Technical Feature Advantage: SQL Hybrid-Based Optimizer

Rule-Based Optimizer (RBO):

  • Concept: Follows pre-defined rules to choose an execution plan, regardless of the actual data size or distribution.

  • Value:

    • Simple and predictable.

    • Can be efficient for well-understood queries and static data.

  • Limitations:

    • Lacks adaptability to different data scenarios.

    • Can lead to suboptimal plans for complex queries or large datasets.

Cost-Based Optimizer (CBO):

  • Concept: Analyzes statistics about the data (cardinalities, indexes, etc.) to estimate the cost (e.g., execution time) of different execution plans and chooses the most efficient one.

  • Value:

    • More efficient and adaptable for complex queries and diverse data.

    • Can significantly improve query performance.

  • Limitations:

    • Requires accurate data statistics.

    • Can be resource-intensive and complex to optimize.

Hybrid-Based Optimizer:

  • Concept: Combines elements of both RBO and CBO.

    • Leverages rules for specific situations where they are known to be effective.

    • Uses CBO for more complex scenarios where cost estimation is beneficial.

  • Value:

    • Aims to combine the strengths of both RBO and CBO.

    • Can improve performance and predictability for a wider range of queries and data.

  • Limitations:

    • Requires careful tuning and configuration to balance the strengths of each approach.

StarRocks Hybrid-Based Optimizer:

  • Features:

    • Uses a multi-stage cost-based approach with rule-based hints for specific cases.

    • Analyzes different query execution paths considering factors like data locality, storage format, and cost.

    • Continuously monitors and learns from query execution to improve future optimizations.

  • Claimed benefits:

    • Improved query performance compared to pure RBO or CBO.

    • More efficient resource utilization.

    • Adaptability to diverse workloads and data characteristics.