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Sheriff Adepoju, a large-scale automation engineer, challenges how performance claims are reported in database systems research, arguing that many papers omit practical details required to independently verify results that can materially change outcomes in real deployments. In a peer-reviewed paper recently accepted for publication, Adepoju proposed a minimum reporting standard called MR-1, along with a disclosure-based compliance score, to make database performance claims more interpretable and comparable, especially when the work depends on kernel behavior and eBPF, the Linux mechanism for running verified programs at kernel hook points.

Adepoju’s critique is aimed at the experimental “condition,” not the idea that research is conducted in good faith. This paper argues that when authors do not report kernel versions, hardware topology, attachment points, export paths, and measurement controls, readers cannot separate a technique’s contribution from invisible system variables. In practice, this can turn headline numbers into results that are difficult to replicate outside the original laboratory environment. A framework that separates three different kinds of “eBPF for databases” Adepoju’s paper begins by arguing that the research conversation often bundles different goals into one label “eBPF for databases”even though those goals have different feasibility constraints and failure modes.

He organized the database-kernel integration via eBPF into three modes:

Mode 1: Observability. Using eBPF to derive database-relevant signals from kernel and user-space hooks without changing the kernel policy. The concern here is that the measurement stack can alter latency and throughput, and that export mechanisms can drop events under load unless loss behavior is measured and reported.

Mode 2: Policy injection. Using eBPF to install workload-specific cache or networking policies at kernel choke points. Adepoju treats this category as operationally riskier than observability because it can change the shared kernel behavior and cause externalities under mixed workloads.

Mode 3: Kernel-resident state. Maintaining a structured, semantics-bearing state in the kernel context for fast-path decisions that require coordinated updates beyond raw maps. This study argues that this mode expands the correctness and security boundaries and therefore demands the most explicit reporting of assumptions and failure modes. The purpose of the taxonomy is not to declare a winner. This is to prevent category errors in evaluation, such as comparing a tracing pipeline to a kernel fast path as if both were the same kind of “optimization”and to force papers to disclose the constraints that actually determine feasibility.

MR-1: minimum disclosure, not a “publish your entire stack” demand MR-1 is Adepoju’s attempt to define a baseline: a minimum set of experimental disclosures that should accompany database–kernel integration papers making performance or feasibility claims using the eBPF. MR-1 does not require authors to release proprietary codes, internal datasets, or customer workloads. It focuses on system variables that can decide whether the results are transferred: kernel and platform, kernel version and relevant configuration flags, CPU model and topology (including NUMA), and key device details that affect the measured path. Program and attachment specifics: the exact hook points and attach types; program build/toolchain; and map types and resource footprints, where kernel-resident state is part of the claim. Export and backpressure: how data moves out of kernel context, whether events can be dropped, and how loss or backpressure is detected and reported.

Runtime controls: CPU pinning/affinity; interrupt or networking affinity, where relevant; sampling and aggregation windows for observability work. Workloads and baselines: workload configuration (dataset size, skew, scan behavior, request mix), baseline settings, warm-up procedures, and steady-state criteria. Metrics and variability: definitions for throughput and tail latency, repetitions and variability reporting, and overhead accounting when the eBPF path can meaningfully consume CPU or memory. Adepoju argues that these items are not administrative minutiae. These were the experimental conditions. Without them, he says, “X% faster” can be impossible to interpret because the reader cannot tell whether the effect came from the proposed technique or from unreported kernel behavior, topology, or measurement stack choices. A compliance score designed to make omissions visible To prevent MR-1 from becoming a purely qualitative checklist, Adepoju proposes a compliance score ranging from 0 to 10, computed strictly from what a paper explicitly discloses. Points are assigned for the disclosure of the kernel/platform, attachment details, state/export behavior, runtime controls, workloads/baselines, and metrics/variability.

This study frames the score as a disclosure index, not a verdict on scientific quality. A higher score indicates that the paper’s results are easier to interpret and compare. A lower score indicates that readers should treat quantitative comparisons cautiously because key variables are missing. This distinction is central to Adepoju’s argument: missing disclosures do not prove that a result is wrong; they make it hard to validate or translate into practice. Why this matters outside academia Adepoju’s paper connects the reproducibility debate to enterprise reality: database and infrastructure teams regularly rely on academic and industry research when evaluating architectures, observability approaches, and performance trade-offs. If performance evidence is under-specified, organizations may spend months chasing results that do not transfer to their kernels, fleets, workload shapes, or operational constraints. He also argued that under-reporting can distort the research record over time by making “best results” appear comparable when they were produced under incompatible conditions. The limits of MR-1 and the pushback it may face Adepoju’s proposal is likely to attract criticism from multiple directions, and his paper acknowledges several practical constraints.

First, not every laboratory has access to identical hardware, and strict reporting standards can make it more difficult to publish performance work if reviewers treat missing resources as missing rigor. Adepoju’s counter is that MR-1 does not require identical hardware, only explicit reporting, so that comparisons can be made honestly. Second, some researchers and industry authors may resist disclosing details they consider sensitive, such as exact hardware configurations or deployment constraints. Adepoju’s approach attempts to thread that needle by focusing on minimal technical conditions rather than proprietary implementation. Third, checklists can be gamed by the user. A paper can disclose a long list of parameters while still making questionable comparisons between them. Adepoju positions the MR-1 as a floor: it prevents invisible variables from hiding in plain sight, but it does not replace careful evaluation design. Fourth, the compliance score measures disclosure, not replicability. A paper can be fully disclosed and still be difficult to reproduce, and a paper can be under-disclosed and still be correct. The claim is narrower: disclosure is a prerequisite for independent verification and responsible comparisons. What Adepoju is asking for next Adepoju is proposing MR-1 as a minimum standard that journals, conferences, and reviewers can require for database-kernel integration work that makes performance claims using eBPF. He also outlined a path to reduce author burden: machine-checkable reporting templates and tooling that can auto-collect the kernel and attachment metadata MR-1 demands. His broader objective is to shift performance discourse away from results that read as universally transferable and toward results that clearly state the conditions under which they hold, so practitioners can decide when to adopt, when to test, and when to disregard a headline number that was never reproducible outside a specific laboratory setup.

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