Cost Based Optimizer (CBO)

What is a Cost Based Optimizer?

How a Cost Based Optimizer Works

• Query Analysis: Initially, the CBO breaks down the SQL query to understand the required operations, like table joins, data sorting, and indexing.
• Execution Plan Generation: It then explores numerous ways to execute the query, each constituting a different execution plan.
• Cost Estimation: For each plan, the CBO estimates a cost, considering factors like estimated I/O and CPU usage. This cost is not measured in traditional units but in an abstract measure that might be referred to as "Query Bucks."
• Optimal Plan Selection: After evaluating the costs, the CBO selects the execution plan with the lowest estimated resource usage.

Cost Based vs. Rule Based Optimizers

• Rule-Based Optimizers (RBOs): These are earlier generation optimizers that use a fixed set of predefined rules to choose an execution plan. They don’t consider the actual data distribution or size, which can lead to suboptimal performance, especially in complex queries or large databases.
• Cost-Based Optimizers (CBOs): in contrast, use actual data statistics to estimate the cost of different query execution plans. This approach allows them to adapt to varying data distributions and sizes, making them more efficient and reliable for complex queries and large datasets.

Benefits of Using a Cost Based Optimizer

• . Improved Query Performance
  • Optimal Execution Plans: By considering various execution strategies and their associated costs, CBOs select the most efficient plan for a given query. This leads to faster query execution times, especially in complex databases.
  • Dynamic Plan Selection: CBOs can adapt to changing data patterns and select plans that are optimized for the current state of the data, ensuring consistently high performance.
• Efficient Resource Utilization
  • Balanced Resource Use: CBOs estimate the cost of different execution plans in terms of CPU, memory, and I/O resources. This helps in selecting plans that make optimal use of available resources, preventing over-utilization or under-utilization of system capabilities.
  • Reduced Operational Overhead: Efficient query plans mean less strain on database resources, which can reduce operational costs related to hardware, energy, and maintenance.
• Scalability and Flexibility
  • Handling Large Datasets: CBOs are particularly effective in environments with large and complex datasets. They can scale efficiently as data volume grows, maintaining performance without the need for constant manual tuning.
  • Adaptability to Data Changes: CBOs continuously update their understanding of data distribution and statistics, making them adept at handling evolving data landscapes without manual intervention.
• Enhanced Accuracy in Query Optimization
  • Data-Driven Decisions: Unlike Rule-Based Optimizers, CBOs rely on actual data statistics and distributions, leading to more accurate and effective optimization decisions.
  • Reduced Guesswork: The reliance on data over fixed rules reduces guesswork in query planning, leading to more consistent and predictable query performance.
• Better Support for Complex Queries
  • Handling Sophisticated Query Structures: CBOs are adept at optimizing complex queries involving multiple joins, subqueries, and advanced functions, where rule-based systems might struggle.
  • Customization for Specific Workloads: CBOs can tailor execution plans to fit specific workload patterns, which is particularly beneficial in specialized or analytical query environments.
• Integration with Advanced Database Features
  • Compatibility with Other Optimizations: CBOs often work in tandem with other database optimizations like vectorization, parallel processing, and advanced indexing, further enhancing overall performance.
  • Future-Proofing: As database technologies evolve, CBOs are well-positioned to integrate with emerging techniques and algorithms, ensuring that the database remains at the forefront of performance optimization.

Practical Implications and Challenges

• Arbitrary Cost Assignments: The CBO uses estimated values for CPU and IO work, which are not directly translatable to actual time or resource usage. These estimations, while not perfect, guide the optimizer in choosing efficient execution plans.
• Hardware Independence: The costs used in CBOs are not tailored to specific hardware configurations, ensuring broad applicability across various system types.
• Excluding Some Query Aspects: Certain elements like memory grants may not be factored into the cost, potentially affecting the optimizer's accuracy.
• Inaccuracies in Complex Scenarios: For very complex queries or those involving atypical operations (like linked server queries), the CBO might not always estimate costs accurately, leading to less-than-optimal plans.

Introducing the StarRocks Cost Based Optimizer (CBO)

Conclusion