Free tools Windows power users keep installed
One-click scans. No signup required.
To catch performance regressions in Rust eBPF, benchmark the same workload and measurement layer before and after a change, then rerun suspicious results. A Rust helper microbenchmark can reveal a slowdown in that function, but it does not measure the complete cost of loading, attaching, or executing an eBPF program in the kernel.
First decide what you are measuring
A benchmark is useful only when its result matches the question you want answered. Keep measurements from different layers separate rather than treating one as a proxy for another.
As an Amazon Associate I earn from qualifying purchases.
- Rust function: times an isolated helper or other user-space Rust logic. It can show whether that code changed in cost.
- User-space and kernel interaction: includes the operations needed to communicate with or manage eBPF from user space. Name the operations included in the timing.
- Attached kernel program: concerns work performed by the eBPF program in the kernel. An ordinary Rust function benchmark does not establish this cost.
- End-to-end workflow: may include loading, attaching, event handling, event transfer, and user-space processing. State exactly which of those are timed.
Label each result with its measurement layer. In particular, do not turn a faster helper-function result into a claim that the attached program runs faster.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsChoose a Rust benchmark harness
Cargo’s cargo bench documentation explains that the command compiles and runs package benchmarks, supports filtering, and uses the optimized bench profile by default.
#1 Best Overall
- Criterion: a stable-channel option identified in Cargo’s documentation. It is used in Everett Pompeii’s presentation to benchmark a Rust
SourceAddr::newfunction. - Built-in
#[bench]: the benchmark attribute documented by Rust is nightly-only.
Pompeii’s Linux Foundation-hosted presentation, “Rust eBPF Micro-Benchmarking”, contrasts microbenchmarks with broader macro or integration benchmarks. Its example is a benchmark of a Rust function in an eBPF project—not a measurement of the full cost of executing an attached program in the kernel.
Build a repeatable comparison
- Define the unit under test. Record whether you are timing a Rust function, a user-space/kernel interaction, or an attached program. For an end-to-end test, list the included operations, such as load, attach, per-event execution, event transfer, and user-space handling.
- Fix the workload. Use the same inputs and input distribution, event volume, and workload pattern for both revisions. A changed workload can change the result even when the code has not regressed.
- Keep the environment and build settings consistent. Record the host, kernel, Rust toolchain, eBPF library version, build profile and flags, and measurement procedure. For kernel-side tests, include relevant program and attachment context.
- Run the same benchmark on both revisions. Use the same harness and settings, and preserve the commands or configuration needed to repeat the run. With
cargo bench, use its filtering support when you need to run a specific benchmark. - Inspect the distribution, not just a single number. Report the harness’s measurements and outliers where available. Rerun a suspicious result under the same conditions; one small difference from one run is not, by itself, a reliable regression signal.
Report the dimensions that matter
Latency alone may not describe an eBPF change. For meaningful comparisons, report the dimensions your workload needs and do not imply that an unmeasured dimension stayed constant.
Rank #2
- Latency: identify what one operation means and report per-operation timing separately from sustained event rate.
- Throughput: state the workload and event rate tested rather than inferring capacity from a microbenchmark.
- Resource use: report CPU, memory, and event-transport overhead only when measured.
- Fidelity: for tracing workloads, note whether events were lost under the tested conditions.
- Environment: provide kernel, toolchain, library version, hardware, build flags, and workload details so readers can interpret the result.
A 2025 preliminary study of storage-I/O tracing compared BCC, bpftrace, libbpf, ebpf-go, and Aya across performance, resource use, and event fidelity. It found no library that consistently led across all measured dimensions; tradeoffs depended on workload. The study used Aya 0.13.1, so its results are not a current benchmark of Aya 0.14.0 and do not establish a general ranking of Rust. Read the study.
How to interpret a published timing
In the presentation’s 2023 SourceAddr::new demonstration, the reported timing interval was 538.49–542.58 ns, with 13 outliers among 100 measurements. Those figures describe that example run only. They are not a general Rust eBPF baseline, a current-machine target, or a measurement of kernel execution cost.
Rank #3
Set up an Aya project without freezing old prerequisites
Aya’s development guide lists stable and nightly Rust, bpf-linker, cargo-generate, and bpftool among its setup tools. LLVM versions and platform-specific linker installation steps can change, so consult the live guide for current requirements instead of relying on copied commands that may have aged. The guide gives this project scaffolding command:
cargo generate https://github.com/aya-rs/aya-templateThe Aya 0.14.0 API reference documents aya::sys::enable_stats, which enables global eBPF statistics and returns a file-descriptor handler. The existence of that API does not, on its own, define a regression-testing procedure or show that a particular benchmark is valid. See the API reference.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
