Database Partitioning via Sequential Key Sharding
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Solution Overview
Problem
Conventional database systems face challenges in scaling efficiently, as vertical scaling is expensive and horizontal scaling requires significant computational overhead, especially during data migration and partitioning processes.
Innovation Solution
A system is implemented that optimizes data migration by partitioning databases across multiple systems, using a transparent front end to manage record distribution, and minimizing data movement by leveraging sequential database keys to create new shards with minimal overhead, thereby reducing network traffic and computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vertical scaling is used to increase processing power, then database performance is improved, but system cost increases exponentially
Solution Approach 1:
The database is divided into multiple partitions distributed across multiple computer systems, transforming a single large database into smaller, manageable segments that can be processed in parallel, thereby increasing processing power without requiring a single expensive high-capacity system
Solution Approach 2:
The system transitions from vertical scaling (adding more resources to a single system) to horizontal scaling (adding more systems), changing the dimension of scalability from depth to breadth, which provides more cost-effective processing power expansion
2Device complexity
If horizontal scaling is implemented to increase processing power, then system cost is reduced, but computational overhead increases
Solution Approach 1:
The system automatically detects sequential database keys and self-optimizes the partitioning strategy without requiring manual intervention, reducing the computational overhead associated with complex partition management while maintaining cost-effective horizontal scaling
Solution Approach 2:
The system changes the partitioning parameter from arbitrary or hash-based distribution to sequential key-based distribution, which reduces computational overhead by enabling more efficient data placement and retrieval operations across the distributed system
3Productivity
If conventional partitioning is used to distribute data, then database operations can be spread over multiple systems, but network traffic increases due to data migration
Solution Approach 1:
The system performs preliminary detection of sequential database keys before partitioning, allowing it to pre-organize data in a manner that minimizes future data migration needs, thereby reducing network traffic during database operations while maintaining effective distribution across multiple systems
Data Source
AI summary
According to one aspect, provided is a horizontally scaled database architecture. Partition a database enables efficient distribution of data across a number of systems reducing processing costs associated with multiple machines. According to some aspects, the partitioned database can be managed as a single source interface to handle client requests. Further, it is realized that by identifying and testing key properties, horizontal scaling architectures can be implemented and operated with minimal overhead. In one embodiment, databases can be partitioned in an order preserving manner such that the overhead associated with moving the data for a given partition can be minimized during management of the data and/or database. In one embodiment, splits and migrations operations prioritize zero cost partitions, thereby, reducing computational burden associated with managing a partitioned database.