Key Value Store Data Structure Conversion for Dynamic Workloads
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Solution Overview
Problem
Key Value Store (KVS) systems face challenges in maintaining performance when use conditions change, as they are unable to dynamically convert between data structures, leading to inefficient resource utilization and suboptimal performance.
Innovation Solution
An information storage system with a data structure converting unit that adapts the data structure of key-value data based on changing use conditions, allowing conversion between flat and cluster structures, and potentially other data structures, to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If tabular data is converted into KV data and stored into a KVS using a fixed conversion method, then the data can be stored and accessed, but it becomes impossible to change the conversion method afterward even if the workload changes
Solution Approach 1:
The patent implements dynamic data structure conversion by allowing the KVS to transform stored data between different key formats (original key, composite key, inverted key) based on changing workload requirements. The system monitors access patterns and automatically reorganizes data structures to match current query needs, making the conversion method flexible rather than fixed.
Solution Approach 2:
The system changes the parameters of data storage by modifying key composition and structure. It transforms keys from simple identifiers to composite keys combining multiple table identifiers and column names, or inverts them to enable different query patterns. This parameter transformation allows the same data to serve multiple access patterns without physical replication.
2Productivity
If a wrong conversion method is defined or the workload changes, then resource utilization becomes inefficient and performance deteriorates
Solution Approach 1:
The patent incorporates feedback mechanisms that monitor access patterns and query workloads continuously. Based on this feedback, the system automatically adjusts data structure conversion methods to optimize performance. When access patterns change, the system detects this through monitoring and triggers appropriate data reorganization to maintain efficient query processing.
Solution Approach 2:
The system performs preliminary data structure conversion in anticipation of workload changes. By pre-organizing data in multiple key formats or maintaining conversion capabilities ready, the system can quickly adapt to changing query patterns without significant performance degradation, effectively preparing ahead for potential workload shifts.
3Adaptability or versatility
If data is stored in a fixed data structure, then storage is simple, but the system cannot adapt to changing use conditions
Solution Approach 1:
The patent makes the KVS multi-functional by enabling it to store and access data through multiple conversion methods. The same stored data can be accessed using original keys, composite keys, or inverted keys depending on the query type. This universality allows a single storage system to handle diverse access patterns without requiring separate storage structures for each use case.
Data Source
AI summary
An information storage system includes: a data storing unit storing key value data in which a key is one of a plurality of elements of record data composed of the elements and the key is associated with a value including one or a plurality of record data; and a data structure converting unit converting a data structure of the key value data stored by the data storing unit into another data structure by changing the key. The data structure converting unit performs conversion of the data structure of the key value data stored by the data storing unit in accordance with a use condition of the key value data.