bcc README

The project's README from github.com/iovisor/bcc. As one markdown file: README.md.

BCC Logo

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 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.

# ./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. 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 for installation steps on your platform.

FAQ

See FAQ.txt for the most common troubleshoot questions.

Reference guide

See 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

Tools

Memory and Process Tools
Performance and Time Tools
CPU and Scheduler Tools
Network and Sockets Tools
Storage and Filesystems Tools
Filesystems Tools

Networking

Examples:

BPF Introspection

Tools that help to introspect BPF programs.

Motivation

BPF guarantees that the programs loaded into the kernel cannot crash, and cannot run forever, but yet BPF is general purpose enough to perform many arbitrary types of computation. Currently, it is possible to write a program in C that will compile into a valid BPF program, yet it is vastly easier to write a C program that will compile into invalid BPF (C is like that). The user won't know until trying to run the program whether it was valid or not.

With a BPF-specific frontend, one should be able to write in a language and receive feedback from the compiler on the validity as it pertains to a BPF backend. This toolkit aims to provide a frontend that can only create valid BPF programs while still harnessing its full flexibility.

Furthermore, current integrations with BPF have a kludgy workflow, sometimes involving compiling directly in a linux kernel source tree. This toolchain aims to minimize the time that a developer spends getting BPF compiled, and instead focus on the applications that can be written and the problems that can be solved with BPF.

The features of this toolkit include:

In the future, more bindings besides python will likely be supported. Feel free to add support for the language of your choice and send a pull request!

Tutorials

Networking

At Red Hat Summit 2015, BCC was presented as part of a session on BPF. A multi-host vxlan environment is simulated and a BPF program used to monitor one of the physical interfaces. The BPF program keeps statistics on the inner and outer IP addresses traversing the interface, and the userspace component turns those statistics into a graph showing the traffic distribution at multiple granularities. See the code here.

Contributing

Already pumped up to commit some code? Here are some resources to join the discussions in the IOVisor community and see what you want to work on.

Looking for more information on BCC and how it's being used? You can find links to other BCC content on the web in LINKS.md.

bcc (iovisor/bcc)

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22.7K ·GitHub
这是什么东西?BCC(BPF Compiler Collection)是一套工具包,用于在 Linux 上通过 eBPF 编写高效的内核跟踪和操作程序。它还自带大量现成工具,可用于 I/O 分析、网络和监控。
它是用来自己写跟踪器的框架,还是直接就能跑的工具集?两者都是。你可以在 C 里编写内核插桩代码(通过一个封装 LLVM 的 C 包装层),再用 Python 或 Lua 作为前端——仓库里还包含几十个可以直接运行的独立工具,比如 opensnoop、biolatency 和 tcptop。
我的内核版本有要求吗?有——它天生只能用在 Linux 上:eBPF 最早出现在 Linux 3.15,而 BCC 用到的很多功能需要 Linux 4.1 及以上版本,所以内核越新越好。
把它挂到正在运行的内核上安全吗?这正是 eBPF 的核心承诺:程序是用户定义的、经过沙箱隔离的字节码,由内核执行——可以直接在运行中的内核上做插桩,绝不会导致内核崩溃、卡死或产生负面影响。
自带的这些工具实际能做什么?都是日常用得上的东西,比如 execsnoop(新进程)、opensnoop(文件打开)、biolatency(磁盘 I/O 延迟直方图)、profile(CPU 采样)、offcputime、memleak(排查内存泄漏)、tcpconnect 和 tcptop(TCP),还有用来找 MySQL/PostgreSQL 慢查询的 dbslower。
写自己的跟踪器必须会写 C 代码吗?用自带工具不用。要写自定义工具的话,内核部分用 C 编写,再通过 Python 或 Lua 前端来驱动;示例要么是 C 和 Python 混在同一个文件里,要么是 .c/.py 成对出现。
它除了观测,还能做别的事吗,比如控制网络流量?可以。除了跟踪,README 还把网络流量控制列为一项主要用途,并给出了示例,比如 HTTP 过滤器、分布式网桥,以及按 IP 分类并限速。
它能深入跟踪哪些应用的运行时?可以通过 USDT 探针跟踪 MySQL 和 Node.js,还有一族面向高级语言的工具——ucalls、uflow、ugc、ustat、uthreads——能汇总 Java 这类运行时中的方法调用、GC 事件、对象分配、异常和线程创建情况。
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