Database Write Optimizer Combining Parallel Operations
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Solution Overview
Problem
Existing database systems face performance issues due to the sequential nature of write operations, which leads to increased transaction time and row locks, preventing efficient scaling.
Innovation Solution
The method involves combining write operations that target the same row into a single, functionally equivalent operation, allowing these operations to be performed in parallel and eliminating row locks by ensuring no two operations write to the same row.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If write operations are performed in series, then data consistency is maintained, but transaction time increases and database performance deteriorates
Solution Approach 1:
The patent segments write operations into groups based on their target rows, identified through hash values of primary keys. Operations targeting different rows are executed in parallel while operations on the same row are combined, thus maintaining consistency without sequential execution of all operations.
Solution Approach 2:
The patent combines multiple write operations that target the same row into a single consolidated operation. This merging reduces the total number of operations while preserving data consistency, allowing other operations to proceed in parallel and improving overall transaction speed.
2Productivity
If operations are performed in parallel, then transaction time is reduced, but row locks are created which prevent other operations
Solution Approach 1:
The patent segments operations into parallel-executable groups by hashing primary keys and grouping operations with different hash values. This segmentation ensures that parallel operations target different rows, eliminating row lock conflicts while maintaining high transaction speed.
3Productivity
If the number of write operations is reduced, then parallel execution is enabled, but operation complexity increases
Solution Approach 1:
The patent introduces a write optimizer as an intermediary component that automatically analyzes write operations, computes hash values of primary keys, groups operations by target row, combines operations on the same row, and schedules parallel execution. This intermediary handles the complexity internally, allowing the rest of the system to benefit from parallel execution without dealing with the complexity directly.
Data Source
AI summary
Methods and systems disclosed herein may optimize write operations in a transaction to reduce the number of operations to the point where each of the write operations may be performed in parallel. The writer optimizer may review a first write operation and a second write operation to determine whether the first write operation and the second write operation write to the same row in the same table. When the first write operation and the second write operation write to the same row in the same table, the first write operation and the second write operation may be combined to generate a functionally-equivalent third write operation. The third write operation may reduce the number of operations in the transaction by replacing both the first write operation and the second write operation.