[转帖]BPF Compiler Collection (BCC)

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**Bcc Tools Introduction** **Overview** Bcc (Bytecode Compiler) is a tool that allows programmers to perform various operations on the kernel, including memory manipulation, network programming, and system call invocation. The bcc toolkit provides various tools and functionalities to help programmers write, debug, and analyze BPF programs. **Features** * End-to-end BPF workflow in a shared library * Modified C language for BPF backends * Integration with llvm-bpf backend for JITDynamic * Support for BPF kernel hooks * Binding for Python * Tutorials and resources for learning about Bcc **Getting Started** * Install the bcc toolkit and its dependencies. * Set up a BPF environment, including the kernel and kernel modules. * Write a BPF program using the Python interface. * Run the program and observe its behavior. **Tools and Functions** * `bbf` - BPF compiler * `bcppy` - BPF memory bpper * `bcore` - BPF kernel hooks and functions * `bpy` - Python bindings for BPF * `bvt` - BPF virtual machine * `bp` - BPF kernel plugin **Usage** * Write BPF programs using the `bbf` compiler. * Run the compiled program. * Use the BPF tools and functions to manipulate memory, network, and system calls. * Write and run Python programs to interact with BPF programs. **Example** ```python # Create a BPF virtual machine bvt = bvt.new() # Get the kernel and kernel modules kernel = bvt.kernel modules = kernel.modules # Load the BPF program bbf = bbf.new() bbf.load(kernel, modules, 'myprogram.bbf') # Execute the program bbf.run() ``` **Benefits** * Bcc provides a powerful and flexible tool for programmers to solve performance, troubleshooting, and networking issues. * It integrates with various tools and libraries, making it easy to write and execute BPF programs. * The toolkit offers tutorials and resources to help beginners get started with Bcc. **Additional Resources** * Bcc Documentation: `tutorial.md` * Bcc Tutorial: `tutorial_bcc_python_developer.md` * IOVisor Community: `Mailing List` * GitHub Issues: `Links` **Note:** This is a high-level overview of Bcc tools. For more detailed information and specific examples, refer to the documentation and tutorials provided.

正文

https://github.com/iovisor/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:

Networking

Examples:

BPF Introspection:

Tools that help to introspect BPF programs.

  • introspection/bps.c: List all BPF programs loaded into the kernel. 'ps' for BPF programs. Examples.

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:

  • End-to-end BPF workflow in a shared library
    • A modified C language for BPF backends
    • Integration with llvm-bpf backend for JIT
    • Dynamic (un)loading of JITed programs
    • Support for BPF kernel hooks: socket filters, tc classifiers, tc actions, and kprobes
  • Bindings for Python
  • Examples for socket filters, tc classifiers, and kprobes
  • Self-contained tools for tracing a running system

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.

External links

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.

bpf compiler collection bcc

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