Logical Data Chunking for Distributed Database Sharding
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Solution Overview
Problem
In distributed database environments, managing data distribution across multiple shards is challenging, particularly when retrieving data sets split across multiple shards, as it requires joining, aggregating, or merging data from multiple sources, which can be inefficient.
Innovation Solution
A system and method that analyze transactions between applications and data storage systems to identify logical data chunks and define data distribution policies, allowing for efficient distribution of data across multiple data storage systems, with the ability to simulate and optimize these policies for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data sets are split across multiple shards to increase scalability and performance, then data storage capacity and system scalability are improved, but data retrieval efficiency deteriorates due to the need for joining and aggregating data from multiple shards
Solution Approach 1:
The patent segments data into logical data chunks that are further divided into physical shards for distribution across multiple servers. This segmentation allows the system to maintain scalability by distributing data while optimizing retrieval efficiency by ensuring related chunks are stored together in the same or adjacent shards, reducing the number of joins required.
Solution Approach 2:
The patent applies local quality by creating data distribution policies that optimize the placement of specific logical data chunks in particular shards based on access patterns and relationships. This allows different parts of the data to be stored with different optimization strategies, improving retrieval efficiency for frequently accessed data while maintaining overall scalability.
2Adaptability or versatility
If data is distributed across multiple shards to improve scalability, then system capacity is increased, but the complexity of managing data distribution and ensuring optimal placement increases
Solution Approach 1:
The patent implements self-service through automated data distribution policies that are generated and applied without manual intervention. The system analyzes data access patterns, relationships, and requirements to automatically determine optimal shard placements for logical data chunks, reducing the operational complexity of managing large-scale data distribution.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting data distribution policies based on changing system conditions, access patterns, and data characteristics. This allows the system to adapt to evolving requirements and optimize data placement automatically, reducing the complexity of manual management while maintaining high system capacity.
3Reliability
If data from multiple shards needs to be joined and aggregated for retrieval, then data completeness is maintained, but query processing time and operational overhead increase
Solution Approach 1:
The patent merges related logical data chunks into the same or adjacent physical shards, allowing queries to retrieve data from a single shard or nearby shards rather than requiring joins across multiple distributed shards. This merging approach maintains data completeness for related information while significantly reducing query processing time and operational overhead.
Solution Approach 2:
The patent applies preliminary action by pre-organizing data into logical chunks and pre-determining optimal shard placements based on anticipated access patterns and relationships. This preliminary organization reduces the need for complex join operations during query processing, maintaining data completeness while minimizing query processing time.
Data Source
AI summary
A system and method for analysis and management of data distribution in a distributed database environment. A method may include analyzing information related to transactions between an application and a plurality of data storage systems to produce analysis results. Analysis results may be used to produce a set of rules for defining logical data chunks. A logical data chunk may include at least a portion a data set stored in the plurality of data storage systems. A data distribution policy may be defined based on the set of rules. Other embodiments are described and claimed.