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

This article explores the widespread industry trend of migrating data‑intensive analytics applications from on‑premises to cloud‑native environments, highlighting that the unique cost model of cloud storage demands a more detailed understanding of performance optimization.

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

The research 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 traditional data systems.

Based on this case study, the paper provides logical I/O optimization strategies tailored for cloud environments, aiming to help readers design efficient I/O solutions that significantly improve cost‑performance ratios for data‑intensive applications.

Readers will gain a new perspective on system design in the cloud computing domain and practical guidance for addressing the rapid growth of data‑intensive workloads.