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A Cost-Based Optimizer (CBO) is an advanced type of query optimizer that enhances query performance by evaluating multiple query execution plans and choosing the one with the lowest estimated cost. Unlike simpler optimizers that rely solely on heuristic or rule-based approaches, CBOs use detailed statistical information about the data to make informed decisions. By leveraging data statistics such as data distribution, table sizes, and index availability, the CBO can estimate the cost of various execution plans and select the most efficient one, reducing both processing time and resource consumption. This sophisticated approach allows the CBO to handle complex queries effectively, optimizing the performance and overall efficiency of the database system.
What Exactly Is a Query Optimizer and Why Do We Need Query Optimization?
SQL, or Structured Query Language, allows users to specify what data they want but doesn't tell them how to retrieve it. The process of determining the "how" is managed by the database's "brain," known as the query optimizer. A query optimizer is a crucial component of a Database Management System (DBMS) that determines the most efficient way to execute a SQL query. It analyzes various potential execution plans and selects the one that minimizes resource usage and execution time, ensuring that database operations are performed as efficiently as possible.
Query optimization is vital for several reasons. First, it significantly impacts the overall performance of a database system. Efficient query execution leads to faster response times, which is crucial for applications that require real-time data access, such as e-commerce platforms, financial systems, and social media services. Second, query optimization helps in reducing the computational load on the database server. By selecting the most efficient execution plan, the optimizer minimizes the use of CPU, memory, and I/O resources, thereby preventing system bottlenecks and improving throughput. Third, optimization is essential for cost management, especially in cloud-based environments where resource usage directly translates to operational costs. Without effective query optimization, even simple queries can become resource-intensive, leading to slow performance, increased costs, and a poor user experience. Therefore, query optimization is not just a technical necessity but also a critical factor in the economic and practical sustainability of database operations.
Challenges Faced by Query Optimizers
Query optimizers are a core part of relational database systems. They are crucial to database kernel development and serve as a "litmus test" for gauging the maturity of an entire database system. Despite decades of research and development, key challenges remain in the realm of query optimization. The central question persists: how can we use limited system resources to select the best execution plan for a query?
Challenge 1: Accurate Statistics and Cost Models
Accurate statistics and cost models are foundational to query optimizers, as they are responsible for calculating the costs of various execution plans. However, achieving this accuracy is challenging due to several reasons:
- Statistics Collection: There are two main issues with collecting statistics in database systems. First, if statistics are gathered through sampling, there will inevitably be sampling errors. Second, the collection of statistics is inherently delayed. This means that when optimizing a SQL query, the statistics used are from a previous point in time.
- Selectivity Calculation and Intermediate Result Estimation: Calculating selectivity has always been a difficult task for database systems. Both academia and industry have been researching methods to make selectivity calculations more accurate, such as dynamic sampling and multi-column histograms. However, these methods have not fully solved the problem. For instance, calculating the selectivity of join predicates remains an unsolved issue.
- Cost Models: Most modern database systems use static cost models, which rely on fixed assumptions about I/O, CPU, and memory usage. These models do not adapt to changing system conditions.
Challenge 2: Vast Plan Space
The number of potential execution plans for complex queries is enormous. In many cases, the optimizer cannot even enumerate all possible plans. Efficiently navigating this vast plan space to find the best execution plan remains a significant challenge for query optimizers.
How a Cost Based Optimizer Works
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Query Analysis: Initially, the CBO breaks down the SQL query to understand the required operations, like table joins, data sorting, and indexing.
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Execution Plan Generation: It then explores numerous ways to execute the query, each constituting a different execution plan.
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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."
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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
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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.
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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
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. Improved Query Performance
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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.
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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.
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Efficient Resource Utilization
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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.
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Reduced Operational Overhead: Efficient query plans mean less strain on database resources, which can reduce operational costs related to hardware, energy, and maintenance.
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Scalability and Flexibility
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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.
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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.
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Enhanced Accuracy in Query Optimization
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Data-Driven Decisions: Unlike Rule-Based Optimizers, CBOs rely on actual data statistics and distributions, leading to more accurate and effective optimization decisions.
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Reduced Guesswork: The reliance on data over fixed rules reduces guesswork in query planning, leading to more consistent and predictable query performance.
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Better Support for Complex Queries
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Handling Sophisticated Query Structures: CBOs are adept at optimizing complex queries involving multiple joins, subqueries, and advanced functions, where rule-based systems might struggle.
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Customization for Specific Workloads: CBOs can tailor execution plans to fit specific workload patterns, which is particularly beneficial in specialized or analytical query environments.
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Integration with Advanced Database Features
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Compatibility with Other Optimizations: CBOs often work in tandem with other database optimizations like vectorization, parallel processing, and advanced indexing, further enhancing overall performance.
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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.
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Practical Implications and Challenges
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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.
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Hardware Independence: The costs used in CBOs are not tailored to specific hardware configurations, ensuring broad applicability across various system types.
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Excluding Some Query Aspects: Certain elements like memory grants may not be factored into the cost, potentially affecting the optimizer's accuracy.
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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)
StarRocks has innovatively developed a new Cost-Based Optimizer (CBO), utilizing the principles of Cascades and Optimal Reciprocal Collision Avoidance (ORCA). This CBO is specifically designed and improved to work effectively with the capabilities of StarRocks' execution engine and scheduler, representing a notable progress in database optimization.
The CBO optimizer in StarRocks leverages a range of statistics, which are collected periodically. These include data such as row count, average column size, cardinality, the presence of NULL values, and the range of MAX/MIN values.
For the collection of statistics, StarRocks offers options for either full or sampled data gathering. This can be conducted either manually or on a regular schedule, aiding the CBO optimizer in improving cost estimation and in choosing the most efficient execution plan.
With its advanced Cost-Based Optimizer, StarRocks enhances CPU efficiency, especially when handling complex queries. This efficiency is achieved via branched execution planning with cascading logic. This approach allows for the concurrent processing of different query parts, eliminating the need for sequential completion. Optimization of the query execution plan is automatic and increasingly effective, as it incorporates actual statistics from your data and queries.
Furthermore, StarRocks boasts rapid performance on object storage platforms like S3. When integrated with data lakes such as Hudi, Iceberg and Delta Lake, the statistical data provided by these data lake modules also contribute to the efficiency of StarRocks' cost-based optimizer. This is achieved without the necessity of loading data directly into StarRocks.
Conclusion
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