Database Tool Segmentation for Read Write Efficiency
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Solution Overview
Problem
Conventional database systems face a performance trade-off where they cannot be both read-efficient and write-efficient simultaneously, leading to resource-intensive multiple table joins during read operations and substantial resource consumption during write operations.
Innovation Solution
The database tool maintains the same data in both a write-efficient and a read-efficient database, assigning processors to different transaction types and segments to process transactions simultaneously, and uses a machine learning algorithm to determine which fields to read and send based on user device channels, reducing processor resources required for read operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a normalized database model is employed to write data to minimal locations, then write efficiency is improved, but read operations require multiple table joins which consume substantial resources
Solution Approach 1:
The patent divides the database system into two separate database instances: a write-efficient database using normalized model and a read-efficient database using denormalized model with pre-computed views. This segmentation allows each database to be optimized for its specific purpose without compromise
Solution Approach 2:
The patent introduces a view layer as an intermediary between the normalized database and read operations. Materialized views pre-compute and store aggregated data in denormalized format, acting as a mediator that satisfies read queries without requiring complex joins on the base normalized tables
2Productivity
If data is stored in redundant locations to avoid multiple table joins, then read efficiency is improved, but writing data to multiple locations consumes substantial resources
Solution Approach 1:
The patent separates read and write operations into different database instances. The write-efficient database maintains normalized data with minimal redundancy, while the read-efficient database handles all read queries using denormalized views, isolating write complexity from read optimization
3Device complexity
If a single processor handles both read and write operations, then system simplicity is maintained, but processor bottlenecks prevent simultaneous transaction processing
Solution Approach 1:
The patent assigns different processors to different database instances and transaction types. Write processors handle transaction processing on the normalized database, while read processors handle queries on the denormalized database, enabling parallel processing without bottlenecks
Solution Approach 2:
The patent creates a multi-functional system where separate processors can simultaneously handle different types of operations (read and write) on different database instances, with each processor specialized for its function but collectively providing comprehensive database service
Data Source
AI summary
A system includes a first database, a second database, a first processor, a second processor, a third processor, and a fourth processor. The first database includes a first segment assigned to the first processor and a first transaction type and a second segment assigned to the second processor and a second transaction type. Each of the first and second processors receives a request, assigned to its associated transaction type, from the third hardware processor, to store data in the first database. The processor then writes the data into the segment of first database assigned to the processor/transaction type. The processor further stores a command to write the data into the second database in a queue. The fourth processor then executes the commands stored in the queue, thereby writing the data into the second database.