Decoupling Inference from State Updates in Low-Latency Feature Engines via Probabilistic Thinning

Problem
The paper addresses the challenge of high latency and operational costs in streaming data systems that require frequent state updates due to incoming events. In production environments, each event typically necessitates read-modify-write operations on persistent storage, leading to contention and inefficiencies. The authors propose a solution to decouple inference from state persistence, a gap in the literature that has not been adequately addressed, particularly in the context of low-latency feature engines. This work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution is the introduction of a probabilistic thinning mechanism that selectively triggers durable state updates based on the informativeness of incoming events. The authors utilize approximate statistics from disk-backed key-value stores to manage persistence-path control without requiring a high-frequency in-memory control plane or cross-worker coordination. The method involves modeling stochastic processes to derive bounds on filtering rates and proving that common time-based aggregations remain unbiased under variance-aware formulations. This ensures that systemic error accumulation is mitigated. The authors conducted experiments in a controlled setting to isolate per-event costs, focusing on storage Input/Output and serialization overhead.

Results
The proposed method demonstrated significant performance improvements, with up to 90% of events excluded from the persistence path while maintaining or enhancing downstream utility. The evaluation showed substantial reductions in storage I/O and serialization overhead compared to traditional methods. Specific baseline comparisons were not detailed in the abstract, but the results indicate a marked efficiency gain in processing streaming data.

Limitations
The authors acknowledge that their approach may not be universally applicable to all types of streaming data systems, particularly those with different latency requirements or operational constraints. Additionally, the reliance on approximate statistics may introduce variability in certain scenarios, which could affect the robustness of the results. The paper does not discuss potential impacts on system complexity or the overhead introduced by the probabilistic thinning mechanism itself.

Why it matters
This work has significant implications for the design of low-latency streaming ML systems, particularly in environments where operational costs and latency are critical factors. By decoupling inference from state updates, the proposed method allows for more efficient processing of high-frequency events, which is essential for real-time applications. The findings contribute to the ongoing discourse on optimizing streaming data architectures and can inform future research in this area, as published in arXiv cs.LG.