Gleaner: A Semantically-Rich and Efficient Online Sampler for Microservice Diagnostics

TL;DR

Gleaner is an efficient online trace sampler for microservice diagnostics that improves fidelity and RCA accuracy by representing traces as set-based edge bags, enabling fast processing and better anomaly detection.

Contribution

Gleaner introduces a novel set-based trace representation and sampling strategy that replaces graph analysis, enabling high-fidelity, real-time trace sampling for microservice diagnostics.

Findings

Gleaner processes traces in 0.74ms each, enabling real-time sampling.

It improves trace pattern coverage by up to 128.7%.

At 1% sampling rate, it boosts RCA accuracy by 42%-107%.

Abstract

Distributed tracing in microservices is critical for diagnostics but generates overwhelming data volumes, necessitating intelligent sampling. To maximize fidelity, state-of-the-art (SOTA) tail-based samplers analyze complete (or even log-enriched) traces by modeling them as graphs. However, this reliance on computationally expensive graph analysis creates a performance bottleneck that prohibits their use in online settings. To this end, we propose Gleaner, an online tail-sampling framework that breaks this trade-off. It is founded on the key insight that explicit graph structures are unnecessary for high-fidelity trace grouping. Instead, Gleaner represents each trace as a "bag-of-edges" augmented with log semantics, replacing slow graph algorithms with highly efficient set-based operations. It also employs an alarm-driven quota and a diversity-preserving strategy to prioritize anomalous and rare traces for downstream Root Cause Analysis (RCA). Experimentally, Gleaner processes traces at 0.74ms each, improving Trace Pattern Coverage by up to 128.7% and Shannon Entropy by up to 32.9% over baselines. At just a 1% sampling rate, Gleaner improves RCA accuracy by 42%-107% over the next-best sampler. Moreover, RCA on Gleaner's sampled data is more accurate than with the entire, unsampled dataset. This result reframes intelligent sampling from a data reduction technique to a powerful signal enhancement paradigm for automated operations.