Optimizing I/O for Data‑Intensive Analytics in Cloud‑Native Environments: Insights from Uber Presto
This whitepaper examines the industry shift of moving data‑intensive analytics workloads to cloud‑native platforms, revealing how cloud storage cost models affect I/O optimization and presenting Uber Presto case‑study findings that highlight fragmented access patterns and associated financial impacts.
This article explores the widespread industry trend of migrating data‑intensive analytics applications from on‑premises to cloud‑native environments, emphasizing that the unique cost model of cloud storage demands a more nuanced understanding of performance optimization.
Through empirical research on Uber's production Presto workload, the study discovers 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 carries different cost implications in the cloud compared to traditional data platforms.
The paper presents a case‑study‑driven analysis of I/O optimization logic and strategies, aiming to help readers design efficient I/O solutions tailored for cloud environments and significantly improve cost‑performance ratios for data‑intensive processing.
Core Highlights:
• Adjust cognition and strategies based on varying cloud storage scenarios and their impact on application design and performance.
• Using Uber data as a case study, illustrate the additional costs that common I/O optimization techniques may incur during cloud migration.
• Offer a fresh perspective on system design in the cloud computing domain to assist stakeholders in addressing the rapid growth of data‑intensive applications.
The whitepaper serves as a starting point for further research, providing readers with actionable I/O optimization logic to develop efficient strategies for cloud‑based, data‑intensive workloads.
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