Optimizing I/O for Data‑Intensive Analytics in Cloud‑Native Environments: Insights from Uber Presto
This whitepaper investigates the migration of data‑intensive analytics to cloud‑native environments, using Uber’s Presto workload to expose how cloud storage cost models and fragmented I/O patterns affect performance, and proposes optimized I/O strategies to improve cost‑effectiveness and system design.
The whitepaper examines the industry trend of moving data‑intensive analytics applications from on‑premises to cloud‑native environments, highlighting that cloud storage introduces a distinct cost model that requires finer‑grained performance optimization.
By observing Uber’s production Presto workload, the study reveals that traditional I/O optimizations ignore the financial cost of storage API calls; fragmented access patterns (over 50% of accesses under 10 KB and over 90% under 1 MB) can lead to high expenses in the cloud.
The paper presents a case‑study‑driven logical framework and strategies for I/O optimization tailored to cloud settings, aiming to help designers create cost‑effective I/O solutions for data‑intensive applications.
Readers will gain a new perspective on system design in cloud computing, enabling them to address the rapid growth of data‑intensive workloads with efficient I/O strategies.
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