Developing Custom eBPF Programs

eBPF (Extended Berkeley Packet Filter) is an incredibly powerful technology that allows developers to run sandboxed programs in the Linux kernel without changing the kernel source code or loading kernel modules. With eBPF, you can write custom programs to monitor network traffic, analyze system performance, and respond to specific events—all while maintaining the stability and reliability of your system.

In this article, we'll walk you through the process of writing custom eBPF programs tailored for specific use cases, focusing on enhancing performance and ensuring reliability. We'll cover the steps of setting up your environment, writing your first eBPF program, and deploying it effectively.

Prerequisites

Before we dive into coding eBPF programs, it's essential to set up a suitable development environment. Make sure you have the following prerequisites:

Linux Kernel: Ensure you're running at least version 4.1, as eBPF support is continuously improving with each kernel release.
BPF Compiler Collection (BCC): This is a toolkit providing BPF-related functionality. You can install it using your package manager.
```
sudo apt install bpfcc-tools linux-headers-$(uname -r)
```
Clang and LLVM: These compilers can generate BPF bytecode from your C code. Install them via:
```
sudo apt install clang llvm
```
Basic Knowledge of C: Since eBPF programs are typically written in C, prior knowledge will be very helpful.

Setting Up the Environment

Once your development environment is in place, it’s time to set up a project directory for our eBPF program.

mkdir ebpf_example
cd ebpf_example

Writing Your First eBPF Program

Let’s create a simple eBPF program that counts dropped packets. To do this, we'll use BCC to load our eBPF code.

Create the C Source File:

Create a file called packet_count.c in the project directory.

#include <uapi/linux/bpf.h>
#include <uapi/linux/ptrace.h>
#include <uapi/linux/bpf_common.h>
#include <linux/if_ether.h>
#include <linux/ip.h>

BPF_HASH(packet_count, u32, u64);

int count_packets(struct __sk_buff *skb) {
    u32 key = 0;
    u64 *value;

    // Increment the packet count
    value = packet_count.lookup(&key);
    if (value) {
        __sync_fetch_and_add(value, 1);
    } else {
        u64 init_value = 1;
        packet_count.update(&key, &init_value);
    }
    return 0;
}

In the code above:

We include necessary headers for BPF and Linux networking.
We declare a hash map to hold the packet count.
The count_packets function increments the count every time a packet is dropped.

Create a Python Script to Load the BPF Program:

Now, let’s create a Python script to load and attach our eBPF program to the kernel.

Create a file called load_packet_count.py:

#!/usr/bin/env python3

from bcc import BPF

# Load BPF program
b = BPF(src_file="packet_count.c")
# Attach to the socket filter
b.attach_kprobe(event="dev_kfree_skb", fn_name="count_packets")

# Print dropped packets count
print("Tracing dropped packets... Hit Ctrl-C to end.")

try:
    while True:
        pass
except KeyboardInterrupt:
    print("Detaching...")

# Output results
for k, v in b["packet_count"].items():
    print(f"Dropped packets: {v.value}")

Compiling and Running the Program

Make Sure the Script is Executable:

chmod +x load_packet_count.py

Run the Script:

sudo ./load_packet_count.py

Now, if the system drops packets, your eBPF program will count them. You can stop the execution using Ctrl + C, and the script will print the count of dropped packets.

Performance and Reliability Considerations

While developing eBPF programs, it's vital to balance performance and reliability. Here are some best practices to keep in mind:

1. Avoid Long Execution Times

eBPF programs run in the kernel context. High execution times can lead to performance bottlenecks, so keep your code short and avoid performing complex computations. Aim for efficiency in design.

2. Batching Events

When counting events (e.g., dropped packets), consider aggregating data in batches rather than performing writes for every single event. This reduces overhead and context switches between user mode and kernel mode, leading to better performance.

3. Use Lock-Free Data Structures

When handling shared data in eBPF, prefer lock-free data structures like hash maps. BPF Hash Maps, as seen in our example, utilize atomic operations, reducing the risk of blocking.

4. Be Modest with Memory

Keep memory usage to a minimum, as memory allocation in the kernel can fail. Use statically sized storage structures and avoid dynamic memory allocations.

5. Test Extensively

Given that eBPF runs in kernel space, thorough testing is vital. Use different scenarios and loads to ensure reliability without unexpected crashes or panics.

Deploying eBPF Programs

After developing and testing your eBPF program, consider how you want to deploy it. Here are some deployment strategies:

1. Systemd Service

If your eBPF program runs as a background service, consider creating a systemd service file. This would enable your program to start on boot.

Example Service File:

[Unit]
Description=eBPF Packet Count

[Service]
ExecStart=/path/to/load_packet_count.py
Restart=always
User=root

[Install]
WantedBy=multi-user.target

Enable the service with:

sudo systemctl enable your_service_name

2. Integration with Monitoring Tools

Using tools like Prometheus or Grafana can help visualize the data collected by your eBPF program. Consider exporting your results to these tools to leverage sophisticated monitoring solutions.

Conclusion

Writing custom eBPF programs tailored to your specific use cases is a rewarding experience that can significantly enhance system performance and reliability. While the initial learning curve can be steep, the power and flexibility eBPF provides are worth the effort.

Armed with the fundamental techniques outlined in this tutorial, you're ready to tackle more complex tasks with eBPF. From network observability to performance profiling, the potential applications are virtually limitless. Happy coding!

Networking & Infrastructure - Linux eBPF