How StarRocks' Cost-Based Optimizer Enables High-Performance Joins in Distributed Systems
By
HermitX
Hot, fresh, and worth queueing round the block for.
Summary
This technical deep dive explores how StarRocks, a distributed database system, achieves high-performance joins through its cost-based optimizer. The article explains the engineering approach of keeping data normalized while making joins fast enough to run on-the-fly, addressing the challenge of join planning in distributed systems where search spaces are huge. It covers four main areas: join fundamentals and optimization challenges, logical join optimizations, join reordering, and distributed join planning, concluding with real-world case studies from companies like NAVER and Demandbase.
Key quotes
· 5 pulledStarRocks takes the opposite approach: keep data normalized and make joins fast enough to run on the fly.
In a distributed system, the join search space is huge, and a good plan can be orders of magnitude faster.
This deep dive explains how StarRocks' cost-based optimizer makes that possible, in four parts: join fundamentals and optimization challenges, logical join optimizations, join reordering, and distributed join planning.
The engineering choices that turn joins into a strength.
Finally, we examine real-world case studies from NAVER, Demandbase, and...
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