What problem does it solve?
This Skill solves slow or inefficient StarRocks analytical performance caused by poorly chosen schema shapes, keys, and indexing strategies, especially when translating requirements into practical DDL and loading patterns.
Core Features & Use Cases
- Choose the right table shape: Compare star schema, wide tables, and aggregation tables to match BI dashboard access patterns and minimize expensive joins.
- Design for StarRocks storage mechanics: Select sort keys (ORDER BY), distribution strategy (DISTRIBUTED BY), partitioning, and compression so queries skip irrelevant granules and scan/aggregate efficiently.
- Accelerate common filters: Add bitmap indexes for low-cardinality predicates and bloom filter columns for high-cardinality equality filters.
- Implement robust history and BI acceleration: Use SCD Type 2 patterns with Primary Key tables for point-in-time joins and design aggregation tables (Aggregate Key) for repeated metric queries.
- Validate and avoid anti-patterns: Prevent over-normalization, incorrect index usage, and mismatched grain that leads to expensive queries or frequent rebuilds.
Quick Start
Ask the AI to design a StarRocks data model for your BI workload and provide the preferred table type (star schema, wide table, or aggregation table) including DDL choices for sort key, distribution, partitioning, and relevant indexes.
