SQL Query Suspension Control for Distributed Database Testing
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Solution Overview
Problem
Existing database systems face limitations in processing speed due to hardware constraints, data storage methods, and restricted co-processing options, particularly when handling large volumes of data.
Innovation Solution
A database system architecture that utilizes parallelized data input, storage, retrieval, and query processing systems, enabling distributed execution of queries across multiple computing devices and nodes, with independent processing core resources to manage administrative and configuration operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed parallel processing is implemented across multiple computing devices, then processing speed and throughput are improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent divides the database processing workload into multiple independent segments that can be executed in parallel across different computing devices. Each segment represents a discrete unit of work that can be processed independently, allowing the system to achieve parallel processing without requiring complex inter-device coordination for each operation.
Solution Approach 2:
The patent introduces an intermediary layer (such as a query coordinator or distributed transaction manager) that manages the parallel processing operations. This intermediary handles the complexity of coordination, task distribution, and result aggregation, shielding individual processing nodes from the overall system complexity while enabling high-throughput parallel execution.
2Quantity of substance
If more co-processing resources are allocated to handle large data volumes, then data processing capacity is improved, but hardware costs and system resource requirements increase
Solution Approach 1:
The patent designs co-processing resources to perform multiple functions - they can handle different types of database operations (queries, updates, analytics), work with various data formats, and serve multiple applications simultaneously. This multi-functionality allows the system to process large data volumes using a versatile resource pool rather than requiring specialized hardware for each function.
Solution Approach 2:
The patent enables dynamic adjustment of processing parameters such as parallelism degree, buffer sizes, and memory allocation based on workload characteristics. By changing these parameters, the system can optimize data processing capacity for different scenarios without requiring proportional increases in physical hardware resources.
3Speed
If data is stored in a traditional centralized manner, then data consistency is maintained, but retrieval speed and query execution time deteriorate for large datasets
Solution Approach 1:
The patent segments the centralized data storage into distributed data partitions across multiple nodes. Each partition maintains local consistency through standard transaction protocols, while the overall system achieves consistency through coordinated metadata management and distributed transaction protocols, enabling fast local retrieval without sacrificing global data integrity.
Solution Approach 2:
The patent implements preliminary data organization and indexing strategies at the distributed level, where data is pre-grouped and indexed according to query patterns before retrieval occurs. This preliminary organization enables faster query execution by reducing the scope of data that needs to be accessed and processed, while distributed transaction protocols maintain consistency across partitions.
Data Source
AI summary
A method for execution by a database system to improve testing, the method includes creating a synchronization database object (SDO) indicating a set of conditions for suspension of execution of queries. The method further includes establishing a first and second session, where the first session applies the SDO to a query expression and the second session monitors the SDO. The method further includes initiating, via the first session, execution of queries, and suspending the execution of queries when detecting a condition is met. The method further includes updating, via the first session, at least one relational database table to include monitoring data indicating suspension of the queries. The method further includes the second session verifying the suspension and instructing the SDO to resume suspended queries. The method further includes resuming, via the first session, the queries until a next condition is met or execution of the queries is finished.