Challenges in Join Optimization
2 months ago
- #joins
- #database
- #optimization
- StarRocks optimizes join performance by keeping data normalized and making joins fast enough to run on the fly.
- The article explains StarRocks' cost-based optimizer in four parts: join fundamentals, logical join optimizations, join reordering, and distributed join planning.
- Common join types include Cross Join, Full/Left/Right Outer Join, Anti Join, Semi Join, and Inner Join, each with different performance characteristics.
- Join optimization challenges include multiple join implementation strategies, join order selection, difficulty in estimating join effectiveness, and distributed system complexities.
- StarRocks uses Hash Join as its primary join algorithm, with optimizations like predicate pushdown, predicate extraction, equivalence derivation, and limit pushdown.
- Join reordering strategies in StarRocks include exhaustive, greedy, and dynamic programming approaches to determine the optimal join order.
- Distributed join planning in StarRocks involves shuffle join, broadcast join, bucket shuffle join, colocate join, and replicate join to minimize network overhead.
- Global Runtime Filters in StarRocks help reduce join input size by filtering irrelevant rows early using Min/Max filters, IN predicates, and Bloom filters.
- Case studies from NAVER, Demandbase, and Shopee demonstrate significant performance improvements and cost reductions by leveraging StarRocks' efficient join execution.