bcc

Introduction to BPF Compiler Collection (BCC)

The BPF Compiler Collection, commonly known as BCC, is a powerful toolkit designed for efficiently creating programs that can trace and manipulate the Linux kernel. It primarily leverages the capabilities of extended BPF (eBPF), a feature that empowers users to define safe and sandboxed bytecode that is executed in the kernel. This feature, first introduced in Linux 3.15, has matured significantly, with BCC relying on functionalities available from Linux 4.1 onwards.

What is eBPF?

eBPF, which stands for "Extended Berkeley Packet Filters," allows for attaching user-defined programs to the kernel. Ingo Molnár, a well-known kernel developer, described eBPF as a fascinating feature enabling live kernel instrumentation without compromising system stability. eBPF programs can operate without crashing or negatively interfering with the kernel, creating vast opportunities for real-time performance analysis and network traffic control.

Benefits of Using BCC

BCC simplifies the development of BPF programs by providing a set of tools that make it easier to write and manage these programs. Here are some critical aspects of BCC:

• Ease of Use: By offering kernel instrumentation in C (and a C wrapper around LLVM), BCC makes complex tasks more approachable. Additionally, it features front-ends in Python and Lua, which cater to different developer preferences.
• Performance Optimization and Analysis: BCC is a suitable choice for tasks like performance optimization, providing insights into how efficiently the kernel and its components are running.
• Network Traffic Control: Real-time monitoring and controlling of network traffic become feasible using BCC tools.

Examples and Capabilities

BCC comes packed with numerous examples and tools designed for various needs:

Tracing Examples

• Disk I/O Histogram: As demonstrated in the example using a tool called bitehist.py, BCC can trace disk I/O functions and present a summary of activity in a histogram format. This helps visualize data distributions efficiently.
• Process and Network Tracing: Tools like disksnoop.py trace I/O latency on devices, while tcpv4connect.py monitors TCP IPv4 connections, showcasing the versatility of BCC in handling different tracing requirements.

Tool Categories

1. Memory and Process Tools: Tools like execsnoop.py trace new process executions, while memleak.py helps identify memory leaks by tracking memory allocations.
2. Performance Tools: Tools such as funclatency.py measure function execution times, providing insights into potential performance bottlenecks.
3. CPU and Scheduler Tools: The suite includes tools like cpudist.py for summarizing CPU time usage and profile.py for sampling stack traces and understanding CPU utilization.
4. Network Tools: These include tcpconnect.py for tracing TCP connections and tcplife.py to summarize TCP session lifespans.
5. Storage Tools: Tools such as biosnoop.py trace block device I/O, offering detailed data about read and write operations.

Installing BCC

Installation instructions are detailed in the INSTALL.md file, guiding users through setting up BCC on their systems effectively.

Additional Resources

• FAQ: Common troubleshooting questions are answered in the FAQ.txt.
• Reference Guide: The complete BCC and BPF API reference can be found in the reference guide.

Conclusion

BCC is an indispensable toolkit for developers and systems administrators interested in advanced performance analysis and real-time kernel tracing. By abstracting the complexities of eBPF, it allows for effective program development to meet diverse monitoring and instrumentation needs. Whether it’s optimizing performance, controlling network traffic, or simply understanding kernel operations, BCC provides the necessary tools to get the job done seamlessly.