Rule-Based Optimizer (RBO):
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Concept: Follows pre-defined rules to choose an execution plan, regardless of the actual data size or distribution.
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Value:
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Simple and predictable.
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Can be efficient for well-understood queries and static data.
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Limitations:
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Lacks adaptability to different data scenarios.
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Can lead to suboptimal plans for complex queries or large datasets.
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Cost-Based Optimizer (CBO):
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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.
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Value:
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More efficient and adaptable for complex queries and diverse data.
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Can significantly improve query performance.
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Limitations:
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Requires accurate data statistics.
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Can be resource-intensive and complex to optimize.
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Hybrid-Based Optimizer:
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Concept: Combines elements of both RBO and CBO.
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Leverages rules for specific situations where they are known to be effective.
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Uses CBO for more complex scenarios where cost estimation is beneficial.
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Value:
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Aims to combine the strengths of both RBO and CBO.
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Can improve performance and predictability for a wider range of queries and data.
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Limitations:
- Requires careful tuning and configuration to balance the strengths of each approach.
StarRocks Hybrid-Based Optimizer:
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Features:
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Uses a multi-stage cost-based approach with rule-based hints for specific cases.
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Analyzes different query execution paths considering factors like data locality, storage format, and cost.
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Continuously monitors and learns from query execution to improve future optimizations.
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Claimed benefits:
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Improved query performance compared to pure RBO or CBO.
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More efficient resource utilization.
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Adaptability to diverse workloads and data characteristics.
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