Multi-Model Database Clustering for Intelligent Query Routing
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Solution Overview
Problem
Current databases focus on specific data types, requiring organizations to build complex data pipelines and strategies for replication, leading to delays, performance bottlenecks, and increased operational expenditure when transitioning between on-premises and cloud environments, while multi-model databases fail to account for user technical needs, impacting productivity and viewing data as a liability.
Innovation Solution
A multi-model and clustering database system that integrates and manages different data types and models, enabling seamless integration of on-premises and cloud databases, using machine learning to analyze SQL queries and dynamically manage and replicate data across various database types, with intelligent cluster management and replication pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If organizations use separate databases for different data types, then data management flexibility is improved, but system complexity and operational expenditure increase
Solution Approach 1:
The patent combines multiple specialized databases (relational, document, graph, key-value) into a single unified database system that can handle different data types through a common architecture. This consolidation eliminates the need for separate data pipelines between databases while maintaining the ability to store and query different data models, thereby reducing system complexity while preserving data management flexibility.
Solution Approach 2:
The unified database system provides multi-functionality by supporting multiple data models (relational, document, graph, key-value) within a single database instance. This universal approach allows the same database system to perform various data storage and query operations that previously required different specialized databases, reducing both complexity and operational overhead.
2Reliability
If organizations implement complex data replication pipelines between databases, then data consistency is improved, but productivity and operational efficiency deteriorate
Solution Approach 1:
By consolidating multiple databases into a unified system, the patent eliminates the need for complex inter-database replication pipelines. Data consistency is maintained through the shared underlying architecture and storage engine, while operational efficiency improves by removing redundant data synchronization processes and reducing the overhead of managing multiple separate systems.
3Adaptability or versatility
If organizations transition between on-premises and cloud database environments, then scalability is improved, but operational expenditure and complexity increase
Solution Approach 1:
The unified database system provides a consistent platform that can operate in both on-premises and cloud environments. This universal architecture allows organizations to scale flexibly by deploying the same database system across different infrastructure types without requiring environment-specific adaptations or complex migration strategies, thereby improving scalability while reducing operational complexity.
Data Source
AI summary
Techniques are disclosed for enabling a multi-model and clustering database system. For example, a query from a client is received at a primary cluster in a clustering database system implemented over one or more processing platforms. The primary cluster analyzes the query to determine an intent of the query and, based on the intent of the query, to identify at least one secondary cluster from a plurality of secondary clusters in the clustering database system to execute the query. The primary cluster manages the plurality of secondary clusters, and one or more of the plurality of secondary clusters support a data model type that is different than a data model type of one or more others of the plurality of secondary clusters.