Introduction
Extended Berkeley Packet Filter (eBPF) has revolutionized Linux kubernetes-observability-stack-production-implementation" title="Kubernetes Observability Stack: A Production Implementation Guide" class="internal-link">observability. Originally designed for packet filtering, eBPF now enables powerful tracing, profiling, and monitoring capabilities without kernel modifications.
What Is eBPF?
eBPF allows you to run sandboxed programs in the tuning-high-performance-workloads" title="Linux Kernel Tuning for High-Performance Workloads" class="internal-link">Linux kernel without changing kernel source code or loading kernel modules. These programs can:
- Trace system calls and kernel functions
- Monitor network traffic
- Enforce security policies
- Profile application performance
Why eBPF for Performance Monitoring?
Traditional Approaches
- strace: High overhead, limited scope
- perf: Powerful but complex
- kernel modules: Risky and maintenance-intensive
eBPF Advantages
- Low Overhead: JIT-compiled, runs efficiently
- Safety: Verified before execution
- Flexibility: Programmable for custom metrics
- Dynamic: No reboot required
Getting Started with BCC
BCC (BPF Compiler Collection) provides easy-to-use eBPF tools:
# Install BCC tools (Ubuntu/Debian)
sudo apt-get install bpfcc-tools linux-headers-$(uname -r)
# Or on RHEL/CentOS
sudo yum install bcc-tools
Essential BCC Tools
execsnoop - Track new process execution:
sudo execsnoop-bpfcc
opensnoop - Monitor file opens:
sudo opensnoop-bpfcc
biolatency - Block I/O latency histogram:
sudo biolatency-bpfcc
tcpconnect - Track TCP connections:
sudo tcpconnect-bpfcc
Using bpftrace
bpftrace provides a high-level tracing language:
# Install bpftrace
sudo apt-get install bpftrace
One-Liners
Count syscalls by process:
bpftrace -e 'tracepoint:raw_syscalls:sys_enter { @[comm] = count(); }'
Histogram of read sizes:
bpftrace -e 'tracepoint:syscalls:sys_exit_read /args->ret > 0/ { @bytes = hist(args->ret); }'
Trace slow disk I/O:
bpftrace -e 'kprobe:blk_account_io_done { @us[comm] = hist((nsecs - @start[arg0]) / 1000); } kprobe:blk_account_io_start { @start[arg0] = nsecs; }'
Production Use Cases
1. Latency Analysis
Identify sources of application latency:
# Trace function latency
sudo funclatency-bpfcc /path/to/binary:function_name
2. Memory Leak Detection
Track memory allocations:
# Show outstanding allocations
sudo memleak-bpfcc -p $(pidof your_app)
3. Off-CPU Analysis
Find where applications block:
sudo offcputime-bpfcc -p $(pidof your_app) 5
4. Network Debugging
Analyze TCP behavior:
# TCP retransmit tracing
sudo tcpretrans-bpfcc
# Connection latency
sudo tcpconnlat-bpfcc
Building Custom eBPF Programs
For advanced use cases, write custom eBPF programs:
#!/usr/bin/env python3
from bcc import BPF
# eBPF program
prog = """
int hello(void *ctx) {
bpf_trace_printk("Hello from eBPF!\n");
return 0;
}
"""
# Load and attach
b = BPF(text=prog)
b.attach_kprobe(event="sys_clone", fn_name="hello")
# Read output
b.trace_print()
Integration with Monitoring Systems
Prometheus Integration
Use eBPF exporters to send metrics to Prometheus:
- ebpf_exporter: General-purpose eBPF metrics
- cloudflare/ebpf_exporter: Production-ready exporter
Grafana Dashboards
Visualize eBPF metrics in Grafana:
- Configure Prometheus data source
- Import eBPF dashboard templates
- Customize for your workload
Best Practices
- Start with Existing Tools: BCC provides 70+ ready-to-use tools
- Measure Overhead: eBPF is efficient but not free
- Version Compatibility: Ensure kernel version compatibility
- Security Review: Audit custom eBPF programs carefully
Conclusion
eBPF transforms Linux observability, enabling deep insights with minimal overhead. Start with BCC tools for immediate value, then explore bpftrace and custom programs as needs evolve.