Database Query Filtering by Source and Time Partitions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Database queries often return irrelevant data due to lack of source and time filtering, leading to inefficient execution and resource utilization, as they are not optimized to exclude data based on the query's source and time of execution.
Innovation Solution
A system modifies database queries by appending filters based on the source and time of execution, ensuring that only relevant data is retrieved from the appropriate storage partitions, thereby optimizing query execution and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If database queries execute on all data partitions without filtering, then complete data coverage is achieved, but execution time and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by determining the source and time of execution of database queries before execution, and pre-filters the query based on these parameters. This allows the query to be executed only on relevant data partitions that match the source and time criteria, avoiding unnecessary scanning of irrelevant partitions while ensuring all relevant data is covered.
2Loss of information
If database queries execute on all data partitions without filtering, then all relevant data is retrieved, but resource expenditure increases
Solution Approach 1:
The system determines the source and time of execution of the database query in advance, and applies a filter to the query based on these parameters before execution. This preliminary filtering ensures that the query is executed only on data partitions that are relevant to the specific source and time context, thereby retrieving all necessary information while minimizing computational resource consumption by avoiding processing of irrelevant data.
3Productivity
If database queries are modified with source and time filters, then query execution efficiency improves, but query complexity increases
Solution Approach 1:
The system introduces an intermediary component that automatically determines the source and time of execution of database queries and applies appropriate filters. This intermediary handles the complexity of query modification internally, allowing the filtering logic to be embedded without requiring changes to the user-facing query interface. The query structure appears simple to users while the system automatically adds necessary filters based on source and time parameters.
Data Source
AI summary
Techniques for modifying a database query are disclosed. A source and/or time associated with an initial database query for execution on a database are determined. A modification of the initial database query is determined based on the source and/or time. The modification includes adding a filter to the initial database query. The modified database query is executed to return a set of results. Optionally, partitions of the database that are relevant to the modified database query may be selected. The modified database query may be executed on only the relevant partitions. The datasets included in the results to the modified database query may be more important, relevant, and/or valuable to a user than the datasets that were excluded based on the filter. The datasets included in the results may be retrieved from faster data storage than the excluded datasets.