Database Query Feedback Mechanism for Row-Level Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current relational database management systems (RDBMSs) face inefficiencies when processing unsorted data, as they often need to perform computationally expensive sort operations to execute queries effectively, leading to increased computational workload.
Innovation Solution
The method involves generating filtering predicates based on evaluated data during query execution and updating predicate values dynamically to reduce the computational workload by eliminating rows from the sort process earlier in the query processing stage, thereby optimizing data filtering and access path selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If RDBMS processes unsorted data, then data filtering can be performed, but computationally expensive sort operations are required
Solution Approach 1:
The system performs preliminary actions by evaluating predicates early in the query execution process and using those evaluations to filter data before it reaches the sort operation. This reduces the amount of data that needs to be sorted, thereby decreasing the computational workload of the sort operation while maintaining query execution efficiency.
Solution Approach 2:
The system uses feedback from predicate evaluation during query execution to dynamically adjust and refine filtering predicates. This feedback mechanism allows the system to optimize data filtering in real-time, reducing the computational burden on subsequent sort operations while maintaining high query execution efficiency.
2Reliability
If full data set is processed to ensure query accuracy, then query results are complete, but processing time increases
Solution Approach 1:
The system performs preliminary filtering actions by evaluating predicates before the main query processing loop. This preliminary action identifies and filters out irrelevant data early, ensuring query result accuracy while reducing the overall processing time by avoiding unnecessary processing of filtered-out data.
Solution Approach 2:
The system applies partial filtering action by using feedback from evaluated data to refine predicates dynamically. This partial action is sufficient to achieve the required query accuracy without the need to process the entire data set, thereby reducing processing time while maintaining reliability.
3Productivity
If data is sorted to optimize query performance, then query execution is efficient, but sort operations are computationally expensive
Solution Approach 1:
The system performs preliminary filtering actions that reduce the size of the data set before it reaches the sort operation. By filtering out irrelevant data early, the system decreases the computational cost of the sort operation while maintaining efficient query execution speed on the reduced data set.
Solution Approach 2:
The system uses feedback from predicate evaluation to optimize filtering before sorting. This feedback mechanism allows the system to dynamically adjust filtering criteria to maximize the reduction of data volume before sorting, thereby minimizing the computational cost of sort operations while maintaining query execution efficiency.
Data Source
AI summary
Methods, systems, and computer readable media of executing a database query are provided. One or more predicates are generated based on implicit filtering present within the database query, wherein each predicate specifies a condition with respect to a predicate value. An access path is selected for the database query based on the one or more predicates and the one or more predicates are integrated within the selected access plan. The database query is executed in accordance with the selected access plan. At least one predicate value based on data accessed is updated, as the database query is being executed, to increase filtering of data during remaining execution of the database query.