OpenTelemetry eBPF: Observability in Modern Applications

OpenTelemetry traditionally relies on instrumentation within application code to collect traces and metrics. While effective, this approach can introduce performance overhead and require modifying the application code. By integrating OpenTelemetry and eBPF, OpenTelemetry gains the ability to collect observability data at a lower level, without modifying the application codebase extensively. This makes it particularly valuable for environments where minimal overhead and non-intrusive monitoring are priorities.

Benefits of Integrating OpenTelemetry with eBPF

Integrating OpenTelemetry with eBPF offers several key advantages for observability in modern applications:

Real-Time Monitoring: eBPF allows developers to monitor and trace application activities in real-time, providing immediate visibility into performance bottlenecks, resource utilization, and system interactions.

Low Overhead: eBPF programs run in a safe and controlled environment within the kernel, minimizing the performance impact on the applications being monitored. This makes it suitable for production environments where performance is critical.

Dynamic Tracing: With eBPF, developers can dynamically trace and analyze activities in both user space and the kernel without altering application code or restarting processes. This capability is essential for troubleshooting complex issues and optimizing application performance.

Comprehensive Observability: By combining eBPF's low-level insights with OpenTelemetry's standardized telemetry collection, teams can achieve comprehensive observability across distributed systems. This includes metrics, traces, and logs that provide a holistic view of application health and performance.

How OpenTelemetry eBPF Works

To illustrate how OpenTelemetry leverages eBPF for observability, consider the following examples:

Example 1: Tracing System Calls

#include <linux/bpf.h>
#include <linux/version.h>
#include <linux/ptrace.h>

SEC("tracepoint/syscalls/sys_enter_openat")
int trace_sys_openat(struct trace_event_raw_sys_enter *ctx) {
    char filename[256];
    bpf_probe_read_str(&filename, sizeof(filename), ctx->args->filename);
    
    // Emit trace event with OpenTelemetry
    emit_trace_event("sys_openat", filename);
    
    return 0;
}

In this example, an eBPF program traces the openat system call and emits a trace event using OpenTelemetry whenever this system call is invoked.

Example 2: Monitoring Network Traffic

#include <linux/bpf.h>
#include <linux/version.h>
#include <linux/in.h>
#include <linux/if_ether.h>
#include <linux/ip.h>
#include <linux/tcp.h>

SEC("xdp")
int xdp_prog(struct xdp_md *ctx) {
    void *data_end = (void *)(long)ctx->data_end;
    void *data = (void *)(long)ctx->data;

    struct etpdr *eth = data;
    struct iphdr *ip = data + sizeof(struct etpdr);
    struct tcphdr *tcp = data + sizeof(struct etpdr) + sizeof(struct iphdr);
    
    // Collect metrics using OpenTelemetry
    collect_network_metrics(ip->saddr, ip->daddr, ntohs(tcp->source), ntohs(tcp->dest));
    
    return XDP_PASS;
}

This eBPF program monitors incoming network traffic, extracts relevant metrics such as source and destination IP addresses, and port numbers, and then uses OpenTelemetry to collect and export these metrics for analysis.

Example 3: Profiling Application Performance

#include <linux/bpf.h>
#include <linux/version.h>
#include <linux/sched.h>

SEC("kprobe/do_sys_open")
int kprobe_do_sys_open(struct pt_regs *ctx) {
    const char __user *filename;
    filename = (const char __user *)PT_REGS_PARM1(ctx);
    
    // Profile application performance using OpenTelemetry
    profile_application("do_sys_open", filename);
    
    return 0;
}

In this example, an eBPF program profiles the do_sys_open kernel function, capturing parameters like filenames to provide insights into application behavior and performance characteristics using OpenTelemetry.

Practical Examples of OpenTelemetry eBPF Integration

Let's delve into three practical examples to illustrate how OpenTelemetry eBPF can be implemented to enhance observability:

Example 1: Monitoring HTTP Request Latencies

from opentelemetry import trace
from bpf_programs import http_latency_monitor

# Initialize OpenTelemetry tracer
tracer = trace.get_tracer(__name__)

# Load and attach eBPF program to monitor HTTP request latencies
http_latency_monitor.attach(tracer)

In this example, an eBPF program (http_latency_monitor) is loaded and attached to an OpenTelemetry tracer. This program captures and measures latency metrics for HTTP requests in real-time. By analyzing these metrics, developers can identify slow-performing endpoints or network issues affecting application performance.

Example 2: Tracking File System Operations

from opentelemetry import metrics
from bpf_programs import fs_operations_monitor

# Initialize OpenTelemetry metrics
meter = metrics.get_meter(__name__)

# Load and attach eBPF program to monitor file system operations
fs_operations_monitor.attach(meter)

Here, another eBPF program (fs_operations_monitor) is employed to monitor file system operations such as reads, writes, and deletions. By integrating this program with OpenTelemetry metrics, operations teams can gain insights into file system utilization patterns, detect potential bottlenecks, and optimize disk I/O performance.

Example 3: Dynamic Tracing of Kernel Functions

from opentelemetry import trace
from bpf_programs import kernel_trace_monitor

# Initialize OpenTelemetry tracer
tracer = trace.get_tracer(__name__)

# Load and attach eBPF program for dynamic tracing of kernel functions
kernel_trace_monitor.attach(tracer)

In this scenario, an eBPF program (kernel_trace_monitor) is utilized for dynamic tracing of kernel functions. By attaching this program to an OpenTelemetry tracer, developers can capture detailed traces of system calls, interrupts, and other kernel-level events. This deep visibility into kernel activities is invaluable for diagnosing low-level issues, optimizing system performance, and ensuring the stability of critical infrastructure.

Enhancing Operational Insights

Integrating OpenTelemetry with eBPF not only enhances monitoring capabilities but also provides operational insights that are crucial for maintaining application health and performance. By continuously monitoring metrics, traces, and logs across distributed systems, teams can proactively identify and mitigate issues before they impact end-users or business operations. This proactive approach not only improves system reliability but also enhances the overall user experience.

Future Trends and Considerations

As software applications evolve, the need for advanced observability tools like OpenTelemetry and eBPF is growing. Organizations should invest in these technologies to address modern operational challenges. Notably, the number of eBPF-based projects listed on ebpf.io has increased from 9 to 41 over the past two years, highlighting its rapid adoption and significance. As eBPF continues to advance, it promises to offer deeper insights and more efficient management of complex applications.

Conclusion
The integration of OpenTelemetry with eBPF represents a significant advancement in the field of observability for modern applications. By combining OpenTelemetry's standardized telemetry collection with eBPF's dynamic tracing capabilities, organizations can achieve unparalleled visibility into their software stack—from the application layer to the kernel level. This enhanced observability not only facilitates proactive monitoring and rapid issue resolution but also empowers teams to deliver more reliable, performant, and scalable applications.