Database Addendum Parts Using Compressed Row Deletion Lists
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Solution Overview
Problem
Existing database systems are limited by hardware, data storage methods, and co-processing options, leading to inefficiencies in processing speed and execution time.
Innovation Solution
A database system architecture that employs parallelized data input, storage, and query processing systems, utilizing segmenting and restructuring of data segments for efficient storage and processing, along with optimized query plans to enhance processing speed and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional database systems use sequential processing methods, then system complexity remains manageable, but processing speed and execution time become limiting factors
Solution Approach 1:
The database system divides data into segments that can be processed in parallel across multiple computing devices. Each segment can be independently processed, allowing concurrent execution of queries and operations that would traditionally be sequential, thereby improving processing speed while managing complexity through modular architecture
Solution Approach 2:
The system transitions from single-threaded sequential processing to multi-threaded parallel processing by utilizing multiple computing devices simultaneously. This dimensional shift from one processing dimension to many enables concurrent operations and significantly improves execution time without proportionally increasing perceived system complexity
2Speed
If data is stored in traditional formats, then storage simplicity is maintained, but execution speed is limited by hardware constraints
Solution Approach 1:
Data is segmented into manageable portions that can be distributed across multiple computing devices and processed concurrently. This segmentation enables parallel execution of operations on different data portions, improving execution speed while maintaining storage structure through standardized segment formats
Solution Approach 2:
The system performs preliminary data reorganization and structuring before queries are executed. By pre-processing and optimizing data storage structures in advance, the system reduces the complexity of query execution and improves overall execution speed without requiring complex operations during actual query processing
3Productivity
If limited co-processing options are used, then system simplicity is preserved, but processing efficiency and scalability are restricted
Solution Approach 1:
The database system implements a universal co-processing architecture where multiple computing devices can perform various processing functions concurrently. The system supports diverse operations (select, insert, update, delete, aggregate functions) being executed simultaneously across different devices, greatly improving processing efficiency and scalability while managing complexity through standardized co-processing interfaces
Solution Approach 2:
The system merges multiple processing operations into concurrent executions across multiple computing devices. By combining and coordinating operations that would traditionally be executed sequentially, the system achieves parallel processing efficiency and enhanced scalability without requiring complex coordination mechanisms for each individual operation
Data Source
AI summary
A database system is operable to generate an addendum part indicating deletion of a set of rows of a plurality of rows of a dataset based on generating a row list based on identifying the set of rows for deletion from the dataset and compressing the row list in a set of compressed blocks written to disk memory resources. Based on determining to process the addendum part, the addendum part is read based on decompressing the row list based on access in the set of compressed block in the disk memory resources and emitting the row list for processing.