Optimizing I/O for Data-Intensive Analytics in Cloud-Native Environments: A Case Study of Uber Presto

The article explores the trend of migrating data‑intensive analytics applications to cloud‑native environments and notes that the unique cost model of cloud storage requires a more detailed understanding of performance optimization.

Through empirical research on Uber’s production Presto environment, the study finds that data access patterns are highly fragmented—over 50% of accesses are smaller than 10 KB and over 90% smaller than 1 MB—meaning that traditional I/O optimizations that ignore storage‑API financial costs can lead to high expenses in the cloud.

The whitepaper, presented as a case study, provides logical I/O‑optimization strategies to help designers create efficient I/O solutions tailored for cloud environments, thereby improving cost‑performance ratios for data‑intensive processing.

• Adjust cognition and strategy based on different cloud storage scenarios and their impact on application design and performance.

• Using Uber data as a case study, illustrate the additional costs that widely used I/O‑optimization techniques may incur during enterprise‑level cloud migration.

• Offer a new perspective on system design in the cloud computing domain to help stakeholders cope with the rapid growth of data‑intensive applications.

This whitepaper presents the I/O‑optimization logic and ideas as a case study, serving as a starting point for readers to design efficient I/O strategies specifically for data‑intensive applications in cloud environments.