Optimizing I/O for Data‑Intensive Analytics in Cloud‑Native Environments: Insights from Uber Presto

This article explores the growing industry trend of migrating data‑intensive analytics applications to cloud‑native environments, emphasizing that the unique cost model of cloud storage demands a more nuanced understanding of performance optimization.

Through an empirical study of Uber's production Presto workload, the authors discover that traditional I/O optimizations often overlook the financial cost of storage API calls, which can lead to unexpectedly high expenses in cloud settings.

The study shows that over 50% of data accesses are smaller than 10 KB and more than 90% are under 1 MB, indicating a highly fragmented access pattern that has different implications for cloud platforms compared to on‑premise systems.

Based on these observations, the whitepaper provides a logical framework and practical strategies for I/O optimization tailored to cloud environments, helping readers design efficient I/O solutions that improve cost‑performance ratios for data‑intensive applications.

Overall, the paper offers a fresh perspective on system design in the cloud computing domain, guiding stakeholders to address the rapid growth of data‑intensive workloads with cost‑aware, performance‑driven approaches.