eBPF Observability in Production: Deep Kernel Insights Without Overhead

Introduction

Remember when getting deep visibility into production systems meant choosing between three equally bad options: heavy instrumentation that tanks performance, sampling that misses critical events, or invasive kernel modules that make your SREs nervous? Yeah, those days are thankfully behind us.

eBPF (Extended Berkeley Packet Filter) has fundamentally changed the observability game. In 2025, it’s become the de facto standard for production-grade monitoring, security, and performance analysis - and for good reason. It gives you kernel-level visibility with overhead so low you can run it everywhere, all the time, without the paranoia that used to come with deep instrumentation.

I’ve been running eBPF-based observability in production for the past two years across Kubernetes clusters handling millions of requests daily. The insights it provides have been game-changing for debugging, security monitoring, and performance optimization. In this guide, I’ll share what I’ve learned about deploying eBPF observability tools, the real-world value they deliver, and the gotchas you need to watch out for.

What Makes eBPF Different: The Technical Edge

Traditional Observability vs eBPF

Traditional approach problems:

• Performance overhead: Instrumentation libraries add latency and memory bloat
• Code changes required: Adding tracing means modifying and redeploying services
• Incomplete visibility: You only see what you explicitly instrumented
• Kernel blind spots: User-space tools can’t see network stack, syscalls, or scheduler behavior
• Sampling bias: To reduce overhead, you sample - and miss the anomalies you care about

eBPF advantages:

• No application changes: eBPF programs run in the kernel, observing without touching your code
• Sub-microsecond overhead: Validated in production at companies like Netflix, Cloudflare, and Meta
• Complete system visibility: See everything from network packets to file I/O to CPU scheduling
• Safety guarantees: The eBPF verifier ensures programs can’t crash the kernel
• Dynamic instrumentation: Attach/detach probes without restarts

How eBPF Actually Works

In simple terms:

1. You write a small program (in C or using high-level frameworks)
2. The eBPF verifier ensures it’s safe (bounded loops, no invalid memory access)
3. It’s JIT-compiled to native machine code
4. It attaches to kernel events (syscalls, network packets, function calls)
5. Data is efficiently passed to user space via maps or ring buffers

Think of it as running sandboxed code inside the kernel, with performance comparable to native kernel modules but with safety guarantees.

Production-Ready eBPF Observability Tools

1. Cilium Hubble: Network Observability Done Right

Cilium is primarily a CNI (Container Network Interface), but its Hubble component provides incredible network observability.

What it gives you:

• Layer 7 visibility: See HTTP, gRPC, Kafka, DNS traffic without sidecars
• Service dependency mapping: Auto-generated from actual traffic flows
• Network policy visualization: Understand what’s allowed and what’s blocked
• Latency breakdown: Where time is spent in the network stack

Quick setup:

# Install Cilium with Hubble enabled
helm install cilium cilium/cilium --version 1.15.0 \
  --namespace kube-system \
  --set hubble.relay.enabled=true \
  --set hubble.ui.enabled=true

# Enable Hubble CLI
cilium hubble enable --ui

# Watch live traffic
hubble observe --namespace default --protocol http

Real use case:

We had mysterious 500ms latency spikes on checkout requests. Traditional APM showed “network delay” - super helpful, right? Hubble revealed that DNS lookups for a payment service were timing out and retrying. The service discovery config had stale endpoints. Five-minute fix.

2. Pixie: Zero-Instrumentation Application Monitoring

Pixie is my go-to for application-level observability without touching code.

What it captures automatically:

• HTTP/HTTPS request traces (yes, even encrypted traffic via eBPF SSL hooks)
• Database queries (MySQL, PostgreSQL, Redis, MongoDB)
• DNS lookups and responses
• gRPC and Kafka messages
• Resource usage per service

Installation:

# Install Pixie
kubectl apply -f https://withpixie.ai/install.yaml

# Or via Helm
helm install pixie pixie-operator/pixie-operator-chart \
  --set clusterName=production \
  --set deployKey=<your-deploy-key>

Why I love it:

You get distributed tracing, service maps, and request-level debugging without adding a single line of instrumentation code. For legacy apps or third-party services you can’t modify, it’s a lifesaver.

Example query:

# PxL (Pixie Language) - find slow database queries
import px

# Get MySQL queries taking > 100ms
df = px.DataFrame(table='mysql_events', start_time='-5m')
df = df[df.latency_ns > 100000000]
df = df.groupby(['req', 'service']).agg(
    count=('latency_ns', px.count),
    avg_latency_ms=('latency_ns', px.mean)
)
px.display(df)

3. Falco: Runtime Security with eBPF

Security monitoring is where eBPF really shines. Falco detects anomalous behavior in real-time.

What it catches:

• Unexpected process execution (crypto miners, reverse shells)
• Sensitive file access (reading /etc/shadow, AWS credentials)
• Network connections from suspicious processes
• Container escapes and privilege escalations
• Configuration tampering

Setup:

# Install Falco with eBPF driver
helm repo add falcosecurity https://falcosecurity.github.io/charts
helm install falco falcosecurity/falco \
  --set driver.kind=ebpf \
  --set falco.grpc.enabled=true \
  --set falco.grpc_output.enabled=true

Custom rules example:

- rule: Unauthorized Process in Container
  desc: Detect processes not in the approved list
  condition: >
    container and proc.name not in (node, nginx, python)
  output: >
    Unexpected process in container
    (user=%user.name command=%proc.cmdline container=%container.name)
  priority: WARNING

Real incident:

Falco alerted us to a compromised container running curl to download a shell script. The pod had been exploited via an unpatched Log4j vulnerability. We isolated it within 60 seconds of initial access. That’s the kind of speed you need.

4. BPF-Based Performance Tools (BCC and bpftrace)

For deep performance troubleshooting, BCC (BPF Compiler Collection) and bpftrace are essential.

BCC provides ready-made tools:

# Track slow filesystem operations
biolatency -m  # Block I/O latency histogram

# Find which processes are causing CPU cache misses
llcstat 5 1  # Last-level cache stats

# Trace TCP retransmits
tcpretrans

bpftrace is a high-level scripting language:

# Trace slow syscalls
bpftrace -e '
  tracepoint:raw_syscalls:sys_enter {
    @start[tid] = nsecs;
  }
  tracepoint:raw_syscalls:sys_exit /@start[tid]/ {
    $duration_us = (nsecs - @start[tid]) / 1000;
    if ($duration_us > 10000) {
      printf("%s took %d us\n", comm, $duration_us);
    }
    delete(@start[tid]);
  }
'

When to use it:

When you’re past monitoring dashboards and need to dig into kernel-level behavior. I use bpftrace for performance investigations and capacity planning deep dives.

Designing Your eBPF Observability Stack

The Layered Approach

Don’t try to use every tool at once. Build incrementally:

Layer 1: Network visibility

• Cilium Hubble for service-to-service flows
• DNS query monitoring
• Network policy verification

Layer 2: Application observability

• Pixie for auto-instrumented tracing
• HTTP/gRPC request analysis
• Database query performance

Layer 3: Security monitoring

• Falco for runtime threat detection
• Process execution tracking
• File integrity monitoring

Layer 4: Performance deep-dives

• BCC/bpftrace for kernel-level investigation
• On-demand, not always-on

Integration with Existing Tools

eBPF doesn’t replace your existing observability - it complements it.

My stack:

• Metrics: Prometheus (eBPF exporters for custom metrics)
• Logs: Grafana Loki (enriched with eBPF context)
• Traces: Pixie feeds into Jaeger for long-term storage
• Security: Falco alerts to PagerDuty and Slack
• Network: Hubble provides service maps for Grafana

Integration pattern:

# Example: Falco → Fluentd → Elasticsearch
# falco-config.yaml
json_output: true
json_include_output_property: true
http_output:
  enabled: true
  url: "http://fluentd:8888/falco"

Performance Considerations: Yes, Even eBPF Has Limits

Overhead Reality Check

eBPF is low-overhead, but “low” isn’t “zero.” Here’s what I’ve measured:

Best practices:

1. Start with one tool - don’t deploy everything at once
2. Monitor the monitors - watch your eBPF tools’ resource usage
3. Use targeted probes - don’t attach to every syscall, be selective
4. Set limits - use Kubernetes resource limits on eBPF pods
5. Test in staging first - validate overhead before production

When eBPF Might Not Be Right

Be honest about constraints:

• Kernel version requirements: eBPF needs Linux 4.9+ (5.8+ recommended)
• Cloud restrictions: Some managed Kubernetes services limit eBPF (check your provider)
• Regulatory constraints: Some compliance frameworks prohibit kernel-level monitoring
• Extreme scale: At massive scale, even 2% overhead matters

Troubleshooting eBPF Observability Tools

Common Issues I’ve Hit

1. eBPF programs not loading

# Check kernel version and config
uname -r
cat /boot/config-$(uname -r) | grep CONFIG_BPF

# Verify eBPF support
bpftool feature

# Check loaded programs
bpftool prog list

2. Performance degradation

# Check how many eBPF programs are loaded
bpftool prog show | wc -l

# Look for programs with high event counts
bpftool prog show --json | jq '.[] | {id, run_cnt, run_time_ns}'

# Detach problematic probes if needed
bpftool prog detach id <program-id>

3. Missing data or events

• Check buffer sizes: eBPF ring buffers can overflow under high load
• Verify probe attachment: Ensure probes are on the right kernel functions
• Look for verifier errors: dmesg | grep -i bpf shows verification failures

Debugging Pro Tips

# Enable eBPF debug logging
echo 1 > /sys/kernel/debug/tracing/events/bpf/enable

# Watch for verification errors
dmesg -w | grep bpf

# Check map usage (can cause memory issues)
bpftool map list
bpftool map dump id <map-id>

Security Best Practices

eBPF is Powerful - Guard It Carefully

The risk:

eBPF can read any kernel memory, intercept any syscall, and modify network packets. In the wrong hands, it’s a rootkit.

How to lock it down:

1. Restrict CAP_BPF and CAP_SYS_ADMIN

Only specific pods/users should load eBPF programs:

# Falco deployment
securityContext:
  capabilities:
    add:
      - BPF
      - SYS_ADMIN  # Required for some operations
    drop:
      - ALL
  privileged: false

2. Use signed eBPF programs

With kernel 5.13+:

# Sign your eBPF object files
sign-file sha256 kernel-key.priv kernel-key.pub program.o

3. Audit eBPF program loading

# Enable audit logging
auditctl -a always,exit -F arch=b64 -S bpf

4. Network isolation for eBPF tools

Use network policies to restrict where observability data flows:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: pixie-egress
spec:
  podSelector:
    matchLabels:
      app: pixie
  policyTypes:
  - Egress
  egress:
  - to:
    - podSelector:
        matchLabels:
          app: pixie-cloud
    ports:
    - protocol: TCP
      port: 443

Real-World Case Studies

Case 1: Cutting Incident Response Time by 80%

Problem: Microservices with 50+ interdependent APIs. When something broke, we spent hours correlating logs.

eBPF solution:

• Pixie for automatic request tracing
• Hubble for service dependency maps
• Falco for security anomalies

Result:

• Mean time to detection (MTTD): 45 min → 3 min
• Mean time to resolution (MTTR): 2 hours → 25 min
• We could replay failing requests without repro steps

Case 2: Finding a 6-Year-Old Performance Bug

Problem: Random 10-second pauses in our API gateway under load.

eBPF solution:

Used bpftrace to trace kernel scheduler events:

bpftrace -e '
  kprobe:finish_task_switch {
    @[comm] = hist(nsecs - @start[tid]);
    delete(@start[tid]);
  }
  kprobe:schedule {
    @start[tid] = nsecs;
  }
'

Discovery: The gateway process was being descheduled for 10+ seconds due to CPU cgroup throttling. A misconfigured limit from 2019 that no one had noticed.

Fix: Adjusted CPU limits. Problem gone.

Getting Started: Your First eBPF Observability Project

Week 1: Network visibility

# Install Cilium with Hubble
helm install cilium cilium/cilium \
  --namespace kube-system \
  --set hubble.relay.enabled=true \
  --set hubble.ui.enabled=true

# Watch traffic
hubble observe --namespace default

Week 2: Application monitoring

# Deploy Pixie
kubectl apply -f https://withpixie.ai/install.yaml

# Explore in the UI
px live default  # Live debugging

Week 3: Security monitoring

# Install Falco
helm install falco falcosecurity/falco --set driver.kind=ebpf

# Check alerts
kubectl logs -n falco -l app=falco

Week 4: Performance deep-dive

# Install BCC tools
apt-get install bpfcc-tools  # Ubuntu/Debian
yum install bcc-tools  # RHEL/CentOS

# Start exploring
/usr/share/bcc/tools/execsnoop  # Trace new processes
/usr/share/bcc/tools/tcplife    # TCP connection lifetimes

Best Practices Checklist

• Verify kernel version compatibility (5.8+ recommended)
• Deploy one tool at a time to understand overhead
• Set resource limits on eBPF monitoring pods
• Restrict CAP_BPF and CAP_SYS_ADMIN capabilities
• Enable audit logging for eBPF program loads
• Integrate eBPF data with existing observability stack
• Create runbooks for common eBPF troubleshooting
• Test in non-production first
• Monitor the monitors (watch eBPF tool resource usage)
• Document your eBPF observability architecture

Resources & Further Learning

• Cilium and Hubble Documentation
• Pixie Docs
• Falco Rules and Configuration
• BCC Tutorial
• bpftrace Guide
• eBPF Summit Talks

Related articles on INFOiYo:

• Building Resilient Microservices: Circuit Breakers & Retry Patterns
• Container Supply Chain Security
• GitOps Continuous Deployment

Final Thoughts

eBPF has moved from “bleeding edge” to “production standard” in 2025. The ability to get deep, kernel-level visibility without performance penalties or code changes is genuinely transformative.

I’ve debugged issues with eBPF that would have been impossible to solve with traditional tools. The combination of network visibility (Hubble), application tracing (Pixie), and security monitoring (Falco) gives you a complete picture of what’s actually happening in production.

The learning curve is real - eBPF isn’t magic, and you need to understand what you’re measuring. But the investment pays off quickly. Start small, pick one tool, learn it deeply, then expand.

The future of observability is kernel-native, low-overhead, and continuous. eBPF is how we get there.

Keep your systems observable and your kernels instrumented.