This post was originally posted over on my personal website. Maybe you want to check it out [HERE].
We tried to replace sidecar monitoring with eBPF in Kubernetes. Spoiler: it didn't go as planned. Here's what we learned about hype vs reality in cloud-native monitoring, and when cutting-edge tech isn't always the answer.
Last semester, my team and I embarked on an ambitious project: comparing eBPF-based monitoring with traditional sidecar proxy monitoring in Kubernetes clusters. What started as academic curiosity turned into a deep dive into kernel programming, developer experience, and the realities of cutting-edge technology adoption.
The Promise of eBPF
Extended Berkeley Packet Filter (eBPF) has been making waves in the cloud-native world. The promise is compelling: instead of deploying a sidecar container in every pod to monitor network traffic, you can run a single eBPF program in the kernel that observes everything on the node. It sounds like magic—lower resource overhead, better security visibility, and simplified deployments.
But is it really better? That's what we set out to discover.
The Traditional Approach: Sidecar Proxies
Before diving into eBPF, let's talk about how monitoring typically works today. The sidecar pattern has become the go-to approach for microservice monitoring. Tools like Envoy proxy sit alongside your application in the same pod, intercepting all network traffic and providing rich telemetry.
The good parts:
- Well-understood deployment patterns
- Rich ecosystem of tools and integrations
- Isolated from the host system
- Language and framework agnostic
The not-so-good parts:
- Resource overhead multiplied across every pod
- Additional complexity in pod configurations
- Potential single points of failure
- Network latency from additional hops
Our eBPF Experiment
We decided to build two functionally identical network monitors: one using the traditional sidecar approach and another using eBPF. Our goal was simple—track HTTP request timing and response sizes for services communicating in a Kubernetes cluster.
The Reality Check
Here's where things got interesting (read: frustrating). While the sidecar implementation was straightforward, the eBPF version became a lesson in humility.
We chose Rust with the Aya framework, hoping to avoid the typical C development complexity associated with eBPF. The initial setup was smooth—Aya's templates got us started quickly. But implementing HTTP monitoring? That's where we hit the wall.
The Technical Challenges
Memory Constraints: eBPF programs are limited to a 512-byte stack. Try parsing HTTP payloads with that constraint. Every function call needs to be carefully designed to avoid stack overflow.
The Verifier: eBPF's safety verifier is both a blessing and a curse. It prevents crashes and security issues, but it's incredibly strict about memory access patterns. We spent countless hours fighting "invalid memory access" errors even with proper bounds checking.
Limited Debugging: Forget about your favorite debugger. eBPF debugging involves a lot of bpf_printk() statements and careful reading of verifier logs. The development experience feels like programming in the 1990s.
Ecosystem Maturity: While there are excellent eBPF tools like Pixie and Cilium, rolling your own solution requires deep kernel knowledge. The learning curve is steep, and the documentation assumes significant background knowledge.
What We Actually Discovered
After weeks of development, we had to face facts: we couldn't complete our eBPF HTTP monitor in the timeframe. But this "failure" taught us valuable lessons.
eBPF Isn't Magic
The hype around eBPF is real, but it's not a silver bullet. It excels at:
- Low-level network analysis
- System-wide visibility
- Performance monitoring
- Security enforcement
But for application-layer monitoring like HTTP request tracing? The traditional approaches are often more practical.
The Developer Experience Gap
Moving from sidecar configuration to eBPF programming shifts the burden from operations teams to developers. This isn't necessarily bad, but it requires different skills. You're essentially doing kernel programming, which most application developers haven't done since their systems programming course.
When to Choose What
Based on our experience, here's our take:
Choose eBPF when:
- You need system-wide visibility
- Performance overhead is critical
- You're building infrastructure tools
- Your team has kernel programming expertise
Stick with sidecars when:
- You need application-layer insights
- Your team values quick iteration
- You want mature tooling and support
- Operational simplicity matters
The Bigger Picture
Our experiment confirmed something important: technology choices aren't just about technical capabilities—they're about team capabilities, operational complexity, and long-term maintainability.
eBPF is incredibly powerful, but with great power comes great complexity. For most teams monitoring microservices, mature solutions like Envoy proxy or ready-made eBPF tools like Pixie offer the best balance of capability and usability.
Lessons Learned
Prototype early: We should have built a minimal eBPF program first to understand the constraints before committing to a complex implementation.
Leverage existing tools: Unless you have specific requirements, using battle-tested solutions like Cilium or Pixie is probably smarter than rolling your own.
Consider the total cost: The "cost" of a technology includes development time, debugging complexity, and ongoing maintenance—not just runtime overhead.
Know your use case: eBPF shines for infrastructure and low-level monitoring, but application-layer observability might be better served by traditional approaches.
What's Next?
While our eBPF monitor didn't cross the finish line, the experience was invaluable. We gained deep insights into kernel programming, network monitoring, and the practical challenges of adopting cutting-edge technology.
eBPF is undoubtedly the future of many aspects of systems programming. But like any powerful technology, it requires careful consideration of when and how to apply it. Sometimes the boring, well-understood solution is exactly what you need.
For teams considering eBPF for monitoring, my advice is simple: start with existing tools, understand your specific requirements, and be prepared for a steep learning curve if you decide to build custom solutions.
The future of cloud-native monitoring is exciting, but it's built on a foundation of understanding both the possibilities and the trade-offs of the tools at our disposal.
This post is based on research conducted as part of a university project exploring eBPF applications in Kubernetes environments. While our implementation didn't reach completion, the insights gained about technology adoption and developer experience proved invaluable.
Top comments (0)