Database Index Clustering and Set Function Calculation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Database operations face performance degradation due to the costs associated with creating and maintaining indexes, particularly during insertion, deletion, and modification operations, as well as inefficiencies in determining index entry positions during queries.
Innovation Solution
The use of differentiated subfields within multi-field database indexes to enforce index clustering, optimize disk I/O, and implement a set function calculation algorithm that reduces the number of records and index entries read during query execution, along with the definition of inferred indexes for circular tables using auto-incremented fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If indexes are created to improve query performance, then search speed is improved, but insertion, deletion, and modification operations become slower due to the need to maintain index structures
Solution Approach 1:
The patent segments the index structure into multiple partitions or levels, allowing query operations to be directed to specific segments rather than maintaining a single monolithic index. This segmentation enables parallel processing and reduces the overhead on insertion operations, as changes can be made in one segment without affecting the entire index structure.
Solution Approach 2:
The patent implements preliminary organization of data into clusters or groups before indexing, so that related records are physically close together in storage. This preliminary action reduces the need for extensive index maintenance during insertions and modifications, as the data is already organized in a query-friendly format.
2Adaptability or versatility
If multiple indexes are created on the same table to handle different query types, then query versatility is improved, but storage space requirements increase
Solution Approach 1:
The patent creates index structures that can serve multiple query types simultaneously. For example, a single multi-level index can support both equality queries and range queries, or an index organized by multiple fields can handle different combination queries without requiring separate indexes for each query pattern.
Solution Approach 2:
The patent merges multiple indexing functions into a unified index structure. Instead of creating separate indexes for different query types, the system combines them into a single index that can handle various query patterns, thereby reducing the total storage space required while maintaining query versatility.
3Speed
If indexes are maintained in sorted tree structures to facilitate efficient searching, then query performance is improved, but the complexity of maintaining these structures during updates increases
Solution Approach 1:
The patent divides the sorted tree structure into multiple segments or levels, where each segment can be independently managed. This segmentation simplifies maintenance operations, as updates can be localized to specific segments without requiring rebalancing of the entire tree structure, thereby reducing the complexity of index maintenance.
4Ease of operation
If full table scans are performed to locate records by field value, then simplicity of operation is maintained, but query response time increases for large tables
Solution Approach 1:
The patent performs preliminary organization of data into clusters or groups based on field values before queries are executed. This preliminary action creates a natural indexing structure that allows the database to quickly locate relevant record clusters without performing full table scans, thereby reducing query response time while maintaining operational simplicity.
Data Source
AI summary
A database system uses indexes to improve performance. The system can use one or more of the following: differentiated subfields for index clustering; set function calculations to reduce the amount of I/O; and/or using an inferred index in a circular table.