How MaxCompute’s Intelligent Data Warehouse Optimizes Queries with AutoMV
This article explains MaxCompute’s intelligent data warehouse architecture, its self‑learning optimization pipeline, the role of intelligent materialized views, the automated recommendation system for materialized views, and the AutoMV feature that automatically creates, updates, and cleans up materialized views to reduce compute costs and improve query performance.
04 AutoMV
AutoMV automates the selection, creation, updating, and cleanup of the most beneficial materialized views, minimizing manual effort and storage waste.
When a query first hits an AutoMV, the view is initially empty; data is written to the view only upon first use, avoiding unnecessary resource consumption.
AutoMV performs precise rewrites only, preventing performance regressions caused by imprecise rewrites.
Cost impact is minimal: storage overhead is negligible compared to the compute savings, with typical projects seeing 16.8% reduction in compute resources and significant CU savings.
Future plans include cross‑project support, fuzzy rewrite capabilities, workflow orchestration improvements, dynamic updates, and enhanced visual interfaces.
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