -> 3        : 0        |                                      |
       4 -> 7        : 211      |**********                            |
       8 -> 15       : 0        |                                      |
      16 -> 31       : 0        |                                      |
      32 -> 63       : 0        |                                      |
      64 -> 127      : 1        |                                      |
     128 -> 255      : 800      |**************************************|
```

The above output shows a bimodal distribution, where the largest mode of
800 I/O was between 128 and 255 Kbytes in size.

See the source: [bitehist.py](examples/tracing/bitehist.py). What this traces,
what this stores, and how the data is presented, can be entirely customized.
This shows only some of many possible capabilities.

## Installing

See [INSTALL.md](INSTALL.md) for installation steps on your platform.

## FAQ

See [FAQ.txt](FAQ.txt) for the most common troubleshoot questions.

## Reference guide

See [docs/reference_guide.md](docs/reference_guide.md) for the reference guide to the bcc and bcc/BPF APIs.

## Contents

Some of these are single files that contain both C and Python, others have a
pair of .c and .py files, and some are directories of files.

### Tracing

#### Examples

- examples/tracing/[bitehist.py](examples/tracing/bitehist.py): Block I/O size histogram. [Examples](examples/tracing/bitehist_example.txt).
- examples/tracing/[disksnoop.py](examples/tracing/disksnoop.py): Trace block device I/O latency. [Examples](examples/tracing/disksnoop_example.txt).
- examples/[hello_world.py](e
阅读完整原文

商业机会分析 规则生成(AI 未配置)

综合评分 45.0 分。该项目在“个人可实施性”维度得 30 分,“付费可能性”维度得 20.0 分,差异化空间 70.0 分(评分依据见评分卡)。

面向细分需求的二次开发产品

产品构想基于 bcc 的核心能力,聚焦一个更窄的场景(如某平台、某行业)做差异化。
目标用户该细分场景用户
难度/周期中 · 2~6 周
收费模式买断制 / 订阅制 / 广告
竞争风险细分需求规模需验证
依据 AI 推测:基于项目主题标签的判断
以上为规则引擎基于仓库公开数据生成的参考结论,包含推测成分,不构成投资或创业承诺;配置 AI 后可获得更深入的分析。
报告会保存到 个人中心

竞品与相似项目 GitHub Search 真实检索

以上为通过 GitHub 官方 Search API 检索的相似项目,功能差异与优劣势需进一步人工确认,配置 AI 后可生成对比分析。

项目优势与风险

技术成熟度创建于 ,最近推送 ,发布过 30 个 Release
社区活跃Star 22,739 / Fork 0 / Issue 0(数据支持,可自行核实)
许可证未检测到(需人工确认)
主要风险上游项目方向可能变化;同赛道竞争者较多(见竞品表);热度与需求不完全等价

安装与使用

快速开始(以仓库 README 为准)
git clone https://github.com/iovisor/bcc.git
cd bcc
# 请阅读仓库 README 获取确切安装步骤(依赖、配置、启动命令)
阅读原仓库文档
术语说明:Docker=应用容器(把程序和依赖打包运行);API Key=调用外部服务的密钥;Git Clone=把仓库代码下载到本地。常见错误:API Key 未配置 / 端口占用 / 数据库连接失败,多数项目在 README 的 Troubleshooting 章节有说明。