Database Update Method Using No-Operation Operators for Column-Level Changes
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Solution Overview
Problem
Existing database updating methods require updating entire rows of data even if only minor changes occur, leading to inefficiencies and resource wastage, especially in large e-commerce databases that need frequent updates.
Innovation Solution
Implementing a method where only the columns with data changes are updated using no-operation operators, generating a third row of data with no-operation operators for unchanged columns, and utilizing SQL or API functions to update corresponding columns in a second database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire row of data is updated even when only minor changes occur, then data consistency is maintained, but updating time and resource consumption increase significantly
Solution Approach 1:
The patent segments the data row into individual columns and identifies which columns have changed. Instead of treating the entire row as a single update unit, the system divides it into column-level operations, allowing selective updating of only those columns that contain changes while maintaining data consistency.
Solution Approach 2:
The patent applies partial action by updating only the necessary portion of the data (changed columns) rather than the entire row. The no-operation operator mechanism enables this by explicitly marking unchanged columns, allowing the system to perform minimal necessary updates while preserving data integrity.
2Reliability
If entire row of data is updated even when only minor changes occur, then data consistency is maintained, but resource consumption increases significantly
Solution Approach 1:
The patent segments the data row into individual columns and identifies which columns have changed. Instead of treating the entire row as a single update unit, the system divides it into column-level operations, allowing selective updating of only those columns that contain changes while maintaining data consistency.
Solution Approach 2:
The patent applies partial action by updating only the necessary portion of the data (changed columns) rather than the entire row. The no-operation operator mechanism enables this by explicitly marking unchanged columns, allowing the system to perform minimal necessary updates while preserving data integrity.
3Manufacturing precision
If SQL statements are reparsed for each row with data changes at different columns, then data accuracy is maintained, but updating efficiency decreases
Solution Approach 1:
The patent merges multiple individual SQL parsing operations into a single bulk parsing operation. By collecting all rows to be updated and parsing them together in one operation, the system maintains data accuracy through proper SQL processing while eliminating redundant parsing overhead and significantly improving updating efficiency.
Solution Approach 2:
The patent performs preliminary actions by collecting and preparing all rows for update before executing the SQL parsing operation. This pre-processing step allows the system to optimize the parsing operation by knowing in advance which columns need updating across all rows, enabling more efficient query construction and execution.
Data Source
AI summary
The present disclosure relates to the field of data processing, disclosing a method and apparatus of inter-databases data updating. A method of inter-databases data updating of the present disclosure includes the steps of generating a third row of data based on data of a first database, where data of at least one column of the third row of data is a no-operation operator, the no-operation operator indicating no data change; and updating a fourth row of data of a second database based on the third row of data, where no-operation operator columns of data of the third row of data are not utilized to update data of corresponding columns of the fourth row of data, non no-operation operator columns of data of the third row of data are utilized to update data of corresponding columns of the fourth row of data. When there is only data change at some columns of a row of data, only data of the columns where data has changed will be updated, decreasing the amount of data to be updated, increasing the updating efficiency of databases, and reducing the waste of resources and time.