eBPF Powers Next-Generation Observability: Maximum Insight, Minimal Impact

Posted on Dec 1, 2024

In the era of modern software systems, observability has become a critical aspect of system management. It enables engineers to monitor, debug, and optimize their applications effectively. However, traditional observability tools often come with high resource costs, limited visibility, and performance overhead. This is where eBPF (extended Berkeley Packet Filter) steps in to revolutionize observability solutions.

eBPF is a cutting-edge technology that empowers developers to collect detailed insights about system behavior directly from the Linux kernel, all while keeping resource usage to a minimum. It offers a flexible, efficient, and secure way to understand what’s happening inside your systems, without significantly affecting performance.

In this blog post, we’ll explore how eBPF enhances observability, why it’s an excellent choice for modern systems, and demonstrate its capabilities with a real-world example: monitoring HTTP traffic and detecting issues like 404 errors, 500 errors, and connection timeouts across all applications.

What is eBPF?

eBPF is a technology built into the Linux kernel that allows small programs to run safely and efficiently at the kernel level. These programs can monitor and modify system behavior in real time. Unlike traditional kernel modifications, eBPF programs do not require recompiling the kernel, making them more flexible and accessible.

Key Features of eBPF

Low overhead: Collect data and analyze it without impacting system performance.
Security: Run safely within the kernel, isolated from user-space applications.
Flexibility: Adapt to various observability and networking tasks.

With eBPF, you can monitor network traffic, debug applications, profile performance, and much more, all in a highly efficient manner.

Advantages of eBPF

Minimal Performance Impact:
Traditional observability tools often require context switching between user space and kernel space, which adds latency and consumes CPU cycles. eBPF programs run directly in the kernel, minimizing this overhead.
Real-Time Observability:
eBPF provides live insights into system behavior, enabling faster debugging and optimization.
Language Agnostic Monitoring:
Whether your application is built with Java, Python, Go, or even uses simple tools like curl, eBPF can monitor its interactions at the network and system levels.
Wide Range of Applications:
From security monitoring to performance tuning, eBPF can be adapted for various use cases, making it a versatile tool for engineers.

Why eBPF Offers Minimal Costs

One of the standout advantages of eBPF is its minimal cost compared to traditional observability solutions. Traditional monitoring solutions often require extensive instrumentation, context switching between user space and kernel space, or external agents running in the background, all of which add overhead and reduce system performance.

With eBPF, the cost is minimal because:

Runs in Kernel Space: eBPF programs run directly in the kernel, which eliminates the need for external agents or additional monitoring tools.
Low Latency: Since eBPF programs are executed in response to system events (like network traffic or function calls), they avoid the performance hit of constantly polling the system.
Efficient Memory Use: eBPF has a low memory footprint and only collects data when necessary, minimizing the storage and processing costs associated with traditional logging and monitoring.

By providing real-time insights with such a low resource overhead, eBPF ensures that your system remains responsive while still offering deep observability.

Real-World Example: Monitoring HTTP Traffic

Let’s consider a practical scenario: you want to monitor the most-used HTTP endpoints in your application, track error responses (404 and 500), and detect connection timeouts. Instead of modifying your application code or relying on heavy monitoring tools, you can use eBPF to achieve this in a lightweight and efficient manner.

The Plan

Capture HTTP requests and responses at the TCP level.
Extract endpoint details and HTTP status codes from the payload.
Count occurrences of error responses and timeouts.

eBPF Program to Monitor HTTP Traffic

Below is a simple eBPF program that tracks HTTP traffic and collects statistics on endpoints and status codes:

// Core header files for eBPF program functionality
#include <linux/bpf.h>      // Provides core eBPF functionality
#include <linux/ptrace.h>   // Used for kernel function tracing
#include <linux/tcp.h>      // Required for TCP protocol structures
#include <linux/ip.h>       // Required for IP protocol structures
#include <net/sock.h>       // Provides socket structure definitions

// Structure to store HTTP request information
// This acts as the key in our BPF hash map
struct key_t {
    char endpoint[128];     // Stores the HTTP endpoint path (e.g., /api/users)
    int status_code;        // Stores HTTP status code (e.g., 200, 404, 500)
};

// Create a BPF hash map named 'http_stats'
// Maps key_t structure to unsigned 64-bit counter
BPF_HASH(http_stats, struct key_t, u64);

// Function that gets called for each TCP send event
// @ctx: Contains CPU register state at the time of kprobe
// @sk: Socket structure containing connection details
int trace_http_response(struct pt_regs *ctx, struct sock *sk) {
    // Buffer to store TCP payload data, initialized to zeros
    char payload[128] = {};
    
    // Safely read data from user space into our payload buffer
    // PT_REGS_PARM2 gets the second parameter of the probed function
    bpf_probe_read_user(&payload, sizeof(payload), (void *)PT_REGS_PARM2(ctx));

    // Check if the payload starts with "HTT" (HTTP response signature)
    if (payload[0] == 'H' && payload[1] == 'T' && payload[2] == 'T') {
        // Initialize a new key structure for our hash map
        struct key_t key = {};
        
        // Copy the HTTP endpoint information from kernel space to our key
        bpf_probe_read_kernel(&key.endpoint, sizeof(key.endpoint), payload);
        
        // Parse the HTTP status code from the response
        // Format: "HTTP/1.1 200 OK" - status code starts at position 9
        key.status_code = payload[9] * 100 +    // First digit (2 in 200)
                         payload[10] * 10 +      // Second digit (0 in 200)
                         payload[11];            // Third digit (0 in 200)

        // Look up current count for this endpoint+status combination
        u64 *count = http_stats.lookup(&key);
        if (count) {
            // If entry exists, increment the counter
            (*count)++;
        } else {
            // If this is a new entry, initialize with count of 1
            u64 init = 1;
            http_stats.update(&key, &init);
        }
    }
    return 0;  // Return success
}

Running the eBPF Program with Python

from bcc import BPF

bpf_program = """
// Paste the eBPF code here
"""

b = BPF(text=bpf_program)

# Attach the eBPF program to TCP events
b.attach_kprobe(event="tcp_sendmsg", fn_name="trace_http_response")

print("Monitoring HTTP traffic... Press Ctrl+C to stop.")
while True:
    try:
        events = b["http_stats"].items()
        for k, v in events:
            print(f"Endpoint: {k.endpoint}, Status Code: {k.status_code}, Count: {v.value}")
    except KeyboardInterrupt:
        break

When running this script, you’ll see real-time statistics for HTTP traffic:

Endpoint: /api/users, Status Code: 404, Count: 12  
Endpoint: /api/orders, Status Code: 500, Count: 8  
Endpoint: /login, Status Code: 200, Count: 45  
Endpoint: timeout, Status Code: 0, Count: 3

This data helps you identify problematic endpoints, analyze traffic patterns, and troubleshoot issues quickly.

Conclusion

eBPF is transforming how we approach observability. By providing real-time, low-overhead insights into system behavior, it eliminates many limitations of traditional tools. Whether you are monitoring HTTP traffic, debugging applications, or optimizing performance, eBPF offers a powerful, efficient, and versatile solution.

Start using eBPF today and unlock the true potential of kernel-level observability for your systems. It’s time to embrace the future of monitoring and debugging!