Distributed Database Data Replication Near Activity Centers
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Solution Overview
Problem
Conventional distributed databases face performance issues due to the need for frequent access to remote storage locations, leading to high network bandwidth costs and latency, especially for high-frequency queries, as data records become more distributed across multiple storage locations.
Innovation Solution
Data records are replicated to storage locations closest to their center of activity by determining network distances and using query attributes to identify the optimal storage location, allowing database servers to access data from the nearest location, thereby reducing network distance and improving query performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data records are distributed across multiple storage locations to provide load-balancing, then storage capacity and availability are improved, but network bandwidth consumption and access latency increase for high-frequency queries
Solution Approach 1:
The patent applies local quality by creating replicas of data records at multiple storage locations with different access characteristics. Instead of uniformly distributing all data access operations, the system identifies frequently accessed records and creates local replicas near the access points (database servers), while less frequently accessed records remain at remote storage locations. This differentiated approach optimizes network bandwidth usage by serving frequent queries from local replicas rather than repeatedly accessing remote storage.
Solution Approach 2:
The system performs preliminary action by proactively identifying data records that are likely to be frequently accessed based on query patterns and access metadata, and pre-creating replicas at appropriate storage locations before actual high-frequency queries occur. This anticipatory replication prevents future network bandwidth congestion by having data already positioned at optimal locations.
2Quantity of substance
If data records are distributed across multiple storage locations, then storage capacity is improved, but query performance deteriorates due to increased network latency
Solution Approach 1:
The patent implements local quality by placing data replicas at storage locations that are topologically close to the database servers that frequently query them. The system calculates network distance metrics and positions replicas optimally, so that local database servers can access frequently needed data from nearby storage locations rather than remote ones, significantly reducing query latency while maintaining distributed storage capacity.
Solution Approach 2:
The patent introduces an intermediary component (the data placement and replication system) that acts as a mediator between the distributed storage infrastructure and database servers. This intermediary intelligently determines which data records should be replicated to which storage locations based on access patterns and network topology, effectively mediating the trade-off between storage distribution and access speed by optimizing the intermediate data placement layer.
3Adaptability or versatility
If data is stored at remote storage locations to enable distributed storage, then system scalability is improved, but access speed deteriorates due to network distance
Solution Approach 1:
The patent applies segmentation by dividing data records into segments that are strategically replicated across the distributed storage infrastructure. Instead of treating data as a monolithic distributed entity, the system segments frequently accessed records and creates targeted replicas closer to access points, while less frequently accessed data remains at remote locations. This segmented replication strategy maintains system scalability while improving access speed for critical data.
Solution Approach 2:
The patent introduces another dimension to the storage architecture by adding a spatial dimension to data placement decisions. The system considers network topology and physical distance as additional dimensions in the storage hierarchy, creating a multi-dimensional data placement strategy that goes beyond simple logical distribution. This allows the system to maintain scalability while optimizing access speed by positioning data in the optimal spatial location within the distributed network.
Data Source
AI summary
A network provides distributed database services based on replicating data records to a new storage location that is closest to consumers of the data records, based on comparing a network distance between the new storage location and an attribute identifying a center of activity of at least one data element, relative to the network distance between any existing storage location in the center of activity. If the new storage location is closer to the center of activity relative to any of the other existing stores locations, than at least the one data element associated with the center of activity is copied to the new storage location. Multiple centers of activity may be utilized for respective data records, enabling data records to be copied on a per-record basis, or grouped as needed.