Multi-Temperature Database Access Optimization via Segmentation
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Solution Overview
Problem
Multi-temperature databases face challenges in optimizing access time for both frequently and infrequently accessed data, as existing methods often require separate databases for hot and cold data, which may not be feasible or efficient due to technical and regulatory constraints.
Innovation Solution
A method that maps each table, row, or column to a specific query based on data temperature by determining data frequency access measures, creating views, and associating query types with processing rules to prioritize execution of queries accessing hot or cold data, using a massively parallel processing system and management strategies to allocate resources effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If multiple separate databases are used to store hot and cold data, then data access time is optimized, but system complexity and regulatory compliance difficulty increase
Solution Approach 1:
The patent segments data within a single database by creating separate logical views for hot and cold data based on access frequency. This allows the system to apply different processing rules to different data segments while maintaining a unified physical database structure, thus optimizing access time without increasing system complexity.
Solution Approach 2:
The patent introduces an intermediary layer (views and processing rules) between the user and the underlying database. This intermediary automatically routes queries to appropriate data segments based on access frequency, providing optimized access without requiring users to manage multiple separate databases.
2Productivity
If data access frequency is monitored and processed differently, then query execution efficiency improves, but processing complexity increases
Solution Approach 1:
The patent implements self-service by automatically monitoring data access frequency and dynamically creating or updating views based on observed patterns. The system autonomously determines which data is hot or cold and applies appropriate processing rules without requiring manual intervention, thus improving efficiency while keeping processing complexity manageable.
Solution Approach 2:
The patent uses feedback from actual query execution patterns to dynamically adjust data classification and processing strategies. By monitoring access frequency and using this feedback to update views and processing rules, the system optimizes query execution efficiency while adapting to changing data usage patterns.
Data Source
AI summary
An optimization technique is provided that optimizes data access by mapping each table, or row and/or column in a table, to a particular query, depending on whether the data in said table, row or column is ‘hot’ or ‘cold’ data. In one aspect, the invention features a method for optimizing the access time of an SQL query to a database including data. The method includes receiving, for each query type, a data frequency access measure and associating each query type with a processing rule set, according to the received data frequency access measure.