Comprehensive Guide • 25 min read

The Ultimate Guide to eBPF Monitoring

Master kernel-level observability with eBPF. From fundamentals to advanced implementation, this guide covers everything you need to revolutionize your infrastructure monitoring.

Introduction to eBPF

eBPF (extended Berkeley Packet Filter) represents the most significant advancement in Linux observability since dtrace. It enables programmable kernel-level monitoring without modifying kernel source code or loading kernel modules.

What is eBPF?

eBPF is a revolutionary technology that allows you to run sandboxed programs in the Linux kernel without changing kernel source code or loading kernel modules. Think of it as JavaScript for the kernel - safe, efficient, and incredibly powerful.

Key Benefits:

  • Zero Instrumentation: Monitor without modifying applications
  • Kernel-Level Visibility: See everything happening in your system
  • Production Safe: Verified programs with bounded execution
  • High Performance: Minimal overhead (<5% typically)

History and Evolution

Originally introduced in Linux 3.18 (2014), eBPF has evolved from a simple packet filtering mechanism to a comprehensive kernel programming framework. Major milestones include:

  • 2014: eBPF introduced in Linux 3.18
  • 2016: BPF Type Format (BTF) added for better introspection
  • 2018: BPF-to-BPF function calls enabled complex programs
  • 2020: BPF LSM (Linux Security Module) framework
  • 2023: Production adoption by major tech companies

eBPF vs Traditional Monitoring

AspecteBPF MonitoringTraditional APM
Code ChangesNone requiredExtensive instrumentation
Performance Impact<5% overhead5-15% overhead
Deployment TimeMinutesDays to weeks
Language SupportAll languagesSDK-supported only

How eBPF Works

Understanding eBPF's architecture is crucial for effective implementation. Let's explore the core components and execution model.

Architecture Overview

┌─────────────────────────────────────────────────────┐
│                  User Space                         │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐│
│  │ BPF Program │  │   BPF Maps  │  │Control Apps ││
│  │   (C/Rust)  │  │  (Storage)  │  │ (Loaders)   ││
│  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘│
│         │                 │                 │       │
├─────────┼─────────────────┼─────────────────┼───────┤
│         │    System Call Interface         │       │
├─────────▼─────────────────▼─────────────────▼───────┤
│                   Kernel Space                      │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐│
│  │BPF Verifier │  │ BPF Runtime │  │ BPF Helpers ││
│  └─────────────┘  └──────┬──────┘  └─────────────┘│
│                          │                          │
│  ┌───────────────────────▼──────────────────────┐  │
│  │         Kernel Subsystems & Hooks            │  │
│  │  Network  │  Tracing  │  Security  │  Perf   │  │
│  └──────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────┘

Core Components

1. BPF Programs

Small programs written in restricted C that run in kernel space. They're compiled to BPF bytecode and verified before execution.

// Example: Simple packet counter
#include <linux/bpf.h>
#include <bpf/bpf_helpers.h>

struct {
    __uint(type, BPF_MAP_TYPE_HASH);
    __type(key, __u32);
    __type(value, __u64);
    __uint(max_entries, 1024);
} packet_count SEC(".maps");

SEC("xdp")
int count_packets(struct xdp_md *ctx) {
    __u32 key = 0;
    __u64 *count = bpf_map_lookup_elem(&packet_count, &key);
    
    if (count) {
        __sync_fetch_and_add(count, 1);
    }
    
    return XDP_PASS;
}

2. BPF Maps

Key-value data structures for storing state and communicating between kernel and user space. Types include:

  • Hash Maps: General purpose key-value storage
  • Arrays: Fast indexed access
  • Ring Buffers: Efficient event streaming
  • LRU Maps: Automatic eviction of old entries
  • Stack/Queue: LIFO/FIFO data structures

3. BPF Verifier

The kernel component that ensures BPF programs are safe to run. It performs:

  • Control flow analysis: No loops or recursion
  • Memory safety: Valid pointer access only
  • Bounded execution: Programs must terminate
  • Privilege checks: Appropriate capabilities required

Execution Flow

  1. 1
    Program Compilation: C code compiled to BPF bytecode using LLVM
  2. 2
    Loading: Bytecode loaded into kernel via bpf() syscall
  3. 3
    Verification: Verifier ensures program safety
  4. 4
    JIT Compilation: Bytecode converted to native machine code
  5. 5
    Attachment: Program attached to kernel hook points
  6. 6
    Execution: Runs when events occur at hook points

eBPF Monitoring Capabilities

eBPF enables comprehensive monitoring across all layers of your infrastructure. Here's what you can observe with eBPF technology.

Network Monitoring

Packet-Level Analysis

  • • TCP/UDP flow tracking
  • • Connection establishment/teardown
  • • Packet drops and retransmissions
  • • Latency measurements
  • • Bandwidth utilization

Application Protocol Analysis

  • • HTTP/HTTPS request tracking
  • • gRPC method calls
  • • Database query monitoring
  • • Message queue operations
  • • Custom protocol decoding

System Performance

// CPU profiling with eBPF
SEC("perf_event")
int profile_cpu(struct bpf_perf_event_data *ctx) {
    u32 pid = bpf_get_current_pid_tgid() >> 32;
    u64 ts = bpf_ktime_get_ns();
    
    struct stack_trace_t *trace;
    trace = bpf_map_lookup_elem(&stack_traces, &pid);
    
    if (trace) {
        trace->kernel_stack_id = bpf_get_stackid(ctx, 
            &kernel_stacks, KERN_STACKID_FLAGS);
        trace->user_stack_id = bpf_get_stackid(ctx, 
            &user_stacks, USER_STACKID_FLAGS);
        trace->timestamp = ts;
    }
    
    return 0;
}

Application Tracing

eBPF can trace application behavior without modifying code:

Database Queries

MySQL, PostgreSQL, MongoDB wire protocol analysis

HTTP Requests

Full request/response capture with timing

Function Calls

User-space function entry/exit tracing

Security Monitoring

Security Use Cases

Runtime Security
  • • Process execution monitoring
  • • File access tracking
  • • Network connection auditing
  • • Privilege escalation detection
Threat Detection
  • • Suspicious syscall patterns
  • • Kernel rootkit detection
  • • Container escape attempts
  • • MITRE ATT&CK mapping

Container & Kubernetes

eBPF provides deep visibility into containerized environments:

  • Container Metrics: CPU, memory, network, and I/O per container
  • Service Mesh: Sidecar-free service communication tracking
  • Pod Security: Runtime policy enforcement and compliance
  • Resource Tracking: Cross-namespace resource utilization

Implementing eBPF Monitoring

Let's walk through implementing eBPF monitoring in your infrastructure, from environment setup to production deployment.

Prerequisites

System Requirements

  • Linux Kernel: 4.14+ (5.4+ recommended for BTF)
  • Kernel Headers: Matching your kernel version
  • Compiler: Clang/LLVM 10+ for BPF compilation
  • Tools: bpftool, libbpf, bcc or bpftrace
  • Permissions: CAP_SYS_ADMIN or root access

Development Environment Setup

# Install dependencies on Ubuntu/Debian
sudo apt-get update
sudo apt-get install -y     clang llvm libelf-dev libbpf-dev     linux-headers-$(uname -r)     bpftool linux-tools-$(uname -r)

# Install BCC (BPF Compiler Collection)
sudo apt-get install -y bpfcc-tools linux-headers-$(uname -r)

# Install bpftrace
sudo apt-get install -y bpftrace

# Verify installation
bpftool version
bpftrace --version

Your First eBPF Program

Let's create a simple HTTP request monitor:

// http_monitor.bpf.c
#include <vmlinux.h>
#include <bpf/bpf_helpers.h>
#include <bpf/bpf_tracing.h>
#include <bpf/bpf_core_read.h>

#define MAX_MSG_SIZE 256

struct event {
    u32 pid;
    u32 uid;
    u8 comm[16];
    u8 msg[MAX_MSG_SIZE];
};

struct {
    __uint(type, BPF_MAP_TYPE_RINGBUF);
    __uint(max_entries, 256 * 1024);
} events SEC(".maps");

SEC("uprobe/SSL_write")
int trace_ssl_write(struct pt_regs *ctx) {
    struct event *e;
    const char *buf = (const char *)PT_REGS_PARM2(ctx);
    
    e = bpf_ringbuf_reserve(&events, sizeof(*e), 0);
    if (!e)
        return 0;
    
    e->pid = bpf_get_current_pid_tgid() >> 32;
    e->uid = bpf_get_current_uid_gid() & 0xFFFFFFFF;
    bpf_get_current_comm(&e->comm, sizeof(e->comm));
    
    bpf_probe_read_user_str(&e->msg, sizeof(e->msg), buf);
    
    bpf_ringbuf_submit(e, 0);
    return 0;
}

char LICENSE[] SEC("license") = "GPL";

User-Space Loader

// loader.go
package main

import (
    "bytes"
    "encoding/binary"
    "fmt"
    "log"
    "os"
    "os/signal"
    
    "github.com/cilium/ebpf/link"
    "github.com/cilium/ebpf/perf"
    "github.com/cilium/ebpf/rlimit"
)

//go:generate go run github.com/cilium/ebpf/cmd/bpf2go -cc clang -type event bpf http_monitor.bpf.c

func main() {
    // Remove memory limit for eBPF
    if err := rlimit.RemoveMemlock(); err != nil {
        log.Fatal(err)
    }

    // Load pre-compiled eBPF programs
    objs := bpfObjects{}
    if err := loadBpfObjects(&objs, nil); err != nil {
        log.Fatalf("loading objects: %v", err)
    }
    defer objs.Close()

    // Attach to SSL_write
    ex, err := link.OpenExecutable("/usr/lib/x86_64-linux-gnu/libssl.so.1.1")
    if err != nil {
        log.Fatal(err)
    }
    
    l, err := ex.Uprobe("SSL_write", objs.TraceSslWrite, nil)
    if err != nil {
        log.Fatal(err)
    }
    defer l.Close()

    // Read events from ring buffer
    rd, err := perf.NewReader(objs.Events, os.Getpagesize())
    if err != nil {
        log.Fatal(err)
    }
    defer rd.Close()

    // Handle Ctrl-C
    sig := make(chan os.Signal, 1)
    signal.Notify(sig, os.Interrupt)

    fmt.Println("Monitoring HTTPS requests... Press Ctrl-C to exit")
    
    go func() {
        <-sig
        os.Exit(0)
    }()

    // Process events
    var event bpfEvent
    for {
        record, err := rd.Read()
        if err != nil {
            log.Fatal(err)
        }

        if err := binary.Read(bytes.NewBuffer(record.RawSample), 
            binary.LittleEndian, &event); err != nil {
            continue
        }

        fmt.Printf("[%d] %s: %s
", event.Pid, 
            string(event.Comm[:]), string(event.Msg[:]))
    }
}

Using HyperObserve

While building custom eBPF programs is powerful, HyperObserve provides production-ready eBPF monitoring out of the box:

Quick Start with HyperObserve

# Install HyperObserve agent
curl -sSL https://hyperobserve.com/install.sh | sh

# Configure with your license key
hyperobserve configure --key YOUR_LICENSE_KEY

# Start monitoring
sudo systemctl start hyperobserve
sudo systemctl enable hyperobserve

# Verify it's working
hyperobserve status

HyperObserve automatically deploys optimized eBPF programs for:

  • ✓ Application performance monitoring
  • ✓ Network flow analysis
  • ✓ Database query tracking
  • ✓ Container monitoring
  • ✓ Security threat detection

Production Deployment

Deployment Checklist

  • Verify kernel version compatibility
  • Test in staging environment first
  • Monitor resource usage during rollout
  • Configure appropriate map sizes
  • Set up log rotation and storage

Kubernetes Deployment

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: ebpf-monitor
spec:
  selector:
    matchLabels:
      app: ebpf-monitor
  template:
    spec:
      hostNetwork: true
      hostPID: true
      containers:
      - name: monitor
        image: hyperobserve/agent:latest
        securityContext:
          privileged: true
        volumeMounts:
        - name: sys
          mountPath: /sys
        - name: headers
          mountPath: /lib/modules
      volumes:
      - name: sys
        hostPath:
          path: /sys
      - name: headers
        hostPath:
          path: /lib/modules

Performance Considerations

eBPF is designed for production use with minimal overhead. Understanding performance characteristics helps optimize your monitoring deployment.

Performance Metrics

<5%
CPU Overhead

Typical overhead with full monitoring enabled

100K+
Events/Second

Processing capacity per CPU core

<1ms
Latency Impact

Added latency to monitored operations

Optimization Techniques

1. Sampling Strategies

// Adaptive sampling based on load
#define SAMPLE_RATE 10  // Sample 1 in 10 events

SEC("kprobe/tcp_sendmsg")
int trace_tcp_send(struct pt_regs *ctx) {
    // Simple sampling
    if (bpf_get_prandom_u32() % SAMPLE_RATE != 0)
        return 0;
    
    // Or time-based sampling
    u64 now = bpf_ktime_get_ns();
    static u64 last_sample = 0;
    
    if (now - last_sample < 1000000)  // 1ms minimum
        return 0;
    
    last_sample = now;
    // ... rest of monitoring logic
}

2. Map Optimization

Choose the right map types and sizes for your use case:

Map TypeUse CasePerformance
ARRAYFixed-size countersO(1) - Fastest
HASHDynamic key-valueO(1) avg
LRU_HASHBounded memoryO(1) + eviction
RINGBUFEvent streamingLock-free

3. Program Optimization

Best Practices
  • Early Returns: Exit fast for uninteresting events
  • Bounded Loops: Use #pragma unroll for loops
  • Stack Usage: Keep stack usage under 512 bytes
  • Helper Calls: Minimize expensive helper functions
  • Map Lookups: Cache repeated lookups in variables

Benchmarking Results

Real-world performance comparison between eBPF and traditional monitoring:

Metric
HTTP Latency
Baseline
45.2ms
With eBPF
45.7ms (+1.1%)
With APM Agent
52.1ms (+15.3%)
Metric
CPU Usage
Baseline
42%
With eBPF
44% (+4.8%)
With APM Agent
48% (+14.3%)

Security and Safety

eBPF's security model ensures safe execution in production kernels. Understanding these guarantees is crucial for confident deployment.

Security Architecture

eBPF Security Layers

1
Verifier: Static analysis ensures programs are safe before loading
2
Sandbox: Programs run in restricted environment with limited capabilities
3
Resource Limits: Bounded execution time and memory usage
4
Capability Checks: CAP_SYS_ADMIN or CAP_BPF required for loading

Verifier Guarantees

The eBPF verifier ensures programs cannot:

  • Access arbitrary kernel memory
  • Create infinite loops or unbounded recursion
  • Leak kernel pointers to userspace
  • Perform out-of-bounds memory access
  • Call arbitrary kernel functions

Security Use Cases

Runtime Protection

  • • Process behavior monitoring
  • • Syscall filtering and auditing
  • • Network policy enforcement
  • • File integrity monitoring
  • • Container escape detection

Threat Detection

  • • Kernel rootkit detection
  • • Cryptomining detection
  • • Privilege escalation attempts
  • • Lateral movement tracking
  • • Data exfiltration monitoring

Security Best Practices

Production Security Checklist

Real-World Use Cases

Organizations worldwide use eBPF monitoring to solve complex observability challenges. Here are proven patterns and success stories.

Case Study: E-Commerce Platform

Challenge

A major e-commerce platform with 500+ microservices struggled with:

  • • 15% performance overhead from APM agents
  • • Incomplete visibility across polyglot services
  • • 3-month deployment time for new monitoring

Solution with eBPF

  • ✓ Deployed HyperObserve eBPF monitoring in 2 days
  • ✓ Reduced monitoring overhead to <5%
  • ✓ Achieved 100% service coverage automatically
  • ✓ Detected performance bottlenecks in database queries
68%
Overhead Reduction
2 days
Deployment Time
$2.1M
Annual Savings

Case Study: Financial Services

Requirements

  • • Sub-millisecond latency monitoring
  • • Regulatory compliance for all transactions
  • • Zero application modification allowed
  • • 99.999% uptime requirement

eBPF Implementation

// Custom eBPF program for transaction monitoring
SEC("uprobe/process_transaction")
int trace_transaction(struct pt_regs *ctx) {
    struct transaction_event event = {};
    
    // Capture transaction details
    event.timestamp = bpf_ktime_get_ns();
    event.tid = bpf_get_current_pid_tgid();
    
    // Read transaction ID and amount
    bpf_probe_read_user(&event.tx_id, 
        sizeof(event.tx_id), (void *)PT_REGS_PARM1(ctx));
    bpf_probe_read_user(&event.amount, 
        sizeof(event.amount), (void *)PT_REGS_PARM2(ctx));
    
    // Check for anomalies
    if (event.amount > THRESHOLD) {
        event.flags |= ANOMALY_HIGH_VALUE;
    }
    
    // Submit to compliance system
    bpf_perf_event_output(ctx, &events, 
        BPF_F_CURRENT_CPU, &event, sizeof(event));
    
    return 0;
}

Results achieved:

  • ✓ 100% transaction visibility with 0.1ms overhead
  • ✓ Real-time fraud detection with ML integration
  • ✓ Compliance audit trail with cryptographic proof
  • ✓ Zero application changes required

Common Patterns

Database Performance

Monitor all database queries without agents:

  • • Query latency distribution
  • • Slow query detection
  • • Connection pool monitoring
  • • Transaction deadlock detection

Kubernetes Observability

Container and pod-level insights:

  • • Service mesh without sidecars
  • • Cross-namespace communication
  • • Resource usage per container
  • • Network policy violations

Industry Adoption

eBPF in Production

73%
Fortune 500 adoption
5M+
Servers monitored
90%
Cost reduction
24/7
Production ready

Best Practices

Follow these proven practices for successful eBPF monitoring deployment and operations.

Development Best Practices

Code Quality

  • Use CO-RE (Compile Once - Run Everywhere)
  • Implement proper error handling
  • Add comprehensive logging
  • Version your BPF programs
  • Write unit tests for userspace code

Performance

  • Profile BPF programs in development
  • Use per-CPU maps when possible
  • Implement sampling for high-frequency events
  • Batch map operations
  • Monitor BPF program overhead

Operational Excellence

Production Checklist

Deployment
  • □ Gradual rollout strategy
  • □ Rollback procedures documented
  • □ Performance baselines established
  • □ Alert thresholds configured
  • □ Runbooks created
Monitoring
  • □ BPF program metrics tracked
  • □ Map usage monitored
  • □ Event rates dashboarded
  • □ Error rates alerted
  • □ Resource usage tracked

Troubleshooting Guide

Common Issues and Solutions

Verifier rejection: "invalid mem access"

Check pointer bounds, add null checks, use bpf_probe_read_*

High CPU usage from BPF program

Implement sampling, optimize hot paths, reduce map operations

Map full errors

Increase map size, use LRU maps, implement cleanup logic

Missing events

Check ring buffer size, verify attach points, test in isolation

Community Resources

The Future of eBPF

eBPF continues to evolve rapidly. Here's what's coming next and how to prepare for the future of kernel-level observability.

Upcoming Features

Kernel Developments

  • BPF Timers: Scheduled operations in kernel
  • Typed Pointers: Stronger type safety
  • Dynamic Linking: Modular BPF programs
  • Windows eBPF: Cross-platform support
  • Hardware Offload: SmartNIC acceleration

Ecosystem Growth

  • Standardization: Cross-vendor compatibility
  • Cloud Native: Kubernetes integration
  • AI/ML Integration: Smart monitoring
  • Edge Computing: IoT device monitoring
  • WebAssembly: WASM + eBPF convergence

Industry Trends

2025
eBPF becomes default monitoring
80%
Cloud providers adopt eBPF
Zero
Agent-based monitoring decline
AI
Automated root cause analysis

Get Started Today

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Conclusion

eBPF represents a paradigm shift in infrastructure monitoring. By moving observability into the kernel, we achieve unprecedented visibility with minimal overhead.

Key Takeaways

  • 1eBPF enables zero-instrumentation monitoring with <5% overhead
  • 2Production-safe by design with kernel verification
  • 3Complete visibility across network, application, and infrastructure
  • 4Language and framework agnostic monitoring
  • 5The future of observability is kernel-level

Whether you're building your own eBPF programs or leveraging platforms like HyperObserve, now is the time to adopt this transformative technology. The monitoring revolution is here.

Additional Resources

Code Examples

Production-ready eBPF programs for common monitoring scenarios.

Browse Examples →

Interactive Lab

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Start Lab →

PDF Guide

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