Query Governor for Dynamic Data Change Thresholds
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Solution Overview
Problem
Database management systems (DBMS) face challenges in managing query execution efficiently, particularly in determining the estimated amount of data change, which can lead to resource exhaustion and the need for throttling queries to prevent excessive data modification, without adequately scaling thresholds based on user permissions or time-sensitive conditions.
Innovation Solution
Implementing a query governor that calculates the estimated amount of data change for incoming queries and sets dynamic thresholds based on user identity and time, rejecting queries that exceed these thresholds to prevent resource overload and optimize query execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the DBMS executes all incoming queries without restriction, then query processing capability is maintained, but resource exhaustion occurs due to excessive data modification
Solution Approach 1:
The system performs preliminary estimation of the amount of data change before executing the query. The query governor calculates an estimated amount of data change for the received query and compares it against a threshold before allowing execution, preventing resource exhaustion before it occurs.
Solution Approach 2:
A query governor acts as an intermediary component between the query receiver and the database execution engine. It intercepts incoming queries, estimates their data change impact, and selectively permits or rejects queries based on the estimated amount of data change, thus protecting the DBMS from excessive data modification.
2Reliability
If the DBMS rejects queries that may change excessive data, then resource exhaustion is prevented, but query processing capability is reduced
Solution Approach 1:
The system dynamically adjusts the threshold for acceptable data change based on various parameters including user identity, time of day, and database conditions. This allows the DBMS to be more permissive during off-peak hours or for privileged users while maintaining strict protection during critical periods, optimizing both resource protection and query processing capability.
3Device complexity
If a fixed threshold is used for data change limitation, then implementation simplicity is maintained, but adaptability to different users and conditions is lost
Solution Approach 1:
The threshold for data change limitation is transformed from a static fixed value to a dynamic parameter that automatically adjusts based on user identity, time of day, and system conditions. The query governor retrieves appropriate thresholds from storage based on these varying parameters, enabling the system to adapt to different users and conditions without manual reconfiguration.
4Ease of operation
If the DBMS allows unlimited data modification, then ease of operation is maintained, but harmful effects occur through excessive resource consumption
Solution Approach 1:
The query governor applies preliminary anti-action by pre-evaluating incoming queries to identify those that would cause excessive data modification. By calculating the estimated amount of data change before execution and rejecting problematic queries in advance, the system prevents harmful resource consumption while maintaining operational freedom for legitimate queries.
Data Source
AI summary
Techniques are described for estimating and managing an amount of data change for query processing. Embodiments of the present disclosure may generally receive a query to be executed and calculate an estimated amount of data change for the received query. If the initial estimated amount of data change for the received query does not exceed a threshold amount of data change, embodiments of the present disclosure may submit the query for execution. If the initial estimated amount of data change for the received query does exceed a threshold amount of data change, embodiments of the present disclosure may halt the execution of the query.