StarRocks' engineering blog explains how its cost-based optimizer tackles the hardest part of OLAP: joins. Instead of denormalizing data, the system keeps tables normalized and makes joins fast enough for real-time queries. The core challenge is planning in a distributed environment, where the search space for optimal join orders is enormous and execution costs vary wildly.

The optimizer handles four key challenges: choosing the right join algorithm (like Hash Join vs. Sort-Merge), selecting an efficient multi-table join order, accurately estimating join selectivity, and accounting for data reshuffling and network overhead in distributed systems. StarRocks applies heuristic rules to transform inefficient join types, like turning a Cross Join into an Inner Join when a predicate exists.

Real-world deployments at NAVER, Demandbase, and Shopee demonstrate the practical impact. By prioritizing high-selectivity joins and minimizing data movement, StarRocks avoids the expensive backfills and slow schema evolution that plague denormalized systems. The approach lets developers run complex analytical queries on the fly without sacrificing performance.