RDBMS Limit Query Optimization via Cardinality Analysis
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Solution Overview
Problem
Existing methods for optimizing limit queries over analytical functions in relational database management systems (RDBMS) are inadequate, particularly due to the unknown semantic properties of analytical functions and their high computational complexity, leading to inefficient resource utilization and data transfer.
Innovation Solution
The implementation of static compile-time and dynamic run-time optimizations that utilize semantic properties such as 'granularity' and 'input-to-output cardinality' to minimize the input records processed by analytical functions, thereby optimizing limit queries with clauses like LIMIT, TOP, and SAMPLE, and reducing unnecessary data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all input records are processed by analytical functions to ensure complete result set, then result accuracy is improved, but execution time and resource usage increase significantly
Solution Approach 1:
The patent applies preliminary action by pushing the LIMIT clause down through the query plan to the analytical function input stage. This allows the system to pre-determine and limit the number of input records processed by analytical functions based on the desired output size K and the function's input-to-output cardinality ratio, avoiding unnecessary processing of excess records while ensuring the required number of output records are generated
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the LIMIT value applied to analytical function inputs based on the function's semantic properties, specifically its input-to-output cardinality. The system modifies the effective input limit parameter from the original K to K divided by the cardinality ratio, optimizing the balance between processing sufficient records for accuracy and limiting records for performance
2Reliability
If LIMIT clause is applied after analytical function execution, then complete input processing ensures accurate results, but unnecessary data processing increases resource usage
Solution Approach 1:
The patent implements preliminary action by transforming the query execution plan to apply the LIMIT clause at the input stage of analytical functions rather than after execution. This preliminary limitation of input records prevents wasteful processing of data that would be discarded anyway, while maintaining result accuracy through proper cardinality-based calculation of the required input subset
Solution Approach 2:
The patent extracts and utilizes the semantic properties of analytical functions, specifically their input-to-output cardinality ratios, to determine the precise number of input records needed. By extracting this metadata information and applying it to limit the input records before analytical function execution, the system eliminates processing of unnecessary data while preserving the required output accuracy
3Productivity
If semantic properties of analytical functions are utilized for optimization, then query performance is improved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing the input-to-output cardinality ratios of analytical functions in a metadata catalog during system initialization or function registration. This preliminary preparation allows the query optimizer to efficiently retrieve and utilize these semantic properties during query optimization without adding complex computation during query execution, thus improving performance while managing system complexity
Data Source
AI summary
A relational database management system (RDBMS) optimizes limit queries over analytical functions, wherein the limit queries include an output clause comprising a LIMIT, TOP and SAMPLE clause with an expression specifying a limit that is a number K or a percentage α %. The optimizations of the limit queries include: (1) static compile-time optimizations, and (2) dynamic run-time optimizations, based on semantic properties of “granularity” and “input-to-output cardinality” for the analytical functions.