Key-Value Storage Bucketing for Reduced Read Latency
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Solution Overview
Problem
Existing data storage systems face inefficiencies such as wasted space and increased latency due to inefficient data storage and retrieval methods, particularly in large-scale server environments, leading to significant monetary and environmental losses.
Innovation Solution
Implementing a key-value storage database optimization method that uses bucketing techniques to efficiently store and retrieve data by dividing datasets into file-size-based buckets, allowing for bulk writes and minimizing disk seeks through optimized read operations, which reduces space requirements and memory costs while enabling fast value retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is stored in traditional unoptimized formats on disk, then storage capacity is available, but disk seek operations increase and read latency increases
Solution Approach 1:
The patent segments data into fixed-size buckets organized in a tree structure, where each level of the tree represents a segmentation step. This segmentation allows the system to quickly narrow down the search space by traversing the tree hierarchy rather than performing linear disk seeks, thereby reducing read latency and improving retrieval speed.
Solution Approach 2:
The patent introduces a hierarchical tree dimension to the data storage structure, transforming the traditional flat disk storage into a multi-level hierarchical structure. This dimensional change enables the system to perform logarithmic-time lookups instead of linear scans, significantly reducing disk seek operations and improving access speed.
2Speed
If data is stored with fine-grained organization for fast access, then read speed improves, but storage space efficiency decreases
Solution Approach 1:
The patent applies local quality by organizing data into fixed-size buckets at each tree level, where each bucket is optimized for its specific position in the hierarchy. This local optimization allows efficient packing of data within each bucket while maintaining overall space efficiency through the hierarchical structure, preventing both excessive fragmentation and wasted space.
Solution Approach 2:
The patent changes the storage parameter from variable-length records to fixed-size buckets, and from flat structure to hierarchical tree structure. These parameter changes enable more efficient space utilization by eliminating gaps between variable-length records while maintaining fast access through the hierarchical organization, thus resolving the contradiction between space efficiency and access speed.
3Productivity
If bulk data writing is performed without optimization, then data can be loaded efficiently, but space utilization is poor and retrieval is slow
Solution Approach 1:
The patent performs preliminary action by organizing incoming bulk data into the hierarchical bucket structure during the write phase itself, rather than organizing it later for retrieval. Data is immediately placed into appropriate buckets at each tree level as it arrives, enabling efficient bulk loading while simultaneously optimizing space utilization and retrieval performance through the pre-established hierarchical structure.
Data Source
AI summary
Systems and methods are provided for optimizing data storage and improving the speed of data retrieval in a data store, such as a storage server connected to a large network through the use of bucketing techniques to create efficient data structures for storing received key-value datasets at one or more storage servers. Fast key-value read requests and key-value retrievals may be accomplished through the use of multiphase lookup operations on the one or more storage servers. The system is optimized for best performance of retrieval through the separation of the write and read mechanisms. Systems and methods provided herein control the level of wastefulness on the back end of a system and reduce read operation inefficiencies on the front end of a system.