Hierarchical Deterministic Log Sampling for High-Throughput Multi-Layer Distributed Systems: A Consistency Framework for Cross-System Observability

Abstract

This article presents a hierarchical deterministic sampling framework addressing the critical challenge of maintaining trace consistency across multi-layer distributed systems with heterogeneous sampling requirements. Traditional probabilistic sampling methods create fragmented observability, severely hindering cross-system debugging and compliance auditing. The framework leverages cryptographic hash functions to generate deterministic probability distributions from request identifiers, ensuring consistent sampling decisions across system boundaries while respecting service-specific constraints. Through deterministic probability generation, hierarchical sampling coordination, and fan-out aware rate adjustment, the system delivers complete end-to-end traces for targeted requests while enabling varying sampling rates across different components. Evaluation across diverse deployment scenarios demonstrates significant improvements in trace completeness, sampling consistency, and scalability compared to conventional approaches. The framework establishes foundational patterns for deterministic coordination in distributed systems that can transform observability practices in complex enterprise environments while addressing critical requirements across financial services, e-commerce, healthcare, and cloud infrastructure domains.