This directory contains source code for BCC, a toolkit for creating small programs that can be dynamically loaded into a Linux kernel.

The compiler relies upon eBPF (Extended Berkeley Packet Filters), which is a feature in Linux kernels starting from 3.15. Currently, this compiler leverages features which are mostly available in Linux 4.1 and above.

See INSTALL.md for installation steps on your platform.

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!

This toolchain is currently composed of two parts: a C wrapper around LLVM, and a Python API to interact with the running program. Later, we will go into more detail of how this all works.

First, we should include the BPF class from the bpf module:

from bcc import BPF

Since the C code is so short, we will embed it inside the python script.

The BPF program always takes at least one argument, which is a pointer to the context for this type of program. Different program types have different calling conventions, but for this one we don't care so void * is fine.

prog = """
int hello(void *ctx) {
  bpf_trace_printk("Hello, World!\\n");
  return 0;
};
"""
b = BPF(text=prog)

For this example, we will call the program every time fork() is called by a userspace process. Underneath the hood, fork translates to the clone syscall, so we will attach our program to the kernel symbol sys_clone.

b.attach_kprobe(event="sys_clone", fn_name="hello")

The python process will then print the trace printk circular buffer until ctrl-c is pressed. The BPF program is removed from the kernel when the userspace process that loaded it closes the fd (or exits).

b.trace_print()

Output:

bcc/examples$ sudo python hello_world.py 
          python-7282  [002] d...  3757.488508: : Hello, World!

For an explanation of the meaning of the printed fields, see the trace_pipe section of the kernel ftrace doc.

Source code listing

At RedHat 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.

Here is a slightly more complex tracing example than Hello World. This program will be invoked for every task change in the kernel, and record in a BPF map the new and old pids.

The C program below introduces two new concepts. The first is the macro BPF_TABLE. This defines a table (type="hash"), with key type key_t and leaf type u64 (a single counter). The table name is stats, containing 1024 entries maximum. One can lookup, lookup_or_init, update, and delete entries from the table. The second concept is the prev argument. This argument is treated specially by the BCC frontend, such that accesses to this variable are read from the saved context that is passed by the kprobe infrastructure. The prototype of the args starting from position 1 should match the prototype of the kernel function being kprobed. If done so, the program will have seamless access to the function parameters.

#include <uapi/linux/ptrace.h>
#include <linux/sched.h>

struct key_t {
  u32 prev_pid;
  u32 curr_pid;
};
// map_type, key_type, leaf_type, table_name, num_entry
BPF_TABLE("hash", struct key_t, u64, stats, 1024);
// attach to finish_task_switch in kernel/sched/core.c, which has the following
// prototype:
//   struct rq *finish_task_switch(struct task_struct *prev)
int count_sched(struct pt_regs *ctx, struct task_struct *prev) {
  struct key_t key = {};
  u64 zero = 0, *val;

  key.curr_pid = bpf_get_current_pid_tgid();
  key.prev_pid = prev->pid;

  val = stats.lookup_or_init(&key, &zero);
  (*val)++;
  return 0;
}

Source code listing

The userspace component loads the file shown above, and attaches it to the finish_task_switch kernel function. The [] operator of the BPF object gives access to each BPF_TABLE in the program, allowing pass-through access to the values residing in the kernel. Use the object as you would any other python dict object: read, update, and deletes are all allowed.

from bcc import BPF
from time import sleep

b = BPF(src_file="task_switch.c")
b.attach_kprobe(event="finish_task_switch", fn_name="count_sched")

# generate many schedule events
for i in range(0, 100): sleep(0.01)

for k, v in b["stats"].items():
    print("task_switch[%5d->%5d]=%u" % (k.prev_pid, k.curr_pid, v.value))

Source code listing

See INSTALL.md for installation steps on your platform.