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Quick Definition

Plain-English definition: eBPF observability uses the kernel-level eBPF (extended Berkeley Packet Filter) mechanism to collect, filter, and emit high-fidelity runtime telemetry from hosts, containers, and services without changing application code.

Analogy: eBPF observability is like adding invisible, programmable sensors into the plumbing of an entire data center that can inspect water flow, pressure, and leaks without cutting pipes.

Formal technical line: eBPF observability programs attach to kernel tracepoints, kprobes, uprobes, sockets, cgroups, and network hooks to capture events and metrics with minimal overhead and programmable filtering delivered to user-space consumers.

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What is eBPF observability?

What it is / what it is NOT

• It is a low-level, dynamic instrumentation layer inside the Linux kernel used for capturing observability signals.
• It is NOT a full observability platform by itself; it is an instrumentation substrate that exports telemetry to tools.
• It is NOT a replacement for application-level tracing, but it can augment and correlate with traces and logs.

Key properties and constraints

• Low overhead when used correctly; programs run in-kernel sandbox.
• Requires recent Linux kernels and privileged load permission.
• Programmability enables context-rich filtering and aggregation.
• Restricted by verifier limits; complex logic may fail to load.
• Cross-process and cross-container visibility when attached at the kernel level.
• Security controls and auditing required to avoid escalation.

Where it fits in modern cloud/SRE workflows

• Supplemental layer to APM, logs, and metrics: fills blind spots like network visibility, syscall-level latency, and kernel scheduling anomalies.
• Useful for incident response, live debugging, performance tuning, and security monitoring (host and container).
• Works in Kubernetes and cloud VMs; integrates with CI pipelines for testing instrumentation and with SLO processes for targeted SLIs.

A text-only “diagram description” readers can visualize

• Hosts and nodes contain kernel where eBPF programs are loaded.
• eBPF probes attach to syscalls, network sockets, cgroups, and tracepoints.
• Probes collect events, histograms, and counters in kernel maps.
• A user-space agent reads maps and consumes perf events, processes them, and exports to backend observability systems.
• Observability backend stores metrics, traces, and logs and drives dashboards and alerts.

eBPF observability in one sentence

A programmable, low-latency mechanism for capturing kernel-level telemetry and transforming it into actionable metrics, events, and diagnostics for monitoring and incident response.

eBPF observability vs related terms (TABLE REQUIRED)

ID	Term	How it differs from eBPF observability	Common confusion
T1	Tracing	Focus on call stacks and spans at app level	People conflate tracing with kernel probes
T2	Logging	Textual records from apps and systems	Logs are verboser and higher overhead
T3	Metrics	Aggregated numeric series	Metrics lack raw event context by default
T4	Network tapping	Passive packet capture	eBPF can be programmable and filtered in kernel
T5	Security EDR	Focus on threats and alerts	eBPF is an enabler not a full EDR platform
T6	System profiling	CPU and memory profiling	eBPF provides targeted, live sampling
T7	APM	Application performance management	APM often needs app instrumentation
T8	Kernel auditing	Compliance-focused logs	eBPF can implement custom audits
T9	eBPF programs	Implementation code	Observability is the use case not just code
T10	Sidecar agents	User-space per-pod collectors	eBPF can be host-global and non-invasive

Row Details (only if any cell says “See details below”)

• None

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Why does eBPF observability matter?

Business impact (revenue, trust, risk)

• Faster detection of service degradations reduces user-visible outages and churn.
• Root-causing intermittent and network-level issues prevents repeated revenue impact.
• Enables risk reduction by detecting anomalous kernel or network behavior early.
• Supports compliance and forensic needs with high-fidelity event capture.

Engineering impact (incident reduction, velocity)

• Shortens time-to-detect and time-to-resolve for hard-to-reproduce problems.
• Reduces need for code changes to diagnose production issues.
• Enables experimentation and performance tuning with lower risk thanks to precise telemetry.
• Lowers troubleshooting toil by offering immediate visibility into kernel and network events.

SRE framing (SLIs/SLOs/error budgets/toil/on-call)

• eBPF-derived SLIs can monitor syscall latency, socket connect success rate, TCP retransmissions, and container scheduling stalls.
• Use eBPF metrics in SLOs for availability and latency when kernel-level behavior impacts user experience.
• Error budget burn can be tied to SLOs that include eBPF signals for platform health.
• Reduces on-call toil when runbooks include eBPF probes for fast triage.

3–5 realistic “what breaks in production” examples

• Network path flaps due to MTU mismatch causing repeated retransmits.
• Latent syscall blocking in a thread pool causing request queue buildup.
• DNS query failures from a misconfigured DNS proxy leading to cascading outages.
• Container host CPU steal due to noisy neighbor and cgroup misconfiguration.
• Load balancer health-check spikes due to a kernel bug causing delayed ACKs.

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Where is eBPF observability used? (TABLE REQUIRED)

ID	Layer/Area	How eBPF observability appears	Typical telemetry	Common tools
L1	Edge network	Kernel socket hooks for ingress/egress	Packet drops retransmits latency	eBPF agents XDP tracers
L2	Host OS	Syscall probes and scheduler hooks	Syscall latency counters CPU steal	bpftrace libbpf based agents
L3	Container runtime	cgroup probes and network namespacing	Per-pod syscalls network bytes	Container-aware eBPF collectors
L4	Service	Uprobe for user functions plus network	Function latency tcp stats histograms	APM plus eBPF integrations
L5	Data plane	XDP and tc for packet processing	Per-flow metrics and drops	XDP programs and collectors
L6	Kubernetes control	Kube-proxy and kubelet hooks	Kube-proxy conntrack metrics	K8s-aware eBPF operators
L7	Serverless/PaaS	Host observability for managed runtimes	Cold-start syscalls API latency	Platform-integrated eBPF agents

Row Details (only if needed)

• None

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When should you use eBPF observability?

When it’s necessary

• You need kernel-level context to investigate performance or reliability issues.
• Network debugging requires visibility into packets, sockets, or conntrack events.
• Multi-tenant hosts require host-aware telemetry per cgroup/container.
• Security monitoring demands syscall-level detection or behavior-based heuristics.

When it’s optional

• You have strong application-level instrumentation and only need occasional host insights.
• Low-scale workloads where simpler logging and metrics suffice.
• Early-stage projects where operational overhead is a higher priority than deep visibility.

When NOT to use / overuse it

• For simple business metrics and events best captured in application code.
• When kernel privileges and platform constraints prohibit safe deployment.
• When complexity and maintenance overhead would outweigh benefits.

Decision checklist

• If you need cross-container network visibility AND low-latency packet filtering -> use eBPF.
• If you need only business metrics and distributed tracing -> prefer application instrumentation.
• If kernel access is restricted AND you can’t manage eBPF lifecycle -> choose host agent alternatives.

Maturity ladder: Beginner -> Intermediate -> Advanced

• Beginner: Deploy host-level eBPF agent for basic syscall latency and TCP metrics.
• Intermediate: Per-pod tagging, exported histograms, and integrated dashboards.
• Advanced: Dynamic on-demand probes for incident response, automated remediation, and security policies enforced via eBPF.

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How does eBPF observability work?

Explain step-by-step

Components and workflow

1. eBPF program: small, verified bytecode that registers hooks in kernel tracepoints, kprobes, uprobes, or network hooks like XDP/tc.
2. Kernel verifier: static checks ensure safety and bounded resource usage before loading.
3. Kernel maps: in-kernel data structures where the program stores counters, histograms, and small state.
4. Perf events / ring buffers: used to transfer streaming events from kernel to user-space.
5. User-space agent: reads maps, decodes events, enriches with metadata (labels, pod info), and exports to observability backends.
6. Backend and dashboards: long-term storage, correlation with traces/logs, and alerting.

Data flow and lifecycle

• Load program -> attach to hook -> collect events into maps -> agent polls or receives events -> aggregate and export -> store and visualize -> retire program when done.

Edge cases and failure modes

• Verifier rejection due to complex logic or loops.
• Map size exhaustion under bursty workloads.
• Agent crash leaving programs loaded but uncollected.
• Kernel version incompatibility causing incompatible helper calls.

Typical architecture patterns for eBPF observability

1. Host-agent pattern – Use case: Node-level visibility in cloud VMs or Kubernetes nodes. – Notes: Single agent per node reads kernel maps and exports to metrics pipeline.

2. Per-service sidecar pattern – Use case: Service-level deeper inspection requiring per-app context. – Notes: Sidecar attaches to uprobes in process context; limited by sidecar privileges.

3. On-demand troubleshooting pattern – Use case: Ad-hoc incident response. – Notes: Load eBPF probes temporarily during incidents to gather high-res traces, then unload.

4. Data-plane accelerator pattern – Use case: High-performance packet processing and flow telemetry using XDP. – Notes: Use in edge or load balancer nodes for per-packet metrics.

5. Security enforcement pattern – Use case: Real-time policy enforcement and detection. – Notes: eBPF attaches to syscalls and cgroups for behavior-based blocking or alerting.

6. Cloud-native operator pattern – Use case: Kubernetes environments with managed lifecycle. – Notes: Operator manages RBAC, program loading, and upgrades.

Failure modes & mitigation (TABLE REQUIRED)

ID	Failure mode	Symptom	Likely cause	Mitigation	Observability signal
F1	Verifier reject	Program won’t load	Complex code or unsupported helper	Simplify code test smaller programs	Agent logs kernel load error
F2	Map exhaustion	Missing events or OOM	Insufficient map size under burst	Increase map size add eviction	Drop counters in agent
F3	High overhead	CPU spikes on host	Polling too frequently or heavy logic	Reduce sampling use aggregation	Host CPU and runqueue metrics
F4	Agent crash	No telemetry exported	User-space bug or OOM	Auto-restart and healthchecks	Stale timestamps on metrics
F5	Kernel mismatch	Runtime errors or wrong returns	Helper unavailable kernel older	Build conditional programs per kernel	Kernel version mismatch logs
F6	Permission denied	Cannot load programs	Lack of CAP_BPF or sysctl	Grant required caps or use operator	Agent permission errors
F7	Interference with kube-proxy	Connection issues	Incorrect tc/XDP rule ordering	Validate priorities rollback change	Increased connection errors

Row Details (only if needed)

• None

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Key Concepts, Keywords & Terminology for eBPF observability

• eBPF — A sandboxed virtual machine in the Linux kernel used to run small programs safely — Enables programmable in-kernel telemetry and policies — Pitfall: verifier restrictions.
• Kernel verifier — Validator for eBPF programs before load — Ensures memory safety and bounded loops — Pitfall: rejects legitimate complex logic.
• kprobe — Kernel probe attached to kernel functions — Captures kernel function entry/exit — Pitfall: version-dependent symbols.
• uprobe — User-space probe attached to user functions — Enables function-level tracing without code changes — Pitfall: symbol relocation in stripped binaries.
• tracepoint — Stable kernel instrumentation point — Lower risk across kernels — Pitfall: not available for every event.
• XDP — eBPF hook for high-performance packet processing early in networking stack — Enables DDoS mitigation and per-packet metrics — Pitfall: requires NIC driver compatibility.
• tc — Traffic control eBPF hooks for queuing and shaping — Useful for per-flow metrics and policing — Pitfall: more overhead than XDP.
• cgroup — Control group hooks for process grouping and resource policies — Enables per-container observability — Pitfall: requires correct cgroup version handling.
• map — In-kernel data structure for eBPF programs — Used to store counters, histograms, and small state — Pitfall: fixed sizing needs capacity planning.
• ring buffer — Kernel-to-user streaming mechanism — Low-latency event transfer — Pitfall: backpressure handling needed.
• perf event — Event mechanism to send sampled data — Often used for stack traces and perf counters — Pitfall: can add overhead if overused.
• helpers — Kernel-provided functions eBPF programs can call — Provide safe operations like bpf_probe_read — Pitfall: availability varies by kernel.
• libbpf — User-space library for loading and interacting with eBPF programs — Simplifies program lifecycle — Pitfall: version compatibility.
• bpftrace — High-level tracing tool using eBPF — Fast ad-hoc investigation — Pitfall: heavy scripts may be rejected.
• BPF CO-RE — Compile Once Run Everywhere approach — Enables portability across kernels — Pitfall: requires careful use of type info.
• tcplife — Concept: TCP lifetime events for connection health — Useful metric for network SLI — Pitfall: noisy on busy hosts.
• socket filter — eBPF attach point for sockets — Used to filter or inspect socket traffic — Pitfall: limited context on encrypted payloads.
• perfetto / trace processor — Tools for processing structured trace events — Useful for long traces — Pitfall: storage and retention costs.
• RBAC — Role-based access control for eBPF program loading in clusters — Secures who can deploy probes — Pitfall: misconfigured RBAC can block essential probes.
• CAP_BPF — Linux capability to allow eBPF program creation — Required for loading programs — Pitfall: granting broadly increases risk.
• CAP_SYS_ADMIN — Capability often required for attaching some hooks — Powerful permission — Pitfall: security risk if over-granted.
• ebpf verifier logs — Debug output from kernel when reject occurs — Essential for debugging load failures — Pitfall: sometimes cryptic.
• sockops — eBPF hook for TCP socket events — Good for instrumentation of connect/accept — Pitfall: kernel version dependence.
• map pinning — Persisting maps via debugfs or bpffs — Enables sharing between processes — Pitfall: lifecycle management complexity.
• uprobes on containers — Attaching to functions inside container processes — Provides app-level metrics without code changes — Pitfall: PID namespace handling.
• histogram map — eBPF map for distribution capture — Useful for latency buckets — Pitfall: choosing bucket boundaries.
• stack trace map — Map type to store stack IDs — Used for flamegraphs — Pitfall: storage size and unwinding limitations.
• export agent — User-space component that reads eBPF maps and forwards telemetry — Bridge to observability backends — Pitfall: agent becomes single point of failure.
• safety sandbox — Verifier and run-time checks to prevent kernel corruption — Ensures stability — Pitfall: constraints on expressiveness.
• dynamic instrumentation — Load-on-demand probes for troubleshooting — Reduces baseline overhead — Pitfall: need orchestration tooling.
• flow keys — Tuple identifying network flow (5-tuple) — Basis for per-flow metrics — Pitfall: NAT and load balancers change flows.
• conntrack — Connection tracking subsystem observed by eBPF — Useful to detect conntrack table saturation — Pitfall: kernel tuning needed.
• stack unwinding — Process of converting addresses to function names in traces — Key for function-level insight — Pitfall: requires debug symbols.
• sample rate — Frequency of event sampling — Balances fidelity vs overhead — Pitfall: mistaken rate leads to skewed metrics.
• aggregation in kernel — Summarizing metrics inside kernel maps before export — Reduces user-space churn — Pitfall: losing raw event context.
• user-driven probes — Probes defined and controlled by SREs during incidents — Enables targeted data collection — Pitfall: operational discipline required.
• eBPF operator — Kubernetes controller to manage eBPF lifecycle — Automates deployment and upgrades — Pitfall: operator dependencies and security model.
• verifier complexity — Measure of program analysis complexity — Affects load success — Pitfall: increases with dynamic loops and recursion.
• SLO-derived telemetry — Using eBPF metrics as SLIs feeding SLOs — Connects infra signals to customer experience — Pitfall: hard to map to user-visible impact.

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How to Measure eBPF observability (Metrics, SLIs, SLOs) (TABLE REQUIRED)

ID	Metric/SLI	What it tells you	How to measure	Starting target	Gotchas
M1	Syscall latency P95	Kernel call latency affecting app	Histogram of syscall durations via kprobe	P95 < 10 ms	Sampling bias under load
M2	TCP retransmit rate	Network reliability per host	Count retransmits per second via socket hooks	< 0.1% of packets	NAT hides root cause
M3	Connect success rate	Service reachability from hosts	Success vs failures by socket connect	99.9% success	Short spikes may be transient
M4	Per-pod syscall rate	Noisy neighbor detection	Syscall counts per cgroup per minute	Baseline varies per app	High-cardinality in large clusters
M5	Packet drop rate XDP	Data-plane packet drops	Drops and accepts from XDP program	Near zero on healthy nodes	Driver compat issues
M6	Map drop counter	Telemetry loss in maps	Count of events dropped by map	Zero drops	Bursts may overflow map
M7	Agent export latency	Telemetry freshness	Time from event to backend	< 10s for alerts	Backend ingestion delays
M8	eBPF program load failures	Reliability of instrumentation	Count load errors per deploy	Zero per deploy	Verifier messages can be cryptic
M9	Kernel CPU overhead	Observability cost on host	CPU ms spent in eBPF programs	< 2% of host CPU	Aggressive sampling spikes CPU
M10	Stack trace capture rate	Usability of traces	Fraction of events with valid stack	> 90% where needed	Missing symbols reduce value

Row Details (only if needed)

• None

Best tools to measure eBPF observability

Tool — bpftrace

• What it measures for eBPF observability: Ad-hoc tracing of syscalls, functions, and events.
• Best-fit environment: Troubleshooting on Linux hosts and dev nodes.
• Setup outline:
• Install bpftrace package.
• Run one-off scripts for probes.
• Capture output and export manually.
• Strengths:
• Fast to write ad-hoc scripts.
• High expressiveness for one-off diagnostics.
• Limitations:
• Not ideal for production continuous monitoring.
• Scripts may be rejected by verifier for complexity.

Tool — libbpf / bpftool

• What it measures for eBPF observability: Program lifecycle, introspection, and map inspection.
• Best-fit environment: Building production-ready eBPF agents.
• Setup outline:
• Use libbpf-based binaries compiled with CO-RE.
• Use bpftool for debugging.
• Integrate map reading into exporter.
• Strengths:
• Production-grade APIs and stability.
• CO-RE portability.
• Limitations:
• Requires compiled code and build toolchain.
• Higher development effort.

Tool — Cilium (ebpf-based)

• What it measures for eBPF observability: Network and L7 visibility in Kubernetes.
• Best-fit environment: Kubernetes clusters requiring network observability.
• Setup outline:
• Deploy Cilium as CNI.
• Enable observability modules.
• Use Hubble or exporters for metrics.
• Strengths:
• Tight K8s integration and per-pod visibility.
• Built-in policy and flow capture.
• Limitations:
• Introduces dependency on Cilium CNI.
• Operational complexity for upgrades.

Tool — Pixie/tracee-like agents

• What it measures for eBPF observability: Trace-level telemetry and function-level events.
• Best-fit environment: Cloud-native microservices on K8s.
• Setup outline:
• Deploy agent daemonset.
• Collect function-level events and traces.
• Use UI or export to backend.
• Strengths:
• Rich, near-application insights without code changes.
• Good for debugging distributed requests.
• Limitations:
• Data volume; needs aggregation.
• Privacy/security constraints for sampled payloads.

Tool — Falco (eBPF mode)

• What it measures for eBPF observability: Runtime security events from syscalls and file operations.
• Best-fit environment: Host security and container runtime monitoring.
• Setup outline:
• Deploy Falco with eBPF input.
• Define rules for suspicious patterns.
• Route alerts to SIEM.
• Strengths:
• Rule-based security detection with kernel visibility.
• Mature alerting and rule ecosystem.
• Limitations:
• False positives require tuning.
• Requires sysadmin capabilities.

Recommended dashboards & alerts for eBPF observability

Executive dashboard

• Panels:
• Service-level availability combining app SLI and eBPF-inferred influxes.
• Cluster-level network health: retransmit rate, drop rate.
• Top hosts by eBPF CPU overhead.
• Why: Gives leadership one-glance risk view connecting infra to customer impact.

On-call dashboard

• Panels:
• Recent agent load failures and map drops.
• Per-node high syscall latency P95 times.
• Top 10 pods by syscall rate and TCP errors.
• Active on-demand probes and their durations.
• Why: Prioritized, actionable view for triage.

Debug dashboard

• Panels:
• Raw event stream tail (samples).
• Per-flow latency histograms.
• Stack trace frequency for top slow syscalls.
• Map utilization and ring buffer occupancy.
• Why: Deep drill-down for postmortem and live debugging.

Alerting guidance

• What should page vs ticket:
• Page: Service-level SLO breaches involving eBPF-derived SLI (connectivity failure, map exhaustion, agent offline).
• Ticket: Non-urgent map tuning suggestions, low-level load warnings.
• Burn-rate guidance:
• Use error budget burn to escalate from ticket to page when burn rate is sustained above 2-3x expected for 30–60 minutes.
• Noise reduction tactics:
• Dedupe by host and reason, group by service and node, suppress transient spikes with short windowing, sample stack traces only when latency exceeds threshold.

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Implementation Guide (Step-by-step)

1) Prerequisites – Linux kernel recent enough to support required helpers (Varies / depends). – Privileged deployment path or operator with CAP_BPF and CAP_SYS_ADMIN as required. – Observability backend accepting metrics, traces, or events. – CI and testing pipeline for verifying eBPF programs.

2) Instrumentation plan – Identify SLOs and map to kernel-level signals. – Define minimal set of probes needed for SLOs. – Plan map sizing, sampling, and retention.

3) Data collection – Choose host-agent or operator model. – Ensure tagging and pod metadata enrichment. – Configure export frequency and batching.

4) SLO design – Define SLIs that eBPF can realistically measure (e.g., connect success). – Set SLO targets reflecting business intent. – Plan error budget policy and escalation.

5) Dashboards – Build executive, on-call, and debug dashboards. – Add drill-downs for traces, stack traces, and map health.

6) Alerts & routing – Map alerts to severity and on-call rotations. – Use grouping and dedupe rules.

7) Runbooks & automation – Create runbooks for common eBPF incidents. – Automate safe rollback and circuit breakers for probes.

8) Validation (load/chaos/game days) – Load testing with map-sizing scenarios. – Chaos experiments: agent crash, kernel upgrade simulation. – Game days for SLO breaches involving eBPF signals.

9) Continuous improvement – Regularly review map sizing and agent overhead. – Add automation for dynamic probe deployment for recurring issues.

Pre-production checklist

• Verify kernel compatibility and helper availability.
• Test program loads against verifier on staging.
• Validate map sizing with synthetic bursts.
• Ensure RBAC and capability least privilege.
• Confirm backend ingestion and dashboard panels.

Production readiness checklist

• Observability agent healthchecks and auto-restart.
• Alerting for map drops and load failures.
• Runbook accessible and tested.
• Canary rollout of eBPF changes.
• Backout procedures documented.

Incident checklist specific to eBPF observability

• Check agent and eBPF program loading logs.
• Validate map utilization and drops.
• Correlate eBPF signals with application traces.
• If high overhead, disable nonessential probes.
• Capture necessary stacks and exports for postmortem.

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Use Cases of eBPF observability

1) Network performance troubleshooting – Context: Intermittent latency between services. – Problem: Packet drops or retransmits not visible in app logs. – Why eBPF helps: Capture per-packet events and TCP stack behavior. – What to measure: Retransmit rate, RTT distribution, packet drop per interface. – Typical tools: XDP, tcp probe, libbpf-based exporter.

2) DNS reliability diagnosis – Context: Applications failing name resolution intermittently. – Problem: Cascading timeouts across services. – Why eBPF helps: Trace DNS queries at socket level and measure response times. – What to measure: DNS response latency, failures per process. – Typical tools: Socket hooks, uprobes on resolver libs.

3) Noisy neighbor detection – Context: One container impacts host latency for others. – Problem: Shared kernel resources causing stalls. – Why eBPF helps: Per-cgroup syscall and scheduling metrics. – What to measure: Syscall rate per cgroup, runqueue length, cpu steal. – Typical tools: cgroup probes, scheduler tracepoints.

4) Cold start analysis in serverless – Context: Increased cold-start duration. – Problem: Runtime initialization syscalls dominate time. – Why eBPF helps: Capture function-level timing in user runtime without code change. – What to measure: Uprobe timings for startup functions, syscall breakdown. – Typical tools: Uprobes, bpftrace for ad-hoc.

5) Security anomaly detection – Context: Suspicious escalations or unexpected execs. – Problem: Traditional logs too slow or incomplete. – Why eBPF helps: Detect unusual syscalls patterns at kernel level. – What to measure: Execve counts, suspicious socket activity. – Typical tools: Falco in eBPF mode, custom eBPF rules.

6) Connection churn on load balancers – Context: LB experiencing high connection churn. – Problem: Conntrack exhaustion and packet drops. – Why eBPF helps: Monitor conntrack usage and per-flow lifetimes. – What to measure: Conntrack entries, timeouts, flow durations. – Typical tools: conntrack hooks, XDP metrics.

7) Memory leak investigation – Context: Gradual host memory growth causing OOM. – Problem: Hard to attribute to containers/processes. – Why eBPF helps: Track allocations and free patterns via kernel probes. – What to measure: Slab allocations per process, page faults. – Typical tools: kmem tracepoints, bpftrace.

8) Observability for managed PaaS – Context: Platform provider needs host insight without code changes. – Problem: Tenant apps opaque. – Why eBPF helps: Non-invasive telemetry per tenant via cgroups. – What to measure: Per-tenant socket failures and syscall latencies. – Typical tools: eBPF operator and multi-tenant map enforcement.

9) Runtime profiling for performance tuning – Context: Slow tail latency in requests. – Problem: Hard to capture syscall-level tail events. – Why eBPF helps: Capture P99 and P999 syscall distributions and stacks. – What to measure: Tail latency histograms and stack traces. – Typical tools: Histogram maps, stack trace maps.

10) Compliance and audit trails – Context: Need demonstration of runtime behavior for compliance. – Problem: Logs are incomplete. – Why eBPF helps: Capture specific audited syscalls and operations. – What to measure: Occurrence of privileged syscalls over time. – Typical tools: Uprobes and tracepoint-based audit collection.

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Scenario Examples (Realistic, End-to-End)

Scenario #1 — Kubernetes pod network troubleshooting

Context: Clients intermittently get 502 from a microservice running in Kubernetes.
Goal: Identify whether pod-to-pod socket failures or host networking causes 502s.
Why eBPF observability matters here: It captures socket-level failures and conntrack behavior across host and pods without changing app code.
Architecture / workflow: Daemonset agent loads eBPF kprobes and cgroup socket hooks, maps are read and exported to metrics backend, dashboards show per-pod socket failures and retransmits.
Step-by-step implementation:

• Deploy eBPF operator as DaemonSet with minimal RBAC.
• Attach socket connect and accept probes with pod metadata enrichment.

• Export counters to metrics backend and build dashboards. What to measure:

• Connect success rate per pod.

• TCP retransmits per node.

• Conntrack table occupancy. Tools to use and why:

• Cilium or libbpf-based agent for pod context.

• Metrics backend for SLI calculation. Common pitfalls:

• Verifier rejects complex stack capture program.

• High-cardinality labels cause metric explosion. Validation:

• Recreate failure in staging and verify probes capture connect failures. Outcome: Root cause identified as MTU mismatch on a subset of nodes causing fragmentation and retransmits; fix applied and SLO restored.

Scenario #2 — Serverless cold-start investigation (PaaS)

Context: A managed PaaS notices increased cold starts impacting P99 latency.
Goal: Pinpoint kernel-level delays during runtime startup.
Why eBPF observability matters here: Captures syscall timing during startup across tenants without modifying functions.
Architecture / workflow: Temporary uprobes attached to runtime init functions across hosts, aggregated histograms exported.
Step-by-step implementation:

• Identify runtime init function symbols.
• Deploy on-demand eBPF uprobes limited to a small canary batch.
• Collect syscall timings and stack traces for slow starts. What to measure: Uprobe durations, syscall breakdown during init, cold-start P99. Tools to use and why: bpftrace for quick probe scripts, libbpf-based agent for production. Common pitfalls: Stripped binaries lacking symbols hamper uprobes.
  Validation: Canary shows certain startup library performing large mmap; change reduces cold-starts.
  Outcome: Library replaced and P99 cold-starts improved.

Scenario #3 — Incident response / postmortem

Context: A critical incident where a service became unresponsive for 10 minutes.
Goal: Reconstruct timeline and root cause with high fidelity.
Why eBPF observability matters here: Kernel-level telemetry fills gaps where app logs were lost due to storage saturation.
Architecture / workflow: Retrospective loading of preserved eBPF maps and perf samples from forensic nodes, correlate with traces.
Step-by-step implementation:

• Collect preserved eBPF map snapshots and perf logs from affected nodes.
• Correlate high syscall latency spikes with application trace IDs.
• Identify a kernel scheduler bug triggered by high futex contention. What to measure: Syscall latency, runqueue metrics, futex waits.
  Tools to use and why: libbpf for map extraction, trace processor for correlation.
  Common pitfalls: If not preserved, ephemeral maps may be lost.
  Validation: Reproduced in staging with load test showing futex contention.
  Outcome: Kernel upgrade and patch resolved recurrence.

Scenario #4 — Cost/performance trade-off for packet filtering

Context: High ingress costs due to packet processing and telemetry volume at edge nodes.
Goal: Reduce telemetry cost while preserving necessary network observability.
Why eBPF observability matters here: eBPF enables in-kernel aggregation and filtering before export to reduce volume.
Architecture / workflow: XDP programs summarize per-flow metrics and only export anomalies.
Step-by-step implementation:

• Implement XDP filter to count flows and drop duplicates.
• Add threshold logic to export heavy hitters only.
• Route exports to long-term store with compression. What to measure: Export volume, packet drop rate, detection latency.
  Tools to use and why: XDP programs and a libbpf exporter.
  Common pitfalls: Aggressive filtering hides small anomalies.
  Validation: Load test demonstrating 70% telemetry reduction with preserved detection of anomalies.
  Outcome: Cost savings while retaining effective monitoring.

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Common Mistakes, Anti-patterns, and Troubleshooting

List of mistakes with Symptom -> Root cause -> Fix (15–25 entries)

1. Symptom: Program fails to load -> Root cause: Verifier reject -> Fix: Simplify program, remove loops, test smaller pieces.
2. Symptom: High CPU overhead -> Root cause: Too frequent polling or complex in-kernel logic -> Fix: Increase sampling interval, aggregate in kernel.
3. Symptom: Missing events -> Root cause: Map overflow -> Fix: Increase map size or implement eviction.
4. Symptom: Agent reports permission denied -> Root cause: Missing CAP_BPF or RBAC -> Fix: Grant least privilege caps or use operator with controlled permissions.
5. Symptom: No per-pod context -> Root cause: Not enriching with cgroup or pod metadata -> Fix: Add cgroup id mapping and metadata enrichment.
6. Symptom: Metric explosion -> Root cause: High-cardinality labels from user tags -> Fix: Reduce label cardinality and roll up.
7. Symptom: Stack traces unavailable -> Root cause: Missing debug symbols or stripped binaries -> Fix: Add debug symbols or use address-to-symbol mapping offline.
8. Symptom: Data loss under burst -> Root cause: Ring buffer backpressure -> Fix: Increase buffer, add batching, or reduce sampling.
9. Symptom: False security alerts -> Root cause: Overbroad detection rule -> Fix: Tune rules and add whitelist exceptions.
10. Symptom: Kernel panics or instability -> Root cause: Unsafe eBPF helper use or kernel bug -> Fix: Validate with staging and avoid unsupported helpers.
11. Symptom: Inconsistent metrics across upgrades -> Root cause: Kernel helper behavior changed -> Fix: Versioned program builds and CO-RE checks.
12. Symptom: High agent restart churn -> Root cause: Memory leaks in agent -> Fix: Memory profiling and restart thresholds.
13. Symptom: Slow export latency -> Root cause: Backend throttling -> Fix: Backpressure handling and batch exports.
14. Symptom: Rejected in production -> Root cause: Unknown verifier logs -> Fix: Capture verifier logs and iterate in staging.
15. Symptom: Unable to deploy in managed cloud -> Root cause: Kernel access restricted by provider -> Fix: Use provider-managed observability features or alternative approaches.
16. Symptom: Over-reliance on eBPF for business metrics -> Root cause: Misuse of kernel telemetry for business KPIs -> Fix: Keep business metrics in-app and use eBPF for infra signals.
17. Symptom: Probes interfering with networking -> Root cause: Incorrect XDP priorities or tc order -> Fix: Validate ordering and use safe priorities.
18. Symptom: Latency regression after probes -> Root cause: Capturing too many stack traces -> Fix: Sample only when thresholds exceeded.
19. Symptom: Hard-to-reproduce verifier rejection -> Root cause: Kernel header mismatch -> Fix: Use CO-RE and vmlinux debug info.
20. Symptom: Excessive cardinality on dashboards -> Root cause: Exporting raw labels like PID or IP -> Fix: Aggregate and map to stable identifiers.
21. Symptom: Security policy concerns -> Root cause: Broad capability grants -> Fix: Audit capabilities and use operator with RBAC.
22. Symptom: Observability blindspots -> Root cause: Relying only on eBPF and not correlating logs/traces -> Fix: Correlate multi-signal telemetry.
23. Symptom: Long-term storage costs spike -> Root cause: Exporting raw event streams continuously -> Fix: Aggregate in-kernel and export samples.
24. Symptom: Patchwork instrumentation -> Root cause: Uncoordinated probes by multiple teams -> Fix: Centralize program lifecycle and naming conventions.

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Best Practices & Operating Model

Ownership and on-call

• Platform team owns eBPF agent lifecycle and secure capability management.
• Product or service team owns SLOs and interpretation of eBPF-derived SLIs.
• On-call rotations include at least one platform engineer familiar with eBPF runbooks.

Runbooks vs playbooks

• Runbooks: Step-by-step remediation for recurring operational issues.
• Playbooks: Higher-level decision guides for new or complex incidents.
• Keep runbooks accessible and versioned alongside instrumentation code.

Safe deployments (canary/rollback)

• Canary deploy eBPF changes to a subset of nodes.
• Monitor agent CPU, map drops, and error rates during canary.
• Automated rollback when overhead exceeds threshold.

Toil reduction and automation

• Automate map resizing recommendations based on historical peaks.
• Auto-disable noncritical probes during sustained high overhead.
• Use CI to validate verifier acceptance pre-release.

Security basics

• Least privilege: grant only necessary capabilities and RBAC.
• Audit who can load programs and cluster roles.
• Mask sensitive payloads and comply with privacy constraints.

Weekly/monthly routines

• Weekly: Review map drop counters and agent error logs.
• Monthly: Review kernel compatibility and program verifier rejections.
• Quarterly: Run a game day focused on eBPF probes and SLOs.

What to review in postmortems related to eBPF observability

• Whether eBPF telemetry was available and helpful.
• Probe lifecycle: did programs load and unload as expected?
• Any agent-induced overhead during the incident.
• Action items to add or remove probes based on findings.

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Tooling & Integration Map for eBPF observability (TABLE REQUIRED)

ID	Category	What it does	Key integrations	Notes
I1	eBPF runtime	Program loading and map management	Kernel libbpf bpftool	Core for all eBPF workflows
I2	Kernel probes	Attach points for events	kprobe uprobe tracepoint	Version dependent helpers
I3	Network hooks	High-performance packet handling	XDP tc	For data-plane use cases
I4	Kubernetes operator	Manage lifecycle in clusters	K8s API CNI	Automates RBAC and upgrades
I5	Security engine	Runtime detection rules	SIEM Falco	Uses eBPF as input source
I6	Tracing correlate	Correlates eBPF events with traces	Trace processor APM	Enables SLI correlation
I7	Metrics backend	Stores and alerts on metrics	Prometheus Grafana	Sink for aggregated metrics
I8	Export agent	User-space bridge from kernel maps	Collector pipeline	Responsible for enrichment
I9	CI/CD tests	Verifier and functional tests	Build pipeline	Prevents bad programs in prod
I10	Debug tools	Ad-hoc investigators	bpftrace bpftool	For incident response

Row Details (only if needed)

• None

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Frequently Asked Questions (FAQs)

What kernels are required for eBPF?

Varies / depends.

Is eBPF safe to run in production?

Yes when programs pass the verifier and follow least-privilege deployment practices.

Can eBPF be used in managed Kubernetes services?

Often yes but depends on provider permissions and CNI; check provider restrictions.

Does eBPF replace APM?

No; it complements APM by filling kernel and network-level blind spots.

What permissions are required to load eBPF programs?

Typically CAP_BPF and sometimes CAP_SYS_ADMIN; exact permissions vary.

How much overhead does eBPF add?

Typically low if sampled and aggregated; poorly designed probes can spike CPU.

Can eBPF capture encrypted payloads?

No. eBPF can capture metadata but not decrypted payload without key access.

Are eBPF programs portable across kernels?

Use BPF CO-RE to improve portability; some helper availability still varies.

How do you troubleshoot verifier rejects?

Capture verifier logs, simplify program, and test iteratively in staging.

Is eBPF suitable for multi-tenant environments?

Yes with careful cgroup tagging and RBAC to avoid cross-tenant leakage.

How do you manage telemetry volume?

Aggregate in kernel, sample, and export only anomalies.

Can you use eBPF on containers without privileges?

Not directly; you require host-level privileges or an operator to manage probes.

How to ensure privacy when using eBPF?

Mask or avoid capturing payloads and enforce access controls on traces.

Can eBPF be used for enforcement as well as observability?

Yes; eBPF supports enforcement but that increases risk and requires stricter controls.

How to test eBPF programs before production?

Use CI with verifier checks and kernel-compatible test runners.

Will eBPF work on all cloud providers?

Varies / depends.

How are maps persisted across restarts?

Map pinning to bpffs can persist maps; lifecycle management required.

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Conclusion

Summary

• eBPF observability is a powerful, kernel-level instrumentation approach that complements existing observability signals by providing high-fidelity insights into networking, syscalls, and kernel interactions.
• It requires careful planning, security posture, and tooling to be reliable and cost-effective.
• Best used for targeted SRE use cases: network debugging, incident response, performance tuning, and security monitoring.

Next 7 days plan (5 bullets)

• Day 1: Inventory kernel versions and permissions across prod and staging.
• Day 2: Identify 2–3 high-value SLIs that would benefit from eBPF signals.
• Day 3: Prototype one non-intrusive probe in staging and validate verifier acceptance.
• Day 4: Create dashboards for the new SLI and set low-severity alerts.
• Day 5–7: Run a canary deployment, run a small load test, and document a runbook.

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Appendix — eBPF observability Keyword Cluster (SEO)

• Primary keywords
• eBPF observability
• eBPF monitoring
• eBPF tracing
• kernel observability

• Linux eBPF telemetry

• Secondary keywords

• kernel-level monitoring
• eBPF for SRE
• eBPF security monitoring
• XDP observability

• cgroup observability

• Long-tail questions

• what is eBPF observability
• how to measure eBPF performance impact
• eBPF for Kubernetes monitoring
• best practices for eBPF probes
• how to use eBPF for network troubleshooting
• how to debug eBPF verifier rejects
• eBPF syscall tracing tutorial

• using eBPF for cold-start analysis

• Related terminology

• kernel verifier
• kprobe uprobes
• tracepoints
• libbpf
• bpftrace
• CO-RE
• XDP tc
• ring buffer maps
• stack trace map
• histogram map
• perf events
• conntrack
• cgroup id
• CAP_BPF
• CAP_SYS_ADMIN
• eBPF operator
• map pinning
• heap profiling
• syscall latency
• TCP retransmits
• runtime security
• Falco eBPF
• bpftrace scripts
• libbpf collector
• map eviction
• sampling rate
• aggregation in kernel
• high-cardinality labels
• observability SLI
• observability SLO
• error budget
• runbook
• on-demand probes
• packet drops XDP
• kernel compatibility
• verifier logs
• probe lifecycle
• map sizing
• export latency
• agent overhead
• telemetry volume
• encryption and payloads