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To catch performance regressions in Rust eBPF, benchmark the same workload before and after a change, keep the build and host conditions fixed, and label exactly what the timer measures. A Rust function microbenchmark can reveal slower helper logic, but it does not measure the full cost of loading, attaching, or running an eBPF program in the kernel.
Start by deciding what you need to measure
“eBPF performance” can mean several different things. Choose the measurement layer before writing a benchmark; otherwise, a result may be precise but answer the wrong question.
- Rust function: Measures a helper or data transformation in isolation, such as constructing a source address. It can show whether a change slowed that Rust logic.
- User-space/kernel interaction: Measures operations such as loading or attaching a program, or the cost of moving events between kernel and user space.
- Attached kernel program: Measures the program’s behavior while it runs in the kernel, such as per-event runtime or sustained event handling.
- End-to-end workload: Measures a complete tracing or application path. Its result may include kernel execution, event transport, and user-space processing, so state which components are included.
Keep results from these layers separate. A microbenchmark of ordinary Rust code should not be presented as kernel execution latency.
Choose a Rust benchmark harness
Cargo’s benchmark command compiles and runs package benchmarks, supports filtering, and uses the optimized bench profile by default. The built-in #[bench] facility documented by Rust is nightly-only; Cargo points to Criterion as an option for stable Rust.
#1 Best Overall
The Linux Foundation-hosted presentation “Rust eBPF Micro-Benchmarking” by Everett Pompeii discusses microbenchmarks alongside macro and integration benchmarks, and demonstrates a Criterion benchmark around a Rust SourceAddr::new function. That example isolates a Rust function; it does not, by itself, measure an attached eBPF program’s kernel-side runtime.
Build a comparison that can reveal a real change
- Write down the unit under test. Specify the function, operation, or complete path being timed. For an end-to-end test, say whether the timing includes load, attach, per-event execution, event transfer, or user-space processing.
- Fix the workload. Use the same inputs and input distribution, event rate, and test procedure for each revision. A change in workload can look like a code regression or improvement.
- Fix the environment and build. Record and hold constant the host, kernel, Rust toolchain, relevant library versions, build profile, and flags. Use the same measurement procedure for both revisions.
- Run the same benchmark on both revisions. Use the same harness and filtering choices. Save the outputs so the comparison is reproducible rather than relying on remembered timings.
- Inspect distributions and outliers. If the harness reports them, include them in your interpretation. A small difference from one run is not enough to establish a regression; rerun suspicious results under the same conditions.
- Report the result with its scope. Include the measured layer, workload, environment, and relevant latency or throughput figures. If resource use or event loss matters, measure and report those separately rather than assuming a faster timing captures the whole tradeoff.
Interpret benchmark numbers in context
Pompeii’s 2023 presentation reports an example interval of 538.49–542.58 ns for its SourceAddr demonstration, with 13 outliers among 100 measurements. Those figures describe that presentation’s example run, not a general Rust eBPF baseline, a current-machine target, or the cost of running an eBPF program in the kernel.
Rank #2
For a meaningful version comparison, consider latency and throughput separately: latency is the time per operation, while throughput is the sustained event or operation rate. Depending on the workload, also examine CPU and memory use, event-transport overhead, and whether events were lost. These dimensions can move in different directions.
Set up an Aya project with current tooling
Aya’s development guide lists stable and nightly Rust, bpf-linker, cargo-generate, and bpftool among its setup tools or prerequisites. LLVM versions and platform-specific linker installation steps can change, so follow the guide’s current instructions rather than relying on frozen setup commands. The guide also gives this scaffolding command:
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cargo generate https://github.com/aya-rs/aya-templateAya 0.14.0 documents aya::sys::enable_stats as enabling global eBPF statistics and returning a file-descriptor handler in its API reference. The existence of that API does not establish a complete regression-testing method or prove that a particular benchmark measures the intended cost.
Compare eBPF tools and libraries by workload
A 2025 preliminary study compared BCC, bpftrace, libbpf, ebpf-go, and Aya on storage-I/O tracing workloads. Its authors report that no library consistently outperformed the others across performance, resource use, and fidelity; tradeoffs depended on the workload. The Aya tests used version 0.13.1, so those results are not a benchmark of Aya 0.14.0 or a ranking of Rust generally. See the study and its methods for the tested context.
When comparing two implementations, keep the workload and measurement layer the same, and report the relevant dimensions together:
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- Latency per operation and sustained throughput
- CPU and memory use, where measured
- Event transport overhead and event-capture fidelity, including losses under the tested workload
- Kernel, toolchain, library version, hardware, build flags, and workload
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