Add Observability with Pixie eBPF
Have you ever wished you could see exactly what your Kubernetes system does without changing any code?
I’ve been there — frustrated by blind spots and long debug cycles. I want to show a fast path to clear, in-cluster insight that helps developers act now.
With a single install on your platform, this open source tool taps the linux kernel via eBPF to collect rich telemetry data. You get golden signals, service maps, HTTP and database traces, and CPU flame graphs — all without language agents or instrumentation.
We’ll walk through prerequisites, install, validation, and how to integrate with New Relic for long-term storage, alerting, and incident correlation. My goal is practical: help you gain useful visibility today so you can fix issues faster and keep teams focused on building.
Key Takeaways
- You can enable in-cluster visibility with one install and no code changes.
- The linux kernel powers automatic telemetry collection for quick insights.
- This open source platform provides service maps, traces, and flame graphs day one.
- New Relic adds storage, alerts, and correlation for long-term value.
- Follow a simple workflow: prerequisites, install, validate, integrate, optimize.
Why Pixie eBPF observability matters for Kubernetes teams today
Seeing real activity inside a Kubernetes node cuts debug time and reduces guesswork. At the linux kernel level we can capture system and network events without touching app code. That means fewer deploys and faster answers when incidents happen.
This technology runs verified, sandboxed programs in the kernel, so platform owners get strong security guarantees and low overhead. The JIT-compiled approach keeps performance tight while collecting rich data on traffic, errors, and latency.
Compared with manual instrumentation, an event-driven, kernel-based method saves developers time in cloud native environments. It links network behavior to application symptoms so teams can reason end-to-end.
- Safe by design — programs are verified before they run in the kernel.
- Practical — no code changes to start seeing system-level traces.
- Composable — works with open standards and CNCF projects and can feed New Relic for longer-term storage and alerts.
Prerequisites and environment readiness
A quick readiness check saves hours—verify kernel support, permissions, and outbound access first.
Supported Linux kernel and cluster considerations
Confirm your linux kernel versions meet the minimum for kernel probes—widespread support begins around 4.13–4.14+. Check node images and node pools so the in-cluster programs can load safely.
Review your Kubernetes cluster distribution and API versions. Ensure RBAC is enabled and you can grant the required cluster-level permissions for agents and DaemonSets to run.
Access, permissions, and network requirements for in-cluster deployment
Plan outbound network egress for telemetry to community endpoints or for routing to New Relic. Verify DNS and registry access so images pull without interruption.
Keep security tight: use least-privilege RBAC, appropriate PodSecurity settings, and limit elevated capabilities to only what the kernel hooks require.
- Check node resource headroom—CPU and memory—for collection processes so they don’t compete with workloads.
- Understand user space vs kernel space implications: probes attach via kprobes/uprobes, so container runtime and kernel config matter.
- If you’ll route off-cluster, prepare secure keys and outbound connectivity for New Relic integration.
Install and configure Pixie in your cluster
I’ll show a single command to start collecting rich kernel and application signals right away.
Run the installer and the DaemonSet will deploy across nodes so each pod can feed telemetry without touching your application code. With one install the system will automatically collect service-level metrics, unsampled requests, and database traces.
One-command install and initial bootstrap
Execute the official CLI install to bootstrap the control plane and agents. The installer loads verified programs into the kernel and enriches events with Kubernetes metadata.
Verifying agents and data plane health across nodes and pods
Check DaemonSet status and pod readiness with kubectl. Confirm each node reports a healthy agent and that the data path shows HTTP latencies, error rates, and DB call counts.
Tuning for your environment: namespaces, data locality, and retention
Scope collection by namespace to reduce noise and keep data local where possible. Route selected telemetry to New Relic for long-term storage and alerting.
- Quick checks — kubectl get ds, kubectl get pods -n pl, and CLI health commands.
- Noise control — exclude sensitive namespaces and enable sampling for high-traffic services.
- Retention — configure routing to New Relic for retention and incident correlation.
| Step | Command / Check | Expected Result | Notes |
|---|---|---|---|
| Install | installer cli apply | Control plane + DaemonSet running | Minutes to bootstrap; no code changes |
| Node health | kubectl get ds -n pl | All nodes report ready pods | Verify kernel hooks loaded |
| Telemetry check | cli show metrics / UI | HTTP latencies, traces visible | Look for service-level metrics and DB calls |
| Export | route to New Relic | Data forwarded for alerts | Keep in-cluster locality for performance |
Validate telemetry and start exploring key features
Before diving deep, confirm the telemetry pipeline is capturing meaningful service behavior. I like a quick checklist to prove value fast.
First, verify service-level metrics — latency, error rate, and throughput — for a few critical services. If those metrics and traces appear, you can trust the data stream and move on to exploration.
Automatic collection of service-level metrics, requests, and traces
Check that the system will automatically collect HTTP golden signals and request traces across your services. Look for end-to-end traces that show user-facing requests and any backend calls.
Service maps and golden signals for HTTP services
Open the service map to visualize dependencies and unexpected network edges. Use traces to confirm requests flow as expected and to spot noisy or slow links.
Live debugging and database transactions
Use live capture to inspect full-body requests and DB transactions for MySQL, PostgreSQL, Redis, and DNS. These views speed root-cause work without redeploys.
CPU flame graphs for application profiling
Generate CPU flame graphs to find hot paths in Go, C, or Rust binaries. No instrumentation or restarts needed — you’ll see performance hotspots in seconds.
Kubernetes cluster explorer: drill down to pod events
From cluster to namespace to pod, correlate metrics and events with trace spikes. That context helps tie technical findings to user impact and SLOs.
- Quick wins: fix a noisy retry, reduce a chatty dependency, or harden an endpoint.
- Confirm kernel-level visibility by checking timing and metadata when payloads are encrypted.
| Check | How to verify | Expected result |
|---|---|---|
| Service metrics | Look for latency, error rate, throughput | Golden signals visible per service |
| Traces | Inspect end-to-end traces for requests | Requests map across services with span details |
| Live debugging | Capture full requests and DB calls | Transaction bodies and SQL visible for analysis |
| Flame graphs | Run sampling profiler | Hot functions highlighted without redeploy |
Connect Pixie with New Relic for long-term value
Connecting your in-cluster signals to a scalable backend turns quick hits into lasting operational value. I’ll outline the practical steps and what you gain when you route telemetry to New Relic.
Route telemetry to New Relic for storage and alerts
After creating a New Relic account and installing the integration, configure routing so Pixie streams telemetry to New Relic One. That gives durable storage, dashboards, and alerting on the same signals you inspect in-cluster.
Incident correlation and production support
Link logs, metrics, and traces to enrich context. This helps incident commanders correlate symptoms and reduce mean time to resolution.
- Map cluster services and pods to New Relic entities for accurate topology.
- Fine-tune alert policies on critical requests and error rates to cut noise.
- Document how uses ebpf hooks surface system facts and meet security and governance needs.
| Benefit | Action | Result |
|---|---|---|
| Durable storage | Route telemetry | Historical analysis |
| Faster MTR | Correlate logs & traces | Quicker root cause |
| Scale & support | Enable commercial plan | Operational guidance |
Performance, security, and open source considerations
Performance and safety should guide any decision to add kernel-level telemetry in production. I focus on practical numbers and guardrails so teams can adopt this technology without surprises.
eBPF overhead, JIT compilation, and real-world efficiency targets
JIT compilation and the kernel verifier make probes efficient. In practice, CPU overhead is small—typical reports show under 2% and worst-case caps near 5% for continuous collectors.
Large operators have measured sub‑1% cost for focused flow logging. Still, watch node-level resource dashboards and scope collectors to control resource use.
Safety model: verifier, hooks, and sandboxed programs
Probes attach to kernel hooks like kprobes, uprobes, and tracepoints and run in a sandbox. The verifier blocks unsafe programs so security teams can approve deploys with confidence.
Limit which namespaces can be observed, disable unneeded collectors, and apply access controls and audits to reduce risk while keeping useful events and traffic insights.
Open standards and CNCF: the open source project path
This approach follows open standards and a CNCF sandbox path, which helps avoid lock-in and attracts contributions. That community momentum improves the code, lowers risk, and gives developers clear upgrade paths.
| Focus | Action | Result |
|---|---|---|
| Performance | Sample or scope collectors | Lower cpu overhead |
| Security | Restrict namespaces & audit | Safer kernel use |
| Operational | Monitor node resources | Predictable resource budgets |
Next steps to level up your observability
Start small and prove value quickly. I suggest a 30–60 minute pilot in a non‑critical cluster to confirm metrics, traces, and basic CPU impact. Document the results in plain terms so developers see wins fast.
Week one, expand to two key applications, compare before/after performance, and tune instrumentation choices to cut noise. Add alerts for top requests and error budgets so real incidents validate the effort.
Let Pixie use eBPF at the kernel level to reduce manual instrumentation and keep code focused on business logic. Form a small working group, write runbooks, and harden security and governance as you scale.
Follow this roadmap and you’ll lift platform-level kubernetes observability to the next level while keeping teams productive.
