Optimizing I/O for Data‑Intensive Analytics in Cloud‑Native Environments: Insights from Uber Presto
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native environments, analyzes how cloud storage cost models affect performance optimization, and presents Uber Presto case‑study findings that reveal fragmented access patterns and hidden financial costs of traditional I/O strategies.
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 nuanced understanding of performance optimization.
Through empirical research on Uber's production Presto workload, the authors discover that over 50% of data accesses are smaller than 10 KB and over 90% are under 1 MB, indicating a highly fragmented access pattern that carries different financial implications in the cloud compared to traditional 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 during data processing.
Key sections cover adjusting cognition and strategies based on varying cloud storage scenarios, the additional costs associated with widely used I/O optimization techniques during cloud migration, and a fresh perspective on system design in the cloud computing domain to address the rapid growth of data‑intensive applications.
Readers will gain a foundational understanding of cloud‑native I/O optimization, enabling them to develop specialized strategies for high‑efficiency data handling in cloud environments.
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